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Could we control our climate?
Could we control our climate?

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2.2 The world’s climate models

Many of the world’s universities and meteorological institutes have created their own climate models for making predictions. The reason for this is that each model is slightly different. The motivation for having many different climate models is to compare their predictions. The wider the spread of predictions, the greater scientists’ uncertainty about the future.

The IPCC (2013) report included results from 42 different climate models. Their acronym-filled names sound rather technical and obscure, but are often derived from the institute name followed by the model version. For example:

  • GFDL stands for the U.S. National Oceanic and Atmospheric Administration (NOAA) ‘Geophysical Fluid Dynamics Laboratory’
  • GISS stands for the NASA ‘Goddard Institute for Space Studies’
  • the ‘Had’ in HadGEM stands for the UK Met Office ‘Hadley Centre’ (and GEM for Global Environmental Model), and
  • MPI stands for the Max Planck Institutes in Germany.

As an example, you can see their different predictions for global warming under RCP4.5 in Figure 3. Use the magnifying glass to see the variation in predictions in more detail.

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Figure 3 Projected mean surface air temperature change in 2081–2100 displayed as anomalies with respect to 1986–2005 for RCP4.5 from each of the 42 climate models used in IPCC AR5 (IPCC 2013). Use the magnifying glass to see the variation in predictions in more detail.
Interactive feature not available in single page view (see it in standard view).

You will notice that the models give quite a range of different predictions for the future.

  • Recall that all four RCPs had a net positive forcing, i.e. warming the climate. What might seem surprising about some of these predictions?

  • As the forcing is positive, one would expect overall warming relative to today, but many predictions show large regions of cooling.

The maps show cooling in some places because regional changes can be quite different to the global mean. Usually maps of predictions show the mean of all the models, so it is not possible to see all the variation between models. It is important to bear this wide variation in mind.