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ATMOSPHERIC VS OCEAN MODELS There are both atmospheric GCMs (AGCMs) and oceanic GCMs (OGCMs). An AGCM and an OGCM can be coupled together to form an atmosphere-ocean coupled general circulation model (AOGCM). With the addition of other components (such as a sea ice model or a model for evapotranspiration over land), the AOGCM becomes the basis for a full Climate Model . Within this structure, different variations can exist, and their varying response to climate change may be studied (e.g., Sun and Hansen, 2003). MODELING TRENDS: CARBON AND SULPHUR CYCLE A recent trend in GCMs is to extend them to become Earth System Models , that include such things as submodels for Atmospheric Chemistry or a Carbon Cycle Model to better predict changes in Carbon Dioxide concentrations resulting from changes in emissions. In addition this approach allows feedback between these systems to be taken into account. For example, Chemistry-Climate models allow the possible effects of climate change on the recovery of the ozone hole {Link without Title} to be studied. Climate prediction uncertainties depend on uncertainties in both models and the future course of industrial growth and technology (which is currently the largest unknown) (see IPCC scenarios below). Progress has been made in incorporating more realistic physics in the models, but significant uncertainties and unknowns remain. {Link without Title} Note that many simpler levels of Climate Model exist; some are of only heuristic interest; others continue to be scientifically relevant. MODEL STRUCTURE Three-dimensional (more properly four-dimensional) GCMs discretise the equations for fluid motion and integrate these forward in time. They also contain parametrisations for processes - such as convection - that occur on scales too small to be resolved directly. More sophisticated models may include representations of the carbon and other cycles. A simple general circulation model (SGCM), a minimal GCM, consists of a Dynamical Core that relates material properties such as temperature to dynamical properties such as pressure and velocity. Examples are codes that solve the Primitive Equations , given energy input into the model, and energy Dissipation in the form of scale-dependent Friction , so that Atmospheric Wave s with the highest Wavenumber s are the ones most strongly attenuated. Such models may be used to study atmospheric processes within a simplified framework but are not suitable for future climate projections. Atmospheric GCMs (AGCMs) model the atmosphere (and typically contain a land-surface model as well) and impose sea surface temperatures (SSTs). A large amount of information including model documentation is available from AMIP {Link without Title} . They may include atmospheric chemistry.
A GCM contains a number of Prognostic Equations that are stepped forward in time (typically winds, temperature, moisture, and surface pressure) together with a number of Diagnostic Equations that are evaluated from the simultaneous values of the variables. As an example, pressure at any height can be diagnosed by applying the Hydrostatic Equation to the predicted surface pressure and the predicted values of temperature between the surface and the height of interest. The pressure diagnosed in this way then is used to compute the pressure gradient force in the time-dependent equation for the winds. Oceanic GCMs (OGCMs) model the ocean (with fluxes from the atmosphere imposed) and may or may not contain a Sea Ice model. For example, the standard resolution of HadOM3 is 1.25 degrees in latitude and longitude, with 20 vertical levels, leading to approximately 1,500,000 variables. Coupled atmosphere-ocean GCMs (AOGCMs) (e.g. HadCM3 , GFDL CM2.X ) combine the two models. They thus have the advantage of removing the need to specify fluxes across the interface of the ocean surface. These models are the basis for sophisticated model predictions of future climate, such as are discussed by the IPCC . AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. They are the only tools that could provide detailed regional predictions of future climate change. However, they are still under development. The simpler models are generally susceptible to simple analysis and their results are generally easy to understand. AOGCMs, by contrast, are often nearly as hard to analyse as the real climate system. Model grids The fluid equations for AGCMs are discretised using either the , ''T'' , ''Q'' ), though a full count would give more (clouds; soil levels). HadGEM1 uses a grid of 1.25 in latitude and 1.875 degrees in longitude. For a standard finite difference model, the gridlines converge towards the poles. This would lead to computational instabilities (see CFL Condition ) and so the model variables must be filtered along lines of latitude close to the poles. Ocean models suffer from this problem too, unless a rotated grid is used in which the North Pole is shifted onto a nearby landmass. Spectral models do not suffer from this problem. There are experiments using geodesic grids {Link without Title} and icosahedral grids, which (being more uniform) do not have pole-problems. Flux correction Early generations of AOGCMs required a somewhat ''ad hoc'' process of "flux correction" to achieve a stable climate. Flux correction amounts to a linearization of the climate about the current state, so that models of climate change would start from reasonable initial conditions. A world with excessive sea ice, for example, might be unduly sensitive to an increase in greenhouse gasses, while one with no sea ice at all would underestimate the effect of such a change. The danger, however, is that a model may need flux corrections because of unrealistically strong feedback processes that result in a transition to a different climate state. As a result, there has been strong movement away from the use of flux corrections, and the vast majority of models used in the current round of the Intergovernmental Panel On Climate Change do not use them. The model improvements that now make flux corrections unnecessary are various, but include improved ocean physics, improved resolution in both atmosphere and ocean, and a better fit between atmosphere and ocean models. Most recent simulations show "plausible" agreement with the measured temperature anomalies over the past 150 years, when forced by observed changes in "greenhouse" gases and aerosols, but better agreement is achieved when natural forcings are also included [http://www.hadleycentre.gov.uk/research/hadleycentre/pubs/talks/sld017.html . {Link without Title} Convection Moist convection causes the release of latent heat and is important to the Earth's energy budget. Convection occurs on too small a scale to be resolved by climate models, and hence must be parameterised. This has been done since the earliest days of climate modelling, in the 1950s . Akio Arakawa did much of the early work and variants of his scheme are still used although there is a variety of different schemes now in use [http://grads.iges.org/reps/rep27/colarep27.html [http://rainbow.llnl.gov/projects/modeldoc/cmip/table4.html . The behavior of clouds is still poorly understood and is parametrized. Convection velocity and enstrophy conserving parametrization is in use in the UCLA VI AGCM. [http://www.atmos.ucla.edu/~mechoso/esm/agcm.html [http://www-pcmdi.llnl.gov/projects/modeldoc/amip/33ucla.html#RTFToC25] Output variables Most models include software to diagnose a wide range of variables for comparison with observations or study of atmospheric processes. An example is the 1.5 metre temperature, which is the standard height for near-surface observations of air temperature. This temperature is not directly predicted from the model but is deduced from the surface and lowest-model-layer temperatures. Other software is used for creating plots and animations. PROJECTIONS OF FUTURE CLIMATE CHANGE Coupled ocean-atmosphere GCMs use Transient Climate Simulation s to project/predict future temperature changes under various scenarios. These can be idealised scenarios (most commonly, CO2 increasing at 1%/yr) or more realistic (usually the "IS92a" or more recently the SRES scenarios). Which scenarios should be considered most realistic is currently uncertain, as the projections of future CO2 (and sulphate) emission are themselves uncertain. The 2001 IPCC Third Assessment Report figure 9.3 shows the global mean response of 19 different coupled models to an idealised experiment in which CO2 is increased at 1% per year [http://www.grida.no/climate/ipcc_tar/wg1/fig9-5.htm Figure 9.5 shows the response of a smaller number of models to more realistic forcing. For the 7 climate models shown there, the temperature change to 2100 varies from 2 to 4.5 °C with a median of about 3 °C. ACCURACY OF MODELS THAT PREDICT GLOBAL WARMING ]] According to the IPCC , the majority of climatologists agree that important climate processes are imperfectly accounted for by the Climate Models but don't think that better models would change the conclusion. Scientists point out that there are specific flaws in the models, such as albedo errors, and external factors not taken into consideration such as possible indirect solar effects mediated through cosmic rays that could change the conclusion above. GCMs are capable of reproducing the general features of the observed global temperature over the past century {Link without Title} . A debate over how to reconcile climate model predictions that upper air (tropospheric) warming should be greater than surface warming, with observations some of which appeared to show otherwise {Link without Title} now appears to have been resolved in favour of the models, following revisions to the data: see Satellite Temperature Record . The effects of clouds are highly significant in climate and a significant area of uncertainty in climate models. Clouds have ''competing'' effects on the climate; one notes that the local ground temperature drops when a cloud passes overhead on an otherwise hot or sunny day. Hence, one of the roles that clouds play in climate is in cooling the surface by reflecting sunlight back into space. Yet, seemingly opposite phenomena have occurred, such as when clear winter nights become colder, rather than warmer, in contrast to cloudy winter nights. The general concept therefore, is that clouds block the radiation of heat away from the surface (and eventually into space), and radiate it back to the surface of the Earth, moderating otherwise more extreme temperatures. If CO2 changes the amount or distribution of clouds, it could have various complex effects on the climate. In the 2001 IPCC report on climate change, the possible changes in cloud cover were highlighted as one of the dominant uncertainties in predicting future climate change; see also [http://ams.allenpress.com/amsonline/?request=get-document&doi=10.1175%2FJCLI3799.1 . Thousands of climate researchers around the world use climate models to understand the climate system. There are thousands of papers published about model-based studies in peer-reviewed journals - and a part of this research is work improving the models. Improvement has been difficult but steady (most obviously, state of the art AOGCMs no longer require flux correction), and progress has sometimes led to discovering new uncertainties. In 2000 , a comparison between measurements and dozens of GCM simulations of ENSO -driven tropical precipitation, water vapor, temperature, and outgoing longwave radiation found similarity between measurements and simulation of most factors. However the simulated change in precipitation was about one-fourth less than what was observed. Because the behavior of water and energy are closely connected, the precipitation difference should not have been present. If the simulated precipitation is in error, this implies there is another error which causes the simulations of the other factors to produce the observed values. The other possibility is that the satellite-based measurements are in error. Either indicates considerable progress is required in order to monitor and predict such changes. {Link without Title} A rather more complete discussion of climate models is provided by the IPCC TAR chapter 8, Model Evaluation ( 2001 ).
The precise magnitude of future changes in climate is still uncertain {Link without Title} ; for the end of the 21st century (2071 to 2100), for SRES scenario A2, the change of global average SAT change from AOGCMs compared with 1961 to 1990 is +3.0 °C (4.8 °F) and the range is +1.3 to +4.5 °C (+2 to +7.2 °F). Forecasts of climate change are inevitably uncertain. Even the degree of uncertainty is uncertain, a problem that stems from the fact that these climate models do not necessarily span the full range of known climate system behavior. {Link without Title} RELATION TO WEATHER FORECASTING The global climate models used for climate projections are very similar in structure to (and often share computer code with) ''' for details. Most runs a mesoscale model with an 11 km resolution covering the UK, and various agencies in the U.S. also run nested models such as the NGM and NAM models. Like most global numerical weather prediction models such as the GFS, global climate models are often spectral models [http://www-das.uwyo.edu/~geerts/cwx/notes/chap12/nwp_gcm.html instead of grid models. Spectral models are often used for global models because some computations in modeling can be performed faster thus reducing the time needed to run the model simulation. SEE ALSO
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