A climate model is a mathematical representation of the climate system, implemented computationally to simulate its behavior. Models describe interactions among the atmosphere, ocean, land surface, and ice, with complexity ranging from simplified global calculations to three-dimensional simulations. They are used to investigate physical processes, interpret past and present conditions, and explore possible climate change under specified assumptions about external influences. (gfdl.noaa.gov)
Physical and numerical foundations
Comprehensive climate models apply conservation of energy, mass, and momentum. They calculate the evolution of variables such as temperature, winds, humidity, and ocean currents, while exchanging heat, water, and momentum between components. This coupling allows changes in one component to influence others rather than treating the atmosphere and ocean as independent systems. (gfdl.noaa.gov)
The governing partial differential equations are approximated numerically. The globe is divided into horizontal grid cells and vertical layers, and calculations advance through successive time steps. Resolution determines which spatial features can be represented directly. Finer grids generally provide greater detail but require more calculations and computing resources; atmospheric and oceanic components need not use identical grids. (ipcc.ch)
Processes smaller than the grid spacing, or too complex to calculate explicitly, require parameterization. These mathematical schemes estimate their collective effects on resolved variables. Examples include cloud microphysics, turbulent mixing, and aspects of convection. Parameterizations use physical principles and observational evidence, but introduce choices and uncertainties that cannot be removed simply by increasing computing power. (gfdl.noaa.gov)
Types of climate models
Climate modeling uses a hierarchy rather than a single preferred design. Simple energy-balance models represent the relationship between absorbed sunlight, outgoing radiation, and temperature, sometimes including simplified heat transport. Their limited computational requirements make them useful for examining basic mechanisms and exploring many assumptions, although they cannot reproduce detailed circulation. Intermediate-complexity models retain additional processes while simplifying others, permitting simulations over very long timescales. (archive.ipcc.ch)
A general circulation model represents atmospheric or oceanic motion in three dimensions. Coupled versions connect these components with land and sea ice. An Earth system model extends this framework to include additional interactions, particularly the carbon cycle and biological or chemical processes. Such models can represent exchanges of carbon between the atmosphere, ocean, and biosphere, allowing these exchanges to respond to climate. (ipcc.ch)
Regional climate models simulate limited areas at relatively fine resolution, usually receiving boundary information from a global model. They can better represent coastlines, mountains, and local circulation. Their results nevertheless depend on the driving global simulation and on their own physical assumptions. (ipcc.ch)
Experiments, predictions, and projections
Climate models function as experimental tools: researchers alter selected influences while retaining others to investigate their effects. Standard experiments include historical simulations, long control runs under fixed preindustrial conditions, and idealized increases in carbon dioxide. Coordinated protocols make outputs from different modeling groups easier to compare. (wcrp-climate.org)
Future projections specify pathways for greenhouse gases, air pollutants, and land use. These inputs influence radiative forcing and the subsequent climate response. A projection is conditional on its scenario, not a claim that a particular socioeconomic future will occur. Scenarios provide alternative assumptions rather than automatically supplying probabilities for those alternatives. (ipcc.ch)
Climate projections differ from forecasts of individual weather events. Long-term simulations examine changes in distributions, seasonal behavior, and extremes rather than the precise sequence of daily weather decades ahead. Seasonal-to-decadal predictions also use the observed initial state to capture potentially predictable natural fluctuations, including El Niño–Southern Oscillation and variations in ocean heat content. (metoffice.gov.uk)
Evaluation and coordinated comparison
Model evaluation asks whether a simulation is suitable for a particular scientific purpose. Relevant tests concern both the representation of physical processes and agreement with observations. No single performance score establishes reliability for every variable, region, or timescale. Model tuning adjusts uncertain parameters within physically and observationally acceptable ranges; agreement with tuning targets is therefore distinct from independent evaluation. (ipcc.ch)
The Coupled Model Intercomparison Project coordinates shared experiments, documentation, and comparisons across institutions. Its simulations provide important evidence for assessments by the Intergovernmental Panel on Climate Change. Intercomparison exposes differences among models without requiring them to share identical structures or parameterizations. (wcrp-climate.org)
Uncertainty and regional interpretation
Projection uncertainty has three principal sources: uncertain future forcing scenarios, differences in modeled climate responses, and internal variability. Internal variability arises within the coupled system even without changing external influences. Their relative importance depends on the variable, spatial scale, and forecast horizon; short-term regional changes can be strongly influenced by natural fluctuations. (ipcc.ch)
An ensemble contains multiple simulations. Initial-condition ensembles vary starting states to investigate internal variability; multi-model ensembles sample differences in model formulation. Ensemble spread is informative but is not a complete measure of uncertainty or automatically a calibrated probability distribution. Shared errors and incomplete sampling can limit its interpretation. (ipcc.ch)
Downscaling translates large-scale information into regional detail through dynamical simulations or methods based on statistics. Bias adjustment can improve agreement with observed distributions, but cannot repair missing physical mechanisms. Higher resolution likewise does not guarantee accuracy: credible regional interpretation also requires appropriate processes, forcing inputs, and evaluation for the intended application. (ipcc.ch)