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cfr: a Python package for Climate Field Reconstruction

Authors: Feng Zhu; Julien Emile-Geay; Hakim, Gregory J.; Guillot, Dominique; Khider, Deborah; Tardif, Robert; Perkins, Walter A.;

cfr: a Python package for Climate Field Reconstruction

Abstract

The codebase for the paper entitled "cfr (v2024.1.26): a Python package for climate field reconstruction" in GMD. cfr aims to provide a universal framework for climate field reconstruction (CFR). It provides a toolkit for the processing and visualization of the proxy records, climate model simulations, and instrumental observations, the calibration and running of the proxy system models (PSMs, Evans et al., 2013), the preparation and running of the multiple reconstruction frameworks/algorithms, such as LMR (Hakim et al., 2016; Tardif et al., 2019) and GraphEM (Guillot et al., 2015), and the validation of the reconstructions, etc.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
1
Average
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30
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