GCRI · 气候与灾害
气候预估
Climate projections describe how the climate may change under different futures of emissions and land use. They come from ensembles of global models, often downscaled to regions, and are increasingly reported by 全球变暖 level. This page sets out how projections are made, what their uncertainty contains, and how they serve planners who must make long-lived decisions about infrastructure, water and land.
Climate projections: how the risk develops.
Scenario models, ensembles and uncertainty. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Scenarios set the forcing
Shared Socioeconomic Pathways combine emissions and socioeconomic storylines. Projections are conditional on them; they are not forecasts of which future will happen.
Models differ in sensitivity
Climate models differ in how much they warm for the same forcing and in regional patterns of rainfall. Ensembles sample this spread, and some models run hotter than assessed evidence supports.
Natural variability blurs the signal
Year-to-year and decade-to-decade variability can mask or exaggerate the forced trend, especially for rainfall and at small scales. Over the next decades it is a large share of uncertainty.
Downscaling adds detail and assumption
Regional models and statistical methods add local detail. They inherit errors from the driving model and add their own, so downscaled precision is not the same as accuracy.
全球气候信号。
来自 NASA GIBS 的全球日间地表热量、降雨和海洋热浪数据。每个图层都标注各自的日期。
交互式地图需要JavaScript；图层列表和来源见下文。
图层 3
本页的气候信号
关于本图层
- 证据
- 观测（持续云层仍会造成缺口）
- 来源
- VIIRS NOAA-20 Level 3，8 天（NASA LANCE）
- 提供方
- NASA LANCE
- 许可协议
- NASA 开放数据
- 年份版本
- 8 天；卫星 8 天合成；持续云层仍会造成缺口
- 构建时的最新内容
- 2026-09-14
- 处理结果
- 约 1 公里网格
- 不确定性
- 截至所示日期的 8 天内地表的卫星皮肤温度，是高温风险暴露指标；不是气温、热指数，也不是高温预警。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 估计值
- 来源
- GPM IMERG（NASA GES DISC）
- 提供方
- NASA GES DISC
- 许可协议
- NASA 开放数据
- 年份版本
- 每日；近实时卫星估算
- 构建时的最新内容
- 2026-10-12
- 处理结果
- 约 2 公里网格
- 不确定性
- 近实时卫星估算。卫星或模型栅格数据，按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声；数值是指标，而非某地的测量值。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
关于本图层
- 证据
- 观测
- 来源
- VIIRS NOAA-20 校正反射率（NASA LANCE）
- 提供方
- NASA LANCE
- 许可协议
- NASA 开放数据
- 年份版本
- 每日；卫星图像
- 构建时的最新内容
- 2026-10-14
- 处理结果
- 约 250 米网格
- 不确定性
- 卫星图像。卫星或模型栅格数据，按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声；数值是指标，而非某地的测量值。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 模型生成（GIBS 发布的最新年份）
- 来源
- MODIS Terra 和 Aqua 土地覆盖（MCD12Q1）（NASA LP DAAC）
- 提供方
- NASA LP DAAC
- 许可协议
- NASA 开放数据
- 年份版本
- 每年；卫星分类；GIBS 发布的最新年份
- 构建时的最新内容
- 2024-01-01
- 处理结果
- 约 500 米网格
- 不确定性
- 所示年份的卫星土地覆盖类别，含农田；该分类存在已知误差，并非农场登记册。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 观测（云层造成缺口）
- 来源
- VIIRS 全球洪水产品（NASA LANCE）
- 提供方
- NASA LANCE
- 许可协议
- NASA 开放数据
- 年份版本
- 每日；卫星探测；云层造成缺口
- 构建时的最新内容
- 2026-10-14
- 处理结果
- 约 250 米网格
- 不确定性
- 卫星探测；云层造成缺口。卫星或模型栅格数据，按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声；数值是指标，而非某地的测量值。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
键盘操作，地图获得焦点时
按住 Ctrl 或 ⌘ 并滚动以缩放
最新可用
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap贡献者 (ODbL) · Natural Earth
正在加载最新的NASA影像。
拖动以移动。按住 Ctrl 或 ⌘ 并滚动、双指缩放或使用按钮缩放；悬停可查看指针下的数值；重置可返回该区域。
气候信号，以全球为取景。NASA 发布的卫星和模型产品，各带自己的日期。它们是指标，而非对任何地点的评估。
数据：NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap 贡献者 · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERT · NASA AIRS来源与许可
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA 开放数据
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA 开放数据
- VIIRS 近实时产品（洪水、地表温度、积雪覆盖、校正反射率），NASA LANCE；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- GPM IMERG 降水，NASA 降水处理系统；由 NASA 戈达德地球科学数据和信息服务中心（GES DISC）分发；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- GHRSST Level 4 MUR 分析，NASA 喷气推进实验室；由 NASA 物理海洋学 DAAC（PO.DAAC）分发；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- MODIS 陆地产品（植被指数、总初级生产力、土地覆盖），NASA LP DAAC 和 LANCE；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- ASTER GDEM 是 METI 和 NASA 的产品（ASTER Global Digital Elevation Model V003，NASA LP DAAC）；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。引用格式：NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA 开放数据
- 使用 Natural Earth 制作。免费矢量和栅格地图数据，见 naturalearthdata.com。 公有领域
- © OpenStreetMap 贡献者（ODbL），经由 NASA GIBS（海岸线、边界、道路和地名）。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 ODbL
- Blue Marble，NASA Earth Observatory；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- 地球仪和平面地图上的背景信息：海岸线、边界、道路和地名来自 NASA GIBS 参考图层（OpenStreetMap 数据，ODbL，© OpenStreetMap contributors）；Blue Marble 影像可作为可选底图；3D 地形和高程读数来自 ASTER GDEM 高程色彩指数（5 m 至 200 m 分级，为便于辨识而作了夸张处理）。边界按已发布的样式绘制，不代表对任何领土的立场。
- 实时事件：NASA Earth Observatory Natural Event Tracker（EONET）；事件仅供参考，并保留其原始来源链接。
- 实时：NASA EONET。事件仅供参考，不是主管机构的预警。
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Observed (EO): NASA AIRS. We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- 空气质量预报，5 天，PM2.5（CAMS，经由 Open-Meteo）：包含经修改的 Copernicus 大气监测服务 2026 年信息。
- 过去一年的每日气温（ERA5，经由 Open-Meteo）：包含经修改的 Copernicus 气候变化服务 2026 年信息。
- 过去一年的每日气温和日照（NASA POWER）：这些数据来自 NASA 兰利研究中心（LaRC）POWER 项目，该项目由 NASA 地球科学/应用科学计划资助。
- 天气数据来自 Open-Meteo.com (https://open-meteo.com/), 依据 CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). 授权。
- Optional layers (22 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
图层与日期
- 地表温度，日间，8 天合成：VIIRS NOAA-20 3 级产品，8 天，NASA LANCE · 2026年9月14日
- 降水率，日均值：GPM IMERG，NASA GES DISC · 2026年10月12日
- 海面温度距平：GHRSST MUR25，NASA JPL PO.DAAC · 2026年10月12日
- 真彩色：VIIRS NOAA-20 校正反射率，NASA LANCE · 2026年10月13日
- 土地覆盖类型（IGBP），年度：MODIS Terra 和 Aqua 土地覆盖（MCD12Q1），NASA LP DAAC · 2024
- 洪水水体，3 日合成：VIIRS 全球洪水产品，NASA LANCE · 2026年10月14日
Projections reach decisions through each domain’s models.
- 水耦合Hydrological models translate climate projections into flows.What to watchModel chain stated, with spread
- 食品耦合Crop models translate projections into yield ranges.What to watchMulti-model crop ensembles
- 能源耦合Hydropower planning needs projected inflows over asset lifetimes.What to watchInflow range over asset life
Projection-based analyses are designed for the 前瞻展望系列, labelled as projections under named pathways. CMIP6 and ScenarioMIP (O’Neill and others, 2016) provide the climate side; the next phase of CMIP is in preparation.
- Downscaling evaluations for a region.
- Impact model intercomparisons.
多灾种展望。
团队跟踪该主题时会使用的界面：其指标、状态与决策。名称为示例，数值仅作示意。
衡量什么，以及衡量得如何。
每个指标都注明其单位、频率、背后的来源类型以及数值将附带的证据标签。此处不显示数值：数值由计划依据其自身权限提供。
| 指标 | 单位 | 节奏 | 来源类型 | 标签 |
|---|---|---|---|---|
| Change in annual mean temperature | degrees Celsius at a stated warming level | per ensemble release | CMIP6 and regional ensembles | 模型生成 |
| Change in heavy precipitation | percent change in annual maximum daily rainfall | per ensemble release | Model ensembles | 模型生成 |
| Change in hot days | days above a threshold per year | per ensemble release | Model ensembles | 模型生成 |
| Ensemble spread | range across models | per ensemble release | Ensemble statistics | 模型生成 |
| Model agreement on sign of change | percent of models | per ensemble release | Ensemble statistics | 模型生成 |
| Bias against observations | difference over reference period | per model | 模型评估 | 估算 |
存量、流量、延迟和一个临界阈值。
两个存量，即压力和 适应能力，由一个增强的增长回路、一个延迟的平衡响应以及压力超过能力时产生的损害相连；越过阈值后，系统可能发生突变。这是使用明示示例数值的教学模型，不是预测。
控制措施
- 压力增长
- 每年 6.0%
- 能力在降低压力方面的效力
- 每年 4.0%
- 对感知压力的投资响应
- 20%
- 感知延迟
- 4.0 年
- 压力的临界阈值
- 60 项指数
- 越过阈值后的放大
- 2.0 倍
- 模拟年数
- 50 年
读数
- 峰值压力
- 113 指数
- 期末能力
- 186 指数
- 超过阈值的年数
- 15.8 年
数字、方法与来源
| 年份 | 压力 | 能力 | 感知 |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
方法
- 系统动力学 Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- 临界转变与临界点 Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- 因果回路图 Sterman (2000), Business Dynamics, McGraw-Hill
- 不确定性扇形图（参数蒙特卡罗） Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 单因素敏感性（龙卷风图） Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
数据
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
贯穿六大支柱，由 Nexus Consortium 牵头
- 实验 实验室
- 构建于 Foundry 中的气候与灾害
- 检验地点 Universe 中的气候与灾害
- 编入 Standards 中的气候与地球系统
- 发表于 Reports 中的气候与灾害
- 授课于 Academy 中的地球与环境科学学院
证据来自何处。
具名公共数据集与机构记录，各自注明许可类别。依协议获得的来源仅按其条款使用，绝不再次发布。
| 来源 | 它提供什么 | 许可类别 |
|---|---|---|
| CMIP6 archive via the Earth System Grid Federation | Global climate model output for standard experiments | Open, CC BY 4.0 for most models (to check per model) |
| CORDEX regional climate downscaling | Regional model ensembles for defined domains | Open, terms per domain and model (to check) |
| IPCC WGI Interactive Atlas | Regional projections and observations by warming level and scenario | Public, CC BY 4.0 |
| NASA NEX-GDDP-CMIP6 | Statistically downscaled daily projections | Open, NASA data policy |
| Copernicus Climate Data Store projections | Global and regional projections with documentation | Free and open with attribution, Copernicus licence (to check current terms) |
Model climate projections as a system. Run the cascade.
Assembled from the Nexus Standards area profile 气候风险标准: the hazards it names, the 系统 and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
灾害
- 海平面上升
- Coastal flood （在下游引发）
- 风暴潮 （在下游引发）
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Climate-driven migration and relocation (set off downstream)
- 大规模流离失所（在下游引发）
控制措施
NS-0章程与原则NS-1维持社会运转的 UNI 与制品目录（语法 v0.2：领域、部门、区域和环境分段）NS-3风险记录 UNI（语法、旧编码对照、分配机构）NS-3b数据集与产品元数据规范（STAC、DCAT、PROV-O）NS-9情境 UNI（部署、实例、计划实例）NS-11验证与监测（证据、证明、零信任符合性观测）NS-14符合性及与外部标准的对照NRS-H65危害类别：超越行星边界（NHS-1 planet.boundary）NRS-H62危害类别：复合与多危害事件（NHS-1 sys.compound）NRS-H71危害类别：深时与宇宙灾难（NHS-1 planet.deep-time）NRS-H79危害类别：精神卫生危机（NHS-1 health.mental）NRS-H75危害类别：气候敏感性疾病（NHS-1 health.climate-sensitive）NRS-H7危害类别：大尺度环流异常（NHS-1 atmo.climate-mode）
观测
- ERA5 再分析（Copernicus Climate Data Store）（仅列作参考）
- CMIP6 气候预估（Copernicus Climate Data Store）（仅列作参考）
- NGFS Scenario Explorer（仅列作参考）
- INFORM Risk Index（仅列作参考）
- 每100,000人中因灾害死亡、失踪和直接受影响的人数
- Total greenhouse gas emissions per year
- 灾害造成的直接经济损失与全球国内生产总值之比
示例地理范围
- 太平洋岛国：150 个六边形网格
- 今夜夜间灯光与停电（VIIRS 昼夜波段），NASA GIBS
- 穿透云层的地表水，30 m（OPERA DSWx-S1 雷达），NASA GIBS
- 过去半小时降雨率（IMERG），NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
由谁决定，以及证据服务于什么。
Climate and Hazards prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 谁来决定 | 问题 | 本页面服务的证据 |
|---|---|---|
| Infrastructure owners and design standards bodies | 哪些 气候变化 allowances to build into long-lived assets | Projections by warming level with spread and agreement |
| Water resources planners | How to plan supply and storage for a range of futures | Projected changes in rainfall, evaporation and extremes |
| National adaptation planning teams | Which climate futures to plan against | Projection summaries and limitation statements |
该主题产生的产出
- Projection summaries by 全球变暖 level, with ensemble spread
- Guidance on selecting models and downscaled products for a region
- Bias evaluation notes against local observations
- Scenario limitation statements for planners
How climate projections is modelled, measured and tested.
模型、其假设、数据和不确定性，以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意，不是研究发现。
不确定性区间
How far the outlook for climate projections could spread, shown as ranges rather than a single line.
- 中心估计值
- 集合的中位数
- 范围
- IPCC 校准用语中的“可能”和“很可能”
- 集合
- 明示成员、情景和初始条件
- 配合阅读
- 结果对每项输入的敏感性
专家提出的问题。
Projection questions are about what could happen, and how far to trust it.
- Which scenarios are being used, and what do they assume about emissions and society?
- How much do models agree, and where do they disagree?
- Which part of the uncertainty comes from scenarios, from model differences and from natural variability?
- At what spatial scale can projections be trusted, and how were they downscaled?
- How should low-likelihood, high-impact outcomes be handled?
机制与驱动因素。
Global climate models solve equations for the flow of energy, water and momentum on a grid, with processes too small to resolve, such as clouds, represented by approximations. Different models make different approximations, which is one reason they disagree. Scenarios describe possible futures for emissions and land use; they are not forecasts and carry no probability on their own.
Uncertainty has three main sources: the scenario, the model and internal variability, which is the chaotic year-to-year fluctuation of the climate. Over the next few decades internal variability and model differences dominate many regional projections; later in the century the scenario matters most. Downscaling to local scales adds detail but also adds its own assumptions.
如何研究，以及方法止于何处。
| 方法或数据 | 显示内容 | 局限 |
|---|---|---|
| Multi-model ensembles | The range of projections across models | Models share some biases, so spread may understate uncertainty |
| Large single-model ensembles | The role of internal variability | Reflect one model’s physics |
| Statistical and dynamical downscaling | Regional and local detail | Add uncertainty and depend on the driving model |
| Bias adjustment | Projections aligned with observed statistics | Can distort physical relationships between variables |
| Emulators and storylines | Rapid exploration and coherent narratives | Simplify the underlying physics |
如何研究，以及方法无法说明的内容。
Where this page’s references lead.
阅读本页时所对照的机构和文件。状态和版本会变化；每个链接都指向发布机构。
问题与解答。
What is a global warming level?
What is the difference between a projection and a forecast?
Should I use downscaled projections?
Is a scenario a prediction?
Why not just use the average of all models?
Can projections be used at the scale of a single site?
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