Probabilistic versus Deterministic Skill in Predicting the Western North Pacific- East Asian Summer Monsoon Variability with Multi-Model Ensembles

Abstract:

Based on the historical forecasts of three quasi-operational multi-model ensemble (MME) systems, this study assesses the superiorities of the coupled MME over its contributing single-model ensembles (SMEs) and over the uncoupled atmospheric MME in predicting the seasonal variability of the Western North Pacific-East Asian summer monsoon. The seasonal prediction skill of the monsoon is measured by Brier skill score (BSS) in the sense of probabilistic forecast as well as by anomaly correlation (AC) in the sense of deterministic forecast. The probabilistic forecast skill of the MME is found to be always significantly better than that of each participating SME, while the deterministic forecast skill of the MME is even worse than that of some SME. The BSS is composed of reliability and resolution, two attributes characterizing probabilistic forecast skill. The probabilistic skill increase of the MME is dominated by the drastic improvement in reliability, while resolution is not always improved, similar to AC. A monotonous resolution-AC relationship is further found and qualitatively understood, whereas little relationship can be identified between reliability and AC. It is argued that the MME’s success in improving the reliability possibly arises from an effective reduction of biases and overconfidence in forecast distributions. The coupled MME is much more skillful than the uncoupled atmospheric MME forced by persisted sea surface temperature (SST) anomalies. This advantage is mainly attributed to its better capability in capturing the evolution of the underlying seasonal SST anomaly.