The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Global climate-related predictors at kilometer resolution for the past and future
Open accessAbstract
Abstract. A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and understanding how these processes are altered by climate change is central to mitigation efforts. We developed a set of climate-related variables at as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change method. Combining these grids with mechanistically downscaled information on temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), the 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 and when compared to alternative products. Projected variables were also highly correlated with observations, although some variables showed notable biases, e.g., snow cover days. Together, the CHELSA-BIOCLIM+ dataset presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improvement to 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 the associated services on societies.
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WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas
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The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6
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Climate Extremes: Observations, Modeling, and Impacts
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SoilGrids250m: Global gridded soil information based on machine learning
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Cited by
Zemlianskii, V, Ermokhina, K., Rietze, N., Heim, R., Assmann, J., Rüthi, J., Loginova, N., & Schaepman-Strub, G. (2025) · Environmental Research: Ecology · doi:10.1088/2752-664x/adf447 · Cited by 1
Metadata
- Authors
- Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., & Karger, D. N.
- Venue
- Earth System Science Data
- Publisher
- Copernicus GmbH
- Published
- 2022-12-16
- Volume
- 14
- Issue
- 12
- Pages
- 5573-5603
- ISSN
- 1866-3508 (ISSN-L)
- 1866-3516
- OpenAlex
- W4313418629
- Cited by
- 328
- References
- 84
- FWCI
- 22.45
- Type
- journal article
- Language
- en
- Open access
- gold
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- Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., & Karger, D. N. (2022). Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data. https://doi.org/10.5194/essd-14-5573-2022
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