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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2022
- Source ID
- 10.1029/2022pa004509
Entities
People
- Alexander James
- Deborah Khider
- Feng Zhu
- Jordan Landers
- Julien Emile-Geay
- Varun Ratnakar
- Yolanda Gil
Organizations
- Division of Atmospheric and Geospace Sciences
- Nanjing University of Information Science and Technology
- Office of Naval Research
- University of Southern California