Bioclimate Projections: (02) Mean Diurnal 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 mean diurnal range. Diurnal range is a measure of daily daytime to nighttime temperature range. However, this layer provides the mean of the monthly temperature ranges (monthly maximum minus monthly minimum). Since the climate data inputs are monthly or averaged months across multiple years, this calculation uses recorded temperature fluctuation within a month to capture diurnal temperature range. Using monthly averages in this manner is mathematically equivalent to calculating the temperature range for each day in a month, and averaging these values for the month. 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# 退役通知 本Beta版本产品将于2026年12月停止服务，现有新版本可供使用。Esri建议您将地图与应用更新至新版本。 本图层展示了CMIP6（耦合模式比较计划第六阶段）下的未来平均昼夜温差投影结果。昼夜温差是衡量日间与夜间温度差值的指标，而本图层提供的是逐月温度极差（月最高温减月最低温）的平均值。由于气候数据输入为逐月或多年平均月数据，本计算利用月内记录的温度波动来获取昼夜温度范围；该计算方式在数学上等价于先计算每月每日的温度极差，再对该月取平均。 本数据集由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，WorldClim对此的说明如下： "生物气候变量源自逐月温度与降水数据，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）、季节特征（如温度与降水的年度极差）以及极端或限制性环境因子（如最冷/暖月气温、最湿/最干季度降水量）。季度指为期三个月的时段（占全年的1/4）。" ## 基本参数 - 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（deg C） - 像元分辨率：2.5角分（约5公里） - 源类型：拉伸（Stretched） - 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 ## CMIP6气候情景说明 CMIP6气候试验采用共享社会经济路径（Shared Socioeconomic Pathways, SSP）模拟未来气候情景，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 Pathway, 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。 各SSP情景详细参数如下： | SSP情景 | 预估增温（2041-2060年） | 预估增温（2081-2100年） | 2081-2100年极可能升温范围（℃） | | ------- | ---------------------- | ---------------------- | ------------------------------ | | SSP2-4.5（中等温室气体排放情景：CO₂排放量维持当前水平至2050年，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排放量增至三倍） | 2.4℃ | 4.4℃ | 3.3 – 5.7 | 注：尽管SSP5-8.5情景目前已不再被普遍视为大概率情景，但SSP3-7.0仍被纳入，其被视为可能性的上限；SSP5-8.5则予以保留，因诸多机构仍以该阈值为报告基准。SSP2-4.5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（Conference of the Parties 26, COP26）设定的全球温控目标相符。 ## 气候数据处理流程 WorldClim基于24个全球气候模型生成了各SSP的20年平均输出结果。依据Mahony等人2022年的研究，针对每个变量与时段选取了13个模型进行平均，涵盖的模型包括：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无法用于SSP2-4.5及SSP5-8.5情景。 ## 多维信息访问方式 本图层已将时间与SSP情景集成至多维栅格中。启用时间滑块即可浏览各20年平均时段。在ArcGIS Online与ArcGIS Pro中，可使用多维筛选器选择SSP情景（默认值为SSP2-4.5）。 ## 图层应用场景 该多维影像瓦片支持通过ArcGIS Online或ArcGIS Pro进行分析： 1. 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变化量； 2. 在ArcGIS Pro中可使用多维选项卡访问各类实用工具； 3. 可通过影像显示选项对各图层或变量进行样式化设置。 ## 已知质量问题 WorldClim将各模型的分辨率从约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，尤其在格陵兰地区。此外，SSP2-4.5情景在南极洲的部分变量存在数据缺失。 ## 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：平均昼夜温差（即本图层对应变量） 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：气温年极差 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：降水季节变率 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）