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xclim
is an operational Python library for climate services, providing numerous climate-related indicator tools
with an extensible framework for constructing custom climate indicators, statistical downscaling and bias
adjustment of climate model simulations, as well as climate model ensemble analysis tools.
xclim
is built using xarray
and can seamlessly benefit from the parallelization handling provided by dask
.
Its objective is to make it as simple as possible for users to perform typical climate services data treatment workflows.
Leveraging xarray and dask, users can easily bias-adjust climate simulations over large spatial domains or compute indices from large climate datasets.
For example, the following would compute monthly mean temperature from daily mean temperature:
.. code-block:: python
import xclim
import xarray as xr
ds = xr.open_dataset(filename)
tg = xclim.atmos.tg_mean(ds.tas, freq="MS")
For applications where metadata and missing values are important to get right, xclim provides a class for each index
that validates inputs, checks for missing values, converts units and assigns metadata attributes to the output.
This also provides a mechanism for users to customize the indices to their own specifications and preferences.
xclim
currently provides over 150 indices related to mean, minimum and maximum daily temperature, daily precipitation,
streamflow and sea ice concentration, numerous bias-adjustment algorithms, as well as a dedicated module for ensemble analysis.
.. _xarray: https://docs.xarray.dev/ .. _dask: https://docs.dask.org/
xclim
can be installed from PyPI:
.. code-block:: shell
$ pip install xclim
or from Anaconda (conda-forge):
.. code-block:: shell
$ conda install -c conda-forge xclim
The official documentation is at https://xclim.readthedocs.io/
How to make the most of xclim: Basic Usage Examples
and In-Depth Examples
.
.. _Basic Usage Examples: https://xclim.readthedocs.io/en/stable/notebooks/usage.html .. _In-Depth Examples: https://xclim.readthedocs.io/en/stable/notebooks/index.html
In order to provide a coherent interface, xclim
tries to follow different sets of conventions. In particular, input data should follow the CF conventions
whenever possible for variable attributes. Variable names are usually the ones used in CMIP6
, when they exist.
However, xclim will always assume the temporal coordinate is named "time". If your data uses another name (for example: "T"), you can rename the variable with:
.. code-block:: python
ds = ds.rename(T="time")
.. _CF Conventions: http://cfconventions.org/ .. _CMIP6: https://clipc-services.ceda.ac.uk/dreq/mipVars.html
xclim
is in active development and is being used in production by climate services specialists around the world.
If you're interested in participating in the development of xclim
by suggesting new features, new indices or report bugs, please leave us a message on the issue tracker
_.
xclim
to a new language, be sure to check out the existing |discussions| first!If you would like to contribute code or documentation (which is greatly appreciated!), check out the Contributing Guidelines
_ before you begin!
.. _issue tracker: https://github.com/Ouranosinc/xclim/issues .. _Contributing Guidelines: https://github.com/Ouranosinc/xclim/blob/main/CONTRIBUTING.rst
If you wish to cite xclim
in a research publication, we kindly ask that you refer to our article published in The Journal of Open Source Software (JOSS
_): https://doi.org/10.21105/joss.05415
To cite a specific version of xclim
, the bibliographical reference information can be found through Zenodo
_
.. _JOSS: https://joss.theoj.org/ .. _Zenodo: https://doi.org/10.5281/zenodo.2795043
This is free software: you can redistribute it and/or modify it under the terms of the Apache License 2.0
. A copy of this license is provided in the code repository (LICENSE
).
.. _Apache License 2.0: https://opensource.org/license/apache-2-0/ .. _LICENSE: https://github.com/Ouranosinc/xclim/blob/main/LICENSE
xclim
development is funded through Ouranos, Environment and Climate Change Canada (ECCC), the Fonds vert
and the Fonds d'électrification et de changements climatiques (FECC), the Canadian Foundation for Innovation (CFI), and the Fonds de recherche du Québec (FRQ).
This package was created with Cookiecutter and the audreyfeldroy/cookiecutter-pypackage
project template.
.. _audreyfeldroy/cookiecutter-pypackage: https://github.com/audreyfeldroy/cookiecutter-pypackage/ .. _CFI: https://www.innovation.ca/ .. _Cookiecutter: https://github.com/cookiecutter/cookiecutter/ .. _ECCC: https://www.canada.ca/en/environment-climate-change.html .. _FECC: https://www.environnement.gouv.qc.ca/ministere/fonds-electrification-changements-climatiques/index.htm .. _Fonds vert: https://www.environnement.gouv.qc.ca/ministere/fonds-vert/index.htm .. _FRQ: https://frq.gouv.qc.ca/ .. _Ouranos: https://www.ouranos.ca/
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