def read_catalog(model=None, data='cmip5', freq='day', var='tasmax'):
'''
Read in catalogue of climate models
Parameters:
----------
model (string):
None deafult
HadGEM2-CC, BNU-ESM, CanESM2, bcc-csm1
freq (string):
deafult day
other options - month, year
data (string):
deafult cmip5
var (string):
deafult tas
Returns:
--------
data (pandas.DataFrame):
catalogue of all runs from the selected model
'''
if (model is not None):
catl_model = bp.catalogue(dataset=data,
Model=model,
Frequency=freq,
Var=var).reset_index(drop=True)
else:
catl_model = bp.catalogue(dataset=data, Frequency=freq,
Var=var).reset_index(drop=True)
return catl_model
def get_gcm_catalogue(self):
"""
Output: dataframe of baspy catalogue
"""
print("GCM settings: model = {}, \
rcp = {}, \
start = {}, \
end = {}".format(self.settings['model'],
self.settings['future_rcp'],
self.settings['model_start'],
self.settings['model_end'],
))
# Retrieve catalogue
self.catalogue = bp.catalogue(dataset='cmip5',
Model=self.settings['model'],
Frequency='day',
Experiment=['historical'] + self.settings['future_rcp'],
RunID='r1i1p1',
Var=self.settings['variable_name_gcm'],
).reset_index(drop=True)
print(self.catalogue)
return self.catalogue
import baspy as bp
cmip5_cat = bp.catalogue(dataset='cmip5', refresh=True)
happi_cat = bp.catalogue(dataset='happi', refresh=True)
print('done')
import baspy as bp
import xarray as xr
'''
Define scope of CMIP6 that we want (our catalogue)
* amip = atmosphere-only run (with transient/observed sea surface temperatures)
* tasmin,tasmax = minimum/maximum temperature over period
* CMOR (Climate Model Output Rewriter), defines, amongst other things, the temporal
frequency of the data (monthly, daily etc)
see: https://github.com/PCMDI/cmip6-cmor-tables/tree/master/Tables
* Model = our chosen CMIP6 climate model
* RunID = the run ID :-)
'''
catlg = bp.catalogue(dataset='cmip6',
Experiment='amip',
Var=['tasmax', 'tasmin'],
CMOR='day',
Model='CNRM-CM6-1',
RunID='r1i1p1f2')
''' Read Datasets using BASpy wrapper for Xarray '''
tasmin_ds = bp.open_dataset(catlg[catlg.Var == 'tasmin'])
tasmax_ds = bp.open_dataset(catlg[catlg.Var == 'tasmax'])
''' extract DataArray from Dataset '''
tasmin = tasmin_ds.tasmin
tasmax = tasmax_ds.tasmax
'''
Now analyse CMIP6 data using the Xarray framework
[1] http://xarray.pydata.org/en/stable/
[2] https://github.com/scotthosking/notebooks/blob/master/getting_started_with_Xarray%2BDask.ipynb
'''
import baspy as bp
import numpy as np
import xarray as xr
from pathlib import Path
import pandas as pd
import logging
from typing import List
# load the baspy catalogue
df = bp.catalogue(dataset="cmip6", CMOR="Amon")
def get_global_mean(
Model: str,
Experiment: str,
RunID: str = None,
year_ranges: List[slice] = [slice(1850, 1950),
slice(2000, 2010)],
var: str = "tas",
) -> List[float]:
"""Get area weighted global mean values for a CMIP6 run for year slices.
The actual calculation is all done by xarray. Most of this code is a baspy
wrapper.
A match is looked for in the baspy catalogue, then the global surface mean
temperature is calculated from atmospheric monthly data.
Only tested with CMIP6.
Input:
Model, Experiment RunID --- same meaning as in baspy module
year_ranges --- a list of slices
region_bounds = bp.region.Sub_regions.central_england
### Historical period
hist_con = iris.Constraint(year=lambda y: 1979 <= y <= 2004)
erai = iris.load_cube(
'/group_workspaces/jasmin4/bas_climate/data/ecmwf/era-interim/mon/surface/t2m_mon.nc',
callback=edit_erai_attrs,
constraint=hist_con)
erai = bp.region.extract(erai, region_bounds)
hist_catlg = bp.catalogue(Experiment='historical',
Frequency='mon',
Model='HadGEM2-CC',
Var='tas',
RunID='r1i1p1')
hist = bp.get_cube(hist_catlg, constraints=hist_con)
hist = bp.region.extract(hist, region_bounds)
### Future Period
fut_con = iris.Constraint(year=lambda y: 2070 <= y <= 2100)
fut_catlg = bp.catalogue(Experiment='rcp45',
Frequency='mon',
Model='HadGEM2-CC',
Var='tas',
RunID='r1i1p1')
fut = bp.get_cube(fut_catlg, constraints=fut_con)
fut = bp.region.extract(fut, region_bounds)
# -*- coding: UTF-8 -*-
from jasmin.downloader import dataprocessing as dp
from baspy._xarray.util import extract_region
import pandas as pd
import baspy as bp
## _cm ending means climate model
## tas means surface temp
cat_model = bp.catalogue(dataset='cmip5',
Model='HadGEM2-CC',
Frequency='day',
Experiment='rcp45',
RunID='r1i1p1',
Var='tas').reset_index(drop=True)
for index, row in cat_model.iterrows():
cm = bp.open_dataset(row)
tas_cm = cm.tas
lon_cor_cm = dp.roll_lon(tas_cm)
## Extract a specific region
extr_reg_cm = extract_region(lon_cor_cm, kabul)
reg_time_sliced_cm = dp.slice_time(extr_reg_cm, 1979, 2050)
"""
# Required directories
loaddir_CMIP = 'Priestley-Centre/Near_term_warming/analysis_figure_code/'+\
'SuppFig2/saved_arrays'
savedir = 'Priestley-Centre/Near_term_warming/analysis_figure_code/'+\
'SuppFig8/saved_data'
### ------ Load in CMIP6 data ------
# Load models
models = np.load(loaddir_CMIP + '/models_gtas_CMIP6_piControl.npy')
# Load catalogue so can extract runids
var = 'tas'
cat_PI = bp.catalogue(dataset='cmip6',Var=var,Experiment='piControl',\
CMOR='Amon')
years = np.linspace(1, 20, 20)
### Process data, one model and RunID at a time
i = 0
fig,axs = plt.subplots(7,7,sharex=True,sharey=True,\
figsize=(15,12))
fig.suptitle('PDFs of rolling GSAT trends for 20-year segments of CMIP6 '+\
'piControl runs',fontsize=20)
axs = axs.ravel()
for model in models:
## Get data for model
filtmod_PI = cat_PI[cat_PI['Model'] == model]