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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Climate models do not reveal one predetermined future. They calculate how the climate system could respond to specified greenhouse-gas emissions or concentration pathways, using physical laws and assumptions about oceans, atmosphere, land and ice. The result is a conditional projection: if the stated forcing pathway occurs, the modeled response describes a range of plausible outcomes.
That distinction makes models highly useful for global warming trends and scenario comparisons, but it limits what they can say about a particular city, year or weather event. The most defensible interpretation combines model ensembles with observations, climate-sensitivity evidence and an explicit account of uncertainty.
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Projection, forecast and scenario: three different claims
A forecast attempts to state what will happen, usually over a relatively defined period, on the assumption that the system’s near-term drivers can be estimated. A projection asks what could happen if a specified set of conditions holds. Climate projections are therefore conditional, not predictions that society will follow one inevitable path.
Climate researchers run models with different future forcing pathways. Those pathways represent alternative combinations of greenhouse-gas emissions, land-use change, aerosols and other influences. The models then simulate the climate response. If emissions differ from the pathway used, the corresponding projection is no longer the relevant conditional result.
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The distinction is important when reading a graph labeled “future temperature.” A line is not a promise that the world will follow that line. It is an estimate of the response associated with a scenario, model configuration, baseline and time period. A credible comparison keeps all of those labels visible.
NOAA’s introductory explanation makes the same conceptual point: future warming depends on human emissions choices and on how sensitive the climate system is. Its numerical examples are based on older CMIP3-era work and the IPCC’s Fourth Assessment, so they should not be used as current estimates. See NOAA’s climate-model explainer for the scenario concept, not for up-to-date numbers.
What models can tell us about global warming
Direction and broad differences among pathways
Across plausible high- and low-forcing pathways, models consistently show that sustained increases in heat-trapping gases produce additional global warming, while lower forcing produces less warming later in the century. The exact amount depends on the pathway and the modeled climate response. This makes models useful for evaluating the consequences of choices, even though they cannot determine which choices societies will make.
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Ranges are more meaningful than a single model line
Individual models differ in their representation of clouds, aerosols, ocean circulation, land processes and other feedbacks. An ensemble therefore produces a spread rather than one authoritative trajectory. The spread is not simply an error bar around a known answer: it combines different model responses, assumptions about forcing and, especially over shorter periods, internally generated climate variability.
The IPCC’s Sixth Assessment Report (AR6) did not treat an unfiltered model average as the final answer. For assessed global surface-temperature changes, it combined CMIP6 model projections with observational constraints based on how models reproduced past warming and with the AR6 assessment of climate sensitivity. This approach narrows or weights the evidence where observations justify doing so. The method and assessed results are described in the IPCC AR6 Working Group I Summary for Policymakers.
Past performance is evidence, not a guarantee
Historical simulations provide a basic check: when models are driven with observed or reconstructed past forcings, do they reproduce the broad evolution of observed global temperature? The AR6 summary reports that the CMIP6 historical ensemble-mean global surface-temperature change is within 0.2 °C of observations over most of the historical period. That is an ensemble-mean comparison, not a claim that every model matched observations or that future errors will be limited to 0.2 °C.
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Some individual models fall outside the assessed very likely range for historical warming. Their existence does not make the entire modeling enterprise useless; it shows why assessments examine model performance, observations and physical understanding together rather than accepting every simulation equally. Historical fit also cannot test every future process, forcing pathway or regional metric.
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Where projection uncertainty comes from
| Uncertainty source | What it means | When it tends to matter most |
|---|---|---|
| External forcing or scenario | Future emissions, concentrations, land use, aerosols and other human influences are not known in advance. | Its influence grows over longer horizons as pathways diverge. |
| Model response | Models represent feedbacks and processes differently, producing different responses to the same forcing. | It matters across time horizons and is often prominent in assessed projections. |
| Internal variability | Unforced fluctuations generated within the climate system, such as ocean-atmosphere variability, can temporarily raise or lower temperatures around the forced trend. | It is especially important for near-term periods and for regional or seasonal metrics. |
The relative contribution is not fixed. For example, the discussion of IPCC AR6 Figure 8.23 shows that, for precipitation projections, internal variability, model-response uncertainty and scenario uncertainty contribute differently by location and period; model-response uncertainty is influential across the horizons shown, while internal variability is important in the near term. That result is specific to the precipitation analysis and should not be generalized automatically to every climate variable. The figure is available at IPCC AR6 Figure 8.23.
Why the near term and the far term look different
Near-term projections
Over the next several years to a few decades, natural variability can obscure part of the human-caused trend in global averages and dominate the signal in a particular region. Two model runs with the same emissions pathway can therefore diverge temporarily because their simulated oceans and atmosphere begin in different states. A near-term projection is best read as a distribution of possible conditions, not as a precise annual schedule.
Longer-term projections
Over longer periods, the cumulative effect of forcing pathways becomes more important. The choice between pathways can then account for a larger share of the difference between projections. That does not eliminate model-response uncertainty: feedbacks and climate sensitivity still determine how much warming follows a given forcing. Long-range results are consequently most useful for comparing consequences of pathways, not for naming the temperature of one specific future year.
What models can—and cannot—say at regional scale
Why a global result does not translate directly to a city
Global temperature is a broad metric. Regional climate depends on atmospheric circulation, ocean conditions, topography, land cover, local feedbacks and the quality of observations used to evaluate the simulation. Internal variability can be large relative to the forced trend in one basin or country. A model that is useful for global temperature may not be fit for a question about a mountain valley, a coastline or a seasonal rainfall regime.
Higher resolution helps, but does not remove uncertainty
Finer grid spacing can represent some coastlines, mountains, storms and other processes more realistically. It cannot create observations that do not exist, resolve every relevant process or predict internal variability on demand. The IPCC assesses regional confidence using observations, the model’s fitness for the purpose, representation of relevant processes and the scale of the question. It warns that regional uncertainties and even contradictory signs among models can remain substantial. These findings are summarized in IPCC AR6 Working Group I Chapter 10.
Ensemble spread is not the whole uncertainty
A wide spread among models signals disagreement, but a narrow spread does not prove high certainty. Models may share a structural limitation, omit a low-probability process or fail to sample the tails of the distribution. Conversely, a regional ensemble mean can conceal a small chance of a severe change that matters for planning. Chapter 10 notes that ensemble means do not always characterize low-likelihood, high-impact regional outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read a climate-projection chart correctly
- Identify the variable. Check whether the chart shows global mean surface temperature, a regional average, precipitation, extremes, sea level or another metric. Evidence for one variable cannot automatically be transferred to another.
- Find the scenario or forcing pathway. A curve without its emissions or concentration pathway is incomplete. Do not compare curves that assume different pathways as if they were competing forecasts.
- Check the baseline and period. Temperature anomalies are measured relative to a stated reference period. Two values with different baselines may not be directly comparable.
- Check the geographic scale. Global, continental, national and local results answer different questions. A global ensemble mean cannot stand in for a neighborhood projection.
- Read the range and confidence language. Look for assessed ranges, likely or very likely qualifiers, and whether the range reflects model spread, internal variability, scenario uncertainty or a combination.
- Ask what evidence was combined with the models. AR6 global-temperature assessments used observations of past warming and the assessed climate sensitivity alongside CMIP6 simulations. A raw model output should not be presented as equivalent to that assessed result.
- Match the time horizon to the decision. Near-term infrastructure design may require internal-variability information and local observations; long-lived investments may need a set of pathway-based scenarios rather than one best guess.
Common mistakes and the better interpretation
| Mistake | Why it fails | Better practice |
|---|---|---|
| Calling one scenario “the prediction” | The scenario is an assumption about future forcing, not a forecast of policy or emissions. | State the pathway and describe the result as conditional. |
| Using the ensemble mean as a guaranteed outcome | The mean can hide skewed ranges, model disagreement and low-likelihood impacts. | Report the assessed range and relevant confidence information. |
| Judging models by one year or one weather event | Internal variability and weather noise can overwhelm the forced signal at short timescales. | Evaluate the metric over an appropriate period and scale. |
| Assuming more resolution means certainty | Fine grids improve some processes but do not solve missing observations, structural error or variability. | Check regional validation and process-specific evidence. |
| Comparing numbers with different baselines | The same anomaly can represent different absolute temperatures when reference periods differ. | Put baseline, period, variable and region beside every number. |
| Treating historical agreement as proof of future perfection | Past forcing and processes do not test every future condition. | Use historical skill as one line of evidence alongside physical understanding and observations. |
What climate models are useful for in practice
- Scenario analysis: comparing how different forcing pathways alter global warming and associated risks.
- Risk management: testing whether infrastructure, water systems, health plans or ecosystems remain robust across a range of plausible outcomes.
- Attribution and understanding: separating externally forced trends from internal variability and examining the mechanisms behind change.
- Research and observation design: identifying where additional measurements or improved process representation could reduce uncertainty.
For a practical decision, the right output may be a range of conditions, a set of stress tests or a threshold-crossing likelihood rather than one “most likely” number. The choice depends on the decision’s lifetime, vulnerability and tolerance for failure.
What models cannot tell you with confidence
- They cannot specify which emissions pathway governments, companies and households will actually follow.
- They cannot provide a deterministic weather calendar decades in advance.
- They cannot guarantee the exact temperature, rainfall total or extreme-event count for a particular city and year.
- They cannot remove uncertainty simply by adding more models or increasing grid resolution.
- They cannot make a regional projection reliable when observations are sparse or the relevant physical processes are poorly represented.
These limits do not make projections meaningless. They define the questions models are equipped to answer: how climate outcomes differ under stated conditions, how broad trends compare with observations, and which risks remain plausible across the uncertainty range.
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A responsible statement names the scenario, baseline, period, variable, geographic scale and evidence basis. It distinguishes a raw simulation from an assessed result, reports uncertainty rather than hiding it, and explains whether internal variability matters for the time and place in question. For global warming, the strongest conclusions are usually about direction, broad differences among pathways and assessed ranges—not an exact temperature attached to an unspecified future.
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