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Pyleoclim: Paleoclimate Timeseries Analysis and Visualization with Python
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  • Deborah Khider,
  • Julien Emile-Geay,
  • Feng Zhu,
  • Alexander James,
  • Jordan Landers,
  • Varun Ratnakar,
  • Yolanda Gil
Deborah Khider
University of Southern California Information Sciences Institute, University of Southern California Information Sciences Institute

Corresponding Author:[email protected]

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Julien Emile-Geay
University of Southern California, University of Southern California
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Feng Zhu
School of Atmospheric Sciences, Nanjing University of Information Science & Technology, School of Atmospheric Sciences, Nanjing University of Information Science & Technology
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Alexander James
University of Southern California, University of Southern California
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Jordan Landers
University of Southern California, University of Southern California
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Varun Ratnakar
University of Southern California, University of Southern California
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Yolanda Gil
University of Southern California, University of Southern California
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Abstract

We present a Python package geared towards 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: (1) orbital-scale climate variability in a deep-sea core, illustrating spectral, wavelet and coherency analysis in the presence of age uncertainties; (2) correlating a high-resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (3) 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 FAIR 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.