Predictability of Extremely Pluvial Winters Over the Yangtze–Huai River Basin in China

Author:

He Jiaying12ORCID,Doi Takeshi2ORCID,Behera Swadhin2ORCID,Luo Jing‐Jia1ORCID,Yamagata Toshio2ORCID

Affiliation:

1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC–FEMD) Key Laboratory of Meteorological Disaster Institute for Climate and Application Research (ICAR) Ministry of Education (KLME) Nanjing University of Information Science & Technology (NUIST) Nanjing China

2. Application Laboratory (APL) Research Institute for Value‐Added‐Information Generation (VAiG) Japan Agency for Marine‐Earth Science and Technology (JAMSTEC) Yokohama Japan

Abstract

AbstractIn the 2018–2019 winter, an unprecedented pluvial event occurred in the Yangtze‐Huai River Basin (YHRB), which is one of the most developed areas in China. This was a record over the past 40 years. Other notable events during this period were the events of the 2002–2003, 1997–1998, and 1989–1990 winters. This study aims to examine the ability of the Scale Interaction Experiment‐Frontier (SINTEX‐F) to predict these extremely pluvial winters and its predictability source. Extreme precipitation events in the 2018–2019 and 1997–1998 winters were reasonably well predicted 1 month earlier by the ensemble seasonal prediction system from deterministic and probabilistic perspectives. In contrast, the predictions of extremely pluvial winters in 2002–2003 and 1989–1990 were less skillful in some parts of the YHRB. The analysis of co‐variability of inter‐member anomalies suggested that the SST anomalies in the tropical western Indian Ocean (IO) and the tropical Pacific Ocean were responsible for the predictions of the 2018–2019 and 1997–1998 winters through increased low‐level moisture convergences in the YHRB as well as wave trains along the upper westerly jet. Limited performance in predicting the 2002–2003 and 1989–1990 events might be due to less accurate prediction of low‐level circulation around the YHRB associated with the SST anomalies in the tropical eastern IO, western IO, and the Pacific, respectively. Besides, atmospheric internal variability as well as model biases may also limit the deterministic prediction skills.

Funder

National Natural Science Foundation of China

Japan Society for the Promotion of Science

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

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