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ABSTRACT Weather at the mid‐latitudes is governed by cyclones and anticyclones mostly migrating eastward. These weather systems cause the jet streams to undulate; the meandering patterns are known as the Rossby waves. Occasionally, Rossby waves bring forth localised extreme weather phenomena. An example of a finite‐amplitude wave phenomenon is atmospheric blocking, which is often associated with heat waves and droughts. Recent development of a finite‐amplitude local wave activity (FALWA) theory by Nakamura and collaborators enables comprehensive analysis of the dynamics of finite‐amplitude Rossby waves observed in climate data, which helps to understand the drivers of their life cycles. Despite the simplicity of interpretation it brings about, to apply the FALWA diagnostic to climate data requires more involved calculations than the traditional Eulerian framework. This article introduces the open‐source Python packagefalwa,which encapsulates the FALWA diagnostics implemented on gridded climate data presented in the authors' previous publications. It reviews the essence of the FALWA theory, the corresponding components in the package that implement the calculations, and where users can find sample notebooks to start with. It aims to serve as a road map for new users to easily navigate through this package. The latter half of this article documents the practices of the developers, which include the documentation tools, continuous integration practice, and repository maintenance using automated GitHub functionalities. The authors also discuss existing numerical issues and future improvement plans. This open‐source project aims to promote the broader application of FALWA diagnostics on climate data and model outputs by streamlining complex numerical computations.
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Pyleoclim: Paleoclimate Timeseries Analysis and Visualization With Python
Abstract We present a Python package geared toward the intuitive analysis and visualization of paleoclimate timeseries,Pyleoclim. The code is open‐source, object‐oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code's philosophy, structure, and base functionalities and apply it to three paleoclimate problems: (a) orbital‐scale climate variability in a deep‐sea core, illustrating spectral, wavelet, and coherency analysis in the presence of age uncertainties; (b) correlating a high‐resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (c) model‐data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting Findable, Accessible, Interoperable, and Reusable software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud‐executable Jupyter notebooks, to encourage adoption by new users.
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- PAR ID:
- 10375826
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Paleoceanography and Paleoclimatology
- Volume:
- 37
- Issue:
- 10
- ISSN:
- 2572-4517
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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