The Atmospheric River (AR), which is defined as a long, narrow, and transient corridor with intensive horizontal moisture transport in the atmosphere, has been recognized as the significant bridge from the global atmosphere-ocean-land coupled climate system to the regional hydrological extremes, given its closed linkage to both the precipitation and large-scale atmospheric circulation system. As a new concept receiving more and more scientific attention in recent years, the study of AR may provide external insight into a thorough inquiry of how the global climate system modulates the regional hydrological impact and help the numerical models to achieve a better simulation and weather prediction, especially for the prediction of extremes with a longer lead time.
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The Atmospheric River (AR), which is defined as a long, narrow, and transient corridor with intensive horizontal moisture transport in the atmosphere, has been recognized as the significant bridge from the global atmosphere-ocean-land coupled climate system to the regional hydrological extremes, given its closed linkage to both the precipitation and large-scale atmospheric circulation system. As a new concept receiving more and more scientific attention in recent years, the study of AR may provide external insight into a thorough inquiry of how the global climate system modulates the regional hydrological impact and help the numerical models to achieve a better simulation and weather prediction, especially for the prediction of extremes with a longer lead time.
As the first and most crucial step of the AR study, a novel AR identification algorithm, which addresses several crucial research gaps among the current AR algorithms, has been developed in this study. For example, new AR metrics are proposed to probe the structure of AR and distinguish the tropical cyclones and other weather systems from the AR database. The all-season global AR catalogs, including historical and future, are constructed based on the algorithm. Also, our algorithm joined the Atmospheric River tracking method intercomparison project and has outstanding performance among dozens of AR algorithms.
After the AR catalog construction, we zoom in on our study to East Asia (EA), the densely populated region suffered a lot from the hydrological extremes (e.g., extreme precipitation, flooding and drought). As AR is a brand new concept for EA and the EA AR catalog based on our algorithm is the first comprehensive and systematic catalog so far, we started our study from the climatological point of view. The eightstage EA AR annual cycle has been distinguished by the self-organizing map (a neural network-based clustering algorithm) automatically. The eight stages synchronize with the seasonal-to-subseasonal variation of precipitation and their associated atmospheric steering. Particularly, in spring, the extension of the western North Pacific subtropical high (WNPSH) to the south China sea, due to the east-west thermal contrast, steers ARs over Southeast China (SEC), inducing spring precipitation. During the Asian summer monsoon season, the spatial variation of the AR main route echoes the northward displacement of WNPSH, coincides with the prevailing monsoonal wind onset and stepwise propagation of the rain belt. The warm moisture-laden air of the wintertime ARs confronts the dry and cold air over SEC, contributing to the winter precipitation. ARs offer substantial potential predictability of (heavy) precipitation days over the mid-latitude from late-spring to early-summer, and over SEC and south Japan from late-winter to early-spring.
When people talk about the hydrological extremes in EA, except for the ARs, another significant contributor cannot be ignored, which is the tropical cyclone (TC). Thus, in this study, we diagnose the compound hydrological impact of the TCs and ARs over the south china coastal (SCC) region, which is a TC hotspot region in China. A significant precipitation regime transition pattern can be observed. In early summer, when the East Asia summer monsoon (EASM) prevail over the south China, the contribution of AR takes the lead. In late summer, the EASM propagate northward, and the contribution of TC becomes more significant. In the transition/hybrid period (from mid-July to early August), the concurrence of ARs and TCs (AR-TC pairs) play an important role. More importantly, the occurrence of ARs will amplify the hydrological risk of TCs significantly, when AR occur before TC landfall, during TC landfall and after the TC dissipation or leaving the SCC, respectively.
Given the significant role of WNPSH to the AR and precipitation over EA, we assess the climatic modulation and long-lead predictability of EA AR and precipitation by regarding the WNPSH as an anchor. Three predictors have been proposed in this study, and they achieve a promisingly high skill in the WNPSH prediction (temporal correlation coefficient is 0.78) with a two-season long lead in the leave-1-year-out cross-validation over the 40-year analysis period. The three predictors defeat the conventional climate indices (e.g., Niño 3.4/4 or Indian ocean dipole), even though the El Niño-Southern Oscillation (ENSO) and IOD are the major sources of interannual variability and seasonal prediction of EA boreal summer weather condition. This discordance provides external evidence, different insights, and cautions people to revisit and rethink the complex atmosphere-ocean coupled response to the diverse tropical SST patterns, in terms of the long-lead prediction of boreal summer EA weather condition.
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