北美当前和预测的气候数据（CMIP6 ）¶
开发大气-海洋环流模型 (AOGCM) 是为了在广泛的时间尺度上模拟气候变率，并且经常在耦合模拟和数据同化模式下进行测试。 您可以在此处阅读有关 AOGCM 和 CMIP6 的更多信息。 此页面上的数据集由 AdaptWest 开发，该项目由 Wilburforce 基金会资助，旨在为气候适应规划开发信息资源。前言 – 床长人工智能教程
数据是使用 ClimateNA 软件生成的。CMIP6: the next generation of climate models explained - Carbon Brief ClimateNA 使用来自 PRISM 和 WorldClim 的当前气候数据，并缩减了耦合模型比对项目第 6 阶段 (CMIP6) 数据库中对应于第六次 IPCC 评估报告的未来预测数据。
集合预测是来自 13 个 CMIP5 模型（下表）的平均预测，这些模型被选为代表类似 AOGCM 的所有主要集群。 除了集合预测之外，还提供了代表较大集合的 9 个单独的 AOGCM（下表）的数据。 选择了九个单独的模型来代表所有主要的类似 AOGCM 集群。 使用更广泛的 13 个 AOGCM 集来创建集合数据。 与单个 AOGCM 的预测相比，此处还提供了更大范围的时间段和情景的集合预测。
| AOGCM Ensemble Models | AOGCM Individual Models |
| ACCESS-ESM1-5 | ACCESS-ESM1-5 |
| BCC-CSM2-MR | |
| CNRM-ESM2-1 | CNRM-ESM2-1 |
| CanESM5 | |
| EC-Earth3 | EC-Earth3 |
| GFDL-ESM4 | GFDL-ESM4 |
| GISS-E2-1-G | GISS-E2-1-G |
| INM-CM5-0 | |
| IPSL-CM6A-LR | |
| MIROC6 | MIROC6 |
| MPI-ESM1-2-HR | MPI-ESM1-2-HR |
| MRI-ESM2-0 | MRI-ESM2-0 |
| UKESM1-0-LL | UKESM1-0-LL |
Data citation¶
AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.
Paper citation¶
You can read the paper here and cite as as below
AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org. For further information and citation refer to: Wang, T., A. Hamann, D. Spittlehouse, C. Carroll. 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS One 11(6): e0156720 https://doi.org/10.1371/journal.pone.0156720 Mahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 1-21. https://doi.org/10.1002/joc.7566
The current climatic variables included in the datasets for climate normals, AOGCM models and AOGCM ensemble model are listed below
| Monthly Variables | Description |
| tmin | minimum temperature for a given month (°C) |
| tmax | maximum temperature for a given month (°C) |
| tave | mean temperature for a given month (°C) |
| ppt | total precipitation for a given month (mm) |
Earth Engine Snippet Climate variables¶
var climate_models_ppt = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_ppt"); var climate_models_tave = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tave"); var climate_models_tmax = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmax"); var climate_models_tmin = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmin"); var climate_normals_ppt = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_ppt"); var climate_normals_tave = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tave"); var climate_normals_tmax = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmax"); var climate_normals_tmin = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmin"); var aogcm_ensemble_ppt = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_ppt"); var aogcm_ensemble_tave = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tave"); var aogcm_ensemble_tmax = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmax"); var aogcm_ensemble_tmin = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmin");
Google Earth Engine v7.3 的后期处理¶
所有 9 个单独的 AOGCM 模型都添加到与每个气候变量相关的集合中，并命名为 Climate-Models_（变量名称）。 合奏模型与气候平均值一起被摄取。
AOGCM 系综模型和单个模型都有日期范围和排放情景类型 emission_scenario，开始和结束日期作为属性添加。
由于 Climate-Models-(Variable Name) 集合由 9 个单独的模型组成，另一个元数据字段被添加到这些集合中，即 model 以按模型名称过滤。
由于所有气候变量都是每月的，因此将一个名为 month 的额外元数据添加到气候平均值、合奏和单个模型集合中，以便根据需要进一步分割数据。
模型的版本号和 20 年和 30 年月度变量的周期现在包含在元数据中。
Earth Engine Snippet Bioclimatic variables¶
var climate_models_bioclim = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_bioclim"); var aogcm_ensemble_bioclim = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_bioclim"); var climate_normals_bioclim = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_bioclim");
There are a total of 33 bioclimatic variables included for the collections and models , the reference table is included below and you can filter using the metadata property bioclim_variable and the property names from the table.
| Bioclimatic Variables | Description |
| MAT | mean annual temperature (°C) |
| MWMT | mean temperature of the warmest month (°C) |
| MCMT | mean temperature of the coldest month (°C) |
| TD | difference between MCMT and MWMT, as a measure of continentality (°C) |
| MAP | mean annual precipitation (mm) |
| MSP | mean summer (May to Sep) precipitation (mm) |
| AHM | annual heat moisture index, calculated as (MAT+10)/(MAP/1000) |
| SHM | summer heat moisture index, calculated as MWMT/(MSP/1000) |
| DD_0 | degree-days below 0°C (chilling degree days) |
| DD5 | degree-days above 5°C (growing degree days) |
| DD_18 | degree-days below 18°C |
| DD18 | degree-days above 18°C |
| NFFD | the number of frost-free days |
| FFP | frost-free period |
| bFFP | the julian date on which the frost-free period begins |
| eFFP | the julian date on which the frost-free period ends |
| PAS | precipitation as snow (mm) |
| EMT | extreme minimum temperature over 30 years |
| EXT | extreme maximum temperature over 30 years |
| Eref | Hargreave's reference evaporation |
| CMD | Hargreave's climatic moisture index |
| MAR | mean annual solar radiation (MJ m-2 d-1) (excludes areas south of US and some high-latitude areas) |
| RH | mean annual relative humidity (%) |
| CMI | Hogg’s climate moisture index (mm) |
| DD1040 | (10<DD<40) degree-days above 10°C and below 40°C |
| Tave_wt | winter (December to February) mean temperature (°C) |
| Tave_sp | spring (March to May) mean temperature (°C) |
| Tave_sm | summer (June to August) mean temperature (°C) |
| Tave_at | autumn (September to November) mean temperature (°C) |
| PPT_wt | winter (December to February) precipitation (mm) |
| PPT_sp | spring (March to May) precipitation (mm) |
| PPT_sm | summer (June to August) precipitation (mm) |
| PPT_at | autumn (September to November) precipitation (mm) |
| PPT_at | autumn (September to November) precipitation (mm) |
已知的问题：¶
由于两国 PRISM 数据之间的边缘匹配问题，沿美国/加拿大边界出现了一些降水值的不连续性。
年平均太阳辐射 (MAR) 数据是临时数据，计划在即将发布的 ClimateNA 软件中进行修订。
License¶
These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Changelog¶
- Updated to v7.3
- Added 20 year periods apart from 30 year periods
Data Website: You can download the data and description here
Explore the data in R-Shiny apps here
Created by: AdaptWest Project, Wang, T., A. Hamann, D. Spittlehouse, C. Carroll
Curated in GEE by: Samapriya Roy
Keywords: climate change, global circulation models, gridded climate data, north america,emission scenarios,climate variables
Last updated: 2023-03-24
