Statistics and Climate

Do different climate models give different results? And if so, why? The answer to these questions will increase our understanding of the climate models, and potentially the physical phenomena and processes present in the climate system.

We now have many different climate models, many different methods, and get a range of different results. They provide what we call ‘multi-model‘ and ‘multi-method‘ ensembles. But how do we make sense out of all this information?

And, do we really need all these different models? Global climate models tend to give roughly similar estimates for the climate sensitivity, but there is nevertheless a spread between the different model estimates. The models often diverge more radically if we zoom down to a region.

Furthermore, a single model may give different answers for the future temperature over North America, depending on which day is used to describe the weather at the starting point of the model simulation (Deser et al., 2012).

So the question is whether the differences in model set-up affect the range of the results, and whether a mix of models is superior to many simulations with a single model in terms of accounting for the unknowns of climate modelling.

The fuzziness associated with the spread between the model results is often referred to by the catch-all phrase ‘uncertainty‘, referring to (unpredictable) chaotic internal variations, vaguely known forcing estimates, and climate model limitation.

Whereas climate scientists find ‘uncertainty’ difficult, it plays a central role in statistics (Katz et al., 2013). The statisticians are experts at drawing knowledge from a large volume of information, incomplete data samples, and have methods for ‘distilling’ the data (using a phrase coined by Bruce Hewitson). Some interesting methods are regression analysis and factorial design.

It is necessary to bring on board more statisticians to participate on climate research. Hence, the motivation for a Statistics and Climate workshop with a high proportion of statisticians among the participants (supported by the SARMA network, Met Norway, Norwegian computing, and the Bjerknes centre).


Bringing together people from different fields can be challenging, and we sometimes realise that we speak about ‘uncertainty’ or ‘models’, but mean different things. Is ‘uncertainty’ a probability distribution, model error, gaps in observations, inaccuracy, or imprecision?

In statistics, a ‘model’ may be a probability distribution or an equation whose coefficients are estimated from the data (‘best-fit’). We can also define ‘weather’ as a time series describing when and how much, and ‘climate’ as a probability distribution saying something about how typical such an event is (illustration below).

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  1. C. Deser, R. Knutti, S. Solomon, and A.S. Phillips, "Communication of the role of natural variability in future North American climate", Nature Climate Change, vol. 2, pp. 775-779, 2012.
  2. R.W. Katz, P.F. Craigmile, P. Guttorp, M. Haran, B. Sansó, and M.L. Stein, "Uncertainty analysis in climate change assessments", Nature Climate Change, vol. 3, pp. 769-771, 2013.