The effects of model climate bias on ENSO variability and ensemble prediction

Abstract

New methods are presented for determining the role of coupled ocean-atmosphere model climate bias on the strength and variability of the El Nino-Southern Oscillation (ENSO) and on the seasonal ensemble prediction of El Nino and La Nina events. An intermediate complexity model with a global atmosphere coupled to a Pacific basin ocean is executed with parallelised algorithms to produce computationally efficient year-long forecasts of large ensembles of coupled flow fields, beginning every month between 1980 and 1999. Firstly, the model is provided with forcing functions that reproduce the average annual cycle of climatology of the atmosphere and ocean based on reanalysed observations. We also configure the model to generate realistic ENSO fluctuations. Next, an ensemble prediction scheme is employed which produces perturbations that amplify rapidly over a month. These perturbations are added to the analyses and give the initial conditions for the ensemble forecasts. The skill of the forecasts is presented and the dependency on the annual and ENSO cycles determined. Secondly, we replace the forcing functions in our model with functions that reproduce the averaged annual cycles of climatology of two state of the art, comprehensive Coupled General Circulation Models. The changes in skill of subsequent ensemble forecasts elucidate the roles of model bias in error growth and potential predictability.
 
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