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  • Review Article
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Towards objective probabalistic climate forecasting

Nature volume 419, page 228 (2002)Cite this article

At the most basic level, the community needs to agree on what is meant by a 'correct' estimate of risk in climate forecasting. A probabilistic climate forecast (for example, of the risk of global precipitation increasing by more than 10% by 2050) cannot be checked against observations as can a probabilistic weather forecast. If a two-day weather forecast is wrong at the 5% level much more than 5% of the time, then something is amiss with the forecasting system. If a 50-year climate forecast is 'wrong' at the 5% level, we could simply have been unlucky. This apparent difficulty with verification has led to the view that estimates of uncertainty based on expert opinion (for example, the range for climate sensitivities used by ref. 8) are as good as any other9. This leaves the field open to the charge of subjectivism.

Objectively, we need to ask: 'given the observations available now, what range of forecasts might we have obtained had we started again from scratch many times over and made completely independent sets of decisions about model formulation and resolution?' A probabilistic forecast can only be said to have converged when including additional models is unlikely to make much difference to the forecast distribution of a particular variable. But there are complications. For example, we have no way of defining how 'close' two models are solely in terms of their formulation7, so we cannot design a representative sampling strategy over 'all possible AOGCMs' even if we had the resources to do so. In practice, therefore, a probabilistic forecast must begin with a very large ensemble of possible models, obtained by varying parameter values, parameterization schemes, resolution and entire model components, and extracting a sub-sample weighted according to the different models' ability to simulate recent observed climate change. A probabilistic forecast based on this sub-sample will have converged if its spread is determined primarily by the constraint of consistency with observations and not by the choice of models within the original ensemble — this is the crucial distinction between a constrained ensemble and an unconstrained ensemble of opportunity.

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Figure 1: The three-dimensional surface shows a forecast probability distribution of a one-dimensional quantity (global-mean warming above pre-industrial) accounting for uncertainty in the climate response, while the lines show the (smaller) impact of initial condition uncertainty in an ensemble of model simulations.

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  1. Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PU, UK

    Myles R. Allen & David A. Stainforth

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  1. Myles R. Allen
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  2. David A. Stainforth
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Correspondence to David A. Stainforth.

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Allen, M., Stainforth, D. Towards objective probabalistic climate forecasting. Nature 419, 228 (2002). https://doi.org/10.1038/nature01092a

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