Seasonal prediction of winter precipitation anomalies over central Southwest Asia: A canonical correlation analysis approach

No Thumbnail Available

Date

2018

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Central southwest Asia (CSWA; 20�-47�N, 40�-85�E) is a water-stressed region prone to significant variations in precipitation during its winter precipitation season of November-April. Wintertime precipitation is crucial for regional water resources, agriculture, and livelihood; however, in recent years droughts have been a notable feature of CSWA interannual variability. Here, the predictability of CSWA wintertime precipitation is explored based on its time-lagged relationship with the preceding months' (September-October) sea surface temperature (SST), using a canonical correlation analysis (CCA) approach. For both periods, results indicate that for CSWA much of the seasonal predictability arises from SST variations in the Pacific related to El Ni�o-Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). Additional sources of skill that play a weaker predictive role include long-term SST trends, North Atlantic variability, and regional teleconnections. CCA cross-validation skill shows that the regional potential predictability has a strong dependency on the ENSO phenomenon, and the strengthening (weakening) of this relationship yields forecasts with higher (lower) predictive skill. This finding is validated by the mean cross-validated correlation skill of 0.71 and 0.38 obtained for the 1980/81-2014/15 and 1950/51-2014/15 CCA analyses, respectively. The development of cold (warm) ENSO conditions during September-October, in combination with cold (warm) PDO conditions, is associated with a northward (southward) shift of the jet stream and a strong tendency of negative (positive) winter precipitation anomalies; other sources of predictability influence the regional precipitation directly during non-ENSO years or by modulating the impact of ENSO teleconnection based on their relative strengths. � 2018 American Meteorological Society.

Description

Keywords

Drought, Interannual variability, Precipitation, Sea surface temperature, Seasonal forecasting, Statistical forecasting

Citation

4

Endorsement

Review

Supplemented By

Referenced By