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climetrics: An R package to quantify multiple dimensions of climate change
  • Shirin Taheri,
  • Babak Naimi,
  • Miguel B. Araújo
Shirin Taheri

Corresponding Author:[email protected]

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Babak Naimi
University of Utrecht Roosevelt Academy
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Miguel B. Araújo
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Climate change affects biodiversity in diverse ways, necessitating the exploration of multiple climate dimensions using standardized metrics. However, existing methods for quantifying these metrics are scattered and tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed “climetrics” which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are currently implemented, including 1) Standardized Local Anomalies; 2) Changes in Probabilities of Local Climate Extremes; 3) Changes in Areas of Analogous Climates; 4) Novel Climates; 5) Changes in Distances to Analogous Climates; and 6) Climate Change Velocity. For climate change velocity, three different algorithms are implemented and available within the package including; a) Distanced-based Velocity (“dVe”); b) Threshold-based Velocity (“ve”); and c) Gradient-based Velocity (“gVe”). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The climetrics R package is seamlessly integrated with the rts package for efficient handling of raster time-series data. The functions in climetrics are designed to be user-friendly, making them suitable for less-experienced R users. Detailed comments and descriptions in their help pages and vignettes of the package facilitate further customization by advanced users. In summary, the climetrics R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.
18 Oct 2023Submitted to Ecography
19 Oct 2023Submission Checks Completed
19 Oct 2023Assigned to Editor
19 Oct 2023Review(s) Completed, Editorial Evaluation Pending
27 Oct 2023Reviewer(s) Assigned
29 Jan 2024Reviewer(s) Assigned
17 Mar 2024Review(s) Completed, Editorial Evaluation Pending
18 Apr 20242nd Revision Received
23 Apr 2024Editorial Decision: Accept