Bioclimate Projections: (01) Annual Mean Temperature

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 mean annual temperature. 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：本测试版产品将于2026年12月停用，当前已有该产品的新版本可供使用。Esri建议您更新地图与应用程序以适配新版本。 本图层展示耦合模式比较计划第六阶段（CMIP6）的年平均气温未来预估结果，可与近期气候历史数据对比，以更深入地理解未来气候变化可能带来的影响。 本预估数据由WorldClim制作，属于美国地质调查局（USGS）确定的19个生物气候变量系列之一，其官方说明如下：“生物气候变量由月均气温与月降水量数据计算得到，旨在生成更具生物学意义的变量。这类变量常被应用于物种分布建模及相关生态建模技术中，涵盖年度趋势（如年平均气温、年降水量）、季节特征（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/最热月均温、湿季/干季降水量）。其中，季度指为期3个月的时段（即一年的1/4）。” ### 基本参数 时间范围：平均时段涵盖2021-2040年、2041-2060年、2061-2080年及2081-2100年 单位：摄氏度（℃） 像元分辨率：2.5角分（约5公里） 源类型：拉伸型 像元类型：32位浮点型 数据投影：地理坐标系统WGS84（GCS WGS84） 镶嵌投影：地理坐标系统WGS84（GCS WGS84） 覆盖范围：全球 数据源：WorldClim CMIP6生物气候情景数据集 CMIP6气候试验采用共享社会经济路径（SSPs）构建未来气候情景，每一条SSP均将人类/社区行为模式与前代耦合模式比较计划第五阶段（CMIP5）中的典型浓度路径（RCP）温室气体强迫相结合。Esri根据用户需求选取了3种SSP情景纳入本服务：SSP2-4.5、SSP3-7.0及SSP5-8.5。 #### SSP情景详情 | SSP情景 | 预估增温（2041-2060年） | 预估增温（2081-2100年） | 2081-2100年极大概率增温区间（℃） | | ---- | ---- | ---- | ---- | | SSP2-4.5（中等温室气体排放情景） | 2.0 ℃ | 2.7 ℃ | 2.1 – 3.5 | | SSP3-7.0（高温室气体排放情景） | 2.1 ℃ | 3.6 ℃ | 2.8 – 4.6 | | SSP5-8.5（极高温室气体排放情景） | 2.4 ℃ | 4.4 ℃ | 3.3 – 5.7 | 注：SSP2-4.5情景指二氧化碳排放量维持当前水平至2050年，此后逐步下降但未在2100年前实现净零排放；SSP3-7.0情景指二氧化碳排放量至2100年翻倍；SSP5-8.5情景指二氧化碳排放量至2075年增至3倍。 尽管当前普遍认为SSP5-8.5情景已不再具备较高可能性，但SSP3-7.0仍被纳入本服务，作为排放情景的高上限选项；SSP5-8.5则因众多机构以此为基准开展研究而予以保留。SSP2-4.5情景对应的增温幅度与2021年联合国格拉斯哥COP26会议设定的全球温控目标相符。 ### 气候数据处理流程 WorldClim基于24个全球气候模式，为各SSP情景提供了20年平均的输出结果。根据Mahony等人2022年的研究，本数据集针对每个变量与时段选取了13个模式进行平均计算，涉及的模式包括：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模式在SSP2-4.5及SSP5-8.5情景中无可用数据。 ### 多维信息访问方式 本图层采用多维栅格结构集成时间与SSP情景维度。启用时间滑块即可切换不同的20年平均时段；在ArcGIS Online及ArcGIS Pro中，可通过多维过滤器选择所需SSP情景（默认选项为SSP2-4.5）。 ### 应用场景 本多维影像瓦片支持在ArcGIS Online或ArcGIS Pro中开展分析。可通过生物气候基准图层对比像元差异，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调用各类实用工具；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将各原始模式约100公里的分辨率降尺度至约5公里，该过程不可避免会产生部分伪影，在全球尺度下尤为明显。部分变量存在显著的边界过渡现象，在格陵兰地区尤为突出。此外，SSP2-4.5情景下南极洲的部分变量存在数据缺失。 ### 关联图层 1. 生物气候变量1：年平均气温 2. 生物气候变量2：气温日较差均值 3. 生物气候变量3：等温性 4. 生物气候变量4：气温季节变率 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：湿季平均气温 9. 生物气候变量9：干季平均气温 10. 生物气候变量10：暖季平均气温 11. 生物气候变量11：冷季平均气温 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：降水季节变率 16. 生物气候变量16：湿季降水量 17. 生物气候变量17：干季降水量 18. 生物气候变量18：暖季降水量 19. 生物气候变量19：冷季降水量 20. 生物气候基准图层（1970-2000年）