CHELSA Bioclimate Projections - Annual Mean Temperature (Bio1)

Beta Notice: This item is currently in beta and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice. This layer displays global downscaled CMIP6 ISIMIP3b projections of annual mean temperature. The data is hosted by the Swiss Federal Institute for Forest, Snow, and Landscape Research WSL. It is built to provide free access to high resolution climate data for research and application. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change. WSL produced projections of 19 traditional bioclimate predictors (defined by the USGS) as well as several additional variables as part of CHELSA BIOCLIM+ and provides the following description in their documentation: "High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. The CHELSA (Climatologies at high resolution for the earth’s land surface areas) data (Karger et al. 2017) consists of downscaled model output temperature and precipitation estimates at a horizontal resolution of 30 arc sec. The temperature algorithm is mainly based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction." Dataset Summary Phenomenon Mapped: Annual mean temperature: mean annual daily-mean air temperatures averaged over 1 year Geographic Extent: Global Data Projection: GCS WGS84 Cell Size: 30 arc seconds (~1 km) Units: deg C Time Extent: averages over 2011-2040, 2041-2070, and 2071-2100. Pixel Type: 32 Bit Float Source: CHELSA BIOCLIM+ Data vintage: 5/30/2025 Publication Date: 6/4/2025 Climate Scenarios The CMIP6 ISIMIP3b climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcings. Three SSPs are included in these services: SSP1-2.6, SSP3-7.0 and SSP5-8.5. From the IPCC AR6 Summery for Policymakers: SSP Scenario Estimated warming (2041–2060) Estimated warming (2081–2100) Very likely range in °C (2081–2100) SSP1-2.6 low GHG emissions: CO2 emissions cut to net zero around 2075 1.7 °C 1.8 °C 1.3 – 2.4 SSP3-7.0 high GHG emissions: CO2 emissions double by 2100 2.1 °C 3.6 °C 2.8 – 4.6 SSP5-8.5 very high GHG emissions: CO2 emissions triple by 2075 2.4 °C 4.4 °C 3.3 – 5.7 Processing the Climate Data CHELSA provides 30-year averaged outputs for the various SSPs from 5 global climate models: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The 5 models are average into a multi-model ensemble for each variable and time period. Accessing the Multidimensional Information The time and SSP scenarios are built into the layer using a multidimensional raster. In ArcGIS Online, use the Multidimensional tab to select the time period and SSP scenario of your choice. In ArcGIS Pro, use the Multidimensional Filter to select the time period and SSP scenario of your choice. Each SSP scenario includes the baseline period (1981-2010) which shows the same values in each SSP scenario. Note that the time periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100 are denoted in the multidimensional raster by their mid-point years as 1995, 2025, 2055, and 2085. What can you do with this layer? These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Read more about this layer in our blogs: Introducing CHELSA Bioclimate Projections New Authoritative Climate Projections for the United States Expanding the CHELSA Bioclimate Projection Collection Known Quality Issues Each model is downscaled from ~100km resolution to ~1km resolution by CHELSA. This inevitably introduces some artifacts into the data. References Brun, P., Zimmerman, N.E., Hari, C., Pellissier, L., Karger, D.N. (2022) Global climate-related predictors at kilometre resolution for the past and future Earth System Science Data. 14, 5573-5603 https://doi.org/10.5194/essd-14-5573-2022 Brun, P., Zimmermann, N.E., Hari, C., Pellissier, L., Karger, D.N. (2022). CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. EnviDat. https://doi.org/10.16904/envidat.332 Related Layers CHELSA Bioclimate Projections: Annual Mean Temperature (Bio1) CHELSA Bioclimate Projections: Mean Diurnal Range (Bio2) CHELSA Bioclimate Projections: Isothermality (Bio3) CHELSA Bioclimate Projections: Temperature Seasonality (Bio4) CHELSA Bioclimate Projections: Max Temperature of Warmest Month (Bio5) CHELSA Bioclimate Projections: Min Temperature of Coldest Month (Bio6) CHELSA Bioclimate Projections: Annual Temperature Range (Bio7) CHELSA Bioclimate Projections: Mean Temperature of Wettest Quarter (Bio8) CHELSA Bioclimate Projections: Mean Temperature of Driest Quarter (Bio9) CHELSA Bioclimate Projections: Mean Temperature of Warmest Quarter (Bio10) CHELSA Bioclimate Projections: Mean Temperature of Coldest Quarter (Bio11) CHELSA Bioclimate Projections: Annual Precipitation (Bio12) CHELSA Bioclimate Projections: Precipitation of Wettest Month (Bio13) CHELSA Bioclimate Projections: Precipitation of Driest Month (Bio14) CHELSA Bioclimate Projections: Precipitation Seasonality (Bio15) CHELSA Bioclimate Projections: Precipitation of Wettest Quarter (Bio16) CHELSA Bioclimate Projections: Precipitation of Driest Quarter (Bio17) CHELSA Bioclimate Projections: Precipitation of Warmest Quarter (Bio18) CHELSA Bioclimate Projections: Precipitation of Coldest Quarter (Bio19) CHELSA Bioclimate Projections: Growing Degree Days Above 10C CHELSA Bioclimate Projections: Net Primary Productivity CHELSA Bioclimate Projections: Snow Cover Days CHELSA Bioclimate Projections: Snow Water Equivalent Questions? 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