The Global Centre for Risk and Innovation (GCRI)
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 global warming 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.
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Climate projections are simulations of how the climate may change under stated scenarios of emissions and land use. They come from model ensembles such as CMIP6, often downscaled and reported by global warming level. Their uncertainty combines scenario choice, model differences and natural variability, so they inform plans without predicting one future.
For climate projections, in GCRI's spatial-intelligence reading, the map opens on daytime surface heat, rain and true-colour imagery with a past-year temperature chart at the centre, framed on World; 3 further NASA layers wait in the list.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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For climate projections, in GCRI's spatial-intelligence reading, the map opens on daytime surface heat, rain and true-colour imagery with a past-year temperature chart at the centre, framed on World; 3 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
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.
Shared Socioeconomic Pathways combine emissions and socioeconomic storylines. Projections are conditional on them; they are not forecasts of which future will happen.
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.
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.
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.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Shared Socioeconomic Pathways | Scenarios of societal development used with emissions assumptions to drive climate projections. |
| Downscaling | Deriving local climate information from coarser global model output by regional models or statistics. |
| Equilibrium climate sensitivity | The long-term global warming from a doubling of atmospheric carbon dioxide. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Change in annual mean temperature | degrees Celsius at a stated warming level | per ensemble release | CMIP6 and regional ensembles | MODELLED |
| Change in heavy precipitation | percent change in annual maximum daily rainfall | per ensemble release | Model ensembles | MODELLED |
| Change in hot days | days above a threshold per year | per ensemble release | Model ensembles | MODELLED |
| Ensemble spread | range across models | per ensemble release | Ensemble statistics | MODELLED |
| Model agreement on sign of change | percent of models | per ensemble release | Ensemble statistics | MODELLED |
| Bias against observations | difference over reference period | per model | Model evaluation | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| 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) |
Climate and Hazards prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Infrastructure owners and design standards bodies | Which climate change 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 |
Projection questions are about what could happen, and how far to trust it.
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.
| Method or data | What it shows | Limits |
|---|---|---|
| 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 |
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.