Bioclimate Projections: (14) Precipitation of Driest Month

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 total precipitation during the driest month 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: mm 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，现已推出正式新版本。Esri建议您更新地图与应用，以使用新版本。 本图层展示了CMIP6（耦合模式比较计划第6阶段）未来情景下年度最干月总降水量的预估数据。本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。 WorldClim将本投影作为美国地质调查局（USGS）确定的19个生物气候变量（bioclimate variables）序列的一部分进行制作，并提供如下说明："生物气候变量由月度气温与降雨量数据推导而来，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。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（如气温与降水的年较差）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：涵盖2021-2040年、2041-2060年、2061-2080年、2081-2100年的平均值 单位：毫米（mm） 像元分辨率：2.5弧分（约5千米） 源类型：拉伸 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生物气候气候情景 CMIP6气候试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。每条SSP将人类/社区行为组分与此前CMIP5（耦合模式比较计划第5阶段）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，从SSPs中选取了3种应用于本服务：SSP2-4.5、SSP3-7.0及SSP5-8.5。 | 情景名称 | 2041-2060年预估增温 | 2081-2100年预估增温 | 2081-2100年极大概率增温范围（℃） | | ---- | ---- | ---- | ---- | | SSP2-4.5 | 2.0℃ | 2.7℃ | 2.1–3.5 | | SSP3-7.0 | 2.1℃ | 3.6℃ | 2.8–4.6 | | SSP5-8.5 | 2.4℃ | 4.4℃ | 3.3–5.7 | 尽管当前学界普遍不再认为SSP5-8.5情景具有较高发生概率，但本服务仍纳入了SSP3-7.0（被视为可能性区间的上限情景），并保留了SSP5-8.5，因众多机构仍以该情景作为分析阈值。SSP2-4.5情景对应的增温幅度，与2021年联合国格拉斯哥第26届气候变化大会（COP26）设定的全球温控目标相符。 ### 气候数据处理 WorldClim基于24个全球气候模式，为各类SSP情景提供20年平均结果。根据Mahony等人2022年的研究，本服务针对每个变量与时段，选取了13个模式进行平均。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模式无法获取SSP2-4.5及SSP5-8.5情景的数据。 ### 多维信息访问 本图层采用多维栅格（multidimensional raster）整合了时间维度与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及ArcGIS Pro中，可通过多维筛选器选择SSP情景（默认情景为SSP2-4.5）。 ### 本图层可实现的应用场景 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（Bioclimate Baseline）查看像元差异，并计算历史时期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。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将各模式的分辨率从约100千米降尺度至约5千米。部分数据伪影在所难免，在全球尺度下尤为明显。部分变量存在显著的过渡特征，尤其是在格陵兰地区。此外，SSP2-4.5情景下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（Bioclimate 1 Annual Mean Temperature） 2. 生物气候变量2：气温日较差均值（Bioclimate 2 Mean Diurnal Range） 3. 生物气候变量3：等温性（Bioclimate 3 Isothermality） 4. 生物气候变量4：气温季节变化率（Bioclimate 4 Temperature Seasonality） 5. 生物气候变量5：最热月最高气温（Bioclimate 5 Max Temperature of Warmest Month） 6. 生物气候变量6：最冷月最低气温（Bioclimate 6 Min Temperature Of Coldest Month） 7. 生物气候变量7：气温年较差（Bioclimate 7 Temperature Annual Range） 8. 生物气候变量8：湿季平均气温（Bioclimate 8 Mean Temperature Of Wettest Quarter） 9. 生物气候变量9：干季平均气温（Bioclimate 9 Mean Temperature Of Driest Quarter） 10. 生物气候变量10：暖季平均气温（Bioclimate 10 Mean Temperature Of Warmest Quarter） 11. 生物气候变量11：冷季平均气温（Bioclimate 11 Mean Temperature Of Coldest Quarter） 12. 生物气候变量12：年降水量（Bioclimate 12 Annual Precipitation） 13. 生物气候变量13：最湿月降水量（Bioclimate 13 Precipitation Of Wettest Month） 14. 生物气候变量14：最干月降水量（Bioclimate 14 Precipitation Of Driest Month） 15. 生物气候变量15：降水季节变化率（Bioclimate 15 Precipitation Seasonality） 16. 生物气候变量16：湿季降水量（Bioclimate 16 Precipitation Of Wettest Quarter） 17. 生物气候变量17：干季降水量（Bioclimate 17 Precipitation Of Driest Quarter） 18. 生物气候变量18：暖季降水量（Bioclimate 18 Precipitation Of Warmest Quarter） 19. 生物气候变量19：冷季降水量（Bioclimate 19 Precipitation Of Coldest Quarter） 20. 生物气候基准图层（1970-2000年）（Bioclimate Baseline 1970-2000）