In modeling species distributions and population dynamics, spatially-interpolated climatic data are often used as proxies for real, on-the-ground measurements. In shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections -- particularly among pleustonic and epilimnetic organisms. I used a global database of satellite-derived lake surface water temperatures (LSWT) to assess and correct for the statistical correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high-resolution global maps of air-LSWT offsets, corresponding uncertainty measures, and derived LSWT-based bioclimatic layers for use by the scientific community.

All files are in LZW-compressed geoTIFF formats, some of which include interleaved layers. Many GIS tools can work with these file types, including in R (terra & raster packages), Python (gdal, rasterio, georaster, & geotiff packages), and ArcGIS.