4.2.1.10. Accessing Obs4MIPs and CMIP Data via ESGF

This notebook demonstrates how to access CMIP6 and Obs4MIPs data using the intake-esgf package.

To run this notebook, you’ll need to install intake-esgf. You can install it using the following command:

conda install -c conda-forge intake-esgf

Alternatively, you can follow the official documentation for more installation details and usage instructions.

[1]:

import intake_esgf

The catalog can be configured by following the instructions outlined in the intake-esgf configuration documentation. This allows you to set up indices that provide access to specific datasets.

For instance, the following example demonstrates how to access the Lawrence Livermore National Laboratory (LLNL) Solr index, which supports a wide range of ESGF datasets. By configuring the index in this way, you can easily query and retrieve the data you need from the ESGF network.

Refer to the configuration guide for more details on setting up additional data sources or modifying the existing configuration to suit your needs.

[2]:

intake_esgf.conf.set(indices={"esgf-node.llnl.gov":True})

[2]:

<contextlib._GeneratorContextManager at 0x75e3bb3add30>

The following cell shows which projects are supported.

[3]:

intake_esgf.supported_projects()

[3]:

['cmip6', 'cmip5', 'cmip3', 'obs4mips', 'drcdp']

4.2.1.10.2. Access obs4MIPS

In this section, we show how to access the ‘temperature at surface’ (tas) dataset from obs4MIPS (Observations for Model Intercomparison Projects). obs4MIPS is a project that provides observational datasets for comparison with climate model outputs.

We first use the search method to get the desired dataset.

[9]:

cat.search(
    project="obs4MIPs",
    institution_id="ECMWF",
    variable_id="tas",
)

[9]:

Summary information for 1 results:
activity_id       [obs4MIPs]
institution_id       [ECMWF]
source_id            [ERA-5]
frequency              [mon]
variable_id            [tas]
grid_label              [gn]
dtype: object

We load the search results into a dictionary and extract the xarray.Dataset containing the data in the subsequent cell.

[10]:

dsd = cat.to_dataset_dict()

[11]:

dsd["tas"]

[11]:

<xarray.Dataset> Size: 2GB
Dimensions:    (time: 528, bnds: 2, lat: 721, lon: 1440)
Coordinates:
  * time       (time) datetime64[ns] 4kB 1979-01-16T12:00:00 ... 2022-12-16T1...
  * lat        (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.5 89.75 90.0
  * lon        (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
    height     float64 8B 2.0
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) datetime64[ns] 8kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds   (time, lat, bnds) float64 6MB dask.array<chunksize=(12, 721, 2), meta=np.ndarray>
    lon_bnds   (time, lon, bnds) float64 12MB dask.array<chunksize=(12, 1440, 2), meta=np.ndarray>
    tas        (time, lat, lon) float32 2GB dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
Attributes: (12/41)
    CONVERSION_DATE:             Mon Nov 11 07:48:24 MST 2019
    CONVERSION_PLATFORM:         Linux casper02 3.10.0-693.21.1.el7.x86_64 #1...
    Conventions:                 CF-1.11; ODS-2.5
    DATA_SOURCE:                 ECMWF: https://cds.climate.copernicus.eu, Co...
    NCO:                         netCDF Operators version 4.7.9 (Homepage = h...
    NETCDF_COMPRESSION:          NCO: Precision-preserving compression to net...
    ...                          ...
    tracking_id:                 hdl:21.14102/003d2533-c769-4a37-b5de-8a0cd03...
    variable_id:                 tas
    variant_info:                obs4MIPs-compliant product prepared by PCMDI...
    variant_label:               PCMDI
    license:                     License to use Copernicus products available...
    cmor_version:                3.9.0

In the following cell, we calculate the mean temperature over time, convert it to degrees Celsius, and plot the global surface temperature variable.

[12]:

ds_plot = dsd["tas"]["tas"].mean(dim="time") - 273.15
ds_plot.plot(cmap="bwr", vmin=-40, vmax=40)

[12]:

<matplotlib.collections.QuadMesh at 0x75e3b22eb610>

../../_images/notebooks_Accessing_Data_10-ECT_Access_ESGF_22_1.png

4.2.1.10. Accessing Obs4MIPs and CMIP Data via ESGF

4.2.1.10.1. Access CMIP Data

4.2.1.10.2. Access obs4MIPS