Global climate models

On this page

General circulation models

The Earth’s climate results from interactions between many processes in the atmosphere, ocean, land surface and cryosphere (snow, ice and permafrost). The interactions are complex and extensive, so that quantitative predictions of the impact on the climate of greenhouse gas increases cannot be made just through simple intuitive reasoning. For this reason, computer models have been developed which try to mathematically simulate the climate, including the interaction between the component systems. An ideal model would simulate all of the physical, chemical and biological mechanisms shown in the diagram below, on a computational grid in which the points were close enough together to resolve the development of clouds and the influence of hills and mountains but which also covered the whole globe. However this is computationally impossible, even with today’s fastest computers, and judicious simplifications and parameterisations must be made. (A parameterisation is a way of representing processes which occur on smaller spatial scales than the model grid).

A schematic view of many of the processes and interactions in the global climate system (From Le Treut et al., 2007).

Figure 1: A schematic view of many of the processes and interactions in the global climate system (From Le Treut et al., 2007).

Three dimensional models which simulate the atmosphere are called Atmospheric General Circulation Models (AGCMs), and have been developed from weather forecasting models. Similarly, Ocean General Circulation Models (OGCMs) have been developed to simulate the ocean. These models typically divide the atmosphere or ocean into a horizontal grid with a horizontal resolution of 2° to 4° latitude and longitude, with 10 to 20 layers in the vertical. Both AGCMs and OGCMs have been used in "stand-alone" mode, with ocean surface and sea ice extent being prescribed for AGCM runs, and with prescribed surface temperatures, winds and salinities in the case of the OGCMs. (Harvey et al, 1997).

Because the oceans have such a large heat capacity, and can transfer heat around the globe, it is vital to couple atmosphere and ocean models in order to simulate climate variability and changes. This has led to the development of coupled atmosphere ocean models. There have been several ocean representations in global coupled models.

Most research centres around the world operate the models in a "transient" mode, in which the greenhouse gas concentration is gradually increased from present day levels, using Atmosphere Ocean General Circulation models AOGCMs. This compares to the old equilibrium models where CO2 is held constant. The “transient” model shows that the warming of the oceans around Antarctica takes a long time and for many decades the equatorial region warms faster than the higher southern latitudes. This produces an increase in westerly winds, which has been observed during the late 20th century (Salinger et al., 2005). However, AOGCMs are as yet imperfect representations of the real world. For example, small systematic errors can result from coupled interactions between imperfect component models, leading to a slow drift in the coupled system over long time scales (100 years or so). In some models the air-sea interactions are modified by a so-called "flux – correction" term chosen to remove any climate drift if the atmospheric greenhouse gas conditions remained at today’s levels. (Meehl, 1992). As noted in the introduction to this page, various small-scale processes have to be parameterised (Cubasch and Cess, 1996), including development of sub-grid scale clouds and their subsequent precipitation, boundary layer processes (momentum, energy and water exchange at the land-air and sea-air boundaries), and small eddies in the oceans.

Finally, we note that the fluxes between the atmosphere, oceans and land of several greenhouse gases, including carbon dioxide and methane are themselves sensitive to climate and environmental changes. Also reactions influencing concentrations of some radiatively active gases (e.g. ozone), the transport of these gases, and the wash-out of aerosols are influenced by climatic conditions. Thus an ideal AOGCM would also include coupled biosphere / carbon cycle / atmospheric chemistry models. However given the limitations imposed by current computer resources, greenhouse gas concentration time series for use in transient AOGCM runs are generally specified separately, based on assumptions about anthropogenic emission rates and separate modelling of biosphere interactions.

An example of simulations with a state of the art global climate model is given on the website of the Hadley Centre for Climate Prediction and Research. This is a simulation of temperature changes between 1900 and 2100, using historic values of greenhouse gas (carbon dioxide) forcing up to 1990 and then a 1% increase per year in equivalent carbon dioxide concentrations, with sulphate aerosol forcing also included. A substantial scientific effort continues on improving AOGCMs, including testing and improving the parameterisations against observations and removing the errors which require flux corrections. This effort, together with the continuing speed increase of computers, will lead to further improvements to AOGCMs. More information on work to validate and improve global climate models is available in the brochure of the Hadley Centre.

Hadley Centre for Climate Prediction and Research 

GCMs are not used only for climate change studies. For example, at NIWA we are using the atmospheric component of the Hadley Centre’s model to investigate causes of natural climate variability in the Southern Hemisphere. Several groups around the world are using their models to make climate forecasts.

Computer power is still a limitation for GCM simulations extending over hundreds of years. As a consequence the resolution is fairly coarse (typically 1° to 5° latitude grid size in the IPCC 2007 models). One solution to this is to run a regional climate model (RCM), which represents a limited area of the globe at a much higher resolution (10 to 30 km). NIWA uses a RCM to simulate climate change over New Zealand (see Regional Impacts page for more information).

More information on regional impacts

For a summary of improvements to GCMs please refer to the Executive Summary in the IPCC's Climate Models and Their Evaluation (Randall et al., 2007).

IPCC: Climate Models and Their Evaluation 


Figure 2: How the complexity of climate models has increase over time (from Le Treut et al., 2007).

How Well do Models Simulate Observed Features of the Climate?

If we are to have confidence in future projections of global climate models, it is vital to first test them against observed climate and also to see whether they realistically simulate different climatic conditions which were observed during the earth’s past. Since the 1990 IPCC report, there has been a major international effort, known as the Atmospheric Model Intercomparison Project (Gates, 1992), to document the comparative performance of GCMs in simulating the contemporary climate. Thirty atmospheric GCMs were forced by observed sea surface temperatures and sea ice for the decade 1979-1988. Reports from 26 diagnostic subprojects were described at the First AMIP Scientific Conference (AMIP, 1995), covering a wide range of atmospheric features that included cyclone frequencies, tropical 30-60 day oscillation, large-scale southern hemisphere circulation, soil moisture, cloudiness, extreme events, and many others. Results have also been reported extensively in the open literature and in IPCC assessments.

IPCC assessments 

The material presented on this page is not designed as an exhaustive evaluation / verification of global climate models. We have provided examples of some of the comparisons which have been made against observations, but refer readers to the 2007 IPCC Fourth Assessment for a more detailed up-to-date assessment.

2007 IPCC Fourth Assessment 

Confidence in the ability of climate models to estimate future climate changes comes from the fact that they are based on accepted physical laws such as conservation of mass, energy and momentum, as well as a wealth of observations for their more empirically-based components such as cloud reflectivities or infrared absorptive properties of greenhouse gases. Model simulations are also routinely compared against observations of the atmosphere, ocean and land surface. Figure 3 is such an example where the model surface temperatures are compared to observed global temperature changes over the 20th century. In this example, the climate models have been forced by known changes in natural factors (solar insolation and volcanic aerosols) and in human-caused factors (increasing greenhouse gas concentration and man-made aerosols). There is excellent agreement at the global scale between models and observations. Note how well the models simulate short-term cooling following large volcanic eruptions: this is because the models are ‘told’ when the eruption occurred and how much aerosol was deposited into the stratosphere. Irregular short-lived warmings are also apparent in the observations (e.g., early 1940s, and 1998), that lie within the model range but are well above the model average. These warmings occurred as a consequence of El Niño events in the equatorial Pacific, and are part of what is called ‘internal variability’ which cannot be specified in the way the computer simulations are set up. Thus, the climate models will develop El Niño events through the period of record, but they will not line up in time with their occurrence in the real world, any more than the simulated daily weather patterns will agree with observed weather sequences over long integrations.


Figure 3: Global-mean surface temperature, relative to 1901-1950 average, from observations (black line) and from 58 simulations (orange lines) by 14 global climate models reported on in the IPCC Fourth Assessment. The climate models are driven by both natural and human-caused factors that influence climate. The average over all model runs is given by the red line, with the vertical grey bars signifying the dates of large volcanic eruptions. [Figure from IPCC (2007), Chapter 8, FAQ8.1].

There have been ongoing model improvements and comprehensive evaluations of their ability to represent current climate and its variability (Randall et al., 2007). As an example of complex climatic features that can be generated by models, Figure 4 shows simulations from the UK Met Office Hadley Centre global climate model (Hadgem1) of 30-year average precipitation (1970-1999 from 20c3m run) for the months of December through to February, together with the corresponding satellite derived observed precipitation pattern from the Tropical Rainfall Measuring Mission (TRMM).

Figure 4: The December to February mean rainfall across the surface of the earth: bottom, 30-year average precipitation (1970-1999 from 20c3m run) as simulated by the climate model (model resolution 2.5 degree latitude by 3.75 degrees longitude); top, satellite derived precipitation is Dec-Feb average over nine seasons from Dec 1998 to Feb 2007 (analysis resolution 0.25 x 0.25 degrees). 

Numerous model intercomparisons with observations, such as shown in Figure 3 (above), have led the IPCC to make a very strong statement about attribution of climate change in their latest Fourth Assessment: “Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.”  Model simulations similar to those in Figure 3, but where greenhouse gas concentration does not increase over the century, do not show any significant warming of the global climate. Many other predictions from climate models are becoming more evident in the observations in many parts of the world as time progresses, such as: changing wind patterns, increases in sea-level, reduction in sea-ice, changes in the distribution of daily rainfall with more extreme events occurring.

Projections from General Circulation Models

Advances in climate change modelling have enabled the IPCC to make best estimates and likely assessed uncertainty ranges for projected air warming and sea level rise for different greenhouse gas emission scenarios. The IPCC fourth assessment (Climate Change 2007) relies on a larger number of climate models of increasing complexity and realism, as well as new information regarding the nature of feedbacks from the carbon cycle and constraints on climate response from observations.

IPCC Fourth Assessment: Climate Change 2007 

After considering results from a number of Atmospheric Ocean General Circulation Models (AOGCMs), Earth System Models of Intermediate Complexity (EMICs) and Simple Climate Models (SCMs) for a range of emissions scenarios, the IPCC came up with estimates of surface temperature and sea level changes over the next century (IPCC, 2007). The most widely quoted predictions are:

  • for the mid-range IPCC emission scenario, A1B, assuming the "best estimate" value of climate sensitivity and including the effects of future increases in aerosol, models project an increase in global mean surface air temperature relative to 1990 of about 2.8°C by 2100, with a likely range of 1.7 – 4.4 °C.
  • average sea level is expected to rise as a result of thermal expansion of the oceans and melting of glaciers and ice-sheets. For the A1B scenario, assuming the "best estimate" value of climate sensitivity and of ice melt sensitivity to warming, and including the effects of future changes in aerosol, models project an increase in sea level of about 21 - 48 cm from the present to 2100. Note the important caveat that the IPCC place on sea level projections: “The projections do not include uncertainties in climate-carbon cycle feedbacks nor the full effects of changes in ice sheet flow, therefore the upper values of the ranges are not to be considered upper bounds for sea level rise. They include a contribution from increased Greenland and Antarctic ice flow at the rates observed for 1993-2003, but this could increase or decrease in the future.”

Information on the IPCC and a summary of the IPCC projections for future global climate change 

Information regarding climate change scenarios and climate change projections for New Zealand 

Interaction between climate change and ENSO

The El Nino-Southern Oscillation (ENSO) is the major contributor to interannual variability in global climate. Global-average surface air temperature increases in the year following an El Nino (Jones, 1989), due to additional heat input from the warmer sea surface temperatures in the central and eastern equatorial Pacific. The ENSO cycle also affects atmospheric and oceanic carbon dioxide distributions (Feely et al., 1987), and so is an intrinsic component of the global climate system. Since ENSO events have dramatic regional effects in many parts of the globe, including New Zealand (Gordon, 1985), the correct simulation of ENSO variations could be critical to climate change projections. Coupled atmosphere-ocean GCMs do not have the "fractions of a degree" resolution required to accurately locate the ocean upwelling and strong gradients occurring during El Ninos. Thus, while recent GCMs do have ENSO-like fluctuations that capture the main features (Knutson and Manabe, 1997), the simulated ENSO is weaker than observed.

More information on the El Niño-Southern Oscillation

There has been steady progress in the last decade in simulating and predicting ENSO (Randall et al., 2007). However, there remain substantial systematic errors in the simulation of the mean climate of the equatorial Pacific and its interannual variability. For example, most models do not have a strong enough west-east gradient in sea surface temperature, and the equatorial oceanic “cold tongue” is too equatorially confined and extends too far to the west, compared to observations. These errors in the mean state influence the simulated ENSO variability. Figure 5 shows the ENSO temperature teleconnection patterns (the correlation of surface air temperature with the Southern Oscillation Index) from observations and from one of the AR4 models for the current climate.


Figure 5: Correlation from Southern Oscillation Index and seasonal surface air temperature anomalies, all 4 seasons combined over 1970-1999, for: observations (top, NCEP reanalysis data) and AR4 climate model (gfdl_cm21, one of the ‘better’ Fourth Assessment models in terms of its ENSO teleconnection patterns and ENSO statistics).

A number of experiments have attempted to estimate how ENSO fluctuations might change in a warmer world (Meehl et al, 1993; Knutson and Manabe, 1994, 1997; Tett, 1994; Merryfield, 2006; Meehl et al., 2007), but there is little agreement beyond the fact that El Niños are almost certain to continue in some form or another.  For example, Oldenborgh et al. (2005) assessed 19 Fourth Assessment climate models. Only six models had a good representation of the amplitude, irregular periodicity and skewness of the interannual sea surface temperature variations.  In these models, forecasts of the trend in the mean state of ENSO in 2051-2100 under the SRES A2 scenario ranged from no change (4 models) to a small shift to more El Niño-like conditions (2 models). The trend in year-to-year variability varied from a slight increase (3 models), through no change (2 models) to a slight decrease (1 model). IPCC concluded (Meehl et al., 2007) that “there is no consistent indication at this time of discernible future changes in ENSO amplitude or frequency”.

The observed ENSO cycle seems to undergo multi-decadal changes: for example, the El Niño signal in global climate anomalies was quite weak between the World Wars, but strong since 1950 (Allan et al., 1996). Since 1976, El Niño events have also predominated over the opposite cold SST phase of La Niña, including the unprecedented long-running El Niño of 1990-1995. This multi-decadal variability adds another complication to interpreting climate model simulations: are future model changes a consequence of greenhouse forcing, or are they just a manifestation of model internal multi-decadal variability? Trenberth and Hoar (1996) generated long time series of the Southern Oscillation using a simple statistical model, and concluded that the extended El Niño of the early 1990s was so rare that anthropogenic greenhouse changes could be influencing ENSO occurrence. There is some support for this speculation from model experiments by Meehl and Washington (1996) who found that cloud feedbacks resulted in tropical Pacific SST increases that were greater east of the dateline than to the west. There were attendant shifts in large-scale precipitation patterns and mid-latitude circulation anomalies that resembled some aspects of El Niño events. However, Harrison and Larkin (1997) present an opposing viewpoint. Their analysis of the 1876-1996 Darwin sea level pressure record concludes that a long run like the 1990-1995 episode might be expected every 150-200 years at the 95% confidence level, and therefore favours natural variability rather than global warming as an explanation.


Allan, R.; Lindesay, J.; Parker, D.E. (1996). El Nino-Southern Oscillation and climatic variability. CSIRO Publishing. 416 p.

AMIP (1995). Proceedings of The First International AMIP Scientific Conference. W.L. Gates (ed.), WCRP-92, WMO/TD-No. 732. World Meteorological Organisation, Geneva. 532 p.

Crowley, T.J. (1993). Geological assessment of the Greenhouse Effect. Bulletin of the American Meteorological Society 74: 2363-2373.

Crowley, T.J.; Kim, K.-Y. (1996). Comparison of proxy records of climate change and solar forcing. Geophysical Research Letters 23: 359-362.

Cubasch. U.; Cess, R.D. (1996). Processes and Modelling. In: Houghton, J.T.; Jenkins, G.J.; Ephraums, J.J. (eds). Climate Change – the IPCC Scientific Assessment, pp. 69-91. Cambridge University Press.

England, M.H. (1995). Using chlorofluorocarbons to assess ocean climate models. Geophysical Research Letters 22: 3051-3054.

Feely, R.A.; Gammon, R.H.; Taft, B.A.; Pullen, P.E.; Waterman, L.S.; Conway, T.J.; Gendron, J.F.; Wisegarver, D.P. (1987). Distribution of chemical tracers in the eastern equatorial Pacific during and after the 1982-1983 El Nino/Southern Oscillation event. Geophysical Research 92: 6545-6558.

Gates, W.L. (1992). AMIP: The Atmospheric Model Intercomparison Project. Bulletin of the American Meteorological Society 73: 1962-1970.

Gates, W.L.; Henderson-Sellers, A.; Boer, J.; Folland, C.K.; Kitoh, A.; McAveney, B.J.; Semazzi, F.; Smith, N.; Weaver, A.J.; Zheng, Q.-C. (1996). Climate models – Evaluation. In: Houghton, J.T.; Jenkins, G.J.; Ephraums, J.J. (eds). Climate Change – the IPCC Scientific Assessment, pp. 233-284. Cambridge University Press.

Gent, P.R.; McWilliams, J.C. (1990). Isopycnal mixing in ocean circulation models. Journal of Physical Oceanography 20: 150-155.

Gordon, N.D. (1985). The Southern Oscillation: a New Zealand perspective. Journal of the Royal Society New Zealand 15: 137-155.

Hadley Centre (1997). Climate Change and its impacts: a global perspective. Some recent results from the UK Research Programme, December 1997. Hadley Centre for Climate prediction and Research, Bracknell, UK.

Harrison, D.E.; Larkin, N.K. (1997). Darwin sea level pressure, 1876-(1996). Evidence for climate change? Geophysical Research Letters 24: 1779-1782.

Harvey, D.; Gregory, J.; Hoffert, M.; Jain, A.; Lal, M.; Leemans, R.; Raper, S.; Wigley, T.; de Wolde, J. (1997). An introduction to simple climate models used in the IPCC Second Assessment Report. IPCC Technical Paper II, Intergovernmental Panel on Climate Change, Geneva. 47 pp.

Hirst, A.C.; Gordon, H.B.; O'Farrell, S.P. (1996). Global warming in a coupled climate model including oceanic eddy-induced advection. Geophysical Research Letters 23: 3361-3364.

IPCC (1996). Climate Change 1995 – The Science of Climate Change. Summary for Policymakers, and Technical Summary of the Working Group I Report. Intergovernmental Panel on Climate Change, Geneva. 56 p.

IPCC, 2007: Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jones, P.D. (1989). The influence of ENSO on global temperatures. Climate Monitor 17: 80-89.

Kiehl, J.T.; Briegleb, B.P. (1993). The relative roles of sulfate aerosols and greenhouse gases in climate forcing. Science 260: 311-314.

Knutson, T.R.; Manabe, S. (1994). Impact of increased CO2 on simulated ENSO-like phenomena. Geophysical Research Letters 21: 2295-2298.

Knutson, T.R.; Manabe, S. (1997). Simulated ENSO in a global coupled ocean-atmosphere model: Multidecadal amplitude modulation and CO2 sensitivity. Geophysical Research Letters 21: 2295-2298.

Le Treut, H., R. Somerville, U. Cubasch, Y. Ding, C. Mauritzen, A. Mokssit, T. Peterson and M. Prather, 2007: Historical Overview of Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Meehl, G.A. (1992). Global coupled models: atmosphere, ocean, sea ice. In: Trenberth, K. (ed.). Climate system modeling, pp. 555-581. Cambridge University Press.

Meehl, G.A.; Branstator, G.W.; Washington, W.M. (1993). Tropical Pacific interannual variability and CO2 climate change. Joural of Climate 6: 42-63.

Meehl, G.A.; Washington, W.M. (1996). El Nino-like climate change in a model with increased atmospheric CO2 concentrations. Nature 382: 56-60.

Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver and Z.-C. Zhao, 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Merryfield, W.J. (2006). Changes to ENSO under CO2 Doubling in a Multimodel Ensemble. Journal of Climate 19: 4009-4027.

Mitchell, J.F.B.; Johns, T.C.; Gregory, J.F.B.; Tett, S.F.B. (1995). Transient climate response to increasing sulphate aerosols and greenhouse gases. Nature 376: 501-504.

van Oldenborgh, G. J., Philip, S. Y., and Collins, M. (2005). El Niño in a changing climate: a multi-model study, Ocean Sci., 1, 81-95.

Randall, D.A.; Wood, R.A.; Bony, S.; Colman, R.; Fichefet, T.; Fyfe, J.; Kattsov, V.; Pitman, A.; Shukla, J.; Srinivasan, J.; Stouffer, R.J.; Sumi, A.; Taylor, K.E. (2007). Climate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.  [ Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Robitaille, D.Y. and Weaver, A.J. (1995). Validation of sub-grid-scale mixing schemes using CFCs in a global ocean model. Geophysical Research Letters 21: 2917-2920.

Salinger, M.J.; Griffiths, G.M.; Gosai, A. (2005). Extreme pressures differences at 0900 NZST and winds across New Zealand. International Journal of Climatology 25: 1203-1222.

Santer, B.D.; Taylor, K.E.; Wigley, T.M.L.; Johns, T.C.; Jones, P.D.; Karoly, D.J.; Mitchell, J.F.B.; Oort, A.H.; Penner, J.E.; Ramaswamy, V.; Schwarzkopf, M.D.; Stouffer, R.J.; Tett, S. (1996). A search for human influences on the thermal structure of the atmosphere. Nature 382: 39-46.

Schimel, D. and 26 co-authors (1996). Radiative forcing of climate. In: Houghton, J.T.; Jenkins, G.J.; Ephraums, J.J. (eds). Climate Change – the IPCC Scientific Assessment, pp. 69-131. Cambridge University Press.

Tett, S.F.B. (1994). Simulation of El-Nino/Southern Oscillation like variability in a global AOGCM and its response to CO2 increase. Climate Research Tech. Note 45. U.K. Met Office, March 1994.

Trenberth, K.E.; Hoar, T.J. (1996). The 1990-1995 El Nino-Southern Oscillation event: Longest on record. Geophysical Research Letters 23: 57-60.

Whetton, P.H.; England, M.H.; O'Farrell, S.P.; Watterson, I.G.; Pittock, A.B. (1996). Global comparison of the regional rainfall results of enhanced coupled and mixed layer ocean experiments: Implications for climate change scenario development. Climatic Change 33: 497-519.

Prepared by David Wratt and Brett Mullan