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Emile-Geay et al. (2024)
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This section shows the usage of `pens` as in the paper entitled *Temporal Comparisons Involving Paleoclimate Data Assimilation: Challenges & Remedies* (Emile-Geay et al., 2024, doi:10.1175/JCLI-D-24-0101.1).
Paleoclimate Ensembles and Offline Data Assimilation
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This page gathers notebooks showing the usage of the `pens` package [#pens]_ as used in the article *"Temporal Comparisons Involving Paleoclimate Data Assimilation: Challenges & Remedies"* [#article]_, by Julien Emile-Geay, Gregory J. Hakim, Frederi Viens, Feng Zhu and Daniel E. Amrhein.

There are two main uses to these notebooks:

1. **Understand the genesis and properties of a new tool** introduced in the paper, called **"plume distance"**, which is broadly applicable to all manner of ensembles as used in climate science (broadly defined). Indeed, while the article focused on paleoclimate reconstruction ensembles (particularly those generated by "offline" data assimilation methods), the plume distance can help quantify distance between timeseries generated by initial condition ensembles (e.g. like [#initial-conditions]_), observational ensembles obtained by [#resampling]_, or `any other kind` [#other-ensembles]_ (e.g. perturbed parameter ensembles).

2. **Reproduce results from the paper**, which showcases several useful Python packages to do paleoclimatology in the Cloud, including `pens` [#pens-pypi]_, `xarray` [#xarray]_, and `Pyleoclim` [#pyleoclim]_.

.. [#pens] https://doi.org/10.5281/zenodo.14397028
.. [#article] https://doi.org/10.1175/JCLI-D-24-0101.1
.. [#initial-conditions] https://doi.org/10.1038/s41558-020-0731-2
.. [#resampling] https://doi.org/10.1175/JCLI-D-17-0901.1
.. [#other-ensembles] https://doi.org/10.1098/rsta.2007.2076
.. [#pens-pypi] https://pypi.org/project/pens/
.. [#xarray] https://docs.xarray.dev/en/stable/index.html#
.. [#pyleoclim] https://github.com/LinkedEarth/Pyleoclim_util
.. toctree::
:maxdepth: 1
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