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from IPython.display import Image
Image(url='https://vesg.ipsl.upmc.fr/thredds/fileServer/IPSLFS/jservon/CliMAF_Notebooks_html/CliMAF-logo-small.png')
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A science-oriented framework to ease the analysis of climate model simulations¶
WP5 ANR Convergence¶
Development team: Stéphane Sénési (CNRM-GAME), Gaëlle Rigoudy (CNRM-GAME), Jérôme Servonnat (LSCE-IPSL), Ludivine Vignon (CNRM-GAME), Laurent Franchisteguy (CNRM-GAME), Patrick Brockmann (LSCE-IPSL)¶
Beta-testing: Olivier Marti (LSCE-IPSL), Marie-Pierre Moine (CERFACS), Emilia Sanchez-Gomez (CERFACS)¶
¶
contact: climaf@meteo.fr¶
users list: climaf-users@meteo.fr¶
The goals of CliMAF are to provide the scientists with simplified and science-oriented means for :
- accessing both model and references data
- pre-tretament (period and geographical selections, regridding, averaging like seasonal mean computations...)
- plotting maps, cross-sections and time series
- building atlases
- plugging personal scripts in an atlas or in an analysis workflow
- sharing such scripts
- handlling ensembles (multi-model, multi-realization) CliMAF provides full managment of the outputs by handling the naming of the output files; it also stores the information on "how I obtained this file" under the form of an expression called CRS (CliMAF Reference Syntax) that allows checking for the existing files and avoid recomputing the same files twice (or more).
Access to CMIP data at CLIMERI, and much more¶
In this notebook we will see:
- A simple example with a CMIP5 dataset
- Explore an archive with the .explore() built-in method
- the CMIP6 archive
- use the magical CMIP5_extent project to extend the CMIP5 historical runs with a bit of scenario
- CORDEX
- libIGCM simulations
- some references #
First, import climaf¶
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from climaf.api import *
And set verbosity ('critical' -> minimum ; 'debug' -> maximum)¶
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clog('critical') # min verbosity = critical < warning < info < debug = max verbosity
1. Access to CMIP5 data: simple example¶
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# -- We use ds() to get the dataset
dat_cmip5 = ds(project='CMIP5',
model='CNRM-CM5',
variable='tos',
experiment='historical',
period='1980-1981',
frequency='monthly',
)
summary(dat_cmip5)
# -- summary() gives the list of files found by ds() and the pairs 'facets':'values' associated with the request
# -> The user can then refine the request to select only one file
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# -> in this case,
dat_cmip5.explore('choices')
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# -- We use ds() to get the dataset
dat_cmip5 = ds(project='CMIP5',
model='CNRM-CM5',
variable='*',
experiment='historical',
period='1980-1981',
frequency='monthly',
)
dat_cmip5.explore('choices')
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dat_cmip5 = ds(project='CMIP5',
model='CNRM-CM5',
variable='*',
experiment='historical',
period='1980-1981',
frequency='monthly',
table='Amon'
)
dat_cmip5.explore('choices')
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Note that you could also use summary(), that will make use of .explore('choices') and provide you with the same result:¶
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summary(dat_cmip5)
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dat_cmip5 = ds(project='CMIP5',
model='CNRM-CM5',
variable='tos',
experiment='historical',
period='1980-1981',
frequency='monthly'
)
dat_cmip5.crs
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dat_cmip5 = dat_cmip5.explore('resolve')
summary(dat_cmip5)
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dat_cmip5.crs
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3. Explore CMIP6 data¶
¶
Important info concerning CMIP6: use 'table' and not 'frequency' to specify the frequency with the CMIP6 CliMAF project. Here is the list of tables for CMIP6:¶
https://github.com/WCRP-CMIP/CMIP6_CVs/blob/master/CMIP6_table_id.json
Basically, a table is a combination of the realm considered (A for atmosphere, O for ocean, SI for Sea Ice, etc.) and the frequency (mon for monthly, day for daily, etc.)
¶
At the same time, we illustrate the use of period='*' to get the full period available¶
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dat_cmip6 = ds(project='CMIP6', variable='pr', period='*', table='Amon', model='CNRM-CM6-1')
dat_cmip6.explore('choices')
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CliMAF found data covering the period '1850-2014' for your request; you can specify your own period now that you know what is available, or use dat_cmip6 as is to use the full available period¶
One value per wildcard: use explore('resolve') to make a well-defined object¶
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ok_dat_cmip6 = dat_cmip6.explore('resolve')
summary(ok_dat_cmip6)
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Same request but with realization='*' (default value is r1i1p1f1)¶
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dat_cmip6 = ds(project='CMIP6', variable='pr', period='*', table='Amon', model='CNRM-CM6-1', realization='*')
dat_cmip6.explore('choices')
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Geographical region selection from ds()¶
for all the data requests with ds(), you can specify a geographical domain:¶
--> domain = domain=[latmin,latmax,lonmin,lonmax]¶
Warning: it is really effective on regular grids (based on cdo sellonlatbox)¶
--> For irregular grids, it is recommended to regrid first an then use CliMAF llbox()¶
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test = ds(project='CMIP5',
model='CNRM-CM5',
variable='tas',
experiment='historical',
period='1980-1981',
frequency='monthly',
domain=[20,80,-80,40] # or domain='20,80,-80,40'
)
implot(test)
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req_cmip5_extent = ds(project='CMIP5_extent', variable='tas', experiment='historical',
model='IPSL-CM5A-MR', period='1990-2010', table='Amon', version='latest')
req_cmip5_extent.explore('choices')
ok_req_cmip5_extent = req_cmip5_extent.explore('resolve')
summary(ok_req_cmip5_extent)
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Same with all the models!¶
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req_cmip5_extent = ds(project='CMIP5_extent', variable='tas', experiment='historical',
model='*', period='1990-2010', table='Amon', version='latest')
ens_cmip5_extent = req_cmip5_extent.explore('ensemble')
summary(ens_cmip5_extent)
Safety step: check the consistency between request and actual available period with check_time_consistency_CMIP()¶
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# -- ok_ens_cmip5_extent will be the checked ensemble
ok_ens_cmip5_extent = ens_cmip5_extent.copy()
for elt in ens_cmip5_extent:
print elt
if not check_time_consistency_CMIP(ens_cmip5_extent[elt]):
print 'period not covered for ',elt
ok_ens_cmip5_extent.pop(elt)
Check that the time check actually worked:¶
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for elt in ok_ens_cmip5_extent:
print elt
if not check_time_consistency_CMIP(ok_ens_cmip5_extent[elt]):
print 'period not covered for ',elt
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summary(ok_ens_cmip5_extent)
-- More data access at CLIMERI through CliMAF¶
List the available projects with 'projects()'¶
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projects()
5. Some examples with CORDEX¶
Let's start with a 'blank page':¶
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req_cordex = ds(project='CORDEX', variable='tas', frequency='daily', period='1980-2005',root='/bdd')
req_cordex.explore('choices')
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--> You now have all the possible choices + the list of keywords that need to be specified¶
Try to specify your data request yourself!¶
Important note: at the moment, ds() is not able to produce an ensemble on two keywords at the same time:¶
--> we thus have to build an ensemble by hand to deal with the GCM/RCM (driving_model/model) couples¶
--> See more details in the notebooks on 'handling ensembles' (part 2)¶
And also note that you have CORDEX-Adjust and CORDEX_extent available¶
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# -- Example with CORDEX_extent and one RCM (model)
cordex_extent = ds(project='CORDEX_extent', period='1990-2010', variable='tas', frequency='daily',
model_version='v1', CORDEX_domain='EUR-44', model='SMHI-RCA4')
cordex_extent.explore('choices')
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cordex_adjust = ds(project='CORDEX-Adjust', period='*', variable='tasmax')
cordex_adjust.explore('choices')
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req_ipsl = ds(project='IGCM_OUT',
login='p86denv',
model='IPSLCM6',
simulation='CM6013-pi-01',
experiment='piControl',
DIR='OCE',
frequency='seasonal',
clim_period='*',
variable='tos')
req_ipsl.explore('choices')
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7. Some references (used by the C-ESM-EP for routine evaluation)¶
Pre-computed climatologies:¶
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ref_climato = ds(project='ref_climatos', variable='pr')
print 'explore("choices"):'
print ref_climato.explore('choices')
print ' -- '
print 'Summary:'
summary(ref_climato)
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And 'time series' references (with a temporal dimension)¶
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from climaf.api import *
ref_ts = ds(project='ref_ts', variable='pr', period='*')
print 'explore("choices"):'
print ref_ts.explore('choices')
print ' -- '
print 'Summary:'
summary(ref_ts)
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Tip: check the period available for each product: explore('choices', group_periods_on='product', operation=None)¶
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ref_ts.explore('choices', group_periods_on='product', operation=None)
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Last example: E-OBS, a Climserv archive stored on /bdd¶
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eobs = ds(project='E-OBS')
eobs.explore('choices')
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summary(eobs)
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And see how to add your own data in Adding_access_to_my_own_data.ipynb¶
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