Preprints
https://doi.org/10.5194/essd-2022-212
https://doi.org/10.5194/essd-2022-212
 
27 Jun 2022
27 Jun 2022
Status: this preprint is currently under review for the journal ESSD.

Global climate-related predictors at kilometre resolution for the past and future

Philipp Brun1, Niklaus E. Zimmermann1, Chantal Hari1,2,3,4, Loïc Pellissier1,5, and Dirk N. Karger1 Philipp Brun et al.
  • 1Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
  • 2Wyss Academy for Nature at the University of Bern, 3011 Bern, Switzerland
  • 3Climate and Environmental Physics, Physics Institute, University of Bern, 3012 Bern, Switzerland
  • 4Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland
  • 5Landscape Ecology, Institute of Terrestrial Ecosystems, Department of Environmental System Science, ETH Zürich, 8092 Zürich, Switzerland

Abstract. A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally, from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981–2010, 2011–2040, 2041–2070, and 2071–2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations. Projected variables were also highly correlated to observations, although some variables showed notable biases, e.g., snow cover days (scd). Together, the CHELSA-BIOCLIM+ data set presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.

Philipp Brun et al.

Status: open (until 14 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-212', Anonymous Referee #1, 26 Jul 2022 reply

Philipp Brun et al.

Data sets

CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, Dirk N. Karger https://doi.org/10.16904/envidat.332

Philipp Brun et al.

Viewed

Total article views: 767 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
591 166 10 767 7 9
  • HTML: 591
  • PDF: 166
  • XML: 10
  • Total: 767
  • BibTeX: 7
  • EndNote: 9
Views and downloads (calculated since 27 Jun 2022)
Cumulative views and downloads (calculated since 27 Jun 2022)

Viewed (geographical distribution)

Total article views: 691 (including HTML, PDF, and XML) Thereof 691 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Discussed

Latest update: 08 Aug 2022
Download
Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year-averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.