CHELSA-TraCE21k v1.0. Downscaled transient temperature and precipitation data since the last glacial maximum

Abstract

Abstract. High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm. The data were validated by comparisons with the glacial extent of the Laurentide ice shield based on expert delineations, proxy data from Greenland ice cores, historical climate data from meteorological stations, and a dynamic simulation of species a distribution throughout the Holocene. Validations show that CHELSA TraCE21k output creates a reasonable representation of the distribution of temperature and precipitation through time at a high spatial resolution, and simulations based on the data are capable of detecting effective LGM refugia of species.

Predicted changes in the functional structure of earthworm assemblages in France driven by climate change

“…All climatic variables were computed with functions in the “dismo” (Hijmans et al, 2020) and “envirem” (Title & Bemmels, 2018) R packages, using temperature and precipitation data obtained for different time periods from the CHELSA project (Karger et al, 2017). The CHELSA‐TraCE21k dataset (Karger et al, 2021) was used to produce LGM variables, while the CHELSAcruts dataset (Karger & Zimmermann, 2018) served as a source of contemporary climate data: 1950–1960, in order to describe climatic conditions during the sampling period, and 2000–2016 to describe current climate. In addition, we downloaded future projections of the same variables for the years 2041–2060 and 2061–2080 (respectively referred to as 2050 and 2070 later on) according to two representative concentration pathways (RCP 4.5 and RCP 8.5), modelled under the MIROC5 global circulation model.…”

“…All climatic variables were computed with functions in the "dismo" (Hijmans et al, 2020) and "envirem" (Title & Bemmels, 2018) R packages, using temperature and precipitation data obtained for different time periods from the CHELSA project (Karger et al, 2017). The CHELSA-TraCE21k dataset (Karger et al, 2021) was used to produce LGM variables, while the CHELSAcruts dataset (Karger & Zimmermann, 2018) is a very pessimistic scenario of climate change, which assumes that no mitigation measures will limit greenhouse gases emissions during the 21st century (Burgess et al, 2020;Schwalm et al, 2020). RCP 4.5, on the other hand, is a more optimistic scenario in which temperature rise starts to level-off before the end of the century (Thomson et al, 2011).…”

Towards understanding diversity, endemicity and global change vulnerability of soil fungi

“…Thus, we evaluated the relative vulnerability of soil fungal functional groups by estimating the percentage of species occurring at their upper niche limits to three major global change drivers – land use (land cover change), heat (maximum monthly temperature), and drought (lowest quarterly precipitation). We projected to the year 2070 relative to the 2015 baseline, using the average vulnerability index (Smith et al 2020b), land use extrapolations of the LUH2 global dataset (Hurtt et al 2020), and climatic extrapolations based on the CCS8.5 scenario (Karger et al 2021). For all fungi taken together, predicted vulnerability to heat (best predictor: maximum monthly temperature; R 2 adj =0.583) and drought (precipitation seasonality; R 2 adj =0.456) were the greatest in the tropical and subtropical latitudes.…”

“…Based on geographical coordinates, we assigned the following climatic and land cover metadata to the samples: i) CHELSA v2.1 bioclimatic variables for the period 1981-2010 (Karger et al, 2020), ii) CHELSA-TraCE21k v1.0. for the LGM (Karger et al 2021), and iii) CHELSA v2.1 climate extrapolations for the year 2070 following the RCP8.5 global warming scenario with SSP5 socioeconomic conditions and the GFDL-ESM4 global circulation model (Karger et al, 2020); iv) normalized difference vegetation index (NDVI; Filipponi et al 2018); v) SoilGrids v.2 soil pH from 0-5 cm depth (Poggio et al, 2021); vi) land cover type using Copernicus classification v.3 (Buchhorn et al 2020) for the year 2015; and vii) human footprint index based on the Land-Use Harmonization (LUH2; Hurtt et al, 2020) or the year 2015 and 2070 extrapolation. Based on original descriptions of vegetation (age, cover, relative abundance of species, fire history) or remote sensing data (Google Maps), samples were assigned to biomes (Olson et al 2001) and land cover types.…”

Addressing alpine plant phylogeography using integrative distributional, demographic and coalescent modeling

“…A cross-scale comparison revealed that the low resolution model overestimated the thermal tolerance of mountain plants because cold high altitude areas were averaged out in the coarser model. While higher resolution layers (30 arc seconds) do exist for the present (Karger et al 2017) and the LGM (Karger et al 2021) from CHELSA, their usefulness may be limited for several reasons. Firstly, to our knowledge only González-Serna et al (2019) have been able to achieve a computationally tractable resolution for the ENM smaller than 5 arc minutes (González-Serna et al 2019 used 2.5 arc minutes) in a study applying iDDC.…”