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This content will become publicly available on April 30, 2025

Title: cfr (v2024.1.26): a Python package for climate field reconstruction
Abstract. 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.  more » « less
Award ID(s):
2202777 2303530 1948822
PAR ID:
10518165
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Geoscientific Model Development
Date Published:
Journal Name:
Geoscientific Model Development
Volume:
17
Issue:
8
ISSN:
1991-9603
Page Range / eLocation ID:
3409 to 3431
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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