Philosophical Transactions of the Royal Society A, 366, 2561-2579, 10.1098/rsta.2008.0033

Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model

Judith Berner, Francisco J. Doblas-Reyes, Tim N. Palmer, Glenn Shutts and Antje Weisheimer
ECMWF, Shinfield Park
RG2 9AX, Reading, UK
The impact of a nonlinear-dynamic cellular automaton model, as a representation of the partially-stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium-Range Weather Forecasts coupled ocean-atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the cellular automaton at the near gridscale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the cellular automaton's quasi-stochastic representation of inherent model uncertainty.

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