Abstract
The Köppen-Geiger climate classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1-km Köppen-Geiger climate classification maps for ten historical periods in 1979–2017 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1-km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of Köppen-Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim, is publicly available at http://doi.org/10.5281/zenodo.4546140 for historical climate and http://doi.org/10.5281/zenodo.4542076 for future climate.
Highlights
As a convenient and integrated tool to identify spatial patterns of climatic variables and to examine relationships between climates and biological systems, the Köppen classification has been widely applied in biological science, earth and planetary sciences, and environmental science (Rubel & Kottek, 2011)
We provided a heuristic example which used climate classification map series to detect the long-term area changes of climate zones, showing how the Köppen-Geiger climate classification map series can be applied in climate change studies
Future Köppen-Geiger classification and confidence map for 2070-2099 under RCP8.5 with resolution of 1km for the central Rocky Mountains in North America. (a) Climate maps based on 30 GCMs, (b) the final climate map derived from the most common climate class among all the 30 climate maps, and (c) confidence level distribution of the final climate map
Summary
25 Climate is a key driver of ecosystem functioning and processes, and has a direct impact on the distribution of species (Chen, Hill, Ohlemüller, Roy, & Thomas, 2011; Ordonez & Williams, 2013; Pinsky, Worm, Fogarty, Sarmiento, & Levin, 2013; Thuiller, Lavorel, Araújo, Sykes, & Prentice, 2005). Compared with other human expertise based climate mapping methods (e.g., Holdridge, 1947; Thornthwaite, 1931; Walter & Elwood, 1975) and clustering approaches (e.g., Netzel & Stepinski, 2016), which suffer from a lack in meteorological basis, the Köppen classification demonstrates stronger correlation with distributions of biomes and soil types (Bockheim, Gennadiyev, Hammer, & Tandarich, 2005; Rohli, Joyner, Reynolds, & Ballinger, 2015) It provides an ecologically relevant and effective method to characterize climate conditions by 35 incorporating the seasonal cycles of surface air temperature and precipitation. We provided a heuristic example which used climate classification map series to detect the long-term area changes of climate zones, showing how the Köppen-Geiger climate classification map series can be applied in climate change studies
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