Google Cloud CMIP6 Public Data: Basic Python Example

This notebooks shows how to query the catalog and load the data using python

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from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import zarr
import fsspec

%matplotlib inline
%config InlineBackend.figure_format = 'retina' 
plt.rcParams['figure.figsize'] = 12, 6

Browse Catalog

The data catatalog is stored as a CSV file. Here we read it with Pandas.

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df = pd.read_csv('')

The columns of the dataframe correspond to the CMI6 controlled vocabulary. A beginners' guide to these terms is available in this document.

Here we filter the data to find monthly surface air temperature for historical experiments.

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df_ta = df.query("activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'")

Now we do further filtering to find just the models from NCAR.

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df_ta_ncar = df_ta.query('institution_id == "NCAR"')

Load Data

Now we will load a single store using fsspec, zarr, and xarray.

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# get the path to a specific zarr store (the first one from the dataframe above)
zstore = df_ta_ncar.zstore.values[-1]

# create a mutable-mapping-style interface to the store
mapper = fsspec.get_mapper(zstore)

# open it using xarray and zarr
ds = xr.open_zarr(mapper, consolidated=True)

Plot a map from a specific date.

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Create a timeseries of global-average surface air temperature. For this we need the area weighting factor for each gridpoint.

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df_area = df.query("variable_id == 'areacella' & source_id == 'CESM2'")
ds_area = xr.open_zarr(fsspec.get_mapper(df_area.zstore.values[0]), consolidated=True)
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total_area = ds_area.areacella.sum(dim=['lon', 'lat'])
ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area

By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly.

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%time ta_timeseries.load()
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ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean')
plt.title('Global Mean Surface Air Temperature')
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