cfr (v2024.1.26): a Python package for climate field reconstruction

• Published in: Geoscientific Model Development Vol. 17; no. 8; pp. 3409 - 3431
• Main Authors: Zhu, Feng, Emile-Geay, Julien, Hakim, Gregory J., Guillot, Dominique, Khider, Deborah, Tardif, Robert, Perkins, Walter A.
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 30.04.2024; Copernicus Publications
• Subjects: Algorithms; Application programming interface; Archives & records; Climate change; Climate models; Climatic analysis; Climatic data; Data analysis; Data assimilation; Data collection; Data processing; Design; General circulation models; Meteorologists; Paleoclimate; Paleoclimatology; Precipitation; Proxies; Reconstruction; Reproducibility; Statistical methods; Surface temperature; Variables; Visualization
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-17-3409-2024

Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively.