Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer

Yan Wang,Pei-Qun Zhang,Jie Wu,Wei-Hua Jie,Quan-Liang Chen,Joshua-Xiouhua Fu,Fang Zhou,Hong-Li Ren

doi:10.3390/atmos11050515

Abstract

The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.

Highlights

The Maritime Continent (MC), located at the junction of the Indian Ocean and the Pacific Ocean, consists of several islands and shallow seas [1]
This paper aims at constructing model ensemble (MME) forecast and revealing the predictability of sub-seasonal precipitation during boreal summer in the MC
Temporal Correlation Coefficient (TCC) can represent the temporal agreement of model forecast statistical significance and is able to acquire a distribution of prediction skill by calculating the TCC of each grid point

Summary

Introduction

The Maritime Continent (MC), located at the junction of the Indian Ocean and the Pacific Ocean, consists of several islands and shallow seas [1]. To better promote the development of MME forecast for short-term climate (seasonal to interannual) prediction, several research projects, such as ENSEMBLES [45], the US National Multi-Model Ensemble (NMME) [46], and China MME (CMME) [47] have been launched. The implementations of the MME technique have effectively improved the prediction of weather (e.g., tropical cyclones) and climate variability on both seasonal [48,49,50,51] and sub-seasonal timescales [52,53]. With the S2S database, Specq et al [54] first attempted to assess the balanced MME forecast skill of precipitation over the southwest tropical Pacific at the sub-seasonal timescale. This paper aims at constructing MME forecast and revealing the predictability of sub-seasonal precipitation during boreal summer in the MC. We chose the median of four ensembles as the ensemble of CMA, and the MME was composed of the respective median ensemble of five models

Forecasting Skill Metrics

Prediction

Association ofofMME

The skillphase for each skill for different

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Summary and Summary