Papers › KrigR -- A tool for downloading and statistically downscaling climate reanalysis data
KrigR -- A tool for downloading and statistically downscaling climate reanalysis data
Erik Kusch, Richard Davy
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Advances in climate science have rendered obsolete gridded observation data sets commonly used in macroecological analyses. Novel climate reanalysis products outperform legacy data products in accuracy, temporal resolution, and provision of uncertainty metrics. Consequently, there is an urgent need to develop a workflow through which to integrate these improved data into analyses. The ERA5 product family are the latest and most advanced global reanalysis products created by the ECMWF. These data products offer up to 83 essential climate variables at hourly intervals for the time-period of 1981 to today with preliminary back-extensions being available for 1950-1981. Spatial resolutions range from 30km (ERA5) to 11km (ERA5-Land) and can be statistically downscaled to study-requirements at finer spatial resolutions. Kriging is one such method to interpolate data to finer resolutions and has the advantages that one can leverage additional covariate information and obtain the uncertainty associated with the downscaling. The KrigR R Package enables users to (1) download ERA5(-Land) climate reanalysis data for a user-specified region, and time-period, (2) aggregate these climate products to desired temporal resolutions and metrics, (3) acquire topographical co-variates, and (4) statistically downscale spatial data to a user-specified resolution using co-variate data via kriging. This R package provides a workflow for implementation of state-of-the-art climate data into biological analyses while avoiding issues of storage limitations at high temporal and spatial resolutions by providing data according to user-needs rather than in global data sets. Consequently, KrigR provides a toolbox to obtain a wide range of tailored climate data at unprecedented combinations of high temporal and spatial resolutions thus enabling the use of world-leading climate data in the R-environment.
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