T The Climate Explainer
How Climate Works

How Do Climate Models Work?

A The Climate Explainer guide · Updated 2026-09-04 · 7 min read
How Do Climate Models Work?
Quick answerClimate models divide the atmosphere, ocean, land and ice into a three-dimensional grid, then repeatedly solve equations describing how energy, water and matter move through the system. Scientists evaluate models against observations and past climates. For future runs, they supply different greenhouse-gas and land-use scenarios. The results are conditional projections with quantified uncertainty, not promises of exact weather on a future date.
Contents

Climate models in one paragraph

Climate models are computer-based representations of the atmosphere, ocean, land and ice. They divide Earth into a three-dimensional grid, apply equations grounded in physics, chemistry and biology, and calculate how energy, water and matter move between cells over successive time steps. Scientists test models against observed and past climates, then run them under different assumptions about future greenhouse gases and other influences. The output is a conditional projection: what the climate would tend to do if those inputs occurred.

A model is necessarily simpler than the planet. That is not a defect concealed in the small print; simplification is what makes a model usable. The serious questions are which processes it represents, how it represents them, how it performs against observations, and whether it is fit for the question being asked.

What is inside a global climate model?

A global climate model, often abbreviated GCM, couples several parts of the Earth system. NOAA's Geophysical Fluid Dynamics Laboratory describes the major components as atmosphere, land surface, ocean and sea ice. Modern Earth system models can also represent atmospheric chemistry, aerosols, vegetation and the carbon cycle.

Within each component, equations describe conservation of energy, mass and momentum, along with fluid motion and other physical or chemical processes. The components exchange quantities such as heat, water and momentum. Ocean circulation can move heat; sea ice changes how much sunlight a surface reflects; vegetation exchanges water and carbon with the atmosphere. The model calculates those interactions repeatedly as simulated time advances.

The model does not “learn” a future temperature by extending a line on a graph. It produces a response from the encoded physical system when researchers change an input, such as atmospheric greenhouse-gas concentrations, volcanic aerosols or solar output. That distinction matters: climate models are experiments with physical relationships, not elaborate trend lines.

The basic energy mechanism those equations must respect is explained in what the greenhouse effect is.

Why do models divide Earth into grid cells?

No computer can calculate every air molecule, cloud droplet, leaf and ocean eddy. Models therefore divide the atmosphere, ocean and land into a three-dimensional grid. Equations are solved for variables within each cell, and the results pass to neighbouring cells at the next time step.

Grid-cell size is the model's spatial resolution. Smaller cells can represent finer geographic detail, but they multiply the number of calculations and require more computing power. Shorter time steps likewise add detail and cost. Resolution should not be confused with accuracy: a map containing smaller pixels can still inherit imperfect inputs or an imperfect representation of a process.

Some important processes occur at scales smaller than a grid cell. Clouds are a familiar example. Instead of resolving every cloud directly, a model uses a parameterization: a mathematically defined way to represent the average effect of a smaller-scale process on the larger cell. The IPCC notes that more complex parameterizations and increasing spatial and temporal resolution have accompanied advances in modelling. Parameterization is a declared approximation, not an arbitrary adjustment made after seeing the desired answer.

How are climate models tested?

One test is a historical simulation, sometimes called a hindcast. Researchers start the model in an earlier climate state and supply known changes in external influences, including greenhouse gases, solar intensity and volcanic activity. They then compare the simulated climate with observations the model was not simply asked to reproduce value by value.

Useful evaluation goes beyond global average temperature. Researchers compare spatial patterns, seasonal cycles, ocean behaviour, rainfall, sea-level pressure and responses to major events. The IPCC's Sixth Assessment evaluated successive generations in the Coupled Model Intercomparison Project—CMIP3, CMIP5 and CMIP6—against observations for variables including surface air temperature, precipitation and sea-level pressure.

Models are also tested against climates unlike the recent present, including evidence from ancient climates. No test proves that every regional projection is exact. Testing instead reveals where models reproduce relevant behaviour, where biases persist and how much confidence is appropriate for a particular use.

It is also possible to test old projections after time has passed. The IPCC reports that climate models published since around 1970 projected global surface warming in reasonable agreement with observations when researchers account for the difference between the forcing assumed in the projection and the forcing that actually occurred. That qualifier is essential: a model cannot be marked wrong for a future emissions path that society did not follow.

Why do scientists run many models?

Research groups build models with different resolutions and representations of unresolved processes. They then run coordinated experiments using common inputs through the Coupled Model Intercomparison Project, or CMIP. Comparing the results reveals both robust responses and areas where model structure matters.

An ensemble is a collection of simulations, not a vote in which the most popular outcome automatically becomes true. Ensembles can vary the initial conditions, the model formulation, the assumed future pathway or all three. Their spread helps researchers examine distinct sources of uncertainty.

The ensemble average can reduce some effects of random internal variability, but it does not make every model equally suitable for every task. The IPCC says no universal, robust method exists for weighting a multi-model projection ensemble in all cases; expert judgement remains part of assessment. For a regional question, researchers also consider whether the selected models represent the processes important to that region.

What is the difference between a projection and a prediction?

A projection states what a model produces under specified assumptions. A prediction is an attempt to state what will actually happen, often initialized from current conditions. The terms sometimes overlap in public writing, but the conditional nature of a long-range climate projection should remain explicit.

Future human emissions, land use and technology are not laws of physics. Researchers therefore use scenarios: coherent possible pathways for greenhouse gases, aerosols and other drivers. If a high-emissions and a low-emissions scenario yield different late-century climates, that difference is information about consequences under different inputs. It is not evidence that the model failed to choose one future.

This is why a responsible chart names the scenario, time period, baseline and geographic scale. A single coloured line with those details removed is not a complete climate projection.

Climate models also differ from daily weather forecasts. Weather prediction tries to follow the precise evolution of the atmosphere from today's measured state. Small initial differences grow, limiting detailed forecasts far ahead. Climate projections usually ask about the statistics of the system—averages, ranges, frequencies and long-term responses to forcing. A roulette wheel cannot tell you the next number, but its long-run distribution can still be described; the analogy is imperfect, yet it captures why uncertain sequences can coexist with informative statistics.

For the vocabulary around long-term change, see climate change versus global warming and the broader Climate Terms collection.

Where does uncertainty come from?

Three broad sources recur:

  1. Internal variability. The climate system fluctuates naturally, so simulations begun from slightly different states can trace different short-term paths.
  2. Model uncertainty. Models simplify reality and differ in their treatment of unresolved processes.
  3. Scenario uncertainty. Future emissions and land-use choices are not known in advance.

Observations have uncertainty too. Thermometers, satellites, ocean instruments and reconstructed past climates each have measurement or sampling limits. Model evaluation must account for those limits rather than treating an observational dataset as perfect.

The balance among uncertainties changes with the question. Internal variability can dominate some near-term regional outcomes, while scenario choice becomes more important for many long-term global outcomes. A precise-looking local number therefore deserves more scrutiny than a robust large-scale direction shared across models and lines of evidence.

What can climate models tell us—and what can they not?

Models are strong tools for testing mechanisms, attributing observed changes and estimating conditional climate responses. Confidence is generally greater where results agree with physical understanding, observations and independent evidence, and where multiple models show a consistent large-scale response.

They cannot identify society's exact future emissions path, reproduce every cloud or storm, or guarantee the weather in one place on one future date. Downscaling can add useful local structure, but it does not erase uncertainty inherited from the global model, the scenario or the downscaling method.

The right reading is neither “the model is a crystal ball” nor “the model is imperfect, so it says nothing.” Ask what experiment was run, what assumptions were supplied, how the model was evaluated, what range the ensemble produced and whether the scale matches the claim. That is how climate scientists read model output too.

Sources

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FAQ

Are climate models based only on past temperature trends?

No. Climate models encode physical, chemical and biological processes, including conservation of energy and mass, fluid motion and exchanges among the atmosphere, ocean, land and ice. Observations are used to initialize and evaluate them, not merely to extend a trend line.

How do scientists test a climate model?

They compare simulations with observations of present and past climates, including spatial patterns, seasonal cycles and responses to known influences such as volcanic eruptions and greenhouse gases. Coordinated model intercomparisons reveal both shared results and persistent biases.

Why do climate projections show a range?

The range reflects natural internal variability, differences among model representations and uncertainty about future emissions and land use. Those sources are studied with ensembles that vary initial conditions, models and scenarios rather than relying on one simulation.

Is a climate projection the same as a weather forecast?

No. A weather forecast follows the detailed evolution of the atmosphere from current conditions. A long-term climate projection describes statistical changes under stated forcing assumptions, such as shifts in averages, ranges or frequencies across decades.

Does higher model resolution guarantee greater accuracy?

No. Smaller grid cells can represent finer geographic detail, but accuracy also depends on observations, model physics, parameterizations and fitness for the question. Downscaled or high-resolution output can still carry uncertainty from the global model and scenario.

Why are future emissions scenarios used?

Human emissions, land use and technology cannot be derived from physical equations alone. Scenarios provide coherent possible inputs, allowing researchers to compare what the climate system would do under lower, intermediate or higher forcing pathways.