Dr Antje Weisheimer (she): Publications

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

Chapter in Stochastic Physics and Climate Modelling, Cambridge University Press (2009) 15

Authors:

J Berner, FJ Doblas-Reyes, TN Palmer, GJ Shutts, A Weisheimer

Stochastic parametrization and model uncertainty. ECMWF Tech Memo.

(2009) 598

Authors:

TN Palmer, R Buizza, FJ Doblas-Reyes, T Jung, M Leutbecher, GJ Shutts, M Steinheimer, A Weisheimer

Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts Reply

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 90:10 (2009) 1551-1554

Authors:

TN Palmer, FJ Doblas-Reyes, A Weisheimer, MJ Rodwell

Towards the Probabilistic Earth-System Model

(2008)

Authors:

TN Palmer, FJ Doblas-Reyes, A Weisheimer, GJ Shutts, J Berner, JM Murphy

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

Philos Trans A Math Phys Eng Sci 366:1875 (2008) 2561-2579

Authors:

J Berner, FJ Doblas-Reyes, TN Palmer, G Shutts, A Weisheimer

Abstract:

The impact of a nonlinear dynamic cellular automaton (CA) 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 CA at the near-grid scale 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 CA's representation of inherent model uncertainty.

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