NetCDF Zarr Sequential Recipe: CMIP6¶
This tutorial describes how to create a suitable recipe for many of the CMIP6 datasets. The source data is a sequence of NetCDF files accessed from the ‘s3://esgf-world’ bucket. The target is a Zarr store.
Background¶
The s3://esgf-world bucket has about 250,000 datasets stored in about 950,000 netcdf files (for an average of about four netcdf files per dataset). This is a small subset of the WCRP-CMIP6 collection available at the Federated ESGF-COG nodes such as https://esgf-node.llnl.gov/search/cmip6, but it is faster and easier to work with.
Each CMIP6 dataset can be identified by a 6-tuple consisting of:
(model,experiment,ensemble_member,mip_table,variable,grid_label)
and so a convenient name for a particular dataset is a string of these values joined with a ‘.’ separator:
dataset = model.experiment.ensemble_member.mip_table.variable.grid_label
There can be multiple versions of a dataset, designated by a string beginning with ‘v’ and then an 8 digit date, loosely associated with its creation time
import pandas as pd
import xarray as xr
import s3fs
Step 1: Get to know your source data¶
The CMIP6 collection is very heterogeneous, so getting to know the source data is rather complicated. We first need to identify a dataset and learn how to list the set of netcdf files which are associated with it. Fortunately, you can explore the data here: https://esgf-world.s3.amazonaws.com/index.html#CMIP6/ or download a CSV file listing all of the netcdf files, one per line.
Here we will read the CSV file into a pandas dataframe so we can search, sort and subset the available datasets and their netcdf files.
netcdf_cat = 's3://cmip6-nc/esgf-world.csv.gz'
df_s3 = pd.read_csv(netcdf_cat, dtype='unicode')
df_s3.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1056266 entries, 0 to 1056265
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 project 1056266 non-null object
1 institution_id 1056266 non-null object
2 source_id 1056266 non-null object
3 experiment_id 1056266 non-null object
4 frequency 559718 non-null object
5 modeling_realm 559718 non-null object
6 table_id 1056266 non-null object
7 member_id 1056266 non-null object
8 grid_label 1056266 non-null object
9 variable_id 1056266 non-null object
10 temporal_subset 1027893 non-null object
11 version 1056266 non-null object
12 path 1056266 non-null object
dtypes: object(13)
memory usage: 104.8+ MB
# So there are 956,306 entries, one for each netcdf file. We can see the first five here:
# The 'path' column is the most important - you may need to scroll the window to see it!
df_s3.head()
| project | institution_id | source_id | experiment_id | frequency | modeling_realm | table_id | member_id | grid_label | variable_id | temporal_subset | version | path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CMIP6 | AS-RCEC | TaiESM1 | histSST-piNTCF | NaN | NaN | AERmon | r1i1p1f1 | gn | ps | 185001-201412 | v20200318 | s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES... |
| 1 | CMIP6 | AS-RCEC | TaiESM1 | histSST-piNTCF | NaN | NaN | CFmon | r1i1p1f1 | gn | ta | 185001-201412 | v20200318 | s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES... |
| 2 | CMIP6 | AS-RCEC | TaiESM1 | histSST-piNTCF | NaN | NaN | LImon | r1i1p1f1 | gn | snc | 185002-201412 | v20200318 | s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES... |
| 3 | CMIP6 | AS-RCEC | TaiESM1 | histSST-piNTCF | NaN | NaN | LImon | r1i1p1f1 | gn | snd | 185002-201412 | v20200318 | s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES... |
| 4 | CMIP6 | AS-RCEC | TaiESM1 | histSST-piNTCF | NaN | NaN | LImon | r1i1p1f1 | gn | snw | 185002-201412 | v20200318 | s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES... |
# We will add a new column which is our short name for the datasets (may take a moment for all 956306 rows)
df_s3['dataset'] = df_s3.apply(lambda row: '.'.join(row.path.split('/')[6:12]),axis=1)
# the number of unique dataset names can be found using the 'nunique' method
df_s3.dataset.nunique()
239268
# The value in the `path` column of the first row is:
df_s3.path.values[0]
's3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiESM1/histSST-piNTCF/r1i1p1f1/AERmon/ps/gn/v20200318/ps_AERmon_TaiESM1_histSST-piNTCF_r1i1p1f1_gn_185001-201412.nc'
# which has the short name:
df_s3.dataset.values[0]
'TaiESM1.histSST-piNTCF.r1i1p1f1.AERmon.ps.gn'
# some datasets have multiple versions: (will just check one in each 500 of them ...)
for dataset in df_s3.dataset.unique()[::500]:
df_dataset = df_s3[df_s3.dataset==dataset]
if df_dataset.version.nunique() > 1:
print(dataset,df_dataset.version.unique())
EC-Earth3-LR.piControl.r1i1p1f1.Omon.mlotst.gn ['v20200409' 'v20200919']
FIO-ESM-2-0.piControl.r1i1p1f1.Amon.rsds.gn ['v20190911' 'v20191010']
IPSL-CM6A-LR.piControl.r1i1p1f1.Amon.o3.gr ['v20181022' 'v20181123']
CESM2.1pctCO2.r1i1p1f1.day.zg.gn ['v20190425' 'v20190826']
NorCPM1.historical.r1i1p1f1.Omon.thetao.gr ['v20190914' 'v20200724']
NorESM2-LM.piControl.r1i1p1f1.Ofx.areacello.gn ['v20190815' 'v20190920']
NorESM2-LM.hist-GHG.r1i1p1f1.Emon.va.gn ['v20190909' 'v20191108']
CESM2.deforest-globe.r1i1p1f1.Amon.rsuscs.gn ['v20190401' 'v20191122']
# So pick a dataset, any dataset, and try it! N.B. some datasets are VERY large - especially the day, 6hourly, etc.
#dataset = df_s3.dataset[10450]
# or:
dataset = 'GFDL-CM4.historical.r1i1p1f1.Amon.tas.gr1'
df_dataset = df_s3[df_s3.dataset==dataset]
df_dataset
| project | institution_id | source_id | experiment_id | frequency | modeling_realm | table_id | member_id | grid_label | variable_id | temporal_subset | version | path | dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 603842 | CMIP6 | NOAA-GFDL | GFDL-CM4 | historical | mon | atmos | Amon | r1i1p1f1 | gr1 | tas | 185001-194912 | v20180701 | s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/... | GFDL-CM4.historical.r1i1p1f1.Amon.tas.gr1 |
| 603843 | CMIP6 | NOAA-GFDL | GFDL-CM4 | historical | mon | atmos | Amon | r1i1p1f1 | gr1 | tas | 195001-201412 | v20180701 | s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/... | GFDL-CM4.historical.r1i1p1f1.Amon.tas.gr1 |
So is this what we expect?
this dataset is split over 3 netcdf files - see any trouble here?
lets do a quick sanity check (make sure one and only one variable is specified) and get only the latest version of the files
dvars = df_dataset.variable_id.unique()
assert len(dvars) > 0, 'no netcdf files found for this dataset'
assert len(dvars) == 1, f"trouble with this dataset, too many datasets found: {dvars}"
var = dvars[0]
print('The variable is:',var)
# make sure we are looking at the last available version:
last_version = sorted(df_dataset.version.unique())[-1]
dze = df_dataset[df_dataset.version == last_version].reset_index(drop=True)
input_urls = sorted(dze.path.unique())
input_urls
The variable is: tas
['s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001-194912.nc',
's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_195001-201412.nc']
There are only two files - one netcdf file was from an older version!
We want to look at the first netcdf file to make sure we know what to expect
To use
xarray.open_dataset, we need to turn the input_url (starting with ‘s3://’) into an appropriate file_like object.
# Connect to AWS S3 storage
fs_s3 = s3fs.S3FileSystem(anon=True)
file_url = fs_s3.open(input_urls[0], mode='rb')
ds = xr.open_dataset(file_url)
print(ds)
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 180, lon: 288, time: 1200)
Coordinates:
* bnds (bnds) float64 1.0 2.0
height float64 ...
* lat (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
* lon (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4
* time (time) object 1850-01-16 12:00:00 ... 1949-12-16 12:00:00
Data variables:
lat_bnds (lat, bnds) float64 ...
lon_bnds (lon, bnds) float64 ...
tas (time, lat, lon) float32 ...
time_bnds (time, bnds) object ...
Attributes: (12/46)
external_variables: areacella
history: File was processed by fremetar (GFDL analog of CM...
table_id: Amon
activity_id: CMIP
branch_method: standard
branch_time_in_child: 0.0
... ...
variable_id: tas
variant_info: N/A
references: see further_info_url attribute
variant_label: r1i1p1f1
branch_time_in_parent: 36500.0
parent_time_units: days since 0001-1-1
Step 2: Deciding how to chunk the dataset¶
For parallel I/O and subsetting the dataset in time, we will chunk the data in the time dimension
In order to figure out the number of time slices in each chunk, we do a small calculation on the first netcdf file
Here we set the desired chunk size to 50 Mb, but something between 50-100 Mb is usually alright
ntime = len(ds.time) # the number of time slices
chunksize_optimal = 50e6 # desired chunk size in bytes
ncfile_size = ds.nbytes # the netcdf file size
chunksize = max(int(ntime* chunksize_optimal/ ncfile_size),1)
target_chunks = ds.dims.mapping
target_chunks['time'] = chunksize
target_chunks # a dictionary giving the chunk sizes in each dimension
{'bnds': 2, 'lat': 180, 'lon': 288, 'time': 241}
Step 3: Define a pre-processing function¶
This is an optional step which we want to apply to each chunk
Here we change some data variables into coordinate variables, but you can define your own pre-processing step here
# the netcdf lists some of the coordinate variables as data variables. This is a fix which we want to apply to each chunk.
def set_bnds_as_coords(ds):
new_coords_vars = [var for var in ds.data_vars if 'bnds' in var or 'bounds' in var]
ds = ds.set_coords(new_coords_vars)
return ds
Step 4: Create a recipe¶
A
FilePatternis the starting place for all recipes. These Python objects are the “raw ingredients” upon which the recipe will act. They describe how the individual source files are organized logically as part of a larger dataset. To create a file pattern, the first step is to define a function which takes any variable components of the source file path as inputs, and returns full file path strings.Revisting our input urls, we see that the only variable components of these paths are the 13-character numerical strings which immediatly precede the .nc file extension:
for url in input_urls:
print(f'''{url}
''')
s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001-194912.nc
s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_195001-201412.nc
What do these strings refer to?
If it was not immediately apparent, comparison to our dataset coordinates makes it clear that these numerical strings are time ranges; the string
'185001-194912'from the first url, e.g., represents a time range from Jan 1850 through Dec 1949:
print(ds.coords)
Coordinates:
* bnds (bnds) float64 1.0 2.0
height float64 ...
* lat (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5
* lon (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4
* time (time) object 1850-01-16 12:00:00 ... 1949-12-16 12:00:00
Let’s define a function that takes these strings as input
… and returns full file paths!
def make_full_path(time):
'''
Parameters
----------
time : str
A 13-character string, comprised of two 6-character dates delimited by a dash.
The first four characters of each date are the year, and the final two are the month.
e.g. The time range from Jan 1850 through Dec 1949 is expressed as '185001-194912'.
'''
base_url = 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/'
return base_url + f'tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_{time}.nc'
# And let's be sure to test our function before moving on.
test_url = make_full_path('185001-194912')
print(test_url)
# If our function works, inputting '185001-194912' should have returned a url identical to
# the first of the two urls in the list named `input_urls` defined in cell 10, above:
test_url == input_urls[0]
s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001-194912.nc
True
Combining dimensions
Before we initialize our file pattern, we need to define how we want files to be combined in our eventual zarr store
We have two options:
Concatenating dimensions with a
ConcatDiminstanceMerging dimensions with a
MergeDiminstance
Our current dataset requires only concatenation, which we can achieve by instantiating
ConcatDimwith our variable name ("time") as a positional argument, followed by akeyskwarg, which is a list containing all of the ways which this variable appears in our set of source file paths.
Note: This example reads from only two source files, so we can simply copy-and-paste their respective time variables into a list. If the number of source files was much larger, we might consider finding a way to create this
keyslist programatically.
from pangeo_forge_recipes.patterns import ConcatDim
time_concat_dim = ConcatDim("time", keys=['185001-194912', '195001-201412'])
Instantiating the file pattern
Now that we have a both file path function and our “combine dimensions” object, we can move on to instantiating to file pattern, passing these two objects as arguments.
Note that we will use
fsspec.openunder the hood for most file opening, so if there are any special keyword arguments we want to pass to this function, now is the time to do it.Here we specify
fsspec_open_kwargs={'anon':True}as a keyword argument in theFilePattern, because we want to access the source files anonymously.
from pangeo_forge_recipes.patterns import FilePattern
pattern = FilePattern(make_full_path, time_concat_dim, fsspec_open_kwargs={'anon':True})
pattern
<FilePattern {'time': 2}>
By inspecting our instantiated pattern we see that our pattern has indexed our two files chronologically according to the concatenation key we provided it, and assigned the correct url to each file using the file path function:
for index, fname in pattern.items():
print(index, fname)
Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001-194912.nc
Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) s3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_195001-201412.nc
Time to make the recipe!
In it’s most basic form,
XarrayZarrRecipecan be instantiated using a file pattern as the only argument. Here we’ll be using some of the optional arguments to specify a few additional preferences:
from pangeo_forge_recipes.recipes.xarray_zarr import XarrayZarrRecipe
recipe = XarrayZarrRecipe(
pattern,
target_chunks=target_chunks,
process_chunk=set_bnds_as_coords,
xarray_concat_kwargs={'join':'exact'},
)
Step 5: Execute the recipe¶
Here we use the basic function executor. For details on all execution modes, see:
https://pangeo-forge.readthedocs.io/en/latest/pangeo_forge_recipes/recipe_user_guide/execution.html
from pangeo_forge_recipes.recipes import setup_logging
setup_logging() # setup execution logs
recipe.to_function()() # compile and execute recipe as single Python function
[05/10/22 16:27:20] INFO Caching input 'Index({DimIndex(name='time', xarray_zarr.py:153 index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
INFO Caching file 's3://esgf-world/CMIP6/CMIP/NOAA-GFD storage.py:161 L/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v2018 0701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_18 5001-194912.nc'
INFO Copying remote file 's3://esgf-world/CMIP6/CMIP/N storage.py:172 OAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr 1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1 _gr1_185001-194912.nc' to cache
[05/10/22 16:27:47] INFO Opening input with Xarray xarray_zarr.py:253 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}): 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/ GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20 180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_ gr1_185001-194912.nc'
INFO Opening 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFD storage.py:267 L-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/ tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001- 194912.nc' from cache
INFO Caching metadata for input xarray_zarr.py:167 'Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
INFO Caching input 'Index({DimIndex(name='time', xarray_zarr.py:153 index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
INFO Caching file 's3://esgf-world/CMIP6/CMIP/NOAA-GFD storage.py:161 L/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v2018 0701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_19 5001-201412.nc'
INFO Copying remote file 's3://esgf-world/CMIP6/CMIP/N storage.py:172 OAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr 1/v20180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1 _gr1_195001-201412.nc' to cache
[05/10/22 16:28:02] INFO Opening input with Xarray xarray_zarr.py:253 Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}): 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/ GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20 180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_ gr1_195001-201412.nc'
INFO Opening 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFD storage.py:267 L-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/ tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_195001- 201412.nc' from cache
INFO Caching metadata for input xarray_zarr.py:167 'Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
//pangeo-forge-recipes/pangeo_forge_recipes/recipes/xarray_zarr.py:115: RuntimeWarning: Failed to open Zarr store with consolidated metadata, falling back to try reading non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:
1. Consolidating metadata in this existing store with zarr.consolidate_metadata().
2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or
3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.
return xr.open_zarr(target.get_mapper())
INFO Creating a new dataset in target xarray_zarr.py:511
INFO Opening inputs for chunk xarray_zarr.py:390 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})
INFO Opening input with Xarray xarray_zarr.py:253 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}): 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/ GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20 180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_ gr1_185001-194912.nc'
INFO Opening 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFD storage.py:267 L-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/ tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001- 194912.nc' from cache
INFO Combining inputs for chunk xarray_zarr.py:408 'Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
INFO Storing dataset in /var/folders/tt/4f941hdn0z xarray_zarr.py:553 q549zdwhcgg98c0000gn/T/tmpkxb_4ar2/rP3aoHSm
INFO Expanding target concat dim 'time' to size xarray_zarr.py:569 1980
INFO Opening inputs for chunk xarray_zarr.py:390 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})
INFO Opening input with Xarray xarray_zarr.py:253 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}): 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/ GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20 180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_ gr1_185001-194912.nc'
INFO Opening 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFD storage.py:267 L-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/ tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_185001- 194912.nc' from cache
INFO Combining inputs for chunk xarray_zarr.py:408 'Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
[05/10/22 16:28:03] INFO Storing variable tas chunk xarray_zarr.py:632 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) to Zarr region (slice(0, 1200, None), slice(None, None, None), slice(None, None, None))
INFO Storing variable time chunk xarray_zarr.py:632 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) to Zarr region (slice(0, 1200, None),)
INFO Storing variable time_bnds chunk xarray_zarr.py:632 Index({DimIndex(name='time', index=0, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) to Zarr region (slice(0, 1200, None), slice(None, None, None))
INFO Opening inputs for chunk xarray_zarr.py:390 Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})
INFO Opening input with Xarray xarray_zarr.py:253 Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}): 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/ GFDL-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20 180701/tas_Amon_GFDL-CM4_historical_r1i1p1f1_ gr1_195001-201412.nc'
INFO Opening 's3://esgf-world/CMIP6/CMIP/NOAA-GFDL/GFD storage.py:267 L-CM4/historical/r1i1p1f1/Amon/tas/gr1/v20180701/ tas_Amon_GFDL-CM4_historical_r1i1p1f1_gr1_195001- 201412.nc' from cache
[05/10/22 16:28:04] INFO Combining inputs for chunk xarray_zarr.py:408 'Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)})'
INFO Storing variable tas chunk xarray_zarr.py:632 Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) to Zarr region (slice(1200, 1980, None), slice(None, None, None), slice(None, None, None))
INFO Storing variable time chunk xarray_zarr.py:632 Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) to Zarr region (slice(1200, 1980, None),)
INFO Storing variable time_bnds chunk xarray_zarr.py:632 Index({DimIndex(name='time', index=1, sequence_len=2, operation=<CombineOp.CONCAT: 2>)}) to Zarr region (slice(1200, 1980, None), slice(None, None, None))
INFO Consolidating dimension coordinate arrays xarray_zarr.py:649
INFO Consolidating Zarr metadata xarray_zarr.py:673
Step 6: Check the resulting Zarr store¶
# Check to see if it worked:
ds = xr.open_zarr(recipe.target_mapper)
print(ds)
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 180, lon: 288, time: 1980)
Coordinates:
* bnds (bnds) float64 1.0 2.0
height float64 ...
* lat (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
lat_bnds (lat, bnds) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>
* lon (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4
lon_bnds (lon, bnds) float64 dask.array<chunksize=(288, 2), meta=np.ndarray>
* time (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00
time_bnds (time, bnds) object dask.array<chunksize=(241, 2), meta=np.ndarray>
Data variables:
tas (time, lat, lon) float32 dask.array<chunksize=(241, 180, 288), meta=np.ndarray>
Attributes: (12/46)
Conventions: CF-1.7 CMIP-6.0 UGRID-1.0
activity_id: CMIP
branch_method: standard
branch_time_in_child: 0.0
branch_time_in_parent: 36500.0
comment: <null ref>
... ...
table_id: Amon
title: NOAA GFDL GFDL-CM4 model output prepared for CMIP...
tracking_id: hdl:21.14100/e4193a02-6405-49b6-8ad3-65def741a4dd
variable_id: tas
variant_info: N/A
variant_label: r1i1p1f1
ds[var][-1].plot()
<matplotlib.collections.QuadMesh at 0x7f7c80eb4e20>
Postscript¶
If you find a CMIP6 dataset for which this recipe does not work, Please report it at issue#105 so we can refine the recipe, if possible.
# Troubles found:
dataset = 'IPSL-CM6A-LR.abrupt-4xCO2.r1i1p1f1.Lmon.cLeaf.gr' # need decode_coords=False in xr.open_dataset, but using xarray_open_kwargs = {'decode_coords':False}, still throws an error when caching the input