Preprints
https://doi.org/10.5194/essd-2021-53
https://doi.org/10.5194/essd-2021-53

  24 Mar 2021

24 Mar 2021

Review status: this preprint has been withdrawn by the authors.

A 1-km global dataset of historical (1979–2017) and future (2020–2100) Köppen-Geiger climate classification and bioclimatic variables

Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu Diyang Cui et al.
  • Department of Geographical Sciences, University of Maryland, College Park, 20740, USA

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.

This preprint has been withdrawn.

Diyang Cui et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Plagiarism', Anonymous Referee #1, 25 Mar 2021
    • AC1: 'Reply on RC1', Diyang Cui, 25 Mar 2021
  • EC1: 'Comment on essd-2021-53', David Carlson, 26 Mar 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Plagiarism', Anonymous Referee #1, 25 Mar 2021
    • AC1: 'Reply on RC1', Diyang Cui, 25 Mar 2021
  • EC1: 'Comment on essd-2021-53', David Carlson, 26 Mar 2021

Diyang Cui et al.

Data sets

KGClim future: A 1-km global dataset of future (2020-2100) Köppen-Geiger climate classification and bioclimatic variables Cui, Diyang, Liang, Shunlin, Wang, Dongdong, and Liu, Zheng http://doi.org/10.5281/zenodo.4542076

KGClim historical: A 1-km global dataset of historical (1979-2017) Köppen-Geiger climate classification and bioclimatic variables Cui, Diyang, Liang, Shunlin, Wang, Dongdong, and Liu, Zheng http://doi.org/10.5281/zenodo.4546140

Diyang Cui et al.

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This preprint has been withdrawn.

Short summary
The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.