Abstract

We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen. Design Type(s) modeling and simulation objective Measurement Type(s) climate change Technology Type(s) computational modeling technique Factor Type(s) Sample Characteristic(s) Earth (Planet) • climate system Design Type(s) modeling and simulation objective Measurement Type(s) climate change Technology Type(s) computational modeling technique Factor Type(s) Sample Characteristic(s) Earth (Planet) • climate system Machine-accessible metadata file describing the reported data (ISA-Tab format)

Keywords

Climate changeVegetation (pathology)Anomaly (physics)Elevation (ballistics)ClimatologyEnvironmental scienceClimate modelGeographyCartographyPhysical geographyRemote sensingGeologyMathematicsPhysics

Affiliated Institutions

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Publication Info

Year
2018
Type
article
Volume
5
Issue
1
Pages
180214-180214
Citations
5830
Access
Closed

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5830
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32
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Cite This

Hylke E. Beck, Niklaus E. Zimmermann, Tim R. McVicar et al. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data , 5 (1) , 180214-180214. https://doi.org/10.1038/sdata.2018.214

Identifiers

DOI
10.1038/sdata.2018.214
PMID
30375988
PMCID
PMC6207062

Data Quality

Data completeness: 81%