Introduction. Stochastic physics and climate modelling

Abstract

Finite computing resources limit the spatial resolution of state-of-the-art global climate simulations to hundreds of kilometres. In neither the atmosphere nor the ocean are small-scale processes such as convection, clouds and ocean eddies properly represented. Climate simulations are known to depend, sometimes quite strongly, on the resulting bulk-formula representation of unresolved processes. Stochastic physics schemes within weather and climate models have the potential to represent the dynamical effects of unresolved scales in ways which conventional bulk-formula representations are incapable of so doing. The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic. The latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.

Extremes and Recurrence in Dynamical Systems

“…The basic results are that one can approximately treat the effect of fast modes on the slow dynamics by adding suitably defined deterministic and stochastic (with white spectrum) forcing terms in the evolution equations of the slow variables [327,328]. While the use of deterministic, mean field parametrizations for processes like convection, which cannot yet be captured by the relatively coarse grids of most weather and climate models is a standard practise in geophysical fluid dynamical modelling [329,330,24], currently weather and climate modelling centres are moving in the direction of introducing stochastic parametrizations [331,332], as mounting evidences suggest that they are more effective than usual deterministic methods [333,334]. It is indeed not clear to what extent such methods, which aim at being able to describe the typical behaviour of the slow variables, perform in terms of providing a good representation of extreme events.…”

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

“…Atmospheric instability with abundant moisture transport in the tropical monsoon region and active synoptic-scale convections produce significant 'climate noise' for the seasonal mean ISMR in numerical simulations. The systematic biases are inevitable and endemic (Palmer and Williams, 2008). It was estimated that about 50% of the interannual variability of the seasonal mean summer monsoon climate is governed by atmospheric internal processes (Goswami and Xavier, 2005).…”

Sources of uncertainty in deterministic dynamics: an informal overview

“…Ever since, 'a butterfly flapping its wings' has remained constant-but the location of the butterfly, the consequences and the location of the consequences have varied widely. Moreover, it is almost certain that the topic of this lecture was Lorenz [9], which discussed a stronger form of uncertainty, related to a limit on the scale of observable disturbances that cannot be avoided however accurate the measurements may be [18].…”