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Commit 19c6c8c9 authored by Gieße, Dr. Céline's avatar Gieße, Dr. Céline
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delete joypy install

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%% Cell type:code id:e4e31af2-b088-4035-a13a-06d7ddcdfb25 tags:
``` python
pip install joypy
```
%% Output
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: joypy in /home/u/u241297/.local/lib/python3.10/site-packages (0.2.6)
Requirement already satisfied: numpy>=1.16.5 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from joypy) (1.23.4)
Requirement already satisfied: matplotlib in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from joypy) (3.5.3)
Requirement already satisfied: pandas>=0.20.0 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from joypy) (2.0.0)
Requirement already satisfied: scipy>=0.11.0 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from joypy) (1.8.1)
Requirement already satisfied: python-dateutil>=2.8.2 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from pandas>=0.20.0->joypy) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from pandas>=0.20.0->joypy) (2023.3)
Requirement already satisfied: tzdata>=2022.1 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from pandas>=0.20.0->joypy) (2023.3)
Requirement already satisfied: pillow>=6.2.0 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from matplotlib->joypy) (9.2.0)
Requirement already satisfied: fonttools>=4.22.0 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from matplotlib->joypy) (4.39.3)
Requirement already satisfied: kiwisolver>=1.0.1 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from matplotlib->joypy) (1.4.4)
Requirement already satisfied: pyparsing>=2.2.1 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from matplotlib->joypy) (3.0.9)
Requirement already satisfied: packaging>=20.0 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from matplotlib->joypy) (23.1)
Requirement already satisfied: cycler>=0.10 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from matplotlib->joypy) (0.11.0)
Requirement already satisfied: six>=1.5 in /sw/spack-levante/mambaforge-22.9.0-2-Linux-x86_64-kptncg/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=0.20.0->joypy) (1.16.0)
Note: you may need to restart the kernel to use updated packages.
%% Cell type:code id:fb7d6d3e-7726-49d2-a8c5-85bad5854f10 tags:
``` python
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import pandas as pd
import joypy
from scipy import signal
SMALL_SIZE = 10
MEDIUM_SIZE = 12
BIGGER_SIZE = 14
plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.rc('font', size=MEDIUM_SIZE)
#colors
colors_gwl=['black',(253/255, 141/255, 60/255),(189/255, 0/255, 38/255)]
years = np.linspace(1850, 2100, 251)
Tglob = ["T0","T15","T27"]
Tglob_values = [0,1.5,2.7]
data_path = '/work/uo1075/u241297/data/large_ensembles/CMIP6/MPI-ESM1-2-LR/'
```
%% Cell type:markdown id:6528eeee-cbb0-44f2-9621-1fb397b2fb3d tags:
# Global warming levels
%% Cell type:code id:88e247a5-07b5-4210-9e78-46f9c663d96c tags:
``` python
#MPI-ESM
path=data_path+'global-mean/'
GSAT = np.zeros((50,251))
for i in range(50):
file = path+"tas_hist_ssp585_r"+str(i+1)+"_global-mean.nc"
GSAT[i,:]=np.squeeze(xr.open_dataset(file)['tas'])-273.15
GSAT_rm = np.array(pd.DataFrame(GSAT).rolling(20,axis=1,center=True).mean())
Tzero_GSAT = np.mean(GSAT[:,0:51]) #1850-1900
GSATa = GSAT-Tzero_GSAT
GSATa_rm = GSAT_rm-Tzero_GSAT
```
%% Cell type:code id:fe024f74-6e29-4b7f-a60d-7214aa82e639 tags:
``` python
GWLcross_ind = dict()
for Ti in range(3):
GWLcross_ind[Tglob[Ti]] = np.empty(50,dtype=int)
GWLcross_ind["T0"] = 50*[20]
for Ti in range(1,3):
for i in range(50):
j = 20
while (GSATa_rm[i,j] < Tglob_values[Ti]):
j = j+1
GWLcross_ind[Tglob[Ti]][i] = j
GWL_ind = dict()
for Ti in range(3):
GWL_ind[Tglob[Ti]] = np.zeros((50,20),dtype=int)
for Ti in range(3):
for i in range(50):
GWL_ind[Tglob[Ti]][i,:] = np.linspace(GWLcross_ind[Tglob[Ti]][i]-10,GWLcross_ind[Tglob[Ti]][i]+9,20)
```
%% Cell type:markdown id:01777080-578e-45d5-b047-8a94556cd51d tags:
# Daily Arctic temperatures
%% Cell type:code id:d95e9c5a-7b80-4e46-a023-d1e669faffbc tags:
``` python
gridarea=np.array(xr.open_dataset(data_path+'gridarea.nc')['cell_area'])
```
%% Cell type:code id:4834ae76-c68e-4492-9046-a5f3187952a6 tags:
``` python
path=data_path+'arctic/'
ASATday = np.zeros((50,251,365,13,192))
for i in range(50):
file = path+"tas_hist_ssp585_r"+str(i+1)+"_arctic.nc"
ASATday[i,:,:]=np.array(xr.open_dataset(file)['tas']).reshape((251,365,13,192))-273.15
```
%% Cell type:code id:d91655c0-91ba-407a-bf4e-5e8bfc6d925f tags:
``` python
ASATday_samples = dict()
for Ti in range(3):
ASATday_samples[Tglob[Ti]] = list()
for i in range(50):
ASATday_samples[Tglob[Ti]].append(ASATday[i,GWL_ind[Tglob[Ti]][i,:],:,:,:])
#free up space
del ASATday
```
%% Cell type:markdown id:a51c44c0-cd57-40c6-bef8-42436a92585d tags:
## Detrending
%% Cell type:code id:8b52e180-52d4-4a5a-9818-4523855b0d02 tags:
``` python
#ensemble mean
ASATday_ensmean_samples = dict()
for Ti in range(3):
ASATday_ensmean_samples[Tglob[Ti]] = np.mean(ASATday_samples[Tglob[Ti]],axis=0)
#daily trend
ASATday_trend_samples = dict()
for Ti in range(3):
ASATday_trend_samples[Tglob[Ti]] = ASATday_ensmean_samples[Tglob[Ti]]-signal.detrend(ASATday_ensmean_samples[Tglob[Ti]],axis=0)-np.mean(ASATday_ensmean_samples[Tglob[Ti]],axis=0)[np.newaxis,:,:,:]
#daily detrended
ASATday_detrend_samples = dict()
for Ti in range(3):
ASATday_detrend_samples[Tglob[Ti]] = ASATday_samples[Tglob[Ti]]-ASATday_trend_samples[Tglob[Ti]][np.newaxis,:,:,:,:]
```
%% Cell type:markdown id:d62c36b9-0912-42dc-b769-529b18510167 tags:
## Arctic mean + seasonal samples
%% Cell type:code id:2f131159-f470-4068-a7e1-329ce67462f6 tags:
``` python
#area-weighted Arctic mean
ASATday_detrend_arcticmean_samples = dict()
for Ti in range(3):
ASATday_detrend_arcticmean_samples[Tglob[Ti]] = np.average(ASATday_detrend_samples[Tglob[Ti]].reshape(50,20,365,13*192),axis=3,weights=np.ravel(gridarea))
```
%% Cell type:code id:ec372a8d-4bd8-45fa-8887-2a3b7a1fa43f tags:
``` python
#seasonal
ASATday_detrend_arcticmean_DJF_samples = dict()
ASATday_detrend_arcticmean_MAM_samples = dict()
ASATday_detrend_arcticmean_JJA_samples = dict()
ASATday_detrend_arcticmean_SON_samples = dict()
for Ti in range(3):
ASATday_detrend_arcticmean_DJF_samples[Tglob[Ti]] = np.append(ASATday_detrend_arcticmean_samples[Tglob[Ti]][:,:,-31:],ASATday_detrend_arcticmean_samples[Tglob[Ti]][:,:,:59],axis=2)
ASATday_detrend_arcticmean_MAM_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_samples[Tglob[Ti]][:,:,59:151]
ASATday_detrend_arcticmean_JJA_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_samples[Tglob[Ti]][:,:,151:243]
ASATday_detrend_arcticmean_SON_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_samples[Tglob[Ti]][:,:,243:334]
```
%% Cell type:markdown id:5607d7f9-6410-4453-94ef-8b45cde1200b tags:
## Anomalies
%% Cell type:code id:9a37cbc2-a542-4d62-9892-0c357a1f73c5 tags:
``` python
#MPI-ESM1-2-LR
path=data_path+'arctic-mean/'
ASATday = np.zeros((50,251,365))
ASATday_DJF = np.zeros((50,251,90))
ASATday_MAM = np.zeros((50,251,92))
ASATday_JJA = np.zeros((50,251,92))
ASATday_SON = np.zeros((50,251,91))
for i in range(50):
file = path+"tas_hist_ssp585_r"+str(i+1)+"_arctic-mean.nc"
ASATday[i,:,:]=np.array(xr.open_dataset(file)['tas']).reshape((251,365))-273.15
file = path+"tas_hist_ssp585_r"+str(i+1)+"_arctic-mean_DJF.nc"
ASATday_DJF[i,:]=np.array(xr.open_dataset(file)['tas']).reshape((251,90))-273.15
file = path+"tas_hist_ssp585_r"+str(i+1)+"_arctic-mean_MAM.nc"
ASATday_MAM[i,:]=np.array(xr.open_dataset(file)['tas']).reshape((251,92))-273.15
file = path+"tas_hist_ssp585_r"+str(i+1)+"_arctic-mean_JJA.nc"
ASATday_JJA[i,:]=np.array(xr.open_dataset(file)['tas']).reshape((251,92))-273.15
file = path+"tas_hist_ssp585_r"+str(i+1)+"_arctic-mean_SON.nc"
ASATday_SON[i,:]=np.array(xr.open_dataset(file)['tas']).reshape((251,91))-273.15
Tzero_ASATday = np.mean(ASATday[:,0:51,:])
ASATaday = ASATday-Tzero_ASATday
Tzero_ASATday_DJF = np.mean(ASATday_DJF[:,0:51,:])
ASATaday_DJF = ASATday_DJF-Tzero_ASATday_DJF
Tzero_ASATday_MAM = np.mean(ASATday_MAM[:,0:51,:])
ASATaday_MAM = ASATday_MAM-Tzero_ASATday_MAM
Tzero_ASATday_JJA = np.mean(ASATday_JJA[:,0:51,:])
ASATaday_JJA = ASATday_JJA-Tzero_ASATday_JJA
Tzero_ASATday_SON = np.mean(ASATday_SON[:,0:51,:])
ASATaday_SON = ASATday_SON-Tzero_ASATday_SON
#anomalies
ASATaday_detrend_arcticmean_samples = dict()
ASATaday_detrend_arcticmean_DJF_samples = dict()
ASATaday_detrend_arcticmean_MAM_samples = dict()
ASATaday_detrend_arcticmean_JJA_samples = dict()
ASATaday_detrend_arcticmean_SON_samples = dict()
for Ti in range(3):
ASATaday_detrend_arcticmean_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_samples[Tglob[Ti]] - Tzero_ASATday
ASATaday_detrend_arcticmean_DJF_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_DJF_samples[Tglob[Ti]] - Tzero_ASATday_DJF
ASATaday_detrend_arcticmean_MAM_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_MAM_samples[Tglob[Ti]] - Tzero_ASATday_MAM
ASATaday_detrend_arcticmean_JJA_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_JJA_samples[Tglob[Ti]] - Tzero_ASATday_JJA
ASATaday_detrend_arcticmean_SON_samples[Tglob[Ti]] = ASATday_detrend_arcticmean_SON_samples[Tglob[Ti]] - Tzero_ASATday_SON
```
%% Cell type:markdown id:4bf105f6-6ca9-42aa-97aa-2de480671210 tags:
## Remove seasonal cycle
%% Cell type:code id:dfce4f1b-72a1-4011-994b-4a2b92e7952a tags:
``` python
#seasonal cycle detrend_arcticmeaned
ASATaday_detrend_arcticmean_SC_samples = dict()
ASATaday_detrend_arcticmean_SC_DJF_samples = dict()
ASATaday_detrend_arcticmean_SC_MAM_samples = dict()
ASATaday_detrend_arcticmean_SC_JJA_samples = dict()
ASATaday_detrend_arcticmean_SC_SON_samples = dict()
for Ti in range(3):
ASATaday_detrend_arcticmean_SC_samples[Tglob[Ti]] = np.mean(ASATaday_detrend_arcticmean_samples[Tglob[Ti]],axis=(0,1))
ASATaday_detrend_arcticmean_SC_DJF_samples[Tglob[Ti]] = np.mean(ASATaday_detrend_arcticmean_DJF_samples[Tglob[Ti]],axis=(0,1))
ASATaday_detrend_arcticmean_SC_MAM_samples[Tglob[Ti]] = np.mean(ASATaday_detrend_arcticmean_MAM_samples[Tglob[Ti]],axis=(0,1))
ASATaday_detrend_arcticmean_SC_JJA_samples[Tglob[Ti]] = np.mean(ASATaday_detrend_arcticmean_JJA_samples[Tglob[Ti]],axis=(0,1))
ASATaday_detrend_arcticmean_SC_SON_samples[Tglob[Ti]] = np.mean(ASATaday_detrend_arcticmean_SON_samples[Tglob[Ti]],axis=(0,1))
#seasonal cycle detrend_arcticmeaned (anomalies)
ASATday_detrend_arcticmean_SC_samples = dict()
ASATday_detrend_arcticmean_SC_DJF_samples = dict()
ASATday_detrend_arcticmean_SC_MAM_samples = dict()
ASATday_detrend_arcticmean_SC_JJA_samples = dict()
ASATday_detrend_arcticmean_SC_SON_samples = dict()
for Ti in range(3):
ASATday_detrend_arcticmean_SC_samples[Tglob[Ti]] = np.mean(ASATday_detrend_arcticmean_samples[Tglob[Ti]],axis=(0,1))
ASATday_detrend_arcticmean_SC_DJF_samples[Tglob[Ti]] = np.mean(ASATday_detrend_arcticmean_DJF_samples[Tglob[Ti]],axis=(0,1))
ASATday_detrend_arcticmean_SC_MAM_samples[Tglob[Ti]] = np.mean(ASATday_detrend_arcticmean_MAM_samples[Tglob[Ti]],axis=(0,1))
ASATday_detrend_arcticmean_SC_JJA_samples[Tglob[Ti]] = np.mean(ASATday_detrend_arcticmean_JJA_samples[Tglob[Ti]],axis=(0,1))
ASATday_detrend_arcticmean_SC_SON_samples[Tglob[Ti]] = np.mean(ASATday_detrend_arcticmean_SON_samples[Tglob[Ti]],axis=(0,1))
#subtract seasonal cycle
ASATaday_detrend_arcticmean_deseasonalized_samples = dict()
ASATaday_detrend_arcticmean_deseasonalized_DJF_samples = dict()
ASATaday_detrend_arcticmean_deseasonalized_MAM_samples = dict()
ASATaday_detrend_arcticmean_deseasonalized_JJA_samples = dict()
ASATaday_detrend_arcticmean_deseasonalized_SON_samples = dict()
for Ti in range(3):
ASATaday_detrend_arcticmean_deseasonalized_samples[Tglob[Ti]] = ASATaday_detrend_arcticmean_samples[Tglob[Ti]]-ASATaday_detrend_arcticmean_SC_samples[Tglob[Ti]][np.newaxis,np.newaxis,:]+np.mean(ASATaday_detrend_arcticmean_samples[Tglob[Ti]])
ASATaday_detrend_arcticmean_deseasonalized_DJF_samples[Tglob[Ti]] = ASATaday_detrend_arcticmean_DJF_samples[Tglob[Ti]]-ASATaday_detrend_arcticmean_SC_DJF_samples[Tglob[Ti]][np.newaxis,np.newaxis,:]+np.mean(ASATaday_detrend_arcticmean_DJF_samples[Tglob[Ti]])
ASATaday_detrend_arcticmean_deseasonalized_MAM_samples[Tglob[Ti]] = ASATaday_detrend_arcticmean_MAM_samples[Tglob[Ti]]-ASATaday_detrend_arcticmean_SC_MAM_samples[Tglob[Ti]][np.newaxis,np.newaxis,:]+np.mean(ASATaday_detrend_arcticmean_MAM_samples[Tglob[Ti]])
ASATaday_detrend_arcticmean_deseasonalized_JJA_samples[Tglob[Ti]] = ASATaday_detrend_arcticmean_JJA_samples[Tglob[Ti]]-ASATaday_detrend_arcticmean_SC_JJA_samples[Tglob[Ti]][np.newaxis,np.newaxis,:]+np.mean(ASATaday_detrend_arcticmean_JJA_samples[Tglob[Ti]])
ASATaday_detrend_arcticmean_deseasonalized_SON_samples[Tglob[Ti]] = ASATaday_detrend_arcticmean_SON_samples[Tglob[Ti]]-ASATaday_detrend_arcticmean_SC_SON_samples[Tglob[Ti]][np.newaxis,np.newaxis,:]+np.mean(ASATaday_detrend_arcticmean_SON_samples[Tglob[Ti]])
```
%% Cell type:markdown id:763c3184-d121-457d-9ca3-07079b1b9779 tags:
# PDF plots
%% Cell type:code id:b7b745bf-25b4-4345-962f-56fb7cd4dc0d tags:
``` python
df_annual = pd.DataFrame(data={'T0': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_samples["T0"]), 'T15': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_samples["T15"]), 'T27': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_samples["T27"]), 'Data': 'annual'})
df_DJF = pd.DataFrame(data={'T0': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_DJF_samples["T0"]), 'T15': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_DJF_samples["T15"]), 'T27': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_DJF_samples["T27"]), 'Data': 'DJF'})
df_MAM = pd.DataFrame(data={'T0': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_MAM_samples["T0"]), 'T15': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_MAM_samples["T15"]), 'T27': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_MAM_samples["T27"]), 'Data': 'MAM'})
df_JJA = pd.DataFrame(data={'T0': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_JJA_samples["T0"]), 'T15': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_JJA_samples["T15"]), 'T27': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_JJA_samples["T27"]), 'Data': 'JJA'})
df_SON = pd.DataFrame(data={'T0': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_SON_samples["T0"]), 'T15': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_SON_samples["T15"]), 'T27': np.ravel(ASATaday_detrend_arcticmean_deseasonalized_SON_samples["T27"]), 'Data': 'SON'})
df = pd.concat([df_annual,df_DJF,df_MAM,df_JJA,df_SON])
df = df.groupby('Data', sort=False)
# Draw Plot
plt.figure()
fig, axes = joypy.joyplot(df,
column=['T0','T15','T27'],
by='Data',
x_range=[-7,17],
color=colors_gwl,
overlap=0.2,
alpha=0.5,
figsize=(8,6))
plt.text(-6.5,0.95,'Global warming level:',ha='center',color='black',fontsize=12)
plt.text(-0.2,0.95,'pre-industrial',ha='center',color=colors_gwl[0],fontsize=12,fontweight='bold')
plt.text(4,0.95,'1.5°C',ha='center',color=colors_gwl[1],fontsize=12,fontweight='bold')
plt.text(7,0.95,'2.7°C',ha='center',color=colors_gwl[2],fontsize=12,fontweight='bold')
plt.xlabel('Arctic mean SAT anomaly [°C]')
#plt.savefig('ASATaday_pdfs_allseasons.png',dpi=300)
plt.show()
```
%% Output
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