TITLE

Improving Reliability of Coupled Model Forecasts of Australian Seasonal Rainfall

AUTHOR(S)
Langford, Sally; Hendon, Harry H.
PUB. DATE
February 2013
SOURCE
Monthly Weather Review;Feb2013, Vol. 141 Issue 2, p728
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Seasonal rainfall predictions for Australia from the Predictive Ocean Atmosphere Model for Australia (POAMA), version P15b, coupled model seasonal forecast system, which has been run operationally at the Australian Bureau of Meteorology since 2002, are overconfident (too low spread) and only moderately reliable even when forecast accuracy is highest in the austral spring season. The lack of reliability is a major impediment to operational uptake of the coupled model forecasts. Considerable progress has been made to reduce reliability errors with the new version of POAMA2, which makes use of a larger ensemble from three different versions of the model. Although POAMA2 can be considered to be multimodel, its individual models and forecasts are similar as a result of using the same perturbed initial conditions and the same model lineage. Reliability of the POAMA2 forecasts, although improved, remains relatively low. Hence, the authors explore the additional benefit that can be attained using more independent models available in the European Union Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project. Although forecast skill and reliability of seasonal predictions of Australian rainfall are similar for POAMA2 and the ENSEMBLES models, forming a multimodel ensemble using POAMA2 and the ENSEMBLES models is shown to markedly improve reliability of Australian seasonal rainfall forecasts. The benefit of including POAMA2 into this multimodel ensemble is due to the additional information and skill of the independent model, and not just due to an increase in the number of ensemble members. The increased reliability, as well as improved accuracy, of regional rainfall forecasts from this multimodel ensemble system suggests it could be a useful operational prediction system.
ACCESSION #
85401277

 

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