Bioclimate Projections: (10) Mean Temperature of Warmest Quarter
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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 temperature during the three warmest months of the 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 本测试版（beta）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，以采用新版本。 本图层代表耦合模式比较计划第六阶段（Coupled Model Intercomparison Project Phase 6, CMIP6）对一年中最暖三个月的平均温度所做的未来预估。该图层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。 本预估由世界气候（WorldClim）制作，属于美国地质调查局（United States Geological Survey, USGS）确定的19个生物气候变量系列之一，其描述如下："生物气候变量由月度温度与降水数据推导而来，旨在生成更具生物学意义的变量。这类变量常应用于物种分布建模及相关生态建模技术中。生物气候变量涵盖年度趋势（如年平均温度、年降水量）、季节性特征（如温度与降水的年际波动范围）以及极端或限制性环境因子（如最冷月、最暖月温度，以及湿季与干季的降水量）。季度指为期三个月的周期（即一年的1/4）。" ## 数据基本参数 ### 时间范围 2021-2040、2041-2060、2061-2080、2081-2100年的平均值 ### 单位 摄氏度（deg C） ### 像元大小 2.5分（约5公里） ### 源类型 拉伸（Stretched） ### 像素类型 32位浮点型 ### 数据投影 GCS WGS84 ### 镶嵌投影 GCS WGS84 ### 覆盖范围 全球 ### 数据源 WorldClim CMIP6 生物气候情景 ## CMIP6气候情景说明 CMIP6气候实验采用共享社会经济路径（Shared Socioeconomic Pathways, SSPs）来模拟未来气候情景。每个SSP将人类/社区行为维度与此前CMIP5中使用的传统辐射强迫浓度路径（Representative Concentration Pathways, RCP）温室气体强迫相结合。Esri根据用户需求选取了三种SSP纳入本服务：SSP2-4.5、SSP3-7.0及SSP5-8.5。 | SSP情景 | 2041–2060年预估升温 | 2081–2100年预估升温 | 2081–2100年极可能升温范围（℃） | | ---- | ---- | ---- | ---- | | SSP2-4.5 中等温室气体排放：2050年前CO₂排放量维持当前水平，之后逐步下降但未在2100年达到净零 | 2.0℃ | 2.7℃ | 2.1 – 3.5 | | SSP3-7.0 高温室气体排放：到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：到2075年CO₂排放量增至当前三倍 | 2.4℃ | 4.4℃ | 3.3 – 5.7 | 尽管8.5情景目前已不再被普遍认为是大概率发生的场景，但Esri仍保留了SSP3-7.0（作为高排放可能性的上限）与SSP5-8.5（因众多机构以此为基准开展研究）。SSP2-4.5对应的升温幅度与2021年联合国格拉斯哥第26次缔约方大会（COP26）设定的全球温控目标一致。 ## 气候数据处理流程 WorldClim提供了24个全球气候模式针对各SSP的20年平均输出结果。根据Mahony等人2022年的研究，针对每个变量与时间周期，选取13个模式的结果进行平均。所选用的模式包括：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与SSP5-8.5情景中无可用数据。 ## 多维信息访问 本图层采用多维栅格集成了时间与SSP情景维度。启用时间滑块即可浏览各20年平均周期的数据。在ArcGIS Online与ArcGIS Pro中，可通过多维过滤器选择所需SSP情景（默认选中SSP2-4.5）。 ## 可使用该图层开展的操作 此类多维影像瓦片支持通过ArcGIS Online或ArcGIS Pro进行分析。可使用生物气候基准图层对比像素差异，并计算历史时段至未来时段的变化量。在ArcGIS Pro中，可通过多维选项卡访问各类实用工具。各图层或变量可通过图像显示选项进行样式配置。 ## 已知质量问题 所有模式均由WorldClim从约100km分辨率降尺度至约5km分辨率。全球尺度下难免出现伪影，格陵兰地区存在较为明显的过渡特征。此外，SSP2-4.5情景下南极洲部分变量存在数据缺失。 ## 相关图层 1. 生物气候1：年平均温度 2. 生物气候2：平均日较差 3. 生物气候3：等温性 4. 生物气候4：温度季节性 5. 生物气候5：最暖月最高温度 6. 生物气候6：最冷月最低温度 7. 生物气候7：温度年较差 8. 生物气候8：湿季平均温度 9. 生物气候9：干季平均温度 10. 生物气候10：最暖季平均温度 11. 生物气候11：最冷季平均温度 12. 生物气候12：年降水量 13. 生物气候13：最湿月降水量 14. 生物气候14：最干月降水量 15. 生物气候15：降水季节性 16. 生物气候16：湿季降水量 17. 生物气候17：干季降水量 18. 生物气候18：暖季降水量 19. 生物气候19：冷季降水量 20. 生物气候基准图层：1970-2000年时段
