Potential predictability of seasonal extreme precipitation accumulation in China

Wei, Wenguang, Yan, Zhongwei and Jones, P. D. ORCID: https://orcid.org/0000-0001-5032-5493 (2017) Potential predictability of seasonal extreme precipitation accumulation in China. Journal of Hydrometeorology, 18 (4). 1071–1080. ISSN 1525-755X

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Abstract

The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960–2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	http://journals.ametsoc.org/doi/10.1175/...
Depositing User:	Pure Connector
Date Deposited:	21 Feb 2017 02:22
Last Modified:	22 Oct 2022 02:19
URI:	https://ueaeprints.uea.ac.uk/id/eprint/62651
DOI:	10.1175/JHM-D-16-0141.1

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