From c13fd84c3b40cd8d6113e820934d14ea7d9d98b5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20H=C3=B6rner?= <johannes.hoerner@univie.ac.at>
Date: Fri, 16 Jul 2021 17:19:02 +0200
Subject: [PATCH] added ICON runscripts, reworked plots
---
ICON_runscripts/exp.mlo_aqua_1500ppmv.run | 1201 ++++++++++++
.../exp.mlo_aqua_1500ppmv_hice_unlim.run | 1202 ++++++++++++
ICON_runscripts/exp.mlo_aqua_1594ppmv.run | 1201 ++++++++++++
.../exp.mlo_aqua_1594ppmv_hice_unlim.run | 1202 ++++++++++++
.../exp.mlo_aqua_1688ppmv_hice_unlim.run | 1202 ++++++++++++
.../exp.mlo_aqua_1875ppmv_hice_unlim.run | 1202 ++++++++++++
.../exp.mlo_aqua_1875ppmv_winton.run | 1203 ++++++++++++
.../exp.mlo_aqua_2250ppmv_hice_unlim.run | 1201 ++++++++++++
.../exp.mlo_aqua_2250ppmv_winton.run | 1203 ++++++++++++
.../exp.mlo_aqua_2437ppmv_winton.run | 1203 ++++++++++++
.../exp.mlo_aqua_2625ppmv_winton.run | 1203 ++++++++++++
.../exp.mlo_aqua_3000ppmv_74sic_winton.run | 1203 ++++++++++++
...exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run | 1202 ++++++++++++
.../exp.mlo_aqua_3000ppmv_winton.run | 1203 ++++++++++++
.../exp.mlo_aqua_3000ppmv_winton_50sic.run | 1203 ++++++++++++
...exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run | 1202 ++++++++++++
...exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run | 1202 ++++++++++++
...a_4219ppmv_71sic_winton_semtnerrestart.run | 1203 ++++++++++++
.../exp.mlo_aqua_4219ppmv_winton_50sic.run | 1203 ++++++++++++
...exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run | 1202 ++++++++++++
.../exp.mlo_aqua_5000ppmv_37sic_winton.run | 1203 ++++++++++++
.../exp.mlo_aqua_5000ppmv_74sic_winton.run | 1203 ++++++++++++
...exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run | 1202 ++++++++++++
.../exp.mlo_aqua_5000ppmv_winton.run | 1203 ++++++++++++
python_scripts/.gitkeep | 0
..._Semtner_lores_comparison_PAPERPLOTS.ipynb | 1709 +++++++++++++++++
.../Winton_artificial_forcing.ipynb | 1581 +++++++++++++++
.../__pycache__/functions.cpython-36.pyc | Bin 0 -> 1253 bytes
.../bifurcation_Semtner_unlim-lim5.ipynb | 244 +++
.../bifurcation_Winton_Semtner_unlim.ipynb | 226 +++
python_scripts/cdo_pp.ipynb | 347 ++++
.../energyflux_icelimit_comparison.ipynb | 1198 ++++++++++++
python_scripts/ice_thickness.ipynb | 630 ++++++
python_scripts/overview.ipynb | 655 +++++++
..._5000_and_3000ppmv_and_1500ppmv_plot.ipynb | 205 ++
35 files changed, 35652 insertions(+)
create mode 100755 ICON_runscripts/exp.mlo_aqua_1500ppmv.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_1500ppmv_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_1594ppmv.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_1594ppmv_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_1688ppmv_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_1875ppmv_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_1875ppmv_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_2250ppmv_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_2250ppmv_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_2437ppmv_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_2625ppmv_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_3000ppmv_74sic_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_3000ppmv_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_3000ppmv_winton_50sic.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_4219ppmv_winton_50sic.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_5000ppmv_37sic_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_5000ppmv_74sic_winton.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run
create mode 100755 ICON_runscripts/exp.mlo_aqua_5000ppmv_winton.run
create mode 100644 python_scripts/.gitkeep
create mode 100644 python_scripts/Winton_Semtner_lores_comparison_PAPERPLOTS.ipynb
create mode 100644 python_scripts/Winton_artificial_forcing.ipynb
create mode 100644 python_scripts/__pycache__/functions.cpython-36.pyc
create mode 100644 python_scripts/bifurcation_Semtner_unlim-lim5.ipynb
create mode 100644 python_scripts/bifurcation_Winton_Semtner_unlim.ipynb
create mode 100644 python_scripts/cdo_pp.ipynb
create mode 100644 python_scripts/energyflux_icelimit_comparison.ipynb
create mode 100644 python_scripts/ice_thickness.ipynb
create mode 100644 python_scripts/overview.ipynb
create mode 100644 python_scripts/paper_5000_and_3000ppmv_and_1500ppmv_plot.ipynb
diff --git a/ICON_runscripts/exp.mlo_aqua_1500ppmv.run b/ICON_runscripts/exp.mlo_aqua_1500ppmv.run
new file mode 100755
index 0000000..9818200
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1500ppmv.run
@@ -0,0 +1,1201 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1500ppmv.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1500ppmv/LOG.exp.mlo_aqua_1500ppmv.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1500ppmv/LOG.exp.mlo_aqua_1500ppmv.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1500ppmv.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1500ppmv"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0200-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1500e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 5.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_1500ppmv_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_1500ppmv_hice_unlim.run
new file mode 100755
index 0000000..5148ca1
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1500ppmv_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1500ppmv_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1500ppmv_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1500ppmv_hice_unlim/LOG.exp.mlo_aqua_1500ppmv_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1500ppmv_hice_unlim/LOG.exp.mlo_aqua_1500ppmv_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1500ppmv_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1500ppmv_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0300-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1500e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_1594ppmv.run b/ICON_runscripts/exp.mlo_aqua_1594ppmv.run
new file mode 100755
index 0000000..49470b3
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1594ppmv.run
@@ -0,0 +1,1201 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1594ppmv.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1594ppmv/LOG.exp.mlo_aqua_1594ppmv.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1594ppmv/LOG.exp.mlo_aqua_1594ppmv.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1594ppmv.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1594ppmv"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0200-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1594e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 5.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_1594ppmv_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_1594ppmv_hice_unlim.run
new file mode 100755
index 0000000..1f77586
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1594ppmv_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1594ppmv_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1594ppmv_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1594ppmv_hice_unlim/LOG.exp.mlo_aqua_1594ppmv_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1594ppmv_hice_unlim/LOG.exp.mlo_aqua_1594ppmv_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1594ppmv_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1594ppmv_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0230-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1594e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_1688ppmv_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_1688ppmv_hice_unlim.run
new file mode 100755
index 0000000..8e20926
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1688ppmv_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1688ppmv_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1688ppmv_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1688ppmv_hice_unlim/LOG.exp.mlo_aqua_1688ppmv_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1688ppmv_hice_unlim/LOG.exp.mlo_aqua_1688ppmv_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1688ppmv_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1688ppmv_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0190-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1688e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_1875ppmv_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_1875ppmv_hice_unlim.run
new file mode 100755
index 0000000..5ee7a7d
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1875ppmv_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1875ppmv_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1875ppmv_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1875ppmv_hice_unlim/LOG.exp.mlo_aqua_1875ppmv_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1875ppmv_hice_unlim/LOG.exp.mlo_aqua_1875ppmv_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1875ppmv_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1875ppmv_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0150-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1875e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_1875ppmv_winton.run b/ICON_runscripts/exp.mlo_aqua_1875ppmv_winton.run
new file mode 100755
index 0000000..38ad8f9
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_1875ppmv_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_1875ppmv_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1875ppmv_winton/LOG.exp.mlo_aqua_1875ppmv_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_1875ppmv_winton/LOG.exp.mlo_aqua_1875ppmv_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_1875ppmv_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_1875ppmv_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0100-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 1875e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_2250ppmv_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_2250ppmv_hice_unlim.run
new file mode 100755
index 0000000..84dc134
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_2250ppmv_hice_unlim.run
@@ -0,0 +1,1201 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_2250ppmv_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2250ppmv_hice_unlim/LOG.exp.mlo_aqua_2250ppmv_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2250ppmv_hice_unlim/LOG.exp.mlo_aqua_2250ppmv_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_2250ppmv_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_2250ppmv_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0121-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P1M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 2250e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_2250ppmv_winton.run b/ICON_runscripts/exp.mlo_aqua_2250ppmv_winton.run
new file mode 100755
index 0000000..98b414e
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_2250ppmv_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_2250ppmv_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2250ppmv_winton/LOG.exp.mlo_aqua_2250ppmv_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2250ppmv_winton/LOG.exp.mlo_aqua_2250ppmv_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_2250ppmv_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_2250ppmv_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0200-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 2250e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_2437ppmv_winton.run b/ICON_runscripts/exp.mlo_aqua_2437ppmv_winton.run
new file mode 100755
index 0000000..bc747c1
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_2437ppmv_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_2437ppmv_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2437ppmv_winton/LOG.exp.mlo_aqua_2437ppmv_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2437ppmv_winton/LOG.exp.mlo_aqua_2437ppmv_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_2437ppmv_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_2437ppmv_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0200-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 2437e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_2625ppmv_winton.run b/ICON_runscripts/exp.mlo_aqua_2625ppmv_winton.run
new file mode 100755
index 0000000..4f3cf18
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_2625ppmv_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_2625ppmv_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2625ppmv_winton/LOG.exp.mlo_aqua_2625ppmv_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_2625ppmv_winton/LOG.exp.mlo_aqua_2625ppmv_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_2625ppmv_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_2625ppmv_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0200-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 2625e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_3000ppmv_74sic_winton.run b/ICON_runscripts/exp.mlo_aqua_3000ppmv_74sic_winton.run
new file mode 100755
index 0000000..e9a34ec
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_3000ppmv_74sic_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_3000ppmv_74sic_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_74sic_winton/LOG.exp.mlo_aqua_3000ppmv_74sic_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_74sic_winton/LOG.exp.mlo_aqua_3000ppmv_74sic_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_3000ppmv_74sic_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_3000ppmv_74sic_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0120-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 3000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run
new file mode 100755
index 0000000..77be276
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_77sic_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:55:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_3000ppmv_77sic_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_3000ppmv_77sic_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0340-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 3000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_3000ppmv_winton.run b/ICON_runscripts/exp.mlo_aqua_3000ppmv_winton.run
new file mode 100755
index 0000000..cf4f61c
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_3000ppmv_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_3000ppmv_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_winton/LOG.exp.mlo_aqua_3000ppmv_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_winton/LOG.exp.mlo_aqua_3000ppmv_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_3000ppmv_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_3000ppmv_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0121-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P1M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1M"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 3000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_3000ppmv_winton_50sic.run b/ICON_runscripts/exp.mlo_aqua_3000ppmv_winton_50sic.run
new file mode 100755
index 0000000..f2d61ba
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_3000ppmv_winton_50sic.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_3000ppmv_winton_50sic.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_winton_50sic/LOG.exp.mlo_aqua_3000ppmv_winton_50sic.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3000ppmv_winton_50sic/LOG.exp.mlo_aqua_3000ppmv_winton_50sic.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_3000ppmv_winton_50sic.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_3000ppmv_winton_50sic"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0130-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 3000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run
new file mode 100755
index 0000000..20e5450
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3750ppmv_77sic_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3750ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_3750ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_3750ppmv_77sic_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_3750ppmv_77sic_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0450-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 3750e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run
new file mode 100755
index 0000000..cf8a8fe
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4063ppmv_77sic_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4063ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4063ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:55:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_4063ppmv_77sic_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_4063ppmv_77sic_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0490-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 4063e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run b/ICON_runscripts/exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run
new file mode 100755
index 0000000..19ff66b
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4219ppmv_71sic_winton_semtnerrestart/LOG.exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4219ppmv_71sic_winton_semtnerrestart/LOG.exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_4219ppmv_71sic_winton_semtnerrestart.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_4219ppmv_71sic_winton_semtnerrestart"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0480-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 4219e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_4219ppmv_winton_50sic.run b/ICON_runscripts/exp.mlo_aqua_4219ppmv_winton_50sic.run
new file mode 100755
index 0000000..ccc3c9c
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_4219ppmv_winton_50sic.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_4219ppmv_winton_50sic.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4219ppmv_winton_50sic/LOG.exp.mlo_aqua_4219ppmv_winton_50sic.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4219ppmv_winton_50sic/LOG.exp.mlo_aqua_4219ppmv_winton_50sic.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_4219ppmv_winton_50sic.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_4219ppmv_winton_50sic"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0130-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 4219e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run
new file mode 100755
index 0000000..2f49d4e
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4375ppmv_77sic_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4375ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_4375ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_4375ppmv_77sic_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_4375ppmv_77sic_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0450-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 4375e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_5000ppmv_37sic_winton.run b/ICON_runscripts/exp.mlo_aqua_5000ppmv_37sic_winton.run
new file mode 100755
index 0000000..1990937
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_5000ppmv_37sic_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_5000ppmv_37sic_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_37sic_winton/LOG.exp.mlo_aqua_5000ppmv_37sic_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_37sic_winton/LOG.exp.mlo_aqua_5000ppmv_37sic_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_5000ppmv_37sic_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_5000ppmv_37sic_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0100-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT8M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT8M"
+ mpi_phy_config(1)%dt_cnv = "PT8M"
+ mpi_phy_config(1)%dt_cld = "PT8M"
+ mpi_phy_config(1)%dt_gwd = "PT8M"
+ mpi_phy_config(1)%dt_sso = "PT8M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT8M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 5000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_5000ppmv_74sic_winton.run b/ICON_runscripts/exp.mlo_aqua_5000ppmv_74sic_winton.run
new file mode 100755
index 0000000..4007d19
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_5000ppmv_74sic_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_5000ppmv_74sic_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_74sic_winton/LOG.exp.mlo_aqua_5000ppmv_74sic_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_74sic_winton/LOG.exp.mlo_aqua_5000ppmv_74sic_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:50:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_5000ppmv_74sic_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_5000ppmv_74sic_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0130-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 5000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run b/ICON_runscripts/exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run
new file mode 100755
index 0000000..5634f49
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run
@@ -0,0 +1,1202 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run
+#SBATCH --partition=compute
+#SBATCH --workdir=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_77sic_hice_unlim
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_77sic_hice_unlim/LOG.exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:52:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_5000ppmv_77sic_hice_unlim.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_5000ppmv_77sic_hice_unlim"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0450-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT6M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT6M"
+ mpi_phy_config(1)%dt_cnv = "PT6M"
+ mpi_phy_config(1)%dt_cld = "PT6M"
+ mpi_phy_config(1)%dt_gwd = "PT6M"
+ mpi_phy_config(1)%dt_sso = "PT6M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT6M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 5000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/ICON_runscripts/exp.mlo_aqua_5000ppmv_winton.run b/ICON_runscripts/exp.mlo_aqua_5000ppmv_winton.run
new file mode 100755
index 0000000..5dc0730
--- /dev/null
+++ b/ICON_runscripts/exp.mlo_aqua_5000ppmv_winton.run
@@ -0,0 +1,1203 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1092
+
+#SBATCH --job-name=exp.mlo_aqua_5000ppmv_winton.run
+#SBATCH --partition=compute
+#SBATCH --nodes=20
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_winton/LOG.exp.mlo_aqua_5000ppmv_winton.run.%j.o
+#SBATCH --error=/pf/b/b380905/icon-aes/runscripts/mlo_aqua_5000ppmv_winton/LOG.exp.mlo_aqua_5000ppmv_winton.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:45:00
+#SBATCH --mail-user=b380905
+#SBATCH --mail-type=FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /pf/b/b380905/icon-aes/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.mlo_aqua_5000ppmv_winton.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin" # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export EXPNAME="mlo_aqua_5000ppmv_winton"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#--------------------------------------------------------------------------------------------------
+#
+# AMIP experiment
+#
+author_list="Marco Giorgetta, MPIM"
+#
+#--------------------------------------------------------------------------------------------------
+#
+# This file describes an AMIP experiment based on the non-hydrostatic atmosphere and the
+# ECHAM physics. The experiment is intialized from IFS analysis files and uses transient
+# boundary conditions for:
+# - SST and sea ice
+# - spectral solar irradiation
+# - well mixed greenhouse gases CO2, CH4, N2O, CFCs
+# - O3 concentration
+# - tropospheric aerosol optical properties
+# - stratospheric volcanic aerosol optical properties
+#
+#--------------------------------------------------------------------------------------------------
+
+# (0) unset some setting of create_target_header for mistral
+
+unset OMPI_MCA_coll_fca_enable
+unset OMPI_MCA_coll_fca_priority
+
+#--------------------------------------------------------------------------------------------------
+
+# (1) Variables provided by the scripting mechanism
+
+# EXPNAME = name of exp. in 'exp.<name>'
+# basedir = base directory, where src/, run/ etc exist
+# icon_data_poolFolder = base directory, where grids/, input/ and setup/ exist
+# nproma = blocking length for array dimensioning and inner loop lengths
+
+# overwrite the default setting with the new path and handle daint (CSCS)
+
+if [ -d /users/icontest ]
+then
+ poolFolder_prefix=/users/icontest
+else
+ poolFolder_prefix=
+fi
+
+icon_data_poolFolder="/work/bb1092/inputdata"
+
+#--------------------------------------------------------------------------------------------------
+
+# (2) Set variables needed by the scripting mechanism
+
+# horizontal grid(s)
+grid_name=icon_grid_0005_R02B04_G
+grids_folder=${icon_data_poolFolder}/grids
+atmo_dyn_grids=${grid_name}.nc
+
+resolution_a=$(awk -F'_' '{print $4}' <<< $grid_name)
+resolution_b=${resolution_a/0/}
+resolution_c=${resolution_a//0/}
+
+# start and end date+time
+start_date=${start_date:="0001-01-01T00:00:00Z"}
+ end_date=${end_date:="0030-01-01T00:00:00Z"}
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="P1D"
+output_interval_inst="PT6H"
+file_interval="P1M"
+
+# namelist files
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+lnd_namelist=NAMELIST_${EXPNAME}_lnd
+
+# JSBACH settings
+run_jsbach=yes
+jsbach_with_hd=no
+jsbach_with_lakes=no
+jsbach_usecase=jsbach_lite # jsbach_lite or jsbach_pfts
+jsbach_check_wbal=no # check water balance
+output_lnd=none # amount of output: min/full/no
+#
+[[ $jsbach_with_lakes == yes ]] || jsbach_usecase=${jsbach_usecase}_nolake
+[[ $jsbach_with_hd == yes ]] && jsbach_usecase=${jsbach_usecase}_with_hd
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+if [[ $jsbach_usecase == *pfts* ]]
+then
+ pft_file_tag="11pfts_"
+ nproma=64
+else
+ pft_file_tag=""
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+¶llel_nml
+ nproma = ${nproma}
+/
+&grid_nml
+ dynamics_grid_filename = "${atmo_dyn_grids}",
+/
+&run_nml
+ num_lev = 45 ! number of full levels
+ modelTimeStep = "PT10M"
+ ltestcase = .FALSE. ! run testcase
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .TRUE. ! transport
+ ntracer = 3 ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output = 'nml'
+ msg_level = 5 ! level of details report during integration
+ restart_filename = "${EXPNAME}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo = 1 ! 1: read topography from the grid file
+ l_emiss = .FALSE.
+/
+&initicon_nml
+ init_mode = 2 ! 2: initialize from IFS analysis
+/
+&nonhydrostatic_nml
+ ndyn_substeps = 5 ! dtime/dt_dyn
+ damp_height = 50000. ! [m]
+ rayleigh_coeff = 0.10
+ vwind_offctr = 0.2
+ divdamp_fac = 0.004
+ htop_moist_proc = 75000. ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn = 40. ! [m]
+ top_height = 72226. ! [m]
+ stretch_fac = 0.949
+ decay_scale_1 = 4000. ! [m]
+ decay_scale_2 = 2500. ! [m]
+ decay_exp = 1.2
+ flat_height = 16000. ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac = 0.015001 ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0 ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0 ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0 ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0 ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+! hus,clw,cli, o3
+ ivadv_tracer = 3, 3, 3, 3
+ itype_hlimit = 3, 4, 4, 4
+ ihadv_tracer = 52, 2, 2, 52
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = "PT10M"
+ mpi_phy_config(1)%dt_cnv = "PT10M"
+ mpi_phy_config(1)%dt_cld = "PT10M"
+ mpi_phy_config(1)%dt_gwd = "PT10M"
+ mpi_phy_config(1)%dt_sso = "PT10M"
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = "PT10M"
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice = .TRUE.
+ mpi_phy_config(1)%lmlo = .TRUE.
+ mpi_phy_config(1)%llake = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o = 1 ! 1: prognostic vapor, liquid and ice
+ irad_co2 = 2 ! 4: from greenhouse gas scenario
+ vmr_co2 = 5000e-6
+ irad_ch4 = 0 ! 4: from greenhouse gas scenario
+ irad_n2o = 0 ! 4: from greenhouse gas scenario
+ irad_o3 = 4 ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2 = 2 ! 2: horizontally and vertically constant
+ irad_cfc11 = 0 ! 4: from greenhouse gas scenario
+ irad_cfc12 = 0 ! 4: from greenhouse gas scenario
+ irad_aero = 0 ! 0: no aerosol
+ !13: only Kinne\'s tropospheric aerosols
+ !14: only Stenchikov\'s volcanic aerosols
+ !15: Kinne aerosol optics for troposphere
+ ! +Stenchikov aerosol optics for stratosphere
+ !18: Kinne background aerosols of 1865 (natural
+ ! background + Stenchikov\'s volc. aerosols
+ ! + simple plumes (anthropogenic)
+ ighg = 0 ! 1: transient well mixed greenhouse gas concentrations
+ isolrad = 2 ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442 ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm = 0.66
+ albsnow_cold = 0.79
+ albice_warm = 0.38
+ albice_cold = 0.45
+/
+&psrad_nml
+ rad_perm = 1 ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm = 2 ! =1 Semtner (default), =2 Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05 ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+ usecase = "${jsbach_usecase}"
+ fract_filename = "bc_land_frac.nc"
+ l_compat401 = .TRUE. ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+ use_alb_veg_simple = .TRUE. ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+ l_heat_cap_map = .FALSE.
+ l_heat_cond_map = .FALSE.
+ l_heat_cap_dyn = .TRUE.
+ l_heat_cond_dyn = .TRUE.
+ l_snow = .TRUE.
+ l_dynsnow = .TRUE.
+ l_freeze = .FALSE.
+ l_supercool = .FALSE.
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+ bc_filename = 'bc_land_soil.nc'
+ ic_filename = 'ic_land_soil.nc'
+ bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+ active = .FALSE. ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+ scheme = 'climatology' ! scheme = logrop / climatology; use climatology for jsbach_lite
+ bc_filename = 'bc_land_phys.nc'
+ ic_filename = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+ cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+ active = .TRUE.
+ routing_scheme = 'full'
+ bc_filename = 'bc_land_hd.nc'
+ diag_water_budget = .TRUE.
+ debug_hd = .FALSE.
+ enforce_water_budget = .TRUE. ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+
+# (4) Define the input
+
+# model files
+#
+add_link_file ${basedir}/data/rrtmg_lw.nc ./
+add_link_file ${basedir}/data/rrtmg_sw.nc ./
+add_link_file ${basedir}/data/ECHAM6_CldOptProps.nc ./
+
+# namelist files
+#
+add_required_file ${basedir}/runscripts/${EXPNAME}/${atmo_namelist} ./
+add_required_file ${basedir}/runscripts/${EXPNAME}/${lnd_namelist} ./
+
+# dictionary file for output variable names
+#
+dict_file="dict.${EXPNAME}"
+cat ${basedir}/run/dictfiles/dict.iconam.mpim > ${dict_file}
+add_required_file ${basedir}/runscripts/${EXPNAME}/${dict_file} ./
+
+# initial conditions
+#
+# - atmosphere: ECMWF analysis, 1979-01-01T00:00:00Z
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file $datadir/ifs2icon_1979010100_${resolution_a}_G.ape.nc ./ifs2icon_${resolution_b}_DOM01.nc
+#
+# - land: source?, date+time?
+datadir=${icon_data_poolFolder}/initial_condition
+add_link_file ${datadir}/ic_land_soil_1976.ape.nc ./ic_land_soil.nc
+
+# boundary conditions
+#
+# - ozone
+datadir=${icon_data_poolFolder}/ozone/cmip6/${resolution_a}
+add_link_file $datadir/ape_o3_iconR2B04-ocean_aqua_planet.nc ./o3_icon_DOM01.nc
+#
+# - sst and sic
+datadir=${icon_data_poolFolder}/sst_and_seaice/1.1.2
+add_link_file $datadir/sic_${resolution_a}.year0.ref0.zero.nc ./bc_sic.nc
+add_link_file $datadir/sst_${resolution_a}.zm.eqsym.year0.ref0.enlarged.timmean.series.nc ./bc_sst.nc
+#
+# - land parameters
+datadir=${icon_data_poolFolder}/boundary_condition
+add_link_file $datadir/bc_land_frac_${pft_file_tag}1976.ape.nc ./bc_land_frac.nc
+add_link_file $datadir/bc_land_phys_1976.ape.nc ./bc_land_phys.nc
+add_link_file $datadir/bc_land_soil_1976.ape.nc ./bc_land_soil.nc
+add_link_file $datadir/bc_land_sso_1976.ape.nc ./bc_land_sso.nc
+# The following bc_land_hd-file is not yet available...
+if [[ ${jsbach_with_hd} = yes ]]; then
+ add_link_file $datadir/bc_land_hd.nc ./bc_land_hd.nc
+fi
+#
+# - lctlib file for JSBACH
+add_link_file ${basedir}/externals/jsbach/data/lctlib_nlct21.def ./lctlib_nlct21.def
+#--------------------------------------------------------------------------------------------------
+
+# (5) Define the output
+
+# Parameters for all output files
+# -------------------------------
+cat >> ${atmo_namelist} << EOF
+&io_nml
+ output_nml_dict = "${dict_file}"
+ netcdf_dict = "${dict_file}"
+ itype_pres_msl = 4
+ restart_file_type= 5
+/
+EOF
+
+# Define output files
+# -------------------
+#
+# output_<xyz>=yes : yes --> output files for <xyz>, any other value --> no files for <xyz>
+#
+# 3-dimensional files include 'ps' and 'pfull' to allow the vertical
+# interpolation to pressure levels by cdo ap2pl.
+
+output_atm_cgrid=no
+#
+if [[ "$output_atm_cgrid" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_cgrid"
+ filename_format = "<output_filename>_<levtype_l>"
+ filetype = 5
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}" ! output_start = output_end
+ output_end = "${start_date}" ! --> write once only irrespective of
+ output_interval = "${output_interval}" ! the output interval and
+ file_interval = "${file_interval}" ! the file interval
+ ml_varlist = 'clon', 'clat', 'areacella', 'zghalf', 'zg'
+/
+EOF
+fi
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d_mean"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pfull' ,
+ 'rho' ,
+ 'clw' , 'cli' ,
+ 'hur' , 'cl' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_atm_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval_inst}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'zg' ,
+ 'ta' ,
+ 'ua' , 'va' , 'wap' ,
+ 'hus' ,
+/
+
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_atm_2d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ taxis_tunit = 9
+ mode = 1
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'psl' ,
+ 'rsdt' ,
+ 'rsut' , 'rsutcs' , 'rlut' , 'rlutcs' ,
+ 'rsds' , 'rsdscs' , 'rlds' , 'rldscs' ,
+ 'rsus' , 'rsuscs' , 'rlus' ,
+ 'ts' ,
+ 'sic' , 'sit' ,
+ 'albedo' ,
+ 'clt' ,
+ 'prlr' , 'prls' , 'prcr' , 'prcs' ,
+ 'pr' , 'prw' , 'cllvi' , 'clivi' ,
+ 'hfls' , 'hfss' , 'evspsbl' ,
+ 'tauu' , 'tauv' ,
+ 'sfcwind' , 'uas' , 'vas' ,
+ 'tas' , 'dew2' , 'ts_icecl' , 'hs_icecl',
+ 'dhsdt_icecl', 'dhidt_icecl',
+ 'qtop_icecl', 'qbot_icecl',
+ 'fluxres_i_icecl', 'fluxres_w_icecl',
+ 't1_icecl', 't2_icecl'
+/
+EOF
+fi
+
+
+output_phy_3d=no # "yes" increases the output volume significantly!
+#
+if [[ "$output_phy_3d" == "yes" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_phy_3d"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 1
+ reg_def_mode = 1
+ reg_lat_def = ${reg_lat_def_reg}
+ reg_lon_def = ${reg_lon_def_reg}
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'ps' , 'pfull' ,
+ 'tend_ta' , 'tend_ta_dyn' , 'tend_ta_phy' ,
+ 'tend_ta_rlw' , 'tend_ta_rsw' ,
+ 'tend_ta_vdf' , 'tend_ta_gwd' , 'tend_ta_sso' ,
+ 'tend_ta_cnv' , 'tend_ta_cld' ,
+ 'tend_ua' , 'tend_ua_dyn' , 'tend_ua_phy' ,
+ 'tend_ua_vdf' , 'tend_ua_gwd' , 'tend_ua_sso' ,
+ 'tend_ua_cnv' ,
+ 'tend_va' , 'tend_va_dyn' , 'tend_va_phy' ,
+ 'tend_va_vdf' , 'tend_va_gwd' , 'tend_va_sso' ,
+ 'tend_va_cnv' ,
+ 'tend_qhus' , 'tend_qhus_dyn', 'tend_qhus_phy',
+ 'tend_qhus_cld', 'tend_qhus_cnv', 'tend_qhus_vdf'
+/
+EOF
+fi
+
+
+#
+if [[ "$output_lnd" == "min" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box' ,
+ 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_lw_srf_down_box' ,
+ 'rad_alb_vis_box' , 'rad_alb_nir_box' ,
+ 'rad_rad_srf_net_box' , 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'pheno_lai_box' ,
+ 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_evapotranspiration_box', 'hydro_w_soil_column_box'
+/
+EOF
+fi
+
+if [[ "$output_lnd" == "full" ]]; then
+ #
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_a2l"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'fract_box',
+ 'a2l_t_air_box' , 'a2l_q_air_box' , 'a2l_rain_box' , 'a2l_snow_box',
+ 'a2l_press_srf_box' , 'a2l_drag_srf_box' , 'a2l_pch_box' ,
+ 'a2l_swvis_srf_down_box', 'a2l_swnir_srf_down_box' , 'a2l_swpar_srf_down_box',
+ 'a2l_lw_srf_down_box' ,
+ 'turb_rough_m_box' , 'turb_rough_h_box' ,
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_rad"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'rad_alb_vis_box' , 'rad_alb_nir_box' , 'rad_rad_srf_net_box' ,
+ 'rad_lw_srf_net_box' , 'rad_sw_srf_net_box' ,
+ 'rad_alb_vis_veg' , 'rad_alb_nir_veg' , 'rad_alb_vis_snow_veg', 'rad_alb_nir_snow_veg',
+ 'rad_alb_vis_soil_veg', 'rad_alb_nir_soil_veg', 'rad_alb_vis_can_veg' , 'rad_alb_nir_can_veg',
+ 'rad_rad_srf_net_veg' , 'rad_lw_srf_net_veg' , 'rad_sw_srf_net_veg'
+ 'rad_alb_vis_glac' , 'rad_alb_nir_glac' ,
+ 'rad_rad_srf_net_glac', 'rad_lw_srf_net_glac' , 'rad_sw_srf_net_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_seb"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'seb_t_box' , 'seb_t_eff_box' , 'seb_qsat_star_box' ,
+ 'seb_latent_hflx_box' , 'seb_sensible_hflx_box' , 'seb_forc_hflx_box'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_sse"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'sse_grnd_hflx_land' , 'sse_hcap_grnd_land' , 'sse_hcap_grnd_veg' , 'sse_grnd_hflx_veg',
+ 'sse_hcap_grnd_glac' , 'sse_grnd_hflx_glac' ,
+ 'sse_t_soil_veg' , 'sse_t_soil_acoef_veg' , 'sse_t_soil_bcoef_veg' ,
+ 'sse_t_soil_glac' , 'sse_t_soil_acoef_glac', 'sse_t_soil_bcoef_glac',
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_hydro"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_q_snocpymlt_box' , 'hydro_w_skin_box' , 'hydro_w_snow_box' ,
+ 'hydro_snowmelt_box' , 'hydro_w_soil_column_veg', 'hydro_w_soil_sl_veg' ,
+ 'hydro_evapotranspiration_veg' , 'hydro_evapopot_veg' , 'hydro_trans_veg' ,
+ 'hydro_fract_water_veg' , 'hydro_fract_snow_veg' , 'hydro_w_skin_veg' ,
+ 'hydro_w_snow_veg' , 'hydro_q_snocpymlt_veg' ,
+ 'hydro_fract_snow_soil_veg' , 'hydro_w_snow_soil_veg' , 'hydro_snow_accum_veg' ,
+ 'hydro_fract_snow_can_veg' , 'hydro_w_snow_can_veg' ,
+ 'hydro_water_stress_veg' , 'hydro_canopy_cond_veg' , 'hydro_canopy_cond_limited_veg'
+ 'hydro_evapotranspiration_glac', 'hydro_evapopot_glac' ,
+ 'hydro_snowmelt_glac' , 'hydro_q_snocpymlt_glac'
+/
+&output_nml
+ output_filename = "${EXPNAME}_lnd_pheno"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'pheno_lai_veg' , 'pheno_fract_fpc_veg' ,
+ 'pheno_fract_fpc_max_veg', 'pheno_fract_forest_veg'
+/
+EOF
+fi
+
+if [[ "$jsbach_check_wbal" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename = "${EXPNAME}_lnd_wbal"
+ filename_format = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype = 5
+ remap = 0
+ operation = 'mean'
+ output_grid = .TRUE.
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .FALSE.
+ ml_varlist = 'hydro_water_flux_box', 'hydro_water_content_box', 'hydro_water_budget_box',
+EOF
+ if [[ "$jsbach_with_hd" == "yes" ]]; then
+ cat >> ${atmo_namelist} << EOF
+ 'hydro_runoff_box' , 'hydro_drainage_box',
+ 'hd_water_budget_box', 'hd_water_budget_old_box', 'hd_water_flux_box', 'hd_water_error_box'
+EOF
+ fi
+ cat >> ${atmo_namelist} << EOF
+/
+EOF
+fi
+
+#--------------------------------------------------------------------------------------------------
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+#
+ICONDIR=${ICON_BASE_PATH}
+RUNSCRIPTDIR=${ICONDIR}/runscripts/${EXPNAME}
+if [ x$grids_folder = x ] ; then
+ HGRIDDIR=${ICONDIR}/grids
+else
+ HGRIDDIR=$grids_folder
+fi
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=${OUTDIR}/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+#else
+# rm -rf ${EXPDIR}
+# mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+if [ x$ocean_namelist != x ]; then
+ run_ocean="true"
+fi
+
+#-----------------------------------------------------------------------------
+# add grids to required files
+all_grids="${atmo_dyn_grids} ${atmo_rad_grids} ${ocean_grids}"
+for gridfile in ${all_grids}; do
+ ls -l ${HGRIDDIR}/$gridfile
+ check_error $? "${HGRIDDIR}/$gridfile does not exist."
+ add_required_file ${HGRIDDIR}/$gridfile ./
+done
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+if [ x$restart_ocean_from != "x" ] ; then
+ rm -f restart_oce.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+ ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+
+calendar='360 day year'
+calendar_type=2
+
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+ experimentStartDate = "$start_date"
+ restartTimeIntval = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+ calendar = $calendar_type
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+#-----------
+#get namelist
+ if [ -f ${RUNSCRIPTDIR}/$model_namelist_filename ] ; then
+ mv -f ${RUNSCRIPTDIR}/$model_namelist_filename ${EXPDIR}
+ check_error $? "mv -f ${RUNSCRIPTDIR}/$model_namelist_filename"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/$model_namelist_filename does not exist"
+ fi
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+# Add JSBACH part to master_namelist
+
+if [[ $run_jsbach == @(yes|true) ]]; then
+ cat >> $master_namelist << EOF
+&jsb_control_nml
+ is_standalone = .false.
+ restart_jsbach = .false.
+ debug = 0
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = 'JSBACH'
+ model_shortname = 'jsb'
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+/
+EOF
+ if [[ -f ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd && -f ${EXPDIR}/NAMELIST_${EXPNAME}_lnd ]] ; then
+ # namelist file has already been copied to expdir by copy_required_files above
+ rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd
+ check_error $? "rm ${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd"
+ else
+ check_error 1 "${RUNSCRIPTDIR}/NAMELIST_${EXPNAME}_lnd does not exist"
+ fi
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+#
+#if [ x$target = "xblizzard" ] ; then
+# run_model
+#else
+ date
+ ${START} ${MODEL}
+ date
+#fi
+#
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+if [[ "x$use_hamocc" = "xyes" ]]; then
+# store HAMOCC log file
+strg="$(ls -rt ${EXPNAME}_hamocc_EU*.nc* | tail -1 )"
+prefx="${EXPNAME}_hamocc_EU_tendencies"
+foo=${strg##${prefx}}
+foo=${foo%%.*}
+bgcout_file="bgcout_${foo}"
+mv bgcout $bgcout_file
+fi
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/python_scripts/.gitkeep b/python_scripts/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/python_scripts/Winton_Semtner_lores_comparison_PAPERPLOTS.ipynb b/python_scripts/Winton_Semtner_lores_comparison_PAPERPLOTS.ipynb
new file mode 100644
index 0000000..8370ded
--- /dev/null
+++ b/python_scripts/Winton_Semtner_lores_comparison_PAPERPLOTS.ipynb
@@ -0,0 +1,1709 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal distribution of albedo and shortwave radiation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<xarray.core.options.set_options at 0x2ab57c937a90>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import sys\n",
+ "from scipy import stats\n",
+ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+ "import sys\n",
+ "import xarray as xr\n",
+ "xr.set_options(display_style=\"html\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'0.16.2'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xr.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "def load_experiment(expname): #loads the dataset of a simulation\n",
+ " fname = expname +\"_atm_2d_ml.mm.gm.nc\" #filename of global yearly mean\n",
+ " dpath = \"/work/bb1092/pp_JH/\" +expname +\"/\" #simulation path\n",
+ " DS = xr.open_dataset(dpath +fname, decode_times=True) #loading of dataset\n",
+ " print(dpath +fname)\n",
+ " return DS # returns the name of the experiment & the actual dataset\n",
+ "\n",
+ "def load_experiment_zm(expname): #loads the dataset of a simulation\n",
+ " fname = expname +\"_atm_2d_ml.mm.zm.nc\" #filename of global yearly mean\n",
+ " dpath = \"/work/bb1092/pp_JH/\" +expname +\"/\" #simulation path\n",
+ " DS = xr.open_dataset(dpath +fname, decode_times=True) #loading of dataset\n",
+ " print(dpath +fname)\n",
+ " return DS # returns the name of the experiment & the actual dataset\n",
+ "\n",
+ "def get_var(dataset, varname, offsettime=True): #gets the dataarray with one specific variable\n",
+ " da=getattr(dataset,varname) #read dataarray\n",
+ " da.squeeze() #squeeze dataarray (time is the only dimension)\n",
+ " #if offsettime:\n",
+ " #da=da.assign_coords(time=((da.time-da.time[0])/360)) #change time units from days to years & move the origin to 0\n",
+ " #else:\n",
+ " #da=da.assign_coords(time=((da.time)/360)) #change time units from days to years \n",
+ " return np.squeeze(da)\n",
+ "\n",
+ "def plot_simulation(axes,co2, startlat, endlat, col, stable): #plot a simulation into the bifurcation diagram\n",
+ " handle, =axes.plot([co2,co2],[np.sin(np.radians(startlat)),np.sin(np.radians(endlat))],color=col,linestyle='--') #plot the line\n",
+ " #plt.plot(co2,np.sin(np.radians(startlat)),'bo',fillstyle='none')#\n",
+ " \n",
+ " if stable==2: # metastable\n",
+ " axes.plot(co2,np.sin(np.radians(endlat)),markeredgecolor=col,marker='o',markerfacecolor=\"none\")\n",
+ " elif stable==1: # stable\n",
+ " axes.plot(co2,np.sin(np.radians(endlat)),color=col,marker='o')\n",
+ " elif stable==3: # towards Snowball\n",
+ " axes.plot(co2,np.sin(np.radians(endlat)),color=col,marker='v')\n",
+ " elif stable==4: # towards icefree\n",
+ " axes.plot(co2,np.sin(np.radians(endlat)),color=col,marker='^')\n",
+ " return handle\n",
+ "\n",
+ "def get_albedo(DS, offsettime=True):\n",
+ " sw_out = get_var(DS,\"rsuscs\", offsettime) \n",
+ " sw_in = get_var(DS,\"rsdscs\", offsettime)\n",
+ " return np.squeeze(sw_out/sw_in.where(sw_in!=0))\n",
+ "\n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos, fontsize=fontsize)\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " for text in legend.get_texts():\n",
+ " if text.get_text()=='Winton':\n",
+ " text.set_color('C1')\n",
+ " if text.get_text()=='3L-Winton':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='0L-Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='Semtner_5m':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-lim5':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='1438ppmv':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='1500ppmv':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='3000ppmv':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='5000ppmv':\n",
+ " text.set_color('C3')\n",
+ " \n",
+ " return legend\n",
+ "\n",
+ "def equivalent_co2_factor(heatflux): # heatflux in W/m²\n",
+ " alpha = 5.35\n",
+ " return np.exp(heatflux/alpha)\n",
+ "\n",
+ "def selmonmean(DA, nyears):\n",
+ " return DA.where(DA['time.year'] >= DA['time.year'][-nyears*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ "def seltimemean(DA, nyears):\n",
+ " return DA.where(DA['time.year'] >= DA['time.year'][-nyears*12], drop=True).mean(dim='time')\n",
+ "\n",
+ "\n",
+ "def icebordermean(DA, l_iceborder, r_iceborder, inside):\n",
+ " if inside:\n",
+ " indeces = np.r_[l_iceborder:r_iceborder+1]\n",
+ " print(\"indeces equatorwards of iceborder: \" +str(indeces))\n",
+ " else:\n",
+ " indeces = np.r_[0:l_iceborder,r_iceborder+1:np.size(DA[\"lat\"])]\n",
+ " print(\"indeces polewards of iceborder: \" +str(indeces))\n",
+ " weights = np.cos(np.deg2rad(DA.lat))\n",
+ " DA_weighted = DA[indeces]*(weights[indeces]/weights[indeces].mean()) #\n",
+ " return DA_weighted.mean()\n",
+ "\n",
+ "def selmean(DA, nyears):\n",
+ " return DA.where(DA['time.year'] >= DA['time.year'][-nyears*12], drop=True).mean(dim='time')\n",
+ "\n",
+ "def integrate_zonal_data(data, lat):\n",
+ " #function to integrate ICON data as zonal mean over longitude for each latitude\n",
+ " #latitude dimension has to be equally spaced!\n",
+ " dlat_array = np.ediff1d(lat)\n",
+ " dlat = dlat_array.mean()\n",
+ " if not np.all(dlat_array == dlat):\n",
+ " print(\"Latitude array not equally spaced!\")\n",
+ " return\n",
+ " \n",
+ " # upper and lower limit of each longitudinal band\n",
+ " latlim_lower = lat-dlat/2\n",
+ " latlim_upper = lat+dlat/2\n",
+ " \n",
+ " # area from the north pole to lower and upper limit\n",
+ " lower = 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(latlim_lower+90)))\n",
+ " upper = 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(latlim_upper+90)))\n",
+ " return (upper-lower) * data\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "321.23447741995005\n"
+ ]
+ }
+ ],
+ "source": [
+ "albsnow_warm = 0.66\n",
+ "albsnow_cold = 0.79\n",
+ "albice_warm = 0.38\n",
+ "albice_cold = 0.45\n",
+ "\n",
+ "albocean = 0.07\n",
+ "\n",
+ "scale_ssi_preind = 0.9442 \n",
+ "ssi_preind = (11.95005, 20.14612, 23.40302, 22.09443, 55.41679, 102.512, 24.69536, 347.4719, 217.2217, 343.2816, 129.3001, 47.07624, 3.130199, 13.17521)\n",
+ "\n",
+ "tsi = scale_ssi_preind*np.sum(ssi_preind)\n",
+ "print(tsi/4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_winton/mlo_aqua_3000ppmv_winton_atm_2d_ml.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_winton/mlo_aqua_3000ppmv_winton_atm_2d_ml.mm.zm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2250ppmv_hice_unlim/mlo_aqua_2250ppmv_hice_unlim_atm_2d_ml.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2250ppmv_hice_unlim/mlo_aqua_2250ppmv_hice_unlim_atm_2d_ml.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2250ppmv_hice_unlim/mlo_aqua_2250ppmv_hice_unlim_atm_2d_ml.mm.zm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2437ppmv_winton/mlo_aqua_2437ppmv_winton_atm_2d_ml.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2437ppmv_winton/mlo_aqua_2437ppmv_winton_atm_2d_ml.mm.zm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1688ppmv_hice_unlim/mlo_aqua_1688ppmv_hice_unlim_atm_2d_ml.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1688ppmv_hice_unlim/mlo_aqua_1688ppmv_hice_unlim_atm_2d_ml.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1688ppmv_hice_unlim/mlo_aqua_1688ppmv_hice_unlim_atm_2d_ml.mm.zm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "winton_exparray=np.empty(10, dtype=\"U100\")\n",
+ "semtner_exparray=np.empty(10, dtype=\"U100\")\n",
+ "\n",
+ "winton_simarray=np.empty(10,dtype=object)\n",
+ "semtner_simarray=np.empty(10,dtype=object)\n",
+ "\n",
+ "winton_zmsimarray=np.empty(10,dtype=object)\n",
+ "semtner_zmsimarray=np.empty(10,dtype=object)\n",
+ "\n",
+ "\n",
+ "winton_exparray[0]=\"mlo_aqua_3000ppmv_winton\"\n",
+ "winton_exparray[1]=\"mlo_aqua_2437ppmv_winton\"\n",
+ "winton_exparray[2]=\"\"\n",
+ "\n",
+ "semtner_exparray[0]=\"mlo_aqua_2250ppmv_hice_unlim\"\n",
+ "semtner_exparray[1]=\"mlo_aqua_1688ppmv_hice_unlim\"\n",
+ "semtner_exparray[2]=\"\"\n",
+ "#semtner_exparray[3]=\"mlo_aqua_1875ppmv_hice_unlim\"\n",
+ "\n",
+ "for i in range(0,10):\n",
+ " if winton_exparray[i]!=\"\":\n",
+ " winton_simarray[i] = load_experiment(winton_exparray[i])\n",
+ " winton_zmsimarray[i] = load_experiment_zm(winton_exparray[i])\n",
+ " semtner_simarray[i] = load_experiment(semtner_exparray[i])\n",
+ " \n",
+ " if semtner_exparray[i]!=\"\":\n",
+ " semtner_simarray[i] = load_experiment(semtner_exparray[i])\n",
+ " semtner_zmsimarray[i] = load_experiment_zm(semtner_exparray[i])\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate iceborders\n",
+ "count=0\n",
+ "Nmin_iceborderW=np.zeros(2,dtype=\"intc\")\n",
+ "Smin_iceborderW=np.zeros(2,dtype=\"intc\")\n",
+ "Nmax_iceborderW=np.zeros(2,dtype=\"intc\")\n",
+ "Smax_iceborderW=np.zeros(2,dtype=\"intc\")\n",
+ "Nmin_iceborderS=np.zeros(2,dtype=\"intc\")\n",
+ "Smin_iceborderS=np.zeros(2,dtype=\"intc\")\n",
+ "Nmax_iceborderS=np.zeros(2,dtype=\"intc\")\n",
+ "Smax_iceborderS=np.zeros(2,dtype=\"intc\")\n",
+ "for ind in [0,1]:\n",
+ " timemean_sicW = seltimemean(get_var(winton_zmsimarray[ind], 'sic', False),40)\n",
+ " timemean_sicS = seltimemean(get_var(semtner_zmsimarray[ind], 'sic', False),40) \n",
+ "\n",
+ " Nmin_iceborderW[ind] = np.min(np.where(timemean_sicW<1)) # minimum iceborder where there is open ocean\n",
+ " Smin_iceborderW[ind] = np.max(np.where(timemean_sicW<1))\n",
+ "\n",
+ " Nmax_iceborderW[ind] = np.min(np.where(timemean_sicW==0)) # minimum iceborder where there is no ice\n",
+ " Smax_iceborderW[ind] = np.max(np.where(timemean_sicW==0))\n",
+ "\n",
+ "\n",
+ "\n",
+ " Nmin_iceborderS[ind] = np.min(np.where(timemean_sicS<1))\n",
+ " Smin_iceborderS[ind] = np.max(np.where(timemean_sicS<1))\n",
+ "\n",
+ " Nmax_iceborderS[ind] = np.min(np.where(timemean_sicS==0)) # minimum iceborder where there is no ice\n",
+ " Smax_iceborderS[ind] = np.max(np.where(timemean_sicS==0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "axis 0: mlo_aqua_2437ppmv_winton and mlo_aqua_1688ppmv_hice_unlim\n",
+ "axis 1: mlo_aqua_3000ppmv_winton and mlo_aqua_2250ppmv_hice_unlim\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(2,1,figsize=(6,4),sharex=True,sharey=True)\n",
+ "month_name=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
+ "axcount=0;\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "for ind in [1,0]:\n",
+ " albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ " albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ "\n",
+ " albedo_W = get_albedo(winton_simarray[ind])\n",
+ " albedo_S = get_albedo(semtner_simarray[ind])\n",
+ "\n",
+ " SW_out_W = get_var(winton_simarray[ind],\"rsuscs\", True) \n",
+ " SW_out_S = get_var(semtner_simarray[ind],\"rsuscs\", True) \n",
+ "\n",
+ " monmean_Wzm=albedo_Wzm.where(albedo_Wzm['time.year'] >= albedo_Wzm['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ " monmean_Szm=albedo_Szm.where(albedo_Szm['time.year'] >= albedo_Szm['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ " monmean_W=albedo_W.where(albedo_W['time.year'] >= albedo_W['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ " monmean_S=albedo_S.where(albedo_S['time.year'] >= albedo_S['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ " SW_outmonmean_W=SW_out_W.where(SW_out_W['time.year'] >= SW_out_W['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ " SW_outmonmean_S=SW_out_S.where(SW_out_S['time.year'] >= SW_out_S['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ " ax[axcount].vlines(timemean_sicS[\"lat\"][[Nmin_iceborderS[axcount], Smin_iceborderS[axcount]]],0,400,color='darkgray', alpha=1)\n",
+ " ax[axcount].vlines(timemean_sicS[\"lat\"][[Nmax_iceborderS[axcount], Smax_iceborderS[axcount]]],0,400,color='darkgray', alpha=1)\n",
+ "\n",
+ " month = 8\n",
+ " rect_snow=mpl.patches.Rectangle((-90,albsnow_warm),180,albsnow_cold-albsnow_warm, alpha=0.4, color='lightblue')\n",
+ " rect_ice=mpl.patches.Rectangle((-90,albice_warm),180,albice_cold-albice_warm, alpha=0.2, color='blue')\n",
+ " ax[axcount].hlines(albocean,-90,90,linewidth=1,color='grey',alpha=0.5)\n",
+ " ax[axcount].add_patch(rect_snow)\n",
+ " ax[axcount].add_patch(rect_ice)\n",
+ " lW=ax[axcount].plot(monmean_Wzm['lat'],monmean_Wzm.sel(month=month),c='C1',label='Winton')\n",
+ " lS=ax[axcount].plot(monmean_Szm['lat'],monmean_Szm.sel(month=month),c='C0',label='Semtner')\n",
+ " print(\"axis \" +str(axcount) +\": \" +winton_exparray[ind] +\" and \" +semtner_exparray[ind])\n",
+ " #ax[axcount].annotate(month_name[month-1],(0,0.55),horizontalalignment='center')\n",
+ " axcount+=1;\n",
+ "#ax.annotate(equivalent_co2_factor((SW_outmonmean_W.sel(month=month)-SW_outmonmean_S.sel(month=month)).values).round(3),(40,0.37),horizontalalignment='center',verticalalignment='top')\n",
+ "ax[1].set_xlim(-90,90)\n",
+ "ax[1].set_ylim(0,0.82)\n",
+ "ax[1].set_yticks([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8])\n",
+ "legend_color(ax[0],['3L-Winton', '0L-Semtner'],9, labelsize)\n",
+ "ax[1].set_xlabel('Latitude [°]', fontsize=labelsize)\n",
+ "ax[0].set_ylabel('Surface albedo []', fontsize=labelsize)\n",
+ "ax[1].set_ylabel('Surface albedo []', fontsize=labelsize)\n",
+ "ax[0].tick_params(labelsize=ticksize) \n",
+ "ax[1].tick_params(labelsize=ticksize) \n",
+ "#fig.suptitle('monthly mean zonal mean surface albedo over the last 40 years\\n' +winton_exparray[ind] +' & ' +semtner_exparray[ind])\n",
+ "#plt.subplots_adjust(left=0, bottom=0, right=1, top=0.9, wspace=0.05, hspace=0.1)\n",
+ "\n",
+ "ax[0].text(-0.12,1,\"a)\", transform=ax[0].transAxes, fontsize=labelsize)\n",
+ "ax[1].text(-0.12,1,\"b)\", transform=ax[1].transAxes, fontsize=labelsize)\n",
+ "\n",
+ "plt.savefig(\"plots/surface_albedo_July_multi.pdf\",dpi=500)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mlo_aqua_2437ppmv_winton and mlo_aqua_1688ppmv_hice_unlim\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x165.6 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,1,figsize=(6,2.3))\n",
+ "month_name=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
+ "axcount=0;\n",
+ "ind=1\n",
+ "albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ "albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ "\n",
+ "albedo_W = get_albedo(winton_simarray[ind])\n",
+ "albedo_S = get_albedo(semtner_simarray[ind])\n",
+ "\n",
+ "SW_out_W = get_var(winton_simarray[ind],\"rsuscs\", True) \n",
+ "SW_out_S = get_var(semtner_simarray[ind],\"rsuscs\", True) \n",
+ "\n",
+ "monmean_Wzm=albedo_Wzm.where(albedo_Wzm['time.year'] >= albedo_Wzm['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "monmean_Szm=albedo_Szm.where(albedo_Szm['time.year'] >= albedo_Szm['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ "monmean_W=albedo_W.where(albedo_W['time.year'] >= albedo_W['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "monmean_S=albedo_S.where(albedo_S['time.year'] >= albedo_S['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ "SW_outmonmean_W=SW_out_W.where(SW_out_W['time.year'] >= SW_out_W['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "SW_outmonmean_S=SW_out_S.where(SW_out_S['time.year'] >= SW_out_S['time.year'][-40*12], drop=True).groupby('time.month').mean(dim='time')\n",
+ "\n",
+ "ax.vlines(timemean_sicS[\"lat\"][[Nmin_iceborderS[axcount], Smin_iceborderS[axcount]]],0,400,color='darkgray', alpha=1)\n",
+ "ax.vlines(timemean_sicS[\"lat\"][[Nmax_iceborderS[axcount], Smax_iceborderS[axcount]]],0,400,color='darkgray', alpha=1)\n",
+ "\n",
+ "month = 8\n",
+ "rect_snow=mpl.patches.Rectangle((-90,albsnow_warm),180,albsnow_cold-albsnow_warm, alpha=0.4, color='lightblue')\n",
+ "rect_ice=mpl.patches.Rectangle((-90,albice_warm),180,albice_cold-albice_warm, alpha=0.2, color='blue')\n",
+ "ax.hlines(albocean,-90,90,linewidth=1,color='grey',alpha=0.5)\n",
+ "ax.add_patch(rect_snow)\n",
+ "ax.add_patch(rect_ice)\n",
+ "lW=ax.plot(monmean_Wzm['lat'],monmean_Wzm.sel(month=month),c='C1',label='Winton')\n",
+ "lS=ax.plot(monmean_Szm['lat'],monmean_Szm.sel(month=month),c='C0',label='Semtner')\n",
+ "print(winton_exparray[ind] +\" and \" +semtner_exparray[ind])\n",
+ "#ax[axcount].annotate(month_name[month-1],(0,0.55),horizontalalignment='center')\n",
+ "\n",
+ "#ax.annotate(equivalent_co2_factor((SW_outmonmean_W.sel(month=month)-SW_outmonmean_S.sel(month=month)).values).round(3),(40,0.37),horizontalalignment='center',verticalalignment='top')\n",
+ "ax.set_xlim(-90,90)\n",
+ "ax.set_ylim(0,0.82)\n",
+ "ax.set_yticks([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8])\n",
+ "legend_color(ax,['3L-Winton', '0L-Semtner'],9,labelsize)\n",
+ "ax.set_xlabel('Latitude [°]',fontsize=labelsize)\n",
+ "ax.set_ylabel('Surface albedo []',fontsize=labelsize)\n",
+ "ax.tick_params(labelsize=ticksize) \n",
+ "#fig.suptitle('monthly mean zonal mean surface albedo over the last 40 years\\n' +winton_exparray[ind] +' & ' +semtner_exparray[ind])\n",
+ "#plt.subplots_adjust(left=0, bottom=0, right=1, top=0.9, wspace=0.05, hspace=0.1)\n",
+ "\n",
+ "plt.savefig(\"plots/surface_albedo_July.pdf\",bbox_inches=\"tight\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mlo_aqua_2437ppmv_winton: SWin=321.23425 SWout=98.25365; LWout=222.16487; total=0.81573486\n",
+ "mlo_aqua_1688ppmv_hice_unlim: SWin=321.23425 SWout=97.2439; LWout=223.32451; total=0.6658478\n",
+ "\n",
+ "mlo_aqua_3000ppmv_winton: SWin=321.23425 SWout=94.45228; LWout=225.86537; total=0.9166107\n",
+ "mlo_aqua_2250ppmv_hice_unlim: SWin=321.23425 SWout=93.05864; LWout=227.3154; total=0.86021423\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import csv\n",
+ "## get components of TOA energy balance\n",
+ "\n",
+ "fieldnames=[\"simulation\", \"SW in\", \"SW out\", \"LW out\"]\n",
+ "f = open(\"plots/TOA_balance.csv\", 'w')\n",
+ "writer = csv.writer(f)\n",
+ "writer.writerow([\"simulation\", \"SW in\", \"SW out\", \"LW out\", \"SW cs out\", \"LW cs out\"])\n",
+ "for ind in [1,0]:\n",
+ " \n",
+ " SW_in_W = get_var(winton_simarray[ind],\"rsdt\", True) \n",
+ " SW_in_S = get_var(semtner_simarray[ind],\"rsdt\", True) \n",
+ " SW_inmean_W=seltimemean(SW_in_W, 40).values\n",
+ " SW_inmean_S=seltimemean(SW_in_S, 40).values\n",
+ " \n",
+ " SW_out_W = get_var(winton_simarray[ind],\"rsut\", True) \n",
+ " SW_out_S = get_var(semtner_simarray[ind],\"rsut\", True) \n",
+ " SW_outmean_W=seltimemean(SW_out_W, 40).values\n",
+ " SW_outmean_S=seltimemean(SW_out_S, 40).values\n",
+ " \n",
+ " LW_out_W = get_var(winton_simarray[ind],\"rlut\", True) \n",
+ " LW_out_S = get_var(semtner_simarray[ind],\"rlut\", True) \n",
+ " LW_outmean_W=seltimemean(LW_out_W, 40).values\n",
+ " LW_outmean_S=seltimemean(LW_out_S, 40).values\n",
+ " \n",
+ " SWcs_out_W = get_var(winton_simarray[ind],\"rsutcs\", True) \n",
+ " SWcs_out_S = get_var(semtner_simarray[ind],\"rsutcs\", True) \n",
+ " SWcs_outmean_W=seltimemean(SWcs_out_W, 40).values\n",
+ " SWcs_outmean_S=seltimemean(SWcs_out_S, 40).values\n",
+ " \n",
+ " LWcs_out_W = get_var(winton_simarray[ind],\"rlutcs\", True) \n",
+ " LWcs_out_S = get_var(semtner_simarray[ind],\"rlutcs\", True) \n",
+ " LWcs_outmean_W=seltimemean(LWcs_out_W, 40).values\n",
+ " LWcs_outmean_S=seltimemean(LWcs_out_S, 40).values\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " print(winton_exparray[ind] +\": \" +\" SWin=\"+str(SW_inmean_W) +\" SWout=\"+str(SW_outmean_W) +\"; LWout=\" +str(LW_outmean_W) +\"; total=\" +str(SW_inmean_W-SW_outmean_W-LW_outmean_W))\n",
+ " print(semtner_exparray[ind] +\": \" +\" SWin=\"+str(SW_inmean_S) +\" SWout=\"+str(SW_outmean_S) +\"; LWout=\" +str(LW_outmean_S)+\"; total=\" +str(SW_inmean_S-SW_outmean_S-LW_outmean_S))\n",
+ " print(\"\")\n",
+ " \n",
+ " \n",
+ " writer.writerow([winton_exparray[ind], str(SW_inmean_W), str(SW_outmean_W), str(LW_outmean_W), str(SWcs_outmean_W), str(LWcs_outmean_W)])\n",
+ " writer.writerow([semtner_exparray[ind], str(SW_inmean_S), str(SW_outmean_S), str(LW_outmean_S), str(SWcs_outmean_S), str(LWcs_outmean_S)])\n",
+ " \n",
+ "f.close()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "clear sky:\n",
+ "637.0255380792839 PW; 1.2489118006869346 W/m^2 = 1.2489128 W/m^2\n",
+ "0.23056602641189058\n",
+ "0.8042223095666693\n",
+ "-0.03478833597856455\n",
+ "all sky:\n",
+ "515.034057095216 PW; 1.0097430530043008 W/m^2 = 1.0097504 W/m^2\n",
+ "0.16410614491583508\n",
+ "0.709068870009622\n",
+ "0.12682498507453116\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mlo_aqua_2437ppmv_winton\n",
+ "mlo_aqua_1688ppmv_hice_unlim\n"
+ ]
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(2,1,figsize=(6,4),sharex=True, sharey = True)\n",
+ "\n",
+ "\n",
+ "\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "nyears = 40\n",
+ "\n",
+ "\n",
+ "\n",
+ "dN1=np.zeros(3)\n",
+ "dN2=np.zeros(3)\n",
+ "dE=np.zeros(3)\n",
+ "dglob=np.zeros(3)\n",
+ "\n",
+ "ind=1\n",
+ "for sky_ind in range(2):\n",
+ " SW_out_W_cs = get_var(winton_zmsimarray[ind],\"rsutcs\", True) \n",
+ " SW_out_S_cs = get_var(semtner_zmsimarray[ind],\"rsutcs\", True)\n",
+ "\n",
+ " LW_out_W_cs = get_var(winton_zmsimarray[ind],\"rlutcs\", True) \n",
+ " LW_out_S_cs = get_var(semtner_zmsimarray[ind],\"rlutcs\", True)\n",
+ "\n",
+ " SW_out_W = get_var(winton_zmsimarray[ind],\"rsut\", True) \n",
+ " SW_out_S = get_var(semtner_zmsimarray[ind],\"rsut\", True)\n",
+ "\n",
+ " SW_in_W = get_var(winton_zmsimarray[ind],\"rsdt\", True) \n",
+ " SW_in_S = get_var(semtner_zmsimarray[ind],\"rsdt\", True) \n",
+ "\n",
+ " LW_out_W = get_var(winton_zmsimarray[ind],\"rlut\", True) \n",
+ " LW_out_S = get_var(semtner_zmsimarray[ind],\"rlut\", True)\n",
+ "\n",
+ "\n",
+ " timemean_SWinW = seltimemean(SW_in_W, nyears)\n",
+ " timemean_SWinS = seltimemean(SW_in_S, nyears)\n",
+ " timemean_SWoutW_cs = seltimemean(SW_out_W_cs, nyears)\n",
+ " timemean_SWoutS_cs = seltimemean(SW_out_S_cs, nyears)\n",
+ " timemean_LWoutW_cs = seltimemean(LW_out_W_cs, nyears)\n",
+ " timemean_LWoutS_cs = seltimemean(LW_out_S_cs, nyears)\n",
+ " timemean_SWoutW = seltimemean(SW_out_W, nyears)\n",
+ " timemean_SWoutS = seltimemean(SW_out_S, nyears)\n",
+ " timemean_LWoutW = seltimemean(LW_out_W, nyears)\n",
+ " timemean_LWoutS = seltimemean(LW_out_S, nyears)\n",
+ "\n",
+ " SW_out_W_integrated = integrate_zonal_data(timemean_SWoutW,timemean_SWoutW.lat)\n",
+ " SW_out_S_integrated = integrate_zonal_data(timemean_SWoutS,timemean_SWoutS.lat)\n",
+ " SW_out_W_cs_integrated = integrate_zonal_data(timemean_SWoutW_cs,timemean_SWoutW_cs.lat)\n",
+ " SW_out_S_cs_integrated = integrate_zonal_data(timemean_SWoutS_cs,timemean_SWoutS_cs.lat)\n",
+ " \n",
+ " \n",
+ " if sky_ind==0:\n",
+ " N1_W = SW_out_W_cs_integrated[0:Nmin_iceborderS[ind]].sum()\n",
+ " N2_W = SW_out_W_cs_integrated[Nmin_iceborderS[ind]:Nmax_iceborderS[ind]].sum()\n",
+ " E_W = SW_out_W_cs_integrated[Nmax_iceborderS[ind]:Smax_iceborderS[ind]+1].sum()\n",
+ " S2_W = SW_out_W_cs_integrated[Smax_iceborderS[ind]+1:Smin_iceborderS[ind]+1].sum()\n",
+ " S1_W = SW_out_W_cs_integrated[Smin_iceborderS[ind]+1:].sum()\n",
+ "\n",
+ " N1_S = SW_out_S_cs_integrated[0:Nmin_iceborderS[ind]].sum()\n",
+ " N2_S = SW_out_S_cs_integrated[Nmin_iceborderS[ind]:Nmax_iceborderS[ind]].sum()\n",
+ " E_S = SW_out_S_cs_integrated[Nmax_iceborderS[ind]:Smax_iceborderS[ind]+1].sum()\n",
+ " S2_S = SW_out_S_cs_integrated[Smax_iceborderS[ind]+1:Smin_iceborderS[ind]+1].sum()\n",
+ " S1_S = SW_out_S_cs_integrated[Smin_iceborderS[ind]+1:].sum()\n",
+ " \n",
+ " dglob[sky_ind] = SW_out_W_cs_integrated.sum()-SW_out_S_cs_integrated.sum()\n",
+ " elif sky_ind==1:\n",
+ " N1_W = SW_out_W_integrated[0:Nmin_iceborderS[ind]].sum()\n",
+ " N2_W = SW_out_W_integrated[Nmin_iceborderS[ind]:Nmax_iceborderS[ind]].sum()\n",
+ " E_W = SW_out_W_integrated[Nmax_iceborderS[ind]:Smax_iceborderS[ind]+1].sum()\n",
+ " S2_W = SW_out_W_integrated[Smax_iceborderS[ind]+1:Smin_iceborderS[ind]+1].sum()\n",
+ " S1_W = SW_out_W_integrated[Smin_iceborderS[ind]+1:].sum()\n",
+ "\n",
+ " N1_S = SW_out_S_integrated[0:Nmin_iceborderS[ind]].sum()\n",
+ " N2_S = SW_out_S_integrated[Nmin_iceborderS[ind]:Nmax_iceborderS[ind]].sum()\n",
+ " E_S = SW_out_S_integrated[Nmax_iceborderS[ind]:Smax_iceborderS[ind]+1].sum()\n",
+ " S2_S = SW_out_S_integrated[Smax_iceborderS[ind]+1:Smin_iceborderS[ind]+1].sum()\n",
+ " S1_S = SW_out_S_integrated[Smin_iceborderS[ind]+1:].sum()\n",
+ " \n",
+ " dglob[sky_ind] = SW_out_W_integrated.sum()-SW_out_S_integrated.sum()\n",
+ " \n",
+ " dN1[sky_ind] = (N1_W.values+S1_W.values) - (N1_S.values+S1_S.values)\n",
+ " dN2[sky_ind] = (N2_W.values+S2_W.values) - (N2_S.values+S2_S.values)\n",
+ " dE[sky_ind] = E_W.values - E_S.values\n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " #areas\n",
+ " NA1 = -2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[ind]+90))) + 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Nmin_iceborderS[ind]]+90)))\n",
+ " NA2 = -2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Nmin_iceborderS[ind]]+90))) + 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Nmax_iceborderS[ind]]+90)))\n",
+ " E = -2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Nmax_iceborderS[ind]]+90))) + 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Smax_iceborderS[ind]+1]+90)))\n",
+ " SA2 = -2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Smax_iceborderS[ind]+1]+90))) + 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Smin_iceborderS[ind]+1]+90)))\n",
+ " SA1 = -2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Smin_iceborderS[ind]+1]+90))) + 2*np.pi*6371000**2 * (1-np.cos(np.deg2rad(SW_out_W_integrated.lat[Smin_iceborderS[ind]+1]+90)))\n",
+ " \n",
+ " \n",
+ " if sky_ind!=2:\n",
+ " ax[sky_ind].vlines(timemean_sicS[\"lat\"][[Nmin_iceborderS[ind], Smin_iceborderS[ind]]],0,400,color='darkgray', alpha=1)\n",
+ " ax[sky_ind].vlines(timemean_sicS[\"lat\"][[Nmax_iceborderS[ind], Smax_iceborderS[ind]]],0,400,color='darkgray', alpha=1)\n",
+ " else:\n",
+ " ax[sky_ind].vlines(timemean_sicS[\"lat\"][[Nmin_iceborderS[ind], Smin_iceborderS[ind]]],-200,200,color='darkgray', alpha=1)\n",
+ " ax[sky_ind].vlines(timemean_sicS[\"lat\"][[Nmax_iceborderS[ind], Smax_iceborderS[ind]]],-200,200,color='darkgray', alpha=1)\n",
+ " \n",
+ " if sky_ind== 0:\n",
+ " lW_SW=ax[sky_ind].plot(timemean_SWoutW_cs['lat'],timemean_SWoutW_cs,c='C1',label='Winton')\n",
+ " lS_SW=ax[sky_ind].plot(timemean_SWoutS_cs['lat'],timemean_SWoutS_cs,c='C0',label='Semtner')\n",
+ " elif sky_ind==1:\n",
+ " lW_SW=ax[sky_ind].plot(timemean_SWoutW['lat'],timemean_SWoutW,c='C1',label='Winton')\n",
+ " lS_SW=ax[sky_ind].plot(timemean_SWoutS['lat'],timemean_SWoutS,c='C0',label='Semtner')\n",
+ " elif sky_ind==2:\n",
+ " lW_SW=ax[sky_ind].plot(timemean_SWoutW['lat'],-1 * (timemean_SWoutW_cs-timemean_SWoutW),c='C1',label='Winton')\n",
+ " lS_SW=ax[sky_ind].plot(timemean_SWoutS['lat'],-1 * (timemean_SWoutS_cs-timemean_SWoutS),c='C0',label='Semtner')\n",
+ " \n",
+ " if sky_ind==0:\n",
+ " print(\"clear sky:\")\n",
+ " total = selmean(get_var(winton_simarray[ind],\"rsutcs\", True), nyears) - selmean(get_var(semtner_simarray[ind],\"rsutcs\", True), nyears)\n",
+ " else:\n",
+ " print(\"all sky:\")\n",
+ " total = selmean(get_var(winton_simarray[ind],\"rsut\", True), nyears) - selmean(get_var(semtner_simarray[ind],\"rsut\", True), nyears)\n",
+ " \n",
+ " print(str(dglob[sky_ind]*1e-12) +\" PW; \" +str(dglob[sky_ind]/(4 * np.pi *6371000**2)) +\" W/m^2 = \" +str(total.values)+\" W/m^2\" )\n",
+ " print(dN1[sky_ind] / dglob[sky_ind])\n",
+ " print(dN2[sky_ind] / dglob[sky_ind])\n",
+ " print(dE[sky_ind] / dglob[sky_ind])\n",
+ " \n",
+ "ax[0].set_xlim(-90,90)\n",
+ "ax[1].set_xlim(-90,90)\n",
+ "ax[0].set_ylim(0,150)\n",
+ "legend_color(ax[0],['3L-Winton', '0L-Semtner'],9, labelsize)\n",
+ "ax[1].set_xlabel('Latitude [°]', fontsize=labelsize)\n",
+ "ax[0].set_ylabel('TOA SW out cs\\n[W m$^{-2}$]', fontsize=labelsize)\n",
+ "ax[1].set_ylabel('TOA SW out as\\n[W m$^{-2}$]', fontsize=labelsize)\n",
+ "#ax[2].set_ylabel('CRE [W m$^{-2}$]')\n",
+ "ax[1].annotate(\"dF = \" +str(round(total.values.tolist(),2)) +\"Wm$^{-2}$\",(-20,40), fontsize=labelsize) # 1.01 Wm$^{-2}$\")\n",
+ "ax[1].annotate(f'{100*round(dN1[sky_ind] / dglob[sky_ind],2):.0f}' +\"%\",(70,20), fontsize=labelsize)\n",
+ "ax[1].annotate(f'{100*round(dN2[sky_ind] / dglob[sky_ind],2):.0f}' +\"%\",(45,20), fontsize=labelsize)\n",
+ "ax[1].annotate(f'{100*round(dE[sky_ind] / dglob[sky_ind],2):.0f}' +\"%\",(20,20), fontsize=labelsize)\n",
+ "\n",
+ "#ax[1].annotate(\"dF = 1.01 Wm$^{-2}$\",(-20,40))\n",
+ "#ax[1].annotate(\"16%\",(70,20))\n",
+ "#ax[1].annotate(\"71%\",(45,20))\n",
+ "#ax[1].annotate(\"13%\",(20,20))\n",
+ "\n",
+ "#ax[0].annotate(\"dF = 1.25 Wm$^{-2}$\",(-20,50))\n",
+ "#ax[0].annotate(\"23%\",(70,20))\n",
+ "#ax[0].annotate(\"80%\",(45,20))\n",
+ "#ax[0].annotate(\"-3%\",(20,20))\n",
+ "\n",
+ "ax[0].text(-0.15,1,\"a)\", transform=ax[0].transAxes, fontsize=labelsize)\n",
+ "ax[1].text(-0.15,1,\"b)\", transform=ax[1].transAxes, fontsize=labelsize)\n",
+ "ax[0].tick_params(labelsize=ticksize) \n",
+ "ax[1].tick_params(labelsize=ticksize) \n",
+ "\n",
+ "plt.savefig(\"plots/TOA_EB.pdf\")\n",
+ "plt.savefig(\"plots/TOA_EB.png\", dpi=500)\n",
+ "plt.show()\n",
+ "print(winton_exparray[ind])\n",
+ "print(semtner_exparray[ind])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mlo_aqua_2437ppmv_winton and mlo_aqua_1688ppmv_hice_unlim\n",
+ "clear sky:\n",
+ "637.0255380792839 PW; 1.2489118006869346 W/m^2 = 1.2489128 W/m^2\n",
+ "0.23056602641189058\n",
+ "0.8042223095666693\n",
+ "-0.03478833597856455\n",
+ "all sky:\n",
+ "515.034057095216 PW; 1.0097430530043008 W/m^2 = 1.0097504 W/m^2\n",
+ "0.16410614491583508\n",
+ "0.709068870009622\n",
+ "0.12682498507453116\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(winton_exparray[ind] +\" and \" +semtner_exparray[ind])\n",
+ "for sky_ind in range(2):\n",
+ " if sky_ind==0:\n",
+ " print(\"clear sky:\")\n",
+ " total = selmean(get_var(winton_simarray[ind],\"rsutcs\", True), 40) - selmean(get_var(semtner_simarray[ind],\"rsutcs\", True), 40)\n",
+ " else:\n",
+ " print(\"all sky:\")\n",
+ " total = selmean(get_var(winton_simarray[ind],\"rsut\", True), 40) - selmean(get_var(semtner_simarray[ind],\"rsut\", True), 40)\n",
+ " \n",
+ " print(str(dglob[sky_ind]*1e-12) +\" PW; \" +str(dglob[sky_ind]/(4 * np.pi *6371000**2)) +\" W/m^2 = \" +str(total.values)+\" W/m^2\" )\n",
+ " print(dN1[sky_ind] / dglob[sky_ind])\n",
+ " print(dN2[sky_ind] / dglob[sky_ind])\n",
+ " print(dE[sky_ind] / dglob[sky_ind])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'latitude [°]')"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x864 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# hovmoeller plots of reflected sw radiation\n",
+ "fig, ax = plt.subplots(2,2,figsize=(8,12), sharey = True, sharex = True, constrained_layout=True)\n",
+ "axind=0\n",
+ "\n",
+ "colors1 = np.linspace(0.0, 1.0, 12)\n",
+ "colors = np.append(\"darkblue\",colors1.astype(\"str\"))\n",
+ "\n",
+ "#ticks=[0,0.01, 0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,10,20]\n",
+ "ticks=[0, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]\n",
+ "\n",
+ "cmap= mpl.colors.ListedColormap(colors)\n",
+ "norm = mpl.colors.BoundaryNorm(ticks, cmap.N)\n",
+ "\n",
+ "\n",
+ "for ind in [1,0]:\n",
+ " albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ " albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ " \n",
+ " sic_Wzm = get_var(winton_zmsimarray[ind], \"sic\")\n",
+ " sic_Szm = get_var(semtner_zmsimarray[ind], \"sic\")\n",
+ " \n",
+ " albedo_Wzmmean = selmonmean(albedo_Wzm, 40)\n",
+ " albedo_Szmmean = selmonmean(albedo_Szm, 40)\n",
+ " sic_Wzmmean = selmonmean(sic_Wzm, 40)\n",
+ " sic_Szmmean = selmonmean(sic_Szm, 40)\n",
+ " \n",
+ " f1 = ax[axind,0].contourf(albedo_Wzmmean.lat,albedo_Wzmmean.month, albedo_Wzmmean,levels = ticks,cmap= cmap,norm=norm)\n",
+ " f2 = ax[axind,1].contourf(albedo_Szmmean.lat,albedo_Szmmean.month, albedo_Szmmean,levels = ticks,cmap= cmap,norm=norm)\n",
+ " \n",
+ " c1 = ax[axind,0].contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999],colors=\"orange\",linewidths=1)\n",
+ " c2 = ax[axind,1].contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999],colors=\"orange\",linewidths=1)\n",
+ " plt.clabel(c1,fmt='%1.1f',colors=\"orange\")\n",
+ " plt.clabel(c2,fmt='%1.1f',colors=\"orange\")\n",
+ " \n",
+ " ax[axind,0].set_xlim(35,85)\n",
+ " ax[axind,0].set_title(winton_exparray[ind])\n",
+ " ax[axind,1].set_title(semtner_exparray[ind])\n",
+ " cbar = plt.colorbar(f2,ax=ax[axind,1])\n",
+ " cbar.set_label(\"surface albedo\")\n",
+ " axind+=1\n",
+ "ax[0,0].set_ylabel(\"month\")\n",
+ "ax[1,0].set_ylabel(\"month\")\n",
+ "ax[1,0].set_xlabel(\"latitude [°]\")\n",
+ "ax[1,1].set_xlabel(\"latitude [°]\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'latitude [°]')"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x864 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# hovmoeller plots of snow thickness\n",
+ "fig, ax = plt.subplots(2,2,figsize=(8,12), sharey = True, sharex = True, constrained_layout=True)\n",
+ "axind=0\n",
+ "\n",
+ "colors1 = np.linspace(0.0, 1.0, 12)\n",
+ "colors = np.append(\"darkblue\",colors1.astype(\"str\"))\n",
+ "\n",
+ "#ticks=[0,0.01, 0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,10,20]\n",
+ "ticks=np.linspace(0,2,11)\n",
+ "\n",
+ "cmap= mpl.colors.ListedColormap(colors)\n",
+ "norm = mpl.colors.BoundaryNorm(ticks, cmap.N)\n",
+ "\n",
+ "\n",
+ "for ind in [1,0]:\n",
+ " hs_Wzm = get_var(winton_zmsimarray[ind], \"hs_icecl\")\n",
+ " hs_Szm = get_var(semtner_zmsimarray[ind], \"hs_icecl\")\n",
+ " \n",
+ " sic_Wzm = get_var(winton_zmsimarray[ind], \"sic\")\n",
+ " sic_Szm = get_var(semtner_zmsimarray[ind], \"sic\")\n",
+ " \n",
+ " hs_Wzmmean = selmonmean(hs_Wzm, 40)\n",
+ " hs_Szmmean = selmonmean(hs_Szm, 40)\n",
+ " sic_Wzmmean = selmonmean(sic_Wzm, 40)\n",
+ " sic_Szmmean = selmonmean(sic_Szm, 40)\n",
+ " \n",
+ " f1 = ax[axind,0].contourf(hs_Wzmmean.lat,hs_Wzmmean.month, hs_Wzmmean,levels = ticks)\n",
+ " f2 = ax[axind,1].contourf(hs_Szmmean.lat,hs_Szmmean.month, hs_Szmmean,levels = ticks)\n",
+ " \n",
+ " c1 = ax[axind,0].contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999],colors=\"orange\",linewidths=1)\n",
+ " c2 = ax[axind,1].contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999],colors=\"orange\",linewidths=1)\n",
+ " plt.clabel(c1,fmt='%1.1f',colors=\"orange\")\n",
+ " plt.clabel(c2,fmt='%1.1f',colors=\"orange\")\n",
+ " \n",
+ " ax[axind,0].set_xlim(35,85)\n",
+ " ax[axind,0].set_title(winton_exparray[ind])\n",
+ " ax[axind,1].set_title(semtner_exparray[ind])\n",
+ " cbar = plt.colorbar(f2,ax=ax[axind,1])\n",
+ " cbar.set_label(\"snow thickness [m]\")\n",
+ " axind+=1\n",
+ "ax[0,0].set_ylabel(\"month\")\n",
+ "ax[1,0].set_ylabel(\"month\")\n",
+ "ax[1,0].set_xlabel(\"latitude [°]\")\n",
+ "ax[1,1].set_xlabel(\"latitude [°]\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'latitude [°]')"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x864 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# hovmoeller plots of reflected sw radiation\n",
+ "fig, ax = plt.subplots(2,2,figsize=(8,12), sharey = True, sharex = True, constrained_layout=True)\n",
+ "axind=0\n",
+ "\n",
+ "colors1 = np.linspace(0.0, 1.0, 12)\n",
+ "colors = np.append(\"darkblue\",colors1.astype(\"str\"))\n",
+ "\n",
+ "#ticks=[0,0.01, 0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,10,20]\n",
+ "ticks=[0, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]\n",
+ "\n",
+ "cmap= mpl.colors.ListedColormap(colors)\n",
+ "norm = mpl.colors.BoundaryNorm(ticks, cmap.N)\n",
+ "\n",
+ "\n",
+ "for ind in [1,0]:\n",
+ " albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ " albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ " \n",
+ " sic_Wzm = get_var(winton_zmsimarray[ind], \"sic\")\n",
+ " sic_Szm = get_var(semtner_zmsimarray[ind], \"sic\")\n",
+ " \n",
+ " rsutcs_Wzm = get_var(winton_zmsimarray[ind], \"rsutcs\")\n",
+ " rsutcs_Szm = get_var(semtner_zmsimarray[ind], \"rsutcs\")\n",
+ " \n",
+ " albedo_Wzmmean = selmonmean(albedo_Wzm, 40)\n",
+ " albedo_Szmmean = selmonmean(albedo_Szm, 40)\n",
+ " sic_Wzmmean = selmonmean(sic_Wzm, 40)\n",
+ " sic_Szmmean = selmonmean(sic_Szm, 40)\n",
+ " rsutcs_Wzmmean = selmonmean(rsutcs_Wzm, 40)\n",
+ " rsutcs_Szmmean = selmonmean(rsutcs_Szm, 40)\n",
+ " \n",
+ " #f1 = ax[axind,0].contourf(albedo_Wzmmean.lat,albedo_Wzmmean.month, albedo_Wzmmean,levels = ticks,cmap= cmap,norm=norm)\n",
+ " f2 = ax[axind,1].contourf(albedo_Szmmean.lat,albedo_Szmmean.month, albedo_Szmmean-albedo_Wzmmean)\n",
+ " \n",
+ " c1 = ax[axind,0].contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999],colors=\"orange\",linewidths=1)\n",
+ " c2 = ax[axind,0].contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999],colors=\"red\",linewidths=1)\n",
+ " plt.clabel(c1,fmt='%1.1f',colors=\"orange\")\n",
+ " plt.clabel(c2,fmt='%1.1f',colors=\"orange\")\n",
+ " \n",
+ " ax[axind,0].set_xlim(35,85)\n",
+ " ax[axind,0].set_title(winton_exparray[ind])\n",
+ " ax[axind,1].set_title(semtner_exparray[ind])\n",
+ " cbar = plt.colorbar(f2,ax=ax[axind,1])\n",
+ " cbar.set_label(\"surface albedo\")\n",
+ " axind+=1\n",
+ "ax[0,0].set_ylabel(\"month\")\n",
+ "ax[1,0].set_ylabel(\"month\")\n",
+ "ax[1,0].set_xlabel(\"latitude [°]\")\n",
+ "ax[1,1].set_xlabel(\"latitude [°]\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-70. -65. -60. -55. -50. -45. -40. -35. -30. -25. -20. -15. -10. -5.\n",
+ " 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55. 60. 65.\n",
+ " 70.]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 360x864 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# hovmoeller plots of reflected sw radiation\n",
+ "from matplotlib.colors import LinearSegmentedColormap\n",
+ "fig, ax = plt.subplots(2,1,figsize=(5,12), sharey = True, sharex = True, constrained_layout=True)\n",
+ "axind=0\n",
+ "\n",
+ "cmap1 = plt.get_cmap(\"Reds\")\n",
+ "cmap2 = plt.get_cmap(\"Blues_r\")\n",
+ "\n",
+ "cmap = LinearSegmentedColormap.from_list(\"blub\", colors, N=12)\n",
+ "ticks=np.linspace(-70,70,29)\n",
+ "print(ticks)\n",
+ "\n",
+ "for ind in [1,0]:\n",
+ " albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ " albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ " \n",
+ " sic_Wzm = get_var(winton_zmsimarray[ind], \"sic\")\n",
+ " sic_Szm = get_var(semtner_zmsimarray[ind], \"sic\")\n",
+ " \n",
+ " rsutcs_Wzm = get_var(winton_zmsimarray[ind], \"rsut\")\n",
+ " rsutcs_Szm = get_var(semtner_zmsimarray[ind], \"rsut\")\n",
+ " \n",
+ " albedo_Wzmmean = selmonmean(albedo_Wzm, 40)\n",
+ " albedo_Szmmean = selmonmean(albedo_Szm, 40)\n",
+ " sic_Wzmmean = selmonmean(sic_Wzm, 40)\n",
+ " sic_Szmmean = selmonmean(sic_Szm, 40)\n",
+ " rsutcs_Wzmmean = selmonmean(rsutcs_Wzm, 40)\n",
+ " rsutcs_Szmmean = selmonmean(rsutcs_Szm, 40)\n",
+ " \n",
+ " #f1 = ax[axind,0].contourf(rsutcs_Wzmmean.lat,rsutcs_Wzmmean.month, rsutcs_Wzmmean, levels=ticks)\n",
+ " f2 = ax[axind].contourf(rsutcs_Szmmean.lat,rsutcs_Szmmean.month, rsutcs_Szmmean-rsutcs_Wzmmean, levels=ticks,cmap=\"seismic\")\n",
+ " \n",
+ " c1 = ax[axind].contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999],colors=\"C1\",linewidths=1.5)\n",
+ " c2 = ax[axind].contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999],colors=\"C0\",linewidths=1.5)\n",
+ " plt.clabel(c1,fmt='%1.1f',colors=\"C1\")\n",
+ " plt.clabel(c2,fmt='%1.1f',colors=\"C0\")\n",
+ " \n",
+ " ax[axind].set_xlim(35,85)\n",
+ " ax[axind].set_title(str(winton_exparray[ind]) +\"-\" +str(semtner_exparray[ind]),fontdict={'size': 10})\n",
+ " cbar = plt.colorbar(f2,ax=ax[axind])\n",
+ " cbar.set_label(\"$\\Delta$TOA SW cs out [Wm$^{-2}$]\")\n",
+ " axind+=1\n",
+ "ax[0].set_ylabel(\"month\")\n",
+ "ax[1].set_ylabel(\"month\")\n",
+ "ax[1].set_xlabel(\"latitude [°]\")\n",
+ "ax[1].set_xlabel(\"latitude [°]\")\n",
+ "plt.savefig(\"plots/hovmoeller_rsutcs_multi.png\",dpi=300)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-35. -32.5 -30. -27.5 -25. -22.5 -20. -17.5 -15. -12.5 -10. -7.5\n",
+ " -5. -2.5 0. 2.5 5. ]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# hovmoeller plots of reflected sw radiation\n",
+ "fig, ax = plt.subplots(1,1,figsize=(6,6))\n",
+ "var=\"rsutcs\"\n",
+ "ind=1\n",
+ "\n",
+ "ticks=np.linspace(-35,5,17)\n",
+ "#ticks=np.linspace(-55,5,13)\n",
+ "print(ticks)\n",
+ "reds = mpl.cm.get_cmap('Reds', np.size(ticks)-3)\n",
+ "blues = mpl.cm.get_cmap('Blues_r', np.size(ticks)-3)\n",
+ "\n",
+ "\n",
+ "newcolors2 = reds(np.linspace(0, 1, np.size(ticks)-3))\n",
+ "newcolors = blues(np.linspace(0, 1, np.size(ticks)-3))\n",
+ "newcolors = np.vstack((newcolors, newcolors2[:2,:]))\n",
+ "\n",
+ "cmap = mpl.colors.ListedColormap(newcolors, name='newcmap')\n",
+ "\n",
+ "\n",
+ "albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ "albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ "\n",
+ "sic_Wzm = get_var(winton_zmsimarray[ind], \"sic\")\n",
+ "sic_Szm = get_var(semtner_zmsimarray[ind], \"sic\")\n",
+ "\n",
+ "rsutcs_Wzm = get_var(winton_zmsimarray[ind], var)\n",
+ "rsutcs_Szm = get_var(semtner_zmsimarray[ind], var)\n",
+ "\n",
+ "albedo_Wzmmean = selmonmean(albedo_Wzm, 40)\n",
+ "albedo_Szmmean = selmonmean(albedo_Szm, 40)\n",
+ "sic_Wzmmean = selmonmean(sic_Wzm, 40)\n",
+ "sic_Szmmean = selmonmean(sic_Szm, 40)\n",
+ "rsutcs_Wzmmean = selmonmean(rsutcs_Wzm, 40)\n",
+ "rsutcs_Szmmean = selmonmean(rsutcs_Szm, 40)\n",
+ "\n",
+ "#f1 = ax[axind,0].contourf(rsutcs_Wzmmean.lat,rsutcs_Wzmmean.month, rsutcs_Wzmmean, levels=ticks)\n",
+ "f2 = ax.contourf(rsutcs_Szmmean.lat,rsutcs_Szmmean.month, rsutcs_Szmmean-rsutcs_Wzmmean, levels=ticks,cmap=cmap)\n",
+ "\n",
+ "c1 = ax.contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999999],colors=\"C1\",linewidths=1.5)\n",
+ "c2 = ax.contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999999],colors=\"C0\",linewidths=1.5)\n",
+ "plt.clabel(c1,fmt='%1.1f',colors=\"C1\")\n",
+ "plt.clabel(c2,fmt='%1.1f',colors=\"C0\")\n",
+ "\n",
+ "ax.set_xlim(35,70)\n",
+ "cbar = plt.colorbar(f2)\n",
+ "cbar.set_label(\"$\\Delta$TOA SW as out [Wm$^{-2}$]\")\n",
+ "\n",
+ "ax.set_ylabel(\"Month\")\n",
+ "ax.set_xlabel(\"Latitude [°]\")\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.savefig(\"plots/hovmoeller_\" +var +\".png\",dpi=300)\n",
+ "plt.savefig(\"plots/hovmoeller_\" +var +\".pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x216 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# hovmoeller plots of reflected sw radiation\n",
+ "fig, ax = plt.subplots(1,3,figsize=(6,3), sharey = True, sharex = True)\n",
+ "\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "ind=1\n",
+ "var=\"rsut\"\n",
+ "ticks1=[0, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]\n",
+ "colors1 = np.linspace(0.0, 1.0, 12)\n",
+ "colors = np.append(\"darkblue\",colors1.astype(\"str\"))\n",
+ "cmap1= mpl.colors.ListedColormap(colors)\n",
+ "\n",
+ "\n",
+ "ticks2=np.linspace(-35,5,17)\n",
+ "reds = mpl.cm.get_cmap('Reds', np.size(ticks2)-3)\n",
+ "blues = mpl.cm.get_cmap('Blues_r', np.size(ticks2)-3)\n",
+ "newcolors2 = reds(np.linspace(0, 1, np.size(ticks2)-3))\n",
+ "newcolors = blues(np.linspace(0, 1, np.size(ticks2)-3))\n",
+ "newcolors = np.vstack((newcolors, newcolors2[:2,:]))\n",
+ "\n",
+ "ticks2=np.linspace(-5,35,17)\n",
+ "reds = mpl.cm.get_cmap('Reds', np.size(ticks2)-3)\n",
+ "blues = mpl.cm.get_cmap('Blues_r', np.size(ticks2)-3)\n",
+ "newcolors2 = reds(np.linspace(0, 1, np.size(ticks2)-3))\n",
+ "newcolors = blues(np.linspace(0, 1, np.size(ticks2)-3))\n",
+ "newcolors = np.vstack((newcolors[-2:,:], newcolors2))\n",
+ "\n",
+ "cmap2 = mpl.colors.ListedColormap(newcolors, name='newcmap')\n",
+ "\n",
+ "\n",
+ "albedo_Wzm = get_albedo(winton_zmsimarray[ind])\n",
+ "albedo_Szm = get_albedo(semtner_zmsimarray[ind])\n",
+ "\n",
+ "sic_Wzm = get_var(winton_zmsimarray[ind], \"sic\")\n",
+ "sic_Szm = get_var(semtner_zmsimarray[ind], \"sic\")\n",
+ "\n",
+ "var_Wzm = get_var(winton_zmsimarray[ind], var)\n",
+ "var_Szm = get_var(semtner_zmsimarray[ind], var)\n",
+ "\n",
+ "\n",
+ "albedo_Wzmmean = selmonmean(albedo_Wzm, 40)\n",
+ "albedo_Szmmean = selmonmean(albedo_Szm, 40)\n",
+ "sic_Wzmmean = selmonmean(sic_Wzm, 40)\n",
+ "sic_Szmmean = selmonmean(sic_Szm, 40)\n",
+ "var_Wzmmean = selmonmean(var_Wzm, 40)\n",
+ "var_Szmmean = selmonmean(var_Szm, 40)\n",
+ "\n",
+ "f0 = ax[0].contourf(albedo_Wzmmean.lat,albedo_Wzmmean.month, albedo_Wzmmean,levels = ticks1,cmap= cmap1)\n",
+ "f1 = ax[1].contourf(albedo_Szmmean.lat,albedo_Szmmean.month, albedo_Szmmean,levels = ticks1,cmap= cmap1)\n",
+ "f2 = ax[2].contourf(var_Szmmean.lat,var_Szmmean.month, var_Wzmmean-var_Szmmean, levels=ticks2,cmap=cmap2)\n",
+ "\n",
+ "\n",
+ "divider = make_axes_locatable(ax[0])\n",
+ "cax0 = divider.new_horizontal(size='5%', pad=0.03)\n",
+ "fig.add_axes(cax0)\n",
+ "cbar0 = fig.colorbar(f0, cax = cax0, orientation = 'vertical')\n",
+ "\n",
+ "divider = make_axes_locatable(ax[1])\n",
+ "cax1 = divider.new_horizontal(size='5%', pad=0.03)\n",
+ "fig.add_axes(cax1)\n",
+ "cbar1 = fig.colorbar(f1, cax = cax1, orientation = 'vertical')\n",
+ "\n",
+ "divider = make_axes_locatable(ax[2])\n",
+ "cax2 = divider.new_horizontal(size='5%', pad=0.03)\n",
+ "fig.add_axes(cax2)\n",
+ "cbar2 = fig.colorbar(f2, cax = cax2, orientation = 'vertical')\n",
+ "\n",
+ "\n",
+ "c0 = ax[0].contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999],colors=\"C1\",linewidths=1)\n",
+ "c1 = ax[1].contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999],colors=\"C0\",linewidths=1)\n",
+ "\n",
+ "c21 = ax[2].contour(sic_Wzmmean.lat,sic_Wzmmean.month, sic_Wzmmean, levels=[0,0.5,0.999999999],colors=\"C1\",linewidths=1.5)\n",
+ "c22 = ax[2].contour(sic_Szmmean.lat,sic_Szmmean.month, sic_Szmmean, levels=[0,0.5,0.999999999],colors=\"C0\",linewidths=1.5)\n",
+ "\n",
+ "plt.clabel(c21,fmt='%1.1f',colors=\"C1\", fontsize=ticksize)\n",
+ "plt.clabel(c22,fmt='%1.1f',colors=\"C0\", fontsize=ticksize)\n",
+ "plt.clabel(c0,fmt='%1.1f',colors=\"C1\", fontsize=ticksize)\n",
+ "plt.clabel(c1,fmt='%1.1f',colors=\"C0\", fontsize=ticksize)\n",
+ "\n",
+ "\n",
+ "cbar0.ax.set_title(r'$\\alpha$', fontsize=labelsize)\n",
+ "cbar1.ax.set_title(r'$\\alpha$', fontsize=labelsize)\n",
+ "cbar2.ax.set_title(r'$\\Delta$F$_{sw}$ [Wm$^{-2}$]', fontsize=labelsize)\n",
+ "\n",
+ "\n",
+ "ax[0].set_title(\"3L-Winton\", fontsize=labelsize)\n",
+ "ax[1].set_title(\"0L-Semtner\", fontsize=labelsize)\n",
+ "ax[2].set_title(\"Difference\", fontsize=labelsize)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "cbar0.ax.tick_params(labelsize=ticksize) \n",
+ "cbar1.ax.tick_params(labelsize=ticksize) \n",
+ "cbar2.ax.tick_params(labelsize=ticksize) \n",
+ "ax[0].tick_params(labelsize=ticksize) \n",
+ "ax[1].tick_params(labelsize=ticksize) \n",
+ "ax[2].tick_params(labelsize=ticksize) \n",
+ "\n",
+ "\n",
+ "\n",
+ "ax[0].set_xlim(35,70)\n",
+ "\n",
+ "ax[0].set_ylabel(\"Month\", fontsize=labelsize)\n",
+ "ax[0].set_xlabel(\"Latitude [°]\", fontsize=labelsize)\n",
+ "ax[1].set_xlabel(\"Latitude [°]\", fontsize=labelsize)\n",
+ "ax[2].set_xlabel(\"Latitude [°]\", fontsize=labelsize)\n",
+ "\n",
+ "\n",
+ "ax[0].text(-0,1.04,\"a)\", transform=ax[0].transAxes, fontsize=labelsize)\n",
+ "ax[1].text(-0,1.04,\"b)\", transform=ax[1].transAxes, fontsize=labelsize)\n",
+ "ax[2].text(-0,1.04,\"c)\", transform=ax[2].transAxes, fontsize=labelsize)\n",
+ "plt.savefig(\"plots/hovmoeller_albedo_\" +var +\".png\",dpi=300)\n",
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+ "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'rsut' ()>\n",
+ "array(31.93481445)\n",
+ "Coordinates:\n",
+ " lon float64 0.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'rsut'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5532f940-bd3b-4677-9523-5c8c56facc0c' class='xr-array-in' type='checkbox' checked><label for='section-5532f940-bd3b-4677-9523-5c8c56facc0c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>31.93</span></div><div class='xr-array-data'><pre>array(31.93481445)</pre></div></div></li><li class='xr-section-item'><input id='section-f921da2b-933c-4b76-ab88-f12b4d01001b' class='xr-section-summary-in' type='checkbox' checked><label for='section-f921da2b-933c-4b76-ab88-f12b4d01001b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-89700667-9956-4927-a6de-1766cf6085b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89700667-9956-4927-a6de-1766cf6085b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7e5974b-ae35-4438-93aa-a961231721cd' class='xr-var-data-in' type='checkbox'><label for='data-c7e5974b-ae35-4438-93aa-a961231721cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e329dbd2-ef2d-4230-b54a-f189584e8140' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e329dbd2-ef2d-4230-b54a-f189584e8140' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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+}
diff --git a/python_scripts/Winton_artificial_forcing.ipynb b/python_scripts/Winton_artificial_forcing.ipynb
new file mode 100644
index 0000000..a49fd66
--- /dev/null
+++ b/python_scripts/Winton_artificial_forcing.ipynb
@@ -0,0 +1,1581 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Offline sea-ice models: Semnter 0-layer and reformulated Winton 3-layer\n",
+ "\n",
+ "Sea-ice models are written to resemble the models as implemented in ICON-AES 1.3.00. The Winton model is almost exactly structured as in Winton (2000), except for small fixes (which are also in the ICON code).\n",
+ "The Semnter 0-layer models is further simplified, especially as brine pockets are neglected (as in the ICON code). \n",
+ "Calculcation of surface fluxes is not a part of either model, it's implemented in the same way as in ICON.\n",
+ "\n",
+ "Semtner, A. J. (1976) ‘A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate’, Journal of Physical Oceanography, 6(3), pp. 379–389. doi: 10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2.\n",
+ "\n",
+ "Winton, M. (2000) ‘A Reformulated Three-Layer Sea Ice Model’, Journal of Atmospheric and Oceanic Technology, 17(4), pp. 525–531. doi: 10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "import matplotlib.pyplot as plt\n",
+ "import math\n",
+ "import pylab as pl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.27\n"
+ ]
+ }
+ ],
+ "source": [
+ "# physical constants from ICON\n",
+ "dtime = 600 # model time step [s]\n",
+ "Ki = 2.1656 # heat conductivity ice [J/(msK)]\n",
+ "Ks = 0.31 # heat conductivity snow [J/(msK)]\n",
+ "rhoi = 917.0 # density of sea ice [kg/m^3]\n",
+ "rhos = 300 # density of snow [kg/m^3]\n",
+ "rhow = 1025.022 # density of ocean water [kg/m^3]\n",
+ "ci = 2106 # heat capacity of ice [J/(kgK)]\n",
+ "cs = 2090 # heat capacity of snow [J/(kgK)]\n",
+ "Tf = -1.9 # freezing temperature of ocean water [°C]\n",
+ "Tmelt = 273.15 # melting temperature of water [K]\n",
+ "zemissdef = 0.996 # longwave emissivity factor [] ICON\n",
+ "#zemissdef = 1-0.7 # longwave emissivity factor [] Abbot(2010)\n",
+ "sigma = 5.6704e-8 # Stefan-Boltzman constant [W/(m^2K^4)]\n",
+ "# albedo values defined in runscript!\n",
+ "albsnow_warm = 0.66\n",
+ "albsnow_cold = 0.79\n",
+ "albice_warm = 0.38\n",
+ "albice_cold = 0.45\n",
+ "I0 = 0.17 # ice surface penetrating radiation []\n",
+ "L = 2.8345e6 - 2.5008e6 # latent heat of fusion [J/kg]\n",
+ "muS = 0.054 * 5.0 # constant of linear salt - freezing point relationship * Sea ice bulk salinity [°C]\n",
+ "\n",
+ "hcilayer = 0.1 # thickness of stabilizing constant heat capacity layer [m]\n",
+ "\n",
+ "print(muS)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# functions\n",
+ "def E(T): \n",
+ " return ci*(T+muS)-L*(1+muS/T) # enthalpy of melting for upper layer [J/kg] #(1)\n",
+ "def E2(T):\n",
+ " return ci*(T+muS)-L # enthalpy of melting for lower layer [J/kg] #(25)\n",
+ "\n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos, fontsize=fontsize)\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " for text in legend.get_texts():\n",
+ " if text.get_text()=='Winton':\n",
+ " text.set_color('C1')\n",
+ " if text.get_text()=='3L-Winton':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='0L-Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='Semtner_5m':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-lim5':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='1438ppmv':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='1500ppmv':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='3000ppmv':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='5000ppmv':\n",
+ " text.set_color('C3')\n",
+ "\n",
+ " return legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# surface fluxes (not actually part of the Winton Model!)\n",
+ "def surface_fluxes(Tsurfold, hsold, rsds, rlds, lhflx, shflx, albedo):\n",
+ " #print(\"calculate surface fluxes\")\n",
+ " # check for snow on ice\n",
+ " if hsold>0: \n",
+ " I=0 # fraction of penetrating radiation []\n",
+ " #print(\"snow on ice\")\n",
+ " else: \n",
+ " I=I0\n",
+ " #print(\"bare ice\")\n",
+ " if hsold>1e-6: # ICON checks diferently for Tsurfmelt and I!\n",
+ " Tsurfmelt = 0 # melting temperature of surface [°C]\n",
+ " #print(\"snow on ice\")\n",
+ " else: \n",
+ " Tsurfmelt = - muS\n",
+ " #print(\"bare ice\")\n",
+ "\n",
+ " # surface energy flux balance \n",
+ " # longwave incoming + longwave outgoing + shortwave balance + latent heat flux + sensible heat flux\n",
+ " # in ICON: addition of zemissdef\n",
+ " SWnet = rsds * (1-albedo)\n",
+ " FS = 1* ( zemissdef *(rlds - sigma* (Tsurfold+Tmelt)**4 ) + SWnet*(1-I) + lhflx + shflx ) #UPWARD flux!\n",
+ " dFS = -4 * zemissdef * sigma * (Tsurfold+Tmelt)**3\n",
+ " \n",
+ " #print(\"FS = \" +str(FS) +\" W/m^2\")\n",
+ " #print(\"dFS = \" +str(dFS) +\" + \" +str(rhoi * hcilayer /dtime *ci) +\" W/(m^2K)\")\n",
+ " \n",
+ " return FS, dFS, SWnet, I, Tsurfmelt "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 2.a) temperature calculation\n",
+ "def set_ice_temp(Tsurfold, T1old, T2old, hiold, hsold, FS, dFS, SWnet, I, Tsurfmelt, c_stability=True):\n",
+ "# if hsold>1e-6: \n",
+ "# I=0 # fraction of penetrating radiation []\n",
+ "# Tsurfmelt = 0 # melting temperature of surface [°C]\n",
+ "# print(\"snow on ice\")\n",
+ "# else: \n",
+ "# Tsurfmelt = - muS\n",
+ "# I=I0 # fraction of penetrating radiation []\n",
+ "# print(\"bare ice\")\n",
+ " \n",
+ " # Factor B + stabilizing ice layer in ICON ?\n",
+ " # stabilizing ice layer dominates the sensitivity of temperature on surface flux balance -> surface temperature is far less sensitive\n",
+ " B = -dFS #(8)\n",
+ " if c_stability:\n",
+ " B = B+rhoi * hcilayer /dtime *ci \n",
+ " A = -FS - Tsurfold * B #(7)\n",
+ "\n",
+ " #conductivities\n",
+ " K12 = (4*Ki*Ks) / (Ks*hiold + 4*Ki*hsold) # coupled conductivity of snow-ice layer (upper layer) [W/(mK)] (5)\n",
+ " K32 = 2*Ki/hiold # coupled conductivity of both ice layers (lower layer) [W/(mK)] (10)\n",
+ "\n",
+ "\n",
+ " A1 = (rhoi*hiold)/(2*dtime) * ci + K32*(4*dtime*K32+rhoi*hiold*ci)/(6*dtime*K32+rhoi*hiold*ci) + (K12*B)/(K12+B) # (16)\n",
+ " B1 = -1*(rhoi*hiold)/(2*dtime) * (ci*T1old - (L*muS)/T1old) - SWnet * I - \\\n",
+ " K32 * (4*dtime*K32*Tf + rhoi*hiold*ci*T2old)/(6*dtime*K32 + rhoi*hiold*ci) + (A*K12)/(K12+B) # (17)\n",
+ " C1 = -1 * (rhoi*hiold)/(2*dtime) * L *muS\n",
+ "\n",
+ " # temperature T1\n",
+ " T1 = -1 * (B1+ (B1**2 - 4*A1*C1)**0.5)/(2*A1) # (21)\n",
+ "\n",
+ " # temperature T2 (not necessary to calculate here?)\n",
+ " T2 = (2 * dtime * K32 * (T1 + 2*Tf) + rhoi*hiold*ci*T2old) / (6 * dtime * K32 + rhoi * hiold * ci) #(15)\n",
+ "\n",
+ " # surface temperature\n",
+ " Tsurf = (K12 * T1 - A) / (K12 + B) # (6)\n",
+ " #print(K12,A,B)\n",
+ "\n",
+ " if Tsurf>Tsurfmelt: #check for melting of upper snow or ice surface\n",
+ " #print(\"surface melting\")\n",
+ " Tsurf = Tsurfmelt\n",
+ " \n",
+ " # recalculate A1 & B1 for surface melting\n",
+ " A1 = (rhoi*hiold)/(2*dtime) * ci + K32*(4*dtime*K32+rhoi*hiold*ci)/(6*dtime*K32+rhoi*hiold*ci) + K12 # (19)\n",
+ " B1 = -1*(rhoi*hiold)/(2*dtime) * (ci*T1old - (L*muS)/T1old) - SWnet * I- \\\n",
+ " K32 * (4*dtime*K32*Tf + rhoi*hiold*ci*T2old)/(6*dtime*K32 + rhoi*hiold*ci) + K12*Tsurf # (20)\n",
+ "\n",
+ " # recalculate T1 & T2\n",
+ " T1 = -1 * (B1+ (B1**2 - 4*A1*C1)**0.5)/(2*A1) # (21)\n",
+ " T2 = (2 * dtime * K32 * (T1 + 2*Tf) + rhoi*hiold*ci*T2old) / (6 * dtime * K32 + rhoi * hiold * ci) #(15)\n",
+ "\n",
+ " Qtop = K12 * (T1-Tsurf) - (A+B*Tsurf) # (22)\n",
+ " else:\n",
+ " Qtop = 0\n",
+ "\n",
+ " Qbot = -4 * Ki * (Tf-T2)/hiold # (23)\n",
+ "\n",
+ " # calculated values \n",
+ " #print(\"Tsurf: \" +str(Tsurfold) +\" -> \" +str(Tsurf) +\" °C\")\n",
+ " #print(\"T1: \" +str(T1old) +\" -> \" +str(T1) +\" °C\")\n",
+ " #print(\"T2: \" +str(T2old) +\" -> \" +str(T2) +\" °C\")\n",
+ "\n",
+ " #print(\"Qbot= \" +str(Qbot) +\" W/m^2\")\n",
+ " #print(\"Qtop= \" +str(Qtop) +\" W/m^2\")\n",
+ "\n",
+ " # Temeperatures may change in the ice growth scheme!\n",
+ " # ICON output values\n",
+ " #print(\"ICON: \")\n",
+ " #print(\"Tsurf: \" +str(Tsurfold) +\" -> \" +str(Tsurf_ICON) +\" °C\")\n",
+ " #print(\"T1: \" +str(T1old) +\" -> \" +str(T1_ICON) +\" °C\")\n",
+ " #print(\"T2: \" +str(T2old) +\" -> \" +str(T2_ICON) +\" °C\")\n",
+ " \n",
+ " return Tsurf, T1, T2, Qbot, Qtop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 2.b) ice thickness calculation\n",
+ "\n",
+ "def ice_growth(hiold, hsold, Tsurf, T1, T2, Qbot, Qtop, prls, prcs):\n",
+ " # snow thickness calculation\n",
+ " #print(\"calculate snow accumulation\")\n",
+ " hs=hsold +(prls+prcs)*dtime / rhos\n",
+ "\n",
+ " #print(\"hs: \" +str(hsold) +\" -> \" +str(hs) +\" m\")\n",
+ "\n",
+ " # initialize h1 & h2\n",
+ " h1=hiold/2\n",
+ " h2=hiold/2\n",
+ "\n",
+ "\n",
+ " #print(\"calculate ice thickness change\")\n",
+ " # bottom accretion\n",
+ " if Qbot<0:\n",
+ " #print(\"bottom accretion\")\n",
+ " dh2 = Qbot * dtime / (rhoi * E2(Tf)) #(24) \n",
+ " T2 = (dh2*Tf + h2*T2)/(dh2 + h2) #temperature change of lower ice layer due to added mass (26)\n",
+ " h2 += dh2 \n",
+ " \n",
+ " hi = h1+h2\n",
+ " \n",
+ " fluxres_w=0\n",
+ " # surface melting\n",
+ " if Qtop>0:\n",
+ " #print(\"surface melting\")\n",
+ " dhs = -min( (Qtop*dtime) /(L *rhos) ,hs) #(27)\n",
+ " dh1 = -min(max((Qtop*dtime - L*hs*rhos) /(-1*E(T1) *rhoi),0 ),h1) #(28)\n",
+ " dh2 = -min(max((Qtop*dtime - L*hs*rhos + E(T1)*h1*rhoi)/(-1*E2(T2)*rhoi),0 ),h2) #(29)\n",
+ "\n",
+ " fluxres_w = max(Qtop*dtime - L*hs + E(T1)*hiold + E2(T2)*h2 ,0) #(30)\n",
+ " h1 += dh1\n",
+ " h2 += dh2\n",
+ " hs += dhs\n",
+ "\n",
+ " # bottom melting\n",
+ " if Qbot>0:\n",
+ " #print(\"bottom melting\")\n",
+ " dh2 = -min( (Qbot*dtime) /(-E2(T2)*rhoi) ,h2) #(31)\n",
+ " dh1 = -min(max((Qbot*dtime + E2(T2)*h2*rhoi) /(-E(T1)*rhoi) ,0),h1) #(32)\n",
+ " dhs = -min(max((Qbot*dtime + E2(T2)*h2*rhoi + E(T1)*h1*rhoi)/(L*rhos) ,0),hs) #(33)\n",
+ "\n",
+ " fluxres_w += max(Qbot*dtime - L*hs + E(T1)*hiold + E2(T2)+h2, 0)\n",
+ " h1 += dh1\n",
+ " h2 += dh2\n",
+ " hs += dhs\n",
+ " \n",
+ " hi = h1+h2\n",
+ "\n",
+ " # snow ice conversion\n",
+ " dhs = -max((hs - ((rhow-rhoi)/rhos)*hi)*(rhoi/rhow),0)\n",
+ " dh1 = -dhs\n",
+ " if dhs<0:\n",
+ " print(\"convert snow to ice\")\n",
+ "\n",
+ " # adjust T1 due to incorporation of zero heat capacity snow\n",
+ " f1 = h1/(h1+dh1)\n",
+ " Tbar = f1 * (T1- (L/ci) * (muS/T1)) + (1-f1)*(-muS) # (39) with T2=-muS\n",
+ " T1 = (Tbar - (Tbar**2 + 4*muS*L / ci)**0.5)/2 # (38)\n",
+ "\n",
+ "\n",
+ " hs += dhs\n",
+ " h1 += dh1\n",
+ " hi = h1+h2\n",
+ "\n",
+ " # even up h1 & h2 and change temperature accordingly\n",
+ " if h1>h2:\n",
+ " #print(\"convert h1 to h2\")\n",
+ "\n",
+ " f1 = h1/(0.5*hi)-1\n",
+ " Tbar = f1 * (T1- (L/ci) * (muS/T1)) + (1-f1)*(T2) # (39) \n",
+ " T2 = Tbar # (40)\n",
+ " elif h2>h1:\n",
+ " #print(\"convert h2 to h1\") \n",
+ "\n",
+ " f1 = h1/(0.5*hi) \n",
+ " Tbar = f1 * (T1- (L/ci) * (muS/T1)) + (1-f1)*(T2) # (39) \n",
+ " T1 = (Tbar - (Tbar**2 + 4*muS*L / ci)**0.5)/2 # (38)\n",
+ "\n",
+ "\n",
+ " # check if T2>-muS through ice conversion\n",
+ " # not explicitly defined in Winton (2000), taken from the ICON code\n",
+ " # available energy h2*ci*(T2 + muS) is used t euqally melt upper and lower layer with enthalpies E(T1) and E2(-muS)\n",
+ " if T2>-muS:\n",
+ " print(\"T2 over bulk melting temperature: melting\")\n",
+ " #hi = hi - h2*ci*(T2 + muS) / (0.5*L- 0.5*(ci*(T1 +muS) - L * (1+muS/T1)))\n",
+ " hi = hi - h2*ci*(T2 + muS) / (-0.5* E(T1) - 0.5* E2(-muS))\n",
+ " T2=-muS\n",
+ " \n",
+ " #print(\"hi: \" +str(hiold) +\" -> \" +str(hi) +\" (\" +str(hi_ICON) +\") m\")\n",
+ " #print(\"hs: \" +str(hsold) +\" -> \" +str(hs) +\" (\" +str(hs_ICON) +\") m\")\n",
+ " #print(\"Tsurf: \" +str(Tsurfold) +\" -> \" +str(Tsurf) +\" (\" +str(Tsurf_ICON) +\") °C\")\n",
+ " #print(\"T1: \" +str(T1old) +\" -> \" +str(T1) +\" (\" +str(T1_ICON) +\") °C\")\n",
+ " #print(\"T2: \" +str(T2old) +\" -> \" +str(T2) +\" (\" +str(T2_ICON) +\") °C\")\n",
+ "\n",
+ " #print(\"\")\n",
+ " return hi, hs, T1, T2, fluxres_w"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# surface fluxes \n",
+ "def surface_fluxes_semtner(Tsurfold, rsds, rlds, lhflx, shflx, albedo):\n",
+ " #print(\"calculate surface fluxes\")\n",
+ "\n",
+ " # surface energy flux balance \n",
+ " # longwave incoming + longwave outgoing + shortwave balance + latent heat flux + sensible heat flux\n",
+ " # in ICON: addition of zemissdef\n",
+ " SWnet = rsds * (1-albedo) # = swflx_ice\n",
+ " nonsolar_ice = 1* ( zemissdef *(rlds - sigma* (Tsurfold+Tmelt)**4 ) + lhflx + shflx ) #UPWARD flux # =nonsolar_ice!\n",
+ " dnonsolarT = -4 * zemissdef * sigma * (Tsurfold+Tmelt)**3 # dnonsolarT\n",
+ " \n",
+ " #print(\"FS = \" +str(FS) +\" W/m^2\")\n",
+ " #print(\"dFS = \" +str(dFS) +\" + \" +str(rhoi * hcilayer /dtime *ci) +\" W/(m^2K)\")\n",
+ " \n",
+ " return nonsolar_ice, dnonsolarT, SWnet\n",
+ "\n",
+ "\n",
+ "# set ice temperature\n",
+ "def set_ice_temp_semtner(Tsurfold, hiold, hsold, nonsolar_ice, dnonsolarT, SWnet, c_stability=True):\n",
+ " \n",
+ " # effective heat conductivity of ice&snow\n",
+ " k_eff = (Ki * Ks) / (Ks*hiold + Ki*hsold) # in Semtner paper not a variable but used in the formula for Fs\n",
+ " \n",
+ " F_A = -1*nonsolar_ice - SWnet # Flux from atmosphere, not in Semtner paper\n",
+ " \n",
+ " F_S = k_eff * (Tf - Tsurfold) # Flux into ice\n",
+ " \n",
+ " deltaTdenominator = k_eff - dnonsolarT \n",
+ " if c_stability:\n",
+ " deltaTdenominator = deltaTdenominator +rhoi*hcilayer*ci/dtime # addition of constant heat capacity to stabilize surface temperture (not in Semtner paper)\n",
+ " \n",
+ " deltaT = (F_S - F_A) / deltaTdenominator\n",
+ " \n",
+ " if Tsurfold + deltaT > 0: # Tsurf > 0°C -> Surface melting\n",
+ " deltaT = -Tsurfold\n",
+ " Tsurf = 0\n",
+ " \n",
+ " Qtop = (F_S - F_A) - deltaT * deltaTdenominator # Qtop >0 -> melting \n",
+ " # (F_S - F_A) -> flux imbalance from old surface temperature Tsurfold\n",
+ " # Tsurfold * deltaTdenominator -> flux imbalance resulting from warming Tsurf to 0°C (Tsurfold in °C!)\n",
+ " \n",
+ " Qbot = - F_S + deltaT * k_eff # originally Qbot = -F_S\n",
+ " else:\n",
+ " Tsurf = Tsurfold + deltaT\n",
+ " \n",
+ " Qtop = 0\n",
+ " Qbot = k_eff * (Tsurf - Tf)\n",
+ " \n",
+ " return Tsurf, Qbot, Qtop, F_A, F_S\n",
+ "\n",
+ "\n",
+ "def ice_growth_semtner(hiold, hsold, Tsurf, Qbot, Qtop, prls, prcs):\n",
+ " \n",
+ " #print(\"calculate snow accumulation\")\n",
+ " hs=hsold +(prls+prcs)*dtime / rhos\n",
+ " \n",
+ " \n",
+ " if hs>0:\n",
+ " hs = max(hs - dtime * Qtop / (L*rhos),0) # alf*rhos = qs\n",
+ " hi = hiold\n",
+ " else:\n",
+ " hi = max(hiold - dtime* Qtop / (L*rhoi),0) # alf*rhoi = qi\n",
+ " \n",
+ " hi = hi - dtime * Qbot / (L*rhoi) # Qbot=-F_S; alf*rhoi = qi\n",
+ " \n",
+ " return hi, hs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def set_albedo(hi, hs, Tsurf): # after set_ice_albedo in mo_sea_ice.f90 in ICON\n",
+ "\n",
+ " if hs>1e-2*rhow/rhos: # snow has to be thicker than 0.034m\n",
+ " if Tsurf==0:\n",
+ " albedo=albsnow_warm\n",
+ " elif Tsurf <=-1:\n",
+ " albedo=albsnow_cold\n",
+ " else:\n",
+ " albedo = (1 + Tsurf)*albsnow_warm - Tsurf*albsnow_cold\n",
+ "\n",
+ " else: # bare ice\n",
+ " if Tsurf==0:\n",
+ " albedo=albice_warm\n",
+ " elif Tsurf <=-1:\n",
+ " albedo=albice_cold\n",
+ " else:\n",
+ " albedo = (1 + Tsurf)*albice_warm - Tsurf*albice_cold\n",
+ " \n",
+ " \n",
+ " return albedo\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ -86400. -85800. -85200. ... 2677200. 2677800. 2678400.]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x2abbf1b7eeb8>]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# initial conditions\n",
+ "init_Tsurf = -10\n",
+ "init_T1 = -10\n",
+ "init_T2 = -6\n",
+ "init_Qbot = 0\n",
+ "init_Qtop = 0\n",
+ "init_hi = 1\n",
+ "init_hs = 0\n",
+ "\n",
+ "prls = 0\n",
+ "prcs = 0\n",
+ "albedo_init = albice_cold\n",
+ "\n",
+ "# boundary conditions\n",
+ "lhflx = 0\n",
+ "shflx = 0\n",
+ "\n",
+ "initial_conditions=\"melting_icon\"\n",
+ "if initial_conditions==\"melting\":\n",
+ " # melting -> melt ratched effect with abbot (2010) zemissfac\n",
+ " rlds = 0 \n",
+ " shflx = 60 \n",
+ " rsds = 500\n",
+ " init_T1 = -1.9\n",
+ " init_T2 = -1.9\n",
+ " init_Tsurf = -3\n",
+ " init_hi = 4\n",
+ "elif initial_conditions==\"melting_icon\":\n",
+ " # melting -> melt ratched effect with ICON zemissfac\n",
+ " rlds = 0\n",
+ " shflx = 150\n",
+ " rsds = 750\n",
+ " init_T1 = -1.9\n",
+ " init_T2 = -1.9\n",
+ " init_Tsurf = -3\n",
+ " init_hi = 4\n",
+ " \"\"\"\n",
+ " rlds = 0\n",
+ " shflx = 150 \n",
+ " rsds = 800\n",
+ " init_T1 = -4.5\n",
+ " init_T2 = -2.5\n",
+ " init_Tsurf = -3\n",
+ " init_hi = 1\"\"\"\n",
+ "elif initial_conditions==\"melting_snow\":\n",
+ " rlds = 0\n",
+ " shflx = 150\n",
+ " rsds = 750\n",
+ " init_T1 = -20\n",
+ " init_T2 = -15\n",
+ " init_Tsurf = -5\n",
+ " init_hi = 20\n",
+ " init_hs = 1\n",
+ "elif initial_conditions==\"freezing\":\n",
+ " # freezing\n",
+ " rlds = 0 \n",
+ " shflx = 20\n",
+ " init_hi = 2\n",
+ " init_hs = 0.5\n",
+ " rsds = 500\n",
+ " #rlds = 100 \n",
+ " #rsds = 200\n",
+ " init_T1 = -9\n",
+ " init_T2 = -5\n",
+ " init_Tsurf = -5\n",
+ "elif initial_conditions==\"freezing_2\":\n",
+ " # freezing\n",
+ " init_hi = 0.05\n",
+ " init_hs = 0.0\n",
+ " prls = 0\n",
+ " rlds = 0 \n",
+ " shflx = 20\n",
+ " rsds = 500\n",
+ " #rlds = 100 \n",
+ " #rsds = 200\n",
+ " init_T1 = -1.9\n",
+ " init_T2 = -1.9\n",
+ " init_Tsurf = -1.9\n",
+ "elif initial_conditions==\"freezing_cold\":\n",
+ " # freezing\n",
+ " init_hi = 10\n",
+ " init_hs = 0.0\n",
+ " prls = 0\n",
+ " rlds = 0 \n",
+ " shflx = 20\n",
+ " rsds = 500\n",
+ " #rlds = 100 \n",
+ " #rsds = 200\n",
+ " init_T1 = -40\n",
+ " init_T2 = -50\n",
+ " init_Tsurf = -20\n",
+ "elif initial_conditions==\"melting_cold\":\n",
+ " # freezing\n",
+ " init_hi = 10\n",
+ " init_hs = 1.0\n",
+ " prls = 0\n",
+ " rlds = 0 \n",
+ " shflx = 150\n",
+ " rsds = 750\n",
+ " #rlds = 100 \n",
+ " #rsds = 200\n",
+ " init_T1 = -100\n",
+ " init_T2 = -100\n",
+ " init_Tsurf = -60\n",
+ "elif initial_conditions==\"melting_2\":\n",
+ " rlds = 120 \n",
+ " rsds = 300\n",
+ " init_hi = 1\n",
+ " init_T1 = -5\n",
+ " init_T2 = -3\n",
+ " init_Tsurf = -8\n",
+ "elif initial_conditions==\"snowball\":\n",
+ " shflx = -50\n",
+ " rlds = 0\n",
+ " rsds = 800\n",
+ " init_T1 = -7\n",
+ " init_T2 = -5\n",
+ " init_Tsurf = -10\n",
+ " init_hi = 20\n",
+ "elif initial_conditions==\"abbot\":\n",
+ " shflx = 80 \n",
+ " rlds = 0\n",
+ " rsds = 40/ albedo_init\n",
+ " init_T1 = -3.5\n",
+ " init_T2 = -2.8\n",
+ " init_Tsurf = -8\n",
+ "else:\n",
+ " rlds=0\n",
+ " rsds=0\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "#artificial radiative forcing\n",
+ "days = 31\n",
+ "spinupdays = 1\n",
+ "time_artificial=np.linspace(-int(spinupdays),int(days),(days+spinupdays)* int((60 * 60 * 24)/dtime)+1) # timestep has to be 600s = 10m = 1/6h = 1/144d \n",
+ " # = dtime / (60 * 60 * 24)\n",
+ "print(time_artificial*24*60*60)\n",
+ "rsds_array_artificial = rsds*np.sin(time_artificial*2*np.pi-np.pi/2)\n",
+ "rsds_array_artificial[rsds_array_artificial<0]=0\n",
+ "plt.plot(time_artificial,rsds_array_artificial)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Winton model\n",
+ "\n",
+ "Initial conditions are defined here. Albedo calculation is activatred or deactivated by commenting out the set_albedo function. The model breaks if the ice thickness is below 0m, there is no handling of minimal ice thickness implemented as in the ICON sea-ice scheme!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-1.7990559582288546\n",
+ "-1.8775725110044958\n"
+ ]
+ }
+ ],
+ "source": [
+ "# run Winton model\n",
+ "\n",
+ "# initial conditions\n",
+ "Tsurf = init_Tsurf\n",
+ "T1 = init_T1\n",
+ "T2 = init_T2\n",
+ "Qbot = init_Qbot\n",
+ "Qtop = init_Qtop\n",
+ "hi = init_hi\n",
+ "hs = init_hs\n",
+ "albedo = albedo_init\n",
+ "\n",
+ "# result array\n",
+ "hi_result=[]\n",
+ "hi_result.append(hi)\n",
+ "\n",
+ "hs_result=[]\n",
+ "hs_result.append(hs)\n",
+ "\n",
+ "Tsurf_result=[]\n",
+ "Tsurf_result.append(Tsurf)\n",
+ "\n",
+ "T1_result=[]\n",
+ "T1_result.append(T1)\n",
+ "\n",
+ "T2_result=[]\n",
+ "T2_result.append(T2)\n",
+ "\n",
+ "Qbot_result=[]\n",
+ "Qbot_result.append(Qbot)\n",
+ "\n",
+ "Qtop_result=[]\n",
+ "Qtop_result.append(Qtop)\n",
+ "\n",
+ "albedo_result=[]\n",
+ "albedo_result.append(albedo)\n",
+ "\n",
+ "FS_result=[]\n",
+ "\n",
+ "SWnet_result=[]\n",
+ "\n",
+ "for i in range(1,rsds_array_artificial.size):\n",
+ " FS, dFS, SWnet, I, Tsurfmelt = surface_fluxes(Tsurf, hs, rsds_array_artificial[i], rlds, lhflx, shflx, albedo)\n",
+ " Tsurf, T1, T2, Qbot, Qtop = set_ice_temp(Tsurf, T1, T2, hi, hs, FS, dFS, SWnet, I, Tsurfmelt, True)\n",
+ " #Qtop=0\n",
+ " hi, hs, T1, T2, fluxres_w = ice_growth(hi, hs, Tsurf, T1, T2, Qbot, Qtop, prls, prcs)\n",
+ " #albedo = set_albedo(hi, hs, Tsurf)\n",
+ " #print(\"time=\", str(time_artificial[i]), \" Tsurf=\", str(Tsurf))\n",
+ " \n",
+ " \n",
+ " #hi, hs, T1, T2, fluxres_w = ice_growth(hi, hs, tsurf_array.values[tind+i,lind], t1_array.values[tind+i,lind], t2_array.values[tind+i,lind], qbot_array.values[tind+i,lind], qtop_array.values[tind+i,lind], prls_array.values[tind+i,lind], prcs_array.values[tind+i,lind])\n",
+ " \n",
+ " # assign results to final array\n",
+ " hi_result.append(hi)\n",
+ " hs_result.append(hs)\n",
+ " Tsurf_result.append(Tsurf)\n",
+ " T1_result.append(T1)\n",
+ " T2_result.append(T2)\n",
+ " Qbot_result.append(Qbot)\n",
+ " Qtop_result.append(Qtop)\n",
+ " albedo_result.append(albedo)\n",
+ " SWnet_result.append(SWnet)\n",
+ " FS_result.append(FS)\n",
+ " \n",
+ " if hi<=0: \n",
+ " break\n",
+ "print(T1)\n",
+ "print(T2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Semtner model\n",
+ "\n",
+ "Initial conditions are defined here. Albedo calculation is activatred or deactivated by commenting out the set_albedo function. The model breaks if the ice thickness is below 0m, there is no handling of minimal ice thickness implemented as in the ICON sea-ice scheme!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# run semtner model \n",
+ "\n",
+ "# initial conditions\n",
+ "Tsurf = init_Tsurf\n",
+ "T1 = init_T1\n",
+ "T2 = init_T2\n",
+ "Qbot = init_Qbot\n",
+ "Qtop = init_Qtop\n",
+ "hi = init_hi\n",
+ "hs = init_hs\n",
+ "albedo = albedo_init\n",
+ "\n",
+ "# result array\n",
+ "hi_result_Semtner=[]\n",
+ "hi_result_Semtner.append(hi)\n",
+ "\n",
+ "hs_result_Semtner=[]\n",
+ "hs_result_Semtner.append(hs)\n",
+ "\n",
+ "Tsurf_result_Semtner=[]\n",
+ "Tsurf_result_Semtner.append(Tsurf)\n",
+ "\n",
+ "T1_result_Semtner=[]\n",
+ "T1_result_Semtner.append(T1)\n",
+ "\n",
+ "T2_result_Semtner=[]\n",
+ "T2_result_Semtner.append(T2)\n",
+ "\n",
+ "Qbot_result_Semtner=[]\n",
+ "Qbot_result_Semtner.append(Qbot)\n",
+ "\n",
+ "Qtop_result_Semtner=[]\n",
+ "Qtop_result_Semtner.append(Qtop)\n",
+ "\n",
+ "albedo_result_Semtner=[]\n",
+ "albedo_result_Semtner.append(albedo)\n",
+ "\n",
+ "FS_result_Semtner=[]\n",
+ "FA_result_Semtner=[]\n",
+ "\n",
+ "SWnet_result_Semtner=[]\n",
+ "\n",
+ "for i in range(1,rsds_array_artificial.size):\n",
+ " nonsolar_ice, dnonsolarT, SWnet = surface_fluxes_semtner(Tsurf, rsds_array_artificial[i], rlds, lhflx, shflx, albedo)\n",
+ " Tsurf, Qbot, Qtop, FA, FS = set_ice_temp_semtner(Tsurf, hi, hs, nonsolar_ice, dnonsolarT, SWnet, True)\n",
+ " #Qtop=0\n",
+ " hi, hs = ice_growth_semtner(hi, hs, Tsurf, Qbot, Qtop, prls, prcs)\n",
+ " #albedo = set_albedo(hi, hs, Tsurf)\n",
+ " #hi, hs = ice_growth(hi, hs, tsurf_array.values[tind+i,lind], qbot_array.values[tind+i,lind], qtop_array.values[tind+i,lind], prls_array.values[tind+i,lind], prcs_array.values[tind+i,lind])\n",
+ "\n",
+ " \n",
+ " \n",
+ " #hi, hs, T1, T2, fluxres_w = ice_growth(hi, hs, tsurf_array.values[tind+i,lind], t1_array.values[tind+i,lind], t2_array.values[tind+i,lind], qbot_array.values[tind+i,lind], qtop_array.values[tind+i,lind], prls_array.values[tind+i,lind], prcs_array.values[tind+i,lind])\n",
+ " \n",
+ " # assign results to final array\n",
+ " hi_result_Semtner.append(hi)\n",
+ " hs_result_Semtner.append(hs)\n",
+ " Tsurf_result_Semtner.append(Tsurf)\n",
+ " Qbot_result_Semtner.append(Qbot)\n",
+ " Qtop_result_Semtner.append(Qtop)\n",
+ " albedo_result_Semtner.append(albedo)\n",
+ " SWnet_result_Semtner.append(SWnet)\n",
+ " FS_result_Semtner.append(FS)\n",
+ " FA_result_Semtner.append(FA)\n",
+ " if hi<=0: \n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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5JSkCQgBpO+awMy+Uezq2MjuKKIKOdSrwSIdQpm+O52ePbhDeFVb8B84eNDua05EiIFxeTvJRKqTuZW9AZ8IrljY7jiii57rWoXFwAC/M3UPiHe+ARymYNwZys82O5lSkCAiXd2T1DwBUbTvI5CTiVnhY3Ph0cBNQ8NjCk2T3+ABO7oC1H5gdzalIERAuz33/QvaqMNpGNTM7irhFweV8ePv+huw6cZH3EupCgwGw+m1I3G52NKchRUC4tGOHYqmVfZCLNXvibpH/Ds7o7gaVGdqyOpPWHGVt7efBryLMe0QWqC8i+akXLu3Ab98DULfzMJOTCGu80qseEZVK8+T8OC50/QiSD8LK182O5RSkCAiXdSkjm6qJS4n3jqBstdpmxxFW8Paw8NmQJlzJyuWxjf7kNX8YNk2Ao6vNjubwpAgIl7Vs3WYaqKN4NOhrdhRRDMIqlOa1PvXZePQcn1sehMAwmP8opJ83O5pDkyIgXFJenub8llkAVG4jo4JKiv7NqnF/02p8sPoEW5u9C5fPwKInZJK5m5AiIFzSusPJtMxYy4WASCgbYnYcUUyUUrzZN5K6lcowank2F1q/APsWwfZpZkdzWFIEhEtavHoTjd2OUrppP7OjiGLm7WFh4jBjuO+w2Obk1uxgTDInTxMXSIqAcDnx59Ipe/xnANylP6BEqh7ow8eDmhB76jL/dX8S7VEK5jwEOZlmR3M4UgSEy/l+83HudttCdoVG0hRUgnWKqMCTXcL5dk8GqyP+A6f2yLDRAkgREC7lSlYua7dso5HbETwa3md2HGFjT3YJp2OdIB7eHMTZug/Cxs+MhWjEH6QICJeyYGci7bLXG1/Uv9fcMMLm3NwUHw1sTMUy3vQ/0oOc8nVh3qNw+azZ0RyGFAHhMrTWTN14nPu9o9GVG0tTkIsI8PFk4rBmJKUr/uX2FDojxXh+QIaNAlIEhAuJPn6B1FNHiMg9iJK7AJcSWdWfN+6NZFZ8aZZWHQ+Hf4XNE82O5RCkCAiXMXVDHH29thpf1JMi4Gr6RwUzvHUNHj3YlMSKnWD5KzLbKFIEhIs4fSmDpTGnGOS7HSo3hnI1zY4kTPBKr3q0CwuiT8IQMksFwewRcOWi2bFMJUVAuIQfNsdTSZ+hatpe6RB2Ye4WNz4f0pTSZSsw5sp49KVEWDDOpfsHpAiIEi8rJ4/pm+N5vNJeY4M0Bbk0fx8Pvhoexfa8MCZ7Dof9i126f0CKgCjxfolJIvlyJj3cNkLlRtIUJKgV5MfnQ5ryVkpndvq0QS9/BRK2mR3LFFIERIk3dUMcrcqlUebcbrkLEH+4o3YQr/Sqz4PnR3DJo3x+/8AFs2PZnRQBUaLtSUhhe/xFnqm6z9gg/QHiOiPahNCzRV0evPQoeZdOwnzX6x+QIiBKtGkb4/DxtND08ur8pqBQsyMJB6KU4rV7IvEKacH/5QyBAz8bK5K5ECkCosS6kJbFgl0nGVnfgnvSNmkKEgXydHdj4rBmLPPryypaoH/9N5zYanYsu5EiIEqsGVtPkJWTx4iAXcYGaQoSN1DO15NvH2rBq+pRTuly5M4aDmnJZseyC6uKgFKqv1IqVimVp5SK+strLyqlDiulDiilulkXU4hbk5un+X7TcVqHBhIU/wtUaihNQeKmQoP8+HBERx7Lfpqc1LPkzh4JuTlmx7I5a+8EYoD7gDXXb1RK1QMGAfWB7sAEpZTFynMJUWQr950m8eIVHmnsAYnRUF8WjxGFa1ajHI8M6stL2SOxxK0hzwXWH7CqCGit92mtDxTwUh9ghtY6U2t9DDgMtLDmXELcimkbj1PF35v22RuMDdIUJIqoe2Rl6vV4lO9zuuC24WPYu8DsSDZlqz6BqsCJ675OyN/2N0qpMUqpaKVU9NmzMse3sN7hM6msO5zM0FY1sOxbIE1B4pY91K4m8S3+zY68MLLmjIWzBf2tWzIUWgSUUiuUUjEFfPS52dsK2Fbg4Fut9Zda6yitdVRQUFBRcwtxQ9M2HsfT4saQOgoStspdgLgtL/RqxKzQN7mU487lqQMh45LZkWyi0CKgtb5Tax1ZwMfN7pESgODrvq4GnLQ2rBCFSc3IZs62BHo1qkzZuF+MjTI0VNwGNzfFf4bexSflXsI79Tjnp48ukQ+S2ao5aCEwSCnlpZSqCYQDW2x0LiH+MHd7ImlZuQxvHQJ75xtNQYG1zI4lnJS3h4WnRz/EZO8RlItfxqklb5kdqdhZO0S0r1IqAWgN/KyUWgagtY4FZgF7gaXAOK11rrVhhbiZvDzN1I1xNA4OoFGZy9IUJIpFWV9P+ox9kxVu7Qja+g5J25eYHalYWTs6aJ7WuprW2ktrXVFr3e26197UWtfSWtfRWv9ifVQhbm79kWSOnk1jeJsa10Z0SFOQKAZVyvpQa9QUjlEVn4UPczpun9mRio08MSxKjKkbjhPo68ndDSpD7Dyo1ECagkSxqVm1InkDfwCtSZ82gHPnz5kdqVhIERAlwonz6azcf5rBLarjlZZkNAXJXYAoZrXrNiKp2ySCcxM49MVgUq9kmh3JalIERInw3abjuCnF0FbVrzUFyVPCwgYi2vTmaLOXaJW9mV8/f4KMbOfu7pQiIJxeelYOM7bE0z2yEpX9S0HsfGkKEjZVu/ezHK/Rj/suz+DbL98nOzfP7Ei3TYqAcHrzdiRyKSOHkW1C4OIJSNgiTUHCtpSixgNfcKZsE4afeZf3v51JjpMWAikCwqlprfl2fRyRVcvQrEZZ49kAgMj7zA0mSj53TyqMmk2udzkejP8X/56+yikLgRQB4dQ2HDnHoTOXGdGmJkopiJkLlRvLXEHCPvyC8BsxiyBLOvcfeoEXZkWTm+dcTxVLERBO7Zv1cQT6etKrYWU4fwxObpe7AGFflRvhcf9EmrkdotXe//L87F1OVQikCAinFX/OGBY6pGV1vD0s15qCpD9A2Fv9vtDhBfpZ1lBx9+e8OHc3eU5SCKQICKc1bWMcFqUY2rKGsSFmLlSNgrI1TM0lXFTHF6BBf573mEX69tm8NH+PUxQCKQLCKaVl5jAz+gQ9GlSmkr83nDsCp3ZLU5Awj1Jwz2fo4FZ85DWJA1tX8u+FMWgHn3lUioBwSnN3JJKakcOINiHGhpi5xr/SFCTM5OGNGjQdS0BVvvP9mNWbt/KveTEOfUcgRUA4HWNY6DEaVvOnafUAY2PsXAhuBf4FLmAnhP34BqKGzsbHXTPf/2N+3rKP52bvctjho1IEhNNZdziZI2fTGNEmxBgWemY/nNkrTUHCcZQPRw38nsCsRH6uNJmFO47zxIwdZOU4XiGQIiCczrfr4yjv50nPhpWNDbHzAAX1brbiqRB2VrM93PMJwRe3sDh0Hkv2JDH2+20ON9eQFAHhVOKS01h14AxDWlTHy91iLPcXOxdC2kHpSmbHE+LPGg+B9s8RcXIe8xpuYdX+M4yeGk16Vo7Zyf4gRUA4lWkbjxvDQlvlDwM9HQvJB2UFMeG4Or0Ekf1ocvBjZrWOZ8ORZIZP2UJqRrbZyQApAsKJXM7MYXb0Ce5uUJmKZbyNjbHzQLlBXWkKEg7KzQ3unQAh7Wmx62V+7HyFHfEXGfrVZi6kZZmdToqAcB6zo0+QmpnDiLYhxoarTUE17wC/IFOzCXFT7l4w6AcoX4eWW5/kh56l2H8qlX4TN5B48Yqp0aQICKeQm6eZsv4YTasH0LR6WWNj0i44fxTqy6gg4QS8/WHYT1CqLC03PsKs/pU5k5rJ/RM2cOBUqmmxpAgIp7As9hQnzl/h4fbXzQ4aOxfc3KFub/OCCXErylSBYXMgJ5PGa0Yx58E65GlN/4kb2Bp33pRIUgSEU/hq7VGql/Oha/38EUBaG/0BoR3Bp5yZ0YS4NUF1YPAMSEmg9qrRzB3dmPJ+Xgz7ajO/7j1t9zhSBITD23b8AtvjL/JQ2xAsbsrYmLgdLsZLU5BwTjVaw/1fQUI01VY9zuwxzYmoXIZHvotm5tZ4u0aRIiAc3ldrj1LG253+UcHXNsbOBTcPiOhpXjAhrFG3N9z9LhxYQuDvLzB9VAvahQfxzzl7+GzVIbtNPOdul7MIcZviz6WzLPYUj3Soha9X/o9rXp7RFBTWBUoFmBtQCGu0eBgun4Y17+JbqixfPfgq/5yzm/eWHyTxYgav96mPh8W2f6tLERAObcr6Y1jc1LXZQgHiN8ClRLjzNdNyCVFsOr0EVy7A+o/xLFWW9/s/RWV/byb8foSEC+l8PrQpZbw9bHZ6aQ4SDislPZtZ0Sfo3ajKtYfDAHbPAg9fiLjbvHBCFBeloMe70KA/rHgVtx1Teb57BO/c35CNR87R74sNnDifbrPTSxEQDmv6lnjSs3IZ3e66YaE5mbB3gdEX4OlrXjghipObG9z7BYR3hUVPQcxcBjQPZtpDLTiVkkHfCevZeeKibU5tk6MKYaXMnFy+3XCMtmGB1KtS5toLh1dAxkVoOMC8cELYgsUD+k+F6q1g7hg4vII2YeWZ+1hbSnlaGDhpI7/sSSr200oREA5p3vZETl/KZGyHWn9+Yfcs8CkPoZ3MCSaELXn6GM8QVIiAmQ9A/GbCKvgx/7G21K9Shkd/2M7E1UeKdeSQFAHhcHLzNJPWHCWyahnahZW/9kLGJTi41Fg8xiJjGkQJVSoAhs2F0pVhen84FUOgnxfTH25Fr4aVeeuX/Tw7e1exrUsgRUA4nF9ikjiWnMZjHcOMlcOu2rcIcjKMDjQhSjK/CvDgfPD0g2l94OwBvD0sfDKoCc/cVZu52xMZOGkjSSnWTz5nVRFQSvVXSsUqpfKUUlHXbQ9RSl1RSu3M/5hodVLhErTWTPjtCKHlfelW/y+LxOyZBWVDoFpzU7IJYVcB1eHBhcZU6VPvgXNHcHNTPNElnC8faMbhM5fp/el6th23bs4ha+8EYoD7gDUFvHZEa904/2NsUQ52KiWD3Dz7PCUnHNPqg2fZm3SJsR1qXZsiAiD1FBxbY9wFXH93IERJVj4Mhi+EvGyjEFw4DkDX+pWYN64tfl4WBn25yapTWFUEtNb7tNYHrEpwnbOXMxnxzRaHWGhBmOOL349Q2d+be5tU/fMLMXNB50EDGRUkXEyFuvDgAsi6DFN7Q0oiALUrlmbBuHa0rlW+kAPcnC37BGoqpXYopVYrpdrfaCel1BilVLRSKtrfPZfNR8/T+7N1xCSm2DCacETbjl9g87HzjG4fiqf7X34098yCyo0gqLY54YQwU6UG8MBc48niqb2NO2PA38eDb0ZY1zxaaBFQSq1QSsUU8HGz9fySgOpa6ybAM8B0pVSZgnbUWn+ptY7SWkdVr1iOWWNbk5Oruf+LDczbkXB735VwSl/8fpgAHw8Gtwj+8wvJh+HkDrkLEK6tajMY+pNRAKb1gbRkgD83m96GQouA1vpOrXVkAR8LbvKeTK31ufzPtwFHgCL9Cdc4OIBFj7ejUXAAT8/cxasLY8nKySvq9yOc1IFTqazYd4YRbULw8fzL8M89swBlDA0VwpVVbwlDZxl9A9PuhXTrF6KxSXOQUipIKWXJ/zwUCAeOFvX9QaW9+GF0Sx5qW5NvN8TRf6Jt584Q5vvst8P4eFoY3jrkzy/k5cGuHyG0g7EqkxCuLqQdDJ4OyQfhO+sLgbVDRPsqpRKA1sDPSqll+S/dAexWSu0CfgLGaq0LT5qT8cenHhY3/t27HhOHNeNocho9P1nLsthT1sQVDurg6VQW7z7J8DYhlPX1/POL8RuMxWMaDzUnnBCOqFZnY+H6M/uMpiErWDs6aJ7WuprW2ktrXVFr3S1/+xytdX2tdSOtdVOt9aIiHTD5EJze+6dN3SMr8fPj7Qkp78sj323j9UV7pXmohPl4xSF8PCyMuX794Kt2TgfP0hDRy/7BhHBk4XfBoOlwdr9Vh3GsJ4aVgqm94HTsnzZXD/Rh9tjWjGgTwpT1x+g/aaM0D5UQ+09d4uc9SYxsW/PvdwGZlyF2PkT2NeZUEUL82dVCYAXHKgKB4eDuDd/2glN7/vSSl7uFV++pzxdDm3L0zGVpHiohPvr1EKW93BndvubfX9y3CLLToNEQ+wcTwlmE32XV2x2rCLh7wYjF4OFjjIVN2v23XXo0qMziJ9pRI0EwzoUAABd3SURBVNBoHnpx7h7Ss3JMCCusFXsyhaWxpxjZriYBPp5/32HnD1C2pjG1rhDCJhyrCACUCzUKgaefUQhO7vzbLjUCfZnzaBse6RDKjK3x9PpkHXsS5OEyZ/PRikOU9nZnVLsC7gIuHIe4tUaHsEwTIYTNOF4RAChX0ygEXmVg2j1wYuvfdvF0d+PFHnX5YVRL0rNy6TthPV/8fkTmHnISexJS+HXvaUa3C8W/VAHrp+6aYfzbaKB9gwnhYhyzCIAxW+TIJeATaAyBOlbQHHXQJqw8S59qz131KvL20v0M/WoTJy9aP72qsK2PVhykjLc7I9uF/P1FrWHXdKh5hzGTohDCZhy3CAAEBMPIX4xfBD/0h4PLC97Nx5MJQ5vyTr+G7E5IocfHa1m8+6Sdw4qi2hF/gZX7zzDmjlDKeBdwFxC/ES7EybMBQtiBYxcBgNKVjDuCoAiYMcQYMlgApRQDooJZ8oTxTMH46TsYN30752VGUoeiteZ/S/ZR3s+LEW0L6AsAo0PY0w/q9rZvOCFckOMXAQCfcsac2lWbwU8jjQeIbiCkvC9zxrbmH93qsDz2FF0/XM3SGBlK6iiWxZ5ma9wFnr4rHD+vApaIzEqD2AVQ717w9LV/QCFcjHMUAQBvf2Mq1Zp3wPxHYcvkG+7qbnFjXKcwFj3ejoplvBn7/TaemrGDi+lyV2Cm7Nw83l66n7AKfgyMCi54p70LICsVGsuzAULYg/MUATD+Mhw8E+rcDUueg3Uf3nT3iEplmD+uLU/fWZvFu5O468M1rNx32k5hxV/9uCWeY8lpvNgjAnfLDX70or8xHhqs0ca+4YRwUc5VBAA8vGHANIjsByteheUvGzNN3mh3ixtP3hnO/HFtCfT1ZNTUaJ6ZuVP6CuzsUkY2H604RKvQcnSOqFDwTqdjIWELNBshzwYIYSfOVwQALB5w32RoMQY2fAoLxkFu9k3fElnVn4Xj2/FE5zAW7jrJnR+sZv6ORLSW5wrsYeLvRziflsVLd9dD3egX/LZvweIpTUFC2JFzFgEANzfo8Q50eskYUz5jKGTdfFI5T3c3nulah5+faE+NQB+emrmT4d9slcnobOzkxSt8ve4Y9zauQoNq/gXvlJUOu2ZCvT7GQAAhhF04bxEAo8mgw/PQ8wM4tNxYYOHKhULfVqdSaX4a24bX7qnPtrjzdP1wDV+tPUpOrkxRbQvvLT+ABp7rVufGO8XOg8wUaDbSbrmEEM5eBK5qPgr6f2usQ/vN3XCp8AfFLG6K4W1C+PWZDrQNC+SNn/fRd8IGWeC+mG07foG52xN5qG1NqpW9yXTQ276F8rWlQ1gIOysZRQCg/r3GIswX4+Hrbsbi5EVQJaAUkx+M4vMhTUlKyeCez9bx7wUxpFy5eR+DKFxunubfC2KoVMabxzuH3XhH6RAWwjQlpwiAsQ7tiMWQnQ5f3wUnthTpbUopejaszMpnO/BAqxp8v+k4nd/7nZ+2JUjHsRWmbz5O7MlLvNSzLr4FPRh21dUO4UaD7ZZNCGEoWUUAoEoTGLUcSgUYU1HvK9rKlgD+pTx4rU8kC8e3o0agD8/N3sWASRvZl3TJhoFLpuTLmby77ABtagXSq2HlG+8oHcJCmKrkFQGAwFow6leo1ABmPgCbJt7S2yOr+vPT2Da8c39DjpxNo9en63htUSyXMqSJqKj+u3gvV7Jzeb1P/RsPCQXpEBbCZCWzCAD4locHFxpPFy/9Jyx76aYPlf2Vm5tiQPNgVj3bgcEtgvl2Qxyd3v2d6ZvjZc2CQvx24AwLdp5kXKcwwiqUvvnO276RDmEhTFRyiwAYi5MP/M54qGzjZ8bkc9kZt3SIAB9P3ri3AQvHtSM0yJd/zdtDz0/WsuFIso1CO7e0zBxenhdDWAU/Hu1Y6+Y7J2yDhK0Q9ZB0CAthkpJdBADcLMZDZV3fgL3zjWcJ0s/f8mEaVPNn1iOt+XxIU1IzchgyeTNjpkUTl5xmg9DO652l+0m8eIW37muAl7vl5jtv/sJYPa7JMPuEE0L8TckvAmD8ldnmceg3BRK3GSOHzh25jcNcG0X0j251WH84mbs+XM3/luyT/gJg/eFkpm48zsi2IUSFFNLJe+mk0R/Q5AHwKqTJSAhhM65RBK6KvN/oJ7hyASZ3hmNrb+sw3h4WxnUK47fnOtK3SVUmrz3KHe/8xldrj5KZk1vMoZ1DypVsnpu9i9AgX/7ZPaLwN2z9CnQetBxj+3BCiBtyrSIAUKM1jF4JfhWNpqHt3932oSqU8eadfo1YNL4dDar688bP++j83mrm7Uggz8U6j19bGMuZ1Ew+GNAYb49CmoGyrxhTRte521hLWghhGtcrAgDlahrPEoS0h4XjYfkrtzRy6K8iq/rz3aiWfDeqBQE+Hjw9cxc9P13H6oNnXeJhsznbEpi7I5HxncJoHBxQ+Bt2z4Ir56HVY7YPJ4S4KdcsAmA8TDb0J4gaBRs+gVkPGEsbWqF9eBCLxrfj40GNSc3IZviULQz7ejO7TlwsptCO5/CZy7w8P4aWNcvxRJfwwt+gNWz6wniGQ4aFCmE61y0CABZ36Pm+MXrowBKY0h1SEq06pJubok/jqqx8tgP/6V2PfUmp9Pl8PaOnbiX2ZMmanO5KVi7jp2+nlKeFjwc1weJWhGGeR3+Hs/uMuwAZFiqE6Vy7CIDxi6jlI8ayleePweRORZ5z6Ga83C2MbFuTNc934tm7arPl2Hl6frKOx37YxsHTqcUQ3Fxaa/7x0y4OnE7lgwGNqOTvXbQ3bvoCfIOMTnohhOmkCFxVuyuM/hU8fODbnrDj+2I5rJ+XO493CWftPzvzROcw1hxMpttHa3hyxg6Onr1cLOcww4Tfj7B4dxL/6FaHjnVusFzkXyUfhkPLoPlocPeybUAhRJFIEbhehbrw8Cqo0dZYsnLJ84UuW1lU/qU8eKZrHdY+34lH7qjF8tjT3PnBap6euZNDTnZnsDz2FO8tP8A9jarwaIdCngq+3qbPjdlCox6yXTghxC1RjjR6JSoqSkdHR5sdA3JzYMV/jKkmQtpD/6ngG1ispzibmsmk1Uf4YXM8GTm5dK9fiXGdwoiseoPlFx3ElmPneeDrzURULsOMh1tRyrOQ4aBXpZ6GjxpA48HQ+2PbhhTCxSiltmmto27nvVbdCSil3lVK7VdK7VZKzVNKBVz32otKqcNKqQNKqW7WnMfuLO7Q7U24d6LRPzC5I5yKKdZTBJX24uVe9Vj/QmfGdQxj3aFken26jhHfbCE67tantbCHfUmXGDV1K1XLluKbEc2LXgDAuAvIy4a2T9ouoBDillnbHPQrEKm1bggcBF4EUErVAwYB9YHuwASl1C38xnAQjQfDyF+MJqGv7zKmOShm5Xw9ea5bHda/2Jl/dKvD7oQU+k3cyMBJG1l7yHGeMzhwKpUHvt6Mr6c70x5qQTlfz6K/+coF2Po11O8L5UJtF1IIccusKgJa6+Va65z8LzcB1fI/7wPM0Fpnaq2PAYeBFtacyzTVmsGY36FiJMweYUxJXUz9BNcr4+3BuE5hrPtnJ17uWZe4c2k88PUW7p2wgWWxp0ydvjomMYVBX27E4qb4fnTLm68VXJAtX0HWZWj3tG0CCiFuW3F2DD8E/JL/eVXgxHWvJeRv+xul1BilVLRSKvrs2bPFGKcYla4EI36+NiX11Hsg9ZRNTuXj6c7o9qGseb4Tb/aN5NzlTB75bhtd3v+d7zbGcSXLvnMTrT+czJDJm/DxdGfWI60Jq+B3awfITIVNEyC8q/GAmBDCoRRaBJRSK5RSMQV89Llun5eAHOCHq5sKOFSBf8pqrb/UWkdpraOCgoJu53uwD3dPuPtduG8yJO2ESXdA3Hqbnc7L3cLQljX4/bmOfDq4Cf6lPHhlQSyt31rJe8sOcCb11tZFuB3fbYzjwSlbqOTvzcxHWlEj0PfWD7LlS2OKiA4vFHs+IYT1brL6t0FrfefNXldKDQd6AV30tQbsBCD4ut2qASdvN6RDaTjAaBqa9YCxhvFdr0Hr8TZ7+tXd4kbvRlXo1bAy0ccvMHnNUT7//TBfrjnKPY2rMLp9TSIqlSnWc17KyObVBbHM3ZFIl4gKfDSoMaW9PW79QBmXYP0nEN7NaFYTQjgcq4aIKqW6Ax8AHbTWZ6/bXh+YjtEPUAVYCYRrrW/aluEwQ0SLIuMSLHjMWMi+Xh/o87nd5sU/lpzGN+uPMTs6gSvZubQPL8/o9qHcEV7+5uv5FsHqg2d5cc5uTqdmMr5TGE90CS/adBAFHuxd+O0NePg3qNrUqlxCiBuzZoiotUXgMOAFnMvftElrPTb/tZcw+glygKe01r8UfJRrnKoIgDEZ2oZPYcWrxqiXgd9DhSLMpV9MLqZn8cPmeL7dEMfZ1ExCg3wZ3jqE+5pWveW/3GMSU3h76X7WHkomtLwvHwxsXLQZQW8kIwU+agjVW8OQGbd/HCFEoUwrAsXN6YrAVXHrYPZIYxbSez6BBv3sevrMnFyW7Eni2w3H2XXiIr6eFu5vVo3+zYKpV6XMDf+Sv5CWxar9Z5ixNZ6tcRcI8PFgfKcwHmhdo/ClIQuz8nVY+z48sgYqN7LuWEKIm5Ii4AguJRkL2cdvhKbDoftbxkL3drbzxEWmbYhj8e4ksnLzKOPtTpPqZalUxpsAXw8ysnI5l5bFgVOpHDpjzF0UWt6XAc2DGdyiOv6lbqPt/68uJcEnTaBuL7j/K+uPJ4S4KSkCjiI3G377H6z7EIIioP83xnxEJjh3OZO1h5LZdPQcuxNSOJeWyYW0bEp5Wijn60mNQB+ah5SjVWggTasHWN2X8CeLnoQdP8Dj0bJymBB2IEXA0RxeCfMegczL0ONtaPqg68ydf/YgTGhlPFPR4y2z0wjhEkybO0jcQFgXGLseglvAoidgzihjNJErWP6yMR33Hc+ZnUQIUQRSBGyldEV4YB50fgVi5xsPlyVuNzuVbR1cZqwX0PGf4Fve7DRCiCKQImBLbhbjL+IRP+dPQtcVNk4whpaWNDmZsPRFCAyHFo+YnUYIUURSBOyhRmsYuxbC74JlL8KPgyAt2exUxWvTF3D+iDEqyv0WZhgVQphKioC9+JSDQdONRe2PrIIJreHgcrNTFY8Lx2H121Dnbgi/6SwjQggHI0XAnq4uav/wb8Zi69P7w+JnICvd7GS3T2tY/BQoN6PACSGcihQBM1SKNNYybj0eor+GSe2dt9N490zjzqbLfyAguPD9hRAORYqAWTy8jSUsH1wI2VeMlctWv2usb+wsUk/D0hegWgtoPsrsNEKI2yBFwGyhHeDR9VDvXmPGzW96wPmjZqcqnNbGLKpZ6XDPp8ZIKCGE05Ei4AhKlYV+X8N9X8HZAzCxPWz/zrGHkm75Eg6vgK5v2HXmVCFE8ZIi4Ega9jfuCqo0gYXj4cfBNlvG0iqnY2H5K8aSkS0eNjuNEMIKUgQcTUCw0U/Q7X9w9Df4vCXsmuk4dwVXLsDMYeDtbyyk4ypzIglRQkkRcERubtB6nDH/UFAdmDfGMe4K8nJhzsNw8QQM/A78KpibRwhhNSkCjqx8GIz85bq7ghawa4Z5dwUrX4fDv8Ld70D1VuZkEEIUKykCjs7Nct1dQV1jiuofBxsLt9jTxgmw/iNoNhKiHrLvuYUQNiNFwFmUD4ORS67dFUxoab+7gp3TjTmP6t4DPd+3/fmEEHYjRcCZXL0reHTDtbuC6QMhJcF259w+DRaMg9COxlKR8jyAECWKFAFnFFgr/67g/yBurTGCaPMko+O2uGgNa96FhY9DaCcY+AO4exXf8YUQDkGKgLNys0Drx+CxTRDcEn55HqZ0g9N7rT92ZirMGwur3oAGA2DwDPDys/64QgiHI0XA2ZWtAcPmwH2TjekmJrWHlf+F7IzbO17iNpjUAfbMgg4vQN9Jsj6AECWYFIGSQCloOADGbYUG/WHtezCxLRxaUfSO44vxMHcMTO4CORkwfBF0etF4ZkEIUWK5mx1AFCPfQOg70SgIi56CH+6H8rWNIZ0174CgiGsdu1obD58dXw97ZhvzALm5Q7unoO1TUCrA3O9FCGEXUgRKolqdYfxWiJkLWyYZ0z0DePpBqXJg8YD0ZMhIMbaXrmKMOmoxBvyrmZdbCGF3UgRKKncvaDwYGg0y+goSthoL12ReMhaF9/aHCvWgckNjPQBp9hHCJUkRKOmUMoaUBtYyCoIQQlxH/vwTQggXJkVACCFcmBQBIYRwYVIEhBDChVlVBJRS7yql9iuldiul5imlAvK3hyilriilduZ/TCyeuEIIIYqTtXcCvwKRWuuGwEHgxeteO6K1bpz/MdbK8wghhLABq4qA1nq51jon/8tNgDxpJIQQTqQ4+wQeAn657uuaSqkdSqnVSqn2N3qTUmqMUipaKRV99uzZYowjhBCiMEoXMsGYUmoFUKmAl17SWi/I3+clIAq4T2utlVJegJ/W+pxSqhkwH6ivtb5UyLlSgQO38X2UROWBZLNDOAi5FtfItbhGrsU1dbTWpW/njYU+May1vvNmryulhgO9gC46v6JorTOBzPzPtymljgC1gehCTndAax1VlOAlnVIqWq6FQa7FNXItrpFrcY1SqrDfrTdk7eig7sA/gXu01unXbQ9SSlnyPw8FwoGj1pxLCCFE8bN27qDPAC/gV6UUwKb8kUB3AK8rpXKAXGCs1vq8lecSQghRzKwqAlrrsBtsnwPMuY1DfmlNnhJGrsU1ci2ukWtxjVyLa277WhTaMSyEEKLkkmkjhBDChUkREEIIF2ZKEVBKdVdKHVBKHVZKvVDA615KqZn5r29WSoXYP6V9FOFaPKOU2ps/P9NKpVQNM3LaQ2HX4rr9+imltFKqxA4PLMq1UEoNyP/ZiFVKTbd3Rnspwv+R6kqp3/IfTt2tlLrbjJy2ppSaopQ6o5SKucHrSin1Sf512q2UalqkA2ut7foBWIAjQCjgCewC6v1ln8eAifmfDwJm2junA12LToBP/uePuvK1yN+vNLAGY5qSKLNzm/hzEQ7sAMrmf13B7NwmXosvgUfzP68HxJmd20bX4g6gKRBzg9fvxpi1QQGtgM1FOa4ZdwItgMNa66Na6yxgBtDnL/v0Aabmf/4T0EXlj0EtYQq9Flrr3/S1ZzBK8vxMRfm5APgv8A6QYc9wdlaUa/Ew8LnW+gKA1vqMnTPaS1GuhQbK5H/uD5y0Yz670VqvAW421L4PME0bNgEBSqnKhR3XjCJQFThx3dcJ+dsK3EcbE9SlAIF2SWdfRbkW1xvFn+dnKkkKvRZKqSZAsNZ6sT2DmaAoPxe1gdpKqfVKqU35D26WREW5Fq8Cw5RSCcAS4HH7RHM4t/r7BDBnofmC/qL/6zjVouxTEhT5+1RKDcOYn6mDTROZ56bXQinlBnwIjLBXIBMV5efCHaNJqCPG3eFapVSk1vqijbPZW1GuxWDgW631+0qp1sB3+dciz/bxHMpt/d40404gAQi+7utq/P327Y99lFLuGLd4JfGJ46JcC5RSdwIvYUzPkWmnbPZW2LUoDUQCvyul4jDaPBeW0M7hov4fWaC1ztZaH8OYeDHcTvnsqSjXYhQwC0BrvRHwxphcztUU6ffJX5lRBLYC4UqpmkopT4yO34V/2WchMDz/837AKp3f81HCFHot8ptAJmEUgJLa7guFXAutdYrWurzWOkRrHYLRP3KP1vq2J85yYEX5PzIfY9AASqnyGM1DJXF+rqJci3igC4BSqi5GEXDFeekXAg/mjxJqBaRorZMKe5Pdm4O01jlKqfHAMoye/yla61il1OtAtNZ6IfA1xi3dYYw7gEH2zmkPRbwW7wJ+wOz8vvF4rfU9poW2kSJeC5dQxGuxDOiqlNqLMT/XP7TW58xLbRtFvBbPApOVUk9jNH+MKIl/NCqlfsRo/iuf3//xH8ADQGs9EaM/5G7gMJAOjCzScUvgtRJCCFFE8sSwEEK4MCkCQgjhwqQICCGEC5MiIIQQLkyKgBBCuDApAkII4cKkCAghhAv7f9pWpr97C2UPAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0, 1)"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "l0, = plt.plot(time_artificial[1:],-np.array(FS_result_Semtner)-np.array(FA_result_Semtner))\n",
+ "l1, = plt.plot(time_artificial,Qtop_result_Semtner)\n",
+ "#l2, = plt.plot(time_artificial,Qbot_result_Semtner)\n",
+ "plt.xlim(0,1)\n",
+ "plt.legend([l0, l1],[\"F$_S$ +F$_A$\", \"Qtop\"])\n",
+ "plt.xlabel(\"time [days]\")\n",
+ "plt.ylabel(\"F [Wm$^2$]\")\n",
+ "plt.title(\"Semtner surface fluxes\")\n",
+ "plt.show()\n",
+ "\n",
+ "#plt.plot(time_artificial,rsds_array_artificial)\n",
+ "plt.plot(time_artificial,Tsurf_result)\n",
+ "plt.plot(time_artificial,Tsurf_result_Semtner)\n",
+ "plt.xlim(0,1)\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(time_artificial,Qtop_result)\n",
+ "plt.plot(time_artificial,Qtop_result_Semtner)\n",
+ "plt.xlim(0,1)\n",
+ "\n",
+ "plt.show()\n",
+ "#plt.plot(time_artificial[1:],FA_result_Semtner)\n",
+ "#plt.plot(time_artificial[1:],SWnet_result)\n",
+ "#plt.plot(time_artificial[1:],SWnet_result_Semtner)\n",
+ "plt.plot(time_artificial[1:],-np.array(FS_result_Semtner)-np.array(FA_result_Semtner))\n",
+ "plt.xlim(0,1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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h4cLAYtqzx0auFyxQci0iIiKRQ8m1EBsL991nUzu8h0WLYP9+Gzles8YS5YED4cYbrZPjWWfBb38Ll19upfcyJSZanex166zFeiCqVLGkf+7cwJ4jIiIiUpyUXAsADz5oe+9tNDpTbCyce67tAerWhU8/zf0ZiYm2T0sLPLkGK+v37ruQnp71+SIiIiIlmaqFyAmcg4kTYdo0W+B46BAsWQK//vWp781MqNPSghPLhRfC3r3Wwl1EREQkEmjkWk5S1JrXp50GdeoEL7n+1a+gTBkbvT7//OA8U0RERCSUQpZcO+cSgFlAfMbnjPfeP5rjmrOAl4GawG5goPd+U8a5vwCXYKPrU4Df+Wjp1R7F/vpXmzoSDDVqQN++MHo0fPihjaoXVZUq8L//QfXqwYlNREREJDehHLk+AqR67/c75+KAOc65j73387NdMxoY671/zTmXCowEbnDOpQAXAK0zrpsDdAZmhDBeCYKrrgru8/7xD0uIf/qp6M/Ytw8mTYLp02HAgODFJiIiIpJTyJLrjFHm/Rlv4zK2nCPPLYC7M15PBz7MvB1IAMoCLuPe7aGKVYLnwAGYNQvatIHatQt//9atNq1k50447zyru/3vfwcW05EjNmVlwQIl1yIiIhJaIV3Q6JyLdc59CewApnjvc/bsWwZckfG6H1DJOVfdez8PS7a3ZmyTvfercnn+MOfcYufc4p07d4buB5EC27QJeveGjz8u3H1jxkD79jZnOzUVrr4a+vULTkzx8ZCUBJ9/HpzniYiIiOQlpMm19z7de98GqAu0d86dk+OSe4DOzrml2LSPzcAx51wToHnGfWcCqc65i3J5/gve+yTvfVLNmjVD+aNIAZ19NlSrZlMw8pKebvWrR4600n9go8rO2bHPPrNqJbNnBy+uTp2sk+ShQ8F7poiIiEhOxVItxHu/xzk3A+gFrMh2fAvQH8A5VxG4wnu/1zk3DJjvvd+fce5jIBlbICklWEyMjTi/845ND2nbFipVgmXLrOrHwoWWSO/fb5VArr8e6teHV16x96HSqROMGmUJe48eofscERERKd1CNnLtnKvpnKuS8boc0B1Iy3FNDedcZgwPYJVDADZiI9plMhZDdgZOmhYiJdNtt8HPP0PnzlYjG2DVKmuNvmuX1cx++22bV12/vp3PmVjv3QsPPxy8Do2pqTbv+rXXLLZAtvT04MQkIiIi0SeUI9e1gdecc7FYEj/Oez/ROTccWOy9nwB0AUY65zw2Kn17xr3jgVTgK2xx4yfe+/+GMFYJovPOg6++soS6ZUs71q+fTcmIiyvYM+LiYMQI26ekBB5TuXJw001WKvCttwJ7Vvny9vM1ahR4XCIiIhJdQlktZDnQNpfjj2R7PR5LpHNekw7cEqrYJPSaNTuxBXp8fOHuL18ezjoreA1pwOZzN2kCe/YU/Rk//WTTS6ZPV3ItIiIiJ1OHRimxEhODm1zHx9uUlUB4b6UBFy60kXARERGR7EJaLUQkEM2bW3J97Fi4I8niHHTooLJ+IiIikjsl11JitWtnc67Xry/a/QcO2PSNd9+F1auDF1eXLvD117BjR/CeKSIiItFBybWUWAMGwI8/2jzpglq9Gr75xl5v3GhVQq65Bvr0CV5cXbva/rPPgvdMERERiQ6acy0lVkEqi6Sn2/znjz6yLS0NBg+2utmNGsHUqTB2rG379lnN7UAlJUG9elZ5pFkzq+1dVBUrFu7Lg4iIiJRsSq6lRJswAe6/H55/3iqIHDhgDWgyR6Lbt4cvvrA62Z07w+23w+WX27n4eOjWzSp8jB1rI9rnnx94TLGxcO+98NvfBud5ixZZwi4iIiKRT8m1lGgNG8K332ZNxQA44wzYts1e33WXjXD36gVVquT+jFat7Hww3XGHJdaBzLs+eNA6VM6apeRaREQkWjjvfbhjCIqkpCS/ePHicIchIbBmDaxcaUl0QoLVv27cONxRBcdZZ0Fysi26FBERkcjgnFvivc91aEwj11LinX22bYFKT7cpHSVJcrKV9fPeyvyJiIhIZFO1ECkVbrnF2rKXNF27wubNsHZtuCMRERGRYFByLaVC1aqwahUcORLuSE6UOZd88uTwxiEiIiLBoWkhUiq0awdHj8KyZVZhpLC2bbOFlMGeutG0KbRpA08/bV8AAi3rd8klgT1DREREAqPkWkqF5GTbz5tX8OT6m2/g/ffhgw+slvb27Vbyb8UKuOii4MTlHAwfDv36wcCBgT/vk0+gZ8/AnyMiIiJFo+RaSoUzz7TGL9Onw+9+l/+18+fDkCE2jQSsTN6IEVap5IUXrMb1rl1QrVpwYuvTB7Zuhd27i/6Mw4dtTvm8eUquRUREwknJtZQaDz1kHRq9t5Hogwdh/Xqro71wodWcHjTIEvHateE3v7GGNPXqZT0jMdH2aWmQkhK82GrWtC0QLVrYzyEiIiLho+RaSo1hw2x//HjWNBGwOcqtWmW9r1cPPvss92eEKrkOhpQUeOedkllyUEREpLRQci2lTkwMTJqU1ZCmXj0oW7Zg9zZoYNempYU0xCLp0sWmrSxdqo6PIiIi4aLkWkql3r2Ldl+ZMtbQpiQm15ll/SZNUnItIiISLkquRQppzJjgLWYMplq1oEcPePbZwKeGlC8Pd9wB5coFLz4REZHSQMm1SCF16RLuCPL25JNW6/rxxwN/Vr16cM01gT9HRESkNFG7CZFC2rcPXn8d1qwp2v1bt9qCybfegq++Cm5s559vDW+OHy/6duSIzUdX5REREZHCU3ItUkiHDsGvfw0ffVS4+8aMgQ4doE4d6N7dSv/16xeaGJ0r+la2rNXMXrAgNLGJiIhEMyXXIoV0+unQuLE1pMlLero1o3nqqaxjc+fayPCIETBtGixZAqtXhz7eokhOtvgOHQp3JCIiIpFFc65FiqB/f/jrX2HyZLjgAqhYEZYtg//8x6ZTzJ9v00diY+G666wxzWuvQVxcuCMvmO7d4ZlnYPZsWyQpIiIiBaORa5EiGDbMpk/06mV1pQFWrICRI2HHDhg4EN58E3butMQaTk6sDx6EoUNhwoTijb0gLrrIfr7//jfckYiIiEQWjVyLFEGTJvD117B8ubUdBxgwAK66quCj0wkJ8PbbNup92WWhi7UoKlSwn+df/4Ldu63xTlGVK2dVTGrUCF58IiIiJZWSa5EiatDAtkzx8YW7PyYGmjWDVauCGVXwjBgBGzbYFJeiOn4c1q+H1q2tbraIiEi0U3ItEkaJifD55+GOIncNGsCcOYE/p2FDW8Cp5FpEREoDzbkWCaPmzW10+Kefwh1J6KSmwowZNootIiIS7ZRci4RRu3ZW1m/TpqLdv3s3TJ0Kb7yRtbCypOnaFX780aqpiIiIRDtNCxEJox49YO3awt2zerWV+GvSBL77Di6+2I5Xr26VSgJZfBgKXbvafto0aNs2vLGIiIiEWgn737BI6eKc7dPT874msyHNAw9YZZLERPjLX+zceefBzJnwhz/Arl2weXPoYy6sM8+Epk0tuRYREYl2IRu5ds4lALOA+IzPGe+9fzTHNWcBLwM1gd3AQO/9poxz9YGXgHqAB3p779eHKl6RcPnoI7j5ZnjlFRt9PngQDh+GSy6x8+edZyX/YmOhc2e49dastunOWU1q7+Hpp63ySL164ftZ8nLJJdZ0p2PHU4+sZ37hyE1CArzwAjRqFNz4REREgiWU00KOAKne+/3OuThgjnPuY+999sJeo4Gx3vvXnHOpwEjghoxzY4ER3vspzrmKgJZDSVQ691w4cgT69Mk6VrcufP+9vb7rLksqe/WCqlVzf0aLFnDTTZacl0SPPAI//ABbt+Z9jff5P8N7+Owzqw3+4IPBjU9ERCRYnD/V/9GC8SHOlQfmAL/x3i/IdvxroKf3fpNzzgF7vfenOedaAC947y8s6GckJSX5xYsXBz12keKwcaONTsfFWb3s+vU1Opub1q3h9NNtEaeIiEi4OOeWeO+TcjsX0gWNzrlYYAnQBPhH9sQ6wzLgCuA5oB9QyTlXHWgK7HHOvQ80BKYC93vvT5iZ6pwbBgwDqF+/fih/FJGQql/ftkAcP27zrmvWDE5MJVFqKvz73zbSX9imPSIiIsUhpAsavffp3vs2QF2gvXPunByX3AN0ds4tBToDm4FjWNLfKeN8O6ARMDiX57/gvU/y3ifVjOaMQqQAbrnFpphEs65dbT56IF0jRUREQqlYqoV47/cAM4BeOY5v8d739963BR7MOLYX2AQs9d6v894fAz4EziuOWEUi1dln25zmXbvCHUnodO5sCyKnTw93JCIiIrkLWXLtnKvpnKuS8boc0B1Iy3FNDedcZgwPYJVDABYBVZ1zmcPRqcDKUMUqEg3atbP9woVFuz+/xYYlRZUqVitbc65FRKSkCuWc69rAaxnzrmOAcd77ic654cBi7/0EoAsw0jnnsbJ9t4NNJ3HO3QN8lrHQcQnwYghjFYl47drZqO78+fCrXxXsntWr4YMP4P33YdEi66SYng7vvmvTTGJjQxtzUfTrBw89BLVrBxZfQgKMG2elDkVERIKlWKqFFAdVCxGxUd1KlWDWrPyvmz0bhg2DtIzfJbVrB/37w513WmJ9003WObJx49DHXFhHjsDIkUVvGZ9p7FhrvjNyZHDiEhGR0iNs1UJEpHj96U82IpvZ1fHQIdiwwRLlxYthyBC49lqoU8c6J952m40E162b9YzmzW2/alXJTK7j4+GxxwJ/TlqaukaKiEjwKbkWiSKXXWb7Q4fgwmxV4mNjoVUrK9cHljTnNW+5WTPbp6XBpZeGLtZwS02FESNg716oXDnc0YiISLQolmohIlK84uNh8mSYORPWrbPydUuXwvXXn/reatWsUUta2qmvjWRdu9qXjdmzwx2JiIhEE41ci0ShmBjo0aPo9ycmRn9y3bGjfQmZPj26R+hFRKR4aeRaRE7y4oswfny4owithARISbER/uPHwfv8NxERkYJQci0iJ2naFGrVCncUoXfNNfD11zYnPSYm/825vLeyZeGzz8L904iISEmgaSEicpKffoIxY2zRX3Jy4e/fvBmWL7fGNImJNkJcEt18M8TFwcaNgT3nL3+BDz+Ebt2CE5eIiEQuJdcicpK4OCt3t29f4ZLrv/4V3nrLyv5lSkqCBQts9LekiYmBG28M/Dmff26LR0VERErg/+5EJNzKlbOGNPnVgT52DObNg2efzTo2a5YlrH/+s1XhWLfO6m2XxMQ6mDp3hq++gl27wh2JiIiEm0auRSRX/fvD/fdbx8ZLL4UKFaxF+nvv2cj0ggWwf7+Ncl9/PdSsae3E4+LCHXnx69zZ9rNnw+WXhzcWEREJrygfTxKRoho0CKpXt0V/q1bZsWXL4OmnYfduO//OO7BjhyXWcHJifewYXHwxPP988cZe3Nq1s+ojmhoiIiLOR0mNqaSkJL84+0RPEQnYtm3w5Zc277pKFThyxCprlCnE77zq1bOGLWPHhi7OkiA11RZGTply6tF75/I+V7Zs1pcVEREpmZxzS7z3Sbmd07QQEclTrVrQq1fW+/j4wj+jNDSkAbj9dhgwABo1CvxZ48bBlVcG/hwRESl+Sq5FJKSaN4dXX7VGLPmN2Ea6K66AqVNh/fr8m86c6peF//d/8L//KbkWEYlUSq5FJKSaN7eSft9/D/Xrhzua0ApGneuPP9bcbRGRSKYFjSISUh07WtL5009Fu3/nTpg0yVqyz5oV3NhKos6d4bvv7MuIiIhEHo1ci0hItWlj0yUKY9Uqq7XdoIHN1770UjseH2+VSsqXD3qYJUZmWb+ZM2HgwPDGIiIihaeRaxEpFvk1WDl2DObOtfnGzZpBixZZzWlSUqxZzdNPW7WSb74pnnjDpVUrq8yiqSEiIpFJI9ciEnLvvQdXXWULG+vXh0OH4Phx6N3bzicmwrffWom/Ll3gzjuhXz87FxtrpQArVLD3aWk2Gh6tYmPhoovgv/+FOXNyr9CS18LQnMfj4uCcc6K/Q6aISEmiOtciEnI//GBJ3vbtWceaNoXVq+31iy/CaadBz542apubw4dtNLtvX1skGc1mzLC62cH4z/MLL8DQoYE/R0REsuRX51rJtYgUi507rSFNmTI2Gg/HT7UAACAASURBVFuvnm2Su6+/hg0bAivrd+uttqD0P/8JbmwiIqWdmsiISNjVrGmt0AOxcyesWwcdOgQnppKsZUvbAtG1K3z6afTXGBcRKUk0E09EIsaoUTYn+/jxcEcSGTp3hh07SkeHTBGRkkLJtYhEjObNbe71t9+GO5LIkL2sn4iIFA8l1yISMdq3t/2CBUW7f/Pm4MUSCZo0gdq1S0fzHRGRkkJzrkUkYrRoARUrwvz5BWuw4r01pHn/fduWLbMygJs3w9/+BsOHQ6VKoY87XJyzedeTJlk5xISE3K/J697sypSxEoG5lQYUEZEsSq5FJGJk1ryeNu3U106ZArffDmvW2PuOHW3Otvfw1VdW1u/aa7NGw6PVvfdaYj1gQODPGj0a/vCHwJ8jIhLNlFyLSER58kmoXBl+/tmarBw6BN9/D2vXwpIl8LvfweWXwxlnQMOGcPfdVhu7Tp2sZyQm2j4tLfqT6zZtYP162LQpsLJ+AwfCZ58puRYRORUl1yISUdq1s/2OHdCtW9bxsmWtdXhmJZHWrWHy5Nyf0bChdS9ctSq0sZYUtWrZFohu3eDNNyE93X6DICIiuVNyLSIRqWpVq4IRHw9169rCvYK2+Y6Ls8V+KlFXcBddBP/6lzUCOv/8cEcjIlJyKbkWkYgUF2cJX1ElJsLGjcGLJ9plL+un5FpEJG8hK8XnnEtwzi10zi1zzn3tnPtTLtec5Zz7zDm33Dk3wzlXN8f505xzm51zY0IVp4iUTq+/DosWhTuKyFGnjo32q6yfiEj+QjlyfQRI9d7vd87FAXOccx977+dnu2Y0MNZ7/5pzLhUYCdyQ7fzjgNofiEjQVagQ7ggiT7duMHasTQ8pV+7k84Up63fJJXDaacGPUUQk3EKWXHvvPbA/421cxpZzPXoL4O6M19OBDzNPOOfOB84APgGSQhWniJRO+/bBnXdaJZF+/Qp//3ffwdKlsGULNGoEvXsHP8aS5t574d134Te/CfxZjz4Kjz0W+HNEREqakM65ds7FAkuAJsA/vPc5+6otA64AngP6AZWcc9WBH4GnsVHsboiIBFmFCjBhglUZKUxy/eSTMG6cNaTJNGAAXHyxzQOPZo0bW0m/nTtPLt2XWym/vMr7DRgAM2YEPTwRkRIhpMm19z4daOOcqwJ84Jw7x3u/Itsl9wBjnHODgVnAZuAYcBvwP+/99y6v3zMCzrlhwDCA+vXrh+aHEJGoFBNjjWWmTbMkMLf/1Bw9CvPmwfLlcMcddmz6dOsS+cwztsivbl2oVs2mOpQGFSoEPqUmNRX+8Q84fDj3rpEiIpHM+VN1DgjWBzn3KHDAez86j/MVgTTvfV3n3JtAJ+A4UBEoCzzvvb8/r+cnJSX5xYsXhyByEYlWL78MN90Ef/+77cuVg9mz4YMPbLHj4sWWAJYvD9u3W1J99GjuI9THjxe8FGBpN2GCTceZOTOwii8iIuHinFvivc912nIoq4XUzBixxjlXDugOpOW4poZzLjOGB4CXAbz313vv63vvG2Cj22PzS6xFRIriyiuhQQObe71pkx1btAief96S6FtvhfHjYds2S6zh5MTae6uice+9xRp6ROvUyX5ToMojIhKNQvmLzNrAaxnzrmOAcd77ic654cBi7/0EoAsw0jnnsWkht4cwHhGRE1SqZE1Rli2DM8+0Y3fcAXfdVfBRaOcs8VZDmoKrWtW6ac6cCQ89FO5oRESCq9imhYSapoWISLhcc42NeH/7bbgjiRz33APPPWdVQ3Kbw13Qsn6xsXD11VCzZvBjFBHJS37TQkrJEhwRkdBJTLQKIlqgV3C//739mT38cODP2rgR/vKXwJ8jIhIMSq5FRALUooXNvV65Es47L9zRRIY6daxW+IEDgZX1u+QSW4QqIlJSKLkWEQlQSorN1c5c9FhYmzfD55/bvkGDojW1iUSxsYF3aezcGUaPhoMHraqLiEi4KbkWEQlQ3bpWzq+gvIevvrL62HXrWqfHq6/OOr9tG5xxRvDjjEYXXgh//jMsXAhduoQ7GhGREJbiExEpTY4ft4Yzx4/nfv7nn60Bzd13W6fDc8+FF1+0cxdfbFVL/vMfe79iRe7PkJOlpNgixzlzwh2JiIjRyLWISBCMH2+jz089BS1b2uLGcuWgVy9LuM88E374AeLjoVs3uP9+uPxyuzc+3pLtzIoXaWl2jZxa1apwzjn2xUVl/USkJMg3uXbOVSvAM4577/cEKR4RkYh06aVw9tknNpNp396S65gY+NOfbBFf9+55z82uXRsmToSkXIs7SV6uuAIee8x+K1C5cu7X5FbaL7eyfjfcAPXrBz1EESlF8q1z7Zw7DGwB8qg4CkCs9z7s/ylSnWsRCbf9+2H5ckvSypa10erTTw93VNHvxx9tYeNXXwX+rNtvhzFjAn+OiES3QOpcr/Letz3Fw5cWOTIRkShSsaLNAQ7E8uUwfz4MGxacmEqDqlXtzy03hSnr17OnyvqJSOBOtaCxYwGeUZBrRESkACZNgltugX37wh1JdHDu5C0mJvetUycb/d6jiY4iEoB8k2vv/eHM1865qs651s658zK3nNeIiEhgmje3vSqGFL8LL7RR7Xnzwh2JiESyApXic849DiwH/gY8nbGNDmFcIiKlUvv2tl+woGj379gRvFhKmw4doEwZTQ0RkcAUtM71VUBj730X733XjC01lIGJiJRGdepAvXo277ogvLcmNA8/bG3YmzeHo0dh2TJITYXvvw9tvNGkQgVrXz99ergjEZFIVtA61yuAKoDGREREQiwlxRJm73MvIZfpgw+s/NyGDTZnuHNnuO02OHbMKpdMn25ziOvVK77YI92VV1o5xSFDbKFkbgpS1i8mBm66CZo2DX6MIlKyFTS5Hgksdc6tAI5kHvTeXxaSqERESrGnn7bW6Pv3w4wZ1pBm0yZYtw4WLYKRI6FrV6hRA1q3tlHrvn3tfabERNunpUHv3mH5MSLSrbfCf/9rTYFyVhXJq8pIbscPHoSdO+Hll4Mfo4iUbAVNrl8DRgFfAXk09xURkWA480zbr1oFl2UbwqhUCdq0gfR0e9+pk225qV7dku20tNDGGm0qVoSZMwN/Tt++askuUloVNLn+wXv/t5BGIiIiJ2jQwEaq4+NtLna1avlPE8kpMVHJdbhceCFMmADbt8MZZ4Q7GhEpTgVNrpc450YCEzhxWsgXIYlKREQoVy6wVugpKbBmTfDikYK78ELbf/459O8f3lhEpHgVNLnO7NKYnO2YB1QxRESkhBo1KtwRlF7nnw8JCTY1RMm1SOlSoOTae9811IGIiIhEi7JlrW72Z5+duuqLiESXfJNr59yl3vuJgV4jIiLF7/BhSE6GgQPhnnsKd6/3sHIlLFlilUrq1IHBg0MSZtS6+morjXjllSdWcsmuoGX9hg2zyjAiUvKdauT6KefcZiC/79xPAkquRURKmIQEOHDA5v0WNLlOT4eHHoL334dvvsk6/sQTdi42NjSxRqMbb4T//Q/mzoXjOepsFaas3+7dtr31VvBjFJHgO1VyvR145hTXaLmMiEgJdcEFVrf5559tqkJOBw/avOANG2DoUEuep06Fs86yBjWpqVC/vlUs0dSGwklIsD/7QF19tcr6iUSSfJNr732XYopDRERCYMAAeO01GD4cHnsMypSBiRNh0iQr87dsmXV0PP10G2ktUwbmzbN9dlu3Wlv1+vXD8mOUap06wbhxsHGj/vxFIkFMuAMQEZHQ6dnTKleMGGEdH8GS53fftaY0995rifb69VkJdc7EGqwk4MMPF1vYkk1mWT+NXotEhoKW4hMRkQgUF2dzrleutO6DYKPYI0YU7jnNm6shTbi0amVfhObMgeuuC3c0InIqGrkWEYly8fHQtm3WiHRRFiVmdnvMayGehE5srDUEyizrJyIl26lK8aV676c553Itge+9fz80YYmISEmSmAg//QTbtkHt2uGOpvS59lorhdi7t82Pzy7nQtP83jsHt9wC7duHJEwR4dTTQjoD04A+uZzzgJJrEZFSoEUL23/1lZLrcLjuOvjkE1i48MTpOTlHsk/1fvt22LMH3nsvNHGKyKmrhTyasb+xeMIREZGSqF07eOUVOPfcot2/bp1Na9iyxUZeb71Vpf0KIy4O3n478Of8+tcwebK6RoqEUoEWNDrn4oErgAbZ7/HeDw9NWCIiUpJUqlS4Do3Hj1upv0aNoGZNmDnTugxmuugiaNky6GHKKXTqBK+/DmvXwtlnhzsakehU0AWNHwF9gWPAgWybiIiUEj/8AGPGWFv13OzfDx9/bKPS9epZ6/V337VzAwbAt99aO3WAFSuKJ2Y5kcr6iYReQUvx1fXe9yrMg51zCcAsID7jc8ZnTjPJds1ZwMtATWA3MNB7v8k51wb4J3AakA6M8N6/W5jPFxGR4Fq6FO68E777Djp0sCS7Vi3o0cParFerZo1mKlSAXr2gb1+49FK7t1Il2w4ftukI334b3p+ltEpMhOrVYfZsaxokIsFX0OR6rnOulff+q0I8+wiQ6r3f75yLA+Y45z723s/Pds1oYKz3/jXnXCowErgBOAj82nu/xjlXB1jinJvsvd9TiM8XEZEgSk21durPPJN17JJLLLmuUAFGj4ZmzaBzZ2v9nZuEBBsBr1ateGKWEzlnf4fTpkF6etHKMopI/k5Viu8rrCpIGeBG59w6LGl2gPfet87rXu+9BzL6gRGXseWs0NkCuDvj9XTgw4x7v8n2nC3OuR3Y6LaSaxGRMImNhenTYfVqS9LKlj2xcshvf1uw5yixDq/rr4cJE6B7dzjjjBPPFaWsX6dOoYlTJFKdauT60kAe7pyLBZYATYB/eO8X5LhkGbZQ8jmgH1DJOVfde78r2zPaA2WBk36J6JwbBgwDqF+/fiChiohIAcTFwTnnBPaMKVNg7Fh47TWIUSuzYnfFFTBkCMydC1u3Zh0vbFm/TZusrJ+Sa5ETOV8M7Z6cc1WAD4A7vfcrsh2vA4wBGmLzs68AWnrv92acrw3MAAblmE5ykqSkJL948eLQ/AAiIhI0L75olUPWr4ezzgp3NFJUN98MH3wAO3fqS5KUPs65Jd77pNzOFcu/DhlzpWcAvXIc3+K97++9bws8mHEsM7E+DZgEPHSqxFpERCJHZkOa5cvDG4cEJiUFdu+Gb7459bUipUnIkmvnXM2MEWucc+WA7kBajmtqOOcyY3gAqxyCc64sNtI91nv/n1DFKCIixa9tW5u/vSDnRMEC8N66DGZ6//2TpytI8UhJsf3cueGNQ6SkCeXIdW1gunNuObAImOK9n+icG+6cuyzjmi7AaufcN8AZwIiM41cBFwGDnXNfZmxtQhiriIgUk/LlrdPj/AL+TvL4cUvg7rkHGje2Ws3ew/ffw6OPWolAKX5Nm9riVCXXIicqaCm+QvPeLwfa5nL8kWyvxwPjc7nmDeCNUMUmIiLhdeGFkJZ26jbcL70EDz4IO3bYYsru3aF/f0u4DxywZjQrVsB55xVf7GJiYmwx48cfw7FjUCZkGYVIZNG/CiIiUuyeecamhmzbBp99BkeOwObNWV0c33wTWre21uldu1pDmt69oXLlrGc0bmwJXVpa3p8joXXjjfDRR9bOvk6dE88Vtqzf0KHQrVto4hQpTkquRUSk2GU2L0lLg4EDs47XqWNJ9dGj9r5vX9tyExcHTZoouQ6nPn3grrvsC1L2v4fClvXbuNEWRyq5lmig5FpERMKmfXurNlG2rDU0yauzY14SE5Vch1NMDPz1r4E/57bb4I031DVSooMqU4qISNiULw9nn231rgubWIM1RBkwIPhxSfFKSYF9++Drr8MdiUjgNHItIiIRK/uUEolc2cv6tW4d3lhEAqWRaxERiWj79sEPP4Q7CglEw4Y2LUhl/SQaaORaREQi1rFjUKuWzdl96qnC3eu9VSaZM8cqlVSpAg88oFbe4eAcdO4Mn3wCP/9sc/BFIpWSaxERiVhlyhSuIQ3A/v1WO/uDD6wRDUDVqvDuu/nX3JbQuvFGGDfOaqDXrXviuZxl+/J77RwMGQI9e4YuVpH8KLkWEZGIduGF8NxzsGePjT7ntHs3zJxpSfUNN9giyokTrQ37449bEnbGGUqsw61nT7jvPpg8GdauzTpelLJ+27cruZbwcT7nP5URKikpyS9evDjcYYiISDFbtMhK+t1yC/zrX3bslVdg6lSb9rF6tR1r1QqWL7fXuZV8+/BDS7x79Ci+2CX47r4b/v1v2LvXaqGLhIJzbon3Pim3c5pZJiIiES0pCS67DMaOzTo2dy58/rmV+XvySRu5XrIk63xutZQfe8xGwCWypaTAoUOwbFm4I5HSStNCREQkojln86c3bsw69sILhZ/mkZhoo+AS2Tp2tP3cufbFS6S4aeRaREQiXkwMNGiQ9b4o86cTE+G77+Dw4aCFJWFQty7Uq6eyfhI+Sq5FRESw5Np7WLMm3JFIoDp3hilT9EVJwkPTQkRERIAWLWy/YoUtfpTIdeON8MYbVkmmfv0TzxWmrB/AoEFwySWhiVOik5JrERERoGVLG7Vu3Lho9y9bZmXkNm2C006zBZJl9H/ZsOjaFf74Ryu5mFnWL3txtPzK+WV//f339vep5FoKQ6X4REREiuDoUatC0rEjVKgAI0daQpdp2jRL8iRy3XefVZDZuxcSEsIdjZQkKsUnIiJSAF9/DddcAzt35n5++3brInj99XD66XDxxfDxx3bu1lth1y5rpQ6wcmXxxCyhk5Ji7di/+CLckUgk0S+sREREMhw/bm3QY2NtUdyRIzb/uksXWLcua8pIjRpw+eXQt29WJ8CqVW3vvTW1iY8Py48gQZS9rF9KSnhjkcih5FpERCRDq1Y2cv3WW7aBdX7s0gUaNoS//hU6dLDkObdGNGCL4RYsKLaQJYTOOMO+UKmsnxSG5lyLiIhk4z1s3Wqvy5a1Eem8EmmJfkOGwPvvw5YtUL58uKORkiK/OdcauRYREcnGOahTJ7BnvPmmLYZLS4NKlYITl4TH4MHwyis2TSh7oyIofFm/666DPn1CEaWUJEquRUREgqx8eRvpXL1aLbgjXadO8NBD8OGHJy5Sza+cX27vN2+2soBKrqOfkmsREZEgy2xIs2yZkutI5xw8/rhtgXjwQRg1Cg4csNKNEr1Uik9ERCTIzj4bqlQp2sLG9PSsOd9gSd3PPwcvNgmPlBT7u9XysOin5FpERCTIYmKsqsj8+QW7/sgRq5c9dCjUrg3XXmvHN2+GESOsIY1EtuRk26vySPTTtBAREZEQ6N/f2md7f+Kitpwefxyeegr27YOKFeHSS2HAADuXkGCJ98qV0KtX8cQtoVG9OiQmKrkuDZRci4iIhMCwYbZftw4mTbIkecsWe790KSxZYs1ozjwTrr7amtJ063Zim+3q1aFmTas6IpGvUyd4+23Yv9++SEl0UnItIiISQtOmwW9/a6/LlbNmNMnJlmDVqGF1lIcMyfv+xEQl19Fi8GB48UVITc3q9gm5l+47VWm/K69U5ZGSSsm1iIhICF17LfTrB3FxVvM6vykiuUlMtDJwEvk6drSyfuPHwxdf2LH8Svjl9Xr7dli+XMl1SaUOjSIiIiXYl1/Crl022lnYxFyi02OP2Vz9H3+E004LdzSlU34dGlUtREREpARr08bmYiuxlkwpKXD8OCxcGO5IJDchS66dcwnOuYXOuWXOua+dc3/K5ZqznHOfOeeWO+dmOOfqZjs3yDm3JmMbFKo4RURESrLjx2HixKLVzJbo1KGDfdmaNy/ckUhuQjlyfQRI9d6fC7QBejnnknNcMxoY671vDQwHRgI456oBjwIdgPbAo865qiGMVUREpERyDm6+GZ5/vvD3Hj0KU6bYPN/Bg9XAJFpUrgwtW6qsX0kVsuTam/0Zb+MytpwTvFsAn2W8ng70zXjdE5jivd/tvf8RmAKowqeIiJQ6zll1kcKMUm7ZAoMGwRlnQI8e8Oc/w2efwd69oYtTilenTjBrFvz0U7gjkZxCWi3EORcLLAGaAP/w3uf8pdYy4ArgOaAfUMk5Vx04E/g+23WbMo7lfP4wYBhA/fr1gx6/iIhISdCpE3z0kdXIbtToxHPew3ffwcyZNqLZv79VJZkyxapJ9O9vCXa5cuGJXUJj0CD45z+tuVDTplnHi1LWr29fa14kwVEs1UKcc1WAD4A7vfcrsh2vA4wBGgKzsES7JZYwx3vvn8i47mHgoPf+6bw+Q9VCREQkWm3YAA0awA03wNixdmz4cGuv/sUXVpoNLJmeMMFeHz9ubdizu/9+S7wffLDYQpcQ8d6m+7z9tv1dZx7Lec2pXu/ebTW3v/oqdLFGo/yqhRRLnWvv/R7n3AxsaseKbMe3AP0zgqwIXOG93+uc2wR0yfaIusCM4ohVRESkpDnrLLj7bvjkk6xjX34JW7fCxRdb9YiUFGjdOut8zsQarCvkTz8puY4GzsGIEbYFYvhwK+33008q6xcsIUuunXM1gaMZiXU5oDswKsc1NYDd3vvjwAPAyxmnJgNPZlvE2CPjvIiISKn0zDMnJlLvv1/4ZyQm2si39yrtJyY52f55WLTISj5K4EJZLaQ2MN05txxYhC1QnOicG+6cuyzjmi7AaufcN8AZwAgA7/1u4PGM+xYBwzOOiYiIlFqBzptOTLQRym3bghOPRL727W0/f35444gmIRu59t4vB9rmcvyRbK/HA+PzuP9lskayRUREJECJibZftQpq1w5vLFIyVKkCzZurrF8wqUOjiIhIKdGyJTRsCIcPhzsSKUm6dYNp01TWL1iKZUGjiIiIhF+tWlbOr6imTYOPP4bvv4f4eBgzxqqPSGS7/nr7u+zfH84+O+t4QUv5ZX/du7eVByzNlFyLiIiUMgVd0Lh/P0ydanWQnYNx4+DVVyEuzs717g1XXx3ycCXEkpPhD3+AN97IKslXkDJ+Od/v3w+ffgppaaGLNRJoWoiIiEgp8skncOaZsHLlyee8t8To3/+GSy6BGjWgX7+stukjRliXx507Ldletap4Y5fQGT3aFrpu327bjh1Z286dWdsPP5y47dqVtT3yCKxeDT/+GO6fJrw0ci0iIlKKtGljydMdd9hc2yNHrPlMu3bWIv3ii+26Ro3gttts1LptRnmC6tWznvPEE9CxY/HHLyVXcrLtFy6Enj3DG0s4KbkWEREpRWrVsiYyjz8O06fbsSpVLLnu0AFeegkuuACaNct/6sgf/1g88UrkaNfO/pmZP790J9fF0v68OKj9uYiISMEdPWr7MmWK1lDm0CGbAtC6de7dIKV0atXKph1l7yYajfJrf65/HUREREqhuDjbitqp8c03bbrIhg3BjUsiW7duMGOGzc0vrTQtRERERAotsyFNWprVzhYBuO46eO45uPZaaNEi63hRyvp17x6ZLdmVXIuIiEihZSZOX34Jv/pVeGORkqN9e7jzTnjlFZg1q2Al/TJfZz929CiMHw9r1oQ23lDQtBAREREptGrVrOHIggWFv/fgQdi61V4fPgwDBsDu3cGNT8Lnb3+Dffus7vWBA1nbwYNZ26FDWdvhw7YdOZK1jRwJa9daib9Io+RaREREiqRjR6sMUZDaCHv22DztK66w+tkPPGDHd+yA996DiRNDG6tElsyyfkX58hZuSq5FRESkSG65xX51n5vsCfeNN0LNmjBwoCXjQ4bATTfZudq1bWGlGtJIdklJVoVm/vxwR1J4mnMtIiIiRZKSYvs5c2DyZPt1/rZt8N13sHGj7WNioHlza6/dt6/V0s5eui8uDpo0UctsOVGFClbWT8m1iIiIlDoTJlj77Lg4OP106+7Yu7fNs61UCe67L//7mzfPvR27lG5du8I//2ll/SpXDnc0BafkWkRERAIyahT85S9Fvz8x0Vqvp6dDbGzw4pLIdt118OyzNq3o3HPtWM6Sfn37WiOjkkQdGkVERCSsDh2C+Hh1epQTeQ+33govvJD3NWPHwg03FF9MmfLr0KjkWkRERERKrJx1sLOnrs6F50uZ2p+LiIhIieU93HYbvPhiuCORksi5rCQ6JsamDmVuJfG3HSUwJBERESlNnLOKI++/X/h7f/oJ3nkHhg2DXr1gw4bgxydSGFrQKCIiImHXsSOMGwfHjxdsNHL5cnjwQfj0U/j5Z+sY2ahR1kI3kXDRyLWIiIiE3UUXWRfH3OoaHz0KixbB00/D9Ol2LCEBVqyAO+6wUe8dO+yaevVsgaRIuGjkWkRERMKuTx9LmJ97zprTeG8dHdessSQ6M2G+916rf9y0Kaxbd/JIdWqqPefjj4v/ZxABJdciIiJSApx2mo1Mf/GFvXcONm+GKlWszXpKim1nnpl1T25TQGrXhnnziidmkdwouRYREZES4bbbTnw/Y0bhn5GYaAscDx6E8uWDEpZIoWjOtYiIiESNxESbUrJmTbgjkdJKybWIiIhEjcRE26elhTcOKb2UXIuIiEjUaNoUfv97aN483JFIaaU51yIiIhI1EhJsYWRRvfEGTJ4MGzfagsnXX7fyfiIFpZFrERERiSo//wwLF8KxY6e+dutW+PDDrPevvgpTp1rN7ZkzTzwnUhAauRYREZGo8t//woABliR363biuUOHYMkSS5wnTLAkPCYGtm+HGjWsS2TVqnZttWqauy2FF7Lk2jmXAMwC4jM+Z7z3/tEc19QHXgOqALHA/d77/znn4oCXgPMy7h3rvR8ZqlhFREQkevTuDZUqwZ13WnL988/WfKZJExuZziz5164dPPEE9O0L1avbsWrVsp4ze7amhEjhhXLk+giQ6r3fn5Esz3HOfey9z97Y9CFgnPf+n865FsD/gAbAlUC8976Vc648sNI597b3fn0I4xUREZEoUK4c/P3vcN998OabEBcH/ftbct2njzWi6dgRatbM/znnnFM88Up0CVly7b33wP6Mt3EZm895MGpJkgAADmpJREFUGXBaxuvKwJZsxys458oA5YCfgZ9CFauIiIhEl0GDbMupbl3bCuLrr60hzb33WgdJkYII6YJG51ysc+5LYAcwxXu/IMcljwEDnXObsFHrOzOOjwcOAFuBjcBo7/3uUMYqIiIikt3atTZtZNWqcEcikSSkybX3Pt173waoC7R3zuX8Bcu1wKve+7pAb+B151wM0B5IB+oADYE/OOca5Xy+c26Yc26xc27xzp07Q/mjiIiISCmT2ZBGybUURrGU4vPe7wFmAL1ynLoJGJdxzTwgAagBXAd84r0/6r3fAXwOJOXy3Be890ne+6Sap5o4JSIiIlIIjRvb/O1ly8IdiUSSkCXXzrmazrkqGa/LAd2BnAVtNgLdMq5pjiXXOzOOpzpTAUjO5V4RERGRkClTBpKSYP78U1+b07Zt8MMP9nrRInvO2rXBjU9KplCOXNcGpjvnlgOLsDnXE51zw51zl2Vc8wdgqHNuGfA2MDhjIeQ/gIrAiox7X/HeLw9hrCIiIiInSU6G9eshPf3U165fD88+C506QZ068O9/2/HTTrPa2h98EMpIpaRwlstGvqSkJL948eJwhyEiIiJR5OBBa6kek8tw5LFjNrp9/LiV9lu40I63bm2l/665Bpo1s2O1asEll8D/+3/FF7uEjnNuiff+pCnLoA6NIiIiInkqX972L70ECxbAkSPWzXHdOms8M3++Jd5dusBVV1lDmiZNTn5O8+bq9lhaKLkWEREROYWZM2HaNGtIU6MGnHcetGqVdX7UqPzvT0yEd98F78G50MYq4aXkWkREROQUXn89sPu7doVDh6wVe3x8cGKSkknJtYiIiEiIXXWVbRL9iqXOtYiIiEhp5z0cPhzuKCTUlFyLiIiIhJj3cNZZcPfd4Y5EQk3TQkRERERCzDlb1FiUhjQbN1qN7ClTYPNmWLo0+PFJ8GjkWkRERKQYJCfD8uWwf3/Brv/f/6BdOxvxvusuK//XuHFoY5TAKbkWERERKQZdu1rDmY8/Pvncrl0wcSLcdx+sXGnHjh+3GtqjRsE339jx8eNh9mz45JPijV0KTtNCRERERIrBRRdZW/QpU+DKK2H3bujXz0akN22ya8qUgZYtoUULuPRS23J64glLxnv1Kt74pWCUXIv8//buP1bL8jzg+PeSH8UhDqlIrHhw4OghWpR6tGANI4hWTZxbsxlJWmuyDJc428W1ZSFm2iZL7Dorf9TV0K6ts63OTsrarQ1aQqckVkB+ScU5ZwBRBDp/QbVaeq798TyE0+PhDMrzvM97zvv9JG/e572fH7m4csN78bz3c9+SJLXAiBHFSo8vv3y4rbcX5s8vVnC8+GLo6Tm8KuSRzJhRXMcFadqTxbUkSVKLXHnl4e0JE4ohHsequxt+8Yvi4cbJk6uLTdVwzLUkSdIQ0t1dvG/b1mwcGpjFtSRJ0hByqLh+5plm49DAHBYiSZI0hEyaBOvXw8yZTUeigXjnWpIkaQiJgAsugFGjjv3cX/4S7rijeIhy6lTo6ioKdVXH4lqSJGmIefrpYin1V18d/LhM2LSpmEMbYPRouPtueP31okB/4QVYsaL+eDuJw0IkSZKGmNdfh6VLYdYsuP7639y3ezesWQOPPgo/+AHs2AFnnFEU0iecUBTm48YVx37kI3DSSa2PfziLzGw6hkr09PTken/XkCRJHSATzj4b9u+HSy6Bd94pVm8cMwZuvhm+/OVi+7LL4Jpr4Oqr4bTTmo56+IiIJzOzZ6B93rmWJEkaYiLgG9+AT38annuuGH+9bx+ceSbcdFNxN/u884phIGoti2tJkqQhaO5cWLv23e2Hpuo7GitXwqJFsGpVcSdcx88HGiVJkjrUySfDzp3OmV0li2tJkqQO9f73F++u9lgdi2tJkqQONWECTJ4MGzY0HcnwYXEtSZLUwWbPhp/+9NjOyYQtW+Ctt4rP99wD731vMf1fp7O4liRJ6mAf/WgxVd/Bg4Mf19sLjz8OixfD9OnFbCQrVxb7Zs2CV14p5tXudM4WIkmS1MEWLixe/WXCm2/C2LHw4ovQ0wMvvwwjR8Kll8JnPlPMsQ3woQ/BBz7gg5FgcS1JktTxMuGuu+Dhh4sFafbtg+efh2uvLebTft/7isVo5s6Fq66C8ePffY3ubti4sfWxtxuLa0mSpA63Y0cxpOOtt4oFaaZOhQUL4OKLi/0RxbjqwcyYAQ89BG+/De95T/0xtyuLa0mSpA531lmwevXxXePjH4f58+GEDn+iz+JakiRJx+3ss13lEZwtRJIkSRVZvhzWrGk6imbVVlxHxJiIWBsRmyPiZxHxuQGO6YqI1RGxMSK2RMRVffbNjIjHy3OfiogxdcUqSZKk47d4Mdx5Z9NRNKvOO9dvA/Mz8zzgfOCKiJjd75hbgQczcxZwHfCPABExEvgW8BeZeQ4wD/hVjbFKkiTpOM2ZUyxIk3n052TCunWwZEkxd/bVV9cXXyvUVlxn4UD5cVT56p/qBE4ut38XeKncvhzYkpmby2v9b2b+uq5YJUmSdPxmzy7mwt6+/eiOX7oUpkyBiy6CL34RJk4sCvShrNYx1xExIiI2AXuBRzLziX6H3A58LCJ2AT8Ebi7bpwMZESsjYkNEfLbOOCVJknT8Fiwo3les+M32gwdh61ZYtgyuvx4OlLdfe3uL1R3vvRf27IEf/7i4g71kCdx4Y2tjr0qts4WUd5vPj4jxwPci4tzM3NrnkIXANzPzzoiYA9wXEeeWcV0CXAi8CayKiCczc1Xf60fEImARQFdXV51/FEmSJP0/pk+HCy+EJ8rbqY89BjfcADt3Hl5efeLEYiXHnh645Zbi1d+uXcc/NWBTWjIVX2a+FhE/Aa4A+hbXf1a2kZmPlw8tngrsAv4zM38OEBE/BD4IrOp33WXAMoCenp5jGN0jSZKkOnz3u8WKjgAnnlgM+bjuumIFxzlzYNq0YlGawXR3w333wf79MG5c/TFXqbbiOiImAr8qC+sTgQXAF/odthO4FPhmRMwAxgD7gJXAZyPid4B3gD8A7qorVkmSJFVjypTD2z09cP/9x36N7u7i/dln4YILqomrVeocc306sDoitgDrKMZc/3tEfD4i/rA85q+BP4+IzcD9wA3lg5CvAl8qz9sEbMjM/6gxVkmSJLWJGTOK92eeaTaO30Ztd64zcwswa4D2v+2z/TTw4SOc/y2K6fgkSZLUQaZNK8Zujx7ddCTHzuXPJUmS1FZGj4a1a5uO4rfj8ueSJElqS729x7YgTTuwuJYkSVLb+dGP4JRTht64a4trSZIktZ1zzoE33oDly9+9b+NG2Ly59TEdDYtrSZIktZ2uLpg3D267rZiO7/LLD+/75CfhC/0neG4TPtAoSZKktvS1r8GttxbLpU+adLj97ruLISPtyOJakiRJbWnatIEXoZk5s/WxHC2HhUiSJEkVsbiWJEmSKmJxLUmSJFXE4lqSJEmqiMW1JEmSVBGLa0mSJKkiFteSJElSRSyuJUmSpIpYXEuSJEkVsbiWJEmSKmJxLUmSJFXE4lqSJEmqiMW1JEmSVJHIzKZjqERE7AN2NB1HBU4Fft50EB3K3DfH3DfH3DfH3DfH3DdrOOR/SmZOHGjHsCmuh4uIWJ+ZPU3H0YnMfXPMfXPMfXPMfXPMfbOGe/4dFiJJkiRVxOJakiRJqojFdftZ1nQAHczcN8fcN8fcN8fcN8fcN2tY598x15IkSVJFvHMtSZIkVcTiuk1ExBUR8V8R8VxE/E3T8XSaiNgeEU9FxKaIWN90PMNZRHw9IvZGxNY+bRMi4pGI+O/y/ZQmYxyujpD72yPixbLvb4qIq5qMcbiKiDMjYnVEbIuIn0XEp8p2+37NBsm9fb9mETEmItZGxOYy958r238vIp4o+/2/RMTopmOtksNC2kBEjACeBS4DdgHrgIWZ+XSjgXWQiNgO9GTmUJ93s+1FxFzgAPDPmXlu2fb3wCuZeUf5n8tTMnNxk3EOR0fI/e3Agcz8hyZjG+4i4nTg9MzcEBHjgCeBPwJuwL5fq0Fyfy32/VpFRABjM/NARIwC1gCfAm4BlmfmAxFxD7A5M7/SZKxV8s51e7gIeC4zn8/Md4AHgGsajkmqRWY+CrzSr/ka4N5y+16KLz5V7Ai5Vwtk5u7M3FBu7we2AWdg36/dILlXzbJwoPw4qnwlMB/417J92PV7i+v2cAbwQp/Pu/Avfqsl8HBEPBkRi5oOpgNNyszdUHwRAqc1HE+n+cuI2FIOG3FYQs0i4ixgFvAE9v2W6pd7sO/XLiJGRMQmYC/wCPA/wGuZebA8ZNjVPBbX7SEGaHO8Tmt9ODM/CFwJ3FT+fC51gq8A04Dzgd3Anc2GM7xFxEnAQ8BfZeYbTcfTSQbIvX2/BTLz15l5PjCZ4pf6GQMd1tqo6mVx3R52AWf2+TwZeKmhWDpSZr5Uvu8FvkfxD4BaZ085LvLQ+Mi9DcfTMTJzT/nl1wt8Fft+bcoxpw8B387M5WWzfb8FBsq9fb+1MvM14CfAbGB8RIwsdw27msfiuj2sA36/fHp2NHAd8P2GY+oYETG2fMiFiBgLXA5sHfwsVez7wCfK7U8A/9ZgLB3lUGFX+mPs+7UoH+z6J2BbZn6pzy77fs2OlHv7fv0iYmJEjC+3TwQWUIx5Xw38SXnYsOv3zhbSJsopgJYCI4CvZ+bfNRxSx4iIqRR3qwFGAt8x//WJiPuBecCpwB7gNmAF8CDQBewE/jQzffCuYkfI/TyKn8UT2A7ceGgMsKoTEZcAjwFPAb1l8xKKsb/2/RoNkvuF2PdrFREzKR5YHEFxQ/fBzPx8+b37ADAB2Ah8LDPfbi7SallcS5IkSRVxWIgkSZJUEYtrSZIkqSIW15IkSVJFLK4lSZKkilhcS5IkSRWxuJYkSZIqYnEtSZIkVcTiWpIkSarI/wGwt+qL80g98QAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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Acxiech0bG51/1EkrVxZVAkzdMsI1MU/CsiQx09Sq/YblfqwoLNMDTpWY79rFM+3jyuKe9gkrnMpU+8ZGx7gWOQGmYJl1FFY4dXQ4X64aGujSUQoDzDW1LUOSb0lipqlVr80j7res2qZ8IHnKzY3l3riTsiqVHmNuWJxSSMoTnvLAktrnm2VMCqcE2KwfRUKXBxZVvaT24VjNzc57nioBNmkytwTYfFZQGWBOLEsSM00typ5pYUUTZfpeqTitMRxTSGEJy4vV2OiYAS5Jq7CEVVZWv350R1EAx+hzSoC5sixJzDS10kqAOZnWsrGSzmO9K7wkaYvhbugoWZRJK9d1LIsBpkwhua4jV5bUPv8spWjmkeOJpGKmRX1k+okpTuCpx4p7iKesLMrac5jHSsUx1BQsM7a0WEk+ENNkUayjMcAULCD54ds0WRS1T4PFtV5Se2GVZR4bGmhOJK3dRyZhWZKYaWqZN4X55gckN4duVlJDVzYWZe2LNo+VivOPgmXGRlEvN6u5mY5FkQAbFkCzY8sDi6L2abC41ktqLyyZx+gsgOZEUksSM00t86Yw/U1AcuMkLGFxZ2nttLJQsMzYmpt7LqFFwQJoPvSFJSxhCUtY9CyKHnNLEjNNLbdBSdrvKSxhZcWiNOamJy+JMe/q6vmgTton6P7QF5awhCUsYfFnaR2fZ0FipqnV1eX8pDh0Lixh+bHMhw8FK6kBdrOAZOlCdzcdy4xNWMISlrCElS4rrgGuZZnHciQx09TiehheWDxYSQ0wZQLsZgHJPly5srq7nQ94zoc2hSUsYQnLFiuuAa5lAfENcD1Wzlo9xExTq55xSnrimrCKwzJnP1OwzE9hZcMyJ2xSsMzYhCUsYQkrCmvXLhoDbH6az0gKVpLPaSqWJYmZppb5ZuY2YXG/rVGy6plDDuPKA0spxwBT1N78pBiXsLJlUc5jPWOe5LCmu8e8qKzaHS6XcQkrmso6j0laIGqNJkUCTLGP5MiyJDHT1DI73Kamnh6gpDtvCpa7p5XTuNJiNTQ4JpiCZX5S1D4pi3JcwoomynmkZJkTNilYgFMzjqwyrGMZWGWcR8D58kzFSvK5Q8WiHBcly5LETFOLMgGux0pySCbNcVGwGhvpWOansIRVy6LYhtIYF4f0XWva5FBYxWTF3YbSHhclyxw9omABPNLkMrAsScw0tdw7SfMNi2LnTZXapjUuCpZSDo/K7AhLWPVYVAaY8zrGZZkrqVCwzNiEVTwWlWlNY1wU/cQU+9taFkVvsrDYSsw0tdyHbyn6iKhY7sPdlONK2k9ce7ibIqHjxqptNeAyLmFFk8xjNFUqjrGhYAG9DwVT9LQKyw4rzXkEer4QUrAo2ik4sWprz2VclCxLEjNNLa4JsPtQsOkn5pCElYFVexiey7iEFU0yj/ZYtSdsck5theWvrq7eLHP3VIpxmceEVV+1tecyLkmmRX3k3oFQpcnNzdJPLCxhCau8LOq+8O5uGhbQ2wiUhSX9xMLiyrIkMdPUcn/oGwNMkYRx6iemTOjKwJLaF4Ml82iPxbX25koqaaW2lCxzAyMKlnksjrgmrWVglaH2liRmmlruvkqq6xOn0e9JcSKWsISVN5ZsQ8KiZJn2BAqWGVstK8kJdW4WwKPXNq1xCUtYFiVmmlppHialTGO4HL4tA0tqz4cl21A+WWWovUmOaw93c2unoEwhObUHlIFVhtpbkphpapkPGNPiQXlok4upEJaw8sqSbUhYabPi9hPXY7kfj6J6J2zGZZnXcTTmwhIWE4mZplZXV0+LB0CTxrgPfyT90Kc43F07LmH5S2rPhyXbUD5Zadc+bj9xPZb78SKwzAmbtYfhk2xHVC0jZn8rrHAqQ+0tScw0tdzfsIDkO+/Gxh5jTtEnmIdkR1jCSpMl21BxWHEMsFcCHOd9QZkmc2XVttgkqVe9Ezbjjsu8TljCYiAx09SqNdNJ05haVtw3WFdXb2POpU+wDKwy9HvmiZVkG0pzXMLyVr3aAzTtFEmSsDKz4mxHtXfYTJqYp9V+UlSW9EynJjHT1HK/WYHkJzzVsqiMedJxNTQ4/yhYAE16xZVFmdBxXcc8sTgk03mqFyUr7g06skhahRWNZTvJN1+kqBJNysScK4syAea6jpaUqplWSg1TSt2nlHpZKbVYKXWUUmovpdTjSqml1Z97VpdVSqnrlVLLlFIvKaXGpTm21OTuSQKS77zdrKQpUVrjojAobmNO1VcpLGHVsiiSVu7rSM1KekJdba9tEhMmLPssipSb2zqa9zgFy4xNWHZYlpR2Mv2/AB7VWh8M4HAAiwFcBuBJrfUYAE9W/waAEwGMqf47H8DNKY8tHaXZ5kGZclO3nyRlUbafUBonafMoFosyaeW6joZF0U9sflKkyUlMmFebB9cWiCKzKFNuruuYtP2k9g6bcVlmbNLmwV6pmWml1FAAHwFwGwBorTu11psBTAQwrbrYNACnVH+fCOB27eg5AMOUUm9La3ypidIAczXmnFlNTb1P2EzamuH+tsyhzUNYyVhcjHnaLMB+O4WwisniYszLwJITNnOjNJPpAwG0AZiilJqrlJqslBoE4C1a69cAoPpzn+ry+wFY43p9a/WxfKmri641o5ZFYTTTGFdRWe6+cC6JuXmdsOKxKL9gUa+j1vHaKeqx3I8XgUXZasB1HfPCojCa3NeRC6v2DptJE3O3D6BIuTmyLClNM90EYByAm7XW7wOwHT0tHfWk6jzW5zilUup8pdRspdTstrY2mpFSSpJpYdVjUZ2wmad0lCOLOpmOe31irkmYsIQVxJJkOp+s2iupcEqTJZn2VSuAVq31P6t/3wfHXL9h2jeqP9e5lt/f9fqRANbWQrXWt2qtx2utx48YMSK1wccWVwPMvWe6yCzq2ivVc4dNSnNYBlZTU/x+4jL0e3JlSe35sKRnOp8sytqbEIGCBfTe30oy3Vta69cBrFFKHVR96GMAFgGYAeCs6mNnAXig+vsMAF+sXtXjSABbTDtIrlRrnLgaYMpxCctfnGsP9BhzypPz0mBRGmDb/cT11lFY3pLa82FxNebC8hfX2psrqeQ8mW4KXiSRvg7gTqVUPwArAJwNx8Dfq5Q6F8BqAKdVl30EwCcBLAPQXl02f3L3JAG0fc7UrCTGXFjFYVHeYdPdV0nJApwPXGP8k7Jq65iERdEDLCxh5Y2VxNDlZR2FlT7Lqyc/Z8l0qmZaaz0PwPg6T32szrIawIVpjicT1WsP2LGDhpXUoHBsgRBWcVi1d9g0iUNDxANgfoc245rp2tTD9iFXYQkrryzZhoTFlWVRcgdEanFt8+DaAiEsXqwk/cS1LPN4HFbtCZuA/UPUXA/fCktYWbJkGxIWV5ZFiZmmVu3h46RpX1ptHpTjElZxWED8fuJ6rLhJBSULoDHmtYc2KcYlLGHljSXbkLDSZJkT7CWZLrnqtWYkMcBpHdKnHJew7LLMHbcoWObxorHc7SdAsg999wmbScblHo+whJUXVhotI9zWUVjRWA0NzmcsBcuMTZLpkisvBphyXMKKzjL9xBQsIP6OrWwsbsacMtnh1r8orGKyuBpzYUVjmVCAgmV+t82yKDHT1OrqomvNqGUleYOlOa6ysOLeoKMeyzxOxYqznmVkJdkZUY/LfcImRc8hxWF4YQkriJV0G0prXGVgGQNMwVIq/j6ylmV+t82yKDHT1PI6qYuKlcQcpjkuziwOJ9QJixcr7oc+13Wsd8JmErNTm0IKS1gUyXTe1pEbK0k/cS3LjK1ILIsSM00tkzgZJU2ThZWMBcRrp/Bixf2wEJaw8sICeow5Bct9t05hCatsLPNFl4JlxiYsdhIzTa3ubrrDFdQs95uV07hqWXH7ieuxgHhjo2bV1p4Li2u9uLLKUHuq9hNzUizF4W7Dojj5Mw0W5TqWgcW99ubLJAXL/M6FReED0mBJm4eolzintrW9o5TjStJPzDWho2Sl1bfLaR3LwJLaR2MBtCl3Gn27XBPNIrPSqD1FO4VhKcUjaU2DReED0mBJMi3qpbwl03EMsFcSFufNzzmh45poliEd5cqS2ttjAZJMF4WVRu1NP7HtdJQ7S5LpVCRmmlppJtNJmvK9UjXpJ86GJck0D1bS9Epqb49lrmVLwQIkmY7LokiA3e0U0k+cLUuS6VQkZppa1L3JtW8wreP3E3NNR4UlrKxYSUxFXtZRWMEsgOaumNwT8zRYpqc+7jbkZpmxcUlHhZVvlkWJmaZWbXrV1BT/hLp6LID2kCtFQicsf2XRFmN7HfPGkm0oXyzqbajeSZYUCR1XljEqVCkk9SF9ynEJq74oa891Hi1KzDS10kzCDCvJTSIoWJTjKgOLsvYyj/ZYUnt7LM7bEND3JEuKhI6SleQGHfWSw+bmZNeZpmBRjqsMLMrac51HixIzTa3aNMa8QcwbxharNtnhMq4ysChrL/NojyW1t8cqyzbkZijl/EvCqh2bbVa9RJPDuMrAoqw913m0KDHT1KpNY5J+6FOxatMYLuMqA4uy9jKP9lhSe3ussmxDSvWk3IaXxOzUjs02q16iyWFcZWBR1p7rPFqUmGlKmZMDi95zyLWvkitLeqaLwZLa22PlZRsyZpiCZcaWpA/VbVA49LRy7bUtA0t6plOVmGlKeX3Dcj8XlVeGNKborLKkakVnSe3tsdLchkwSTLGOZmwcWO5LCSZlmdcnZXFNNMvAkmQ6VYmZppTXNywg+hujUnGSbgqWeQ0VyytxElZ9UdZe5tEeS2pvj5XmNmRu9kGxjmZsRWMBvdtP4rK8Es0k4xJWOFHWnus8WpSYaUr5JdNRD1lQsrT2ToniHuIRVnh5pWq2WVzrxZUltbfH8kqTKVhmbBTrWGRWbcpN1X6SZFzCCi/K2nOdR4sSM00pymS6HivuoU1zjWuOiVMZWJJM82ElbVvIwzpyZVHW3ozNNotzmpwmi7L9hEOiWQaWJNOpSsw0pdJOps2bLSrL61tkHJZpP+GYXnFlSTLNhxV3GzKvycM6cmUlPTmPKmmVZDo5K8nJZpTjAvpe/cR2OsqVJcl0qhIzTSnKNJmSVe9bJAcWQJtecWXVJmFcWFzrxZVVxtonOTmPOtHkyKJcxzKwqGtfr/2EKh0tGstr322bRbmOFiVmmlKUabIfK64xT3tccVJuKpYZG0dWbRrDhcW1Xlmwkuy80xwXN5Y5OY9josmBRbmOeWNRbENpjIsq5U7Kqt0mbbO8fIBtFuU6WpSYaUpllUzHNcDcEnNKljnJkmPaJ8k0P1bcD/08rCOXNJlroinJNA2LYhtKY1wcWOb1VCxuabIk030kZppSlGlyVsm0zXFRsvxS7rgfFmknmrZZlOtYBlaatU/6BYsqOeTK8ko0bbO41osrqyy1r72TZdIT/Wo/d6hMPgcW5TpalJhpSvklwJRpsrDSZdW7k2UaiaawhGVkrnBAmfYJS1jCyj/LvJ6KVfsZViSWRYmZphRlAux1qIiKxSHl5sqiTMzN2ITFg5W0nSItFsAj7ROWsMKwkhzSz8s6cmFRX0qQWzJNybIoMdOUSjtpTWoOuSXAZWDVu8Y313S0DKykrRluVpJLvXFOwoQlrCBWkkP6eVnHIrJq2084pMmSTIv6KO00Oa6pyGpcwkqX5XcnS25JK1dWGnfO45j2CUtYabNkGyo3y7yeE8uixExTKu00WVjlZlHfyTLN1JaSlcRMp52qCUtYwhJWGVlU7Sde+0gx0z1SSq1USs1XSs1TSs2uPraXUupxpdTS6s89q48rpdT1SqllSqmXlFLj0hxbKsoqTRaWsJKyzEmWFCzzmrRYSU7OyyJVE5awhCUsYcVnATTtJxaVRTJ9rNZ6rNZ6fPXvywA8qbUeA+DJ6t8AcCKAMdV/5wO4OYOx0SqrPue4LPebn0PSWiYWZe25zaN5TV7S5LiHEGtbbKjHJSx/FmXtZR6TsWQbyieL6zbU2EiTcluUjTaPiQCmVX+fBuAU1+O3a0fPARimlHqbhfHFV9opZFyzY1j1zE5RklbuLMrac5vHSsVJut2sJCfnpZ2gxP2g7uqiOxmIc0rElUVZe5nHZCzZhvLJKvo2ZFFpm2kN4C9KqTlKqfOrj71Fa/0aAFR/7lN9fMfMLawAACAASURBVD8Aa1yvba0+1ktKqfOVUrOVUrPb2tpSHHoMZZVMR32TcU00y8TKQzId98559Vjm76iseneyjMsyY6P60JdUzS6La6rGtV6yDQmrlpWnbci0IuZETcGLJNLRWuu1Sql9ADyulHrZZ1lV5zHd5wGtbwVwKwCMHz++z/NWxTWZpjTm3BNgbqw0vhSlNY9KxdtJ1mOZv+Ok3FQsM7Y0P/Q5pDFlYFEburTnsbOTjhV3XM3NdCzZhvLPyts2ZP6fhnxcJyPVUWqt11Z/rgNwP4APAHjDtG9Uf66rLt4KYH/Xy0cCWJvm+MjlZ3a4GmDKcQmrryhrX49FnSY3NhaHVe9OloZF+aHPoQ+16CzK2ss80rBkG8oXK4ttCIieJnuto3kuJ0rNTCulBimlhpjfARwPYAGAGQDOqi52FoAHqr/PAPDF6lU9jgSwxbSD5EZp9DmnZYApxyUsb6Vt8s3fFOtYNFa9thjzt+1DrtR9qEVncT10zrVeabNkG8ofy6/2OuJBfi+WeS6KvNYxDsui0mzzeAuA+5VzhmYTgN9rrR9VSv0LwL1KqXMBrAZwWnX5RwB8EsAyAO0Azk5xbOko7UP6aRjzorQtcGV5pclxLvVG2U7hlwAXnRVnZ1TvJMu44zJj43jIlSvLy9Dt2kXHsr2OeWJRG3OO61g0VlCaXPtcXFa9FiM/+SXTOToJMTUzrbVeAeDwOo9vAPCxOo9rABemNZ5MlFUyzc2Yl4HF4eQ8SaZpWLaNeb07WcZlAXyTsCxStZ076Vi21zFPrCTmsH//9MZVFpZpYYvSTxyUJkcx05JM91E+OrvzIkmmi8vicHKeJNM0LNvGvN6dLOOyAL5JWBapmm0W13pJMl1slnkuioLSZFusgiTTYqYpJck0L5Zpp+DQtyvJNB8WV2MeN+Wud4iWc6pmu9dWkunkLK7GnDMLiH5yHmVqS5kmSzLdR2KmKZVV0srNmHNlGZ5NliTTPFkcjbltluFxZEkyzY/F0ZinsY4UJ+dxSG0lmU5VYqYp5Wdabaa2WRlzCpZStGkytXGiSpNts7jWKwsW5VEBrusYh+V3kiWlcaJKkzmwKNcxTyzKowJc19H8TZEmp9GaQZEmc2BRrqNFiZmmFGU7BWVqy9WYc22B4HpIvwztJ1mwKI8KcF1H2yzqO1lyPaTPtf0kbRblUQHqdTRfCilY5rkisCjbKbi2n1iUmGlKUbZTZGWAKcallNOfXJQWiDIc0udaryxYXI15kVjUd7Lk2gLBtf0kbRZXY55GOirtFOmyKNfRosRMUypPyXTRLvXGlSXJND8WV2MurPrifJJlntJkrga4qalnjpOOi0M6ypUlyXSqEjNNqbSTacp2iqJd6o0rS5JpniyOxlxY9UVZe8OTZDo5q7ub9uQ8SZPTZUkynarETFMq7X7iNO6cR2kqhNVXlLWXeaRhNTXJNpQnFmXtqU+y9DIVRWeZv22enEdpzMvAyqotxuY6WpSYaUqlnUybvzmaCmHVF2XtZR5pWLIN5YvFdRvyu5MlZQsEJSvOyXlpGyfzN0WiKSxvUdae6zxalJhpSnm1UzQ08OzbtZ04lYElyTQ/lmxD+WJx3Yb87mTJMU1OwwAXJdEsA0uS6VQlZppSlCcpZdFzSDkuYdUXZe1lHmlYsg3li1WGbcjvJMs4J+f5pX2Uxoky0aQYl7C8RVl7rvNoUU3Bi4hCyy+NoTzphqOpEFZ9leGkLj9WZyfPcck2lB9W2bch83xDhOzLL+3r6gL696dhcTPmwvIWQe137dqF1tZW7Lz6aqC5GVi8uOfJt70N+POf+z4epKuucsbhfs3eezusgQOjsQjV0tKCkSNHorm5OdTyYqYpZd6QtR961KmHpGr5YeUxVaPsj+U6j7IN5YeVx20IcNLksAY4iNXd7ZiUMPI6yTKN5FCS6fywCGrf2tqKIUOGYPRb3wrV0gK88509T775pvPee8c7gCFDwo9La+e9PWZMz2Nbtzpjesc7gKFDw7OIpLXGhg0b0NraigMOOCDUa6TNg1L13qxAsqQi7T5BrmlMUVhlT9W4smQbyg8rq22ouzvayXlBaXKUsXFled3JUpLp/LEIar9z504MHz4cCnDOB6OQ1n1Z5u+oJ8sSSSmF4cOHY+fOnaFfI2aaUvXerED8nbc5edGtJGlM2sZcWH1FWXuZRxqWbEP5YmW1DQHRepP91tH9f+WZ5XUnyyTpKMW4AH9zKKy+Iqq9UorWANdjMZCKOCYx05TySqbj7rypUu4yHHLlyjK158biWq8sWEm2obysY5FYfrWPeqk3PxYQbWx+61h0VtyUu1Kha/OgbBkpA6uri45VT5RpsuVkOo7ETFPKL5mmOCQDxN+xmRu+UIzLvFZYwfJLwmyyuNYrCxZ1Ms1xHYvECkqT45hDilaDoAS4yKwkxpyqzYOyZaQMLL9kOiqLMk1Ouc1j5cqVOPTQQxNzgiRmmlKck2nKcVG3n3BLwiSZLjZLkmm7LCBeOwVFqibJtL1kmrL2Xtf45pAAc2VRJtNM2jy01qhEvWxkSqrjsESxlUUyHbdPMItxFSUJ82NFOCEBgCTTXFmSTEdjdXdH2+mFSUf79QvHCpNohr3UmyTTtKwiHBXwupMlhzSZazIN0KbJEVgrV67EiSeeiGOPPRb/+Mc/MGzYMLS1tUEphXPOOQcXX3wx5syZg3POOQcDBw7Ehz70od2vXbhwIc4++2x0dnaiUqlg+vTpGOO+ikgCiZmmVBbJdNFY9dpPqJMwqmsdc077hCWsNFlA/ZuKxGVxS1qFVV6WpNzxkukf/hBYsaLnsUoF2L4dGDCgvkfw0rZtzvItLcDYscB11wUa8yVLlmDKlCk499xzcdlll2HBggUAgM2bNwMAzj77bNxwww045phj8J3vfGf362655RZcdNFFmDRpEjo7O9Eddb19JG0elMoqmRZWfRbQt/2Ec9onLGGlyYp7ch635FBYwsoLy+tOlhySaUpWVlfg8Pj8GjVqFI488kgceOCBWLFiBb7+9a/j0UcfxdChQ7FlyxZs3rwZxxxzDADgzDPP3P26o446Cj/5yU/ws5/9DKtWrcKAAQPIhirJNKX8+pxtJ8Bpj4ua1dERndXYWP8kS8peW2Hln0Xdf82VZZ4PmxKFYVGOS1j5ZaXRfy2s+jyOLK2BH/wA2H//nsd27gQWLAAOOAAYPjw8a948YK+9gLe/veexAKM+aNAgAMCee+6JF198EY899hhuvPFG3Hvvvbj22ms9L2v3hS98AR/84Afx8MMP44QTTsDkyZPx0Y9+NPxYfSTJNKW8ktY4O+8sEmDKcRWNBaR/J0th2WFRJsDU62iSLQoWEG1swhJWWBZlAsx1HW2zsryTJUW7Q5ITEGOy1q9fj0qlgs9+9rP40Y9+hBdeeAHDhg3DHnvsgVmzZgEA7rzzzt3Lr1ixAgceeCC+8Y1v4OSTT8ZLL70Ubaw+8o0slFJh/qc2rfXHiMaTb/klrXFOFsgiAaYcV5ze5CzGZZtlXkvFqpcuCCsaizIBphyXeT7sbaiDWHFOzvNjhZWwysGiTG25rmMRWEF3srTZ5uHHCjDTr776Ks4+++zdV/T46U9/CgCYMmXK7hMQTzjhhN3L33PPPbjjjjvQ3NyMt771rfje975Hsw4IbvNoBPBJn+cVgBlko8m7OCetXixJudNnUV1KMG8JcFlYgJMihTXAYdKr5mY6VlgJS1jCKiaL+k6WaV8az8eojx49evcJh4cffjheeOGFPsscccQRePHFF3f//YMf/AAAcPnll+Pyyy+PNr6QCjLTX9Far/JbQCl1AeF48i3O6agXK86l3vK2jkVimddSsfKUJlMn01HSlTApUdRLvXFLr4QlLGEJK0jGLFu6NB5XBUUpf1dK9QcApdQ+9RbQWs8iH1VexTkdFVYxWFSXEsxjApxGmkzFKkJ6JSxhCUtYWStBzzQnBZnp+wHcp5R6H4AfZzCefItzoiksYdWy6rWfcE6To56cxzXZEZawhCWsvLIok2kvVg4VZKZfBzARwLkARqU/nJzLK4U0h5WFJayissxr02QBNGmMsIQlLGEJKx4rCzNdwGR6gda6AuDbALbG+Q+UUo1KqblKqYeqfx+glPqnUmqpUuoepVS/6uP9q38vqz4/Os7/Z1VeyWGcQ9TCElaeWF7tJwBNmhzncKSwhCUsYQmLlpWFimamtdY3VH/u1FqfGvP/uAjAYtffPwPwS631GACb4KTeqP7cpLV+J4BfVpfLl/zSvqiHu4UlLFus7u7od87zYpnno7CA+u0ncVleaYywhCUsYQlL2jwIFOpaTkqpT1XT5Y1KqTeVUluVUm+GeN1IACcBmFz9WwH4KID7qotMA3BK9feJ1b9Rff5jyus2NlzV3U1ndoQlLFss8zwVK2pvX72UOy7L/VphCUtYwhJWMhazNo/Ro0dj/fr1fR7/wQ9+gGuuuSb8WBIq7B0QrwNwFoDhWuuhWushWuuhIV/3XQDmOO9wAJu11mbmWgHsV/19PwBrAKD6/Jbq8vmRn6mIcqhbWMKKytI62odYkAGO2prBlQV4p9xxWF47I2EJS1jCKgMrKykVuE/TWu++YYtthTXTa+D0T4feWyulPgVgndZ6jvvhOovqEM+5uecrpWYrpWa3tbWFHU428jMCcfpQhSWssCyA1rRSJtM2WX4pd5yeQ6+dkbCEJSxhlYFluc1j5cqVePe7340LLrgA48aNw5o1a3Y/d9VVV+Gggw7CcccdhyVLlux+/Prrr8chhxyCww47DKeffnr48UVQnTOQ6uq7AB5RSv0NQId5UGt9rc9rjgZwslLqkwBaAAyFk1QPU0o1VdPnkQDWVpdvBbA/gFalVBOAPQBsrIVqrW8FcCsAjB8/nld3eh5NmLDssSqV6DcOCTKa9Z6PywqrsrAA75S7CDtJYQlLWMIKksssz507ofdzO7YCa/oB6/qHZ7VvA1a2AK82433vm9nznE8yvWTJEkyZMgU33XQTRo8eDQCYM2cO7r77bsydOxddXV0YN24cjjjiCADA1VdfjVdeeQX9+/fH5s2bQ65oNIVNpq8C0A7HFA9x/fOU1vpyrfVIrfVoAKcDeEprPQnAXwGYkxnPAvBA9fcZ1b9Rff6pKEk4C+XR0EVlVSrFZ5UhTfarl00WZzNNlXJ73dq3SCyuRoCSJbWX2qfJyqL2Sjn/ou7XzGstadSoUTjyyCN7PfbMM8/g05/+NAYOHIihQ4fi5JNP3v3cYYcdhkmTJuGOO+5AU72rWBEoLHUvrfXxRP/npQDuVkr9GMBcALdVH78NwO+UUsvgJNLpZPFpqlLpm1wB8c0hR5YxFUVm+dXL9CaH/SDxYwHR0uQwrLDyq5dNFuU6cmWZZSnbTygTcwqWMRVFZkntpfZpsrzqZf6mYJmxxUymeyXJAPDCC8CIEcD++4djdXQA8+cDo0Y5r3PLJ5keNGhQ3ce9rlnx8MMP4+mnn8aMGTPwox/9CAsXLiQ31WGT6SeUUrHNtNZ6ptb6U9XfV2itP6C1fqfW+jStdUf18Z3Vv99ZfX5F3P/PmvySMK4nrgkrGgsozsl5wrLH8ku5i3CSkrCEJax0WObOtRQs81ick/gok+l6rIj8j3zkI7j//vuxY8cObN26FQ8++CAAoFKpYM2aNTj22GPx85//HJs3b8a2bdsoRt1LYa35hQC+q5TqALALzsmCOuQVPcojrx1uQwNde4CworOoepPdiUDYNDkMK6yEJax6LPdrKVi1YxOWsITFh2V4tlh+Jw2GuAJHaJb7+RAaN24cPv/5z2Ps2LEYNWoUPvzhDwMAuru7ccYZZ2DLli3QWuPiiy/GsGHDwo8xpHzNtDlRUGvt2x8tqsovvaIyh0Vj9a9zogL1uADvvt44LArjJCxhUbHkiiXCEhZvVpze5KA02RbLT9RpdR0zPXr0aCxYsGD33ytXrtz9+xVXXIErrriiz2tmzZpFNy4PBSXTzymlWgE8CuBRrfXK1EeUZ3E9oY6alUVvctQ0OcxJcGHNNNeT86hZHPuJOderDLUHaPs9KQwKdxa3fmLu9Spj7c1jRWARpslFugOir5nWWo9XSo0CcCKA65RS+wGYBeDPAP5m+p1FVZmUqFZxk1aurCxSbiB6mtzc7M2Kk/YVncU5aS06i2vtTYJGwQL4ndSVBotrOsq1XnmpfZI0mWJ/y5Xlp6htHlmxMlDgCYha61Va61u01qcA+D8AHgRwHIBnlFIPpz3AXMlvx0Z5EhxXAywn+kVncTVhXOsltc8Py/3aNFncTBj3ekntw7HMY2VgUSbTURTUf50jBfVMnwLg71rrdQCgtd4F4KnqP1STapGR186I8oQ6atNKxTLfeL0OO8dheaXNcVlh5fX/Up5sxsHsUNYrTyypfTaseik35YlYXI0m1xPXylB7836jWEfA7ol+WbGi+gCmJyDaVpDrOQPAXKXUUqXU1OqtvN9jntRav5ru8HKmLPqc4xhzahbXfk+q5DALVtxrhVKyyjqPUntvFtdtyP1aoyTX26Vk5aVvtwy1N3zbLAofkBUrjg/IQkVq89Ban6q13g/AxwH8BcBhAG5XSrUppR7JYoC5Unc33z5nvx1uUQ6dc+2P5cqSebTHktpHYwF8e7m5tkBw7XNO8zKO5jHbLG59zpSsLJPpHCnU8fjqVTxegHPHwnkA1gEYkN6wciq/HVscA5wFyzwvLGEJS1hlYLlfKyz7LPOYsPLB8pOFExBHjx6N9evX93m8o6MDxx13HMaOHYt77rkHEyZMwOzZs2nGVkdBPdP/DeAoACMALAHwHIBfAThfa83wuIBlZWmAKXqT3R+ISXuTOewkhSUsYQkriAWUI5lOk1XENFlY4ZTVCYgJNXfuXOzatQvz5s0DANx8883k/4dbQW7siwDeBuc603cC+L3Weq4YaQ/59QlGPdGPc/9ikMkXVniWzKM9ltS+GCyb8+h+bS0r6jrmiWUeo6hXGizKdSwDKyfJ9Pbt23HSSSfh8MMPx6GHHop77rkHAHDDDTdg3LhxeO9734uXX34Z69atwxlnnIF58+Zh7NixWL58+W7GqlWrMGbMGKxfvx6VSgUf/vCH8Ze//CXxcIOuM32wUmovOJfEmwDgMqXUYAAvwrnKx5TEIyiSurvrp8Vxr3RB2eecNituUlFWFod5rPfByrVeRat9nurFlcVhHgHaK5ZQ9hPXspJc6cKrZnHqlTYr7pUuysqKWntXmjxh6oTez23f7vwfs0J2AXd3A+3twIsDgcZGzPzSTN/FH330Uey77754+GHnqsxbtmzBpZdeir333hsvvPACbrrpJlxzzTWYPHkyJk+ejGuuuQYPPfRQL8aoUaNw6aWX4qtf/So++MEP4pBDDsHxxx8fbrw+CnOd6Y1a64cAfA/A5QD+AOBYAJMT/+9FUxaHNjlc8orr4ds8sWQe7bGk9sVgFW0e3a9Ng2Ueo2JxvtSbsMKzKK/mkfLtxN/73vfiiSeewKWXXopnnnkGe+yxBwDgM5/5DADgiCOO6HV7cS+dd9552Lp1K2655RZcc801JMMN6pk+GU4qfTSA9wBYCODvAC6p/hS5FebDVXqThSUsYQlLWLUsoBxXuhBWvlmuZLpPkrxokeNxxowJx9qyBVi6FDj4YGDw4N7P1THT73rXuzBnzhw88sgjuPzyy3cnyv3796+uSiO6uroC/9v29na0trYCALZt24YhQ4aEG6+PfM00gC/BMc3fBTBHa92Z+H8ssjj39glLWGmy+vXjOS5hCSvPLMOLynKPQ1jCSqNnmuLSeH6sOlq7di322msvnHHGGRg8eDCmTp0a/f8CcOmll2LSpEkYNWoUvvzlL/dpBYmjoJ7pzyT+H8oirZ1/XK/TKixhCUtYwkqfRdkXbsYWJ4WsZ1Disrhd61hY9lh+ZjnudaZDsubPn4/vfOc7aGhoQHNzM26++Waceuqp4f8/AH/729/wr3/9C88++ywaGxsxffp0TJkyBWeffXYkTq2C2jwe0lp/KukypZDfYRT3CYhReFwPR6a9jmVgyTx6s6qH7FIbl9S+GCyu8wg460mxjnFZDQ31zXQcFsCv1SBof0u1jmVgxTXTFi6Nd8IJJ+CEE07o9Zi7R3r8+PGYOXMmAGDChAmYMGHC7ufM4wDw3HPP7f79j3/8Y6QheymozeNDSqkZPs8rAIeQjCTvCnqzupcJkkm5ZcdWXJbMoz2W1L4YLMram505lQGOYw65sgDvBL4I5rDsrKi19xP1pfGoxpWBgsz0xBAM6aMGaM00JcssS8XKW8+h7XpxZZWl9vVSbtvj4lp7zvOYNkup6Fc1oO5z5mqmvVJum+aQaz8xJYurMc/JTVuyVlDP9N+yGkjuZTYiin48Spb0ckdneV0PvEgsqb3UPk1WFrWP05vsd63/uEaTG4tyHalZAL8eYK4synpRsvxEmSZTptwZKPA606KQ4ppMB31TjsIyywpLWMISlm1WnDSZOrUVVjSWUvxSbmHxTKbFTJdUYQxw2G9sXI25Xy931HU0/2/ZWdwMCvd6Se3zzaLsczZj42o0KdeRK8tm7QHauyl6vffLwIpjpuuJ8moeOZOvmVZKvT2rgeRefm/WqIcjuZppv5Q77iFXjqwgI8DVhEntw7Gk9umz/IxTUdJkvz5nyv5rynoVqfZ+VyyJyjJj4MQK8hS2kmmjtK8zXbBk+k/mF6XU9JTHkm9RtlOEMa1hv5VSsoLSAPcyQdKavg81bbOThnGKki5Qsrq7+fbaSu3tsdI25mZslEaTYh7jsLxqD8QzmpS9yVL7aCwzBioWhWnN6sTIqLVPo80jggZX75Q4c+ZMfOpT9a/K/Mwzz+A973kPxo4di8WLF+PQQw9NNMwwCjLT7modmOZAci9Ko8mdlfYXBg6mogwsjqkt53qVtfZxe5M5Gk1qFsfUlnO9uNaeupeb28mMlCw/MUmm77zzTnz729/GvHnzMGDAgFiMqAoy09rjd1GtKI2msKKxTMrN1aAIS1hZs6hbM2ybHWEJKy8s87qishhdGm/btm049dRTcfDBB2PSpEnQWmPy5Mm49957ceWVV2LSpEm9lr/22mtxzjnnAHDupnjooYeivb090SoYBV1n+nCl1JtwEuoB1d9R/VtrrYeSjKII4mo0uabJlCyzQebJ7NhmyfWJi137uNdNbvLYJdg2KBxZZbnONEdWlrXv6IjGKvoVS1xy3WDQ0c59HdagkIBdw4CdA4DBjYACXDcpDJVMz507FwsXLsS+++6Lo48+Gs8++yzOO+88zJo1C5/61Kdw6qmn9rpD4je/+U1MmDAB999/P6666ir8+te/xsCBA0MO1l++ybTWulFrPVRrPURr3VT93fwtRtqtrPqcbZpWv0NFlP3XcVkUtTfLps2ybcK8DrlS9gBL7esri9obHtWVG2z2x3I2rZS1p2JJ7YuzDQHen4dULKvJtPnFo80jQB/4wAcwcuRINDQ0YOzYsb2Mcz01NDRg6tSpOPPMM3HMMcfg6KOPjjxkLwUl06Kw8jOHlKbVsChPGsz7Jfsoax90cfsoLLMsRe3TYHFMbYPWsci1N4mWzeSQa0+r37goT1wrymXjsupz5lr7XbvoWDZrD2TTyx3zRiu9kmQAeOV1YOtW4LDDwgHe2ASsWQOMHVv/qFhAMt3fdYfbxsZGdHV1Bf6XS5cuxeDBg7F27dpwYwwpuc40lbIympSX2SvKJfu4ssyyFLXPisXBTJe19oB9E5Y3llw2zh5Lap8vlnldraLWPuhGK1GU8U1btmzZgosuughPP/00NmzYgPvuu4+MLWaaSlwNHSWLa/tJGq0s3MZlluXYtlDmnuk4aTLXQ+fStyu1zwuLa+05zyNAczWPIIPL+NrQF198MS644AK8613vwm233YbLLrsM69atI2Gn1uahlGoB8DSA/tX/5z6t9feVUgcAuBvAXgBeAHCm1rpTKdUfwO0AjgCwAcDntdYr0xofubiaw6x6pjmwuNWL2kxn0bdrm8XV5GfV72mbxdFUVCpAv378xsV1HrmawyDWzp3hWVxrz3kbomIZUaTJMZLpbdu2AQAmTJiACa4zIH/1q1/t/n3q1Km7fx89ejQWLFgAAPjtb3+7+/H9998fy5YtCz/WAKWZTHcA+KjW+nAAYwF8Qil1JICfAfil1noMgE0Azq0ufy6ATVrrdwL4ZXW5/CiMObRxc5SsTStl/7XNvnBu82hu5Z7VtY4p+69t9iZ7XTc5Tm9yVjtcyt5Rmz2tWbFkHvuypPb2WEXfhiiT56Bbk+dIqZlp7Whb9c/m6j8N4KMATKPKNACnVH+fWP0b1ec/plSOqulnDsvQm8y1l5sry+Y8mmUp1jGPLMD+IVcvFtc+VK4smUd7LKl9MVg2k2k/FsC6ZaRWqfZMK6UalVLzAKwD8DiA5QA2a63NKZetAPar/r4fgDUAUH1+C4DhdZjnK6VmK6Vmt7W1pTn8aMraONlIgLm2QHDtmeY6j9TX5c6iZzqNK13YSq+4Hr7l3O8p8xieJbWXbSgqK0LttbmWdj0xPwExinTE/ztVM6217tZajwUwEsAHALy73mLVn/Vmoc/aaK1v1VqP11qPHzFiBN1gkyorcxg1oaNkmeXy2H7CzeQXZR7Nsmn3TBseFSvODU0oU6I8HjqXeewtrvMotZdtKAorQu1bWlqwob3d/3bYOUqTvaS1xoYNG9DS0hL6NZlcZ1prvVkpNRPAkQCGKaWaqunzSADmYn+tAPYH0KqUagKwB4CNWYyPRNyNJjfTmgaLa724sihqb3q5KdNkrjtJjjtcahbXenFlZTWPUa+bzLVeeax9UepFwBo5ciRa//IXtDU0AIsX911g0ybnOtP1nqunTZuAN9+sv/zmzcCWLeFZxGppacHIkSNDL5/m1TxGANhVNdIDABwH56TCvwI4pyiRAAAAIABJREFUFc4VPc4C8ED1JTOqf/+j+vxTOmrOblPcjaawhJU1yzxelJ2ksISVJst1A4o+rKhXuuC6jsLKNau5uRkHPPEEcNttjgmu1eWXA//zP0BnZ7hxffe7wA03ADt29H3u+98HrrzSSdVzcPpcmsn02wBMU0o1wmknuVdr/ZBSahGAu5VSPwYwF8Bt1eVvA/A7pdQyOIn06SmOjV5+psJ8I8z7nQbDtBpQ9F/bZJV9Hs2HFkXtAadmUXq5vVLuqCyA5c4IQHa3e7bJ8qtXWebR1ntCai/bUBQW53kEnH1Cmc201volAO+r8/gKOP3TtY/vBHBaWuNJXVyNUxqsIrctlIlFcQgxDRbHD/2yHArmWi+uLL/ah03nwrAoU+4y1F62IXusSiW8AQ6qfdAyjMR/hHmR+ZZIYQ7DsKImh5TXTc6TOUyj9tzm0fQmU8yjedyGAfZbx6gsv+tyG1bUxInymq9cd5KUJ8FlxZJ57C2pvWxDUVmU8wiEPwkxqPZmmRxIzDSVsjKact3kYrAoa294NgwwV1aY9hOqHW4Zrt3LlSXzaI8ltS8Gi3oezTJJWWKmS6o8Grq4SWuRWXk105RG0xaLqzHXmu+1aLmy8mgqpPb5YXGtfV7nEYh2ZJPKAAfVKwrLssRMUymMCStDO0XUdcyq/YQbyzxu02hSHPZLg+W3jjZqT/mFwYyN8nB3VofOubJsGpSyz6PUvi8rj9uQWcYGK6hlJMp6WpSYaSpldYMOrqkt1/YTytqbEyqKngBTtoxEYVG2ZnCtfVDKzfXQuc0bdOQxOZTap8viWvss59Esk5RlM00OqlcUlmWJmaZSmKSVMgG2ycpjYk6Z2haFlcfEPO+sNFLurNI+YfVlyTzaY5W99maZvLOojLlliZmmUtZGk1vbAgcznXbKbR6nZOW9BSLPrLzXnrqXm7NBoah91iyu9ZLa22MBdnqTsx6XjXW0LDHTVMqjOYzbAsH1EnR5M3S22ykok9a8JeZFqL25/BTl5bPyZlBsH4bneBk0qb1dVpYGmIJFeQWOOJ6CKuW2LDHTVOLa5yxXuohnDovM4pomc61XGWpveFQsrr22efzCUJR65bX2QP6vdMGVJcm0qI/MhNf7lhX1ZAE/VlzTmnbPdNx19PuGS3VFCa6sOMYpiwQ4zjrmbR6l9r0VlJhHYZmxZcGKY5yySIDjrCMlS2pPxwKibZN+tefIiuMDsuiZjrqOliVmmkpZpbZcWdx7k4UlLGFlwzI8YQnLFsvcAZWCZZYRFm+WZYmZplIeDXCcwztA/W+lStnv2+VmKriypGdaau/FolzHPJqwKCyufbtc65Vl7c3/Z4PF0WhyZUnPtKiPsupzjnN9SS+WuW6yXAWiR1maijiH4SlYUnupfZosajMddL3dKIeBKVmcjWYeaw9ImuxWVn3OlNesjtN+Ism0qJfMhHPtc6ZIk7nuvP3WUVjeLJlHeyypfTiWOVyft75d2yyu9eJ4fWJqFtfeZK4sMdOiXvLbsZnHKNIr7r3JXK/dy5Ul82iPJbUvBourAeZ6DWZKFufeZK7XTebK4vpFRk5ALJmyMsA20+Qwh7vzzgqqVxGSfKm91D5NFtd5NMaPykwHHe62xfIzKJQsmyYsTKtB3llB80jFKso8WpaYaSqF6XOm2rFR7rzjmApuh4KzNDtFYHGdR671KkPtZRuKl0xzbKcI6rWNkiZzvj5x0dsWuNae6zxalphpKvnt2OKmyUXe4abB4lovriyZR3ssqX2+WdQpd5AJozTmZpm8szgmrZzrxZVFVXvLEjNNpSx3krZu7MDdCOSNJfNojyW1LwarCPNoeFRpclZGk/LKDTavAlF2VlHm0bLETFMprzujIhxyletMS+3zwuJae5lHe/UKk3ID/EyYTRbXtgW5znQ0FmW9LEvMNJXCfFAXJb3yO8QTdR0pWVnVXuYxXZbU3h5LtqFoLL/ah02Tw9x8x71cmLEFGZQiJMCU9cpr7WUe2UjMNJWCPhBt3x0wi35P22f8Z1F7mcf0WFJ72YaisjjPo/v/pGCFGVvQdbnjnAxP1R9LyfIzdLavmpFF7WUe2UnMNJW6u50TDc1dBWtlO0ERlrCEJSxh+bPCpslhWO7lsmKFaT8JyzLLCUtYeWBZlphpKvm9KQD6D30bh1y59mjmud9T5rFHUnvZhuKwKOcRCMejNK2ULEpjrnW4nlYbbQtc+5zz3DOd93m0LDHTVKI2034pN9dDwXF2kmW43Bhl7WUeaVhS+/qsPG5DlQpNmhzlsHKY2ttkZWHyubZTxDGaHC/1lhWrKPNoWWKmqeT3DQuInl5RGfOsEyeuJylRJod5TyGl9lL7NFlZz6N7OQoWpWml6JlOYx2zrpfhFT1pzTq1zXuST1kvyxIzTSW/b1hA9PTKjxVn551FLzfXtI8ri2sKWYZ5lNrXZ2W1DUVNk/OWAMdJubNIzG2l72GvWFLkpJUri2uSLycgllR+bwog+g6XI4tr2pfnfk8bLKm91D5Nlo3UljJNjmIOubG41ouSBfBNWvPcM22DJT3Toj7iaoCpWQBtyl3mfk/brLyltkVi5bH2VL3JRTB0eU3Mi8BKI+Uuc8+0bZb0TIt6ibrPOYhF2X9N0Q9mWHk3O1mnfTb6UKX2Uvs4LIDvlS6y7ifmbjSl9vk3mlkn5jZ6uSnrZVmpmWml1P5Kqb8qpRYrpRYqpS6qPr6XUupxpdTS6s89q48rpdT1SqllSqmXlFLj0hpbKvL7hgVE37Fl1X8dlUVtprkmdFmwbPe0cjV0Uns6VlF6k7PoAabsc7adAEvts//yYZbjmphz7eUWMx2oLgCXaK3fDeBIABcqpQ4BcBmAJ7XWYwA8Wf0bAE4EMKb673wAN6c4NnqFMZq2EuCiJ+ZZJ4d5TyHznNpK7aOxgGi3tM4yOSxDAsw1tZXa07PMclmmtjYS4KzT97JfzUNr/ZrW+oXq71sBLAawH4CJAKZVF5sG4JTq7xMB3K4dPQdgmFLqbWmNj1xBptVmAkzJ4pqYm9dQsbxqxjmFLHpqK7WPznIvF4blV3uOLK4JMNfUVmqfHsvcPZOi9ma5LFg2zTRlym1ZmfRMK6VGA3gfgH8CeIvW+jXAMdwA9qkuth+ANa6XtVYfy4ey7pm2weJs8inrZV5TVBZXo8m1XnKli3SSwzyzpPZS+zRZAN+rjHC9YollpW6mlVKDAUwH8E2t9Zt+i9Z5rM/xSaXU+Uqp2Uqp2W1tbVTDTK6gpJUyAbbJ4miAqdcR4HvFkjJcNYNrvfxYRbnSRd56pm2zZB6l9rUsinU0KXfe+q/FTKcjpVQzHCN9p9b6j9WH3zDtG9Wf66qPtwLY3/XykQDW1jK11rdqrcdrrcePGDEivcFHVdZGk7L/mrLPuQjpe0NDNje5iTqPVKw8p7Zcaw/Q9iZLchiNlXU/sdReap8mizrlDmNabfRyy3Wmg6WUUgBuA7BYa32t66kZAM6q/n4WgAdcj3+xelWPIwFsMe0guVCWBpiy/5qyzznqOlKyOH75CEoqbPcTByWttlhZGGDq2ruXC8MKSnaiGBRKFjfjlEbtZR6l9mmyuG1D3Hu5KWrPQE0pso8GcCaA+UqpedXH/hvA1QDuVUqdC2A1gNOqzz0C4JMAlgFoB3B2imOjV1A6GmXnzTm15Wrys6p9VBZVyp1lAmzL5AetIyXL/UHt96UuCqu7G2hu9mdxNygUrDDzSMXimhxK7dNjca19GebR8LJIzHN2AmJqZlprPQv1+6AB4GN1ltcALkxrPKkrTNJKmQB3dtoZV1bGnLpeRWAB0jNNyTLLBZnpKKyw4+J2KDiNdSwDS+ZRal/L4lovql5uvwCC0kxLm0dJxdlocky5ubK41r6720m4uaXccqULewkdVxbX2pdhHrnWqwy1L8M8ck65LUvMNJU4m7AyJMBc68WVBfC9aga3HUiZ0iuu9eLKknmU2teyuNaLG4s65bYsMdNUyto4cT05rwhGswzzSJVy5z1N5npiUZ53kjKP/OsltRdWLSvPV2VhIDHTVAo6kSmK2QlixTkJjoLF2RxmVXuZx74s8xovFhDtw5Xi7O4gVpwTeLK4Q51tg5JF7WUe02NJ7WUbisoqwjwykJhpKgUZFGqzY4OV5RVLbLFkHp11NJdTCsPyS7m5nllvKyXK8ix9WyyZR6l9miyutZd5pJ1Hs0+RZLpk4mqcqFnSM10MVlDtzXJZs9yvKTJL+j2LwZJ5lNrXsrjWiyvLq/ZKRQuvLEvMNJU4GydhlYMFhD8cGYYV9gORkhXUyx2F5X6NsIQlLGEJK10WZWJunhMzXTJxvdRbWS4bx7FeWbMAOwaYikW5jnLJK3ssrrUvwzxyrVcZai/z2POcmGlRbHV3Z9cC0dBAd3JeVBZHA5xl7annEQjXmxyWlfdkmrplJKiXm+LEojgsbjvvoHXkypJ5rM+S2he79pQs8xlJMY/mOYp5NM+JmS6ZgkxFEUxrmCtKUF7pIgorq9pTz6NZLktWmNqb5bJkhak9Fct24kRhKtJgBR2+pdhJFsE4cZ1Hqb1sQ1FZUXqTKQ1wUO3Nc3I1j5KJqwEWFj0r7JUuuJppYdlPibgZFGEJS1j5YdnqTebKYiAx01TKc68tV9NqK32nTIA5sriaVq71oq69+zVFZOW131Nqnw8W19pnOY9R0mTKPmfpmfaUmGkqdXfns2+3CAY4zDpGSZO5XjaOksXRTHOuF3UvN9eUO2/9nrZZ3EwY93qVufZx0mSKfTd3lphpUS9xNpqUqS2VMadmcTWHHFlham+DxbVenGsP0BgUSQ7t174MRrPItadMkymNJldWmJ7pHJnpJtsDyKsmTJ3Q6+/bN63EstXrcWXN40YXL/s3Pty+AZ/xeN6t365fhtbGV/E9j2W/vmQZjt/5Jv4jBOvX6xZjY2c/XO6x7NcWLcfJne04MQTrxtdfQntLE77jsex5C1fg812d+HgI1nVr5wEAvumx7Jfmr8SXtMaEKcd4X5GhqmvWvIj+nRV83YM16cVV+DKA46Z+FF1N/t8fr141H8O27sJXPViff2ENvgbgxNtPwI4Wnw8UAD9+ZQHe+uZOnOfB+uy/WvF1AP9xx0nYOrjZl/X95Ytw4LbtOMuDdfI/1+JbAD5z1ynYuEc/X9YV/16MQ9rfxCQP1iefew3fBXDa3Z9F2/AWX9Z3X16C8Ts34XMerI///Q1cAeALfzgda/cZ4Mv61qJ/40OdWzy3kWP/uQ7fB/DF6Wdg9b6DfFnfWLAUx3W342QP1odnt+FHAM69/2wsnzPYl/W1l5bjZN3puY0cNW8DfgrgKw98GUvmD/VlfXnuCnxOac9tZPyCjbgGwIUPfg0LX97Dl/WlOSvxJQATbv9o3W1k7MubcR2Abz7yDcxbMcyXdca/VuE8AB+743h019lG3rN0C24E8O1Hv4XZrXv5sk7/52p8FcAn7joJO/v33UYOemUrfg3g8r9cin+s+7kv69R/tOK/AHzqnonYNqjvNvKO1dtwG4D/98QVeGbzdb6sic++iosBfPoPp2JTnW3k7Wu343YAV/71B3iq/SZf1klPv4bvADht+ul1t5F91+3A7wFc9bcf4/Fdv/FlHf/s6/hvAF/405l1t5ERG3biDwB+/szVeERN82V99Ll1+B6AL844G6v/NbDP83tu6cT9AK599n8wo/9dvqyPzG7DlQDOeejLWPFi321kyLZdeBDADf/4X0wfMt2XddS89c428sgFWLJ4SJ/nB+zsxp8B3Pz8Tbhn6oO+rPfP34hfALjw0W9g4fK+20hTVwVPAPjN7F/jzqmP+rLGLt7kbCOPX4J5a+psI1pjJoCpL/wWU6c+5cs6dOkW/ArAJU9+F3Nev7ruMo8rjXvm3Y7JU2f5sg5e8SZuAXDZX6/AcxuvrbvMn3UnZrx0F26e+rwv652rt2EygP/7tx9g1rZf1V3mwa52/GXhfbhh6jxf1qhXt2MagB8+82P8tfPWusv8sXMLZi3+E66dusiXtd8bO3AngKv+/jM8jql1l7l350b8a8lD+EXN5+XML830ZduQJNNEaqgA3Q3epq+iFBorIdoMACitUfGZmUoD0BCS1aCBbh8z2t0QflwNFfiOSysVelyNFR1Qr+r/GQLnjMubpavrH2ZsDSFqH5altDPv3iw743JY/u9VAGgMVXv/cWnVs1yYcfm+J6rPNYY4uZu69n7j6q6uf6h6BdR+93s1REtSY0WjW8Hzy2YlSu2ry3iNTUeqF3xZUcaltP+4ds9jyM+JUCyCepl1DPPZGjSuaLV3lun2eItFGZcKmseG8O9Vs47dHp8Vu7chivfq7s/7QNTuzxKvcUEpVFTE94TP545W4cYVtA2Z/ydM7dXuenkvE9ZThBpXQzgf0FMv/3GF9SfWpbXO7b8jjjhCs9EBB2h9xhnez3/961oPGxaOddBBWn/uc97PX3KJ1gMHhmO9971an3KK9/OXX651U1M41rhxWp90kvfz3/ue05lcqQSzjjxS649/3Pv5H//YYXV0BLM+8hGtjznG+/mf/9xhbd0azDruOK2POsr7+euuc1gbNgSzPvlJrf3eozfd5LBeey2Ydcopzlx6afJkh7VqVTDrtNO0Pvhg7+enTXNYy5YFsyZN0vrAA72fv+suh7VoUTDr7LO1HjnS+/np0x3WvHnBrK98Ret99vF+/sEHHdbzzwezgrbdxx5zWLNmBbO+9S2tBw3yfv6ppxzWX/8azLrsMq2bm72fnzXLYT32WDAraNt9/nnn+QcfDGYFbbvz5jnPT58ezAradhctcp6/665gVtC2u2yZ8/y0acGsG290ln399frPr1rlPD95cjDrN79xll29uv7zr73mPH/TTcGsoG13wwbn+euuC2YFbbtbtzrP//znwaygbbejw3n+xz8OZs2Y4Sz7r3/Vf75ScZ7//veDWY8+6iz77LPeyzQ1OfvJIIXZdgcOdPbfQQqz7Q4b5nw2BSnMtrvPPs5nZpDCbLsjRzqf5UFauNBh3X239zJBvsqCAMzWdfyoJNNU4trnnHXPtFmOihW294pjfyxXlvRMS+29WHIr93z37XKtV5a1V8r5l3XfLtcrXXBlFaxnWsw0lbga4DAsuW6ysIQlLEoWIOZQWOFZct1kYSVhMZCYaSplfW1oqrsDRkmTOe+8KZPDrFlhk/yiz6PUvjjbEFXKHfYqIxR3qIs6j2FYtlLbLFkUtTdXusj6NtRcWTauDU0xj1FYYqZFddXdTXdbzLAsiusmR00E/N74Nm8dTXlL66xZYWsv80jPktr3sLhuQ+7XeLEoEnPKdNT2raMpb02e1XWTKS8bR2nMo7IofIANVp7nkYHETFMpTLIT9htW2JQo6ySM613lpGdaap8XFtfac55H0wNLwXK/JglL7g4YrTc576lt1iyuiTnXeWQgMdNUCrMDiZImc92xcb2rHNd6cWXJPNpjSe3tsdyvoWCV+e6A5jlbrCLf0a9MLDkBUdRLYT/0Kc00ZTJdhn5PrvXiypLa22PJNpQOK0zKzbVnmmJcZv0pe1q5srgaTa714soSM10yBV2mKkp/GVdW0I6Ne79nnmuf93nMc73yXnvZhuhZQUYgjZ7psOvofo0XL8+sMCaMc890nmvPdR4ZSMw0lfKeOIXdkIrO4lr7Mswj13qVofYyj9HrRdXLHdQ7KpeN6y2ufc7SMx2NJT3TorrifGiTkhWmR9MGi2u9uLJkHu2xpPbFYFHWHgjmcT0Mb4tFWS+pfb7nkYHETFOJ8nC3sIQlLGEJK78sk1hHSfuyPHQuLGEVicVAYqapxPWQqxy+LUbtyzCPXOtVhtrLPNLWy9yEhIJlxhZ2Hc3yRWWFTUfzzOJae67zyEBipikUpim/CIeCw+6MbLA41osri+s8cq1XGWov21C0kwaDam/GljXLGA+vXu44LG7tFGENnS0Wt3pxZVHWnoHETFMo7OEK97JBPK4syrPhs75CgrB6lpF5tMeS2vNkGR5F7W2yKNcR4HdIP4yhs8niWi+uLIraM1BqZlop9Vul1Dql1ALXY3sppR5XSi2t/tyz+rhSSl2vlFqmlHpJKTUurXGlorCHK4Dgb1lhv62FYZmxZc3ieoiaa724sqT29liyDYVjRb0EHWUCTNWaYWNcZTg5j2ubB9d6cWWV3UwDmArgEzWPXQbgSa31GABPVv8GgBMBjKn+Ox/AzSmOi15hDHDYBIWSZXhZJTvcd7hZ14srS2pfjtqXYR6j3NI6TDsFdZ8zlQEOOy7KdaRmAdmlo1zNoY16cWVR1p6BUjPTWuunAWyseXgigGnV36cBOMX1+O3a0XMAhiml3pbW2MgV9hu8e9ksWFo7/yhYAF+DErbfk+KQaxFYWc5jEeqV19qXYRsyvKxZWZtWG+tIzQKCe7nzbA7D9kxTtUDknUVZewbKumf6LVrr1wCg+nOf6uP7AVjjWq61+lgfKaXOV0rNVkrNbmtrS3WwoUXZ5sGVZXhcd7h5TPtssrKex7zXK8+157YNRb2lNdcWiKxNaxHqleWJkbaMZh4Tc5ssitozEJcTEOttYbreglrrW7XW47XW40eMGJHysEIq7OEd97J5Y5mUm6tByeowfBlYUnt7rDJsQ4ZXdJaNNo+ysMzyXuJqNIty0qC0efRR1mb6DdO+Uf25rvp4K4D9XcuNBLA247HFV5STBoPeZFxZlMbcLJO1QaE45FoEltS+HLW3xaJsW+DKsmHM81yvNFpZ/FJuzuYwbL2KzqKsPQNlbaZnADir+vtZAB5wPf7F6lU9jgSwxbSD5EJhvykD4VszuLEorzJCnXKH7fekPAyfZ1ZYc5g1i2u9ylD7oG3I8ChbIPLMCmsOs2ZxrRfn2gM0KTclK430nRsrrKfISTLdlBZYKXUXgAkA9lZKtQL4PoCrAdyrlDoXwGoAp1UXfwTAJwEsA9AO4Oy0xpWKuLZmpMEKuiZkGFaYD50iJHScWZS1l3mMxqKsV3Oz9/NRb2nN9ZA+V1aQObTF4lovrrU3PfwULCCf7RQ2WUGfhzlJplMz01rr//R46mN1ltUALkxrLKkr7OEdIL8nIEZhUZj8sCyt6dNRYfUsx3FcZWCFrX1Li/fzadzSOu8nwWXNktrbY1HWvqMjHAvIZy83Z1ZOzDSXExDzrbCXeAHCp7bcWFxNPuU6Gl7RWVmawyLUK8vaGxZHgxLWmIc5DJ93ltTeHouzyafq5Q67X8szi9KYM5CYaQqF/UbqXjZvLK4mP2yfWhiWSbkpWGYZjqwsjWYR6pVl7W3dhCRszzTVYfi8s7I2mnmvV55rb2sbMv93UVmUxpyBxExTiGtqmwaLm8nPOuXm3h5AeUIdpdHMe70o02SOBoVz2seVlfU85r1eea69jW0oqJebc2sG5cmMYqZLpDBvsKKcgMjV5HOrV5hebq4n1Nlq88grK0qazNmgZHkYvgwsqb09lmxDvVlAdq0Z0uYhiq0o5pDStHJjcTf5WbKipNyUBpib0eS6jpQsw+NoKqgNirDCs6T29liyDfVmhUm5KQ1w1sacgcRMUyhK327YVgOuLG5Gk3u9qFogwvZyh2VRGU3KK6lQr6MfK8otrcP2QlLU3rAoah+VRbmOZWBlPY95r1eeay/bUA8LoLliSVgfIGa6RMpzahuVRWF20mBxrRcFi/KETbMMZZ+zH4tra4bh2WBR9sdm2WvLNe0rwzwWoV55rb1sQ71ZZvksWNLmUTJFaQ/g1k9cBtPKtV42WIaXdZuHjRP9qNK+rFlcd7hc68WVJbW3x5JtKB8ss7yXpM2jZMqz0cx7b3Ke68U15Y7S5lGWNDnrHS5HFtd6cWVJ7e2xZBvKB8ss7yVp8yiZovTHhjU73Fhck9awG6R7WS9xrT3XeTQ8KjMdtrcvzyzOKRHHenFl2ZjHPNcr77WXbYieRVl7BhIzTSHuRpOCFcbQRU1HKVnc6sWVRVl7rYNZ5pbWeT8cScUKszMKm+xQszjWiysr63nMe73yXHvZhtJhUdaegcRMU4jrIX1bbR7cjCbXenFnUdTe8PJ+ODLrQ9Qcd5Jc68WVJbW3x5JtqBisxkYntDHBDWOJmaZQno1mGUwr13pxZYW9CUlYMx1mZxTmCENYlhlbXlmcd2wc68WVJbW3x5JtqBisKCedW5aYaQpRXrqMKyuKoePGynvts55H8zwVK8zOKKyZpjzkaoMVdmfEkZX32ud5HvNerzzXXuYxOqtSCU6Tw9beLMtcYqYpZCaa8gQxbqwoJ8FR9O2mwcpr7cOwwqbJYeplxkZRe8OiWMewLHNYkPJEmaxP4KHqOaRmZV0vriypvT2WbEP8WUC4/QcVi4HETFMoz4f0pc2jGCzzfJZ9zjbGlTXL8OQQNd96cWVJ7e2xZBuyywLCfU6HbfOQZLokyrPRlDYPeyyl6FhmbJQGmOO4smaZlFsOUfM1KFxZUnt7LNmGorOAbA1wFGMuZrokCtPvGbWdghsrz6kt19qb57Nk2UqA81ovShaQ/8QpzDqGvURYnllSe3ss2YbSYQHhPg+pWJQpNwOJmaaQedNQ9ABzZYXpj416Ql1WLMp6Rb1sHEU/HiUrbJ9zlJMGua0jV1bYXu4oJwNlyYpiKorOktrbY2Vde87zCIQ70Y+qN5mSFbb25v9lLjHTFMpzq4G0eURjGV7RWdLmQc+ylXJzPaxcdJbU3h6rLNuQWbaoLGnzKJmK0AJRZFbUNDnrFghhCSsqS+vwh1y5Hu4WlrDKyApzExLKFogysBhIzDSF8pwml4FFfROSPKejwioGK2zKzTU5FJawysoC+J3ol3cWA4mZplCUvt2wO8mysyh7ps3YhCUsYXnzhCUsYdVnhUmTw7KAcNu3sKTNo3TKcwsE13YKkybn9TB83lkNDTzHVQYWZe3DsKL0cnM9dM6RFab2NsZVBhZl7SnT5Ly3U2TNCluCLy97AAAPzElEQVR7BhIzTaE8t0BwPQHRPJ/Xw/B5Z0nt7bEoa5/1PIbt5eZ8GJ6KFTYdzfM6cmUZY57lLa25tkDkmSXJdMmUtdHkyuKeJnM0KJxZUvti1D7Lecz7FUuoWZSpLdd15MoC+J7ol6XRLAJLkumSKEo/cdhDrnlkmefD7nCzZIXZsQmrR1J7eyzZhnrz8soKm7TmeR25sqKcc5MlK8r+VlhyAmLplOfWDOpbWnNN+7ge0ufKktrbY8k25Chsys251YDjuDizgGInwFzbKbiypM2jZIrSTsHtpEHzvLCEJSxhFZVleMLizQL4nQQnLP4sBhIzTaG8p8lcEzphCUtYwpKUmzeL6iYkXNNRYfFnMZCYaQpF6U0O+6Hvx1KKvheSamdki0W5jmVgyTxK7WtZXOvFjWV4FLVPg0W5jmH7Y8PUn5IVNtHMmsW1XlxZVLVnIFZmWin1CaXUEqXUMqXUZbbHE1pRkum8HiY1LIqTSNJgca0XV5bMo9S+lsW1XtxYJomlOqGuu5uWFWYd835La6qT4KhZXOvFlUVVewZiY6aVUo0AbgRwIoBDAPynUuoQu6MKqbCtGXJLa2EJS1jCElYtL2sWQGOc8t4eIKxisBioyfYAXPoAgGVa6xUAoJS6G8BEAIusjqpW8+YBX/saXpq/ffdD+3Suw1sBfPDo+ehWzZ4vfU43YuPPfo3Xf/mA5zIjdrXhbQA+dMxC7Gwc4LncM7sUtv3yt3jtxj97LrP3rvXYF8Cxxy3C1qahnss91Q50/up3WPubJz2XGb5rA/YDcPyJi7GxeZ3nco9t0cCtd+PVac94LrPXrk0YCeCkk1/GG/02ey730PoK+k2djta7/um5zJ5dm7E/gImfWYJX+2/3XO5Pr3dj0J0PYM19h3kuM6xrM94O4LOf/zdWtezyXO6+NV0Y9sojWD3Dm7VH1xaMAnD6pKVYNlB5LnfXK7uwz5LHsWqwN2to11aMBnDml5Zh8aD+nsvd/u8OjJw/Eyt9WEO6t+EAAOd8eTleGjzEc7nbXt6Jd+x4Fit8WIO7t+FAAF+54BXMGTLPc7lfL9mBg9rn+bIGdbfjHQAu/MZK/HOoN+vGf7fj0O3zsdyHNbB7B94J4KJLVuHZK71Z1y3djiO2vYSlPqwBlZ0YA+Dbl67GzKu9Wb9Yvg1HbXkZ//Zh9a904CAAl//fNXj8Wm/WT1ZsxUc2Lw9gdeIgAN/7YSse+ZU368pX3sRxm1bhZR9Wv8ouHAzgyqtexYxfe7O+t3ILTtz4Khb7sJp1F94N4Kc/X4vpU7xZl6/ahInr27DIh9Wou/EeAL/45Wu4505v1rdXb8SpGzYHsCp4D4DrbngDd/zBm3VR6wZM2roNC3xYDVrjUAA33rIOUx7wZl3w6nqc09GBl3xYAHCY1rj1tjbc+mdv1vlr23B+pRLM6uzEb6etx01PeLPOeW0dLgCwYOhYVJT3Z9Oh27fjjt9vwPVPe7POfP0NXARg4d7vR7fyzuYO2b4Ff/jDRvzPc96s0994Hd8GsHDf/4Nu5W2yDtm+Hn+6fxOufsGb9dm2tbgcwOLRx2CX8rY5725fi0ce2owfjfdmTVz/Kv4fgJfHHIfOBu/9+0Htq/HExoPwfR/WSRta8UMASw75BDoa+nku9672FXj69f3x3z6s4zeuwU8ALBn7H+ho8N4vvKt9Kf6+aji+68M6dtNq/ALA0g98BjsaWjyXG7NjKWYPeT8u9mF9aPMqXAdg2dGfR7uPh3nHjuWYP+gw/JcP64NvrsSNAJYfewa2Nw7c/fhhhw8Bnn3W83U2xMlM7wdgjevvVgAfrF1IKXU+gPMB4O1vf3s2I3OrsREYOhTbG3s+hF4ZMBh/GzYB3QHl/P1bvoAxO5b6LrO9cTCeHfoh7PR5QwPAXft8Ae9uXxzIem7oUdja6G2aAOCefU7HodsXBLLmDHk/NjXt6bvcH0Z8DodvfzGQNW/wWKxv3tt3uekjTsO4bXMCWfMHHYbX+73Fd7k/jjgV79/6fCBr0cD3YG2//QJYn8VRb/49kLVk4MFY0+L/Hv3T3p/Gh7Z4f/EwrKUDx2BlywG+yz2w9ymYsHlmIGt5yzuwfMA7fZebsfdEHLfpiUDWKy0HYumAMb7LPTT8ZHQ2PBbIWtUyCi8PPCiA9R+o+Oy0DWt1y9uxeKD/ga1Hhp+ERvgnHtsbB6O1/0gsHHSo73KP7nUiWio7A1kz+00INESP7fUJDOneGsAC/tbvGMwb/D7f5f6y5/H4/+3de4ycVRnH8e+PXgC7u7NbioZLkUuIGC69rZVLuxQEBDQiBpSLCopiolwM0UgwkYISUSv4lygqihFBBMRyEajQ0qXS0m1paUsRQSpCG4oWpCtYrH38Y07JUrozm5edObszv0/S7LxnznvmmSdnd56eOe9Mx+YNVceaP7qLpS2TK/ab03Es4/77IqLy9oD5pS56Wt9Xsc/9Hcew2+vrqo7VXZrO4ta3vAy8ydyOo9lr07NVx3qobRqL2iqP9WBpBvu+9jQ7DGCsP5UOr9inu9TFe159oupYC9qOYEFpWuU+pekc+O9V7EDl1eQFbYfTXZpeZawjOKR3edW5/3DbYcwvHVmxz8K2Q5mycVrVsR5pez/z2o+q2qe7NJ2RsblKv6k80PGBin16Wqcyv9TFqOh/UQRgcetU/thxbMU+S1qmDGispa1TmNNxXMU+j7ZM5sHSkYyO1yv3a53MvWOPr9hnecsE5pVmsGNsqjLWpKpjrRxzMHPbj6r6N2xZyyT+MPbEin0eH3MgD7Qfzc5bXqvY77GWCdw99kMV+zzxjgO2P1Zb/4uDuSiq7ZuqE0mnAh+MiM+l408BUyPi/P7O6ezsjJ6ennqFaGZmZmZNStKSiOjctn3I7JmmvBI9vs/xnsDaTLGYmZmZmVU1lIrpxcD+kvaRNBo4DZidOSYzMzMzs34NmT3TEbFZ0nnAvcAI4LqIWJU5LDMzMzOzfg2ZYhogIu4G7s4dh5mZmZnZQAylbR5mZmZmZsOKi2kzMzMzs4JcTJuZmZmZFeRi2szMzMysIBfTZmZmZmYFuZg2MzMzMyvIxbSZmZmZWUEups3MzMzMCnIxbWZmZmZWkCIidwyFSXoR+FvuOAbBOOAfuYNoUs59Ps59Ps59Ps59Ps59Po2S+3dHxK7bNg7rYrpRSOqJiM7ccTQj5z4f5z4f5z4f5z4f5z6fRs+9t3mYmZmZmRXkYtrMzMzMrCAX00PDtbkDaGLOfT7OfT7OfT7OfT7OfT4NnXvvmTYzMzMzK8gr02ZmZmZmBbmYzkjS8ZL+LOkpSRfnjqeZSFojaYWkZZJ6csfT6CRdJ2m9pJV92sZKmiPpL+lnR84YG1U/uZ8p6fk0/5dJOjFnjI1I0nhJcyWtlrRK0oWp3fO+xirk3vO+DiTtJOkRSctT/i9L7ftIWpTm/m8kjc4d62DxNo9MJI0AngSOBZ4DFgOnR8TjWQNrEpLWAJ0R0QifeznkSeoCeoFfRsRBqe27wIaIuDL9Z7IjIr6WM85G1E/uZwK9ETErZ2yNTNJuwG4RsVRSK7AE+ChwNp73NVUh9x/H877mJAkYExG9kkYBDwEXAhcBt0XETZJ+BCyPiGtyxjpYvDKdz1TgqYj4a0S8DtwEnJQ5JrOaiIj5wIZtmk8Crk+3r6f8YmeDrJ/cW41FxLqIWJpubwRWA3vgeV9zFXJvdRBlvelwVPoXwNHALam9oea+i+l89gD+3uf4OfzLXk8B3CdpiaRzcwfTpN4VEeug/OIHvDNzPM3mPEmPpW0g3mpQQ5L2BiYBi/C8r6ttcg+e93UhaYSkZcB6YA7wNPByRGxOXRqq5nExnY+20+Y9N/VzRERMBk4AvpTeCjdrFtcA+wETgXXA9/OG07gktQC3Al+OiFdyx9NMtpN7z/s6iYj/RcREYE/K78S/d3vd6htV7biYzuc5YHyf4z2BtZliaToRsTb9XA/8jvIvu9XXC2lv49Y9juszx9M0IuKF9GK3BfgJnv81kfaL3grcEBG3pWbP+zrYXu497+svIl4G5gGHAu2SRqa7GqrmcTGdz2Jg/3R162jgNGB25piagqQx6aIUJI0BjgNWVj7LamA2cFa6fRbw+4yxNJWtxVxyMp7/gy5dhPUzYHVEXNXnLs/7Gusv95739SFpV0nt6fbOwDGU963PBU5J3Rpq7vvTPDJKH8vzA2AEcF1EXJE5pKYgaV/Kq9EAI4FfO/e1JelGYAYwDngBuBS4HbgZ2At4Fjg1Inyh3CDrJ/czKL/VHcAa4Atb9/Ha4JA0DegGVgBbUvMllPfuet7XUIXcn47nfc1JOoTyBYYjKC/a3hwRl6fX3puAscCjwCcjYlO+SAePi2kzMzMzs4K8zcPMzMzMrCAX02ZmZmZmBbmYNjMzMzMryMW0mZmZmVlBLqbNzMzMzApyMW1mlpGkdklf7HO8u6RbavA4MyU9L+nyfu5fI2ncID7eDZI2SDqlem8zs+HLxbSZWV7twBvFdESsjYhaFaBXR8Q3ajT2m0TEmfiLqMysCbiYNjPL60pgP0nLJH1P0t6SVgJIOlvS7ZLukPSMpPMkXSTpUUkLJY1N/faTdI+kJZK6JR1Q7UEl7SLpvjTWjwH1ue/2NNYqSeemtnMkXd2nz+clXZW+UfQuScslrZT0icFOkJnZUOZi2swsr4uBpyNiYkR8dTv3HwScAUwFrgBejYhJwMPAp1Ofa4HzI2IK8BXghwN43EuBh9JYsyl/I99Wn01jdQIXSNqF8jeXfUTSqNTnM8DPgeOBtRExISIOAu4Z6BM3M2sEI3MHYGZmFc2NiI3ARkn/Au5I7SuAQyS1AIcDv5XeWFzecQDjdgEfA4iIuyS91Oe+CySdnG6PB/aPiIWSHgA+LGk1MCoiVkjaBMyS9B3gzojofhvP1cxs2HExbWY2tG3qc3tLn+MtlP+G7wC8HBETC4wd2zZImgEcAxwWEa9KmgfslO7+KXAJ8ATlVWki4klJU4ATgW9Lui8itnuRo5lZI/I2DzOzvDYCrUVPjohXgGcknQqgsgkDOHU+cGY65wSgI7WXgJdSIX0AcGifx1pEeaX6DODGdO7ulLee/AqYBUwu+lzMzIYjF9NmZhlFxD+BBenive8VHOZM4BxJy4FVwEkDOOcyoEvSUuA44NnUfg8wUtJjwDeBhducdzOwICK2bgs5GHhE0jLg68C3Cj4HM7NhSRFveZfPzMwajKSZQG9EzHqb49xJ+SP27h9A319Q3kc96J+bbWY2VHhl2sysOfQC5/b3pS3VpC+XeRJ4bYCF9A3AkcB/ijyemdlw4ZVpMzMzM7OCvDJtZmZmZlaQi2kzMzMzs4JcTJuZmZmZFeRi2szMzMysIBfTZmZmZmYFuZg2MzMzMyvo/5bz2KV59X9CAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plt.plot(hi_result)\n",
+ "#plt.plot(hi_array[0:10,lind])\n",
+ "\n",
+ "\n",
+ "titlestr = \"\"\n",
+ "\n",
+ "# temperatures\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "plt.grid()\n",
+ "ax1.hlines(-1,time_artificial[0],time_artificial[-1],'black') \n",
+ "ax1.hlines(0,time_artificial[0],time_artificial[-1],'black') \n",
+ "ax1.annotate(\"wet surface\",[25,-0.7])\n",
+ "ax1.plot(time_artificial,Tsurf_result,'b-')\n",
+ "ax1.plot(time_artificial,Tsurf_result_Semtner,'b--')\n",
+ "ax1.plot(time_artificial,T1_result,'g-')\n",
+ "ax1.plot(time_artificial,T2_result,'r-')\n",
+ "ax1.legend([\"Tsurf Winton\",\"Tsurf Semtner\", \"T1\", \"T2\"])\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24],\"\")\n",
+ "ax1.set_ylabel(\"T [°C]\")\n",
+ "ax1.set_title(titlestr)\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "# hi\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.plot(time_artificial,hi_result,'b-')\n",
+ "ax1.plot(time_artificial,hi_result_Semtner,'b--')\n",
+ "ax1.legend([\"hi Winton\", \"hi Semtner\"])\n",
+ "ax1.set_ylabel(\"hi [m]\")\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "plt.show()\n",
+ "\n",
+ "# hs\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.plot(time_artificial,hs_result,'b-')\n",
+ "ax1.plot(time_artificial,hs_result_Semtner,'b--')\n",
+ "ax1.legend([\"hs Winton\", \"hs Semtner\"])\n",
+ "ax1.set_ylabel(\"hs [m]\")\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "plt.show()\n",
+ "\n",
+ "# Fluxes\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.hlines(0,time_artificial[0],time_artificial[-1],'black') \n",
+ "ax1.annotate(\"melting\",[-1,30])\n",
+ "ax1.annotate(\"freezing\",[-1,-30])\n",
+ "\n",
+ "ax1.plot(time_artificial,Qbot_result,'b-')\n",
+ "ax1.plot(time_artificial,Qtop_result,'g-')\n",
+ "ax1.plot(time_artificial,Qbot_result_Semtner,'b--')\n",
+ "ax1.plot(time_artificial,Qtop_result_Semtner,'g--')\n",
+ "ax1.legend([\"Qbot Winton\", \"Qtop Winton\", \"Qbot Semtner\", \"Qtop Semtner\"])\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.set_ylabel('F [W/m$^2$]')\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "plt.show()\n",
+ "\n",
+ "# albedo\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.plot(time_artificial,albedo_result,'b-')\n",
+ "ax1.plot(time_artificial,albedo_result_Semtner,'b--')\n",
+ "ax1.legend([\"albedo Winton\", \"albedo Semtner\"])\n",
+ "ax1.set_ylabel(\"albedo []\")\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "plt.show()\n",
+ "\n",
+ "# rsds\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.plot(time_artificial,rsds_array_artificial,'r-')\n",
+ "plt.hlines(rlds,time_artificial[0],time_artificial[-1],'y') #\n",
+ "plt.hlines(shflx,time_artificial[0],time_artificial[-1],'g') # shflx\n",
+ "plt.hlines(lhflx,time_artificial[0],time_artificial[-1],'b') #lhflx\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_ylabel(\"F [W/m²]\")\n",
+ "plt.legend([\"rsds\", \"rlds\", \"shflx\", \"lhflx\"])\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# quantify radiative forcing due to change in albedo\n",
+ "rad_absorbed_winton = rsds_array_artificial * albedo_result\n",
+ "rad_absorbed_semtner = rsds_array_artificial * albedo_result_Semtner\n",
+ "\n",
+ "\n",
+ "\n",
+ "# plot absorbed radiation\n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.plot(time_artificial,rad_absorbed_winton,'r-')\n",
+ "ax1.plot(time_artificial,rad_absorbed_semtner,'r--')\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_ylabel(\"absorbed radiation [W/m²]\")\n",
+ "plt.legend([\"winton\", \"Semtner\"])\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "plt.show()\n",
+ "\n",
+ "rad_forcing_dif = rad_absorbed_winton -rad_absorbed_semtner\n",
+ "\n",
+ "# plot difference \n",
+ "fig, (ax1) = plt.subplots(nrows=1,sharex=True,figsize=(12,6))\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "ax1.plot(time_artificial,rad_forcing_dif,'-')\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_ylabel(\"difference in absorbed radiation [W/m²]\")\n",
+ "plt.legend([\"Winton - Semtner\"])\n",
+ "ax1.set_title(titlestr)\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "plt.show()\n",
+ "\n",
+ "print(np.mean(rad_forcing_dif))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "21.345832983984515\n",
+ "10.570061995624597\n",
+ "-5.320920984823984\n",
+ "0.021282371630332003\n",
+ "3.9265472871559473\n",
+ "3.9442638137473938\n"
+ ]
+ }
+ ],
+ "source": [
+ "# plots for presentation\n",
+ "# qtop\n",
+ "fig, (ax1) = plt.subplots()\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "\n",
+ "ax1.plot(time_artificial,Qtop_result_Semtner,color='C0',linestyle='-')\n",
+ "ax1.plot(time_artificial,Qtop_result,color='C1',linestyle='-')\n",
+ "ax1.legend([\"Semtner\",\"Winton\"],loc=1)\n",
+ "ax1.set_ylabel(\"Qtop [Wm$^{-2}$]\")\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "#ax1.set_ylim(0,400)\n",
+ "ax1.set_xlim(0,days)\n",
+ "\n",
+ "\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_xlim(0,days)\n",
+ "plt.savefig(\"plots/model_qtop_{}.svg\".format(initial_conditions),dpi=500)\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "# plots for presentation\n",
+ "# qbot\n",
+ "fig, (ax1) = plt.subplots()\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "\n",
+ "ax1.plot(time_artificial,Qbot_result_Semtner,color='C0',linestyle='-')\n",
+ "ax1.plot(time_artificial,Qbot_result,color='C1',linestyle='-')\n",
+ "ax1.legend([\"Semtner\",\"Winton\"],loc=1)\n",
+ "ax1.set_ylabel(\"Qbot [Wm$^{-2}$]\")\n",
+ "ax1.hlines(0,time_artificial[0],time_artificial[-1],'black',lw=1) \n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "\n",
+ "\"\"\"if initial_conditions==\"melting\":\n",
+ " ax1.set_ylim(-3.2,3.2)\n",
+ "elif initial_conditions==\"freezing\":\n",
+ " ax1.set_ylim(-20,4)\"\"\"\n",
+ "\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_xlim(0,days)\n",
+ "plt.savefig(\"plots/model_qbot_{}.svg\".format(initial_conditions),dpi=500)\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "# hi\n",
+ "fig, (ax1) = plt.subplots()\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "\n",
+ "ax1.plot(time_artificial,hi_result_Semtner,color='C0',linestyle='-')\n",
+ "ax1.plot(time_artificial,hi_result,color='C1',linestyle='-')\n",
+ "ax1.legend([\"Semtner\", \"Winton\"])\n",
+ "ax1.set_ylabel(\"hi [m]\")\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "\n",
+ "#ax1.set_ylim(3.3,4)\n",
+ "\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_xlim(0,days)\n",
+ "plt.savefig(\"plots/model_hi_{}.svg\".format(initial_conditions),dpi=500)\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "# hs\n",
+ "fig, (ax1) = plt.subplots()\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "\n",
+ "ax1.plot(time_artificial,hs_result_Semtner,color='C0',linestyle='-')\n",
+ "ax1.plot(time_artificial,hs_result,color='C1',linestyle='-')\n",
+ "ax1.legend([\"Semtner\", \"Winton\"])\n",
+ "ax1.set_ylabel(\"hs [m]\")\n",
+ "#ax1.set_xticks([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])\n",
+ "ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "\n",
+ "#ax1.set_ylim(3.3,4)\n",
+ "\n",
+ "ax1.set_xlabel(\"time [days]\")\n",
+ "ax1.set_xlim(0,days)\n",
+ "plt.savefig(\"plots/model_hs_{}.svg\".format(initial_conditions),dpi=500)\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "print(np.nanmean(Qtop_result_Semtner))\n",
+ "print(np.nanmean(Qtop_result))\n",
+ "print(np.nanmean(Qbot_result_Semtner))\n",
+ "print(np.nanmean(Qbot_result))\n",
+ "print(np.nanmean(hi_result_Semtner))\n",
+ "print(np.nanmean(hi_result))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plots for thesis\n",
+ "labelsize = 8\n",
+ "ticksize = 6\n",
+ "\n",
+ "if initial_conditions==\"melting_icon\" or initial_conditions==\"melting\":\n",
+ " hilim=[3.95,4]\n",
+ " qtoplim=[0,280]\n",
+ " qbotlim=[-15,4]\n",
+ "elif initial_conditions==\"melting_snow\":\n",
+ " hslim=[0.8,1]\n",
+ " qtoplim=[0,150]\n",
+ " qbotlim=[-10,10]\n",
+ "elif initial_conditions==\"freezing\" or initial_conditions==\"freezing_2\":\n",
+ " qbotlim=[-240,0]\n",
+ " qtoplim=[0,300]\n",
+ " hilim=[0,0.4]\n",
+ " \n",
+ "\n",
+ "#hi\n",
+ "fig, (ax1, ax2, ax3) = plt.subplots(3,1,figsize=(6,4))\n",
+ "#plt.subplots_adjust(hspace=0)\n",
+ "\n",
+ "\n",
+ "if init_hs==0:\n",
+ " ax1.plot(time_artificial,hi_result_Semtner,color='C0',linestyle='-')\n",
+ " ax1.plot(time_artificial,hi_result,color='C1',linestyle='-')\n",
+ "\n",
+ " legend = legend_color(ax1,['3L-Winton','0L-Semtner'],1, labelsize)\n",
+ " ax1.set_ylabel(\"hi [m]\", fontsize=labelsize)\n",
+ " #ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ " ax1.set_xlim(0,7)\n",
+ " ax1.set_ylim(hilim[0],hilim[1])\n",
+ " ax1.xaxis.set_ticks_position('none')\n",
+ " ax1.set_xticklabels('')\n",
+ " ax1.tick_params(labelsize=ticksize) \n",
+ "else:\n",
+ " ax1.plot(time_artificial,hs_result_Semtner,color='C0',linestyle='-')\n",
+ " ax1.plot(time_artificial,hs_result,color='C1',linestyle='-')\n",
+ "\n",
+ " legend = legend_color(ax1,['3L-Winton','0L-Semtner'],1, labelsize)\n",
+ " ax1.set_ylabel(\"hs [m]\")\n",
+ " #ax1.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ " ax1.set_xlim(0,7)\n",
+ " ax1.set_ylim(hslim[0],hslim[1])\n",
+ " ax1.xaxis.set_ticks_position('none')\n",
+ " ax1.set_xticklabels('')\n",
+ " ax1.tick_params(labelsize=ticksize) \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "# qtop\n",
+ "ax2.plot(time_artificial,Qtop_result_Semtner,color='C0',linestyle='-')\n",
+ "ax2.plot(time_artificial,Qtop_result,color='C1',linestyle='-')\n",
+ "#ax2.legend([\"Qtop Semtner\",\"Qtop Winton\"],loc=1)\n",
+ "ax2.set_ylabel(\"Qtop [Wm$^{-2}$]\", fontsize=labelsize)\n",
+ "#ax2.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "ax2.set_ylim(qtoplim[0],qtoplim[1])\n",
+ "ax2.xaxis.set_ticks_position('none')\n",
+ "ax2.set_xticklabels('')\n",
+ "ax2.tick_params(labelsize=ticksize) \n",
+ "ax2.set_xlim(0,7)\n",
+ "#ax2.hlines(0,0,7,lw=1,color='black')\n",
+ "\n",
+ "\n",
+ "# qbot\n",
+ "ax3.hlines(0,0,7,lw=1,color='black')\n",
+ "ax3.plot(time_artificial,Qbot_result_Semtner,color='C0',linestyle='-')\n",
+ "ax3.plot(time_artificial,Qbot_result,color='C1',linestyle='-')\n",
+ "#ax3.legend([\"Qbot Semtner\",\"Qbot Winton\"],loc=1)\n",
+ "ax3.set_ylabel(\"Qbot [Wm$^{-2}$]\", fontsize=labelsize)\n",
+ "#ax3.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "ax3.set_ylim(qbotlim[0],qbotlim[1])\n",
+ "ax3.ticklabel_format(axis='both', style='plain',useOffset=False)\n",
+ "ax3.tick_params(labelsize=ticksize) \n",
+ "\n",
+ "ax3.set_xlabel(\"Time [days]\", fontsize=labelsize)\n",
+ "ax3.set_xlim(0,7)\n",
+ "\n",
+ "ax1.spines['right'].set_color('none')\n",
+ "ax2.spines['right'].set_color('none')\n",
+ "ax3.spines['right'].set_color('none')\n",
+ "ax1.spines['top'].set_color('none')\n",
+ "ax2.spines['top'].set_color('none')\n",
+ "ax3.spines['top'].set_color('none')\n",
+ "ax1.spines['bottom'].set_color('none')\n",
+ "#ax2.spines['bottom'].set_color('none')\n",
+ "ax3.spines['bottom'].set_color('none')\n",
+ "\n",
+ "\n",
+ "plt.subplots_adjust(hspace=0.05)\n",
+ "ax1.text(-0.16,1,\"a)\", transform=ax1.transAxes, fontsize=labelsize)\n",
+ "ax2.text(-0.16,1,\"b)\", transform=ax2.transAxes, fontsize=labelsize)\n",
+ "ax3.text(-0.16,1,\"c)\", transform=ax3.transAxes, fontsize=labelsize)\n",
+ "\n",
+ "plt.savefig(\"plots/model_hi_qtop_qbot_{}.pdf\".format(initial_conditions))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## compare enthalpyax1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide\n",
+ " This is separate from the ipykernel package so we can avoid doing imports until\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# example of E & E2\n",
+ "plt.subplot()\n",
+ "T_vector=np.linspace(-100,0,401)\n",
+ "plt.plot(T_vector,E(T_vector),'C3')\n",
+ "plt.plot(T_vector,E2(T_vector),'C1')\n",
+ "plt.hlines(-L,T_vector[0],T_vector[-1],'C0')\n",
+ "plt.legend(['Winton upper layer','Winton lower layer','Semtner'])\n",
+ "plt.xlabel('Ice Temperature [°C]')\n",
+ "plt.ylabel('Enthalpy [Jkg$^{-1}$]')\n",
+ "plt.xlim(-8,0)\n",
+ "plt.ylim(-360000,-200000)\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(\"plots/enthalpy.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "E1_array=np.zeros(np.size(T1_result))\n",
+ "E2_array=np.zeros(np.size(T2_result))\n",
+ "for i in range(np.size(T1_result)):\n",
+ " E1_array[i]=E(T1_result[i])\n",
+ " E2_array[i]=E2(T2_result[i])\n",
+ "\n",
+ "plt.plot(E1_array)\n",
+ "plt.plot(E2_array)\n",
+ "\n",
+ "dhi_bot = Qbot_result/(rhoi*E2_array)\n",
+ "dhi_bot_Semtner = -1*np.array(Qbot_result_Semtner)/(rhoi*L)\n",
+ "\n",
+ "dhi_surf = Qtop_result/(rhoi*E1_array)\n",
+ "dhi_surf_Semtner = -1*np.array(Qtop_result_Semtner)/(rhoi*L)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'dhi_surf_Semtner' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-90-b669873ae3d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdhi_surf_Semtner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Semtner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdhi_surf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Winton'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"surface ice thickness change [ms$^{-1}$]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'dhi_surf_Semtner' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "plt.plot(dhi_surf_Semtner,label='Semtner')\n",
+ "plt.plot(dhi_surf,label='Winton')\n",
+ "plt.ylabel(\"surface ice thickness change [ms$^{-1}$]\")\n",
+ "plt.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(dhi_bot_Semtner,label='Semtner')\n",
+ "plt.plot(dhi_bot,label='Winton')\n",
+ "plt.ylabel(\"botom ice thickness change [ms$^{-1}$]\")\n",
+ "plt.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(dhi_surf_Semtner+dhi_bot_Semtner,label='Semtner')\n",
+ "plt.plot(dhi_surf+dhi_bot,label='Winton')\n",
+ "plt.ylabel(\"net ice thickness change [ms$^{-1}$]\")\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/__pycache__/functions.cpython-36.pyc b/python_scripts/__pycache__/functions.cpython-36.pyc
new file mode 100644
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diff --git a/python_scripts/bifurcation_Semtner_unlim-lim5.ipynb b/python_scripts/bifurcation_Semtner_unlim-lim5.ipynb
new file mode 100644
index 0000000..1c9d525
--- /dev/null
+++ b/python_scripts/bifurcation_Semtner_unlim-lim5.ipynb
@@ -0,0 +1,244 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Create CO2 - ice-edge latitude diagrams and calculate stability\n",
+ "Stability is assessed by reading simulation data and applying the stability criterion. CO2 - ice-edge latitude diagrams are created by manual input of stability and equilibrium ice-edge latitude. The actual bifurcation diagram is added afterwards as a best guess."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import xarray as xr\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from scipy import stats\n",
+ "\n",
+ "\n",
+ "def plot_simulation_merged(axes,co2, startlat, endlat, col, stable, offset=0): #plot a simulation into the bifurcation diagram\n",
+ " #plt.plot(co2,np.sin(np.radians(startlat)),'bo',fillstyle='none')#\n",
+ " handle = []\n",
+ " if stable==2: # metastable\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),markeredgecolor=col,marker='o',markerfacecolor=\"none\",clip_on=False)\n",
+ " elif stable==1: # stable from warm\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,marker='o',clip_on=False)\n",
+ " elif stable==3: # towards Snowball\n",
+ " if endlat==0:\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,marker='v',clip_on=False)\n",
+ " else:\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,markerfacecolor='none',marker='v',clip_on=False)\n",
+ " handle, =axes.plot([co2 *(1+offset),co2 *(1+offset)],[np.sin(np.radians(startlat)),np.sin(np.radians(endlat))],color=col,linestyle='-',alpha=0.2,clip_on=False) #plot the line\n",
+ " elif stable==4: # towards icefree\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,markerfacecolor='none',marker='^',clip_on=False)\n",
+ " handle, =axes.plot([co2 *(1+offset),co2 *(1+offset)],[np.sin(np.radians(startlat)),np.sin(np.radians(endlat))],color=col,linestyle='-',alpha=0.2,clip_on=False) #plot the line\n",
+ " return handle\n",
+ "\n",
+ " \n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos, fontsize=fontsize)\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " for text in legend.get_texts():\n",
+ " if text.get_text()=='Winton':\n",
+ " text.set_color('C1')\n",
+ " if text.get_text()=='3L-Winton':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='0L-Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='Semtner_5m':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-lim5':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-limited':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='1438ppmv':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='1500ppmv':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='3000ppmv':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='5000ppmv':\n",
+ " text.set_color('C3')\n",
+ " \n",
+ " return legend\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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bJeUG3AucEZ2QREQkVqpLCnMIlr+uzF+iEIuIiMSYFsQTEUlOWhBPRESOjJKCiIiE1ZgUzGyImb1hZnlmlmpmDzVEYCIi0vAiaSncQ3CLzc3ufhDoE92QRCQeFe4tZNzr49i8T3fNTWaRJIWDoR83M6OOgxcikthmLJtB3uY8ZiybEetQJIoiSQq/Bd4ETgTmhZ6LSCNSuLeQOavnMOXsKbz05UtqLSSxGpOCu+cCgwkWxxvq7nOjHpWIxJUZy2YwuOtgurbqyiU9LlFrIYlVOU/BzJ6kijkC7n5TVILRPAWRuFO4t5DL/nYZDw1+iHbp7WiZ1pLLX7qcOT+YQ4fmHWIdnlSt3ucpTAJ+CewCXie429o8YHddXkhEEtOMZTO49LhLaZfeDoCOLTqqtZDEqlzmwt2/AjCz/u4+IVT8uZnd0iCRiUhcWL5lOUs2LeGZlc8cUj6g04AYRSTRFMm9EhaY2SvAUqAv8HZ0QxKReDLrolkArNmxBoCsjKzYBSNRV2NScPd7zCwT6A486O4F0Q9LRERiocakYGa/oNxgr5nh7pOjGpWIiMREJN1Hb4V+G8Flqd+OWjQiIhJTkXQfLSj39C0zey2K8YiISAxF0n30NP/sPsoEvo5qRCIiEjORdB9NAwoJEsMOtPaRiEjSimjtI3f/yt3XuvsOYHq0gxIRkdiosqVgZpcDw4ETzWxWufqtGyIwERFpeNV1H80HFgNrgccIuo1KAM1TEBFJUtUlhTPcfa6Z5QMXVNimRU9ERJJQdUmhNPT7QIVyrVoqIpKkqlsQb17o4UnuPrGs3Mx+HvWoREQkJqobaM4CjgMuMLPB5eqfD/wq6pGJiEiDq677qDtwFtAGODtUVgL8R7SDEhGR2Kiu+2gBwbLZ97l7SQPGJCIiR6BwbyGDnxv8KnBDXk5era4YjWTy2tVm9o6ZbTGzfDP7uG5hiohIQwjdFe87wM9qu28kSWE8cC6QBxwf+l0tM+tjZv9uZo+YWY6Z3WZm99Q2OBERqZ3CvYXMWT0H4FogJ3tmdmZt9o8kKex194ME4wmdgf417eDuy4GNBAvo9Xf3/wIwszYV65rZGDNbZGaLAN0FXETkCMxYNoPBXQcD5AOzqGVrIZKkcL+ZpQO/BP4APBjJgd39WeAJIKusqIp60919kLsPAjZHcmwRETlc4d5CXvryJUYcP6Ks6LfUsrVQY1Jw93nuXuTuf3f3S4FeNe1jZhea2c+AS4E5ZnZb6FjbIw1MRERqZ8ayGVx63KW0S28HQF5O3gZq2VqIZOnsik6pqYK7vwq8Wodji4hIHS3fspwlm5bwzMpnAFZlz8wu2/RupMeoS1IQEZE4NOuiYEHrNTvWcMmcS3rn5eR9XttjVDej+R0OHwcwoH1tX0RERBJDdZPXzq5qm4iIJCd1H4lIRNKbpMc6BGkASgoiEpHMlrWaAyUJqsZLUs0s1cyuMbN/DT2u8eojERFJTJFMXnuGYKXUH4ZmNt8f3ZBERCRWIkkKHd39UaAo2sGIiEhsRZIUNpnZ1UBzM7sc2BDlmEREJEYiSQo3AR2BxcAxwOioRiQiIjETydVHnYCXQj8Abc1so7uXRi8sERGJhUiSwgyClsIy4CRgK9DMzGaFxhpERCRJRNJ9tAsY4O7XAQOBHQT3br45moGJiEjDi6SlcDRwspnlAX2BTu5eamZ7oxuaiIgciaw7co8GngRuWDNlWET3ao6kpXAtcCPwIpADXG9mqcCougYqIiIN4mfU8l7NkdxkJ9/db3H3i9z91tDzg+7+xZFEKiIi0VO89fSOBF/orwVysu7IjWidkiqTgpn9n5nND90/udDM3g39/qieYhYRkSgp3nLuKIIenlrdq7nKpODu57n7YGAVkOXuZxLcb3n1EUcrIiJRs2V3CX6g1eXAE6Gi3xJhayGSMYUTCNY+AsgAjq9TlCIi0iD+9P5mrMmuF4FCgDVThkV8r+ZIrj4aCzxiZm2A7cCPjiBWERGJss82FOEHMm4AbgDIuiO3bFON92o294p33AxtMGvi7gcqKW/v7lvqGmy1wZgtcvdBFYorD1BERCpVdo/mXSunBM+nDIv4Xs3VdR+9VvbAzGaXK3+u1hGKiEhCqC4pWLnH7asoFxGRJFLdmEJzMzuWIHGkl3/cIJGJiEiDqy4prATuCT3+rMJjERFJQlUmBXe/sfxzM+vr7kujH5KIiMRKJPMUyjwYtShERCQu1CYprI9aFCIiEhciSgpmdirwvJmlmFn3KMckIiIxUuOMZjP7A7AbGOzuL5rZ48CQGvY5GzgDOBGYBxxLsETG7V7VbDkREYm5SFoK33b3O4A9oeepNe3g7u+4+28IFs+70t1/TXA7z34V65rZmNBKrIuADpGHLiIi9S2SpLA71H2EmQ0guB1njczsWoIlW9eUKz6sleDu0919UGh5i82RHFtERKIjkqRwM3AlsBe4HhhT0w5mdiUwEugIfGJmdwF9AF3SKiISxyJZJbUtMNHd3cwM6EkN3+jd/Tm0RpKISMKJpKXwaNngcOj3o9ENSUQkuWzaWcTIGR+yaVdRrEOpUSRJoUXZg1BLoUU1dUVEpIJHF+Tz6dfbeWxBfqxDqVEkSWGWmb1hZr8lWE57VpRjEhFJGpt2FvHcoq958Or+PL/4m7hvLdSYFNz9EeCHwGzgh6HnIiISgUcX5DOkTybd27dg+IBj4r61UOVAs5ndSyWXkJoZ7n5PJbuIiEg5m3YW8fzib3hiZHBDyXHn9GDIg28z9pwedGoVn3chqO7qozcaLAoRkST06IJ8Rgw8hvZHNQWgU+v0cGvh7otPjHF0latu6ewFDRmIiEiyWfrNdhZ9tY0Z7/7jkPJB3dvGKKKaRTJPQURE6mD2j84AIL9wNwA9Oh4Vy3AiUpuls0VEJMkpKYiISJiSgoiIhCkpiIgkicK9hYx7fRzbirbV+RhKCiIiSWLGshnkbc7j+S+er/MxlBRERJJA4d5C5qyew5SzpzB/7Xyo403LlBRERJLAjGUzGNx1MF1bdeW8bucBjKrLcZQUREQSXOHeQl768iVGHD8CgBG9RgBc3qT1J7VuLSgpiEitrN++j/Xb98U6jFo50pjj/ZxnLJvBpcddSrv0dgBlv+ektX2/1q0FzWgWkVopKjkY6xBq7UhjPtL909NSj2j/mizfspwlm5bwzMpnyhffkNJk10dArbKZkoKISJR1btM8qsefdVFwm5s1O9aEyy6Zc0nv5aPf+ry2x1JSkKRVsKcAgMyWmTGOpHbiNe6yuCD+1++RulNSkKRVdCC+73BVlXiN+59xKSkkMw00i4gkmfQm6VDLsYQySgoiIkkms2UmeTl5X9dlXyUFEREJ05iCiEgVrnhkIYu+OnxxuUHd24ZvoJNslBRERKrQ95g29D2mDdef1g0I7pw2+eUVmMU4sChS95GISBXGndOD5xd/w5bdxQBs2lnEC0u+Yew5PWIcWfQoKYiIVKFT63RGDDyG5z7+hvS0VB5dkM+IgcfQqVV6rEOLGiUFEZFqjDunB/OWF7B1T3HStxJASUFEpFplrYXrnvgg6VsJoIFmEZEajTunB6sLdyd9KwGUFEREatSpdTqzbvpOrMNoEFFJCmZ2PHAXMAcoBgYAGcDt7u7ReE0RETlyURlTcPfPgadCT7/n7r8GlgH9KtY1szFmtsjMFlHHe4qKiEj9aOiB5sNaCe4+3d0HufsgYHMDxyMiIuVEq/soE7gCaA68bWZ3EXQfPR2N1xMRkfoRlaTg7gXAj6NxbBERiR7NUxARkTAlBRERCVNSEBGRMCUFEREJU1IQEZEwJQUREQnT2kciIkkivcmRr+CqpCAikiQyW2Ye8THUfSQiImFKCiIiEqakICIiYUoKIiISpqQgIiJhSgoiIhKmpCAiImFKCiIiEqakICIiYUoKIiISpqQgIiJhSgoiIhKmpCAiImFKCiIiEqakICIiYbqfQiOwcWcRNz31EV9s2s2KXw5lw44iLn/4XY7reBRNm6Tw9M2nhuvOzdvA5P9dwXt3ns/y9TuY/PIKANZt38eNZx7LzWcdG6vTEJEGoKTQCGQ0T+OPo05jzNOLwmVn9ezAg9cMOKzuK8sKODojuHtTn84Z/GXs6QCMmrmI80/o1DABi0jMqPuoEUhPSyWjRdohZe/lb+HKRxfyxDv54bL5n23k7J4dSDE7pO7e4gMU7t5PVoeWDRKviMSOkkIj1Kl1M/7vp+fyp9Gn8e7qzazcsBOA5z9ex2UDuhxW/61VhZxzfMeGDlNEYkDdR41Qsyap4ceDv/0tPt+4i217ihnYvS1Nmxz+PWHe8gLGfve4hgxRRGJELYVGaPf+A+HHH6/ZSrd2LVi1cRdvrNjIyBkf8vnGXfznvFUAlBwsZfWm3ZzYuXWswhWRBqSWQiNQcrCUG578kJUbdjJyxod859h2vLFyI01TUzglqx0DurVlQLe23HhmcGXRFY8s5KdDewOw8MstnHFc+1iGLyINyNw91jGEmdkidx9UoTh+ApSEsmbHGgCyMrJiGkdtxWvcZXGVFncAoEfHo2IYTe3kF+4GEivmemA1VzmcWgpJIDc/l2mLp1Gwp4DMlplMGDiBYT2GHVJnzpJ1TJ23ivXb99G5TXMmDu1d6aByMrjipStYtW3VYeW92/Zm9qWzYxBRZOI17rK49uSPp3R/58O2n3h0a16ZcHYMIqvZ96e9w4rQhRTlxXPMsaYxhQSXm5/LpIWT2LBnA46zYc8GJi2cRG5+brjOnCXruPOFPNZt34cTTES784U85ixZF7vAo6hfp36kpRx6CW5aShr9O/WPUUSRide4y+JKbf4VcOCQbWmpxsDubWMTWAQGdmtDWuqhX5jjPeZYi3pSMLOBZvYrM/udmelC93o2bfE0ig4WHVJWdLCIaYunhZ9PnbeKfSUHD6mzr+QgU+cd/q00GYzrO44UO/SjnWIpjOs3LkYRRSZe4y6Lq2mH+WCH9uammjH+/J4xiqxm48/vddi8m3iPOdYaoqXwQ2ASMAf4XsWNZjbGzBaZ2SIgvZL9TT9V/2zYs6HSMZdQuQG2bvu+SuuEymN+DvX907FFR9t/cP8jwP7Qqe7ff3D/wx2ad4h5bIkYd1lcKWm79qdlLKJca2F/0YHShzu1So/5e1fVT6fW6bb/QOkh72m8x1yPP3XSUN1HXuH3Pze4T3f3QaGfkxoonmSyNoLySOokm3v55+etNPQ8EcRr3PcCXqG1EE/xVSde39O41BBJ4c8ELYUfAG82wOs1NncBeyuU7Q2Vkz0ze1JNdcpk3ZE7KSoRxkBeTt4G4EmCPwJP5uXkFcQ4pIjEa9xlcaWk7SpNbfHlMkLxrZkyLC7iq86aKcMOeU8TIeZYinpScPeP3f1ud7/N3XdH+/Uam7ycvD8Co4GvCL4NfQWMDpUD/GLNlGGV1gmVl/eLhom6wdwL/J3E+2YYr3HfC/w9Nb3gOuIzvurE63sad+JqnoLUv+yZ2Z6XkxdR/2LWHbm+ZsqwOvdFikjii/ukYGbLgKIaK0ql0rPSTy5aU/RxJHWbZvY8ubhgdUR1E0gHYHOsg0gije39TOTzTa/LOG0iTF4rqmSWs0Qo1FKI6P0LtRSS6r2uYpa81FFjez8T+XxDV3TWmiaviYhImJJC8vtllOqKSBJKhDGFMe4+PdZxSGLS56d+Nbb3M5HPt66xx31SEBGRhqPuIxERCUuEq4+kCtkzszsD/wucCByVl5N3IFT+78DwvJy8s0LPRwI5QCpwHbAReAb4FvBRXk7ez2IQvojEobhICmZ2PMGSC3OAYmAAkAHcDvyEYHq6Aw8BvwW2AYvcfV5MAo4fW4HzgRfLCrJnZjcD+pV73gU4Jy8n7/xyZVcCn+bl5N2fPTP7oeyZ2f3ycvI+bcC465WZnQ2cQZAc5wHHos9PnZlZH2Ao0At4n+Ba/ZbuPtnMJgG7gE0E/18nA3uA2e7+SWwiPnJmNgwYCzxPAp2vmV0GnAf8A/gC6M8RfvbjovvI3T8Hngo9/Z67/xpYRvDHrau7PwhkhZ4vDW2/MAahxpW8nLyivJy8bRWKRwEzyz0fCqRmz8x+M5QAUoEewNLQ9k+A06MfbfS4+zvu/htgNXClPj9Hxphs7C8AAAVxSURBVN2XE7QmM4H+7v5fAGbWFigNPR8IXEDwhWQScG1soj1yZjaAYIXmfBLvfPcQrGPWErigPj77cZEUquEcvsJqlSuuNnbZM7PTCFoF88sVfwtoGmop7CVYmHAVcE5o+3lAwt9xxMyuJfhPvaZcsT4/deTuzwJPEPxBgerfx0R/Ly8CuhL0UJTd0SghztfdX3f3O4GVwLnlN1HHz368dB9lAlcAzYG3zewugibQ08DXZvZvBP/ZPwWuNbM7gddiFG48+xeg4iJ3O4AFocfzgUHAFOD87JnZbxK8rxsbKsBoMLMrgZHAq8An+vwcGTO7EOgLHAfMMbPbANx9m5mlhp4vBt4gmNsyhMM/dwnD3e8DMLMsYEkina+ZnQucStBl+p/18dnXJalJIHtm9lsETdtfE3zTcYIPyj3AOwSrpt6aPTP7duDrciuokj0zezowOS8n75sGD1xE4o6SQgILdRfNBU4m+CZzV15O3gehbX8vd/XRfxK0EDYT9IV2BJ4lGISalZeT91TDRy8i8UhJQUREwuJ9oFlERBqQkoKIiIQpKYiISJiSgoiIhCkpSMyY2Wozuyb0OMvMBkf59c41s1/Vcp/+ZnZzBPXON7MFZva2mb1oZu1D5UPN7B0ze8vMfmdmqTUc5wYzW2Vm51RX70iZWU8z+6S274ckPyUFiQkz60cwh+KSUFEWENWkUEdL3f1/qqtgZh0I5oRc7O7fJVh3pmmo/D+AC939XKAQGB3Ba0519wU1V6s7d18N/Fs0X0MSk5KCxMpw4GGghZk1A8YA/2Jmb4a+0c8xs7+Z2d/N7LpQea4FmpjZn0Lfyv8Uen6GmX1gZvPN7KbQMV42s7mhsnah180Olb9rZkeFjvdIqE6umbUN7fuSmb0MDC37Nm1mKWb2RKhFMLfcuQwDnnb3XRCs5eXuG8qV7wnVewC4PNI3qLJzqKasuvfrcTP7duiY40MzwEUqpaQgsTLQ3T8iWJriAmA6wR/QstVczd1/ALwCfCdUvo5gfZrLgRWhb+XLgRHA94Hb3X0w8GToGOnufhHwGEHSAcDdLwkd93zgYmBtaL8/AONC1ZqG6u0rF/MPgE3ufg7BH/wyRwMbKjnHo4H15V63CGga4ftTprJzqKysuvdrNsEyMhAshvZKLWOQRkRJQRqcmR0HnGRmrwLXAJdWUm1Z6Pf6Co/bEqzJszhUtgjoSdDquMrMngZOCW1bEvr9SahO+eOuA9oA3wauMbO3CLp6yloUZccv73hgIYC7l5Yr3wB0rqT+IeVmlg6UmNkFZnanmU2rZJ+KKjuH6s6rsvfrTeA8M+sE7CrXchE5jJKCxMIIYJS7X+ju5xF8oy4huAlQGa/isRGshnpy6Pkg4Etgm7vfQtCf/8vQtn7lfn9ZxbFWAbPc/Vx3P4vgvh4QLAFS0SrgNAi6ksqVvwJcb2atQtt6mtnRBEuQjDSzlqF6PwHmuPsb7n5/hfOtSmXnUNN5HXKO7n6AYFG0iQT3BBCpUlyskiqNzjCCm36UWUHwWTzTzP4CPFLD/i8Cz5jZ2wTfxn8D3Gpmw4GjQs8h+Fb+KsFa+SOA7EqO9RLwezMrW278QWBnFa/7EnBJ6HV3E3RZ4e6FZnYv8L9mZgQ3P7rZ3beY2f3Aq2ZWSvANfyqAmf2E4O53NansHCI5r4pmA38lSMAiVdLaR5KULFhS+AJ3/3msY6nIzMYS3M/iQ2Cal/tPaGZXAHcAtxG0ZA45h/o6LzPrSZCUniu7qYwIqKUg0uDc/TGCQeLKts0m+FZflgCiFcNqQl1hIuWppSAiImEaaBYRkTAlBRERCVNSEBGRMCUFEREJU1IQEZEwJQUREQlTUhARkbD/BycrAb3A2ZyzAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "### %matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "save_plot = True\n",
+ "plot_unlim = True\n",
+ "plot_5m = True\n",
+ "\n",
+ "offset=0.01\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6,4))\n",
+ "ax.spines['left'].set_position(('outward',5))\n",
+ "ax.spines['bottom'].set_position(('outward',5))\n",
+ " \n",
+ "# 5m simulations\n",
+ "\n",
+ "if plot_5m:\n",
+ " color_5m='C2'\n",
+ " #b=plot_simulation_merged(ax,1250, 90, 0, color_5m, 1)\n",
+ " #plot_simulation_merged(ax,1375, 90, 0, color_5m, 1)\n",
+ " plot_simulation_merged(ax,1438, 90, 0, color_5m, 1)\n",
+ " plot_simulation_merged(ax,1500, 90, 46.6, color_5m, 1)\n",
+ " plot_simulation_merged(ax,1594, 90, 50.3, color_5m, 1)\n",
+ " #plot_simulation_merged(ax,1250, 90, 0, color_5m, 1)\n",
+ " plot_simulation_merged(ax,3000, 90, 65, color_5m, 1)\n",
+ " plot_simulation_merged(ax,5000, 90, 90, color_5m, 1)\n",
+ " \n",
+ " # Jormungand\n",
+ " #mlo_aqua_2000ppmv_Jor2\n",
+ " plot_simulation_merged(ax,2000, 13.48, 0, color_5m, 3)\n",
+ " #mlo_aqua_2000ppmv_Jor5\n",
+ " #plot_simulation_merged(ax,2000, 32.68, 29.34, color_5m, 3)\n",
+ " #mlo_aqua_2500ppmv_Jor2\n",
+ " plot_simulation_merged(ax,2500, 13.48, 0, color_5m, 3)\n",
+ " #mlo_aqua_2500ppmv_Jor5\n",
+ " #plot_simulation_merged(ax,2500, 32.68, 36.16, color_5m, 4)\n",
+ " #mlo_aqua_3000ppmv_Jor\n",
+ " plot_simulation_merged(ax,3000, 28.03, 36.87, color_5m, 4)\n",
+ " #mlo_aqua_3000ppmv_Jor2\n",
+ " plot_simulation_merged(ax,3000, 13.48, 0, color_5m, 3)\n",
+ " #mlo_aqua_3000ppmv_Jor3\n",
+ " #plot_simulation_merged(ax,3000, 16.26, 22.95, color_5m, 4)\n",
+ " #mlo_aqua_5000ppmv_Jor\n",
+ " #plot_simulation_merged(ax,5000, 28.03, 34.75, color_5m, 4)\n",
+ " #mlo_aqua_5000ppmv_Jor2\n",
+ " plot_simulation_merged(ax,5000, 13.29, 30.00, color_5m, 4)\n",
+ " #mlo_aqua_5000ppmv_Jor4\n",
+ " plot_simulation_merged(ax,5000, 7.47, 15.07, color_5m, 4, -offset)\n",
+ " #mlo_aqua_10000ppmv_Jor\n",
+ " #plot_simulation_merged(ax,10000, 28.03, 41.30, color_5m, 4)\n",
+ " \n",
+ " \n",
+ "#hice_unlim simulations\n",
+ "if plot_unlim:\n",
+ " color_unlim='C0'\n",
+ " a=plot_simulation_merged(ax,1500, 90, 0, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,1594, 90, 49.54, color_unlim, 1, offset)\n",
+ " plot_simulation_merged(ax,1688, 90, 51.07, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,1875, 90, 53.20, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,2250, 90, 56.85, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,3000, 90, 65, color_unlim, 1, offset) #5m limited, but always under 5m\n",
+ " plot_simulation_merged(ax,3000, 14.46, 0, color_unlim, 3, offset)\n",
+ " #plot_simulation_merged(ax,3000, 22.05, 22.83, color_unlim, 4)\n",
+ " plot_simulation_merged(ax,3750, 14.46, 0, color_unlim, 3) \n",
+ " #plot_simulation_merged(ax,3750, 19.2, 18.7, color_unlim, 4)\n",
+ " #plot_simulation_merged(ax,3907, 14.46, 15.1, color_unlim, 2)\n",
+ " plot_simulation_merged(ax,4063, 14.46, 11.5, color_unlim, 3)\n",
+ " #plot_simulation_merged(ax,4219, 14.46, 16.23, color_unlim, 2)\n",
+ " plot_simulation_merged(ax,4375, 14.46, 18.2, color_unlim, 4)\n",
+ " plot_simulation_merged(ax,5000, 14.46, 24, color_unlim, 4, offset)\n",
+ " #plot_simulation_merged(ax,5000, 11.6, 14.3, color_unlim, 2)\n",
+ " plot_simulation_merged(ax,5000, 90, 90, color_unlim, 1, offset) # limited, but no ice at all\n",
+ " #plot_simulation_merged(ax,10000, 14.46, 18, color_unlim, 4) \n",
+ "\n",
+ "\n",
+ "ax.set_yticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+ "ax.set_ylim(-0.1,1.1)\n",
+ "ax.set_xlabel(\"Atmospheric CO$_2$ [ppmv]\",size=labelsize)\n",
+ "ax.set_ylabel(\"Ice-Edge Latitude [°]\",size=labelsize)\n",
+ "\n",
+ "plt.tick_params(axis='both', which='major', labelsize=ticksize)\n",
+ "\n",
+ "ax.spines['right'].set_color('none')\n",
+ "ax.spines['top'].set_color('none')\n",
+ "ax.yaxis.tick_left()\n",
+ "ax.xaxis.tick_bottom()\n",
+ "\n",
+ "\n",
+ "ax.set_xscale('log')\n",
+ "\n",
+ "ax.set_xticks(np.linspace(1000,10000,10) )\n",
+ "ax.set_xticklabels([1000, 2000,3000,4000,5000,'','','','',10000] )\n",
+ "ax.set_xlim(1000,5000)\n",
+ "ax.set_ylim(0,1)\n",
+ "# extra ticks at bifurcation points\n",
+ "#semtner_bif=1641\n",
+ "#semtner_5m_bif=1547\n",
+ "semtner_bif=1547\n",
+ "semtner_5m_bif=1469\n",
+ "ax.vlines([semtner_5m_bif],-0.04,-0.01,color=color_5m,lw=1,clip_on=False)\n",
+ "ax.vlines([semtner_bif],-0.04,-0.01,color=color_unlim,lw=1,clip_on=False)\n",
+ "#ax.axvline(semtner_bif,-20,0.1)\n",
+ "ax.annotate(semtner_bif,(semtner_bif,0.02),color=color_unlim,ha='center',clip_on=False, fontsize=labelsize)\n",
+ "ax.annotate(semtner_5m_bif,(semtner_5m_bif,0.1),xytext=(semtner_5m_bif,-0.05),color=color_5m,ha='center',va='top',clip_on=False, fontsize=labelsize)\n",
+ "ax.set_yticklabels([0,10,20,30,45,60,90])\n",
+ "legend_color(ax,[\"0L-Semtner\",\"0L-Semtner-limited\"],2, labelsize)\n",
+ "\n",
+ "\n",
+ "if save_plot:\n",
+ " plt.savefig(\"plots/bifurcation_5mlim_merged.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/bifurcation_Winton_Semtner_unlim.ipynb b/python_scripts/bifurcation_Winton_Semtner_unlim.ipynb
new file mode 100644
index 0000000..b916b72
--- /dev/null
+++ b/python_scripts/bifurcation_Winton_Semtner_unlim.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Create CO2 - ice-edge latitude diagrams \n",
+ "CO2 - ice-edge latitude diagrams are created by manual input of stability and equilibrium ice-edge latitude. The actual bifurcation diagram is added afterwards as a best guess."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import xarray as xr\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "from scipy import stats\n",
+ "\n",
+ "\n",
+ "def plot_simulation_merged(axes,co2, startlat, endlat, col, stable, offset=0): #plot a simulation into the bifurcation diagram\n",
+ " #plt.plot(co2,np.sin(np.radians(startlat)),'bo',fillstyle='none')#\n",
+ " handle = []\n",
+ " if stable==2: # metastable\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),markeredgecolor=col,marker='o',markerfacecolor=\"none\",clip_on=False)\n",
+ " elif stable==1: # stable from warm\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,marker='o',clip_on=False)\n",
+ " elif stable==3: # towards Snowball\n",
+ " if endlat==0:\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,marker='v',clip_on=False)\n",
+ " else:\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,markerfacecolor='none',marker='v',clip_on=False)\n",
+ " handle, =axes.plot([co2 *(1+offset),co2 *(1+offset)],[np.sin(np.radians(startlat)),np.sin(np.radians(endlat))],color=col,linestyle='-',alpha=0.2,clip_on=False) #plot the line\n",
+ " elif stable==4: # towards icefree\n",
+ " axes.plot(co2 *(1+offset),np.sin(np.radians(endlat)),color=col,markerfacecolor='none',marker='^',clip_on=False)\n",
+ " handle, =axes.plot([co2 *(1+offset),co2 *(1+offset)],[np.sin(np.radians(startlat)),np.sin(np.radians(endlat))],color=col,linestyle='-',alpha=0.2,clip_on=False) #plot the line\n",
+ " return handle\n",
+ "\n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos,fontsize=fontsize)\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " for text in legend.get_texts():\n",
+ " if text.get_text()=='Winton':\n",
+ " text.set_color('C1')\n",
+ " if text.get_text()=='3L-Winton':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='0L-Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='Semtner_5m':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-lim5':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='1438ppmv':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='1500ppmv':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='3000ppmv':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='5000ppmv':\n",
+ " text.set_color('C3')\n",
+ " \n",
+ " return legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "save_plot = True\n",
+ "plot_winton = True\n",
+ "plot_unlim = True\n",
+ "\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "offset=0.01\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6,4))\n",
+ "ax.spines['left'].set_position(('outward',5))\n",
+ "ax.spines['bottom'].set_position(('outward',5))\n",
+ " \n",
+ " \n",
+ "# color_winton simulations\n",
+ "if plot_winton:\n",
+ " color_winton='C1'\n",
+ "\n",
+ " \n",
+ " a=plot_simulation_merged(ax,1875, 90, 0, color_winton, 1)\n",
+ " plot_simulation_merged(ax,2250, 90, 0, color_winton, 1)\n",
+ " plot_simulation_merged(ax,2437, 90, 50.5, color_winton, 1)\n",
+ " plot_simulation_merged(ax,2625, 90, 52.6, color_winton, 1)\n",
+ " plot_simulation_merged(ax,3000, 90, 56.3, color_winton, 1)\n",
+ " plot_simulation_merged(ax,5000, 90, 90, color_winton, 1)\n",
+ " plot_simulation_merged(ax,3000, 15.3, 0, color_winton, 3, -offset)\n",
+ " plot_simulation_merged(ax,5000, 15.3, 0, color_winton, 3)\n",
+ " plot_simulation_merged(ax,5000, 38.9, 54.3, color_winton, 4)\n",
+ " #plot_simulation_merged(ax,10000, 15.3, 6, color_winton, 3)\n",
+ " plot_simulation_merged(ax,3000, 30.2, 0, color_winton, 3)\n",
+ " plot_simulation_merged(ax,4219, 30.2, 0, color_winton, 3)\n",
+ " plot_simulation_merged(ax,4219, 16.6, 0, color_winton, 3, -offset)\n",
+ " \n",
+ " \n",
+ "# hice_unlim simulations\n",
+ "if plot_unlim:\n",
+ " color_unlim='C0'\n",
+ " a=plot_simulation_merged(ax,1500, 90, 0, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,1594, 90, 49.54, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,1688, 90, 51.07, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,1875, 90, 53.20, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,2250, 90, 56.85, color_unlim, 1)\n",
+ " plot_simulation_merged(ax,3000, 90, 65, color_unlim, 1) #5m limited, but always under 5m\n",
+ " plot_simulation_merged(ax,3000, 14.46, 0, color_unlim, 3, offset)\n",
+ " plot_simulation_merged(ax,3750, 14.46, 0, color_unlim, 3) \n",
+ " #plot_simulation_merged(ax,3907, 14.46, 15.1, color_unlim, 2)\n",
+ " plot_simulation_merged(ax,4063, 14.46, 11.5, color_unlim, 3)\n",
+ " #plot_simulation_merged(ax,4219, 14.46, 16.23, color_unlim, 2)\n",
+ " plot_simulation_merged(ax,4375, 14.46, 18.2, color_unlim, 4, )\n",
+ " plot_simulation_merged(ax,5000, 14.46, 24, color_unlim, 4)\n",
+ " #plot_simulation_merged(ax,5000, 11.6, 14.3, color_unlim, 2)\n",
+ " plot_simulation_merged(ax,5000, 90, 90, color_unlim, 1, offset) # limited, but no ice at all\n",
+ " #plot_simulation_merged(ax,10000, 14.46, 18, color_unlim, 4)\n",
+ "\n",
+ "\n",
+ "#ax.hlines([np.sin(np.radians(56.3)),np.sin(np.radians(50.5)),np.sin(np.radians(52.6))],1500,3500)\n",
+ "ax.set_yticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+ "ax.set_ylim(-0.1,1.1)\n",
+ "ax.set_xlim(1000,10500)\n",
+ "ax.set_xlabel(\"Atmospheric CO$_2$ [ppmv]\",size=labelsize)\n",
+ "ax.set_ylabel(\"Ice-Edge Latitude [°]\",size=labelsize)\n",
+ "\n",
+ "plt.tick_params(axis='both', which='major', labelsize=ticksize)\n",
+ "\n",
+ "ax.spines['right'].set_color('none')\n",
+ "ax.spines['top'].set_color('none')\n",
+ "ax.yaxis.tick_left()\n",
+ "ax.xaxis.tick_bottom()\n",
+ "\n",
+ "\n",
+ "ax.set_xscale('log')\n",
+ "\n",
+ "ax.set_xticks([1000,2000,3000,4000,5000,6000,7000,8000,9000,10000] )\n",
+ "ax.set_xticklabels([1000,2000,3000,4000,5000,'','','','',10000] )\n",
+ "ax.set_xlim(1000,5000)\n",
+ "ax.set_ylim(0,1)\n",
+ "\n",
+ "# extra ticks at bifurcation points\n",
+ "winton_bif=2344\n",
+ "#semtner_bif=1641\n",
+ "semtner_bif=1547\n",
+ "\n",
+ "ax.vlines([winton_bif],-0.04,-0.01,color=color_winton,lw=1,clip_on=False)\n",
+ "ax.vlines([semtner_bif],-0.04,-0.01,color=color_unlim,lw=1,clip_on=False)\n",
+ "#ax.axvline(semtner_bif,-20,0.1)\n",
+ "ax.annotate(winton_bif,(winton_bif,0.1),xytext=(winton_bif,-0.05),color=color_winton,ha='center',va='top',clip_on=False, fontsize=labelsize)\n",
+ "ax.annotate(semtner_bif,(semtner_bif,0.1),xytext=(semtner_bif,-0.05),color=color_unlim,ha='center',va='top',clip_on=False, fontsize=labelsize)\n",
+ "\n",
+ "ax.set_yticklabels([0,10,20,30,45,60,90])\n",
+ "legend_color(ax,[\"3L-Winton\", \"0L-Semtner\"],2, labelsize)\n",
+ "#plt.legend((a,b),(\"Winton\", \"Semtner hice_unlim\"))\n",
+ "if save_plot:\n",
+ " if plot_winton:\n",
+ " plt.savefig(\"plots/bifurcation_winton_merged.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/cdo_pp.ipynb b/python_scripts/cdo_pp.ipynb
new file mode 100644
index 0000000..7d1a67e
--- /dev/null
+++ b/python_scripts/cdo_pp.ipynb
@@ -0,0 +1,347 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Postprocessing of ICON-AES output for Snowball experiments\n",
+ "\n",
+ "This scripts merges the 2d output data of ICON into a global and zonal mean. It does that by processing custom subperiods and then merging these output files. This is done so that not all the output data has to be reprocessed if the simulation is continued at a later time, as well as for a better overview of which period is processed and which is not. For this, it is reasonable to *not* delete the output files of the subperiods (*.yXXXX_XXXX.nc) until it's certain that this simulation is not continued.\n",
+ "Additionally, 3d data is copied."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of paths "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/csecfrl_experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/ exists\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os, glob, sys\n",
+ "from datetime import datetime\n",
+ "\n",
+ "#experiment = \"mlo_aqua_4219ppmv_71sic_winton_semtnerrestart\" #experiment name\n",
+ "experiment = \"mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1\" #experiment name\n",
+ "\n",
+ "base_path = \"/scratch/b/b380905/experiments/)\" #base path of experiment output data\n",
+ "#base_path = \"/scratch/b/b380782/experiments/\" #Christoph\n",
+ "data_path = base_path +experiment +\"/\"\n",
+ "\n",
+ "save_path = \"/work/bb1092/pp_JH/csecfrl_experiments/\" #saving path\n",
+ "\n",
+ "if not os.path.isdir(save_path +experiment): #check if experiment folder in saving path exists\n",
+ " print(\"creating folder \" +save_path +experiment)\n",
+ " os.mkdir(save_path +experiment)\n",
+ "else:\n",
+ " print(save_path +experiment +\"/ exists\")\n",
+ " \n",
+ "filename = experiment +\"_atm_2d_ml\" #basename of input and output file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Process subperiod of a simulation into zonal and global mean\n",
+ "Output files are *gm.y0000_9999.nc and *zm.y0000_9999.nc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0347*\n",
+ "cdo mergetime: Processed 330854400 values from 540 variables over 359 timesteps [53.82s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0348*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [50.85s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0349*\n",
+ "cdo mergetime: Processed 330854400 values from 540 variables over 359 timesteps [57.53s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0350*\n",
+ "cdo mergetime: Processed 330854400 values from 540 variables over 359 timesteps [49.51s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0351*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [54.46s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0352*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [56.52s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0353*\n",
+ "cdo mergetime: Processed 330854400 values from 540 variables over 359 timesteps [50.91s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0354*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [44.30s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0355*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [48.27s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0356*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [50.75s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0357*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [48.14s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0358*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [48.34s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0359*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [47.18s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0360*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [46.44s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0361*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [38.40s 362MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0362*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [48.39s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0363*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [47.57s 364MB]\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1/mlo_aqua_5000ppmv_83sic_hice_unlim_csecfrl1_atm_2d_ml_0364*\n",
+ "cdo mergetime: Processed 331776000 values from 540 variables over 360 timesteps [46.41s 364MB]\n",
+ "cdo cat: 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 91cdo cat: Processed 5968281600 values from 810 variables over 6476 timesteps [294.88s 452MB]\n",
+ "cdo fldmean: 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 91cdo fldmean: Processed 1673314304 values from 45 variables over 6476 timesteps [185.01s 306MB]\n",
+ "cdo remapcon: YAC first order conservative weights from unstructured (20480) to lonlat (192x96) grid\n",
+ "cdo remapcon: 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 910cdo remapcon: Processed 1673314304 values from 45 variables over 6476 timesteps [228.43s 482MB]\n",
+ "cdo zonmean: Processed 1076486144 values from 45 variables over 6476 timesteps [84.30s 323MB]\n",
+ "merging and averaging took 0:28:11.264509, 0:01:33.959139per year\n",
+ "removed temporary files\n"
+ ]
+ }
+ ],
+ "source": [
+ "startyear = \"0347\"\n",
+ "endyear = \"0364\"\n",
+ "\n",
+ "currentyear = startyear\n",
+ "\n",
+ "starttime = datetime.now()\n",
+ "for year in range(int(startyear,10),int(endyear,10)+1):\n",
+ " currentyear = str(year)\n",
+ " while len(currentyear)<4:\n",
+ " currentyear = \"0\" +currentyear\n",
+ " print(data_path +filename +\"_\" +currentyear +\"*\")\n",
+ " \n",
+ " checkFileList = glob.glob(data_path +filename +\"_\" +currentyear +\"*\", recursive=True)\n",
+ " if len(checkFileList)!=12:\n",
+ " sys.exit(\"Wrong number of files for year \" +currentyear +\"! \" +len(checkFileList) +\" files\")\n",
+ " \n",
+ " !cdo -O mergetime {data_path +filename +\"_\" +currentyear +\"*\"} {data_path +\"temp_y\" +currentyear +\".nc\"}\n",
+ "\n",
+ "\n",
+ "# merge files\n",
+ "!cdo -O cat {data_path +\"temp_y*.nc\"} {data_path +\"temp_merged.nc\"}\n",
+ "!cdo -O fldmean {data_path +\"temp_merged.nc\"} {save_path +experiment +\"/\" +filename +\".gm.y\" +startyear +\"_\" +endyear +\".nc\"}\n",
+ "!cdo -O remapcon,r192x96 {data_path +\"temp_merged.nc\"} {data_path +\"temp_remap.nc\"}\n",
+ "!cdo -O zonmean {data_path +\"temp_remap.nc\"} {save_path +experiment +\"/\" +filename +\".zm.y\" +startyear +\"_\" +endyear +\".nc\"}\n",
+ "\n",
+ "endtime = datetime.now()\n",
+ "duration = endtime-starttime\n",
+ "print(\"merging and averaging took \" +str(duration) +\", \" +str(duration/(int(endyear)-int(startyear)+1)) +\"per year\")\n",
+ "\n",
+ "\n",
+ "#remove files in SCRATCH directory\n",
+ "fileList = glob.glob(data_path +\"temp_y*.nc\", recursive=True)\n",
+ "\n",
+ "#for filePath in fileList:\n",
+ "# os.remove(filePath)\n",
+ "\n",
+ "os.remove(data_path +\"temp_merged.nc\")\n",
+ "os.remove(data_path +\"temp_remap.nc\")\n",
+ "print(\"removed temporary files\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Merge subperiods\n",
+ "Ouput files are *.gm.nc and *.zm.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cdo mergetime: Processed 1101015 values from 180 variables over 24467 timesteps [730.82s 232MB]\n",
+ "cdo mergetime: Processed 105697440 values from 180 variables over 24467 timesteps [824.56s 266MB]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#merge single files\n",
+ "!cdo -O mergetime {save_path +experiment +\"/\" +filename +\".gm.y*.nc\"} {save_path +experiment +\"/\" +filename +\".gm.nc\"}\n",
+ "!cdo -O mergetime {save_path +experiment +\"/\" +filename +\".zm.y*.nc\"} {save_path +experiment +\"/\" +filename +\".zm.nc\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create monthly and yearly means\n",
+ "Output files are *.mm.gm.nc, *.ym.gm.nc, *mm.zm.nc, *ym.zm.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cdo monmean: Processed 1101015 values from 45 variables over 25282 timesteps [109.40s 209MB]\n",
+ "cdo yearmean: Processed 1101015 values from 45 variables over 24534 timesteps [105.47s 176MB]\n",
+ "cdo yearmean: Processed 105697440 values from 45 variables over 24534 timesteps [132.00s 193MB]\n",
+ "cdo monmean: Processed 105697440 values from 45 variables over 25282 timesteps [141.59s 232MB]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# monthly & yearly means\n",
+ "!cdo -O monmean {save_path +experiment +\"/\" +filename +\".gm.nc\"} {save_path +experiment +\"/\" +filename +\".mm.gm.nc\"}\n",
+ "!cdo -O yearmean {save_path +experiment +\"/\" +filename +\".gm.nc\"} {save_path +experiment +\"/\" +filename +\".ym.gm.nc\"}\n",
+ "!cdo -O yearmean {save_path +experiment +\"/\" +filename +\".zm.nc\"} {save_path +experiment +\"/\" +filename +\".ym.zm.nc\"}\n",
+ "!cdo -O monmean {save_path +experiment +\"/\" +filename +\".zm.nc\"} {save_path +experiment +\"/\" +filename +\".mm.zm.nc\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optional: Merge additional subperiods \n",
+ "If the subperiod files have been deleted but the simulation is continued afterwards, this is used to merge additional subperiods into the final file. It works, but it's not recommended because it's easy to skip a time period or double it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#merge additional single files\n",
+ "!cdo -O mergetime {save_path +experiment +\"/\" +filename +\".zm.y0*.nc\"} {save_path +experiment +\"/\" +filename +\".zm.nc\"} {save_path +experiment +\"/temp.zm.nc\"} \n",
+ "!cdo -O mergetime {save_path +experiment +\"/\" +filename +\".gm.y0*.nc\"} {save_path +experiment +\"/\" +filename +\".gm.nc\"} {save_path +experiment +\"/temp.gm.nc\"} "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Remove subperiod files in work directory\n",
+ "Only do this if you're certain that the simulation will not be continued."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#remove files in WORK directory\n",
+ "fileList = glob.glob(save_path +experiment +\"/\" +filename +\"*.y0*\", recursive=True)\n",
+ "\n",
+ "for filePath in fileList:\n",
+ " os.remove(filePath)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optional: copy 3D files into work directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/mlo_aqua_4219ppmv_77sic_hice_unlim/3D\n",
+ "/work/bb1092/pp_JH/mlo_aqua_4219ppmv_77sic_hice_unlim/3D/ exists\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0291*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0292*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0293*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0294*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0295*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0296*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0297*\n",
+ "/scratch/b/b380905/experiments/mlo_aqua_4219ppmv_77sic_hice_unlim/mlo_aqua_4219ppmv_77sic_hice_unlim_atm_2d_ml_0298*\n"
+ ]
+ }
+ ],
+ "source": [
+ "# copy 3D files\n",
+ "startyear = \"0291\"\n",
+ "endyear = \"0300\"\n",
+ "\n",
+ "currentyear = startyear\n",
+ "filename3D = experiment +\"_atm_3d_ml\"\n",
+ "filename3Dmean = experiment +\"_atm_3d_mean_ml\"\n",
+ "print(save_path +experiment +\"/3D\")\n",
+ "if not os.path.isdir(save_path +experiment +\"/3D\"): #check if experiment folder in saving path exists\n",
+ " print(\"creating folder \" +save_path +experiment +\"/3D\")\n",
+ " os.mkdir(save_path +experiment +\"/3D\")\n",
+ "else:\n",
+ " print(save_path +experiment +\"/3D/ exists\")\n",
+ " \n",
+ "\n",
+ "for year in range(int(startyear,10),int(endyear,10)+1):\n",
+ " currentyear = str(year)\n",
+ " while len(currentyear)<4:\n",
+ " currentyear = \"0\" +currentyear\n",
+ " print(data_path +filename +\"_\" +currentyear +\"*\")\n",
+ " !cp {data_path +filename3D +\"_\" +currentyear +\"*\"} {save_path +experiment +\"/3D/\"}\n",
+ " !cp {data_path +filename3Dmean +\"_\" +currentyear +\"*\"} {save_path +experiment +\"/3D/\"}\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/energyflux_icelimit_comparison.ipynb b/python_scripts/energyflux_icelimit_comparison.ipynb
new file mode 100644
index 0000000..222d4ca
--- /dev/null
+++ b/python_scripts/energyflux_icelimit_comparison.ipynb
@@ -0,0 +1,1198 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Calculation of the artificial heatflux out of zonal data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mlo_aqua_1438ppmv_atm_2d_ml_0137to0338.mm.zm.nc\n",
+ "mlo_aqua_3000ppmv_Jor2_atm_2d_ml_0258to0425.mm.zm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import xarray as xr\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "experiment1 = \"mlo_aqua_1438ppmv\"\n",
+ "filename1 = experiment1 +\"_atm_2d_ml_0137to0338.mm.zm.nc\" \n",
+ "data_path1 = \"/work/bb1092/pp_JH/\" +experiment1 +\"/\"\n",
+ "\n",
+ "experiment2 = \"mlo_aqua_3000ppmv_Jor2\"\n",
+ "filename2 = experiment2 +\"_atm_2d_ml_0258to0425.mm.zm.nc\" \n",
+ "data_path2 = \"/work/bb1092/pp_JH/\" +experiment2 +\"/\"\n",
+ "\n",
+ "experiment3 = \"mlo_aqua_5000ppmv_Jor2\"\n",
+ "filename3 = experiment3 +\"_atm_2d_ml_0258to0306.mm.zm.nc\" \n",
+ "data_path3 = \"/work/bb1092/pp_JH/\" +experiment3 +\"/\"\n",
+ "\n",
+ "experiment4 = \"mlo_aqua_1500ppmv\"\n",
+ "filename4 = experiment4 +\"_atm_2d_ml.mm.zm.nc\" \n",
+ "data_path4 = \"/work/bb1092/pp_JH/\" +experiment4 +\"/\"\n",
+ "\n",
+ "\n",
+ "\n",
+ "print(filename1)\n",
+ "print(filename2)\n",
+ "# load netcdf file\n",
+ "DS1 = xr.open_dataset(data_path1 +filename1, decode_times=False)\n",
+ "DS2 = xr.open_dataset(data_path2 +filename2, decode_times=False)\n",
+ "DS3 = xr.open_dataset(data_path3 +filename3, decode_times=False)\n",
+ "DS4 = xr.open_dataset(data_path4 +filename4, decode_times=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos, prop={'weight':'bold',\n",
+ " 'size': fontsize})\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " n = 16\n",
+ " color = plt.cm.tab20(np.linspace(0, 1,n))\n",
+ " \n",
+ " for text in legend.get_texts():\n",
+ " \n",
+ " \n",
+ " \n",
+ " if text.get_text() == \"1438ppmv\":\n",
+ " text.set_color(color[0])\n",
+ " elif text.get_text() == \"1500ppmv\":\n",
+ " text.set_color(color[1])\n",
+ " elif text.get_text() == \"3000ppmv\":\n",
+ " text.set_color(color[10])\n",
+ " elif text.get_text() == \"5000ppmv\":\n",
+ " text.set_color(color[15])\n",
+ " else:\n",
+ " text.set_color(\"white\") \n",
+ " \n",
+ " \n",
+ " return legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# qbot\n",
+ "da1 = DS1.qbot_icecl.sel()\n",
+ "da1 = da1.assign_coords(time=(da1.time/360))\n",
+ "da1 = da1.squeeze()\n",
+ "\n",
+ "da2 = DS2.qbot_icecl.sel()\n",
+ "da2 = da2.assign_coords(time=(da2.time/360))\n",
+ "da2 = da2.squeeze()\n",
+ "\n",
+ "da3 = DS3.qbot_icecl.sel()\n",
+ "da3 = da3.assign_coords(time=(da3.time/360))\n",
+ "da3 = da3.squeeze()\n",
+ "\n",
+ "da4 = DS4.qbot_icecl.sel()\n",
+ "da4 = da4.assign_coords(time=(da4.time/360))\n",
+ "da4 = da4.squeeze()\n",
+ "\n",
+ "qbot1=da1.values\n",
+ "qbot2=da2.values\n",
+ "qbot3=da3.values\n",
+ "qbot4=da4.values\n",
+ "\n",
+ "mqbot1 = np.pi/2* np.dot(da1.values,np.cos(np.radians(da1.lat))) / np.size(da1.lat)\n",
+ "mqbot2 = np.pi/2* np.dot(da2.values,np.cos(np.radians(da2.lat))) / np.size(da2.lat)\n",
+ "mqbot3 = np.pi/2* np.dot(da3.values,np.cos(np.radians(da3.lat))) / np.size(da3.lat)\n",
+ "mqbot4 = np.pi/2* np.dot(da4.values,np.cos(np.radians(da4.lat))) / np.size(da4.lat)\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# sea ice thickness tendency\n",
+ "da1 = DS1.dhidt_icecl.sel()\n",
+ "da1 = da1.assign_coords(time=(da1.time/360))\n",
+ "da1 = da1.squeeze()\n",
+ "dhidt1 =da1.values*60*60*24*30 # ice growth per month\n",
+ "\n",
+ "da2 = DS2.dhidt_icecl.sel()\n",
+ "da2 = da2.assign_coords(time=(da2.time/360))\n",
+ "da2 = da2.squeeze()\n",
+ "dhidt2 =da2.values*60*60*24*30 # ice growth per month\n",
+ "\n",
+ "da3 = DS3.dhidt_icecl.sel()\n",
+ "da3 = da3.assign_coords(time=(da3.time/360))\n",
+ "da3 = da3.squeeze()\n",
+ "dhidt3 =da3.values*60*60*24*30 # ice growth per month\n",
+ "\n",
+ "da4 = DS4.dhidt_icecl.sel()\n",
+ "da4 = da4.assign_coords(time=(da4.time/360))\n",
+ "da4 = da4.squeeze()\n",
+ "dhidt4 =da4.values*60*60*24*30 # ice growth per month\n",
+ "\n",
+ "\n",
+ "# sea ice cover\n",
+ "da1 = DS1.sic.sel()\n",
+ "da1 = da1.assign_coords(time=(da1.time/360))\n",
+ "da1 = da1.squeeze()\n",
+ "sic1 = da1.values\n",
+ "\n",
+ "da2 = DS2.sic.sel()\n",
+ "da2 = da2.assign_coords(time=(da2.time/360))\n",
+ "da2 = da2.squeeze()\n",
+ "sic2 = da2.values\n",
+ "\n",
+ "da3 = DS3.sic.sel()\n",
+ "da3 = da3.assign_coords(time=(da3.time/360))\n",
+ "da3 = da3.squeeze()\n",
+ "sic3 = da3.values\n",
+ "\n",
+ "da4 = DS4.sic.sel()\n",
+ "da4 = da4.assign_coords(time=(da4.time/360))\n",
+ "da4 = da4.squeeze()\n",
+ "sic4 = da4.values\n",
+ "\n",
+ "# sea ice thickness\n",
+ "da1 = DS1.sit.sel()\n",
+ "da1 = da1.assign_coords(time=((da1.time-da1.time[0])/360))\n",
+ "da1 = da1.squeeze()\n",
+ "sit1 = da1.values\n",
+ "\n",
+ "da2 = DS2.sit.sel()\n",
+ "da2 = da2.assign_coords(time=((da2.time-da2.time[0])/360))\n",
+ "da2 = da2.squeeze()\n",
+ "sit2 = da2.values\n",
+ "\n",
+ "da3 = DS3.sit.sel()\n",
+ "da3 = da3.assign_coords(time=((da3.time-da3.time[0])/360))\n",
+ "da3 = da3.squeeze()\n",
+ "sit3 = da3.values\n",
+ "\n",
+ "da4 = DS4.sit.sel()\n",
+ "da4 = da4.assign_coords(time=((da4.time-da4.time[0])/360))\n",
+ "da4 = da4.squeeze()\n",
+ "sit4 = da4.values\n",
+ "\n",
+ "#temperature\n",
+ "da1 = DS1.ts.sel()\n",
+ "da1 = da1.assign_coords(time=((da1.time-da1.time[0])/360))\n",
+ "da1 = da1.squeeze()\n",
+ "ts1 = da1.values\n",
+ "\n",
+ "da2 = DS2.ts.sel()\n",
+ "da2 = da2.assign_coords(time=((da2.time-da2.time[0])/360))\n",
+ "da2 = da2.squeeze()\n",
+ "ts2 = da2.values\n",
+ "\n",
+ "da3 = DS3.ts.sel()\n",
+ "da3 = da3.assign_coords(time=((da3.time-da3.time[0])/360))\n",
+ "da3 = da3.squeeze()\n",
+ "ts3 = da3.values\n",
+ "\n",
+ "da4 = DS4.ts.sel()\n",
+ "da4 = da4.assign_coords(time=((da4.time-da4.time[0])/360))\n",
+ "da4 = da4.squeeze()\n",
+ "ts4 = da4.values\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# points where sea ice thickness exceeds the sea ice limit\n",
+ "icelim1= np.where(sit1 + dhidt1>5) \n",
+ "icelim2= np.where(sit2 + dhidt2>5)\n",
+ "icelim3= np.where(sit3 + dhidt3>5)\n",
+ "icelim4= np.where(sit4 + dhidt4>5)\n",
+ "\n",
+ "\n",
+ "removed_thickness1 = np.zeros([da1.sizes[\"time\"],96])\n",
+ "removed_thickness2 = np.zeros([da2.sizes[\"time\"],96])\n",
+ "removed_thickness3 = np.zeros([da3.sizes[\"time\"],96])\n",
+ "removed_thickness4 = np.zeros([da4.sizes[\"time\"],96])\n",
+ "\n",
+ "# calculate removed sea ice thickness\n",
+ "removed_thickness1[icelim1] = sit1[icelim1] + dhidt1[icelim1] - 5 # per month\n",
+ "removed_thickness2[icelim2] = sit2[icelim2] + dhidt2[icelim2] - 5\n",
+ "removed_thickness3[icelim3] = sit3[icelim3] + dhidt3[icelim3] - 5\n",
+ "removed_thickness4[icelim4] = sit4[icelim4] + dhidt4[icelim4] - 5\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "fig1, ax1 = plt.subplots()\n",
+ "im1=ax1.contourf(da1.lat, da1.time, removed_thickness1)\n",
+ "\n",
+ "fig1.colorbar(im1)\n",
+ "plt.show()\n",
+ "\n",
+ "fig2, ax2 = plt.subplots()\n",
+ "im2=ax2.contourf(da2.lat, da2.time, removed_thickness2)\n",
+ "\n",
+ "fig2.colorbar(im2)\n",
+ "plt.show()\n",
+ "\n",
+ "fig3, ax3 = plt.subplots()\n",
+ "im3=ax3.contourf(da3.lat, da3.time, removed_thickness3)\n",
+ "\n",
+ "fig3.colorbar(im3)\n",
+ "plt.show()\n",
+ "\n",
+ "fig4, ax4 = plt.subplots()\n",
+ "im4=ax4.contourf(da4.lat, da4.time, removed_thickness4)\n",
+ "\n",
+ "fig4.colorbar(im4)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#physical constants from icon\n",
+ "alv = 2.5008e6 # [J/kg] latent heat for vaporisation\n",
+ "als = 2.8345e6 # [J/kg] latent heat for sublimation\n",
+ "alf = als-alv # [J/kg] latent heat for fusion\n",
+ "rhoi = 917.0 # density of sea ice [kg / m**3]\n",
+ "\n",
+ "#calculate energy flux from the freezing of removed sea ice thickness, dhidt = F/(alf*rhoi) <-> F=dhidt*alf*rhoi\n",
+ "\n",
+ "energy1 = np.zeros([da1.sizes[\"time\"],96])\n",
+ "energy1 = removed_thickness1 * alf * rhoi / (60 * 60 * 24 * 30) # Joule per second per m^2 -> Watt per m^2\n",
+ "\n",
+ "energy2 = np.zeros([da2.sizes[\"time\"],96])\n",
+ "energy2 = removed_thickness2 * alf * rhoi / (60 * 60 * 24 * 30) \n",
+ "\n",
+ "energy3 = np.zeros([da3.sizes[\"time\"],96])\n",
+ "energy3 = removed_thickness3 * alf * rhoi / (60 * 60 * 24 * 30) \n",
+ "\n",
+ "energy4 = np.zeros([da4.sizes[\"time\"],96])\n",
+ "energy4 = removed_thickness4 * alf * rhoi / (60 * 60 * 24 * 30) \n",
+ "\n",
+ "\n",
+ "fig1, ax1 = plt.subplots()\n",
+ "im1=ax1.contourf(da1.lat, da1.time, energy1)\n",
+ "\n",
+ "fig1.colorbar(im1)\n",
+ "plt.show()\n",
+ "\n",
+ "fig2, ax2 = plt.subplots()\n",
+ "im2=ax2.contourf(da2.lat, da2.time, energy2)\n",
+ "\n",
+ "fig2.colorbar(im2)\n",
+ "plt.show()\n",
+ "\n",
+ "fig3, ax3 = plt.subplots()\n",
+ "im3=ax3.contourf(da3.lat, da3.time, energy3)\n",
+ "\n",
+ "fig3.colorbar(im3)\n",
+ "plt.show()\n",
+ "\n",
+ "fig4, ax4 = plt.subplots()\n",
+ "im4=ax4.contourf(da4.lat, da4.time, energy4)\n",
+ "\n",
+ "fig4.colorbar(im4)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#global mean flux \n",
+ "msit1 = np.pi/2* np.dot(sit1,np.cos(np.radians(da1.lat))) / np.size(da1.lat)\n",
+ "msit2 = np.pi/2* np.dot(sit2,np.cos(np.radians(da2.lat))) / np.size(da2.lat)\n",
+ "msit3 = np.pi/2* np.dot(sit3,np.cos(np.radians(da3.lat))) / np.size(da3.lat)\n",
+ "msit4 = np.pi/2* np.dot(sit4,np.cos(np.radians(da4.lat))) / np.size(da4.lat)\n",
+ "\n",
+ "msic1 = np.pi/2* np.dot(sic1,np.cos(np.radians(da1.lat))) / np.size(da1.lat)\n",
+ "msic2 = np.pi/2* np.dot(sic2,np.cos(np.radians(da2.lat))) / np.size(da2.lat)\n",
+ "msic3 = np.pi/2* np.dot(sic3,np.cos(np.radians(da3.lat))) / np.size(da3.lat)\n",
+ "msic4 = np.pi/2* np.dot(sic4,np.cos(np.radians(da4.lat))) / np.size(da4.lat)\n",
+ "\n",
+ "mts1 = np.pi/2* np.dot(ts1,np.cos(np.radians(da1.lat))) / np.size(da1.lat)\n",
+ "mts2 = np.pi/2* np.dot(ts2,np.cos(np.radians(da2.lat))) / np.size(da2.lat)\n",
+ "mts3 = np.pi/2* np.dot(ts3,np.cos(np.radians(da3.lat))) / np.size(da3.lat)\n",
+ "mts4 = np.pi/2* np.dot(ts4,np.cos(np.radians(da4.lat))) / np.size(da4.lat)\n",
+ "\n",
+ "\n",
+ "\n",
+ "#energy1 * np.cos(np.radians(da1.lat))\n",
+ "menergy1= np.pi/2* np.dot(energy1,np.cos(np.radians(da1.lat))) / np.size(da1.lat)\n",
+ "menergy2= np.pi/2* np.dot(energy2,np.cos(np.radians(da2.lat))) / np.size(da2.lat)\n",
+ "menergy3= np.pi/2* np.dot(energy3,np.cos(np.radians(da3.lat))) / np.size(da3.lat)\n",
+ "menergy4= np.pi/2* np.dot(energy4,np.cos(np.radians(da4.lat))) / np.size(da4.lat)\n",
+ "\n",
+ "ymenergy1=np.zeros(int(menergy1.shape[0]/12))\n",
+ "ymenergy2=np.zeros(int(menergy2.shape[0]/12))\n",
+ "ymenergy3=np.zeros(int(menergy3.shape[0]/12))\n",
+ "ymenergy4=np.zeros(int(menergy4.shape[0]/12))\n",
+ "\n",
+ "ymsic1=np.zeros(int(msic1.shape[0]/12))\n",
+ "ymsic2=np.zeros(int(msic2.shape[0]/12))\n",
+ "ymsic3=np.zeros(int(msic3.shape[0]/12))\n",
+ "ymsic4=np.zeros(int(msic4.shape[0]/12))\n",
+ "\n",
+ "for y in range(0,int(menergy1.shape[0]/12)):\n",
+ " ymenergy1[y] = np.mean(menergy1[(y*12):(y*12)+11])\n",
+ " ymsic1[y] = np.mean(msic1[(y*12):(y*12)+11])\n",
+ " \n",
+ "for y in range(0,int(menergy2.shape[0]/12)):\n",
+ " ymenergy2[y] = np.mean(menergy2[(y*12):(y*12)+11])\n",
+ " ymsic2[y] = np.mean(msic2[(y*12):(y*12)+11])\n",
+ " \n",
+ "for y in range(0,int(menergy3.shape[0]/12)):\n",
+ " ymenergy3[y] = np.mean(menergy3[(y*12):(y*12)+11])\n",
+ " ymsic3[y] = np.mean(msic3[(y*12):(y*12)+11])\n",
+ " \n",
+ "for y in range(0,int(menergy4.shape[0]/12)):\n",
+ " ymenergy4[y] = np.mean(menergy4[(y*12):(y*12)+11])\n",
+ " ymsic4[y] = np.mean(msic4[(y*12):(y*12)+11])\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "# sea ice latitude\n",
+ "ice_lat1 = np.arcsin(1-msic1) * (180./np.pi) \n",
+ "ice_lat2 = np.arcsin(1-msic2) * (180./np.pi) \n",
+ "ice_lat3 = np.arcsin(1-msic3) * (180./np.pi) \n",
+ "ice_lat4 = np.arcsin(1-msic4) * (180./np.pi) \n",
+ "\n",
+ "yice_lat1=np.zeros(int(ice_lat1.shape[0]/12))\n",
+ "yice_lat2=np.zeros(int(ice_lat2.shape[0]/12))\n",
+ "yice_lat3=np.zeros(int(ice_lat3.shape[0]/12))\n",
+ "yice_lat4=np.zeros(int(ice_lat4.shape[0]/12))\n",
+ "\n",
+ "menergy1m=np.zeros(int(ice_lat1.shape[0]/12))\n",
+ "msit1m=np.zeros(int(ice_lat1.shape[0]/12))\n",
+ "\n",
+ "for y in range(0,int(ice_lat1.shape[0]/12)):\n",
+ " yice_lat1[y] = np.mean(ice_lat1[(y*12):(y*12)+11])\n",
+ " menergy1m[y] = np.mean(menergy1[(y*12):(y*12)+11])\n",
+ " msit1m[y] = np.mean(msit1[(y*12):(y*12)+11])\n",
+ " \n",
+ "for y in range(0,int(ice_lat2.shape[0]/12)):\n",
+ " yice_lat2[y] = np.mean(ice_lat2[(y*12):(y*12)+11])\n",
+ " \n",
+ "for y in range(0,int(ice_lat3.shape[0]/12)):\n",
+ " yice_lat3[y] = np.mean(ice_lat3[(y*12):(y*12)+11])\n",
+ " \n",
+ "for y in range(0,int(ice_lat4.shape[0]/12)):\n",
+ " yice_lat4[y] = np.mean(ice_lat4[(y*12):(y*12)+11])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5.877783718381085\n",
+ "-0.002561321\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "#plt.plot(da1.time,menergy1)\n",
+ "#plt.plot(da2.time,menergy2)\n",
+ "#plt.plot(da3.time,menergy3)\n",
+ "plt.plot(ymenergy1)\n",
+ "plt.plot(ymenergy4)\n",
+ "plt.plot(ymenergy2)\n",
+ "plt.plot(ymenergy3)\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "#plt.title(\"hice_5m: heat flux from removed ice\")\n",
+ "plt.title(\"\")\n",
+ "plt.xlabel(\"time [years]\")\n",
+ "plt.ylabel(\"qbot_removed [W/m²]\")\n",
+ "plt.xlim(0,210)\n",
+ "plt.ylim(0,8)\n",
+ "plt.savefig(\"plots/heatflux_comparison.svg\",dpi=500)\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.plot(da1.time,-mqbot1)\n",
+ "plt.plot(da2.time,-mqbot2)\n",
+ "plt.plot(da3.time,-mqbot3)\n",
+ "plt.legend([\"1438ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.title(\"hice_5m heat flux at ice-ocean interface\")\n",
+ "plt.xlabel(\"time [years]\")\n",
+ "plt.ylabel(\"qbot [W/m²]\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(ymenergy2[-1])\n",
+ "print(ice_lat2[-1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x2ae3ac3cfb00>]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# co2 equivalent of artifical heat flux\n",
+ "alpha = 5.35\n",
+ "mco21 = 1438 * np.exp(menergy1/alpha)\n",
+ "mco22 = 3000 * np.exp(menergy2/alpha)\n",
+ "mco23 = 5000 * np.exp(menergy3/alpha)\n",
+ "mco24 = 1500 * np.exp(menergy4/alpha)\n",
+ "\n",
+ "mco21r = np.exp(menergy1/alpha)\n",
+ "mco22r = np.exp(menergy2/alpha)\n",
+ "mco23r = np.exp(menergy3/alpha)\n",
+ "mco24r = np.exp(menergy4/alpha)\n",
+ "\n",
+ "plt.plot(da1.time,mco21)\n",
+ "plt.plot(da4.time,mco24)\n",
+ "plt.plot(da2.time,mco22)\n",
+ "plt.plot(da3.time,mco23)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(630,)\n",
+ "(630, 96)\n",
+ "(630,)\n",
+ "0.9916648749327626\n",
+ "0.9420278794069241\n",
+ "0.9797683783641139\n",
+ "(0.0, 1.0)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "pltsize=2\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "print(msit3.shape)\n",
+ "print(sit3.shape)\n",
+ "print(menergy3.shape)\n",
+ "\n",
+ "print(np.corrcoef(msit1,menergy1)[1,0])\n",
+ "print(np.corrcoef(msit2,menergy2)[1,0])\n",
+ "print(np.corrcoef(msit3,menergy3)[1,0])\n",
+ "\n",
+ "# far - sit\n",
+ "fig, ax = plt.subplots()\n",
+ "plt.plot(msit1, menergy1,'.',markersize=pltsize)\n",
+ "plt.plot(msit4, menergy4,'.',markersize=pltsize)\n",
+ "plt.plot(msit2, menergy2,'.',markersize=pltsize)\n",
+ "plt.plot(msit3, menergy3,'.',markersize=pltsize)\n",
+ "plt.grid()\n",
+ "plt.xlim(0,5)\n",
+ "\n",
+ "ylim=plt.ylim(0,8.2)\n",
+ "plt.xlabel(\"$h_I$ [m]\", fontsize=labelsize)\n",
+ "plt.ylabel(\"$F_{a}$ [W/m²]\", fontsize=labelsize)\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv\",\"5000ppmv\"],loc = 2, \n",
+ " ncol=4, labelspacing=0.2,columnspacing=2,handletextpad=0.1, handlelength=2, mode=\"expand\",\n",
+ " borderpad=0.5,borderaxespad=0.2,markerscale=6, fontsize=labelsize)\n",
+ "\n",
+ "\n",
+ "ax2 = ax.twinx()\n",
+ "co2_fac=np.linspace(1,5,9)\n",
+ "print(plt.ylim())\n",
+ "\n",
+ "np.exp(menergy1/alpha)\n",
+ "\n",
+ "ax2.set_yticks(np.log(co2_fac)*alpha)\n",
+ "ax2.set_ylim(0,8.2)\n",
+ "_=ax2.set_yticklabels(co2_fac)\n",
+ "plt.ylabel(\"equivalent CO$_2$ increase\", fontsize=labelsize)\n",
+ "ax.tick_params(labelsize=ticksize) \n",
+ "\n",
+ "plt.savefig(\"plots/heatflux_scatter_flux_sit.svg\",dpi=500)\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.show() \n",
+ "\n",
+ "plt.plot(msic1, msit1,'.',markersize=pltsize)\n",
+ "plt.plot(msic4, msit4,'.',markersize=pltsize)\n",
+ "plt.plot(msic2, msit2,'.',markersize=pltsize)\n",
+ "plt.plot(msic3, msit3,'.',markersize=pltsize)\n",
+ "\n",
+ "plt.xlabel(\"sic []\")\n",
+ "plt.ylabel(\"sit [m]\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(msic1, mco21r,'.',markersize=pltsize)\n",
+ "plt.plot(msic4, mco24r,'.',markersize=pltsize)\n",
+ "plt.plot(msic2, mco22r,'.',markersize=pltsize)\n",
+ "plt.plot(msic3, mco23r,'.',markersize=pltsize)\n",
+ "plt.xlabel(\"sic []\")\n",
+ "plt.ylabel(\"equivalent CO$_2$ factor []\")\n",
+ "#plt.ylim(1,3.1)\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(msit1, mco21r,'.',markersize=pltsize)\n",
+ "plt.plot(msit4, mco24r,'.',markersize=pltsize)\n",
+ "plt.plot(msit2, mco22r,'.',markersize=pltsize)\n",
+ "plt.plot(msit3, mco23r,'.',markersize=pltsize)\n",
+ "plt.xlabel(\"sit [m]\")\n",
+ "plt.ylabel(\"equivalent CO$_2$ factor []\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(menergy1, mco21r,'.',markersize=pltsize)\n",
+ "plt.plot(menergy4, mco24r,'.',markersize=pltsize)\n",
+ "plt.plot(menergy2, mco22r,'.',markersize=pltsize)\n",
+ "plt.plot(menergy3, mco23r,'.',markersize=pltsize)\n",
+ "plt.xlabel(\"heat flux [W/m^2]\")\n",
+ "plt.ylabel(\"equivalent CO$_2$ factor []\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(mts1, menergy1,'.',markersize=pltsize)\n",
+ "plt.plot(mts4, menergy4,'.',markersize=pltsize)\n",
+ "plt.plot(mts2, menergy2,'.',markersize=pltsize)\n",
+ "plt.plot(mts3, menergy3,'.',markersize=pltsize)\n",
+ "plt.ylabel(\"heat flux [W/m^2]\")\n",
+ "plt.xlabel(\"surface temperature [K]\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()\n",
+ "\n",
+ "plt.plot(mts1, msit1,'.',markersize=pltsize)\n",
+ "plt.plot(mts4, msit4,'.',markersize=pltsize)\n",
+ "plt.plot(mts2, msit2,'.',markersize=pltsize)\n",
+ "plt.plot(mts3, msit3,'.',markersize=pltsize)\n",
+ "plt.xlabel(\"surface temperature [W/m^2]\")\n",
+ "plt.ylabel(\"sit [m]\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9938529939485001\n",
+ "0.8676139722084588\n",
+ "0.9821811371639458\n",
+ "(0.0, 1.0)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# far - icelat\n",
+ "ylim=(0,6)\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "# same colors as in overview plots\n",
+ "n = 16\n",
+ "color = plt.cm.tab20(np.linspace(0, 1,n))\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "fig, ax = plt.subplots(figsize=(6,4))\n",
+ "print(np.corrcoef(msic1,menergy1)[1,0])\n",
+ "print(np.corrcoef(msic2,menergy2)[1,0])\n",
+ "print(np.corrcoef(msic3,menergy3)[1,0])\n",
+ "plt.scatter(ymsic1, ymenergy1,marker='o',s=6,color=color[0])\n",
+ "plt.scatter(ymsic4, ymenergy4,marker='o',s=6,color=color[1])\n",
+ "plt.scatter(ymsic2, ymenergy2,marker='o',s=6,color=color[10])\n",
+ "plt.scatter(ymsic3, ymenergy3,marker='o',s=6,color=color[15])\n",
+ "\n",
+ "ax.set_xticks([1-np.sin(np.radians(0)),1-np.sin(np.radians(10)),1-np.sin(np.radians(20)),1-np.sin(np.radians(30)),1-np.sin(np.radians(45)),1-np.sin(np.radians(60)),1-np.sin(np.radians(90))])\n",
+ "ax.set_xticklabels([0,10,20,30,45,60,90])\n",
+ "\n",
+ "plt.xlim(0,1.01)\n",
+ "\n",
+ "plt.ylim(ylim)\n",
+ "plt.xlabel(\"Ice-Edge Latitude [°]\", fontsize=labelsize)\n",
+ "plt.ylabel(\"$F_{a}$ [W/m²]\", fontsize=labelsize)\n",
+ "#plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv\",\"5000ppmv\"],loc = 2, \n",
+ "# ncol=4, labelspacing=0.2,columnspacing=2,handletextpad=0.1, handlelength=2, mode=\"expand\",\n",
+ "# borderpad=0.5,borderaxespad=0.2,markerscale=2)\n",
+ "legend_color(ax,[\"1438ppmv\",\"1500ppmv\",\"3000ppmv\",\"5000ppmv\"],2, labelsize)\n",
+ "\n",
+ "ax2 = ax.twinx()\n",
+ "co2_fac=np.linspace(1,5,9)\n",
+ "print(plt.ylim())\n",
+ "\n",
+ "np.exp(menergy1/alpha)\n",
+ "\n",
+ "ax2.set_yticks(np.log(co2_fac)*alpha)\n",
+ "ax2.set_ylim(ylim)\n",
+ "_=ax2.set_yticklabels(co2_fac)\n",
+ "plt.ylabel(\"Equivalent CO$_2$ Increase []\", fontsize=labelsize)\n",
+ "\n",
+ "ax.tick_params(labelsize=ticksize)\n",
+ "ax2.tick_params(labelsize=ticksize)\n",
+ "\n",
+ "#ax.spines['right'].set_color('none')\n",
+ "ax.spines['top'].set_color('none')\n",
+ "ax2.spines['top'].set_color('none')\n",
+ "\n",
+ "plt.savefig(\"plots/heatflux_scatter_flux_icelat.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9773656235942157\n",
+ "0.9704530181574433\n",
+ "0.9820133574839562\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(msit1, -mqbot1,'.',markersize=pltsize)\n",
+ "plt.plot(msit4, -mqbot4,'.',markersize=pltsize)\n",
+ "plt.plot(msit2, -mqbot2,'.',markersize=pltsize)\n",
+ "plt.plot(msit3, -mqbot3,'.',markersize=pltsize)\n",
+ "plt.xlabel(\"sit [m]\")\n",
+ "plt.ylabel(\"heat flux [W/m^2]\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show()\n",
+ "\n",
+ "print(np.corrcoef(msic1,-mqbot1)[1,0])\n",
+ "print(np.corrcoef(msic2,-mqbot2)[1,0])\n",
+ "print(np.corrcoef(msic3,-mqbot3)[1,0])\n",
+ "plt.plot(msic1, -mqbot1,'.',markersize=pltsize)\n",
+ "plt.plot(msic4, -mqbot4,'.',markersize=pltsize)\n",
+ "plt.plot(msic2, -mqbot2,'.',markersize=pltsize)\n",
+ "plt.plot(msic3, -mqbot3,'.',markersize=pltsize)\n",
+ "plt.xlabel(\"sic []\")\n",
+ "plt.ylabel(\"heat flux [W/m^2]\")\n",
+ "plt.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv_Jor2\",\"5000ppmv_Jor2\"],loc = 2)\n",
+ "plt.show() \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x504 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ "fig1, (ax1, ax2) = plt.subplots(2, 1, sharex=False,figsize=(7,7))\n",
+ "ax1.plot(yice_lat1)\n",
+ "ax1.plot(yice_lat4)\n",
+ "ax1.plot(yice_lat2)\n",
+ "ax1.plot(yice_lat3)\n",
+ "\n",
+ "ax1.set_ylim(0,90)\n",
+ "ax1.set_xlim(0,210)\n",
+ "ax1.set_xlabel(\"time [years]\")\n",
+ "ax1.set_ylabel(\"ice latitude [°]\")\n",
+ "ax1.legend([\"1438ppmv\",\"1500ppmv\",\"3000ppmv\",\"5000ppmv\"],loc = 2, \n",
+ " ncol=4, labelspacing=0.2,columnspacing=2,handletextpad=0.1, handlelength=2, mode=\"expand\",\n",
+ " borderpad=0.5,borderaxespad=0.2)\n",
+ "ax1.text(-0.1,1,\"a)\", transform=ax1.transAxes, fontsize=14)\n",
+ "#plt.title(\"hice_5m: global sea ice border \")\n",
+ "\n",
+ "\n",
+ "ax2.plot(ymenergy1)\n",
+ "ax2.plot(ymenergy4)\n",
+ "ax2.plot(ymenergy2)\n",
+ "ax2.plot(ymenergy3)\n",
+ "\n",
+ "ax2.set_xlabel(\"time [years]\")\n",
+ "ax2.set_ylabel(\"$F_{ar}$ [W/m²]\")\n",
+ "ax2.set_ylim(0,6)\n",
+ "ax2.set_xlim(0,210)\n",
+ "ax2.text(-0.1,1,\"b)\", transform=ax2.transAxes, fontsize=14)\n",
+ "plt.savefig(\"plots/hice5m_sic_qbot.svg\",dpi=500)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[3.19146303e+02 5.37758229e-01 1.06490622e+03]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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KoMruJCEmnPySKobvf4eZIW+xI3wCNxyey22DLYxLjMHhMiQnxnKXLgAMWKOTh4jcAczHXQJ9J+6qul/i7okopVSr8q8/BbBo/T7A8Hz8p8wuf4vMuBTkR68zcW0e1bUutuaWMH/KYOZMSNSih6chkJ7HfNwbNm02xqSIyDnAk80TllJKBca//lRuYSUJ0WH8wvI2V5d/wN4ePyLomj/zwr/3sCGriISYcM9VRosenqZAkke1MaZaRBCRTsaYPSIytNkiU0qpRiqutLN40z6q7C4Wb8plS7aVeRWvcHXwGj4KmcHP837MpR/vZUOWe+ZVfkkVKUPjmTNhgO8zdIZVYAJJHgc8iwT/BawWkRJ0Bz+lVBuwPC2fhWuyAAjCyRtd/8alwan8xXUFz5TfyEVJ3UiIiWB0P/c65/rjHNrjCFwgs62u9rx8QkRSga7AJ80SlVJKBWB2cgLvbt3PoaIy/hr5KhfXbOb52ut5xTmLxLhIRIQlm7+fXfXD4T11fOMMNaaq7lvGmJtFZL4xZiGAMWZt84emlFIn531cBcJTMwYS9uEtjHPuZHXCg5THXcf4I+UAbMgqJDEugouHxBMTEaqPqJpAY3oeY0SkP/AzEVlCva1ntbKuUqq1eB9XdaGCqZF/YLhjNw875rI8cywpFhsXDIxj4ZpMkuIjybZW8uPocH1E1UQakzxew/14aiCwnbrJw3jalVKqxc1OTkDKDzFj52P0qM3nT7GPEjPoSiYWHCM1w8rIvl15dMY5TB3WQ8uONLHGlGR/GXhZRF41xtzdAjEppdRxi/7812J4j83oVc6N395BJ1cZL8T/lutvuImk+Kg613rHNrTsSNMKZMBcE4dSqsV411/Y7A6+OVBGaoaVNbuPEBocxLDeXdiy/r/8JPz3VDvhx/YF7DqQwN6V6SyYOczXy9BB8eajVXGVUm3S2MRYkuIjKSitJjXDSkxECFtzSwA4t3IL74f/FqJ6sHzQi4xx9aQy00pqhhVI9/ypU3CbkyYPpVSb9MfPM8m2VuJwugsclthqSYgJZ0zZah4peZ2iyCQ+HLqQ2Zcmszwtn9wiGylD41kwcxgXDNTxjeamyUMp1eq8YxTege2xibFU1jgYPyCWuy9J4i/rcxjepyu3mJX03fZncjuPYab1birWl2AL3edbKe59VKXjG80v4OQhItOA64FXjDE7RWSuMWZR04emlDpbeMc3Pt9zhC37SujdNYxDZdVclBRHxpFyvsy28ktZTN8D7/Af5ziyR7zAqHybp9yIEBsZqo+oWtjp9DzuAW4DFnh2ExzVtCEppc423kdMn+85CoDLuB9VJXWPYtrgLiRvfpWRBzZSPWYuBzvfyU1jE7kJ6szGUi3LchrXWI0xpcaY/wEuw11pVymlTltspHvV93l9uzJxUDcuG9YTgD7B5US8cyXn2zaROuAhwq54nuvGukuol9jsrRz12S2Q/TzCgEFAnoiEGWOqjTGPiMj9AVy/Dujkue/7xphfi8gAYCkQC+wAbjbG2EWkE7AEGAMUATcYY3I9n/UocDvgBB4wxnza2N9DKdW2eEuMbM8rZUNWoW9RX9WhdG74+iEiHcXMq32QYb1v5DzPvuSpGVbW7bWyMbsIm93Bg9O0wHdLO2XPQ0SCReR3wAFgMXAdkC8ivxOREGPMHxt5rxpgsjHmPNyPuqaLyAXAc8BLxpjBQAnupIDnzxJjzCDgJc95iMgw4EZgODAd+LOIBDUyBqVUG+MtMbIhq5CUofHMTk4gfeNKFhyZT22NjafinmdYyo+5clRvX+JIGRrP8D5dPZ8gJ/181Twa0/N4HugMDDDGlAOISBfg956f+Y25kTHGABWetyGeH4N7J8KfeNoXA08ArwKzPK8B3gf+JCLiaV9qjKkB9olIFjAO966GSql2ZnZyAja7AxCuHNWbnStfZWbGbyiO6MszXZ/kX7nBpESUUmV3kpphZeKgOF643j3UGhepRQ5bS2PGPGYCd3oTB4Ax5hhwN3B5IDcTkSAR2QkcBVYD2UCpMcbhOeUA0Mfzug+Q77mfAygD4vzbG7jG/15zRSRNRNKsVmsgYSqlWkhxpZ3Xvshie14pV57Xi5L/PMXk3Y+zK2Q47418g/uvnULK0HhSM6ykF5QBMKxXV5anuf8KmKd7j7eaxvQ8jKfXUL/RKSLHtZ/ig5zAKM+mUh8C5zZ0mufPhvqi5iTt9e+1CFgEkJycHFCcSqnm47/rX3rBMTZkFRJGDdY3n+aCqnWsi5jG7cU3U7v2MNsOO32L/rxrQGx2p24b2wY0Jnmki8gtxpgl/o0ichOw5wTXnJQxplREvgAuAKJFJNjTu+jL97sTHgAScO9gGIx786liv3Yv/2uUUm1M/SKF/rv+Afwwwcn/lv6WAVVZ/Lb2x4SP+jn3iLA9r5TUDCsXDDziSxJJl7iLHkaEBunjqlbWmORxL/BPEfkZ7pLsBvf03HDg6pNd6E9E4oFaT+IIB6biHgRPxT0IvxSYA3zkuWSF5/2XnuOfG2OMiKwA3hGRF4HewGBga2PjUEq1nGK/2VE2u4OI0GCmDuvBO1v2k1ds47Ku+bxQ+jwhTht32B/CPvAyXr5oYJ3KufWThC4IbBsaU5L9IDBeRCbjnuEkwCpjzJoA79ULWOyZGWUBlhljVopIOrBURJ4GvgLe8Jz/BvCWZ0C8GPcMK4wxu0RkGZAOOIB7PY/DlFJtzPK0fN/sKBCeXbWHoooaenTpxLWhX3J32UsUOLvycvcX+fxAFyb6PYHWJNG2SQPDGXVPEBkE9DDGbKzXPgk4ZIzJbsb4mkRycrJJS0tr7TCUOmvUr1U1OzmB3MJKHn7/a6JChGlH3+C+4I840OV8rjg6j6iYHvSODmfLvmLmTxlERGiwllRvA0RkuzEmuaFjjZlt9QegvIH2Ks8xpZSqY/GmXJ5dtYdff7TLlwT++Hkmh62FPFT6DPcFf8TqsB/yj8Ev0zWuJ/klVQRbhEdnnIO3h+KdUaXapsaMeSQaY76p32iMSRORxCaPSCnVAbifaGzIKmTxpn1EhAZz13AXT+T/mn7mEO/E3M1jBRNhYz5zJw0g82gFC2YO8+0CqAPibV9jkkfYSY6FN1UgSqmOobjSXXNq7qSBAGzPKyU85xNu7vQaVa4gnu/xf1w7+6fM33mQkspa0gvKeXLWcJLi3WXUdayjfWjMY6ttInJn/UYRuR337CullPLxTsWNiwqlW2QQ43L/zF9CX6Q0vD9P9XqVP+/vy9Mr05kzYQD5JTY2ZBXy9Mr01g5bBagxPY+fAx+KyE/5PlkkA6EEMFVXKdVx+Q+Q2+wO5k4aiNNWwq0FTxMRnMp33a/k2v3XkBwTz8RBQmqGleVp+SyYOQxI9/yp2pPGTNU9AkwQkRRghKf5P8aYz5s1MqVUu+HdzGlzThGpGVamxVpZUPEMIZZi9l/4DKtlKmNDy9iQVcjcSQMICRKmDutBUnwUb942rrXDV6eh0SXZjTGpuBf0KaUUULfHATB1WA8uqPwTt1hfoNwSyfU1v+LYdyPItmYzf8ogJg3uhs1T4HBk34M6Jbcd0z3MlVIB8yYNm93JwjWZ2OwOglw1VHzwAPMKP6Ag+nys018nenMZv5o8mG25xb4k4Z1NpTWq2jdNHkqpRqufNOZOGkDK0HjCyvOYtPNhRlhyWRF5HfuGPcTN/Qbw5rnuHkVit8g69a3mXZKkU3LbOU0eSqlTqp805k8ZxKMzzsFmd9Ip8z/cHLoIE2Th9pqHWFM9BlL34bIE+Xb4846JwPe9DJ2S274Fsg3tc8aYX56qTSnVsfgXN7woKY75UwZzyZB4Xl2zi2cil9M99O985RrE/0X+L1uqokiICSe/pAr/3RO8vQvtZXQcgfQ8pgH1E8WMBtqUUh2It7hhUnwkG7OLCA22sD87nXsPPUl3Sw5be9zIK8E38cvLRrAtt7hOPSsv7WV0PKdMHiJyN3APMFBE/MuUdAY2NVdgSqnWV1xpx2Z3MH/KYK4c1ZunV6YTnLmK34b/BVeQi99H/4o/5Z1LytBOJHaLZHT/GMC974bq2BrT83gHWAU8Czzi115ujClulqiUUq2ifjVc9xhHFvOnDCL12zx+F76E+NC3qIz+AU92ephlOcEkxUf6Fv1p7+Ls0ZhFgmVAmYjcBlwDJHqvExGMMU81a4RKqWZXf0B8WVo+2dZKxiXGMHFQN6LKMpj0zaPEWw6wyPEj/llzG3sO2kkZGs+CmcOOe0ylOr5Axjz+BZThLlFS0zzhKKVaknc/8e15pWzIKmT+lEGkDI33jXFszS1mTtB/uS30XSqDI7m5+hEOxV1ItrWSpPhIXyVcfUx19gkkefQ1xkxvtkiUUi3KfxYVQMrQeK4c1QeAkX2juXpIKKXv3smo6q1kdrmINUN+xejQOJ7wjH2kZlj5LP2IJo6zVCDJY5OI/MAY822zRaOUanb1exsXJcUxvHdXwkODeG9rPovW5/DiaCuJy57BVVvGP2LuZf+gm1i0fh/zp8TyWfoRFswcxgUD9VHV2SyQ5DERuE1EcnA/thLAGGNGNktkSqkmV7+3cVFSHMmJsQAsXJNJSlJnFgS/xTXpqyiOTGLlDxby+GbD3EHunklVrYuFa7SkiAosecxotiiUUs2u/mI//6Qxf8ognhlfy2UZDxAfvI/VkVdwX9G1JEfHMn+K+zx3McOuPDrjHO1xqICSx37gp8BAY8xTItIP6AnkNUtkSqkz5p1FNTs5wbfYL2VoPC9cP8pXpDAq2MW1Vcvo8vVLFJouPBLxa5YWDSU2MoSN2UVcPCSe2ckJvjpUWgFXQWDJ48+AC5gMPAWUAx8AY5shLqVUE/CvKTU7OYGiihrSC8opsdmJjQwl1pbLbRnzCD68k385J/B65N3sLgkiMS6C3CIbFyXFYbM7AX1MpeoKJHmMN8aMFpGvAIwxJSKi/wRRqg2rX1NqzZ6jZFsr+fWH33BPxGdcmPsKtRLG/fb5rHKNZ3xMLJQWc/HgeAZ0szG4R2cWrskkIjRIk4eqI5DkUSsiQYABEJF43D0RpVQbFRsZ6ntkVVRhJ9tayQUx5fzm2PMMOPQVmV0v4vMh/48EYpkfGsSVo3r7VpYv2ZynYxzqhAJJHi8DHwLdReQZ4DpgQbNEpZQ6Lf5jHN4xDe8g+aSkGG4KWs3jNe9hgIdr55IdOosd60uZP6UbD04bArjrUvnvtaFjHKohgWxD+7aIbAem4J6me5UxZnezRaaUCtjiTbksXJPJ+sxCXv7x+b5B8mv6V/PzY4/RL+QrcqLG8WKne1iZH8r4YIvnSqnzOVoFV51KQJtBGWP2AHuaKRal1BkorrSTluuuVbohq5CHlu1kweVDODfnS8bnvU61CebZ0Pt4/eiFgGhdKnVGGlOSvRzPOEf9Q7gXCXZp8qiUUgHJtlYwd0ka2dZKEuMi6NEljIK92+lcPJ+Ly9P51JnMgtrbsNbEkBgXwcVD4omJCCUmQnsY6vRYTnWCMaazMaZLAz+dA0kcIpIgIqkisltEdonIfE/7EyJyUER2en4u97vmURHJEpEMEfmhX/t0T1uWiDzS0P2UOlsUV9p9iSM6IphDRWXcJ8v4T9gCujmtHP7ha7zR+ykS+g1g/IBYcots5BfbWLgmk+Vp+a0dvmqnAnpsJSIxwGAgzNtmjFnXyMsdwEPGmB0i0hnYLiKrPcdeMsb8vt69hgE3AsOB3sBnIjLEc/gV3DsbHgC2icgKY0x6IL+LUu1R/f02ZicnsHhTrq/H8dPeR7g44zcMPXSAmmGzeTf2bkoqOrM1L9M3a8p7vdamUmcikD3M7wDmA32BncAFwJe4Fw2ekjGmACjwvC4Xkd1An5NcMgtYaoypAfaJSBYwznMsyxiT44lrqedcTR6qw1u8aR8L12SxPtPKhqwiNucUkRATQRcqeCniPc7P/IjikHgKpi9haem5LPwsk7mTInyJw38gXKvhqjNxysdWfubjXk2eZ4xJAc4HrKdzUxFJ9Fy/xdN0n4h8IyJ/8/RuwJ1Y/PvUBzxtJ2qvf4+5IpImImlW62mFqVSbUVxp56XVGWzOcQ+I1zoNEwd1IzXjKKHpy/m80/8w0vpvPo66lomVz7HCNgLvUGV4qB2Hgz0AACAASURBVIV5lyTplFvVpAJ5bFVtjKkWEUSkkzFmj4gMDfSGIhKFu6zJz40xx0TkVeA3uP+f/hvgBeBn1J876GZoOOEdN6BvjFkELAJITk5uaMBfqXZjeVo+C9dkARAbGcKWfcU8cWEI/8/6POfW7CQ9aAi3VN1KenUiKUPdtahKbHa+OVDm26NDqaYUSM/jgIhE495RcLWIfAQcCuRmIhKCO3G8bYz5J4Ax5ogxxmmMcQF/4ftHUwcA/weyfT33O1G7Uh1StrWCdXutXJ/cl8S4CCorK3kxfiU37fwJQ0wO64Y8RvUtnxAzcAxzLx7oK3q4YudBUjOsrNh5sLV/BdUBBbJI8GrPyydEJBXoCnzS2OtFRIA3gN3GmBf92nt5xkMArga+87xeAbwjIi/iHjAfDGzF3SMZLCIDgIO4B9V/0tg4lGpvnl6ZzsbsIhLjInjw2O+YFbYJyuFD50XsP+8xtlqDcXy6ly37iklOjPGtMP++895QJ16pMxPIgPmDwHJjzAFjzNrTuNdFwM3AtyKy09P2GPBjERmF+9FTLjAPwBizS0SW4R4IdwD3GmOcnljuAz4FgoC/GWN2nUY8SrU5/uVFSmx2nl6ZzpwLE+lWW8Bdx54mKSgNgPeHv0J+9Fi255WyMbsQgKT4yDqbNc2ZkOgrMaJUUwtkzKML8KmIFANLgfeNMUcae7ExZgMN/xPo45Nc8wzwTAPtH5/sOqXaC++WsCBc6bc3OMC6vVa2Zx/ikoN/5Xd8BGIhMy6Fl0Lv4uPtTlKGlvGLaUOwO5wUlFWTba1k5khLgzOrlGpqgTy2ehJ4UkRGAjcAa0XkgDFmarNFp1QH5z8QnpZb7Hs8NfXc7sTnf8JznV6kr6OQDWGXsiz6Tnr2S+LjdTkkxUf6ksyFSd3cW8gOjWfOhAE6q0q1iIAWCXocBQ4DRUD3pg1HqY6vfm/DZndQUllLasZRAEKK9xLy9tNcU5bGkYhB/CbqEd7I7w2lMI4Sd5K4MJEn/r2L1Awrg7tH1eltKNUSAhnzuBt3jyMeeB+4U1d1K3Vq9ccxvKVEACJCg3hw2lBue3MrZSWFPB32T37Mp9grolhQexsF/W7g3inn8O2q3ThcBhHx9Thyi2wAhIcG6+Mp1eIC6Xn0x702Y+cpz1RK+Xi3grXZHaz8poBsayUJMeH0jg7nYGkVCz/dxWNxa4kP+wNdTCXvOCdTPu6X7Dto2Li3iBpXBltz3T2O1AwrSfGR3D95MCP7dgWEORMSW/tXVGehQMY8tAChUqdhdnICNruDzTnFZFsriYkI4cKkOJal5ROd9ym3Br/LQMthNjiH80bE7dhih/HbscOpDjnExuwiBnaLIiTIwv2TBwOQmmFlW24xD04LeI2uUk2mMSXZNxhjJvqVZvefMaUl2ZU6hdjIUCJCg9myz11apMRWS3DBDt4LfY3xlj1kuvrwVOdf82HFcErKHFBWzGfpR3xTbb1bwl4wMI4Xrh/lt45DqdZzyuRhjJno+bNz84ejVMdQv/rt1GE9WLP7CAdz9/JwyHtcVbSJsuCu/L+an7E1ZiaZ1mrAQWJcBJcN64nN7gRg3iVJx20Jq+Mbqi0IZMD88YbajTFPNV04SrUPDe0V7v/efzvYDVmFRJSks+zwTRAGNYTyiuNKtva+BWdoF56bNoSlW/NYvfsoT1wxnIwj5Ty7ag8RoUG+goaaMFRbE8iAeaXf6zBgJqB7mKuzkv8geERoMEUVdhatz8Fmd3DJkO4s/nIfAOdGlPGPsJ+4NzHw+MvIZWwvi2RthhUoZEz/aKwVdkpstSz+MpcXrh8FoI+mVJsWyID5C/7vReT3uOtPKXVWKa60Y7M7mD/FPYD97Ko99I+NAGB52gFeX5tDsKOC3LA7YO/31/3VMYPFneeSEhTP4O7usYwt+4rZnlfqWSnuYnAP99Nh7Wmotu50Fgl6RQADmyoQpdqD4ko7Dy3bSWqGlflTBlFld5IQE05esY2YiBCOllWQFXZLnf+yvu6UzNuDX+TL7CLyS6pY8mUeAHMnDSDYImzIcvc+Lh4Sz7Or9hCnj6lUOxDImMe3fL9vRhDuxYK/aY6glGqrFm/KJTXDSkJMOOszC9mxv9RzxPBv5tM37PvdAQ6aOFam/Jd5lw7iP/9JJ7+kqs5nhYcGk5wYy8bsIkB8j6n0cZVqDwLpecz0e+0AjhhjHE0cj1JtkndAvKTSDkB+SZUnGRgutKTzbugz4Pr+/MHVSxiX1IPkWhfFlXbSC8oBSIgJZ8aIXoSHBvkW9+lMKtUeBZI8rq3f4N6iw81/jw6lOoLiSjuvfZHF1wfKqHW62LG/lISYcADGJ8YwOSyDcXmLON+vSs/I6kUcI4rYyBCG9+nKwjWZRIQG8YtpQygoq+L5685jdP+YOvfRhKHao0CSRzLuPcy9g+RXAOuou5+4Uh2C/9iGv/ySKqaE7eYXRR8yvPY7DpsYHnfMoSDpeh65YhQ3bt3Pqu8Ok19SRVpuEaP7RfP5nqMUVdrJtlbyx88zfTv9KdWeBZI8ugGjjTHlACLyBO7Noe5ojsCUamn+C/u8+2p0ChZqHIbR/aKZ2TmbH2S/yljSOWKP5sWQ21lhmUZ1pxAO7y2jaPlOIkJDSBnanSWb89ixv8z32cEW8dWmWp6Wr70N1e4Fkjz6AXa/93YgsUmjUaqFecujV9ldpBccY0NWIa+uzaLU5iAmIoQSm505Yeu4sWQ95x5N54iJ5gnHLfwn5DKsFRbcAx01AL5kcaDExtxJ7omI1bVOcgor+cW0Iazde5SRfaN1QFx1CIEkj7eArSLyIe5ZV1cDS5olKqVaiP9mTADBFii1OQBDiuzgzrClnMs+qIUnam/hXedkhvfrztX9Y1n1XQH5JVX0jQ6nT0w4W/YV0zU8mNwiG+GhljqFC19fm83CNVk8OuMcfWSlOoRAFgk+IyKrgEmeptuMMV81T1hKtYypw3qwbq+VpO5RrNtrJb+onCstW7gvZAVDnPs5YLrxHlP5vbkJq9P9l/7hsmoWrc9hWM9IEuMiePH6USR2i2R5Wj5FlXYWrcuh/o7LOg1XdTSBrPMQYBjQ1RjzlIj0E5FxxpitzReeUs3D+7hqe14pG7OLCDF27u+ynnGV/yCBI+TQh4fsd/GRawIOv/9MEmLCfes10g+7K/Zsyy1mdP8YXxHDuMjQ45KETsNVHU0gj63+jPsB72TgKaAc+AD3DCyl2oVsawWP/+s7qmqd7NhfSiRV3B+Wyk0HV9JDStnpGsjTjgf5r2sMBgsWYFivziQnxhIWHMTXB0rJL6kiISaclKHdiamXKDRJqLNFIMljvDFmtIh8BWCMKRERfXir2gVvT+OjnYfILbIRTwkPBa/m5qDVRFPJRtdwfmnu4QvHcCJDgjEuJ52CLdQ4XNQ4XMyZkMjTK9PZsq+YlKHxOt1WnfUCSR61IhKEp0SJiMRTZ02tUm2PN2lszilmy75izpH9LAz7hOlmAyHiZJ2M5aXqmXxtBvmuiQoLprLWyZDukRyrdpJtreTXH+1iQ1ahJg6lPAJJHi8DHwLdReQZ4DpgQbNEpdQZ8CaMkspa1mVayS2q5BLLNywJ+ZiLg77FGRzO6tAZPFuSQp7pCcDIPl0osdVy4cA4sqwVHCmvIaJTCBcmxbsHx3t1ZtLgbr4yIkqd7QKZbfW2iGwHpuCeSnKVMUb381BthneRn83uZOGaLDphZ1bQRt4M/4QBJh8rsTxXeyP5fWdzyB5OXrG7qGFCTDhHK+wcLqvGlV3IwdJquoa7t409r29XHp1xjiYNpeoJqCS7MWYPsKeZYlEqYN5eBghVtU4Wrcvh7jGdWRC1glm1HxMvxyjoNIgHj93NSteFREWEU5JVQ99oCwC9u4bVqXZbWO5e8FdW5a75GR4arAPgSjXgTPbzUKpVFVfaeeDdHWzIKgIM07vs5w8h/+ZHu7YSgoPPXaPYEH8jwQMv4cMN7p39Smy1xEaGcKDUPWOq1ukethvZpwv5JVWU2Gp9+4j7V75VStXVYslDRBJwr0jviXugfZExZqGIxALv4S51kgtc75nJJcBC4HLABtxqjNnh+aw5fD/e8rQxZnFL/R6q5fnvDw7w2hdZpBeUMzA+km1ZBcwO2sRtIasZZt/HMUs4bzmmsibqCjaWxsAhuCj8GNOH9+CLDCsD4yNJLyivs14DIOWc7lw5qg9Pr0xnwcxhJMVHtdavq1S7IMaYU5/VFDcS6QX0MsbsEJHOwHbgKuBWoNgY838i8ggQY4z5pYhcDtyPO3mMBxYaY8Z7kk0a7iq/xvM5Y4wxJSe6d3JysklLS2vOX081A/+FfBuyCrkoKc63PqOvWLkn8gtm1K4mRirIcPVlY+zVFA28mtqgCN9neOtVWQRcBqI6BTE2MZayqlp27C+la3gwN4ztx12XJOmYhlL1iMh2Y0xyQ8darOdhjCkACjyvy0VkN9AHmAVc6jltMfAF8EtP+xLjzm6bRSTak4AuBVYbY4oBRGQ1MB14t6V+F9V8GqpsCxATEcKmbCsTLd/xl5DVTAnagXEI64LGsTrqSoIHXkxMZCdun5DI4k25LFyT6SsdUlBWRbbVvRp8YHwUqRlW5k4aQNfwEO1lKHWaWmXMQ0QSgfOBLUAPT2LBGFMgIt09p/Wh7l4hBzxtJ2qvf4+5wFyAfv36Ne0voJrN8rR8nl21h3V7rWzMLiIhJpyakkPMrlnLzRFr6ek6QqklmnXxt/BIXjKHiQMb9HcUkldsY3NOEVV292B3bpGNh9//muevO48X/pvB8D5dmT68J3/8PJMbxvXTpKHUGWjx5CEiUbjLmvzcGHPMfzfC+qc20GZO0l63wZhFwCJwP7Y6vWhVS8q2VrBur5W5Fw9EjJOQfZ9xj2sjY8I2E4SLb4NH8UzFbKoHzWBAjzgO5+XQu2sYCbERDO3ZmSVf5rFlX7Hv88KChWxrJWv3HuXtOy8A3NVtUzOsXDDwCEmXaPJQ6nS1aPIQkRDcieNtY8w/Pc1HRKSXp9fRCzjqaT8A+FeX6wsc8rRfWq/9i+aMWzWt+gPg3qm22/NKyMneyzTrJn4SspbQ0ENYq7uwRGayuOYSnOEDiekVyjd7y7i+S2dShsZz/+TBbMstZmiPzqzba2VcYixhIUHkFFYysFskSzbn4f/vDa1uq1TTaMnZVgK8Aeyut9/5CmAO8H+ePz/ya79PRJbiHjAv8ySYT4Hfioh3I+jLgEdb4ndQp6/Oegy7k0XrczhYYmNjdhF51jJSLDt5IHI9Y8LSCLIbtjnO42/22WwNHU9RNYSHWKgqqcJaXg3ApqwiDpRWsTO/lBJbLUnxkeQW2RjQLZI3bxvnu2efmHAtXKhUM2jJnsdFwM3AtyKy09P2GO6ksUxEbgf2A7M9xz7GPdMqC/dU3dsAjDHFIvIbYJvnvKe8g+eq7fLfdCkhJhyAg3u28ZOK/3J12EZiOcZRezSvOq9kY+cZfFnSBYDR3aMp2l/KgLgIDpZVU1bl3uFvwqA4lqUd8CWO5687jz9+nsmCmcN899REoVTzabGpuq1Jp+q2Hv9tXr8+UErmvlxmBW1kTsQmEmuzqSWIbyMn8ErJONa6zsNBMLdc0I+YyFBAuHJU7zqzrrzmThoAQHpBOU/OGq6D30o1gzYxVVd1TPXHL7yvYyNDKa6089CynWzIKCDF8hW/itvGOWFfEoyTg8Hn8LhtDiucE+gc3p18VxVdwoI5Vu0gJrITD04bQnGlnde+yMJmd3J9cl/yimzUOl3s2F9KeGgwD04b0sq/vVJnL00e6ox4p9ba7A7SckvYmF3E53uOcF6faGrytvKmdT6Euc8tLI/mDcd0PnBeTHyfUWwsKSIxLoJxibHkbz/AsWoHSfGRnJ8QzW1vbmVwj84sWu8uK1JYUUO2tZL5Uwbxw+E9dcBbqVamyUMFxL/cecaRcob27MzcSQNZn1nIjv2lDJIDTMxfxpUHN9HfctR33a32h/k6dDQlDsPEQd34xbQhhHrGKFbsPARAYlyEe++MFbvIK7ZhszuYO2kA6QXl/GLaELblFmt1W6XaCE0e6pT8Z0oBvoFvgC37irkgzkZy6RqeDt3EMEseTixsdA7nZfs17OoykT2lFvdsqWoXiXERjOkfwye7DpOaYWVf4TZevH4UVXYHXx8o47LhPamudbLkyzzO6xvDYz8613ev0f1j6oemlGolmjzOcidac+GtJvvaF1n8N/0IuUU2AEb368roftFYbEWMrFjLNcGbGVH5HYRARvBQ3oy4m1eOjqSQriTGRfiuq6p1J47Lhvdk4ZpMLkqKA9yrwP/4eSbgTkQRoUG8cP0o+kSH66MppdowTR5nucWb9rFwTRaLN+USZBFfpdm/bczBGKiocQIQFmIhvLaUwQdTucKyiYlBuwDIc/bljZCfsLhiLFZnL6oqXO5V3xbhoWlD+NVHuyitqgVg1qjeXDmqD5lHyrl/8mCGf1dAekG53/Rad0VbnWKrVNunyeMs4f/oac6ERN9sqO157mLEh8rci+86hwVhd7gor3YnjTjKmBW2gxTnl1wYlk4wLg5benIkpD9Pmnl8fKw/14/pi/WbAqpqXcRGhvg+63efZlBaVUvvrmHMPK83cyYMYPGmfaRmWBnZtyuP/WhYnRi9i/uUUm2fJo+zhP8ivbTcYob37kpaXjE79pcypHskuYWV2F1QXu0knlJ+GLSNyy1bGG/ZTRCGHOnJa44rWOUczy7Tn5Sh3RncozOsy+HLnGKqat09jp5dw5h6TiSHymqw2R3kl1QxMD6Kxy73jl1IvT+VUu2RJo8OLttawaMffIPDZZg8NJ61nmq1G7OLfOfkFVXSzVXI1KDtzAzeSjJ7sIgh29WLV5yz+Nh5AXtMAp2CLNQYQ0JMOKkZVrKOVjB+QKyvGGFosMWzR0YI/7hjPNnWCt/mSl5zJiQSERqk4xlKtXO6wrwD8R/8/ia/lAeWfoXTGCo94xaCf/lhw3DJY3rwdlJkOyMsuQBkuvrwsWs8/3GO52hYIqVVTjoFCTVO95WxkSFMPbcHy9IOAHBRUhzJiTGAcMmQeF+JEF3xrVT7d7IV5po8OojiSjt3vZXG1twSgsTdC6iqddU5JxgH4y27mWbZztSgHfSVQlwI212D+cw5htWuMZSG96OkyoExECTgyRkkxrl358stsnFRUhzDe3fR0iBKdXBanqQD8fYuxibG8sJ/M0jqHkWZzc4n3x329Q6cBl/iGCE5DJUDXBz0DSmWnXQRG9UmhPWukSx0XcPm4DHk2zt/fwObeyOlYIvgcLkfUcV37kRy/1imj+ipPQulFKDJo11w13jKJr2gjGG9u7JoXQ5hwRaqHa46YxduhgeCPuQXIe/XaS0ynfnEOZbVrjGsd/2Aajq5DziPv1//2AheumEUL67OYENWEf3jIlm0Poe4qFCdEaWUAjR5tDn+e3iv2HkQ//0vAPKKbPTuGuabDgvQV6y8H/oEPaWkwc/8yDmBB2vvwYWlTvuQ7pEcKa+hrMrd2xjdL5qI0GA2ZBWyLbeYMf1j2JBVxLBenZk0uJsOciulfDR5tAH+CePxf33Hxuwinlu1B1cD5+aXVBFsgTlBn/JkyOITfua99gf4j+uCk963e5cw9h6t9Ax6x/pWlfuvOI8IDdZ6Ukqp4+iAeSvxfxTVOzqcZWkHiAi2YHM0lDKgC5VcaEnnlZCFBMvx5+x0DeQ2+/9SQpeT3jcy1ELnsBAuG9aTORcl8ln6EU0OSqkG6YB5G+E/lfYPn+1lyZd5dY77J44QHJwvmUwM+paJlu84T7IJkrqJ/pHaO1jqnHzK+4YFC9UO46s19cCUAb7yH0mX6MC3UipwmjyaUXGlnT+szmBdZiFPXDGc332STvrhSl5es4dKe/2zDYPlIJMs3zLR8i3jLbuJlBqcRvjaJPEn51VsdI7gKzOY2kb8z9a9cyemj+hJjrWSDVmFpAyNZ8HMYb6ehlJKnQlNHs3A28Ow2R0s2bwfgFv/vs133Js4rrRs5OXQV467PtvViw+cF7PBNYLNrmEcI7LR9+7ZpRNXjurDXZck+epX+e/upz0NpVRT0ORxGrwlP0SEq0b15rerduNwOPFMWqJHVAhHKmqPq950tWU9L4W+2uBnHjHRvOCYzUbnCA4Sf9L7R4ZaiI4I5ZHp57B0Wz7dokL5Yq+VsioHN4ztV2d7Vq1Qq5RqDpo8ApBtreB/lu3kq/wyX5u3rpO/IxW1gOHaoHX8PuT1k37mCueFPFw7jxoaN2CdMjSeF64f5RvgvmJUH+D4fTmUUqo5afJoBO/MqOXb8ymx1Z7gLEOKZSdvhj5/0s/6k2MWLzmuw0nQCc/pHtWJoxU1ddrCgoVbJgzwPY6qT3sYSqmWpMnjFIor7dz9jzS27Ku7AC+UWkbIPsZY9jLWksFoSybd5Nhx17/suIqFjmtPmizqG9wjih+PT6Ckspad+SWU2GpZeOP5ug2rUqrN0ORxCos35bJlXwldqGCMJZOxlgzGWPYySrLpJO5eyD5XD75wjSLfFY+DIF53zsQR4FfrrXibGBfBU1eN0NpRSqk2TZPHybhc3PDVLTwYttvXVGuC2GUSWeKcRpprCNtdQymk6xndZsbwHvzP9HN0wZ5Sqt3Q5HEyIsRX7wPgTcs1fFI1nK/NwO+LCjaCtzptkEV4etZwdheU8+muwxhjCAm2cLC0mlH9YkiKj9JptEqpdkOTx8mIUP5QPsvT8vl012F27C9t9KUhFrht4kBuGJvA0yvTSc2wcqzawVNXjeCpq0YAOkNKKdV+WU59StMQkb+JyFER+c6v7QkROSgiOz0/l/sde1REskQkQ0R+6Nc+3dOWJSKPNHfc3llMyZ7B6vjIUEKCjt9/Oyk+kuuT+wLQNTyY9+ZN4LHLzyUpPooXrh/FozPOOS5JeD9bH1Mppdqblux5/B34E7CkXvtLxpjf+zeIyDDgRmA40Bv4TES8K99eAaYBB4BtIrLCGJPenIED3HXpIOKiOjE7OYESm53H/vktxhgemXEu23KLfYkhKT7quHELnUarlOpoWix5GGPWiUhiI0+fBSw1xtQA+0QkC/DuQpRljMkBEJGlnnObPXn4J4DYyFDem3eh75j/FFpNEkqps0GLPbY6iftE5BvPYy3v38J9gHy/cw542k7UfhwRmSsiaSKSZrVamyNupZQ6a7V28ngVSAJGAQXAC5724wcV3MsgTtR+fKMxi4wxycaY5Pj4k9eKUkopFZhWnW1ljDnifS0ifwFWet4eAPxHl/sChzyvT9SulFKqhbRqz0NEevm9vRrwzsRaAdwoIp1EZAAwGNgKbAMGi8gAEQnFPai+oiVjVkop1YI9DxF5F7gU6CYiB4BfA5eKyCjcj55ygXkAxphdIrIM90C4A7jXGOP0fM59wKdAEPA3Y8yulvodlFJKueke5koppRp0sj3MW3vAXCmlVDt0VvQ8RMQK5LXgLbsBhS14vzPV3uKF9hdze4sX2l/MGm/T62+MaXC66lmRPFqaiKSdqKvXFrW3eKH9xdze4oX2F7PG27L0sZVSSqmAafJQSikVME0ezWNRawcQoPYWL7S/mNtbvND+YtZ4W5COeSillAqY9jyUUkoFTJOHUkqpgGnyOE0N7YxY77iIyMueHQ+/EZHRLR1jAzGdKuZLRaTMb2fHx1s6xnrxJIhIqojsFpFdIjK/gXPazPfcyHjbzHcsImEislVEvvbE+2QD53QSkfc83++WAPbkaRaNjPlWEbH6fcd3tEas9WIKEpGvRGRlA8fa1HfcaMYY/TmNH+BiYDTw3QmOXw6swl1G/gJgSzuI+VJgZWvH6RdPL2C053VnYC8wrK1+z42Mt818x57vLMrzOgTYAlxQ75x7gNc8r28E3msHMd8K/Km1v996Mf0CeKeh/+3b2nfc2B/teZwmY8w6oPgkp8wClhi3zUB0vSrCLa4RMbcpxpgCY8wOz+tyYDfHb/7VZr7nRsbbZni+swrP2xDPT/0ZNLOAxZ7X7wNTRKShfXVaRCNjblNEpC/wI+CvJzilTX3HjaXJo/k0etfDNuZCzyOBVSIyvLWD8fJ05c/H/S9Nf23yez5JvNCGvmPP45SdwFFgtTHmhN+vMcYBlAFxLRtlXY2IGeBaz2PM90UkoYHjLekPwP8CrhMcb3PfcWNo8mg+jd71sA3ZgbuWzXnAH4F/tXI8AIhIFPAB8HNjzLH6hxu4pFW/51PE26a+Y2OM0xgzCvfGauNEZES9U9rc99uImP8NJBpjRgKf8f2/6luciMwEjhpjtp/stAba2vrfFZo8mtHJdkNsk4wxx7yPBIwxHwMhItKtNWMSkRDcfxG/bYz5ZwOntKnv+VTxtsXv+P+3dzchWURRGMf/D+VCCmoTVES4DqLAkEDcREErIRAS+qKlm9blUoKKoHX0sYgMoqVUtAixfbXIqAiJVrVJyZLELE+Luaa8qM00vs4Iz281Opfr4TJwvHPvnJti+QqMAEcbbv0dX0kbgS3U5NXncjFHxHhEzKQfbwLtaxzaYp1At6SPwH3gkKTBhja1HeOVOHk0zxBwOu0GOghMRsTnqoNaiaTt8+9aJXWQPR/jFcYj4DbwNiKuLdOsNuOcJ946jbGkbZK2putW4DDwrqHZEHAmXfcAw5FWdquQJ+aGNa9usrWnSkTEhYjYFRFtZIvhwxFxsqFZrcY4r0rPMF/PtPTJiC0AEXEdeEy2E2gM+AGcrSbSBTli7gH6JP0CpoHeih/iTuAUMJrecQP0A7uhluOcJ946jfEO4I6kDWRJ7EFEPJQ0ADyPiCGyZHhX0hjZf8O9FcU6L0/M5yR1k51COkG2+6pWaj7Gubg8iZmZFebXVmZmVpiTh5mZFebkYWZmhTl5mJlZYU4eZmZWmJOHmZkV5uRh1iSSbknak677V2j3O5UO31mg7y5Jb7RMeX2zZvN3HmZrCz1AFwAAASdJREFUQNJURGwueu8ffbaRlfhurO1k1nSeeZiVJGmTpEepUu5rScfT70ckHZB0GWhNs4t7OfqbknRF0gtJTyV1pL4+pC+nzSrn5GFW3lHgU0TsS7OAJ4tvRsR5YDoi9kfEiRz9bQJGIqId+A5cBI4Ax4CB1Q3d7P84eZiVNwocTrOFroiYLNnfTxYS0CjwLCJm03Vbyb7NVoWTh1lJEfGerOz3KHBJ5c8ln11ULHEOmEl/Zw4XM7Wa8INoVlLaJTUREYOSpli6iuuspJY0gzBb95w8zMrbC1yVNAfMAn1LtLkBvJL0Mue6h1mteauuWcW8VdfWI695mFXv2/98JEh2VveX5oVltjzPPMzMrDDPPMzMrDAnDzMzK8zJw8zMCnPyMDOzwv4ABCUg06JqTi8AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1.39134715 -0.84883122]\n",
+ "[-0.1278794 0.54114959 0.2003112 ]\n",
+ "[0.01143275 0.01283542 0.00298047]\n",
+ "[ 2.80987065 0.272662 -3.08239936]\n",
+ "[0.06881618 0.00409829 0.07651853]\n",
+ "[-6.77852209 0.2970746 1.7242942 6.71992723]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# fit\n",
+ "from scipy.optimize import curve_fit\n",
+ "\n",
+ "def exponential_func(x, a, b, c):\n",
+ " return a*np.exp(b*x)+c\n",
+ "def linear_func(x, a, b):\n",
+ " return a*x+b\n",
+ "def arcsin_func(x, a, b,c,d):\n",
+ " return a*np.arcsin(b*x+c)+d\n",
+ "def sin_func(x, a, b,c,d):\n",
+ " return a*np.sin(b*x+c)+d\n",
+ "def poly_func(x, a, b,c):\n",
+ " return a+b*x+c*x**2\n",
+ "\n",
+ "msit1m=msit1\n",
+ "mco21r=mco21\n",
+ "menergy1m=menergy1\n",
+ "\n",
+ "#exponential fit of equivalent co2\n",
+ "popt, pcov = curve_fit(exponential_func, msit1, mco21r, p0=(1, 1e-6, 1))\n",
+ "\n",
+ "print(popt)\n",
+ "plt.plot(msit1,mco21r,'.',markersize=pltsize)\n",
+ "plt.plot(msit1,exponential_func(msit1,popt[0],popt[1],popt[2]))\n",
+ "plt.xlabel(\"sit [m]\")\n",
+ "plt.ylabel(\"equivalent CO$_2$ factor []\")\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "plt.plot(msit1m,menergy1m,'.',markersize=pltsize)\n",
+ "\n",
+ "#linear fit of heat flux\n",
+ "popt, pcov = curve_fit(linear_func, msit1m, menergy1m, p0=(1, 1))\n",
+ "#popt=[4,1/4,0]\n",
+ "print(popt)\n",
+ "#plt.plot(msit1m,menergy1m,'.',markersize=pltsize)\n",
+ "plt.plot(msit1m,linear_func(msit1m,popt[0],popt[1]))\n",
+ "\n",
+ "\n",
+ "\n",
+ "#quadratic fit of heat flux\n",
+ "popt, pcov = curve_fit(poly_func, msit1m, menergy1m, p0=(1, 1,1))\n",
+ "#popt=[4,1/4,0]\n",
+ "print(popt)\n",
+ "print(np.sqrt(np.diag(pcov)))\n",
+ "#plt.plot(msit1m,menergy1m,'.',markersize=pltsize)\n",
+ "plt.plot(msit1m,poly_func(msit1m,popt[0],popt[1],popt[2]))\n",
+ "\n",
+ "\n",
+ "#exponential fit of heat flux\n",
+ "popt, pcov = curve_fit(exponential_func, msit1m, menergy1m, p0=(1, 1e-6, 1))\n",
+ "\n",
+ "print(popt)\n",
+ "print(np.sqrt(np.diag(pcov)))\n",
+ "plt.plot(msit1m,exponential_func(msit1m,popt[0],popt[1],popt[2]))\n",
+ "\n",
+ "\n",
+ "\n",
+ "#sin fit of heat flux\n",
+ "popt, pcov = curve_fit(sin_func, msit1m, menergy1m, p0=(-1, 1, 0, 1))\n",
+ "#popt=[4,1/4,0]\n",
+ "print(popt)\n",
+ "#plt.plot(msit1m,menergy1m,'.',markersize=pltsize)\n",
+ "plt.plot(msit1m,sin_func(msit1m,popt[0],popt[1],popt[2],popt[3]))\n",
+ "\n",
+ "\n",
+ "plt.xlabel(\"sit [m]\")\n",
+ "plt.ylabel(\"heat flux [W/m^2]\")\n",
+ "\n",
+ "plt.legend([\"data\",\"linear\",\"quadratic\",\"exponential\",\"sinusoidal\"],loc = 2)\n",
+ "plt.savefig(\"plots/F_sit_fit.pdf\",dpi=500)\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Tags",
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/ice_thickness.ipynb b/python_scripts/ice_thickness.ipynb
new file mode 100644
index 0000000..f973596
--- /dev/null
+++ b/python_scripts/ice_thickness.ipynb
@@ -0,0 +1,630 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<xarray.core.options.set_options at 0x2b97087da198>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import xarray as xr\n",
+ "xr.set_options(display_style=\"html\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "def load_experiment_zm(expname): #loads the dataset of a simulation\n",
+ " fname = expname +\"_atm_2d_ml.ym.zm.nc\" #filename of global yearly mean\n",
+ " dpath = \"/work/bb1092/pp_JH/\" +expname +\"/\" #simulation path\n",
+ " DS = xr.open_dataset(dpath +fname, decode_times=True) #loading of dataset\n",
+ " print(dpath +fname)\n",
+ " return DS # returns the name of the experiment & the actual dataset\n",
+ "\n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos, fontsize=fontsize)\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " for text in legend.get_texts():\n",
+ " if text.get_text()=='Winton':\n",
+ " text.set_color('C1')\n",
+ " if text.get_text()=='3L-Winton':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='0L-Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='Semtner_5m':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-lim5':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-limited':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='1438ppmv':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='1500ppmv':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='3000ppmv':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='5000ppmv':\n",
+ " text.set_color('C3')\n",
+ " \n",
+ " return legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/mlo_aqua_1500ppmv/mlo_aqua_1500ppmv_atm_2d_ml.ym.zm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1594ppmv_hice_unlim/mlo_aqua_1594ppmv_hice_unlim_atm_2d_ml.ym.zm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "nsim=2\n",
+ "exparray=np.empty(nsim, dtype=\"U100\")\n",
+ "simarray=np.empty(nsim, dtype=object)\n",
+ "\n",
+ "exparray[0]=\"mlo_aqua_1500ppmv\"\n",
+ "exparray[1]=\"mlo_aqua_1594ppmv_hice_unlim\"\n",
+ "\n",
+ "for i in range(0,nsim):\n",
+ " simarray[i] = load_experiment_zm(exparray[i])\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sit=np.empty(nsim, dtype=object)\n",
+ "sic=np.empty(nsim, dtype=object)\n",
+ "\n",
+ "for i in range(0,nsim):\n",
+ " sit[i] = simarray[i].sit\n",
+ " sic[i] = simarray[i].sic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "nyears=40\n",
+ "fig, ax = plt.subplots()\n",
+ "#sit[0][-nyears*12:].mean(dim=\"time\").plot(c=\"C2\")\n",
+ "#sit[1][-nyears*12:].mean(dim=\"time\").plot(c=\"C0\")\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "ax.plot(sic[0].lat,sic[0][-nyears:].mean(dim=\"time\"),c=\"C2\",ls=\"--\",label=\"0L-Semtner\")\n",
+ "ax.plot(sic[1].lat,sic[1][-nyears:].mean(dim=\"time\"),c=\"C0\",ls=\"--\",label=\"0L-Semtner-limited\")\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.xlim(40,70)\n",
+ "plt.ylim(0,1)\n",
+ "\n",
+ "ax2 = plt.twinx(ax)\n",
+ "\n",
+ "\n",
+ "\n",
+ "ax2.plot(sit[0].lat,sit[0][-nyears:].mean(dim=\"time\"),c=\"C2\",ls=\"-\")\n",
+ "ax2.plot(sit[1].lat,sit[1][-nyears:].mean(dim=\"time\"),c=\"C0\",ls=\"-\")\n",
+ "\n",
+ "#ax2.fill_between(sit[1].lat,np.squeeze(sit[1][-nyears*12:].mean(dim=\"time\").values),0,color=\"C0\", alpha=0.5)\n",
+ "#ax2.fill_between(sit[0].lat,np.squeeze(sit[0][-nyears*12:].mean(dim=\"time\").values),0,color=\"C2\", alpha=0.5)\n",
+ "\n",
+ "\n",
+ "#ax2.plot(sit[0].lat,np.squeeze(sit[0][-nyears*12:].max(dim=\"time\").values),color=\"black\")\n",
+ "#ax2.plot(sit[1].lat,np.squeeze(sit[1][-nyears*12:].max(dim=\"time\").values),color=\"black\")\n",
+ "#ax2.plot(sit[0].lat,np.squeeze(sit[0][-nyears*12:].min(dim=\"time\").values),color=\"black\")\n",
+ "#ax2.plot(sit[1].lat,np.squeeze(sit[1][-nyears*12:].min(dim=\"time\").values),color=\"black\")\n",
+ "\n",
+ "ax2.set_ylim(0,20)\n",
+ "\n",
+ "ax2.spines['top'].set_color('none')\n",
+ "ax.spines['top'].set_color('none')\n",
+ "\n",
+ "legend_color(ax,['0L-Semtner', '0L-Semtner-limited'],2, labelsize)\n",
+ "\n",
+ "ax2.set_ylabel(\"Sea Ice Thickness [m]\", fontsize=labelsize)\n",
+ "ax.set_ylabel(\"Sea Ice Cover []\", fontsize=labelsize)\n",
+ "ax.set_xlabel(\"Latitude [°]\", fontsize=labelsize)\n",
+ "ax.tick_params(labelsize=ticksize) \n",
+ "ax2.tick_params(labelsize=ticksize) \n",
+ "\n",
+ "plt.savefig(\"plots/sit_semtner_limit.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
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+ ".xr-attrs dt:hover span {\n",
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+ "}\n",
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+ " grid-column: 2;\n",
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+ " display: inline-block;\n",
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+ " stroke: currentColor;\n",
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+ "}\n",
+ "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sit'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4590c44a-b62a-4142-a7de-670a6a0f16a3' class='xr-array-in' type='checkbox' ><label for='section-4590c44a-b62a-4142-a7de-670a6a0f16a3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.9999986 4.999999 4.9999995 4.9999995 ... 5.0 5.0 5.0 4.999999</span></div><pre class='xr-array-data'>array([4.9999986e+00, 4.9999990e+00, 4.9999995e+00, 4.9999995e+00,\n",
+ " 4.9999995e+00, 4.9999990e+00, 4.9999990e+00, 4.9999990e+00,\n",
+ " 4.9999990e+00, 4.9999990e+00, 4.9999986e+00, 4.9999781e+00,\n",
+ " 4.9993877e+00, 4.9956059e+00, 4.9689317e+00, 4.8669600e+00,\n",
+ " 4.4313717e+00, 2.8757820e+00, 1.3569852e+00, 8.1053650e-01,\n",
+ " 5.6007373e-01, 3.8581243e-01, 2.3335367e-01, 9.6744440e-02,\n",
+ " 1.6559524e-02, 4.8852793e-04, 6.5625835e-07, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 2.6353965e-07, 3.0045537e-04, 1.3050905e-02,\n",
+ " 8.7753430e-02, 2.2167826e-01, 3.7447679e-01, 5.4619443e-01,\n",
+ " 7.8110969e-01, 1.2378209e+00, 2.5323899e+00, 4.2385550e+00,\n",
+ " 4.8234253e+00, 4.9544744e+00, 4.9942698e+00, 4.9996810e+00,\n",
+ " 4.9999952e+00, 4.9999990e+00, 4.9999995e+00, 4.9999990e+00,\n",
+ " 4.9999995e+00, 4.9999995e+00, 4.9999995e+00, 5.0000000e+00,\n",
+ " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00, 4.9999990e+00],\n",
+ " dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-d350f1a2-9dd8-4936-9f42-6fc1f92ed1a9' class='xr-section-summary-in' type='checkbox' checked><label for='section-d350f1a2-9dd8-4936-9f42-6fc1f92ed1a9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-95d8ee33-01c8-454a-9ab0-4cd53f12067f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95d8ee33-01c8-454a-9ab0-4cd53f12067f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ab29542-bd5c-45db-86e2-d92c3f1fda3b' class='xr-var-data-in' type='checkbox'><label for='data-3ab29542-bd5c-45db-86e2-d92c3f1fda3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><pre class='xr-var-data'>array(0., dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.0625 -87.1875 ... 89.0625</div><input id='attrs-95cbdbeb-81db-4944-9c25-448b6cdcd769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95cbdbeb-81db-4944-9c25-448b6cdcd769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92f694ce-b55b-4ec0-aa8b-081838114163' class='xr-var-data-in' type='checkbox'><label for='data-92f694ce-b55b-4ec0-aa8b-081838114163' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><pre class='xr-var-data'>array([-89.0625, -87.1875, -85.3125, -83.4375, -81.5625, -79.6875, -77.8125,\n",
+ " -75.9375, -74.0625, -72.1875, -70.3125, -68.4375, -66.5625, -64.6875,\n",
+ " -62.8125, -60.9375, -59.0625, -57.1875, -55.3125, -53.4375, -51.5625,\n",
+ " -49.6875, -47.8125, -45.9375, -44.0625, -42.1875, -40.3125, -38.4375,\n",
+ " -36.5625, -34.6875, -32.8125, -30.9375, -29.0625, -27.1875, -25.3125,\n",
+ " -23.4375, -21.5625, -19.6875, -17.8125, -15.9375, -14.0625, -12.1875,\n",
+ " -10.3125, -8.4375, -6.5625, -4.6875, -2.8125, -0.9375, 0.9375,\n",
+ " 2.8125, 4.6875, 6.5625, 8.4375, 10.3125, 12.1875, 14.0625,\n",
+ " 15.9375, 17.8125, 19.6875, 21.5625, 23.4375, 25.3125, 27.1875,\n",
+ " 29.0625, 30.9375, 32.8125, 34.6875, 36.5625, 38.4375, 40.3125,\n",
+ " 42.1875, 44.0625, 45.9375, 47.8125, 49.6875, 51.5625, 53.4375,\n",
+ " 55.3125, 57.1875, 59.0625, 60.9375, 62.8125, 64.6875, 66.5625,\n",
+ " 68.4375, 70.3125, 72.1875, 74.0625, 75.9375, 77.8125, 79.6875,\n",
+ " 81.5625, 83.4375, 85.3125, 87.1875, 89.0625], dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-37d7ce69-c8d8-4add-8ee0-32e996ce8216' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37d7ce69-c8d8-4add-8ee0-32e996ce8216' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray 'sit' (lat: 96)>\n",
+ "array([4.9999986e+00, 4.9999990e+00, 4.9999995e+00, 4.9999995e+00,\n",
+ " 4.9999995e+00, 4.9999990e+00, 4.9999990e+00, 4.9999990e+00,\n",
+ " 4.9999990e+00, 4.9999990e+00, 4.9999986e+00, 4.9999781e+00,\n",
+ " 4.9993877e+00, 4.9956059e+00, 4.9689317e+00, 4.8669600e+00,\n",
+ " 4.4313717e+00, 2.8757820e+00, 1.3569852e+00, 8.1053650e-01,\n",
+ " 5.6007373e-01, 3.8581243e-01, 2.3335367e-01, 9.6744440e-02,\n",
+ " 1.6559524e-02, 4.8852793e-04, 6.5625835e-07, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
+ " 0.0000000e+00, 2.6353965e-07, 3.0045537e-04, 1.3050905e-02,\n",
+ " 8.7753430e-02, 2.2167826e-01, 3.7447679e-01, 5.4619443e-01,\n",
+ " 7.8110969e-01, 1.2378209e+00, 2.5323899e+00, 4.2385550e+00,\n",
+ " 4.8234253e+00, 4.9544744e+00, 4.9942698e+00, 4.9996810e+00,\n",
+ " 4.9999952e+00, 4.9999990e+00, 4.9999995e+00, 4.9999990e+00,\n",
+ " 4.9999995e+00, 4.9999995e+00, 4.9999995e+00, 5.0000000e+00,\n",
+ " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00, 4.9999990e+00],\n",
+ " dtype=float32)\n",
+ "Coordinates:\n",
+ " lon float32 0.0\n",
+ " * lat (lat) float32 -89.0625 -87.1875 -85.3125 ... 87.1875 89.0625"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.squeeze(sit[0][-nyears*12:].mean(dim=\"time\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/overview.ipynb b/python_scripts/overview.ipynb
new file mode 100644
index 0000000..0f98659
--- /dev/null
+++ b/python_scripts/overview.ipynb
@@ -0,0 +1,655 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import xarray as xr\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from scipy import stats\n",
+ "import cycler\n",
+ "import glob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_experiment(expname): #loads the dataset of a simulation\n",
+ " fname = expname +\"_atm_2d_ml.ym.gm.nc\" #filename of global yearly mean\n",
+ " dpath = \"/work/bb1092/pp_JH/\" +expname +\"/\" #simulation path\n",
+ " DS = xr.open_dataset(dpath +fname, decode_times=False) #loading of dataset\n",
+ " print(dpath +fname)\n",
+ " DA = get_var(DS,'sic',False)\n",
+ " icelat = np.squeeze(np.arcsin(1-DA) * (180./np.pi) )\n",
+ " time=DA.time\n",
+ " \n",
+ " return time, icelat # returns the name of the experiment & the actual dataset & icelat array\n",
+ "\n",
+ "\n",
+ "def load_experiment2(expname): #loads the dataset of a simulation\n",
+ " fname = expname +\"_atm_2d_ml*.mm.gm.nc\" #filename of global yearly mean\n",
+ " dpath = \"/work/bb1092/pp_icon_aes/MLO/AQUA/\" +expname +\"/\" #simulation path\n",
+ " fpath = glob.glob(dpath +fname)[0]\n",
+ " DS = xr.open_dataset(fpath, decode_times=True) #loading of dataset\n",
+ " print(fpath)\n",
+ " DA = DS[\"sic\"].groupby('time.year').mean('time')\n",
+ " icelat = np.squeeze(np.arcsin(1-DA) * (180./np.pi) )\n",
+ " time=DA.year\n",
+ " return time, icelat # returns the name of the experiment & the actual dataset & icelat array\n",
+ "\n",
+ "\n",
+ "def get_var(dataset, varname, offsettime): #gets the dataarray with one specific variable\n",
+ " da=getattr(dataset,varname) #read dataarray\n",
+ " da.squeeze() #squeeze dataarray (time is the only dimension)\n",
+ " if offsettime:\n",
+ " da=da.assign_coords(time=((da.time-da.time[0])/360)) #change time units from days to years & move the origin to 0\n",
+ " else:\n",
+ " da=da.assign_coords(time=((da.time)/360)) #change time units from days to years \n",
+ " return da\n",
+ "\n",
+ "\n",
+ "def plot_sim_overview(timearray, icelatarray, expname, linearray):\n",
+ " \n",
+ " # get initial ice-edge latitude from filename\n",
+ " startlat=\"90°\"\n",
+ " \n",
+ " splitstr=expname.split('sic')[0][-2:] \n",
+ " if splitstr== \"77\":\n",
+ " startlat=\"14°\"\n",
+ " elif splitstr== \"74\":\n",
+ " startlat=\"15°\"\n",
+ " elif splitstr== \"71\":\n",
+ " startlat=\"17°\"\n",
+ " elif splitstr== \"50\":\n",
+ " startlat=\"39°\"\n",
+ " elif splitstr== \"37\":\n",
+ " startlat=\"39°\"\n",
+ " else:\n",
+ " try: splitstr_Jor=expname.split('_')[3]\n",
+ " except: splitstr_Jor=\"\"\n",
+ " if splitstr_Jor==\"Jor\":\n",
+ " startlat=\"30°\"\n",
+ " elif splitstr_Jor==\"Jor2\":\n",
+ " startlat=\"14°\"\n",
+ " elif splitstr_Jor==\"Jor4\":\n",
+ " startlat=\"7°\"\n",
+ " \n",
+ " # get co2 content and corresponding line color\n",
+ " co2 = expname.split('_')[2]\n",
+ " \n",
+ "\n",
+ " if co2 == \"1438ppmv\":\n",
+ " lc = \"C0\"\n",
+ " elif co2 == \"1500ppmv\":\n",
+ " lc = \"black\"\n",
+ " elif co2 == \"1594ppmv\":\n",
+ " lc = \"C1\"\n",
+ " elif co2 == \"1688ppmv\":\n",
+ " lc = \"C0\"\n",
+ " elif co2 == \"1875ppmv\":\n",
+ " lc = \"C2\"\n",
+ " elif co2 == \"2000ppmv\":\n",
+ " lc = \"C3\"\n",
+ " elif co2 == \"2250ppmv\":\n",
+ " lc = \"C4\"\n",
+ " elif co2 == \"2500ppmv\":\n",
+ " lc = \"C4\"\n",
+ " elif co2 == \"2437ppmv\":\n",
+ " lc = \"C3\"\n",
+ " elif co2 == \"2625ppmv\":\n",
+ " lc = \"C6\"\n",
+ " elif co2 == \"3000ppmv\":\n",
+ " lc = \"C5\"\n",
+ " elif co2 == \"3750ppmv\":\n",
+ " lc = \"C6\"\n",
+ " elif co2 == \"4063ppmv\":\n",
+ " lc = \"C7\"\n",
+ " elif co2 == \"4219ppmv\":\n",
+ " lc = \"C8\"\n",
+ " elif co2 == \"4375ppmv\":\n",
+ " lc = \"C8\"\n",
+ " elif co2 == \"5000ppmv\":\n",
+ " lc = \"C9\"\n",
+ " else:\n",
+ " lc = \"black\" \n",
+ " \n",
+ " n = 16\n",
+ " color = plt.cm.tab20(np.linspace(0, 1,n))\n",
+ " \n",
+ " if co2 == \"1438ppmv\":\n",
+ " lc = color[0]\n",
+ " elif co2 == \"1500ppmv\":\n",
+ " lc = color[1]\n",
+ " elif co2 == \"1594ppmv\":\n",
+ " lc = color[2]\n",
+ " elif co2 == \"1688ppmv\":\n",
+ " lc = color[3]\n",
+ " elif co2 == \"1875ppmv\":\n",
+ " lc = color[4]\n",
+ " elif co2 == \"2000ppmv\":\n",
+ " lc = color[5]\n",
+ " elif co2 == \"2250ppmv\":\n",
+ " lc = color[6]\n",
+ " elif co2 == \"2500ppmv\":\n",
+ " lc = color[7]\n",
+ " elif co2 == \"2437ppmv\":\n",
+ " lc = color[8]\n",
+ " elif co2 == \"2625ppmv\":\n",
+ " lc = color[9]\n",
+ " elif co2 == \"3000ppmv\":\n",
+ " lc = color[10]\n",
+ " elif co2 == \"3750ppmv\":\n",
+ " lc = color[11]\n",
+ " elif co2 == \"4063ppmv\":\n",
+ " lc = color[12]\n",
+ " elif co2 == \"4219ppmv\":\n",
+ " lc = color[13]\n",
+ " elif co2 == \"4375ppmv\":\n",
+ " lc = color[14]\n",
+ " elif co2 == \"5000ppmv\":\n",
+ " lc = color[15]\n",
+ " else:\n",
+ " lc = \"white\" \n",
+ " print(co2)\n",
+ " \n",
+ " for checkline in linearray:\n",
+ " if checkline._label==co2:\n",
+ " print(\"label removed\")\n",
+ " co2=None\n",
+ " break\n",
+ " \n",
+ " if co2!=None: line, = plt.plot(timearray, icelatarray, color=lc, label=co2)\n",
+ " else: line, = plt.plot(timearray, icelatarray, color=lc)\n",
+ " \n",
+ " return line"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_1500ppmv/mlo_aqua_1500ppmv_atm_2d_ml_0001to0150.mm.gm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "expname=\"mlo_aqua_1500ppmv\"\n",
+ "\n",
+ "\n",
+ "fname = expname +\"_atm_2d_ml*.mm.gm.nc\" #filename of global yearly mean\n",
+ "dpath = \"/work/bb1092/pp_icon_aes/MLO/AQUA/\" +expname +\"/\" #simulation path\n",
+ "fpath = glob.glob(dpath +fname)[0]\n",
+ "DS = xr.open_dataset(fpath, decode_times=True) #loading of dataset\n",
+ "print(fpath)\n",
+ "DA = DS[\"sic\"].groupby('time.year').mean('time')\n",
+ "icelat = np.squeeze(np.arcsin(1-DA) * (180./np.pi) )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.DataArray 'year' (year: 150)>\n",
+ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,\n",
+ " 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
+ " 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,\n",
+ " 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
+ " 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,\n",
+ " 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+ " 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,\n",
+ " 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n",
+ " 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
+ " 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,\n",
+ " 141, 142, 143, 144, 145, 146, 147, 148, 149, 150])\n",
+ "Coordinates:\n",
+ " * year (year) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150</pre>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray 'year' (year: 150)>\n",
+ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,\n",
+ " 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
+ " 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,\n",
+ " 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
+ " 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,\n",
+ " 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+ " 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,\n",
+ " 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n",
+ " 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
+ " 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,\n",
+ " 141, 142, 143, 144, 145, 146, 147, 148, 149, 150])\n",
+ "Coordinates:\n",
+ " * year (year) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "DA.year"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Semtner experiments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/mlo_aqua_1500ppmv_hice_unlim/mlo_aqua_1500ppmv_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1594ppmv_hice_unlim/mlo_aqua_1594ppmv_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1688ppmv_hice_unlim/mlo_aqua_1688ppmv_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1875ppmv_hice_unlim/mlo_aqua_1875ppmv_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2250ppmv_hice_unlim/mlo_aqua_2250ppmv_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv/mlo_aqua_3000ppmv_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_77sic_hice_unlim/mlo_aqua_3000ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3750ppmv_77sic_hice_unlim/mlo_aqua_3750ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_4063ppmv_77sic_hice_unlim/mlo_aqua_4063ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_4375ppmv_77sic_hice_unlim/mlo_aqua_4375ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv/mlo_aqua_5000ppmv_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv_77sic_hice_unlim/mlo_aqua_5000ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "nsim = 12\n",
+ "\n",
+ "timearray=np.zeros(nsim,dtype=object)\n",
+ "icelatarray=np.zeros(nsim,dtype=object)\n",
+ "exparray=np.empty(nsim, dtype=object)\n",
+ "\n",
+ "exparray[0]=\"mlo_aqua_1500ppmv_hice_unlim\"\n",
+ "exparray[1]=\"mlo_aqua_1594ppmv_hice_unlim\"\n",
+ "exparray[2]=\"mlo_aqua_1688ppmv_hice_unlim\"\n",
+ "exparray[3]=\"mlo_aqua_1875ppmv_hice_unlim\"\n",
+ "exparray[4]=\"mlo_aqua_2250ppmv_hice_unlim\"\n",
+ "exparray[5]=\"mlo_aqua_3000ppmv\"\n",
+ "exparray[6]=\"mlo_aqua_3000ppmv_77sic_hice_unlim\"\n",
+ "exparray[7]=\"mlo_aqua_3750ppmv_77sic_hice_unlim\"\n",
+ "exparray[8]=\"mlo_aqua_4063ppmv_77sic_hice_unlim\"\n",
+ "exparray[9]=\"mlo_aqua_4375ppmv_77sic_hice_unlim\"\n",
+ "exparray[10]=\"mlo_aqua_5000ppmv\"\n",
+ "exparray[11]=\"mlo_aqua_5000ppmv_77sic_hice_unlim\"\n",
+ "\n",
+ "\n",
+ "for i in range(nsim):\n",
+ " timearray[i], icelatarray[i] = load_experiment(exparray[i])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1500ppmv\n",
+ "1594ppmv\n",
+ "1688ppmv\n",
+ "1875ppmv\n",
+ "2250ppmv\n",
+ "3000ppmv\n",
+ "3000ppmv\n",
+ "label removed\n",
+ "3750ppmv\n",
+ "4063ppmv\n",
+ "4375ppmv\n",
+ "5000ppmv\n",
+ "5000ppmv\n",
+ "label removed\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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z8xV6FxY7najWww8/zPz583n66af5/Oc/z+zZs3nttdeoq6vjs5/9LJDWbbnmmmuGthMGyMkiWGvXrj1FPKs3Qa3e2kaieNZQcKLYWG8iYseoqqqisLAQj8cDwJ49e1iyZAnf/OY3MzNQR9q9dUHUwUsp2R+IUOKyk23vW6RIcZ559ZvQuPPcjlE0C1Z8f8Cb9yY2Nm3aNF588UW++tWv8uabb2aExX784x/z/e9/nw0bNmT2701Y7HSiWnfeeSerV6+mpKSE3bt3YxgGX/7yl3nllVdobW0lGo3ywQcfDEgvJLm7HrMrdmZ9cxKa14H10pI+tzlRBKu4uLiH4NquXbvQdf0UQa3e2mD4xLMePVBHZSh6Ll3FTI+T70wZ0+c2J4uNAb2KiAG89NJL3HbbbZnPBw4cIDs7my9/+cv8+c9/5pZbbhm2e+t0jOgI3pSSlliChmiCcMrIrJmquLjpTWxszpw5zJw5k2uuuYZgMEhhYSEHDhzA7/eTl5fXY//ehMVOJ6p1TDTrkksuyQiPHXt4LF++nDVr1vDiiy+ek+LfYHOiCJbFYhmQCNvphNlGmnjWYHOy2Bj0LiIGZJz4MXJychBCsHLlSiorK4GRd2+N2AheSsnGxg4Oh9IRj0CtdToiOYPIe7DoTWwMYPXq1axevZpvfetbrFixgp07d7J582aWL1/Ojh07+PKXv8wvf/nLXoXFTieqdSwNeDqxsYceeoiGhgZmzZrVr939Rd6DwckiWEII1q1b10M8y2Kx8Itf/KLftkgkMmziWf1F3oPFyWJjx/R6oKeIWGNjIzabLRPRh8NhHA4Huq7zzjvvZP7+w3VvnY4R6+AbowkOh2II0gX0RU4bjnNQVVNcuAxEbMw0Ta699lp0XWfZsmUZAbLbb78dSOc8n3zySaB3YbHTiWr1xbhx46iqquLaa68dgqs+O9asWdNDBOupp57KVMecKJ7lcDj6bduwYcPIE88aZE4WGxs/fnxGGviYiBjAyy+/3EMS+sCBA9x999243W4mTpzIt7/9bWAE3lunE6kZjteJYmOv17XKZw81yEgyJTc2tMtgQokbKQaH3gS0lNjYqQyzeNYFyTDeWyNfbEwCKVPSFk9QF4lzWY4Xp0VnSVF2v/sqFAqFoidDIjZ2tkyaNVv++vUNVIWi2DXBHeMLsesjehxYoVAoRgK91oyPKO9pAFXdpVHXFOco565QKBTnwIj0oMtLcyl2qYoZhUKhOBdGpIMvdCplSIVCoThXRqSD15QEgeIEThYbA/jd737HsmXLqKio4OjRo0QiEW666SYqKiq49dZbicfjvbaNdk4Wy3r11VcHLJT1zDPPcOWVV3LzzTdnJj2NdnoTrvvBD37A4sWLWbVqFclk8ozaRhpD4uCFEFcLIe4XQmwQQnxVCPGwEOLfRT/iMc6EwdKWrqEwSXEBc7LY2NGjR3nrrbdYt24dGzZsoLS0lDVr1rBw4UI2bNjAggULWLNmTa9to52TxbLy8vIGJJSVTCZ58skn2bhxI3/7t3/LL37xi+G8jPPKicJ1LS0trF+/nrfffpvy8nL+9Kc/DbhtJDJUcsFvAf8B7AEmSym/C1QCs/vaT0QNigMJZNLoazPFRcbJYmOvvfYahmGwbNky7rvvPgzDYNKkSZkIvbOzk9zc3F7bRruA1unEsvoTytq/f39GguCY2BiMPPGsoeBE4boPP/yQiooK4Ljo2kDbTncf9daHV1xxBffddx+zZ8/mueee4/bbb6e8vJzt27fzzjvv8OCDDwLpWbArV64862sbyjr4lcArwA0ntJ1Sk3niik4Ti6ahAWZzEL1U1b5fCDzx4RPsbd97Tse4JOcSHlzw4IC3b2pqIpFIsG7dOh588EFefvllli9fzgcffMCMGTMoKCjgiSeeIBaLndK2cePGYRPQevXVV2lsbDyXrqKoqIgVK1b0uU1vYln9CWXl5ub2ECU7pokyXOJZH7QEaI+fW9ojx25lYb6/z21OFq47pmN0Yj90dnae0je9tQG93ke99WFbWxuPPPIIhmEwd+5cqqur2bp1K7/61a/46U9/ykMPPQSk/5YnzqA9U4YyB38jsAZ4QwjxEDAD2HHyRlLKp6SU86WU85MpScQ0SB1qxmgKMpJq9BUjB7/fn5lOfu2117Jnzx5++9vfcuONN7Jr1y5uuukm/vCHP/TaBqNfQKs3saz+hLJOJzY20sSzBpuThesmT558TuJsp7uPTu7D/Pz8TB9NmjQpc591dHQghKC8vJyPP/74nB38kEXwUsp7ut/+tfvVP5pkd0eI+ZpOcmsN1vIxKpIf4ZxJ5D1YXHnllTz99NMAbNu2jQkTJtDZ2ZlZdCEvL49AIICu66e0AcMmoNVf5D0YnCw25nQ6BySUNXXq1EwfrF27NjPeMVziWf1F3oNFb8J1zz77LN/4xjcy/XD55Zfzs5/9rN826P0+6q0P+7u37rzzTn7zm99gGMY5LSYyoqQKhFWyc0+YK//3HBI76kjuqCO5txHhtCKsOpbpxWhq/dWLjpPFxr73ve/hdDqpqKggLy+PBx54gEgkwqc//Wl+//vfY7Vaef7559E07ZS2HTt2jGoBrZPFxm644QaefvrpfoWydF3nH//xH1myZAnZ2dk8++yzwAgUzxpkNm3axKOPPtpDuG7p0qUsXryYsrIy7r//fmw224Da6uvre72PeuvD/liyZAmrVq3iscceO7cLPJ1IzXC8xo2fIv/zS+tkOBCTZjIlY+v3yOhfd8jo+j0y+lqljG3aL03THFyJHsVFhRLQOjOUMNvAOd19NCLFxoQQ/9bHc6FDSvnjc3u0nIpmTQ8J1BxuZPrscVgvn5AecB2fh1nfSXJHHYkPqrBMKkDPVwttKxQKRV+cVmxMCPEx8DV6F7H5vpTyisE2ZtqMWfJ/Lfkpk29yc+MnFvb4TkpJ4oMqZEcEAC3Pg16SBQ4rWrYLoZ06XixTBjKeQlh1hG1EZaMUCoViMOl1jlFfXu9OKeWhXo8kxOcHxaSTjdGt4E3SXBfo7ZzYLp8AiRRGY4BUVQtmayj9ncuGPjYHNIGMpzCbgsjwCbMWdYE+JgcZS2KZmI+W5cp8ZbaHSdW0gQDLlEKEzYKwntnCIqnadszWENZLijCagiBAy3Kj+Z3IpIEMxxF+p1okXKFQnFf6cvDVQoi5wA4pZerEL07n+M8VXQM9V9JVZ2JKE030jMqFroHThmVCfjpt0x4mtaseGY6T2td7fbFlaiFGUxCjpg2ARFsI68xStAJfuhzzUEtm20RDAKw6emkWwm5FL81CGjI9yCsERmsIGYigT8hDxlKYbSHMxkDmQRNv7PlgEn4nMnB84WCR7cJaPhbNpbR2FArF0NOXg/8D6Tr2B4FPnw9jdE1gK3YTrU5S1VjN5OKJp91WCIGe60FbPAXZEcZoDCD8LoRVR8v3QjwFGmlHPT4v7WjtFpI7aklur0W47chQHC3fg3VOGTKSSP8qaAikHwaSzENDeOxoeR6M6vRDwqjvREYTYBxPb1nLxyDjKbRsF9itGNWtmG0h0ATYLBBLIjsiGFUtaDNLh7YjFQqFgr4dvE46r3Pekte6JvAUlRKlmp3vVzP5ttM7+GMITSByPWi5np5fOI9P2Ra6hshxA2CbN57E+4eQoTh6WQ7WGWlnK3xObHPKkNNTYNUxjrRhNAXRC3ykqlsxqtvQ8r1o2W5S+xtB19Dy3Ai/Cz3fg5bt7nF6rXuB5WNjHGZDAOGyIbyqzPNMqa+v5+abb2b37t2EQiESiQSf/OQnCYfD+P1+XnjhBdavX8/3v59eAHzfvn38/Oc/Z+XKlfj9/szEppdeeumcaooViguNvgZZraS1Y3ZIKRPnw5j58+fLx3/5KlWvbyZ1xM6dX7+cwgm+QT+PjCcxQ3G0HPeA8uJSSoilEN0PDRlPgs2icurniVgsRjQa5bbbbmPt2rW88sorVFZWsnr1ar773e8yc+bMHnXeCxcuZN26dXg8HhYvXszbb789jNYrFOeFM17RabyUcktvzl0IMWXQzDqJbI8Nz7UWcCf4839to7MpMujnEHYreq5nwA5aCJFx7sf2V879/HGy2FhvImLH6E9UCy4OAS2FAvpOv7wghPh6L+0C+A5w5VAYlOu1YuuYgPXTr5P6wyX8+T+28cl/vRyH29r/zorzzjttb9KWaD6nY+TaCrgqd+AzHKdMmXKKiNgx+hPVuuWWW4ZNQEuhON/0FcH/CVjSy2sx8PxQGZTns2ExfIwrmAZ37KOrPc5/P7GFd186SCR4XjJFihHO6UTEoH9RLRj9AloKxTFOG8FLKb99Pg05hseh47Rp2COTEaUfU/qZGMYHPravrWX/h01cftN4pi0swmI7s1p1xdBwJpH3YCGl7FVEbCCiWjB8AloKxflmxC3ZJ4SgJMdBoMPJHN8VNJZVMvVLgjv/dT5Or5UNz+zjhe9tZueGOqJdKqK/GEgmk1x33XUZsbF58+bxwgsvUFFRwTPPPMOqVasAePnll08R1br88stZsmQJtbW1mSj7mPjTJz7xiYxY1jEBraVLl2YWW+iPYwJaV1wx6JO6FYpB4bRVNOd0UCEmAF8AQkALkAe4pZR96dswf/58uWXLFjrDSdbvbGf6GDfV1jeojVaxsngVebZCairbeO3pSlIJE6fXyuR5hTg8Vlw+G0UT/eSN8fR1CoWi18oaVW2juMA5Y6mC9F5C3AB8AygE5gA/kVLe189u9wBtgA2YI6V8QAixWgiRJaXsPOn4mRWdysrKAMhyWynMsnGoMULF7OW81PgbNra9zsriVYyflcedD85n16Z6juxuY+eGup4XZNUomuRn2sIijJRJ6dRssgpdhDriCA3cfnt/l6xQKBSjgn4jeCHE28A1wOtSymuEEG9KKftMvAohfgI8DYwBnpRSThBCPAr8x8kO/kSORfAAbV0JNu7qYM4EL9JTxxstLzPTO++UnG84EEfTBbFQkq1rakjGDeoPdhLtOr7cV9mMHBoOBTANSdmlOcTCSZJxg1kVY5i2sAjdohELJ9F0Qc3ONvZ90MjSz07Fl+sEoLWui3gkRcmULFUeqVAoRiK9OqaBOPi3SDv4tcAy4E0pZZ9Fv0KIOaRTNFZgM+kUjUtK+Z2+9jvRwUspWbu9DaddZ/H0bN5pW0dl11ZKHGPJtRUw23c5bkvvksGphEF7QxiLTefP/7GNUHscu8tC3hgPHU0RnB4bpinpaAhjc+hMvCyf/ZubMFPH+0LTBNnFbqx2jcaq9NJcLr+NvFIPM5aWMm5WLro+4oYwFArFxclZO/ibgH8GppNeU/XHUspXB908ejp4gF1HujhQH2HFvHw0PcVHne+xJ7SdhBnHZ8ni9uK/xa73PfW/qz1GKmGQXdRTSiDalWDzX6tpPxqitS6EL89J/jgvNoeFSxYVcWBLE83VQZoOB5mxtJRAc5T2hjDRUIJ4OMWYS7K56s7JWKw6WYUuErEUmiYGvbpHmpKW2i4aqwKUTMki2pWkYLwPu9OCkTTRreoho1AoztLBA4h0XiIPaJVDMSrbzckOviOUZENlO8XZduZN9mHVNaSUNMaP8pfG58mzFVLoKMFr8eO35FDiGEPMjBFMdVJkLz1FjfJ0SCkHnHpJJgz2vtvA2y8cwDTTXZFb6qG9IYymCcbNzKVwoo9pC4o4srudqQsL+430m2vSD5KZV5dm7DAMk8aDAT5ee4SanW09tnf5bLiz7LTUdjHnujKuWDkRTddIRFM0Hg5gd1npaAgTj6Sw2DRmLDkubhbqiNFUHSR/rJfKt44Sj6aYuqCQ/DIvFpuOpqkUlEJxAXJmDl4I8Rug1y+llHcPnl3HOdnBSyl5Y1sb4biB06Zx6VgPZfnpvPju4DY2tb/RY3+P7iNmRknJJC7dzRjnBBbnLMOQBp3JNgrtpac48jNx7ifSWtdF/YEAbXVddDZHKZroJ9AcoW5fB/HIcXVlb46D8bNyQQguXVxCoCXC+Jl5mci7uSbIHx9PX7PFqjFxbj6xUIrGqgCJaArNIphzXRkTZufRVBVESsnW12qIdiXx5jroaosBkF3kwjQlgeboKbYWjPOSXeRmfHkeb/5+D8lYus5baAKLVSMZT3/+9Ee9CsUAACAASURBVCMLVBWSQnFhcsYOflz32/8NvA98BFwGXCWl/F9DYeHJDh4gHEvR1JmgpiVKZzjFomlZFGenK2HaEi1oCCzCSkuiiY8D7+PRvZQ4yzgY3kNzvAGfJYuYESEhExTaS8i1FSClSZFjDEciVVRHDlDkGMPS3BvYG9pJINnB3KxF5NoKzuoapJS89lQlhz5uYdzMXBBQu6e9R34/t9TD5HkFWO0629YewTQl/nwnkUCCQEsUTRNMu6KI8TPzGDM9G5ujZ7FTIpYiFko7+ENbW2iuDtLeGCbUEefSq4oRQpBf5sXptbHl/x1m7/vHtfJzStwUTvDhybIz/aoSHB4rB7c0Ew0luGRRMS6f0qpXKC5Azn6QVUp59ek+Dya9OfhjmKbkzZ1tSAnXzc4dUNS9t2sn77avI89WSI4tn6OxGgLJDnRhISWT2DUHebYCGmJ1mJiZ/Ty6jztL78KunV1JpZSSVNLE2p2PTyUMgq0xDu9oIZU02ftuA6GOtFiW02vlE/fNIb8sPWBcta2FnBI3WQWu0x7/bGg6HKT+QCczK0ozdikUilHDWTv4fwPmkx5gLQc+klI+Oujm0beDBzjaFuPDAwEWTPFTmjswXfUTUzBSSlIyhUTSlmgmx5aPXbPTGDtKTeQgE93TMDF5ueFZBIICewnjXBOZ4b0Mqza4kW0qYdB0OEjuGI8SUlMoFOfKOQ2yFgHjgBopZe9r4w0C/Tn4Y6WThilZOiMHl31oItGq8H4OhfdyNFZD3Izh0j0syr6aHFveGadumuMNGNKg2DFmSGxVKBQKziGCf4yTBlv7kxw4W/pz8ADtoSTv7O5AIinJcVCcbackxz4kE5CklNRED7Gp7XUiRhiALGsuc/2LsGsOUjJJtjUXITSyrDmY0qQ+VktHshWbZieY7GRr4D0APlP6D/it2X2dbkD29HWdUkoMaWDRLD3a2pMtWIQVu+ZAIIiZUSJGmPrYEQAmuS8hy6pWOlIoLmDO2sEfy7cLYBYwXUr5T4NrW5qBOHiAYDTFB/s6iSdNkoZkcrGLmWUDX8DjTEmZSdoSLTTEazkY3tur/vk0zyzaEk20nvTdGMd46mLVjHGM44aClSRlkvZEC/WxWuJmjOmecvLshSTb9vOxrOdwtIpEDMbYJxAzYxR68xCayc7AxySJcXX+cia4JqMLCztat3Gwaz/5rnxKPMXsCm6jIV7LVPcMpnln0RpvYlfXxwRTPScPCwTypAIpj+7lpqJPKUevUFyYnH2KpscOQrwupbxhUEw6iYE6+GNIKdl2uIvq5ii6JhAivca1EAK/y8LMMg/+Qc5vH4vqo0YYDZ3G+FGiRoSa6EFcupup7hkkZJxgMoBNs1GRt4KD4b1sbHut1+NpaNg1OxEjghACwzTRtXS9fzgRwWNPT9BKpBLomo6u6ZimSSKZwmaz9Kj1lyYkzAR2y/HxgiJ7KVM9M0jJFHu7dtCebMWhOZnsnk65bz66ZmFb4ANiRpT5WVfhs2YNan8pFIrzwllH8L/neIqmCKiVUv794NqW5kwdPKQdbkNHnLrWGIFICr/bgq4JmjoTpAyTiUUurLqGz2WhKMs2ZFF+R6INt8WD7TSVN/XRIxyJHiaQ6sAmbCzIXkLMjLK3aycpmWJf7XsYlWvIaz7C1IKpHC4cQ8ifx9ZwM576w9x49DB/8WcTK5mAM7sIzeGmKNjBzIjgDyLM4Xg7gVgXc/JncyhQxYySyRgpSY5ehEBwy6RbmJYzDakZeC0+dHHe1lJXKBRDz1k7+PmkJX8lEOje57SCYefC2Tj409EVTbFhZzsp8/j1leU7mDHWg2Oklgm2HYJALUw4oQp10/+F9Y+DMxs0HXKnQGcNWOyg26C9ipARZ3VhEf786axe+d8cCdfx7J5n2dGyg8q2yh6nsGpWsu3ZPP+J58lz5p2zycfGBYKJID/a8iMWFS9i+YTl53xchUJxRpy1g++hHimEeEFK+alBNg4YXAcPkEiZSAmGKTlQH+ZwUxRNg+ljPUwpdvd/gJGCaaSd+zGMVPqzEJCMQu2H8P7PYf+rMP0WmLwMgg3ID3/B9gVf4Kg3n0O1b/P70AFiZnqRFLfVzcKihVxZciUN4QZaoi2M941n2bhlTPRPzJwqaSRZV7uO16tfp6qzivlF8yl0FRJMBNlYt5G2WBu5jlyqAlWZff7u0r9DFzo3TbyJt4++TSQVodhdjEBwSc4lXJp7qVLlVCgGlzOeyXobcDtwPfB6d7MFyJFSDkmINtgO/mRCsRSVNSEaOuLk+2zEkgZep4VJRS7yLvQZnFKmo/0N3wezWyrBNwaCx/Xy6y06eEs5bIR50ePkDXs6f68j0IRGUhq4LC4+c8ln0IRGQ6ie94+8SZsRw6pZmV84n82NH5KSaWmD8b7xzMibQTAeJGWmWDV9Fc/ufZZ369/tYdqJg7puq5uNn96ITb/A+1uhGFmcsYP3A1mkF+P4RfcBkkCjlNLsdafj+64kLTF8GDhAeqEQP/BgX2JlQ+3gIZ1S2FHTRUN7HJ/LQltXkpQhmVjoJM9nw+ey4HVewPnpaCc0bIPAUZj9Gdj5x3SUP20FfPAkvPdfMPEa0K28174bV7idWeFODKDWauGxggK22dLXn2tK5kajLIlEubLkSgqn30bHuz+GtkPYC2fh1HRE2ZUw7ipo2Qs5E5GFMznUeYD69gPs12HFtDsodhezs3UnUkqSqRiXlyzq3XYpYddLcHAdxAJg90JjZfo6yj8N7rz0r5ZjhFog1ARFM089jvqFoLi4OGMHv0JK+aoQ4u85tQ7+132eSYjrgWtJL9mX172i098BO6SU207a9sQVnebV1NQM8HoGh6Rhsqc2zKHGSKbN69SxaILiHAdTS1yjK52Qiqfz9yfSXpV2qDXvYex6kXfbKimPx/G7CmDJ16HzCGx7FqLtkFUG+ZdARw04s6D2g9Ofy5EFFf8KpfNgy6/h6EfQug/GXA4lcyFrLEz/BBx4A4rK4f2fwe4/gWYBTxEkQuDwp8ccID0OMXZRer/qt6F5d7p93FXph4DVBXlT0ufyj4Gl/zI0fahQjDzO2MHfKKV8TQjxhZO+klLK3w3ojELcDjwqpbys28Fvl1JuP9325yOCPx3tXQkME/YdDdMZTpIyJVLC2DwH8yb5kBLq2mJUN0fJ9lhx2DTyvDay3BaShsRmGSW67FJCw3Zo2gWTrwNvYbo9HoL6rWkHe0IZJnv+DPvXwA3/B3a/nH6ASDPtjN96Iv3wANL3X/e9ZvNCouvUc2sWuOYhuOr+42MOUsLht6B5LzRVwpH3oe1A2qm789PpqObdJ5ynm8UPwLLHVCSvuFg460HWH0gp/+WEz49IKf9PP/tUAAuBCcAm0jIHfuCbw52iGQimKREi7ez31IUpzLIRjhmEumV2jyEE2K0asYTJpCInPpcVn8tCdrfT1zWBfjHrq7cfho9+kx4Unnk75EyEYH3a+UdaIdQMB16HKTekfwlMvwWKy/s/7sm/QkwDqjako/3aD9MR/iU3K+euuJg44wh+PDAJ+L/A17ubLcC/9rdk39kyUhz8MaSU7K4NUdMSw2YRTCpykeu1dU+oEuyo6SIQTmLRBV1R45T9NQF+l4UsjxWnLf0gyPPZegilHev/gaSB4kkTXRNYdOW4FApFD3p1Cn2NJo4DFpMeaF3S3ZYEHh5cu0YuQghmlHmZUdb72q9XTMvK1IEnDZNEUlLfHqOtK0mO10ognCQUM6htjZEy0o68qilKdkMEv8tCNGHQFkyS7bEypcRFY0ccIQSluXY0IWgPJcnvHvht60rw3t5OLLrAZtGw6IIst4XJ3eWeQyW8plAoLlwGkqKxSimT58OYkRbBDybJlIlhSurb4xxqjBBNGOiawGHTCXavAKVrYPRSn+R3WQh0b3Ms0NeEwOiexCWAeZN9jM1zIqWkK2YQCCcpznaoaF+huDg46xz854EvAZeSnsnaIaWcN+jmMbodfF+0BBKE4wZj8xwEIymiCYNESuJ16lQ3RwnH0vX6M8s8GYcthKC+PUZ7V5KmzgShWAqnXSd80jjBuHwHM8q8ROIGPpeFxs44BT4b1u5BYVNKDENmPisUiguSs3bwHwJXAOuA64BfSinvGmzr4OJ18OdKNG7wcVWQpCFpDyWx6oJpY9xU1oR63d7j0Ckf78Vp03l/Xyfh7jVZ87xWFk3LUs5eobjwOOMc/DEiUkpDCJEESkhPWlKMIJx2nSunp7XmQ9EUFj2d+plc5KI1mKShI45hSho745Tm2KlpjvHu3rSckN2qUZJjp749TmtXkjd3tmGaMLEovWTgsaqgEavfo1AoTstAHPzjQggH8G3gP4GfDK1JinPBc8IsXCEE+X4b+f6esgDTx3o42BBBE4IxuXbcDguJlElzZ4KtVQEME3bXHo/+BTCuwMnRthgpU+Jx6MyZ4CPXa0UIkSkrNWVa5C0YSRFPmkwodGLR09LHkvS4gUKhOH+cjR78d6WUQ1JJo1I0w0/KkCRSJm1dCXK96fr/qqYI9e3pRcJtlnRtfzRhdmvvpx27VU8P+p44SKwJcNh0koaJaUJpjh2HTWN8gRO34wKWg1AoRh5nnaI5mcvP0RDFCMaiCyy6jsvuBNLll3k+K/XtcfxuC06bjmlKDjRECEVTHG2PU5hlw6IJzO6Zv16XhUTSpLEjTjRhousCw5DUtsaQwOGmKHk+G9keCxMKXVh1QUswQbbbqvL/CsUgosIoRb+ka/OPT87SNcGlYz0AhGMGLrt26kQtJ6codM6f7CMcN9hdGyIQTtHQEedQYxSPQ6etK4ndqlGUZcNm0SjKtuNx6ITjBjke6+jSA1IozhOndfBCiE2cJDJG+mdA7pBapLigcDsGPvgqhMDjsLBgSnpZwM5wkq2HgrR1JXHaNLxOC7WtMUwJBxqOi7+NL3Dic1kozrarCV0KxRlwxjn4oUTl4C8+UoYkEEn2iNKjcYNgNEVta4z69lgmr2+zCK6Ylk2Od3DX2VUoRgGDloNXKAYNiy7I9fZM5TjtOk67TmGWHfATjRvEkiYf7O9k0+52LpvoY2yeQ6VtFIp+UCNaihGP066T7bFyzaxc7FaNjw4F2V8fHm6zFIoRT78OXgihCyE+I4S4r/v9gKpohBA3CSFeEUJ8QQjxdSHE6nM3V3ExY7dqLJudS77fxu7aMFsOBogn+1xcTKG4qBlIBP8H0oqSn5VSGsDj/e0ghLgMcABVwBwp5Q+727N62fYeIcQWIcSWlpaWMzJecfFh1TXmT/Jh1QW1rTHe29tBMqWcvELRGwNx8PlSyieB2BkcdwUwFriM49IGvY7mSimfklLOl1LOz8/PP4NTKC5WHDadZeW5zJvkozOc4r19nRlFToVCcZyBDLI2CyE+DTiFELcBDf3tIKX8HmQWDflYCPH17vbOszdVoTiO065Tlu9EE4KPDgVYt6ON2eO9GQ0dhUIxMAd/N/APwFZgDPCPAz24lPL+s7RLoRgQY/Ic5PttfHigk911IUpzHditqnZAoYCBpWgKgFeAJ4CXgWwhhPofpBgx2K0a5eO8pAzJR4cCmYVQFIqLnYFE8L8G8oFKYCbQDtiFEL/rzs0rFMOO322lfJyX7dVdbNrdwZwJXrLcakKU4uJmIJF4F3CZlHIVMJf0qk6Lgb8fSsMUijNlYpGLyyf7CUaSfHggwEiapa1QDAcDcfDFwLxuTfh5QIGU0gQife+mUJx/xuQ5mDvRTzhmZCSOFYqLlYGkaD4H/DMwgXRd++eFEDrpgVeFYsRRkmvHe1Sn8kiIfH9anVKhuBjp18FLKauAf+rlqwODb45Cce5oQnDZRB+bdnew+UCAKy7JUqtJKS5KThvaCCHWCyHe7J5l2iKEeKf7383n00CF4mzI9dqYM8FHcyDBx4eCpAyVj1dcfJzWwUspr5FSXgvsA8ZLKa8CxgMHz5NtCsU5Mb7AybRSN0daY7y9p4OUoSQNFBcXA0lOXkJaiwbAD0wdOnMUisHl0rEeFkzx0xFKsqO6a7jNUSjOKwMZZP0S8PNuobBO4CtDa5JCMbiU5jqY1JXkUGOEqaVuPGrBb8VFQl85eAuAlHKLlPIWKeVSKeUtwKHzZp1CMUhMKXGhCdhbp3TkFRcPfaVoXj/2Rgjx3ye0/3HozFEohganTWdKiZva1hgH1GIhiouEvn6rnlhXlnuadoXiguGSUjehWIrKIyGcNp0xeY7hNkmhGFL6cvBOIcQE0lG+48T3/R1UCDEDuBGYArwP5AFuKeW/nbvJCsXZoWmCeZP8xBIdfHQogBDp/LxCMVrpK0WzB1gNPALsPel9n0gpdwFNQBFqRSfFCELXBAunZuFzWfjwQIDq5uhwm6RQDBmnjeCllF888bMQolxKuWOgB5ZSPiOE6OS4pMFpV3QCngKYP3++mo2iGHLsVo2rZ+SwaXcHlUe6yPZY8LuU8qRi9HEmIh0/GeiGQojlQohvALcAf1IrOilGGul0jQ9dCN7fF1DruipGJWdSEFw/0A2llGuANWdujkJx/vA4LSyY6mfT7g42VLYzpcTNuHwHQunWKEYJA4rghRALgReFEJoQYtwQ26RQnDdyvTYWTPEjBHxcFeS9fZ3EkyqaV4wO+nXwQoj/BG4D/rVbB/7pIbdKoTiPlOQ4WFaey+zxXloCCdbvbKMlkFALhigueAYSwU+XUn4TODY7RB9CexSKYUEIwcQiF0tn5ADw9p4OPjoUVOu7jnJihklTNE5NKMqmxg4648nhNmlQGUgOPtSdokEIcRnpJfsUilFJtsfKstm5HKyPsPdomGAkxdg8BxOLXOiays1f6CQMk4hh0BRN0BpLUhuOEe1WGbVpgjKPgyz76KmoGoiD/3vgm6SX6Ps8cM+QWqRQDDNWXWP6WA9+t4XKmhCVR0LUtcW4bKJPLeR9ARE3TLqSKToTKYLJFK2xJPWReKZe265puK0683J9WDRBmdsx6h7iA3Hw2cC/SCmlSJcXTAZah9YshWL4KclxUJLj4EhLlI8OBdmws53ZE7yML3CqSpsRimFKJJItrV0cCEZInTCO4rHozMz2kG2z4LNZyLNbR/3fcSAO/kkp5TKAbif/JLBsaM1SKEYOZflOsj1WPjoUZNvhLurb48yd6MNpV8NRw4UhJfsDEVpiCdwWnahh0JlI0RZPImV6VmWe3cqMbDdWTWOMyz7qnXlvDMTBu4696Y7gXX1sq1CMSrxOC1fPyOZwU5TKI128t6+Tq6ZnY7eqBb2HGlNKWmIJ2uMpWmIJGqMJEqZJ0pQZ5UOHruG3WSh1ORBAmcfBFJ9yVQNx8L8TQqwFtgKXAb8bWpMUipHJsUobl13ng/2drNvRxoIpfvJ8tuE2bdQQThqYSASCzkSSmlCMI+EYse6BUIeuUeS0YdM0xnsclLjsABdldD4QxEBqfYUQ+cAEoEpKOWT59/nz58stW7YM1eEVikEjEEnywb4A4bhBrtfKgil+HDaVsjkbpJS0xpMc7oqyuzPcQ7TKIgRj3HbGeZwUOmy4LJpy5r3Ta6ecNoIXQnyHXgTChBBIKVcPomEKxQWH32WlYlYO1U1R9h4N89audi6b6KPAbx9u00Y0KdNkfzBKwjDpTCTpTKQwJQSSKQQwweuk2GlDkh4ULXTasYyyypbzSV8pmrXnzQqF4gLEZtGYWuom12dly4EAHx4IcF157kUfyccMk0PBCLXhGFk2K3HTpCOeJJhMO/NjUaNT18jprjmfke1mvMeJXVdjGoPJgFI05wuVolFcqAQjKdbvbCPLY2Xx9OxRV0/dF4aZHgRtiiWo6ooSSKSQgEvXiBgmbouOx6KT57CiCUGR00ae3YrDcnE/CAeZM0vRnNOZhFgCXAlcCrxGOn/vBx6UI+mJolAMEj6XhXmT/Ww+EODjqiDzJvlGRa7YlJL6SJyaUAyLJkiaElv3wyucMogZJoFEKjMbtNBhY1a2hyKnjWKXHVOiUizDyJA4eCnlJmCTEOJR4JNSytuEEH8HzAa2DcU5FYrhZkyug65oir11YfxuC1OK3cNt0hkRSxm0xZO0xJKkpCRpmlSH0hUsVk2QMiUOXSNqmAjSKRa3NR2ZT/K6cFo0Ch22Hg825duHlyFx8ABCiM8BVUDOCc29DdreQ7f8QVlZ2VCZo1CcFy4pdRMIp6isCRGNm8wa5xm2SD5pmoSSBpGUQcQwiaYMIqm0FouUEEikCKcMhAAp6THrUwBCQJnbwXiPk7FuB7pIF1mYUiJJKxVeqL9SdoeiJEzJHJ8LQ0oihol3FKaMhiQHL4T4JGkNmzVAB1BKOkXzzb5SNCoHrxgNmKZkZ00XVU1RLpvoY3yBc0jPV90VpS4SJ5xMkW23EkikaIwmejjsY9g0gdOiI6XEZ7XgserEDYldF3itFvw2C8XOdF2/BKza+Rv0lFJinJDSqY8lCBoGU1wO9O4HSdKUWLofNB3JFFuDEWZ70056e1eEqkicLsPAZ9Ep97oo9zrZHAiTMCWN8SQJKXmxsYOPuyIAjHPYSMr0dzM8Tp6dPZF82wWpN3T+cvBSyj8CfxyKYysUIx1NE5SP99IZSbH9cBCHVaMo+9zLJ0PJFIYEiSRmmERTJkcjcQ4EIwjAb7NQ3xnObD/Ok46+XRYNl67jsmhYBtlhxwyTveEYFgEzPE4OROJ4dI1iu5XD0QTbuyLcmOdHSsmOUJQD4Ribg2HKPS5ipokmBFYBvznaSnMiRdgwuSrLw0SXnf9ubCdqSty6RqndRksiSUfKoNBmwW+xUB2NkziLAHWa28EjE4sRQrA1GEZKcGRpdCRTg9o3IwFVRaNQDBHxpMnbezpIpkyum52HRe8/nREz0iWF+Q4rCVNiSklNKMahrihtvWiVW4RgnMfBVYVZ6N3pk0jKwG3RaU6kyLVasGiCsGGwLxSjxGGj0GZBCEFNNI4Axjps1MYS7AnHuC7Xx3udIQ5G4kQNk9sKs3Fqgjfbu8izWhjjsPH/NbTxQSA9IenjYIRktw/Jseq0Jw0A7Jog3q2l79I1YoZJX+tkzfG6mONz4dE1/tTcQV0syVyfi78ryWVnV5TGRBKPrlPmsLE1GMGmCcY7bcz3u3mrvYtxTjuzPE7ybBamuR10Jg3Wtwd5pbmT5fl+XJrGJR4HTk2j7KRxglFCrxekHLxCMYS0BhNs2t3BtFI3l4719LltfSTOhob2jGM8EY9Vp8Bpx2vVaYgnybbq6EIQB3aEosRNSUsyRandirPboX63qgEJFNosdCSNTLSbY9WZ4LTzUTCdpvDqGjFTkpSSLItOZ8rInNcmBHZN0GUcd88CKPc6CaYMbsjzs8Dv5nAkzmutQRZneyiwWzkSjZNvs1Jkt/J+Z4h8m4U5XhdT3A6KbFaaEkl8Fh0BtCRSTD5BDCxlSt4PhJjnc+NUdfEDRTl4hWI42HIwQF1rjCUzssn1HtetaYrGaYwmCCUNasIx4oaJTdfwOKzsD0XRhEACa9qDVEbi/Z7HrWuET3DEHl3j9sJsjkQTXOJxsMDvpjGeZEN7FwciMW4vzKbQZmV3OEbKlIxz2vgwEGaez8Wt+T4SpslzTQE6kgY3F2RhFYLD0TiX+93M8AztuILijFEOXqEYDpIpk/U72zGl5NryXGwWjeZonP9X1wZA3DQ5HE9SnUjyXih6Sl55jr2TFf4Q+0IRUpqbG7PhcDRFqPVV/LKZSRzETpxszzSSjqlY3TOoiZnM9TopLahAaDZaW9YSiR4mGqlBt7gxzQSpZJDc3KW4XBNJpYJIJNFoLaHQHoLB7RhGhClTHqYgfwUWixvTjNPaugEhdLzeGTgcxRkbTTNBPN6IlAaRSDUWi5dotIZw+CCB4Hby8q6htOQzJBKtGEaMeLyBZCqIJqzY7YWYZhyncxxO55jz+rcZRSgHr1AMF+2hJBt3teOy6yyc6ucPtU0kTcnvmhuYahygLF7NNL2OA2YeelJjqes9QqINH0G0TPZaQ0qJEOn/s0YqG493KUWF02ltayIefwfoJJFoPuHMGkJYkDKBptlwOMqIxf7/9u48Rs76vuP4+zv3zszuzuzJ7tprb4zPpLDGwWk4WnMVIpCCkKKakKpOG/UQrdSIKm2AKBFVVDVRK5JKSRUh9QxVVUWqKhpIISlnwMGYGDtg49ve+57ZnfuZ+faPZ2x827CzO+OZ70uyvDP7HL/fs57PPv49v2MI1RLBQAfZ3Mh5ZfX5YkQiHyOfnyGTOYbHEyQe/zS53AQLC++e3i4e+3VWrvxdFhbeZ3T0R2SyJy5Y96amVWQyx3E7Vl6qJR5aWq4HVZQSfn+cQKCDa665n7b4zWdtV4dt6ItlAW9MNY3MZNn5foKj/iniLXH2jz/DnflvoUUPqkEcx48qBIMpstkIQ0MbmZ3pw3ECRKIzpFMxVP309ATIZpVsNkgqlTrnLEo0muOGG26nq9uH37+LUmmOvr7P0xzdiIiXXG4S1QKhUC/5/DSp9BF8vmacQhK/P0Y0ug6AYjHN1NTPmEvsYm72FxRLGVavephweDWTU89z4sRTp88ai22lpeV6AoEO/L4WfL4WotEN+P2t+HwxkvPvMDnxE3y+ZgKBDry+KJHwx1B1mF94j1Cwh0RiNzMzr+HxhhDxUCjMkcmcpFCYwe+PUyrlUC0h4sPrDRMK9eLxBMllR/EH4hSLaSKRtXR33UssdiPz8/uIRNYSCvUu40+5aizgjam2l98d4peeEguFFL8x/Dir2r9A69rbaYm1ApTv0IVsNsuxY8cYGxsjFouxYcMGhoeH6enpIRx2F7IolUocO3aM48eP09vbSyqVYnh4mNnZWY4cOQK4d7rhcJjm5mYcx2FwcJCtW7cSCCx+DvtiMcPRo98lFruRjo7bF328CymVcoyP/w9zc29S0gJebxMgpNNHWVjYj8cToLVlMwUnQbGYIpncc94xwuEBurs/SzSyjqamfgKBjNYmUwAADHlJREFUDoLBziUpbxVZwBtTbcl0gX/e+WO6Qj3cr21oIgNNfnwr2iDgxdPShDSHkEX2Hpmbm2NycpKTJ0+ysLBAMpkkk8kwPDwMQE9PD+vXryedTuP3+4lEImQyGXp7e9mwYcNV2wRSKhU4euzvyecm6ei8k4mJH5NKHWZ+fu9Z2wUCXQSDXbS3b6Ol+RO0tG6m6KSYn99LV9e9V2P9LeCNqQVvHkwwPpfjnhs60ANjFIdnwTmjG2IkiH/LKjyRys4tr6ocOHCAkZERDh8+fDrsz7Vy5Uo2bdpEPB6nu7ubeDxOPp9namoKv99PsVgkGAwSj8cvuH82m8Xv9+P11s7Q/0IhQSZzgvmFd0kk3kbVIZM5SSKxG/e5gHBqJpVYbCtNoRWkUofwB9poi99MKNRHNLqBZHIPTnGBYjFFW/xmotGNtfLLwALemFowPZ/n5V/N0hMP8ql1re4iOoUiWihSmprHOTAGJUVamqBQxNMZxdvfXvHAn5ubIxBwB/3kcjnC4TBvv/02O3fuZGZm5vR27e3tJBIJHOfskZ49PT1s3ryZwcFBDhw4wMGDB4nFYrz++ut0dHTw0EMPEY1euu9/tRUKc6TTR5ieeQ2nMAfiYWrqBYrFDMFAF/nCNLnc2EX393iaiMdupDX2SULBHvL5KSYmnyOdPkJb26309W6npAXGx58hGl1Pc3QTkci1BIPdwAdNchVgAW9MrTg0mmLv8QV+bVWUa8+ZdVIzeZwjk5SmU0jIR2nafZAq4QCenlZ8/e1IaOnmS1FVkskks7OzjI6OcvDgQTo7O+np6aFQKOD3+xkbG+P48eOMjo6et39zczMLCws0NTWxevVqstksjuPg9XpPP1/YsGEDfX19qCqxWIxwOEwoFEJEauWOGHCvRTp9mHx+mvmFd2lpuY5QsBcRH1NTP2Vq+v9IJHZTKHzwCzEa3UhzdCOTUy/gOEkAvN4IxeKpB+JCc3QTqg6p9FFCoV4CgXYikWtZ0fcQhcIc0ehGMpljRCJr8XqvaMI6C3hjaoWqsvP9BKOzOe64rp2W8MWnhSql8zh7h9BcAU3lwe/F292CpyeGpy2MLOOEYGdSVQ4fPsz+/fsZGBhg/fr1TExM0N3dzdjYGK+++ipHjx4lm80SDodJp9N0dnbi8XgYHx8/61gigs/nw+/3E4/HaWtrY8uWLUQiEUZGRujv779ok1AtyOenyeencZwkra1bEBEcJ0UisQtFicc+TXL+HXLZETKZE0zPvIzXEyYY6mF+fh9FJ3XRbqZeb5SPb/o2nZ2/dakiWMAbU0tyhRLP7Z5kVVcTgwMtV7RPKZnBOTRBaXoBnBLSHMK3/hq8nc1LXNqPJp/PUywWaWpq4lTWiAjJZJLp6Wnm5+eZm5ujUCiQTqeZmpoCYGJigkwmc/o4Ho+HtWvXct9999HcXJt1XayZmdcoOAlQJZncQ7R5E9nsMPn8JH29DxKNrr/U7ssX8CKyDngU+C8gD2zmClZ0soA3jWb3kSQnJjLnTWNwOeqUKI0nKOwfg7yDb103vjVdS1jS5ZXL5XjvvfdwHIeuri727dvHW2+9hYiwdetWbrrppppv319my3sHLyLbgBjwm6r65fKKTu+o6kVXdLKAN42m4JR4Yc800SYvt25qu/wO59BSicKeIUpjCTzdLfgH+5E6XUZpenqal156ib1796KqRCIRYrEY27dvr9u7+g/hgj/05W68u+CKTiKyS0R2TU5OLnNxjKkuv8/Dmp4wU8kCC9kPPx+5eDz4P9GHty9OaTxJ/ueH0Hz9zWsObm+eBx54gIcffpht27YxMDDA8PAwTz311OmmHXO2pWqiuQZ4HGgCXsZWdDLmouYzDi/smWZwoJmB7vBHPo5zcgZn3zDe1R34N/Zcfoc6cOLECZ5++mm6u7vZsWNHTfXAWWbLuqLTGPAnS3FsY+pNNOQl5PcwlcwvKuB9K9soTS9QHJrBt6570aNhrwb9/f3cdtttPPvsswwNDbFy5cpqF6mm1P+/AGNqnIjQ3uxndmHxTSveFXFwSpQm5ytQsqvD4OAggUCA3bt3V7soNccC3pga0Brxk8oVKTiXnk73cjztUfB7KU00TsAHg0HWrFnDkSNHqKVu37XAAt6YGtBaHuiUSC/uLl5E8HREKU7NN1TYDQwMkEgkzppiwVjAG1MTTgV8cpEBD+Bpi0LOQTPnL9Jdr/r7+wEYGTl/AZNGZgFvTA0IBdyPYrawuCYaAE/EHTClqcuv41ovOjs78Xq9jI1dfGKwRmQBb0wNEBGCfk9FAl7Ks05qOr/oY10tvF4vXV1dFvDnsIA3pkaE/B5y+eLiDxT0gdfTUHfwAF1dXUxMTFx+wwZiAW9MjQgFKnQHL4KEAw11Bw/uNMWpVKqhHi5fjgW8MTWiUk00ABLyo7nGecgKEIlEKJVKZLPZahelZljAG1MjQn4vuUKpInegEvChufqck+ZiTi1Gnk6nq1yS2mEBb0yNCAU8qLrzxC9a0Af5YkM1V0Qi7spYqVTqMls2Dgt4Y2pEJOguUp3KLf5BqwR8oAqFCjy0vUpYwJ/PAt6YGhEJlQM+W4GAD7oDpxqpmcYC/nxLMpvkmUTkBuABIAx8TVXt6htzAeFg5QKeUwFfp3PDX8ipNvhkMlnlktSOJQ944EHgq8BNwF24y/gZY87h9QhNAQ8HRlIcGl3cg8KI43ATUPjFUTLnTBU+39VKz5b6m1bX5/OxYsUKXnnlFd54440Pvf8jjzxCMBhcgpJVz3IEPHywktMFV3QC/qD8Mici+5apTLWoA2j0pWka/RpY/atU/8cee6wap72Qj3INnlPVe859c8nWZD19ApEtwP24TTRfV9WFS2y7S1U/uaQFqmGNXn+wa2D1b+z6Q2WvwZLfwavqW8BbS30eY4wxZ7NeNMYYU6dqLeB/UO0CVFmj1x/sGlj9TcWuwZK3wRtjjKmOWruDN8YYUyHL1U3ykhpxMJSIrAMexR0XkAc2A63AXwBfBkqAqup3qlbIJSQit+KOjdgE/AQYoIHqDyAiHwfuBtYCb+B2j4uo6hMi8g1gHphQ1X+tXimXlojcC/wh8CMas/73A7cBR4GDwCAV/BzUyh38g8A3cMPuruoWZXmo6vvAP5Vf3qWq3wT2AdcDK1X1SWB1dUq39FT1FVX9G+AQ8LlGqz+Aqv4KGAeuAQZV9W8BRCQOlMqvb6hiEZeUiGwGQsARGrD+ZSkgDUSAOyv9OaiJO/iyiw6GajBKg1wLEfk87oe77Yy3G6b+AKr6QxGZA7506q2L/F2PPoMbbptx71ShseqPqj4PPC8iDwBfO/NbVOAa1MRD1g8zGKpeiMg1wONAE/Ay0If7X7O/BP4M94daUtXvVq2QS0hEPgf8PvAcMEuD1R9ARO4BrgPWAD/HbaIIq+pfndFEMa6q/1a9Ui49EXkSeJsGrL+IbAM+hdtE+Qqwigp+Dmoi4I0xxlRerbTBG2OMqTALeGOMqVMW8MYYU6cs4I0xpk5ZwJurnoi0isiL5T+J8t9a7p212GMfEJF/rEQ5L3GOW0Rkv4h86fJbG3PlrBeNqSsi8qqq3lLDx/OoaukC7+8AfKr6VKXOZUwtDXQypmLK/ahfBRzc/sQCtAPfB34PyAL3lTf/HrAeyABfUNXZCxwvCDyjqneVX/8Md5qBu4Gv4H6WnlDV50TkO7hDzj3AQ6p6QkTewO3rnRGRw8DvlM/3iKrurvwVMMYC3jQGUdXPisijwFZVvUNEfoA7grIPOKGqfywinwH+CPjrcw+gqjkROS4i1wJe3CkWisCfA7fjhvmzuAO3vqqqaRG5E3eelcdwB/F8U1WHROR/gdtUNSMicu65jKkUC3jTCE6t8zsCTJ7xdRzYCGwXkbtxPw+vX+I4PwS24wb8v+OG9kbghfL3u8qB/RURuQPwA++VvzehqkPlr78OfF9E8rjD08cXVz1jLswesppGoBf5WoADwL+o6rZyW/ujlzjOS8Ct5T8v4S6MvBe4Q1W34U4Q1QZsU9VbccP71B36me3uv1TVHcCLwI6PVCNjroDdwZtG99/Ad8tt6gBPlt87j6qWROQd3IehJQAR+TvgpyKiwLvAnwIL5eO9c5Fz/oOIDABB4IuVq4oxZ7NeNMZcgoi8CBxV1S+WX38L+E9VfbOC57gF9xfLt1X1Pyp1XGMs4I25QiLyBLBeVX+72mUx5kpYwBtjTJ2yh6zGGFOnLOCNMaZOWcAbY0ydsoA3xpg6ZQFvjDF1ygLeGGPq1P8DUO1D/gFQ6ssAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 432x216 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6,3))\n",
+ "ax.set_prop_cycle(cycler.cycler('color', color))\n",
+ "\n",
+ "\n",
+ "linearray=[]\n",
+ "for i in range(nsim):\n",
+ " linearray.append(plot_sim_overview(timearray[i], icelatarray[i], exparray[i], linearray))\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.ylim(0,90)\n",
+ "plt.xlim(0,520)\n",
+ "plt.xlabel(\"Time [years]\", fontsize=labelsize)\n",
+ "plt.ylabel(\"Ice-Edge Latitude [°]\", fontsize=labelsize)\n",
+ "plt.tick_params(labelsize=ticksize)\n",
+ "#plt.title(\"hice_unlim global sea ice border\")\n",
+ "plt.legend(ncol=3,edgecolor='none', facecolor='none', \n",
+ " columnspacing=1, labelspacing=0.5, handlelength=2, handletextpad=1, fontsize=labelsize)\n",
+ "ax.spines['top'].set_color('none')\n",
+ "ax.spines['right'].set_color('none')\n",
+ "plt.savefig(\"plots/overview_semtner_unlim.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Winton experiments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/mlo_aqua_1875ppmv_winton/mlo_aqua_1875ppmv_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2250ppmv_winton/mlo_aqua_2250ppmv_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2437ppmv_winton/mlo_aqua_2437ppmv_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_2625ppmv_winton/mlo_aqua_2625ppmv_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_winton/mlo_aqua_3000ppmv_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_winton_50sic/mlo_aqua_3000ppmv_winton_50sic_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_74sic_winton/mlo_aqua_3000ppmv_74sic_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_4219ppmv_winton_50sic/mlo_aqua_4219ppmv_winton_50sic_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_4219ppmv_71sic_winton_semtnerrestart/mlo_aqua_4219ppmv_71sic_winton_semtnerrestart_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv_winton/mlo_aqua_5000ppmv_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv_37sic_winton/mlo_aqua_5000ppmv_37sic_winton_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv_74sic_winton/mlo_aqua_5000ppmv_74sic_winton_atm_2d_ml.ym.gm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "nsim = 12\n",
+ "timearray=np.zeros(nsim,dtype=object)\n",
+ "icelatarray=np.zeros(nsim,dtype=object)\n",
+ "exparray=np.empty(nsim, dtype=object)\n",
+ "\n",
+ "\n",
+ "exparray[0]=\"mlo_aqua_1875ppmv_winton\"\n",
+ "exparray[1]=\"mlo_aqua_2250ppmv_winton\"\n",
+ "exparray[2]=\"mlo_aqua_2437ppmv_winton\"\n",
+ "exparray[3]=\"mlo_aqua_2625ppmv_winton\"\n",
+ "exparray[4]=\"mlo_aqua_3000ppmv_winton\"\n",
+ "exparray[5]=\"mlo_aqua_3000ppmv_winton_50sic\"\n",
+ "exparray[6]=\"mlo_aqua_3000ppmv_74sic_winton\"\n",
+ "exparray[7]=\"mlo_aqua_4219ppmv_winton_50sic\"\n",
+ "exparray[8]=\"mlo_aqua_4219ppmv_71sic_winton_semtnerrestart\"\n",
+ "exparray[9]=\"mlo_aqua_5000ppmv_winton\"\n",
+ "exparray[10]=\"mlo_aqua_5000ppmv_37sic_winton\"\n",
+ "exparray[11]=\"mlo_aqua_5000ppmv_74sic_winton\"\n",
+ "\n",
+ "for i in range(nsim):\n",
+ " timearray[i], icelatarray[i] = load_experiment(exparray[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1875ppmv\n",
+ "2250ppmv\n",
+ "2437ppmv\n",
+ "2625ppmv\n",
+ "3000ppmv\n",
+ "3000ppmv\n",
+ "label removed\n",
+ "3000ppmv\n",
+ "label removed\n",
+ "4219ppmv\n",
+ "4219ppmv\n",
+ "label removed\n",
+ "5000ppmv\n",
+ "5000ppmv\n",
+ "label removed\n",
+ "5000ppmv\n",
+ "label removed\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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TBciKiop6BcbGjRvXW3ednZ0IIZgxYwZvv/32BTv4yy6CP9CV4o2WGKoiWFIatlUih5GBRt4XyskCWjAwsSyfz0c6nSaRSLBnz57eY4dLQOtskfdgkM1me7Ve8vLyEELw6quv8t73vpdVq1bx0EMP4XA4+MlPfnLWslQqxY4dOwCrbo5F+Lt27cIwjD5lp6unYwsp77nnHr797W8zYcKEPo3q6Thb5D1YdHd3EwwGAdiwYQOf+tSn+M1vfsMXvvAFVq1axbXXXss111zDj370o7OWQf91058A2dnq64EHHuAXv/gFhmH0Bi7nw2Xl4BOazlutcYq8LpaVRuzI/QqhPwGtgYplPfHEE9x00014PB5++ctfAiNbQOvFF1/ke9/7HmDp5j/55JO92TEzZ85k3rx5gLWJytnK1q5di9Pp5NZbbyWTybBy5Uq6u7spLi7mnnvuoa2tjV//+tcDsuvuu+/m4x//OM8+++zQPPh5sm7dOr70pS/hdrtZuHAh8+fPZ/HixSxcuJCqqioee+wxXC7XgMoaGxv7rZv+BMjOxqJFi3j/+9/Pv/zLv1zYA55uq6fh+DvTln2macpXGtrl/x5olF057Vy2srKx6UN/W9Md2w7Q5jhr1qyRTzzxRJ+yI0eOyPe///3DZNGlzenq5iJthXjpb9knTYmZ0/t9b1csSV0yy+yCoK3vbmNjYzMALim54LHTZsg1f1lNdVVhn/LD3Wlea+5kTMDDktLIKWOhNjY2Nlc4l75csJCwJZnBPKHRaUplWdfSSYnHxcIS27nb2NjYDJRLaqzDlTPocijsjSVRhKAzp3GkO02e08EN5fk4FNu529jY2AyUS8rBkzMJZ3Q2tVv5pW5FEHE7WVwStuV/bWxsbM6RS8pr6kLnutYEs71u7q0u4n3jyri9opCAPal6RXOy2NgxVq5cSWVlJQDt7e0sWLCAJUuWcNddd5FOp9m+fXuv+NiYMWP4/ve/P1yPYGMzLAyJgxdCLBFCPCaEWCuE+KQQ4gkhxLfFWQbQddUknDOYnNAIu+wFTDYWJ4uNvfPOOwD88Y9/7HXwkUiE9evX89prrzFnzhyef/55Zs2a1Ss+NmPGjF5JAhubK4Whkgt+DfghsBcYL6X8OrALmHlGY5zQlTTItHQPhVk2lykni42pqsoLL7zATTfd1Cs/oKpq7/8Nw2DChAm95yeTSZqbmxk/fjzQv/jT6QTInnjiCebOnctPf/pTPvCBDzBz5kxeeukl6uvrefDBB3vvt2zZsotTGTY258BQjn3cAzwH3HxC2Sk5mUKIR4BHAKomVNLaoVHtyyKltDNmLnHW/M+TtNYevqBrFFePZdlDjwzo2BPFxr785S/zq1/9ip///Oe972/atIlPfOITeDwePve5z/WW//Wvf+XWW2/tfd2f+NPpBMgeeOABVqxYQXl5OXv27MEwDD72sY/x3HPP0d7eTjqdZuPGjZeUvoqNzTGGcgz+FuBF4BUhxOPAVcDOkw+SJ+zoFAwEqUskcQqJ2X5hKoU2I4sTxcZWr17NggULTpETmDdvHlu2bOHee+/t4/j/9Kc/cd999/W+7k/86XQCZMcExiZPntwrDnVMOfDWW2/lxRdfZOXKlQNSSLSxudgMWQQvpTwWlv2l5+/sxggHtc4o6VwEpTaKWhQcKvNsBoGBRt4XysliY7///e957rnnePHFF9m9ezdf/OIXWbFiRa/Dz8vL6xUR0zSNvXv3MnPm8dHB/sSfTidAdqwXeTpBqMcff5ympiamT58+tJVgY3MeXFLpKapwIMfE2HcwzUyXgkznEN6ziz7ZjGz6Exv79Kc/DcDChQv52te+xqZNm/j85z+Poijk5+fz1FNPAdaWfcuXL+9zvf7En04nQHYmqqurOXz48CnXt7G5VLikpArmzp0rP/vbj6P911j+9oZCFJ8TpSyMY1wxwnFJZXTaXMYsXLiQ9evXn7XMxuYyot8Jy9NG8EKIfz3DxTqllP9xwSb1Q2FeAbVlSTbs9bNocQDjcBtmNIFrTjXCbadO2tjY2AyUM4XF7wFWAa/28/feoTKoxF2GMbGVfQcSxIsLcF5djezOkH19P/rBFqRmnP0iNjZnoL9I3Y7ebUYiZxqDf0BKeai/N4QQHxgie5jov4pNE97EVGD36w0s++AUxILx6Ptb0A+0otdEUavyUQuDiLAPYevT2NjY2PTLmRx8jRDiamCnlLKPSPvpHP9g4HMEGFc4noNz2tmzAcZfU0Ll5Hxcc6ox42n0Ay0Yh9owDrWBU0UtDaGOLULx2ZOxNjY2NidypiGaXwHTgYHtyTWIXBWcjVx8FG+hwur/3UsubbUvSsiLa+5o3DdMwTm7CqUwgNHQSe71/Wjv1GO0dXMpTRrb2NjYDCdncvAq1szsRU+lLHGXU+QvxnlnHcnOLOv/eKDP+8LlQC0N4ZpVhXvxJNRRYYymONqWGnIbD2N2pS+2yTY2NjaXHGdy8O/H0o958CLZ0osQgtmh+aRKmxmzLMDeDU3UvNPe/7FeJ87pFbhvmIJj2ihkIktuw0Gyr+8nt7UGbXcDMq1d5CewsbGxGX7O5OBHSym3SClzJ78hhJjQ3wmDyWjfBEKOCMlr3yW/3M8rP9tN06H4aY8XqoKjMh/3ook4ppQhvE5kWsOo7yS7fj/au00YTTHMZNYexrGxsbkiOO1CJyHE28Dn+nsL+KqUcsFgGzN37ly5ZcuW3td7u3fyevQllrruZvOPoyTjOW76yFTGzioa8DXNZBZ9TyNmNHFc6kxVEHkelLAP4VQxmuLIdA6k1SMQLmtUSikIoFbmI9yX1IJfGxsbm5PpN53wTA7+X85wsZiU8geDYdWJnOzgDWnw+4af4VLc3Or7W1740Tu0He1m5g2VXHfvONRzWN0qDROZyGJ2pZFdacyuDDKeBikRIS9K2Gcdl9aQmg6mtN5XBGpZGKU4eNzRO1SrIXCoZ7ijjY2NzUXj3Bz8cHCygwfYn9jNmva/cGPRXYx2TWDDyoO8s7aekjF53PjQVMIlvvO+nzRMyBkIb/8rZM1EBqM2itHQCUY/9eRUET4XwutELQmhlIVsiWMbG5vh4PJ08KY0Wdn4SzJmmvvKPojfEeTg1lbWPLUXLWcy+bpSrlo0iuLq4JA5V6kbyFQOme1ZDqAbyJSGTOeQ6RxmIgsZDRH0INwOhNuJUhZCKQj0LsSSUoJuIpx21G9jYzPoXDwHL4QYA3wYSABtQCHgl1KeSd+mXwcPEM218WzTrwk587mr9O9wKi6S8SxbX6xlz7pGDN2kuDrI9Q9MoHxCeNCf52xIKTHqOzHqOqzXySzoJrhUlKIgwuXAbOtGJrLgdqDkeY6rZCoC4Xdbwz2KQAn5TtujsLGxsTkN5+fghRA3A18ASoBZwPellJ86yznfBKKACyiSUn5WCLEC+E8pZeykY4/v6FRVNae2trbfa9amDvFS658o91Rya/F9OBTLCWZTGge2tLLlLzUkY1lGTQoz+doyAhE3JWNDOF0XP2KWhonZ3o3RGMfsTEJORwS9qCVBzGQOmcgcT900TDD7fgYi5MUxoQQl7MOMpTCOtINTRSkMWBPAhonUDJTCAIrffdGfz8bG5pLjvB38emAZ8LKUcpkQYrWU8owC2EKI7wM/BSqAH0spxwghvgT88GQHfyKni+CPsT+xizXtf6XKO5Zbiu9FEccnWbWcwa61DexcU0eiMwuAN+jk6luqmbm8clg1a860/aCUEjI60jRBMzA7k+g1UcickLvvcYKUkNVPOV8pCiBLArgqCu3xfxubK5dzkws+AaPnTwrLgwzEi/wP8PeAE/iyEOJzAGdy7gNhYmAauqmzruMVNsfWMz9yfB9Mp0tl9s1VzLihgnhrmq72NDtX17HhjweJtaZZ8uDEYXOAZ7qvEAK8zt5KVcI+1KoCzNZuZEaz9HbKQqAIK+rXDFAEKAKzMYZ+NAptCVJbGtASBu5RAdQiP45xxbY+j43NFc5AHPy3sSSCpwAv9bw+I1LK7cD2CzOtf6bmzaI918r2+EYizgImBq7q876qKuSX+ckv81M9rYC3njnEtpeO0h1NM35OMW11CRQhmH/3WJzuS3PCU6iK5dRPLj/JYSsTStDL/LT8ehNen5NAJA+jPQXprDUf4HSiFvSM6ZsSmdWRySzC7UApDKKOCp+isS91AzQTVGFlCdm9Ahuby5YBTbL2RO6FQLscwrSbsw3RHMOQOi80/5GmbB3X59/AVcHZZxwCefvlo2x/tY50Vw7VqWDqJsWj87jjkzPwBkZGlJtJJHj7r38muuYAJb4qKseOxxfyoXoVFJcCQoCqgNuJUCSkciAESr4PFOt9mdOR8dTxBWEOxZoA9rkQThXhcaJWRBBuJ9KUyETGWhwWcCNUxVpr0J2xFpL53baUs43NxeOcFzr9guM/9T5IKR8ePLuOM1AHD6CZGqvanuNo+jD5ziJmh69lnG/SaR29aZh0NCUJF/s4uqeDl3+2m2C+hzsfnYnqEGhZA4dLJZjvGcxHuuhouSydjQ0c3rKJI395iyJPBfmRCiJKEU6tpzFzCILzS3AXOxGGbn3KUoKqoBT4rQwfw7RSQ5NZ6083jw8PuRzWfMCJ3x2HYk0WH5swVgQi6EHpWUQmgh4wJUZrN7IziZnIIlSB8LlR8v0IvxukxIynEX43akkeqAoykbHKfC6Ey9GbmipzhtXouB3gcVrXECekpGZ1ZCrX20hhmFaaq2Yg/K7enovM6pgdSaRh9tojfC7reXqGyPpb0CazOkZjJzhUa2+CfjKfpJSQM5C6gfC6+m3wpGal4Io8T1/7MxoyoyEzuvVvVgMhEF4nSsRvNap278rmOOfs4Kt7/vsPwFvAVmA2cL2U8tNDYeG5OHiwcuT3J3azs2sLnVo7xa4ylhbeRsRVcNZzmw7GeOFHO8mm+k5cjp5RyPX3j7+gBVSXCm1Ha9j92qvU73mH9rpaVEMlklfOnLG3EowFQYIacuGfV0bg+nIUz5lH7MxkFqM2aqWAuh0oQY8V+Scy1k5birBWBBumtVK4K40ZT1uZQicgQl6UoMfqBXRnrKi/903Rt+E4HYI+4YcIelAKA5jRhJWOap7lGm6H1etInSK1dMr1hd/d80JYQ1emtM474R5KYQC1qgClOGjVVVMcbV/z8clyVaBE/CilIavOUllkLI3ZkbSe120ppAq/G+No1HqGE1HE8YYYrF6SS0UdX4yjIv/s9WUz0jnvLJrXpJRLTvd6MDlXB38MU5ocSOzhrc61mNLkmshCvKqPCs9o3OrpI/LO5iT7N7fgD7lxex3EWlO8/cpRTENy7d1jmTC3BE/Qiape/ht+m6ZB/Z7dvP3inzm0ZSNu1cvMq26mTBmNO+ZCc+RIT9ApKxyHI+jBVRlEa0xgJDQUrwPv9EIckXPv3UgpkYms5bCkPJ7qeeIxmnFcCyjoQXanMaNJwNIGUsI+zFTOir69ruMRtm72DCul0fc3IzMaIuJHyfNaEb/PBZqBTGUteQm3AxwqsjuDmciAIa3eQnHQ6pUcW9CWzPVE3U4rgk5kjjtYw7R6J14namU+SDCa49acR1a3rmOa1qK2PA/qqIh1z640ZmvX8fRYASJgNUoi4MFsjlt6SWaPTdX5Vm/C47Ts7lkgJ9NaTyOWQeYM1PIwalHwgr4bNiOC83bw/wrMBXYCM4CtUsovDbp5nL+DP0a3Huellj8R1doA8Cg+rstfygT/1AF3Z5PxLGt/vY+anZY8sdOjMuvGKoL5bjoak1RMzqdySgTlMnb68dYWtr/8Au+sfolsMsmogknM9i/Hr+ZhSrNP+mlvJOsQBOaV4RqdhyPfgxJwofqdaG0psgc6cZT48YwLI5znXi9nSiMd8DV6hofEOegTDSbSlJitXRjNcYRTRQn7UMrDfZ5LSmktglMUa8XzSd8haZjWJHjQYw+/2Jwr57+SVQhRClQDtVLK5kE2rJcLdfBgRfPdepyUkeStjrW05poocZcz3j8FAEWoFLlKKHKXnvYaUkrq3+0k3pambm8Hh9+2GoxjowfeoJPxc0uYubyCUNHlO5Sj5bJo6TS+UBgzq9O0cQ9vvbaSlr37KfCW4Sj14x9VgFcJUNRZSjCeh1RhoiYAACAASURBVDhDlqzicxBYUI57bBhpSoxY1lrYpZkoPidq2I2zxIca8SAUgdQMulbXkVjfgKPYh2dyPt7J+TiKvAiXOuSTtG2ZHKoQ5LvtlcM2lz3nHcH/CydNtp5NcuB8GQwHfyJSSvYldrE5to6UkewtFwiWFt7KxMC0AV2nozGJlJJQsZejuzvYv6mZmp1RpCmZuqicubePxh8aOStKo/VH2bNuDfV7d9PV1oKhaeiahtQMgmqEUKAYjzOAyICm5FCrPYypmkWgPYjadPaAQfE7cBR40ZqTyJyJ56oCzIRG7mhX33F1l4rwqKg+B77ZxXiuKsRMajgKvSgeB9mDnWitKWTWmnBVw27cY/LQ2zNoLUmEqpA9HCO9rxMhwFHiI3TLaLrDLrZ1dtOg61RKhQVJBaM7h+J34p1eiMwaGLEsar7HmpdQzryW4XzRWpJIXaJ4VKRmIiUoLgU1/8wRvJk1rOGus8yZ2FxRnLeDPzbeLrD2aJ0ipfzE4NpmMdgO/hiGNMiZ1kSebuq8Fn2RhsxRqrxjqfKOpcxTQcR5bitBk/EsW16oYc/6RhSHIL88gBBQPa2AMTMLiZT6z0nO+HLh2HCKNE2O7t7Jwc1vUbNjK7HmJgBC3iJKiseiOl3U1+0hlevClDolFeOprJyKR/Phy/pxGz7yJpUSnl+Ne4yV828kNbIHOzG6cpgZA5k1MDM6ejRN7kjXcSMEKF4HZurUlb0nIzwOvFcVIJwKnYdjHB7rp35KHqpmMnpnjMo9cRz6SdlAet9JYRRwRDwIrwN0EzXswVHsxVHoRW9OkWtK4qoM4CwLoHgdx/88DoykhpnMoYbcGJ1ZsofjCKdC7mgXmX2d/dqsRtx4pxTgrLCuJ7MGWksKrTmJ1pLC6MiAAGeZH++MInwzi1ADLnAIMCHX0A2mxFWVd1zszpDkauPkGpN4xodxlvrP4VO3OREzoyOcyilDbKdDa0mS3NiMb24JrvIAAFI30VpSuEYFBsuswREbE0K8LKW8eVBMOomhcvAnY0idLZ0bOJTaR7du7RIVUINMDExjRt7cM07MnkysNcW2F2tJxrPk0gbNh63rKapgwtwSZt5QSUFFAAHkMjouj2NE5od3NjeS7OigZPwEnC6rN5PLpGk5fJCmA/uo3fk20YY6wPomprsth102YTKmYWAaOgiB0+1h8vWLmb7sZoSiYOgaseZmZHsOOk0cQTdmSw4znsMxMUhgcjGqz4XUTPTWFNkjcRz51iSxNCTS76RV1zjYnaamO40pYWxaMt104PU6LUfsc6AGXWiNSVLvtOEIe3AUetA7s8isgdQM9GgGM2sgVIHRmUFrS1sS0g4FZ4kPrTnZv6T0ySiA2TOctbgCZ5EXM20gXApCERhJjcyeKNnDcaRm9jnPUejDWeqznLMpyRyIkas9oeFThHX9ngZL8TlQgi7QTfRYto99Sp4LR4EHR74X4VIwOjI4RwUIXD8K1X/qkJWZM8gejJGtiSPThvUhqgKtwZqId+R7eq/nKPBgdOfINSRwFnmths9n9TaEU8FR7EMIgZHIIRwKwm0tqNM7MmjNSYR6vFGShkRxKTgKvJhZAyORA0PiKPRaQ3k9QZmZ1jHTOjJnYOYMa0gwz4XRmcFMakgJrnKrwTQzOul32snst4IJxe/EMzGCs8SHo8CLEnSeEuyZOYNcbRepba2kdrQhVIGzPIAacSMAI9WTNmzIXluQEkeBl2xtl1X3Atzjw2BIcvXdSN2kfMV1g9UTO+8I/imOd5xLgTop5f8ZDItO5mI5+GNIKenSYzRnGjiY3Et9pgav4mN+ZAnjA1NQxbmvdO3uyNB0KEbzwTh732xCz5moTsWahDMkiioIFngorAhQWBFg1MQIZeMvvgLmcNPd0c7Gp39HtL4ORVVRHA6QkkRHlPa6WgoqqgDobGq0nP9p8ATzGDt7LqHiEkLFpVRNn4k/UsDh7jTvxpO0ZzQkoJoGnsYacm+txoxFcbhcBML5VE2byVVLb8DpHlijbhoGiqpi6ibNO/ZyaO8WWmoPkhcpJuQvxKl4cSlu3A4fPm8evuJ8vAVBUi0xXGE//kmF1mSO4IwRoDQkejTdk+9vObj+JpC11hTZQ7GeHo+ONCSuSiurJrOvE5nRQRWoYavRc5X7yezvJFfXjd6RQe/IILMGasiN3poCh8BZ6kfxOZGaiRqw/s0cjFk9G4dA8TpAgtRMnKV+1JALvTOLEU336VUpvv57Wc4yP0isRhGsrCSXatl6Dih5LhSXipHUkOkBnKsIy+nHrBRUNezGke9B78j0lgEoAScIkJrEVe5H6ia5+p4MJ5eCf24pCMg1JjDiOWu4zN/TKCiitwd37PNxlvkJLqkg+WYT2SNWD85ZHsAzPoxnUv55JSb0w3k7+LlYkr8SiPecc0GaMqfjYjv4k2nPtvB69GXacs341SATAlOp9o6jyF2CKs69lU1356jdFSXakEBRFTwBJ5mERqw1RXt9gq62NAATrilh4d9MwJc3MlbVXghSSt5Z/TJ7163BEwgSKSunsGo0QlHQc1mMnGb9axg4nE5aDh+kZse23h6BWjKK4N/+PyjF5ZjRFrI7N6PX16Ad2oPb7aZ49DhcXi9aJkNXeyux5iY8/gDlk6YQKilFURRaa44Qb23B7fWiOBwIRSFUVEIy1knDvj3kl1egOp20HjmEoqoUVFaT7Owg1RXvN4dfKArSNHH7/ExZtBRD0+horKezqRFvMI/ScROZd88D5JdXkOqKU7NjG7lUiuqZs4mUlh+vG9Oks7mRAxvfQJoms265E0/A6uLrmka0/iix5iacHjeR0nIiZaP61GsunSaT6CIZi+H2+cgfVdknUs01dhPbUIvRmgENVI8TM6GBlHgm5eOZmo97dAjhUEjGOtn6wjMc3Pwm3dEoBRVVuP1+/J48Zi+8k6IJY1DzPZhJDb0tjZHKkeqKo3ekEYd1hEPBO7UAFDBTOkYqh+kHWaAQLChEKNYQiFAFZlpHj2ZQPCpK0FowlmtIkKvtQprWHIajwIvic1jzNi4VM6FhdGdxhD1WL8aUZA/H0DsyOIt9uCdGcFVae0hIKTGiVmOntaXQmpI9zhpyDQmEquAeE8I9Jg/X6DyUS3MLz/N28H3UI4UQv5dSvneQjQOG38GD9UOoSx/hna6tNGaOYmKioFLoKibkjNCabcKpuLgmvAiQdGpRBIJR3ioKXMXndK9cWmf7q3Vs/WsNiiqYen05eYVepJSku3O01yfQsgbl48M4PSqGZpJX6KVkTB4Rewy1D22pDG83ttFgKii5LK4ta8nPJggXlxIuLadkzDhCJaWnpC02vLubXWtW0XxoP93RdkxdJ7+ikvzyCrRsBmmaGLpOrLkRl8dL5bSZtNfVkk0mmLbsZqYsXILb5++9nq7l0DIZ0l1dxFqaiDU3kYp34o8U0LBvDwc2bsATCJJfXkG4tIx0dxf1e95By2bxR/JJRNv7PFfp+IlUTZtJ3e6dtNXWoOeOR5pun5/S8RORpknj/nf7vAcQKilFdTjJJLrJJLoxDaPP+/5whPHzFlBcPYa6Pe9wdNcOUvHjsZtQFHyhMOGSMsZefQ2FldWYpkndrh28s/plDF2javosImXlROvr0LNZOpsaSHd3kV9eYaWFmiZaNkOqK24ppgL55RWUTZgMWO93RdtoPnig135vMI+yCZMorBrNpOsWUTx67Hl/L9qP1tC4/126O6Koqoo3L0TJ2PEUVo3G4RxR2VPnvJL1XuA+4Cbg5Z5iB5Avpbx1KCy8FBz8iWSNDA2ZWlqyTbRlm4jrnRS6SujUor1j98cQCKYGZ3FNeOE5jeEDxFpSbHr+CAe3tPQGgEIR5Jf5UZ0KbUe7rTzvE8gv9zPu6mIqJ0fwBl3kFXlRFEFLTRdttV1k0zrjZhef14pcwzCJNacIF/tQB6f7OCSkdIPD3WkOdaXoyOk4hGBSyMeM/AAe9dIUkjs2xHMiqXiMTc/+kVRXnMLKaqqmzcTt93N46yZ2rnqRjsZ6SsdNYNTkqeSXVzJm9lwyiW62/PlpOpoaMHWDUVOmUj5xCvnlFei5HK1HDlH7znaEIvAG8vAEg3gDQTzBPHyhEMlYJzXbt3F46yZ0LYcvFKZq2kwqplyFw+Uml0mTinWSjHXScuQQrUcO9dqrOhyMm3stCx/8UJ8eBkA2lWLL808TrT+KoqgoqorD5cKbFyJUXApSsvv11XRH2xBC6bEvSPnEKRRUVCFUhYa9u2k5coiOhjpMwyCvqIS8wiIKKioJFhTR3RHF7fVSUFmNN5iH6nCSy6Sp37uLpv3vEmtpwu3z4XR7aa05RH8oqoOCikp8oTCl4yYw86bbCRYU9v2sTIOaHdvYt+F16vbuonziFErHTaCrrbXnudx0tbeSS6eQpmn1kJIJtEyasVfP49r7/w5fnpVAEGtp5s0//Jqju3aQ7u7C6fbgDYV5/9e/h9s3KKnW5+zgQ0AYazOOn/RcQAOapZRmvycdP/ceLA35I8ABrI1CQsA/nUms7FJz8KdDN3UOp/bhU/0UuUrRpc7b8bfY070dj+JlcnAGDuHErwYIOcMUukpJGt00ZepJGt04hYtSzyiKXH0jStMwyWUMhKDPZKyWMxCAUAVdPbn5B7e20nQo3js74g+5CBZ4eyd5ARAwamKYwoogWtbA0E3Gz7GcfjKWJRnLkohlScVzaBkdXTPRsgaNB2NkkzoOp0LpuBCjJoYJl/gxNIMjO6Oku3MoqqBkTB5FlUHcPgexlhTZtE7p2BCl40LnvPrXNEykyYAalKxh8nZHN/vjSQwJhW4n4/N8jA16cV/GC9D6Q0qJlknj8g7NeotsKkWiM0p+2SiEcvq6S3RErR6OYVA8dlzvRPpQkk50s3fdWhr376U72k60rpZsKonb70fLZE7pkagOB6XjJxIuLSeXTpGMxZgw7zomzFtAsKAQ2TPH03LkIC2HDtBeV0syFrMaLwGVU6cxZtZcikaPpX7vbnatfYVEtB2PP8CoKdNoeHc3mUQ3Lq8X0zDRtRzBgkI8gSACgdvnw+33IyUc3rYJ1elk4vzryaaSHHl7C4rqYML8BQQi+WjZLOmuOHd8+vNnrPdz4Jwd/G1Syr8KIf4Pp+bB//yMdxLiJmA51pZ9hT07On0I2NkjJXzisQPa0elyoC3bwvqOV2jNNvUpFwhkP7ptYWc+U4OzqPCMJuzMP+dc62QsS1tdN6muHDU72+lsTnHVonImzC0BYNfrDdTuitLRmMTlVTFNSTZ56mSUw63i8qg4nAoOl2pN/k6KEG1I0LA/RrQ+0XusL+QiUuIjlzFor0+c0rM4dsy4WUXEWlPkMgaBsJtwqY+C8gDhEh/t9QmaDsbIZXS0rEE2pROtTyAUwbQlo5h1Y1XvfIRpSlLxLP6wJa6V0HRebuygK6czPs/H9EiAkOuSHBO1GWSklOi5LE63pze7KpNMYOoaDpebwspqnJ5zl9OIt7bwzuqXOLDpTTp6Mr0QgtEzZjP9hlsYN2ceqsOJoWtkUym8wTyEEJimgaL031OM1tex9YU/se/NdTjdHqYuXs7Vt99NIDJkukHn7OBvkVK+JIT48ElvSSnl/w7ojkLcB3xJSjm7x8HvkFLuON3xl0sEfzaklBjSIGl006lFack24lcDVHhHE3TkkTEy1Gdq2NW1lfZcKwB5jjDj/JMZ7RtPxFmAicQpnH1lAy7AHiEEhm5SuytKLqPjD7sJhN34Q25c3jM7yGxapzuaRppQWHF8I/FcRifeliab1Mgr9OLyOmjY38me9Y3U7+0kf5Qfj99JojNLvC3dpzHwBp14/E6cbhWnx0HhqACpriwHtrbicCiMnllIdzRDtD6BrpkES73k31BOvNSFieRq1cOYwgDenpS2WGuKzuaUVZcFHiKlvstaTsJmeEh0dtBWe4T88gpCxSUXfD3TsFJKT9cQDCLnPcn6HSnl5094/UUp5dfOcs5SYD4wBliHJXMQAv55JAzRDBbH0jQbMrUcTu6nMXP0lEg/zxFirH8yozzVFLlKznl8f7g4WV/G0Ew6W1J0NicJFXkpqgr222PpbE6y9cVaju6OEin1U1QZRJS6OZgHpt+BUZskt7oF2WGpQLo8Kp6Ak672TJ/rCAGeoAvVIXA4Va69eyzjrj63SXAbm8uIc47gRwPjgH8HPtdT7AD+XynlssG378pz8CeTMdIcTR8mZSQQKOTMLG25ZurTNUgkCgozQtdwdehanMrIT6k0TMmOzm52diTwOVTmBf2k93ejqAKX10FXW5p4a4pkPEf5hDClY3smtFqthiTdlcM0JO0NCdrrEhRUBMgmNRRV4HQ7cHlUnB4Vh0vF0E0cDoVAvgdTN8mkdLIpjfzyAJPml1AwKmALgNlcypyzg18CLAUewtpjFaxJ1rVSyjcG3TxsB386Mkaa9lwLBxJ72J/cjYJCoauEScHpTPRfhUMZeWPQzeksb7TEiWs644Je5heFznsC1dBNtvylhubDcQJhN6aUaBmDXMZAy+hoOROHU0HLGiQ6MqguBY/PidOj0tGQxDQlgYibcbOLmbG8grxC7yA/rY3NBXPeQzROKaU2JCadhO3gz05LppGa9EHq0keI5lpxCRflnioirkLyHGGqfWPxqpdnjrwpJbs6E9QkMkSzGgGHyoLiEKP8wzcsle7OcXh7G7W7otS+E0VKSaTMT0G5H1/YTSqeI5PUmLm8kuppxzeayWV0OpqS5BV47QVsNheD83bwHwA+CkzFWsnaKaWcM+jmYTv4c0FKSWOmjkPJd2nI1NKtx5FIBILx/iksyF+OR718Ik3dlLzW3MnRZIZij4sqv5vJYT/OwUkhGxQSnRn2vtFES00XnU1JkvEc3qC1WCbRkcUfslZZHssMAkBApNRPJqkRCLuZOK+E8glh8sv9OJyXZq6+zWXJeTv4TcB1wKvAjcB/SykfGmzrwHbwF4IpTTq1dvYndrOraxtuxcPEwDQqvNX41eB5pWFeDKSUHElk2BbtolszmF8UYmr48uqBGLrJ7nUNtNVZeiVOjwNfnov8Mj/tDQnaarvw5rmI1idore0GQFEEkTIfhZVBiiqDFFUFCBX5cPscKKpAKOKS/LxsLlnO28GvlVIuFUK8Avwf4Dkp5awhMNB28INENNfKxs7XaUjXYmKtSQs5IszIm8vk4IxBSb0cDBKawRutMRpSWSIuB/OKQpT7Ro6ufn90tadpO9pt/dUlaO9Zx3AyikMQCLupvqqASdeV4XSpOFwKHr/zrGmtNlck5+3gbwFew9q27wvA01LK/xls68B28INN1sgQzbUS12O8272T1lwTha5i5oYXMspTPWyTs6aU7O5MsL3DWkA1pzCPySEfyhUasSbjWdrrEnR3ZMimNKQpyWUM4m1pat5px9T7/kYjpT7Gziri6lurcdmbfthYDJoe/NellE8MikknYTv4oUNKyZHUfjZ0vErKSOIQTkZ5qpgcnMFo3/iLZke3pvNacydtGY0qv4d5RXkEnbaTOh3JeJbGA5YAmJ4ze1/X7enAH3IxalKEUJGXGcsq8QScGIaJqio96pE6To8DZQTuP2BzCvaGHzaWjk5jpo6j6UPUpg6RMLq4qeguxvonDel9TSnZF0+xNWrJ+l5fHGZM8PKZBL7UaD4cZ+Nzh+lqT9MdzeDyOvAGXcRaUjjdKhLQswaqQyFY4OnN+fcFXcy5fTQF5YO2k5DNpYHt4G36opkaL7T8nrZsC0sKb2GCf+qgT+xlDJOt7V0cTWbIGCblXhcLSsJ21D6IRBsTbH6+BkM3KawIkOvZOCMQ9pDqzpHoyFg5/1mdjsYkWs5gzi3VzLqxyh7PHzmc80KndXCKQpYACqSUUwfXNgvbwV98Mkaav7aupDXbRLGrjCqftU9toavkgp19PKezqjFKQjcY7fcyOuihyn/mDaVthpZUV451v9/PwS2tuH0OCisC5BV6mTC3hFGTI/ZwzuXL4ETwQ4nt4IcHKSV7EzvZ3bWNDs3acCLkiLC48BbKPZXndb2D3Wk2tsVRhOCGsggl3pGdHXO50XKki51r6ujuyBBtSJJL64SKvMy6sZKxs4vtxVmXH7aDtzk7GSNNTeog2+Mb6dJjTMu7mtG+CRS6SnANQP/GlJKNbXHejaco9bpYVBImYA/HXNLomsGR7e1sX3XUytMXUDU1nwX3jadglD1Wf5lgO3ibgZMzc6yPvsLB5N5ehcsCZxE3FN1JxFXY7zmmtFaj1iQyTIv4mVOQd8WmPl6OSClpr0twZEcbO9fUk0vrXH1LNfPeM8aWXr70Oe88eBX4G6AI+BFwtZRy81nvJsQdWBIHK4FCwC+l/NcznWM7+EuPrJmlKVNHR66NXV3b0KXOooKbGO+f0nc3qhOc+9zCPKZH7MjvciaT1Hhj5UH2vtFEMN+DJ+DEE3ASLvYxbfEo8ssvr9XGVwDn7eD/f6yFTh+SUi4QQqySUt54lnNmA2OBRVgbhHxWCLEC+E8pZeykY0fMjk4jnYTexSutz9Gaa6LEXc6c8AIqPKPRpeT15hhHkxmuKcxjmu3cRwwHNrdwYEsLpmFtBN/RmETXTcbNLmbu7aMprLA/60uE83bwq6SUNwohVksplw/QwT8OpIB7AVNKuUwI8SXghyc7+BOxI/hLH1Oa7E/sYnNsAykjQZFzNOgL6cpJ5l2GOjI250Y6kWPHqjp2rq1HyxiMmVnINXeMoagqONymXemct4P/DfAs8BjwbeA+KeUHB3RHIb4PvI01ROOTUn71TMfbDv7ywZA6+xK72dixEZm7njF5ORYXz0AVtkLilUAmqbFzTT07V9eRS+vMvX00c28fbY/VDx/n7eA9wN8DU4B3gZ9KKTNnPOk8sR385UfKSLK+/VWOpPeR7yzk5uJ7CDkjw22WzUUim9ZZ97v97HurmVETw9zyyDS8ATvFchg4bwdfdVKRBrRIKc1BMqwX28FfvtSmDrGm/S9IJMsL76DaN264TbK5iOx7q4k1v9qHN+hk5g2VTL6uDI/fOdxmXUmc/xg8VgbNLmAa0AG4gf+VUv54MC20HfzlTZcW4+XWZ4hqbUzPm8P8yBJ7yOYKorW2i3W/O0Dz4Thun4Orb61mxrIKe2OTi8N5O/g/AfdLKc2elMmVwH3ARinlNYNpoe3gL390U+etzrXs7n6bSu8Ybiq664rYINzmOG1Hu3nr2cMc3R0lmO9h/t1jmXhNCcKWQRhK+q3cgcyIlAFzesbi5wDFPcMzqUE0zmaE4FAcLCy4kUUFN1OfruEvLSsxpD7cZtlcRIqqgrznUzO567FZuP0OVv1iD3/4ty201yeG27QrjoFE8GOBfwTGAIeB7wK1wFgp5YHBNMaO4EcWBxN7ebX9ecb7p7C88A5bZOwKRJqS/ZtbeOPpgxiayZ2fmknpmNBwmzUSsaUKbC4+22JvsTm2jtmha5kXWTTc5tgME13RNM/+x9ukuzXufHQG5RPsTKtB5tyGaIQQa4QQq4UQW4QQbUKIDT3/nlWmwMbmGLND85kcmMHb8bd4p2vrcJtjM0zkFXi593NzCETc/Pk/d3B0T3S4TboiOK2Dl1Iuk1IuB/YBo6WU1wOjgYMXyTabEYAQgkUFNzHaN543OlazPb5puE2yGSYCETf3/MPVhEp8vPD/7eTdN5uG26QRz0AmWScD4Z7/h4CJQ2eOzUhEEQo3Ft3FWN8kNna+xrbYW8Ntks0w4ctzce/nrqZ8YphXf7mXzS8c4VIaJh5pDESo+6PAfwkhwkAM+PjQmmQzElGFyg1Fd6K0K2yOrSPPGf6/7d15lGP1deDx75WelpJUpVpUS1f1Xr3STW8wNGAaGmho4tgEvGIbYzPeZs5kTjKTnDi2T+LEiRPHmfE4mZksPnECZBsf8CQYH3aMWUxYGnqD3ih6X6q6VtVe2m7+eGroVdV0l/TUqvs5p0+XpFfv3XqSrn76vd/v/lgQXeJ1WMYDoSqHD/3qSp79h128+sg+hnrHueEzi/FbmYMpd84ELyKOqmZUdRNw+0n3N5QkMlNxfOJjfeI2hjOD/Lz7UeJOLY2hFq/DMh7wOz5u/txSquvDbHp0P8MDE9z2peW2RuwUK/SR+eSJH0TkoZPuf7B44ZhK5xeHW5vuIOyP8FT3T5jIFqWskbkEiAhrb5/PjXcv4fCufn707Vc51nHOYrPmAhRK8CcPu2k4x/3GvG9V/gi3NN7OSGaIp7t/QiZnE6Gms8uua+XO/74agH/53mZ2vWwXX6dKoe9DVSIyD/dDIHzyz5PtVESWARuBhcDLnOeKTmb6aA63sq7hVt7oeor9g/8Xx4kQCNVSVTWbcLiFYDCB41ht+elixoJaPvGNq3j8r7fzzH07GR1MsebWOV6Hdck750QnEfm7c/2Sqt476Y5FPgN8DNhvKzqZc+k4/DKhrv34JUc6Okban3z3sXC4lVCoBREhnXZfNpHIPGKxhfj9VV6FbIoom87x9H076Hj9OCs3zOIDH1lgNWzOz8XNZBWRFaq67X0d0V2X9Yuqeqet6GTO5ZWe50gf6GZ17yLQNLnZPtKN4wyPdpBOJ4EsjhNHNUMmMwj4icXaicUWEYnMw+ezsrSVRHPKCw++zfZnD9O+pokNn1+KE7SKlJO46AT/s/zEp/PZ9jZgBdAOvISt6GQKyGmOx7p+TM/wMW4fuZWqo2kIOQQWt+BrrX23ho2qkkr1MDi4naGhXeRy4/h8YeLxlYTDMwgG63GcuNW8qQCqypanD/HS/+9gRnuc239tlZUdLuyiE/w/qOrdUxrSaSzBT18TuQkeOfbPJDMD3BH5KLG3J9DkGL7GagKrZiHOqW9u1RxjY4dIJrcwMvLOu/eLBHCcKI5TQzy+kmi0HREbX32p2vNaJ0/97Q7aVzex8YvLrLvm3C48wYvIWqAVd23WWapalI5yS/DT20hmmIc7/4ms+As2tAAAE+ZJREFUZvlIy92EDk+Q2X0MiYUJXjEHqTp7XflsdoxUqp90upeJiR6y2VHGxzvJZJKIBAiFmolEZhOJzHm3T99cOjY/dZCXftzB0mtnsP7uJfgsyZ/NBS/48X+AYeAmVb1KRJ5U1VuLEKAleENvqpt/PfaP1AcSfLjlLqR3jPSWgyBCYMVM/E0157Uf1RwjI+8wNnaI8fFjTEx0AeD3RwiFmgmHW6ipWY7jVBfzzzFTQFV59ZF9bHp0P+1rGrnlC8ts1uuZLjjBP6OqN4vIs6p644nbxYjQErwB2Deyhye7H6Y9soSbGz+EjqZIbz6IDo3jn12Ps3gG4ry/N3g2O8bo6H5GRvaRSnWTSvUCQlVVG+FwK4FALaFQC6FQojh/lLloW54+yC8e6mDx2hZu/txS66451VlPxvnMCx7Od9EgIquB5CTbG3NR5kUXsTZzPa/0P0/NQC1X1a0jeE07mT1dZPf3kOsbIbB6Dr5Y6Lz36fdXUV29lOrqpQCk00mSyW2MjR2gv/81wG3oRCJziEYXUlU1i2DQapaXk1UbZpOeyPLqI/vwB33c8KnF1l0zifNJ8F8Afht3ib67yY9ZN6aYVtZcRTI9wObky9QG6lkUW0Zg6Qx8jTHSWw6ReqnD7bJpubDVgQKBOInEOmAdqhnS6SGGh/eQTG5hdNS9xBQKNVNTs5zq6iX4fOf/YWKK58oPziWTzvHG4wcYTaa47UvL8Qesu+ZczqeLZiHQoaoq7tWpBVO9VN8J1kVjTpbVLI92PUTn+BFun3EXzaFWAHQsTWrzATQ5hn9OA87CZmSKhtCpKun0ACMj7zA0tINUqgeRADU1y6mtXUMgYMvNlYNtzx7mhR/t4fL1M7n+LqtgzsX2wZ/r9lSyBG9ON54d48dHH0BE+OiMewj53UoZms2R2XWM7ME+CDoEVs7En5jaC6aqysREJ8nkFoaGdgMQj19OXd1aHCc2pccy79+LD77N1mcOsfFLy1lwRZPX4Xjt/S3Zd5LIu3twW/CRAtsaM6XC/io2NH6YkcwQz/c+8e7iEOL3EVjWRvDaBUjQT3rTfjIHpnYZOBEhHJ5Bc/MvMXfuF6ipWU4yuZ0DB/6Wnp7n8rNsjVeuubOd5nk1/OyBnfQdHfE6nLJ0Pgn+ARF5WkS+i1tC+IEix2TMKZrDrfyHunXsHd3DzuGtpzzmi1cRvKYdX2M1mR1HSe88WpQVghynmqamDcye/TlisYUMDLzBgQM/pKvrMdLpwSk/npmc3/Fx25eX4wR9PPpX25gYTXsdUtk534lOjcA8YK+q9hQrGOuiMeeiqjza9RDHJg7zkRmfpT6YOOPxzM5jZA/04murJXD5zKJOaEqnh0gmN5NMbkZViUTmEo+vIhqdW7RjmrM72jHAw/9rMzOX1PPL/2XFdB1Z8/764EXkDzgxduw0qvq7UxfXeyzBm0JGsyM8dOQ+Iv4od7Z+Fr+ceWE183YXmY7j+GfV4yxrLfqs1XR6kGRyM0NDu8lmh6mpWU59/Qes1HGJvfn8EZ77p92s2TiHa+5s9zocL7zvcfBPFykQYy5IxB/l+sRGnjj+L2wa+AVr664/Yxv/giY0p2T3drsFyxY2FzWmQKCGROIGGho+QG/vvzEwsInBwR3EYotIJG6wRF8iy69vo+fQEG88cYCmOdW0r5n2F12BAgleVZ8rZSDGnI+5kQUsjl3O1uSrzKlqpyXcdsrjIoKzqBlSGbIdxxHHh39uougteRGHRGIdNTXLGRzclh9Pv5/m5o1Eo9OyRVly6z6xiJ7Dwzxz/07qW6PUtdiHa1FmCIjIOhH5qojcLyKfFpFviMh3xao8mSlwbf2NRP3VPNvzKOlc6ozHRQRnWRu+5hoyuzrJbD+C5qb+wuvZBIN1JBI3MGvW3QQCcY4de5j+/k1FufBrTuUPuBdd/QEfT/zNW2TTOa9D8lxREryqvqCqfwJ0AB9X1W8DbwIri3E8M70EfSFubPwgg5kBXht48azbiE8IrJ6Nf0ET2SP9ZPZ0ljbGYANtbZ8gFltEb+/z9PY+b0m+BGJ1YW66Zym9h4d59ad7vQ7Hc0Wb4ysinwb2AvtPuvuMV7iIfFlENonIpu7u7mKFYypMa3gWS2IreGtwC8OZsw9TFBECC5vxz64nu6+HbPdQSWP0+QI0N/8y8fgqBgZep6vrMbLZiZLGMB3NW5HgsutaeePJg3QfKu1zXm6K1UXzceAeoBHYIiJfB5YBZyz5p6o/UNUrVfXKxsbGYoRjKtSa2qsBZXPylYLbOUtmINVh0tsOo6lMaYLLExESiRupr7+W4eHdHDr096RS/SWNYTq69qMLCFU5vPbTfV6H4qliddE8qKq3qer3VfV+Vf0jVf2q2ndUM4WqnThLqi9n19A2hs7Riof8rNcVMyGdIb3jaAkjzB9fhPr6q5k585Pkcim6uh5FNVvyOKaTUJXDyptnsW9rD90Hp28r3sqwmUva6vjVAGxNvlpwO19NFU57E7ljSbJd3sw8DYdbaWrawMREF319hb91mIu34qZZhCIOrzwyffviLcGbS1rMqWFRbBm7hrYxmhkuuK2/vcntqnnrCJr2pgUdiy2iunop/f2vMDFx3JMYpotQlcPqW2dzYHsvR/ZMz24xS/DmkrcqvpYcObYNFp4FLT4hsLwNJjIlH1VzskTiRvz+CF1dT1hXTZGtvGkWsboQL/24o2RDZcuJJXhzyYsH6miPLmHH0Nazjos/ma82gn9OA9mDfeSGxksU4an8/jCNjTeRSnXnV5MyxeIE/ay9fT7HDwyxb2vRymiVLUvwpiIsq15NWlO8PbJz0m2dBU3g95Hp8K6LJBZbSCy2mL6+l62rpsgWrW0hVhfirRdLf4Hda5bgTUVoDrVS57SxtdNH79BYwW0l6OCf20CuM+lZKx6gsfEm/P4quroeQ7W0wzenE59PWHx1C4d29DLcP73mIViCNxVBRLgssJxcto4Xj/ZMOmvUmZtwW/HveNd69vuraGq6hVSql4GBNzyLYzpYcs0MVGH3K8e8DqWkLMGbirG4cQlzhjvp8/nZ1Vd4RI0EHfyz68l1JtGxwv32xRSNzicabaev72VbOKSIapsitC6sZedLx6ZVyQhL8KZiBHxBnIY+mkdTvN43xES2cLEpZ04DKFO+1N/7lUisB6C39wVP46h0i9e2kDw+Rs+hwh/+lcQSvKkoLc3zmTl8hDSwo3+SVnxVEF9LnOyhPjTj3XDFQCBOPL6a4eHdpFJ9nsVR6eavasTnEzpe7/I6lJKxBG8qyqyquRyL76dtOMWO/mHSufNoxWdyZDu9XUC7tvYKRBwbNllE4ViAmUvq6Hj9+LTpprEEbyqKXxxCTfUsSA6SAnYlRwtuL3URJBoie9jbmY6OE6Gm5nKGhnZaX3wRtV/RxGDP+LSpT2MJ3lScubGFDIQP0DSaZkf/MNkCrTURwT+zDu0fJTfi7RC6urorAWxETRGd6KZ5e9P0mHtQrHLBi0TkPhG5Q0Q+aCs6mVJqC89mf7yTpQNjjGZz7JtkXLy/tRaEMmjFV1NdvZjBwe1ks96Nz69k4WiA2csbePvVTnLToHRBscoF7wHuy9+8xVZ0MqUU8AUJ1dYSzQ4Sz+R4s3+4YJ+rhAP4EtVkjw543jdbW3sFqmkGB7d7GkclW7y2hZFkiiO7Kr8AWam7aGxFJ1MSc6Lt7Ks+zOLeUfpTGTonGevub4nDeBpNFm7tF1so1ERV1SySya2ef9hUqrkrGghFHHZNg0lPxeqiaQE+BnwYW9HJeGB21Xz213QyZ3iCANAxWPhiq6+p2u2m8ahW/Mmqq5eQyQySSk2/4lil4AT8LLiiib2bu0mNV3aJCKcYO1XVTuBXi7FvY85HTaAWjQYYD00wezzDfhnn6lyOgO/sbRoJOvjqY+7M1kXNeHm5KBKZD8DIyF5CIWv0FMO8lY289cJRug8M0ba4zutwisZG0ZiKVR9s5GD8OHN7RsiocnC48IVLX0sNOppCh70dTeM4UUKhZkZG3vE0jkqWmBUDoOdIZc9qtQRvKlZ9IMGB8FEaxzPERNg72WiaxhoAcr3ev+mj0XYmJjrJZEa8DqUiRWqCVFUH6D3s/XNdTJbgTcWqCzTQHxpCgGaFnol04V8IO+D4UI/Hw4NbhAxgdHSfx5FUJhGhoS1GjyV4Yy5NdcEEaX+GTBBqU1nGszlGC9ScEREkFvK8iwYgGGzE5wszPl75Iz280jAzRt+xEXKTFKW7lFmCNxWrNlCHIIxVpanNt8r7J2nF+6Jhz2e0gvthEwzWkU4PeB1KxUrMjJFN5xg47u3Q2GKyBG8qll8c4oE6BkLDxJPuBda+VOFhcRINwUQGTXu/GHYgUEcqVfmTcbySmOleaK3kfnhL8Kai1QUSHA/2EcrkiPh8k7bgJRYCKIt++ECgjmx2mNwkC4mbC1PXHMXnk4oeSWMJ3lS0ukADnY5bWKpOhL7JEny0fBJ8MOiOz7ZumuLwB3zUzYhU9AIgluBNRasPJhgIuW/g2myOZCpTuLpkJAhCWfTDBwKW4IutaW4NXfuTFVsWwhK8qWjNoVYyviypkBIfz5ADkgX64cUnSKQ8RtIEArUA1g9fRC3z40yMZBjoKlzK4lJlCd5UtJhTQ9ypoz8yRG2fO2no+HjhPm2Jhsqii8bnC+A4MdJpS/DFMqM9DsCxd7xd0atYLMGbijezai67YvuoGU0T9/kmLTwm1W6C18kmRpWAjaQprtqmCKGoQ+deS/DGXJLawnM4GO1EHaF9LE33eLrgaBp/Wx0oZPZ5X80xEKizFnwRiU9omR+n01rwF0ZE1ojIH4rI90QkWuzjGXO61vAsVKC/fpy5nYP4gD0FWvG+aAjfjDjZg33oJOPmiy0UaiSXG6e39xcVeyHQay3z4/R3jjI+4v03tqlWlHLBp/kU8DXgWuAW4F9LcExj3hXyh2kMtvBa1RZuO76W1uFxdqiyp+/EwstnKQ1c5Yc5tbCvq6SxnimBysegT6H34BmPXuZLcuWCFR7EVTlmzHf74e//7V8gvtKWib73T68jEPQXbf+lSPDw3kpOZ13RCfhy/uaEiLxZopguRALw/nv7uZVzfOUcG1h8F8viuwBf+fN3f7zY+B5X1dtOv1OK/bVPRK4A7gAiwDdV9ZyzCkRkk6peWdSALoLFd+HKOTaw+C6WxXdxihVf0Vvwqvo68Hqxj2OMMeZUNorGGGMqVLkl+B94HcAkLL4LV86xgcV3sSy+i1OU+IreB2+MMcYb5daCN8YYM0VKNUyyIBFZA3wEd6TN76hqWaw0LCLrcMfvXwZsBaqBw6r6Q08DyxORO4AbgX3A28AqIA58Vcvgq5mI3ACsxh1F9TBQQxmcPxFZBHwdd05GCjfGOPBV4L8BOUBV9c/KIL4MsBxYBHwF+A5wCDiiqg+WQXxzcc/dYVX9oYh8DnfIX1RVv1UG8WWBduCTwAbgW3h4/k7LKU8A8yjia69cWvCfAn4P9wm5xdtQ3qOqL6jqnwAduMldgXKajTsCjOLGtEFVvw28Caz0NKo8VX0O+N/ATqCPMjl/qroHuC9/85bTztssVf0+buLyxMnxqepPVfU7uM9zEOgCwkCoHOID+jn1eV2lqv8TQERqSx/dGefvEeB+4IV8w9HT83daTvl4sV975ZLgocBkKC+JyKeBvar6+/kWiSMi872OC0BVn1LVr+Em0PUnP+RNRGd1B/ATVb2/3M7fWShl+DoUkV/DPYcjqvrdfMK/QkQCXsdW4Hktm/MH3Mt7Cd/z83cipwD7T7q7KK+9suiiAf4fbgs+AnzT21DeIyIfB+4BHs9/9WwF2oDDngaWJyLrgbW4X/P+h4h8Hffr3t97GddpNgJfEZFfwf1a6vn5E5EW4GNAFfD8aeftkIj8Oqe++TyLT0RWAkvcu+U14MPALCClqp4UTzktvjinvi+2iMhvAKiqJyuVnBbfZmCxqn4v/9g9eHj+Ts4puOeqqK89G0VjjDEVqpy6aIwxxkwhS/DGGFOhLMEbY0yFsgRvjDEVyhK8ueSJSFxEfp7/l8z/r/lS1Re7790i8ndTEWeBY1wnIrtE5IvFPI6ZfmwUjakoIvKiql5XxvvzqWruLPd/HnBU9W+m6ljGlMs4eGOmlIj8HvAi7lT/X8ddl68B+EvgPwLjwIfym/8FsBgYA+5W1TNWuRaREPBTVb0lf/tnuGP8NwK/hfte+paqPi4if4ZbNsIHfEZVD4rIy8BmYExE3gE+mz/eb6jqG1N/BoyxBG+mB1HVX8lPKrlKVW8WkR/g1qBpAw6q6n8WkV8C/hPwx6fvQFUnROSAiCwA/LhTzbPAbwI34Sbzx3AnsHxNVUdFZANu/Zhv4NZn+baqHhaRJ4EbVXVMREq7CKiZVizBm+ngxDq/R4Huk36uA5YCd4nIRtz3w78V2M8/AnfhJvh/xk3aS4Gn84835RP2b4nIzUAAt4wEwHFVPTGD95vAX4pICvgd3Pooxkw5u8hqpgM9x88C7AYeUNX1+b72rxfYz3PAuvy/53AXSd4O3Kyq63ELRtUD61V1HW7yPtFCP7nffYuqfh74OfD5C/qLjDkP1oI3091PgD/P96kDfD9/3xlUNSci23AvhuYAROR7wDMiosAO4L8Cw/n9bTvHMf9KRObhVjS8d+r+FGNOZaNojClARH4O7FPVe/O3vws8qKqvTeExrsP9YPlTVf3RVO3XGEvwxpwnEfkWbmXCT3odizHnwxK8McZUKLvIaowxFcoSvDHGVChL8MYYU6EswRtjTIWyBG+MMRXKErwxxlSofwexTFpTcho+wAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 432x216 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot\n",
+ "color = plt.cm.tab20(np.linspace(0, 1,n))\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6,3))\n",
+ "ax.set_prop_cycle(cycler.cycler('color', color))\n",
+ "\n",
+ "linearray=[]\n",
+ "for i in range(nsim):\n",
+ " linearray.append(plot_sim_overview(timearray[i], icelatarray[i], exparray[i], linearray))\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.ylim(0,90)\n",
+ "plt.xlim(0,210)\n",
+ "plt.xlabel(\"Time [years]\", fontsize=labelsize)\n",
+ "plt.ylabel(\"Ice-Edge Latitude [°]\", fontsize=labelsize)\n",
+ "#plt.title(\"hice_unlim global sea ice border\")\n",
+ "plt.tick_params(labelsize=ticksize)\n",
+ "plt.legend(ncol=3,edgecolor='none', facecolor='none',loc=1,\n",
+ " columnspacing=1, labelspacing=0.5, handlelength=2, handletextpad=1, fontsize=labelsize)\n",
+ "ax.spines['top'].set_color('none')\n",
+ "ax.spines['right'].set_color('none')\n",
+ "plt.savefig(\"plots/overview_winton.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Semtner limited experiments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_1438ppmv/mlo_aqua_1438ppmv_atm_2d_ml_0137to0338.mm.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1500ppmv/mlo_aqua_1500ppmv_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1594ppmv/mlo_aqua_1594ppmv_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_2000ppmv_Jor2/mlo_aqua_2000ppmv_Jor2_atm_2d_ml_0258to0277.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_2500ppmv_Jor2/mlo_aqua_2500ppmv_Jor2_atm_2d_ml_0258to0341.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_3000ppmv/mlo_aqua_3000ppmv_atm_2d_ml_0001to0089.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_3000ppmv_Jor/mlo_aqua_3000ppmv_Jor_atm_2d_ml_0298to0337.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_3000ppmv_Jor2/mlo_aqua_3000ppmv_Jor2_atm_2d_ml_0258to0425.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_5000ppmv/mlo_aqua_5000ppmv_atm_2d_ml_0001to0079.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_5000ppmv_Jor2/mlo_aqua_5000ppmv_Jor2_atm_2d_ml_0258to0306.mm.gm.nc\n",
+ "/work/bb1092/pp_icon_aes/MLO/AQUA/mlo_aqua_5000ppmv_Jor4/mlo_aqua_5000ppmv_Jor4_atm_2d_ml_0421to0490.mm.gm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "nsim = 11\n",
+ "timearray=np.zeros(nsim,dtype=object)\n",
+ "icelatarray=np.zeros(nsim,dtype=object)\n",
+ "exparray=np.empty(nsim, dtype=object)\n",
+ "\n",
+ "\n",
+ "exparray[0]=\"mlo_aqua_1438ppmv\"\n",
+ "exparray[1]=\"mlo_aqua_1500ppmv\"\n",
+ "exparray[2]=\"mlo_aqua_1594ppmv\"\n",
+ "exparray[3]=\"mlo_aqua_2000ppmv_Jor2\"\n",
+ "exparray[4]=\"mlo_aqua_2500ppmv_Jor2\"\n",
+ "exparray[5]=\"mlo_aqua_3000ppmv\"\n",
+ "exparray[6]=\"mlo_aqua_3000ppmv_Jor\"\n",
+ "exparray[7]=\"mlo_aqua_3000ppmv_Jor2\"\n",
+ "exparray[8]=\"mlo_aqua_5000ppmv\"\n",
+ "exparray[9]=\"mlo_aqua_5000ppmv_Jor2\"\n",
+ "exparray[10]=\"mlo_aqua_5000ppmv_Jor4\"\n",
+ "\n",
+ "timearray[0], icelatarray[0] = load_experiment2(exparray[0])\n",
+ "timearray[1], icelatarray[1] = load_experiment(exparray[1])\n",
+ "timearray[2], icelatarray[2] = load_experiment(exparray[2])\n",
+ "timearray[3], icelatarray[3] = load_experiment2(exparray[3])\n",
+ "timearray[4], icelatarray[4] = load_experiment2(exparray[4])\n",
+ "timearray[5], icelatarray[5] = load_experiment2(exparray[5])\n",
+ "timearray[6], icelatarray[6] = load_experiment2(exparray[6])\n",
+ "timearray[7], icelatarray[7] = load_experiment2(exparray[7])\n",
+ "timearray[8], icelatarray[8] = load_experiment2(exparray[8])\n",
+ "timearray[9], icelatarray[9] = load_experiment2(exparray[9])\n",
+ "timearray[10], icelatarray[10] = load_experiment2(exparray[10])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1438ppmv\n",
+ "1500ppmv\n",
+ "1594ppmv\n",
+ "2000ppmv\n",
+ "2500ppmv\n",
+ "3000ppmv\n",
+ "3000ppmv\n",
+ "label removed\n",
+ "3000ppmv\n",
+ "label removed\n",
+ "5000ppmv\n",
+ "5000ppmv\n",
+ "label removed\n",
+ "5000ppmv\n",
+ "label removed\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x216 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plot\n",
+ "\n",
+ "n = 20\n",
+ "color = plt.cm.tab20(np.linspace(0, 1,n))\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6,3))\n",
+ "ax.set_prop_cycle(cycler.cycler('color', color))\n",
+ "\n",
+ "\n",
+ "linearray=[]\n",
+ "for i in range(nsim):\n",
+ " linearray.append(plot_sim_overview(timearray[i], icelatarray[i], exparray[i], linearray))\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.ylim(0,90)\n",
+ "plt.xlim(0,510)\n",
+ "plt.xlabel(\"Time [years]\", fontsize=labelsize)\n",
+ "plt.ylabel(\"Ice-Edge Latitude [°]\", fontsize=labelsize)\n",
+ "plt.tick_params(labelsize=ticksize)\n",
+ "#plt.title(\"hice_unlim global sea ice border\")\n",
+ "plt.legend(ncol=3,edgecolor='none', facecolor='none',loc=1, \n",
+ " columnspacing=1, labelspacing=0.5, handlelength=2, handletextpad=1, fontsize=labelsize)\n",
+ "ax.spines['top'].set_color('none')\n",
+ "ax.spines['right'].set_color('none')\n",
+ "plt.savefig(\"plots/overview_semtner_5m.pdf\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python_scripts/paper_5000_and_3000ppmv_and_1500ppmv_plot.ipynb b/python_scripts/paper_5000_and_3000ppmv_and_1500ppmv_plot.ipynb
new file mode 100644
index 0000000..4cc4b63
--- /dev/null
+++ b/python_scripts/paper_5000_and_3000ppmv_and_1500ppmv_plot.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Temporal evolution of sea-ice edge latitude for selected simulations of 0L-Semtner unlimited and limited"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import xarray as xr\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from scipy import stats\n",
+ "\n",
+ "\n",
+ "def load_experiment(expname): #loads the dataset of a simulation\n",
+ " fname = expname +\"_atm_2d_ml.ym.gm.nc\" #filename of global yearly mean\n",
+ " dpath = \"/work/bb1092/pp_JH/\" +expname +\"/\" #simulation path\n",
+ " DS = xr.open_dataset(dpath +fname, decode_times=False) #loading of dataset\n",
+ " print(dpath +fname)\n",
+ " return expname, DS # returns the name of the experiment & the actual dataset\n",
+ "\n",
+ "def get_var(dataset, varname, offsettime): #gets the dataarray with one specific variable\n",
+ " da=getattr(dataset,varname) #read dataarray\n",
+ " da.squeeze() #squeeze dataarray (time is the only dimension)\n",
+ " if offsettime:\n",
+ " da=da.assign_coords(time=((da.time-da.time[0])/360)) #change time units from days to years & move the origin to 0\n",
+ " else:\n",
+ " da=da.assign_coords(time=((da.time)/360)) #change time units from days to years \n",
+ " return da\n",
+ "\n",
+ "def legend_color(ax, handle_array, pos, fontsize):\n",
+ " legend = ax.legend(handle_array,handlelength=0, handletextpad=0, edgecolor='none', facecolor='none', markerscale=0, loc=pos, fontsize=fontsize)\n",
+ " for item in legend.legendHandles:\n",
+ " item.set_visible(False)\n",
+ " for text in legend.get_texts():\n",
+ " if text.get_text()=='Winton':\n",
+ " text.set_color('C1')\n",
+ " if text.get_text()=='3L-Winton':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='0L-Semtner':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='Semtner_5m':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-lim5':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='0L-Semtner-limited':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='1438ppmv':\n",
+ " text.set_color('C0')\n",
+ " elif text.get_text()=='1500ppmv':\n",
+ " text.set_color('C1')\n",
+ " elif text.get_text()=='3000ppmv':\n",
+ " text.set_color('C2')\n",
+ " elif text.get_text()=='5000ppmv':\n",
+ " text.set_color('C3')\n",
+ " \n",
+ " return legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_77sic_hice_unlim/mlo_aqua_3000ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv_77sic_hice_unlim/mlo_aqua_5000ppmv_77sic_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1500ppmv_hice_unlim/mlo_aqua_1500ppmv_hice_unlim_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_3000ppmv_Jor2/mlo_aqua_3000ppmv_Jor2_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_5000ppmv_Jor2/mlo_aqua_5000ppmv_Jor2_atm_2d_ml.ym.gm.nc\n",
+ "/work/bb1092/pp_JH/mlo_aqua_1500ppmv/mlo_aqua_1500ppmv_atm_2d_ml.ym.gm.nc\n"
+ ]
+ }
+ ],
+ "source": [
+ "experiment1, DS1 = load_experiment(\"mlo_aqua_3000ppmv_77sic_hice_unlim\")\n",
+ "experiment2, DS2 = load_experiment(\"mlo_aqua_5000ppmv_77sic_hice_unlim\")\n",
+ "experiment3, DS3 = load_experiment(\"mlo_aqua_1500ppmv_hice_unlim\")\n",
+ "\n",
+ "experiment1_5m, DS1_5m = load_experiment(\"mlo_aqua_3000ppmv_Jor2\")\n",
+ "experiment2_5m, DS2_5m = load_experiment(\"mlo_aqua_5000ppmv_Jor2\")\n",
+ "experiment3_5m, DS3_5m = load_experiment(\"mlo_aqua_1500ppmv\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da1=get_var(DS1,\"sic\",True)\n",
+ "da2=get_var(DS2,\"sic\",True)\n",
+ "da3=get_var(DS3,\"sic\",True)\n",
+ "da1_5m=get_var(DS1_5m,\"sic\",True)\n",
+ "da2_5m=get_var(DS2_5m,\"sic\",True)\n",
+ "da3_5m=get_var(DS3_5m,\"sic\",True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# sea ice latitude\n",
+ "labelsize=8\n",
+ "ticksize=6\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(6,4))\n",
+ "ice_lat1 = np.arcsin(1-da1) * (180./np.pi) \n",
+ "ice_lat1_5m = np.arcsin(1-da1_5m) * (180./np.pi) \n",
+ "\n",
+ "ice_lat2 = np.arcsin(1-da2) * (180./np.pi) \n",
+ "ice_lat2_5m = np.arcsin(1-da2_5m) * (180./np.pi) \n",
+ "\n",
+ "ice_lat3 = np.arcsin(1-da3) * (180./np.pi) \n",
+ "ice_lat3_5m = np.arcsin(1-da3_5m) * (180./np.pi) \n",
+ "\n",
+ "(1-da1).plot(color='C0',ls='-.')\n",
+ "(1-da1_5m).plot(color='C2',ls='-.')\n",
+ "\n",
+ "(1-da2).plot(color='C0',ls='--')\n",
+ "(1-da2_5m).plot(color='C2',ls='--')\n",
+ "\n",
+ "(1-da3).plot(color='C0',ls='-')\n",
+ "(1-da3_5m).plot(color='C2',ls='-')\n",
+ "plt.xlim(0,310)\n",
+ "plt.xlabel(\"Time [years]\", fontsize=labelsize)\n",
+ "plt.ylabel(\"Ice-Edge Latitude [°]\", fontsize=labelsize)\n",
+ "\n",
+ "ax.spines['right'].set_color('none')\n",
+ "ax.spines['top'].set_color('none')\n",
+ "\n",
+ "ax.set_yticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(40)),np.sin(np.radians(50)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+ "ax.set_yticklabels([0,10,20,30,40,50,60,90])\n",
+ "plt.ylim(0,1)\n",
+ "\n",
+ "plt.title(\"\")\n",
+ "legend_color(ax,['0L-Semtner','0L-Semtner-limited'],1, labelsize)\n",
+ "\n",
+ "ax.annotate('5000ppmv',(42,0.4), fontsize=labelsize)\n",
+ "ax.annotate('3000ppmv',(40,0.17), fontsize=labelsize)\n",
+ "ax.annotate('1500ppmv',(20,0.85), fontsize=labelsize)\n",
+ "ax.tick_params(labelsize=ticksize) \n",
+ "\n",
+ "plt.savefig(\"plots/icelat_comparison.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
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