Using Selected Members of a Large Ensemble to Improve Prediction of Surface Air Temperature Anomalies Over Japan in the Winter Months From Mid-Autumn

Abstract

A large ensemble of 120 predictions of the Scale Interaction Experiment-Frontier Research Center for Global Change Version 2 (SINTEX-F2) coupled general circulation model were evaluated for their skill in predicting the surface air temperature (SAT) anomalies over Japan in the winter months December, January, and February. The predictions were initialized using November initial conditions. The members with skill scores based on anomaly correlation coefficient (ACC) were selected and an average of the selected predictions (SEM) was generated. Comparison of SAT anomaly predictions by the average of all the 120 members (ENS) to the SEM predictions shows SEM to outperform the ENS predictions in all the three winter months with higher ACC skill score, higher hit rate and low false alarm rate over the regions covering central Japan in December and January and over the northern region of Hokkaido in February. The improvement in the skill scores in the SEM is found to be due to improved representation of 200 hPa geopotential height anomalies in SEM compared to ENS predictions. The results indicate SEM to be useful for improving skill in predicting SAT anomalies over parts of Japan in the winter months.