Numerical Extended-Range Prediction: Forecast Skill Using a Low-Resolution Climate Model

Abstract

A pilot study that evaluates the potential forecast skill of winter 10?30-day time-mean flow from a low-resolution (R15) climate simulation model is presented. The hypothesis tested is that low-resolution climate model forecasts might be as skillful as high-resolution numerical weather prediction model forecasts at extended-range timescales, if the low-frequency evolution is primarily a large-scale process and if the systematic error of the climate model is less detrimental than high-resolution forecast model error. Eight forecast cases, each containing four ensemble members, are examined and compared to high-resolution forecasts discussed by Miyakoda et al. The systematic error of the climate model is examined and then used to reduce the forecast error in an a posteriors fashion. The operational utility of these climate model forecasts is also assessed. The low-resolution climate model is quite successful in duplicating the skill of the high-resolution forecast model. If the forecast systematic component of error evaluated from the same eight cases is removed, the climate model forecasts improve in a comparable fashion to the high-resolution results. When information from the low-resolution climate simulation is used to estimate the forecast systematic error, the improvement in skill is less successful. These results show that a low-resolution climate model can be a viable tool for numerical extended-range forecasting and imply that large ensembles can be integrated for the same cost as higher-resolution model integrations.