Bioclimate Projections: (11) Mean Temperature of Coldest Quarter

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 coldest 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：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。 本图层展示了CMIP6（Coupled Model Intercomparison Project Phase 6，耦合模式比较计划第六阶段）下一年中最寒冷三个月的平均气温未来预估数据，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。 WorldClim制作了该预估数据，作为美国地质调查局（USGS，United States Geological Survey）确定的19个生物气候变量系列的一部分，并提供如下说明：“生物气候变量源自月均气温与降雨数据，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。生物气候变量涵盖年度趋势（如年平均气温、年降水量）、季节变化特征（如气温与降水的年度波动范围），以及极端或限制性环境因子（如最冷月、最热月气温，以及湿季、干季的降水量）。一季指为期三个月的时段（即全年的1/4）。” 时间范围：2021-2040年、2041-2060年、2061-2080年、2081-2100年的平均值 单位：摄氏度（℃） 像元分辨率：2.5角分（约5千米） 源类型：拉伸型 像元类型：32位浮点型 数据投影：GCS WGS84（Geographic Coordinate System WGS84，地理坐标系统WGS84） 镶嵌投影：GCS WGS84（Geographic Coordinate System WGS84，地理坐标系统WGS84） 覆盖范围：全球 数据源：WorldClim CMIP6生物气候气候情景 CMIP6气候试验采用共享社会经济路径（SSPs，Shared Socioeconomic Pathways）构建未来气候情景，每条SSP将人类/社区行为模式与前代CMIP5（Coupled Model Intercomparison Project Phase 5，耦合模式比较计划第五阶段）中的传统RCP（Representative Concentration Pathway，典型浓度路径）温室气体强迫相结合。Esri根据用户需求选取了3种SSP纳入本服务：SSP2-4.5、SSP3-7.0及SSP5-8.5。 SSP情景、预估增温（2041–2060年）、预估增温（2081–2100年）及2081–2100年极大概率增温范围如下： - SSP2-4.5（中等温室气体排放情景：2050年前二氧化碳排放量维持当前水平，此后逐步下降，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年二氧化碳排放量翻倍）：2.1℃、3.6℃、2.8–4.6℃ - SSP5-8.5（极高温室气体排放情景：至2075年二氧化碳排放量增至三倍）：2.4℃、4.4℃、3.3–5.7℃ 尽管SSP5-8.5情景目前已不被普遍认为是大概率发生的情景，但Esri仍纳入了SSP3-7.0（该情景被视为未来可能性的上限），并保留了SSP5-8.5，因诸多机构仍以该阈值作为分析基准。SSP2-4.5对应的增温幅度与2021年英国格拉斯哥联合国气候变化大会（COP26，Conference of the Parties 26）设定的全球温控目标相符。 气候数据处理流程：WorldClim基于24个全球气候模型，为各SSP情景提供20年平均的输出结果。根据Mahony等人2022年的研究，针对每个变量与时段，选取13个模型进行平均计算。所选用的模型包括：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模型无法用于SSP2-4.5及SSP5-8.5情景。 多维信息访问方式：本图层采用多维栅格结构集成了时间与SSP情景信息。启用时间滑块即可浏览各20年平均时段的数据。在ArcGIS Online及ArcGIS Pro中，可通过多维筛选器选择SSP情景（默认选中SSP2-4.5）。 本图层的应用场景：这些多维影像瓦片支持通过ArcGIS Online或ArcGIS Pro开展分析。可搭配生物气候基准图层，对比像元差异并计算历史时段至未来时段的气候变化量。在ArcGIS Pro中可通过多维选项卡访问各类实用工具。每个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将各模型的分辨率从约100千米降尺度至约5千米，此过程中不可避免会产生一些伪影，在全球尺度下尤为明显。部分变量存在明显的过渡边界，在格陵兰地区尤为突出。此外，SSP2-4.5情景在南极洲的部分变量存在数据缺失问题。 相关图层： 生物气候变量1：年平均气温 生物气候变量2：平均日较差 生物气候变量3：等温性 生物气候变量4：气温季节变化 生物气候变量5：最热月最高气温 生物气候变量6：最冷月最低气温 生物气候变量7：气温年较差 生物气候变量8：湿季平均气温 生物气候变量9：干季平均气温 生物气候变量10：暖季平均气温 生物气候变量11：冷季平均气温 生物气候变量12：年降水量 生物气候变量13：最湿月降水量 生物气候变量14：最干月降水量 生物气候变量15：降水季节变化 生物气候变量16：湿季降水量 生物气候变量17：干季降水量 生物气候变量18：暖季降水量 生物气候变量19：冷季降水量 生物气候基准图层：1970-2000年