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Commit d1249cef authored by Aiko Voigt's avatar Aiko Voigt
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Adds analysis plots of temperature and precipitation

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......@@ -2,4 +2,7 @@ runs/amip/logfiles/
runs/slab4x-sun/logfiles/
runs/slab4x-vap/logfiles/
runs/slab4x/logfiles/
runs/slabctr/logfiles/
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runs/slabctr/logfiles/
analysis/.ipynb_checkpoints/
analysis/__pycache__/
analysis/core.py 0 → 100644
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import xarray as xr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# load yearly mean dataset
def load_yearmean_dataset(file: str, startdate: str):
ds = xr.load_dataset(file)
ds["time"] = pd.date_range(startdate, freq="ME", periods=ds.time.size)
return ds.groupby(ds.time.dt.year).mean()
# compute global mean
def globalmean(ds):
weights = np.cos(np.deg2rad(ds.lat))
weights.name = "weights"
ds_weighted = ds.weighted(weights)
return ds_weighted.mean(("lon", "lat"))
def beautify_timeseries(ax, yaxis0):
""" Makes plots of time series nicer in terms of axes and labeling"""
# adjust spines
ax = plt.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',yaxis0))
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%% Cell type:markdown id:1d1abe75-9ec9-46a7-b938-7eefaf5e37ad tags:
# Evolution of global mean surface temperature and sea ice area
%% Cell type:code id:80f55624-7ed4-4e11-ab49-4ce095a98d71 tags:
``` python
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
```
%% Cell type:code id:dda27ec2-7f9f-44d5-9c31-cbf8188fe59e tags:
``` python
import core as core
```
%% Cell type:code id:caf0443b-bb04-4952-b0c0-2432a95d991f tags:
``` python
# path to model output
path="/home/voigta80/LEHRE/climmodlab_s2024/output4students/"
```
%% Cell type:code id:115178b5-2b01-4441-a373-eaf55555dc6f tags:
``` python
slabctr = core.load_yearmean_dataset(file=path+"/slabctr/slabctr_atm_2d_ml_197901-202901.r180x90.nc", startdate="1979-01-01")
slab4x = core.load_yearmean_dataset(file=path+"/slab4x/slab4x_atm_2d_ml_199002-203901.r180x90.nc", startdate="1999-02-01")
slabsun = core.load_yearmean_dataset(file=path+"/slab4x-sun/slab4x-sun_atm_2d_ml_199002-203901.r180x90.nc", startdate="1999-02-01")
slabvap = core.load_yearmean_dataset(file=path+"/slab4x-vap/slab4x-vap_atm_2d_ml_199002-203901.r180x90.nc", startdate="1999-02-01")
```
%% Cell type:code id:364a4154-b864-4d60-ba69-4ac5384e05cb tags:
``` python
slabctr_mean = core.globalmean(slabctr)
slab4x_mean = core.globalmean(slab4x)
slabsun_mean = core.globalmean(slabsun)
slabvap_mean = core.globalmean(slabvap)
```
%% Cell type:code id:8dfda055-ea17-4a09-854f-617107fb7c6d tags:
``` python
# some data massaging to remove faulty years
# year 2029 of slabctr is not complete
slabctr_mean = slabctr_mean.where(slabctr_mean.year < 2029, drop=True)
# year 2026 of slabvap has missing values
slabvap_mean = xr.merge([slabvap_mean.where(slabvap_mean.year < 2026, drop=True), slabvap_mean.where(slabvap_mean.year > 2026, drop=True)])
```
%% Cell type:markdown id:16822ce6-5606-4693-9f3b-7359bc89dd92 tags:
Global mean surface temperature
%% Cell type:code id:0e30e8ce-54d8-4aa9-85b8-2d8e5f76bec6 tags:
``` python
# print global mean surface temperatures averaged over 10 years to screen
print("Slab control :", slabctr_mean["ts"].isel(year=slice(-10,-1)).mean().values)
print("Slab 4x :", slab4x_mean["ts"].isel(year=slice(-10,-1)).mean().values)
print("Slab 4x-vapor:", slabvap_mean["ts"].isel(year=slice(-10,-1)).mean().values)
print("Slab 4x-sun :", slabsun_mean["ts"].isel(year=slice(-10,-1)).mean().values)
```
%% Output
Slab control : 287.910243809416
Slab 4x : 294.4904180230014
Slab 4x-vapor: 293.87587189765486
Slab 4x-sun : 290.28140743731694
%% Cell type:code id:179d1984-872d-42bf-8722-7a53b9b87989 tags:
``` python
plt.figure(figsize=(6,4))
ax=plt.subplot(1,1,1)
plt.plot(slabctr_mean.year, slabctr_mean["ts"], color="royalblue")
plt.plot(slab4x_mean.year, slab4x_mean["ts"], color="crimson")
plt.plot(slabvap_mean.year, slabvap_mean["ts"], color="coral")
plt.plot(slabsun_mean.year, slabsun_mean["ts"], color="seagreen")
plt.ylim(287,295)
plt.xlim(1980,2038)
core.beautify_timeseries(ax, yaxis0=286.5)
plt.xlabel("year", loc="right", size=12)
plt.ylabel("global-mean surface temperature / K", loc="top", size=12)
plt.text(2038,288.2, "present-day simulation: 287.9 K", ha="right", color="royalblue", size=12)
plt.text(2038,294.7, "4xCO2: +6.5 K", ha="right", color="crimson", size=12)
plt.text(2038,293.2, "4xCO2-vapor: +6.1 K", ha="right", color="coral", size=12)
plt.text(2038,290.6, "4xCO2-sun: +2.4 K", ha="right", color="seagreen", size=12)
plt.savefig("globalmean_ts.pdf", bbox_inches="tight")
```
%% Output
%% Cell type:markdown id:4dbf37eb-835a-40ed-b2fc-71b91080f4ca tags:
Global mean sea ice cover
%% Cell type:code id:e74f9f0c-b147-45b3-8075-be7efa5eefbb tags:
``` python
plt.figure(figsize=(6,4))
ax=plt.subplot(1,1,1)
# surface area of earth in km2
aearth=4*np.pi*np.power(6371,2)
plt.plot(slabctr_mean.year, 1e-6*aearth*slabctr_mean["sic"], color="royalblue")
plt.plot(slab4x_mean.year, 1e-6*aearth*slab4x_mean["sic"], color="crimson")
plt.plot(slabvap_mean.year, 1e-6*aearth*slabvap_mean["sic"], color="coral")
plt.plot(slabsun_mean.year, 1e-6*aearth*slabsun_mean["sic"], color="seagreen")
plt.ylim(7, 20)
plt.xlim(1980,2038)
core.beautify_timeseries(ax, yaxis0=6)
plt.xlabel("year", loc="right", size=12)
plt.ylabel(r"global sea ice area / 10$^\text{6}$ km$^\text{2}$", loc="top", size=12)
plt.savefig("globalmean_sic.pdf", bbox_inches="tight")
```
%% Output
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