Abstract
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 inclimetrics 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.