Our understanding of the climate system has been revolutionized recently, by the development of sophisticated computer models. The predictions of such models are used to formulate international protocols, intended to mitigate the severity of global warming and its impacts. Yet, these models are not perfect representations of reality, because they remove from explicit consideration many physical processes which are known to be key aspects of the climate system, but which are too small or fast to be modelled. The purpose of this paper is to give a personal perspective of the current state of knowledge regarding the problem of unresolved scales in climate models. A recent novel solution to the problem is discussed, in which it is proposed, somewhat counter-intuitively, that the performance of models may be improved by adding random noise to represent the unresolved processes.
One contribution of 17 to a Triennial Issue ‘Astronomy and earth science’.
↵Spectral approaches, which involve a truncated projection onto a finite set of basis functions, are sometimes used instead of the discretization approach (especially in atmosphere GCMs), but the filtering out of small scales described here still occurs.
↵The use of a trigger function in deterministic convective cloud parameterizations may have the effect of naturally introducing fluctuations about the long-term mean, but such fluctuations are likely to be too small to reproduce the probability distribution of figure 5.
- © 2005 The Royal Society