Bioclimate Projections: (03) Isothermality

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 how large the day-to-night temperatures oscillate relative to the summer-to-winter (annual) oscillations. Isothermality is derived by calculating the ratio of the mean diurnal range (Bio 2) to the annual temperature range (Bio 7), and then multiplying by 100. An isothermal value of 100 indicates the diurnal temperature range is equivalent to the annual temperature range, while anything less than 100 indicates a smaller level of temperature variability within an average month relative to the year. A species distribution may be influenced by larger or smaller temperature fluctuations within a month relative to the year and this predictor is useful for ascertaining such information. 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: % 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Beta测试项将于2026年12月停用，目前已有可用新版本。Esri建议您更新地图与应用，以使用新版本。 本图层展示了第六次耦合模式比较计划（CMIP6）的未来情景，即昼夜温度波动幅度相较于夏冬（年度）温度波动幅度的比值。等温性（Isothermality）的计算方式为：将日均温度日较差（Bio 2）与年温度极差（Bio 7）的比值乘以100。当等温性数值为100时，代表单日温度波动幅度与年度温度波动幅度相等；若数值低于100，则代表单月内的温度变异性相较于全年更小。物种分布可能会受到单月内温度波动相较于全年波动幅度大小的影响，该预测变量可用于获取此类相关信息。本图层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的潜在影响。 本图层属于WorldClim数据集产出的19个生物气候变量序列之一，该序列由美国地质调查局（United States Geological Survey, USGS）确定，WorldClim对生物气候变量的说明如下：生物气候变量源自月度气温与降水数据，旨在生成更具生物学意义的变量。此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（如年均温、年降水量）、季节特征（如温度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）。其中，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20年平均数据 单位：百分比（%） 像元分辨率：2.5角分（约5千米） 源类型：拉伸型 像素类型：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生物气候气候情景数据集 CMIP6气候实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将人类/社区行为组分与此前CMIP5中的传统典型浓度路径（Representative Concentration Pathways, RCP）温室气体强迫相结合。Esri根据用户需求，在本服务中纳入了3种SSP情景：SSP2-4.5、SSP3-7.0与SSP5-8.5。 SSP情景 预估增温（2041–2060年） 预估增温（2081–2100年） 2081–2100年极可能增温范围（℃） SSP2-4.5 中等温室气体排放情景：二氧化碳排放量在2050年前维持当前水平，之后逐步下降，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当前三倍 2.4℃ 4.4℃ 3.3–5.7 尽管当前学界普遍不再认为SSP5-8.5情景具备较高发生概率，但本服务仍纳入了SSP3-7.0（被视为可能性上限情景）与SSP5-8.5。保留SSP5-8.5的原因在于诸多机构仍以该阈值作为报告基准。SSP2-4.5情景对应的增温幅度，与2021年联合国格拉斯哥第26届联合国气候变化大会（COP26）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情景生成了20年平均数据。根据Mahony等人2022年的研究，针对每个变量与时段，研究团队选取了13个模式进行平均。这13个模式分别为：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。 多维信息访问方式：本图层采用多维栅格数据结构，内置了时间与SSP情景信息。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（Multidimensional Filter）选择SSP情景（默认情景为SSP2-4.5）。 本图层应用场景：该多维影像瓦片支持在ArcGIS Online或ArcGIS Pro中进行分析。可通过生物气候基准图层（Bioclimate Baseline）查看像元差异，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将各模式的分辨率从约100千米降尺度至约5千米，此过程中难免产生部分伪影，在全球尺度下尤为明显。部分变量存在明显的边界过渡异常，在格陵兰地区尤为突出。此外，SSP2-4.5情景下南极地区的部分变量存在数据缺失。 相关图层： 1. 生物气候变量1：年均温 2. 生物气候变量2：日均温度日较差 3. 生物气候变量3：等温性（Isothermality） 4. 生物气候变量4：温度季节波动性 5. 生物气候变量5：最热月最高温 6. 生物气候变量6：最冷月最低温 7. 生物气候变量7：年温度极差 8. 生物气候变量8：湿季平均温 9. 生物气候变量9：干季平均温 10. 生物气候变量10：暖季平均温 11. 生物气候变量11：冷季平均温 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：降水季节波动性 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）