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Article: A 1 km global dataset of historical (1979-2013) and future (2020-2100) Köppen-Geiger climate classification and bioclimatic variables

TitleA 1 km global dataset of historical (1979-2013) and future (2020-2100) Köppen-Geiger climate classification and bioclimatic variables
Authors
Issue Date2021
Citation
Earth System Science Data, 2021, v. 13, n. 11, p. 5087-5114 How to Cite?
AbstractThe Köppen-Geiger classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. Significant changes in the Köppen climates have been observed and projected in the last 2 centuries. Current accuracy, temporal coverage and spatial and temporal resolution of historical and future climate classification maps cannot sufficiently fulfill the current needs of climate change research. 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 six historical periods in 1979-2013 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 than existing climate map products 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 the 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 via http://glass.umd.edu/KGClim (Cui et al., 2021d) and can also be downloaded at 10.5281/zenodo.5347837 (Cui et al., 2021c) for historical climate and 10.5281/zenodo.4542076 (Cui et al., 2021b) for future climate.
Persistent Identifierhttp://hdl.handle.net/10722/323142
ISSN
2023 Impact Factor: 11.2
2023 SCImago Journal Rankings: 4.231
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCui, Diyang-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWang, Dongdong-
dc.contributor.authorLiu, Zheng-
dc.date.accessioned2022-11-18T11:55:01Z-
dc.date.available2022-11-18T11:55:01Z-
dc.date.issued2021-
dc.identifier.citationEarth System Science Data, 2021, v. 13, n. 11, p. 5087-5114-
dc.identifier.issn1866-3508-
dc.identifier.urihttp://hdl.handle.net/10722/323142-
dc.description.abstractThe Köppen-Geiger classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. Significant changes in the Köppen climates have been observed and projected in the last 2 centuries. Current accuracy, temporal coverage and spatial and temporal resolution of historical and future climate classification maps cannot sufficiently fulfill the current needs of climate change research. 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 six historical periods in 1979-2013 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 than existing climate map products 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 the 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 via http://glass.umd.edu/KGClim (Cui et al., 2021d) and can also be downloaded at 10.5281/zenodo.5347837 (Cui et al., 2021c) for historical climate and 10.5281/zenodo.4542076 (Cui et al., 2021b) for future climate.-
dc.languageeng-
dc.relation.ispartofEarth System Science Data-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleA 1 km global dataset of historical (1979-2013) and future (2020-2100) Köppen-Geiger climate classification and bioclimatic variables-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5194/essd-13-5087-2021-
dc.identifier.scopuseid_2-s2.0-85118946226-
dc.identifier.volume13-
dc.identifier.issue11-
dc.identifier.spage5087-
dc.identifier.epage5114-
dc.identifier.eissn1866-3516-
dc.identifier.isiWOS:000715850900001-

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