Projected Changes in Abrupt Shifts Between Dry and Wet Extremes Over China Through an Ensemble of Regional Climate Model Simulations

Abstract

The dry-wet abrupt alternation (DWAA) event, which is defined as the phenomenon of dry (or wet) spells abruptly following wet (or dry) spells, magnifies the influence of individual wet and dry events. The dynamic evolution of DWAA events has not been studied for different climate zones of China that is particularly susceptible to dry and wet extremes. This study explores the future changes in the abrupt alternations between dry and wet extremes across 10 climate divisions of China, with a thorough assessment of dry and wet conditions using the Standardized Precipitation Evapotranspiration Index (SPEI). We take advantage of an ensemble of regional climate model simulations including the Providing Regional Climate Impacts for Studies (PRECIS) experiment and five CORDEX East Asia experiments to produce high-resolution climate information for a baseline period of 1975-2004 and a future period of 2069-2098. Our findings disclose that a total of 70% of China's land area suffered from the DWAA events at least once during 1975-2004. The wet-dry alternation event is projected to become more frequent in summer, and a prominent increase in the number of dry-wet alternation events is expected to occur in spring over most parts of China. Moreover, an increasing number of DWAA events with intensified magnitude is projected to strike the North China Plain dominated by warm temperature and humid zone, which is the most densely populated region of the country and is also the largest agriculture production area. Our findings also reveal a strong positive correlation between DWAA and heavy rainfall. The 95th percentile rainfall event contributes most to the wet-dry alternation event for most climate divisions of China. Plain Language Summary The emerging disaster of dry-wet abrupt alternation (DWAA) has been receiving increasing attention due to its compound effects on agricultural production and socioeconomic development. Since China is exposed to dry and wet hydrological extremes as well as their interconnections, a first attempt is made in this study to explore the historical evolution and the future change in the abrupt shifts between dry and wet extremes for different climate divisions of China. Our findings reveal that a total of 70% of China's land area suffered from DWAA events at least once from 1975 to 2004. More importantly, the DWAA is expected to occur more frequently and intense by the end of the 21st century, especially for the North China Plain which is the most densely populated region of the country and is also the largest agricultural production area. Moreover, the summertime wet-dry alternation is expected to become more frequent for most climate divisions of China, while the dry-wet alternation event is projected to occur more frequently in spring over East China. These findings provide meaningful insights into the development of adaptation strategies for enhancing society's resilience to compound extremes in a changing climate.

Adapting to Changing Labor Productivity as a Result of Intensified Heat Stress in a Changing Climate

“…As shown in Figure 1, China is delineated into nine climate divisions according to temperature and precipitation variabilities to better explore the regional climate impacts on labor productivity. Considering the internal regional features of precipitation in China and based on previous studies (Bucchignani et al., 2014; H. Chen et al., 2020; Guo et al., 2017; Li et al., 2015; Luo et al., 2013), we divide the contiguous China domain into nine sub‐regions, namely region 1 in a cold and humid climate, region 2 in a warm and arid climate, region 3 in a cold and arid climate, region 4 in a warm and semi‐arid climate, region 5 in cool and semi‐humid climate, region 6 in a cool and humid climate, region 7 in a warm and humid climate, region 8 in a hot and humid climate, and region 9 in a subtropical hot and humid climate. As shown in Figure 1, these sub‐regions are typically used in weather and climate‐related discussions in China because of the climatic similarities within each sub‐region.…”

“…Note that the provincial and gridded population projection for China is derived from the study by Chen et al. (2020). According to the newly published data set, the population of China is projected to decline from 1.3 billion in 2010 to 509 million by 2100.…”

“…Assuming a constant heat stress effect on the hourly change in the labor capacity across all working hours, we define the annual productivity loss/working hour loss as the 25-year averaged labor capacity reduction multiplied by 8 h and the population of workers involved in a given labor type. Note that the provincial and gridded population projection for China is derived from the study by Chen et al (2020). According to the newly published data set, the population of China is projected to decline from 1.3 billion in 2010 to 509 million by 2100.…”

Probabilistic Projections of Multidimensional Flood Risks at a Convection‐Permitting Scale

“…Although RCMs are largely improved in terms of the horizontal grid spacing (e.g., 10–50 km), they are not fine enough to explicitly resolve deep convection which is a critical subgrid process operating at scales from the microscale to the synoptic scale (Brisson et al., 2016; Fosser et al., 2014; Liu et al., 2017; Prein et al., 2013; Zhu et al., 2019). Thus, RCMs heavily rely on the convection parameterization schemes, leading to a misrepresentation of the diurnal cycle of convective precipitation and a large underestimation of hourly precipitation intensities (Chen et al., 2020a, 2020b; Giorgi, 2019; Wang & Wang, 2019; Zhang et al., 2019). Such underestimation can lead to a considerable bias in projecting future flood risks.…”

Probabilistic Characterization of Extreme Storm Surges Induced by Tropical Cyclones

“…Copulas have been proven to be a powerful tool to assess the joint dependence between hydroclimatic variables regardless of their marginal distributions (Chen et al., 2020b, 2020a; Masina et al., 2015; Moftakhari et al., 2017; Trepanier et al., 2017; Wahl et al., 2015; Wang & Wang, 2019; Wang & Zhu, 2020; Zhang et al., 2019). In order to extend the parametric copulas to higher dimensions, vine copula has been proposed to decompose an arbitrary multivariate probability density into a cascade of bivariate copulas, thereby allowing for flexible simulations of the complex interactions among hydroclimate variables (Aas et al., 2009).…”