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

Abstract

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.

Bryophytes of Europe Traits (BET) data set: A fundamental tool for ecological studies

“…For species extinct (EX) or regionally extinct (RE) according to the European IUCN Red List, IUCN maps of the historical ranges were used. In addition to the 19 traditional bioclimatic variables (Hijmans et al, 2005;Xu & Hutchinson, 2013), we included 6 growing degree days variables from the CHELSA BIOCLIM+ data set (Brun et al, 2022a;2022b). Median values of the extracted bioclimatic variables were calculated for all 25 variables.…”

Effects of Climatic Change on Potential Distribution of Spogostylum ocyale (Diptera: Bombyliidae) in the Middle East Using Maxent Modelling

“…Five general circulation models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0 and UKESM1-0-LL) for the year 2085 (2071–2100) were downloaded from the CHELSA (Climatologies at high resolution for the earth’s land surface areas) database ( , accessed on 25 July 2022) with a high resolution of 30 arc sec (~ 1 km) [ 31 ]. We used these models to predict the effects of climate change on S. ocyale according to the emissions scenarios ssp126; SSP1-RCP2.6 and ssp585; SSP5-RCP8.5.…”

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

“…For life-history and some habitat use traits, we collected traits from panTHERIA (31), mammalDIET (32), and EltonTraits (33). We additionally used IUCN range maps and satellite imagery to extract data associated with species biogeography, including variables related to climate (34, 35), land-cover type (36), and global human modification index, an aggregate measure of anthropogenic pressure on a landscape (37), within each species range (see supplementary table 1 for full description of traits and satellite imagery used). Ranges of endemic species were retrieved from the IUCN database and ranges of invasive species in Mexico, Central America, and South America were constructed by building a concave polygon around specie s occurrence points from (38).…”