Bioclimate Projections: (07) Temperature Annual Range

Retirement Notice: This beta item will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer represents CMIP6 future projections of temperature variation over an entire year. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change. WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description: "Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)." Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100 Units: deg C Cell Size: 2.5 minutes (~5 km) Source Type: Stretched Pixel Type: 32 Bit Float Data Projection: GCS WGS84 Mosaic Projection: GCS WGS84 Extent: Global Source: WorldClim CMIP6 Bioclimate Climate Scenarios The CMIP6 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 forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5. SSP Scenario Estimated warming (2041–2060) Estimated warming (2081–2100) Very likely range in °C (2081–2100) SSP2-4.5 intermediate GHG emissions: CO2 emissions around current levels until 2050, then falling but not reaching net zero by 2100 2.0 °C 2.7 °C 2.1 – 3.5 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 While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate Data WorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional Information The time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer? These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality Issues Each model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica. Related Layers Bioclimate 1 Annual Mean Temperature Bioclimate 2 Mean Diurnal Range Bioclimate 3 Isothermality Bioclimate 4 Temperature Seasonality Bioclimate 5 Max Temperature of Warmest Month Bioclimate 6 Min Temperature Of Coldest Month Bioclimate 7 Temperature Annual Range Bioclimate 8 Mean Temperature Of Wettest Quarter Bioclimate 9 Mean Temperature Of Driest Quarter Bioclimate 10 Mean Temperature Of Warmest Quarter Bioclimate 11 Mean Temperature Of Coldest Quarter Bioclimate 12 Annual Precipitation Bioclimate 13 Precipitation Of Wettest Month Bioclimate 14 Precipitation Of Driest Month Bioclimate 15 Precipitation Seasonality Bioclimate 16 Precipitation Of Wettest Quarter Bioclimate 17 Precipitation Of Driest Quarter Bioclimate 18 Precipitation Of Warmest Quarter Bioclimate 19 Precipitation Of Coldest Quarter Bioclimate Baseline 1970-2000停用通知：此测试版产品将于2026年12月停用，现有新版本可供使用。Esri建议您更新地图与应用，以使用新版本。 本图层代表CMIP6（Coupled Model Intercomparison Project Phase 6）的全年气温变化未来预估数据，可与近期气候历史数据对比，以更好地理解未来气候变化的潜在影响。 本数据集由WorldClim制作，作为美国地质调查局（United States Geological Survey, USGS）确定的19个生物气候变量系列的一部分，其对生物气候变量的定义如下：“生物气候变量源自月均气温与降水数据，旨在生成更具生物学意义的变量，常被应用于物种分布建模及相关生态建模技术。这类变量涵盖年度趋势（如年平均气温、年降水量）、季节变化特征（如气温与降水的年较差）以及极端或限制性环境因子（如最冷月、最热月气温，以及最湿季度、最干季度的降水量）。季度指为期三个月的时段，即一年的1/4。” 时间范围：2021-2040、2041-2060、2061-2080、2081-2100的20年均值；单位：摄氏度（deg C）；像元大小：2.5弧分（约5公里）；源数据类型：拉伸（Stretched）；像元类型：32位浮点型（32 Bit Float）；数据投影：GCS WGS84；镶嵌投影：GCS WGS84；覆盖范围：全球；数据源：WorldClim CMIP6生物气候气候情景数据集。 CMIP6气候试验采用共享社会经济路径（Shared Socioeconomic Pathways, SSPs）对未来气候情景进行建模，每个SSP均将人类/社区行为模式与前代CMIP5的传统典型浓度路径（Representative Concentration Pathways, RCP）温室气体辐射强迫情景相结合。Esri根据用户需求选取了三种SSP纳入本服务：SSP2-4.5、SSP3-7.0与SSP5-8.5。 各SSP情景详情如下： | SSP情景 | 2041-2060预估增温 | 2081-2100预估增温 | 2081-2100极可能增温范围（℃） | |--------|------------------|------------------|------------------------------| | SSP2-4.5（中等温室气体排放情景：2050年前CO₂排放量维持当前水平，2050年后逐步下降，但至2100年未达到净零排放） | 2.0℃ | 2.7℃ | 2.1–3.5 | | SSP3-7.0（高温室气体排放情景：至2100年CO₂排放量翻倍） | 2.1℃ | 3.6℃ | 2.8–4.6 | | SSP5-8.5（极高温室气体排放情景：至2075年CO₂排放量翻三倍） | 2.4℃ | 4.4℃ | 3.3–5.7 | 尽管当前普遍认为SSP5-8.5情景已不太可能发生，但SSP3-7.0仍被保留，其被视为未来气候可能性的上限；SSP2-4.5对应的增温幅度与2021年联合国格拉斯哥COP26会议设定的全球温控目标一致。 气候数据处理说明：WorldClim基于24个全球气候模式，生成了各SSP的20年均值输出。根据Mahony等人2022年的研究成果，针对每个变量与时段，选取13个模式的结果进行平均，所选模式包括：ACCESS-ESM1-5、BCC-CSM2-MR、CanESM5、CNRM-ESM2-1、EC-Earth3-Veg、GFDL-ESM4、GISS-E2-1-G、INM-CM5-0、IPSL-CM6A-LR、MIROC6、MPI-ESM1-2-HR、MRI-ESM2-0、UKESM1-0-LL。其中GFDL-ESM4无法获取SSP2-4.5与SSP5-8.5情景的数据。 多维信息访问方式：本图层采用多维栅格集成了时间与SSP情景维度。启用时间滑块即可在各20年平均时段间切换；在ArcGIS Online与ArcGIS Pro中，可通过多维过滤器选择SSP情景（默认选项为SSP2-4.5）。 本图层可用功能：这些多维影像瓦片支持通过ArcGIS Online或ArcGIS Pro进行分析。可搭配生物气候基准图层，对比像素差异并计算历史时段至未来的气候变化量；在ArcGIS Pro中可通过多维选项卡访问各类实用工具；各图层或变量均可通过影像显示选项进行样式配置。 已知质量问题：WorldClim将各模式的分辨率从约100公里降尺度至约5公里，全球尺度下不可避免会存在部分伪影；部分变量存在明显的过渡边界，尤其是在格陵兰地区。此外，SSP2-4.5情景在南极洲存在部分变量的数据缺失。 相关图层： 1. 生物气候变量1：年平均气温 2. 生物气候变量2：平均气温日较差 3. 生物气候变量3：等温性 4. 生物气候变量4：气温季节变化 5. 生物气候变量5：最热月最高气温 6. 生物气候变量6：最冷月最低气温 7. 生物气候变量7：气温年较差 8. 生物气候变量8：最湿季度平均气温 9. 生物气候变量9：最干季度平均气温 10. 生物气候变量10：最暖季度平均气温 11. 生物气候变量11：最冷季度平均气温 12. 生物气候变量12：年降水量 13. 生物气候变量13：最湿月降水量 14. 生物气候变量14：最干月降水量 15. 生物气候变量15：降水季节变化 16. 生物气候变量16：最湿季度降水量 17. 生物气候变量17：最干季度降水量 18. 生物气候变量18：最暖季度降水量 19. 生物气候变量19：最冷季度降水量 20. 生物气候基准图层：1970-2000年基准