![]() Monthly precipitation sums have been downscaled by applying the CHELSA algorithm directly on bias corrected GCM data. Mean monthly maximum daily temperatures and mean monthly minimum daily temperatures have been downscaled using the delta change method based on the high-spatial-resolution data taken from CHELSA V.1.2. ecological niches 24, soil nutrients 25, 26, or to assess climate change impacts of forests 27, insect pests 28, and biodiversity 29. Data based on the CHELSA algorithm 17 have already been used to infer e.g. The downscaling algorithm is based on the CHELSA algorithm 17, which provides a more accurate representation of temperature and precipitation in highly complex terrain. Here, we present four downscaled global circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5 23) gridded monthly time series for the years 2006–2100 with a spatial resolution of 0.049° resolution (approximately 5 km at the equator). 16), yet high resolution (<10 km) global time series are still lacking. Up to now, only downscaled mean climatological data at high spatial resolution has been available for the future (e.g. Such datasets exist usually only for climatological means calculated for specific time periods, but time series that allow for a more dynamic representation of the climate system are still missing at high resolutions of ca. from CHELSA 17, WorldClim 18, CRU 19, GPCC 20, CHIRPS 21, or PRISM 22. The gap between these spatial scales is often bridged by applying a delta change method 15, 16 to current-time climate data that is available at high spatial resolution of ca. Also, it is common to characterize species ranges by their climatic envelopes using species distribution models (SDMs) and a relatively small set of climatic predictors derived from monthly minimum and maximum temperature, as well as precipitation 12, 13.įor many scientific applications, the representation of the temporal and spatial variability of temperature and precipitation is extremely important 14. In ecological studies for example, precipitation together with minimum and maximum temperatures are often used to analyze occurrences of species 11. Therefore, impact studies do not require a complete representation of all climate processes at high resolution. ![]() 10).Īlthough achieving 1 km resolutions in numerical climate modelling is important for quantifying effects such as deep convection or surface drag 10, impact studies usually rely only on a much simpler set of climate variables compared to what numerical climate models can provide. Even with the largest supercomputers and state-of-the-art climate models, as well as large financial investments, such a shortfall can currently only be reduced by a factor of 20 (ref. Currently, global kilometre-scale models only achieve a simulation throughput of 0.043 SYPD (simulated years per day) 8, which amounts to an 25 x shortfall compared to what would be computationally efficient simulations of 1 SYPD 7, 9. Although global circulation and weather models such as WRF-ARF 4, or ICON 5, 6, for example, can be run at high horizontal resolutions close to 1 km, they are still heavily constrained by computational limits 7. Such a coarse resolution is usually not capable of capturing orographic precipitation in complex terrain 1, 2, 3. While many studies in ecology and environmental sciences are conducted at a relatively fine spatial resolution of just a few kilometres, GCMs represent climatic variation at spatial resolutions of 0.5°–1° (ca. There is, however, a relatively large-scale gap between the spatial resolution at which global circulation models (GCMs) are calculated, and the resolution at which impact studies are conducted. High-resolution future climate projections are essential for many scientific applications ranging from climate change impact studies, environmental planning, and ecological analysis and modelling.
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