CAMMAC https://cammac.readthedocs.io

S.Sénési for Météo-France - sept 2019 to march 2021

Create a basic data versions dictionnary , choosing models "by hand"

This does not allow to finely select e.g. realizations per variable. See sibling notebook for a more complete selection of datasets

Also (see bottom) : create an extract of an existing version dictionnary, by restricting the models list

import sys
import json

from IPython.core.display import display, HTML, Image
display(HTML("<style>.container { width:100% !important; }</style>"))

Tell preferences on some facets, per model (here, we assume this has not to depend on variable)

preferred_grids={ "GFDL-CM4":"gr1"}
preferred_variants={ }
preferred_versions={ }

Tell which is period is covered by piControl, for each model to use (here, we assume this has not to depend on variable)

 One can use CliMAF dataset's method 'explore' for investigating which periods are available

piControl_period={
    "CESM2-WACCM":"0001-0499",
    "CNRM-ESM2-1":"1850-2349",
    "GFDL-ESM4":"0001-0500",
    "IPSL-CM6A-LR" : "1850-3049", 
    "GFDL-CM4":"0151-0650",
}

A function for creating a dict of basic values for facets : grid, variant, version,period for a list of models

def basic_data_versions(variable,models) :
    variables=[variable]
    experiments=["historical","ssp126","ssp245","ssp585"]
    #
    data_versions=dict()
    for model in models :
        grid=preferred_grids.get(model,"g*")
        variant=preferred_variants.get(model,"r1i1p1f*")
        version=preferred_versions.get(model,"latest")
        for variable in variables :
            # For SSPs, do not include the period
            quadruplet=(grid,variant,version,"*")
            for exp in experiments :
                feed_dic(data_versions,quadruplet,exp,variable,model)
            # For piControl, do include the period
            quadruplet=(grid,variant,version,piControl_period[model])
            feed_dic(data_versions,quadruplet,"piControl",variable,model)
    return data_versions

def write_versions_dic(d,tag) :
    jsfile="Data_versions_selection_%s.json"%tag
    with open(jsfile,"w") as f :
        json.dump(d,f,separators=(',', ': '),indent=3,ensure_ascii=True)
    print "Data versions dictionnary written as "+jsfile

Create and write a basic versions dictionnary

models=piControl_period.keys()
d=basic_data_versions("pr",models)
write_versions_dic(d,"5models")

Create a dict for reproducing SOD figure 8.18 for evspsbl except for EC-Earth models (which data was retracted)

# Use original per experiment dict and merge all models
models_to_plot_by_var={
    "ssp585": ["BCC-CSM2-MR", "CAMS-CSM1-0","CanESM5", "CNRM-ESM2-1",
               "IPSL-CM6A-LR",
               "MIROC-ES2L","MIROC6", "UKESM1-0-LL","MRI-ESM2-0",
               "CESM2","CESM2-WACCM", "GFDL-CM4","GFDL-ESM4"],
    "ssp245": ["BCC-CSM2-MR", "CAMS-CSM1-0","CanESM5", "CNRM-ESM2-1",
               "IPSL-CM6A-LR",
               "MIROC-ES2L","MIROC6", "UKESM1-0-LL","MRI-ESM2-0", "HadGEM3-GC31-LL",
               "CESM2","CESM2-WACCM", "GFDL-CM4","GFDL-ESM4"],
    "ssp126": ["BCC-CSM2-MR", "CAMS-CSM1-0","CanESM5", "CNRM-ESM2-1",
               "IPSL-CM6A-LR",
               "MIROC-ES2L","MIROC6","UKESM1-0-LL","MRI-ESM2-0",
               "CESM2","CESM2-WACCM", "GFDL-ESM4"],
}
# Merge model names across SSPs in all_models
ens=set()
for e in models_to_plot_by_var :
    for m in models_to_plot_by_var[e] :
        ens.add(m)
all_models=list(ens)
all_models.sort()

# Read dictionnary used for the february 2019 version of the figure
full_dic=read_versions_dictionnary("20200219","./")

# Feed a dictionnary with same content, but restricted to models in all_models
short_dic=dict()
for m in all_models :
    for exp in ["ssp126","ssp245","ssp585", "piControl","historical"] :
        try :
            feed_dic(short_dic,full_dic[exp]["evspsbl"][m],exp,"evspsbl",m)
        except :
            print "No entry for %s and %s"%(exp,m)
write_versions_dic(short_dic,"20200219_short_ssp585_evspsbl")