Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles

Details Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles

Aug 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011georl..3816703w&link_type=abstract

Geophysical Research Letters, Volume 38, Issue 16, CiteID L16703

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Computational Geophysics: Model Verification And Validation, Global Change: Coupled Models Of The Climate System, Mathematical Geophysics: Probabilistic Forecasting (3238, 4315), Mathematical Geophysics: Uncertainty Quantification (1873, 1990), Atmospheric Processes: Global Climate Models (1626, 4928)

Scientific paper

The probabilistic skill of ensemble forecasts for the first month and the first season of the forecasts is assessed, where model uncertainty is represented by the a) multi-model, b) perturbed parameters, and c) stochastic parameterisation ensembles. The main foci of the assessment are the Brier Skill Score for near-surface temperature and precipitation over land areas and the spread-skill relationship of sea surface temperature in the tropical equatorial Pacific. On the monthly timescale, the ensemble forecast system with stochastic parameterisation provides overall the most skilful probabilistic forecasts. On the seasonal timescale the results depend on the variable under study: for near surface temperature the multi-model ensemble is most skilful for most land regions and for global land areas. For precipitation, the ensemble with stochastic parameterisation most often produces the highest scores on global and regional scales. Our results indicate that stochastic parameterisations should now be developed for multi-decadal climate predictions using earth-system models.

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