Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

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-timescale 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.

Carbon dioxide atmospheric concentration and hydrometeorological disasters

“…Public spending on ex-ante risk reduction and warning projects may lead to reductions in disaster's damages that are almost twice that of the spending on ex-post recovery programs (Davlasheridze et al 2017). This explains the large and growing literature attempting to provide better predictions for different climate change induced disasters (Mora et al 2018, Tan et al 2019, Wei et al 2017, Li and Wang 2018, Pei et al 2016, Jayawardena 2015, Siverd el al. 2020.…”

Summer Regional Daily‐Precipitation Extreme Events in Huang‐Huai Rivers Region of China and Their Relationships With Rossby Wave Packet Activities

“…In recent years, extreme weather and climate events, especially the precipitation extremes have received more and more attention because these events have tremendous societal impacts (Fischer & Knutti, 2015; Liu et al., 2019; Zaz et al., 2019). The frequency and intensity of extreme climate events are increasing but with obvious regional differences as reported from different research groups (e.g., Agel et al., 2015; Tank & Können, 2003; Wei et al., 2017; Zhai, 2005). In different parts of China, it is also found that warming extremes, intense precipitation events are increasing but the cold extremes and Typhoons are decreasing (e.g., Guan et al., 2011; Ning et al., 2015; Ren et al., 2011).…”

Potential Precipitation Predictability Decreases Under Future Warming

“…Potential predictability (G. J. Boer, 2004) is another way to quantify predictability from a variance fraction perspective. It describes the fraction of long‐term variability that may be distinguished from the internally generated natural variability which is not predictable on long time scales and therefore may be considered as “noise.” Potential predictability is similar to signal‐to‐noise ratio and describes the potential predictable ability, which is widely used to evaluate the precipitation predictability in previous studies (G. Boer, 2009; G. Boer & Lambert, 2008; Kang et al, 2004; Lou et al, 2019; W. Wei et al, 2017). Therefore, potential precipitation predictability (PPP) is introduced to estimate precipitation predictability over global lands.…”

“…Potential predictability is similar to signalto-noise ratio and describes the potential predictable ability, which is widely used to evaluate the precipitation predictability in previous studies (G. Boer, 2009;G. Boer & Lambert, 2008;Kang et al, 2004;Lou et al, 2019;W. Wei et al, 2017).…”