Wang, Fan

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management.

Heat waves and air pollution extremes exert compounding effects on human health and food security and may worsen under future climate change. On the basis of reconstructed daily O3 levels in China and meteorological reanalysis, we found that the interannual variability of the frequency of summertime co-occurrence of heat wave and O3 pollution in China is regulated mainly by a combination of springtime warming in the western Pacific Ocean, western Indian Ocean, and Ross Sea. These sea surface temperature anomalies impose influences on precipitation, radiation, etc., to modulate the co-occurrence, which were also confirmed with coupled chemistry–climate numerical experiments. We thus built a multivariable regression model to predict co-occurrence a season in advance, and correlation coefficient could reach 0.81 (P < 0.01) for the North China Plain. Our results provide useful information for the government to take actions in advance to mitigate damage from these synergistic costressors.

Heat waves and air pollution are two prominent threats, both of which have been reported to cause public health and ecosystem crises, particularly under rapid urbanization and global warming (1, 2). Heat waves, defined as consecutive days of excessively high atmosphere-related heat stress (3, 4), adversely influence human health by impacting respiratory and cardiovascular systems. Heat waves are linked with high O3 episodes that harm human health and vegetation (5–7). In warm seasons, heat waves and extreme O3 events often occur simultaneously due to common driving meteorological conditions, i.e., stagnant high-pressure systems that favor accumulation of heat and O3 precursors (8). Besides, complex chemistry–climate feedbacks through biogenic emissions (source) and uptake by plants (sink) could exacerbate co-occurrence of heat wave and O3 extremes (9). It is imperative to understand driving factors for the co-occurrence of heat and O3 extremes, as accumulating evidence suggests amplified health outcomes beyond the sum of individual effects (10–12). Analitis et al. (13) reported that the number of daily deaths during heat wave episodes was 54% higher on high O3 days compared with low O3 days.

Previous studies have linked occurrences of heat waves or O3 extremes, separately, with large-scale atmospheric circulation or sea surface temperature (SST) anomalies (14–20). For instance, Zhu et al. (17) demonstrated that the frequency and variability of summertime heat waves over North America was closely associated with SST anomalies in the tropical Atlantic and tropical western Pacific in spring and El Niño–Southern Oscillation phase change. Shen and Mickley (21) showed that O3 concentration in Eastern United States was linked with warm tropical Atlantic SST and cold northeast Pacific SST, as well as positive sea-level pressure (SLP) anomalies over central Pacific and negative SLP anomalies over the Atlantic and North America. However, the climate factors modulating the co-occurrence of heat and O3 extremes at a regional level remain unclear and had only been the subject of limited studies (8, 22–24).

With roughly one-sixth of the world’s population and rapid energy-intensive development, China is facing the dual challenge of air pollution and climate change (25, 26). Central and Eastern China, especially the North China Plain (NCP), experienced improved PM2.5 air quality over past years due to the implementation of the most stringent clean air policy, but now suffers from largest increases in summertime O3 exposure (27). O3 concentrations in the NCP enhanced at almost twice the average pace across China (28). An amplified upward trend of the joint occurrences of heat and O3 extremes has been identified in China over 2013 to 2020 (29). Understanding the driving climate factors for its interannual variability would contribute to long-term planning of control of costressors. Characterizing interannual variability also enables prediction which could allow sufficient time for mitigation of the interactive damages from joint exposure (21, 30–33). Previously, we demonstrated the possibility of seasonal prediction of wintertime aerosol pollution in India (34). Considering the strong linkages between O3 level and climate patterns, we argue here that it may also be possible to predict co-occurrence of heat waves and O3 pollution, potentially up to several years in advance, considering the active efforts in developing reliable seasonal (months ahead) and even longer prediction of climate variability (35).

In this study, we aim to identify leading patterns that control the spatiotemporal variability of occurrence frequency (days in a year) of joint heat wave and O3 pollution events (HWOP). We focus on Central and Eastern China (17.5°N to 47.5°N, 98°E to 125°E), where over 80% Chinese population reside and co-occurrences of HWOP events are prominent. Climate drivers are identified by empirical orthogonal function (EOF), which decomposes historical spatiotemporal variations of HWOP frequency that inferred with atmospheric reanalysis and reconstructed daily O3 datasets. Findings from statistical analyses are further supported by numerical model experiments using the state-of-the-art Community Earth System Model version 2.1.3 (CESM v2.1.3). Encouraged by the robustness of the identified teleconnections between co-occurrence events and SST anomalies, we further build a regression-based statistical model to predict summertime HWOP a season in advance, improving our capability in the management of these important health and vegetation costressors.