Economic values of the APCN multi-model ensemble categorical seasonal predictions

Abstract

The skill of seasonal mean precipitation and temperature predictions by multi-model ensemble (MME) schemes has been examined using hindcast data from the APEC Climate Network (APCN). The hindcast data of the member models have been collected from several institutions in the APEC region. Skill metrics, namely, anomaly correlation coefficient (ACC), Hanssen-Kuipers skill (HKS), and economic value were used to assess the APCN MME predictions. The member models have a range of skills in predicting the seasonal mean conditions: however, the MME schemes consistently produce improved predictions. Though the weighted multi-model ensemble schemes have better skill scores in terms of ACC, the unbiased multi-model ensemble mean provides better and more useful categorical predictions as seen from the HKS score and the economic values. Prediction skills show distinct seasonality in different regions. Precipitation prediction skill is the lowest in the Asian monsoon region in June-July-August. The APCN MME predictions have positive economic values in most regions and examination of monthly values suggests that the MME predictions are reasonably good and useful even with three-month lead times. An uncalibrated multi-model probabilistic prediction scheme gives an indication of threshold probability for which the seasonal predictions are more beneficial to users. The extreme climatic conditions are predicted well by the MME schemes. However, user sectors for which the extreme climate predictions are beneficial are different to those of regular three-category predictions. These results can be used in planning and preparedness for the extremes of climate in the future, and when developing economic decision support models.