Weather and Climate Extremes最新文献

With global warming accelerating, the heavily populated and rapidly urbanized coastal regions of the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) stand as representative areas with mounting concerns about extreme heat stress. This study analyzes differentiated effects of temperature (TAS) and relative humidity (RH) on human heat stress measured by wet-bulb globe temperature (WBGT) in those urban regions based on machine learning and mathematical derivation, while also examining the impacts of global warming and urbanization on prospective heat risks. To generate fine-scale climate projections targeted at the PRD and YRD, two global projections forced by Representative Concentration Pathway (RCP) 8.5 scenario are dynamically downscaled using non-hydrostatic Regional Climate Model version 4.7 (RegCM4), with the urban density and extent updated every year based on Shared Socioeconomic Pathways 5-8.5 (SSP5) scenario, thereby incorporating the transient urban growth into future projections. The bias-corrected downscaled simulations effectively capture the distinct interdependencies between TAS and RH on WBGT across different regions, similar to the observed patterns during the historical period. While the absolute contribution of TAS to WBGT is larger than RH regardless of warming levels and regions, the relative increase in RH becomes more pronounced with warming. Under RCP8.5 scenario, unprecedentedly extreme WBGT is projected to emerge in the far future (2080–2099). In contrast, the effect of urbanization appears to be more dominant in the near future (2030–2049) as urban density under SSP5 scenario is projected to peak around the 2040s and gradually decrease afterwards. The reduction of RH is found in the intensely urbanized areas locally, but it does not significantly lower WBGT because the positive contribution of increased TAS is more dominant. As a result, highly urbanized regions still exhibit higher WBGT compared to other areas. In addition, urban heat island effect is more pronounced for compact areas with high urban density (i.e., PRD) and at night. Despite the smaller temperature increase from urban heat island effect compared to global warming, it can play a critical role in exacerbating heat stress, adding to the already dangerous humid and hot conditions.

Summer extreme heat events happen frequently in northern China during recent decades, which have serious impacts on the society and ecosystem. The present study reveals that there is a synergistic effect of the preceding winter positive mid-latitude North Atlantic SST anomaly (pMNA SSTA) and summer negative tropical eastern Indian Ocean SST anomaly (nTEI SSTA) on strengthening the summer extreme heat events in northern China. The extreme heat events are stronger and more frequent when the two factors cooccur, and the probability of a strengthened extreme heat events is higher, which indicates a synergistic effect of the two factors. The preceding winter pMNA SSTA and summer nTEI SSTA exert their synergistic effect through a series of coupled oceanic-land-atmospheric bridges. The preceding winter pMNA SSTA could lead to an anomalous anticyclone over central Asia via the eastward propagating Rossby wave, which decreases snowfall and the subsequent snow cover there. The negative snow cover anomaly may persist into spring and induce a local anomalous anticyclone in spring via the snow-hydrological effect, which decreases the precipitation over the southern flank of the anomalous anticyclone. The decreased soil moisture persists into summer and induces the eastward propagating Rossby wave, and favors the increase of atmosphere thickness over northern China. The summer nTEI SSTA can also induce the anomalous anticyclone over northern China via the northeastward Rossby wave propagation. Thus, the two factors exhibit evident synergistic effect on the atmospheric circulation anomaly over northern China. The anomalous anticyclone corresponds to the increased atmosphere thickness, which favors the increase of air temperature in northern China and strengthening of extreme heat events. Therefore, the preceding winter pMNA SSTA and summer nTEI SSTA have significant synergistic effect on strengthening the summer extreme heat events in northern China.

This study investigates the hazard of East Asian Atmospheric Rivers (ARs) by applying the AR scale to AR catalog. When AR scale is categorized into five ranks from Category 1 (Cat1) to Category 5 (Cat5) by considering the duration and intensity of each AR event, with Cat5 having the most hazardous hydrological impact, Cat5 ARs are most frequently found in East Asian summer, along the northwestern boundary of the western North Pacific subtropical high. More frequent Cat5 ARs than Cat1 to Cat4 ARs are robustly found in eastern China, Korea, and western Japan, due to the slowly-varying monsoonal flow during the East Asian summer monsoon, which transports a large amount of moisture to the region. Since Cat5 ARs often lead to large event-total rainfall, it explains a close relationship of East Asian summer ARs to heavy rainfall events. This finding helps to better understand the potential hydrological impacts of ARs in East Asia.

Tropical cyclone (TC) peripheral downdrafts and urbanization can promote extreme heatwave (HW) events in the Greater Bay Area (GBA), a highly urbanized coastal area in China. However, the roles of synoptic patterns and urbanization in the HW events remain unclear, particularly for the joint occurrences of the tropical cyclone and heatwave (TC-HW) extremes. Here, we identify three synoptic patterns closely related to TC-HW events, namely: the northeastern Taiwan TC pattern (P4), the southeastern Taiwan TC pattern (P6), and the eastern Taiwan-Philippine Sea TC pattern (P7), as these patterns could enhance HWs through strong downdrafts, strong solar radiation, and low humidity, thereby favoring the maintenance of TC-HW events. Among the three patterns, P6 is most conducive to the occurrence of TC-HW compound events in the GBA. Moreover, the urban-rural temperature disparities under the TC-HW events are unique than those on the days without TC-HW events, i.e., the daily maximum temperature at rural and suburban stations is higher than that at urban stations. This unique feature is the opposite of the urban heat island and is mainly attributed to the rural subsidence warming induced by the TCs and Foehn effects. These results indicate that the spatial distribution of HW in coastal area is substantially modulated by TCs, which is meaningful to understanding the features and underlying mechanism of compound TC-HW events and adapting to their impacts.

2020 and 1998 are the strongest Meiyu years in recent decades. The characteristics of the super-strong Meiyu precipitation and water vapor sources in 2020 and 1998 were compared, and the atmospheric circulation anomalies and the forcing factor SST were examined. (1) In 2020, the Meiyu duration, accumulated precipitation, and number of rainstorm days were greater than in 1998, and the highest since 1961. The Meiyu period in 2020 experienced 11 rainstorm processes. In 1998, a typical “second Meiyu” phenomenon occurred, and the area of heavy rainfall in 1998 was located further southward than that in 2020 (2) The contribution of the Bay of Bengal-South China Sea (BOB-SCS) to the total supply of water vapor in 2020 and 1998 was 43.0% and 42.0%, respectively, i.e., much higher than that of the climatological mean (25.5%). In 2020, the sources that provide most water vapor were the BOB, SCS, and central Pacific Ocean, while in 1998 were the Arabian Sea, BOB, and the western Pacific Ocean. (3) During the Meiyu period in 2020 and 1998, the position of atmospheric circulation pattern “two ridges and one trough” are different. Analysis of the vertical structure revealed that the specific humidity intensity above the area of heavy rainfall in 1998 was weaker than that in 2020, and the low-level convergence zone was further south and not as strong as in 2020. The positions of the western Pacific subtropical high (WPSH) and the western North Pacific anticyclone (WNPAC) in 1998 were both further south than those in 2020, which resulted in the more southerly locations of the southwesterly jet stream and rain belt. It should be pointed out that, the important contributions of the SST anomalies in the equatorial central eastern Pacific and the tropical Indian Ocean to the anomalous WNPAC in 1998 and 2020, respectively.

Accurate nowcasting is critical for preemptive action in response to heavy rainfall events (HREs). However, operational numerical weather prediction models have difficulty predicting HREs in the short term, especially for rapidly and sporadically developing cases. Here, we present multi-year evaluation statistics showing that deep-learning-based HRE nowcasting, trained with radar images and ground measurements, outperforms short-term numerical weather prediction at lead times of up to 6 h. The deep learning nowcasting shows an improved accuracy of 162%–31% over numerical prediction, at the 1-h to 6-h lead times, for predicting HREs in South Korea during the Asian summer monsoon. The spatial distribution and diurnal cycle of HREs are also well predicted. Isolated HRE predictions in the late afternoon to early evening which mostly result from convective processes associated with surface heating are particularly useful. This result suggests that the deep learning algorithm may be available for HRE nowcasting, potentially serving as an alternative to the operational numerical weather prediction model.

During the summer of 2021, the North American Pacific Northwest was affected by an extreme heatwave that broke previous temperature records by several degrees. The event caused severe impacts on human life and ecosystems, and was associated with the superposition of concurrent drivers, whose effects were amplified by climate change. We evaluate whether this record-breaking heatwave could have been foreseen prior to its observation, and how climate change affects North American Pacific Northwest worst-case heatwave scenarios. To this purpose, we use a stochastic weather generator with empirical importance sampling. The generator simulates extreme temperature sequences using circulation analogues, chosen with an importance sampling based on the daily maximum temperature over the region that recorded the most extreme impacts. We show how some of the large-scale drivers of the event can be obtained form the circulation analogues, even if such information is not directly given to the stochastic weather generator.

The Arctic is experiencing amplified climate warming, decreasing sea ice extent, increasingly earlier springtime snowmelt, and a related increase in fire activity. The transition from cold to warm season in the Arctic strongly varies between years, but our understanding of temperature and surface energy budget changes over the springtime is limited. Here we investigate intraseasonal variability of Arctic springtime temperature and surface energy budget components and their interannual trends over 40 years (1981–2020) across the terrestrial Arctic (above 60° N) using ERA5-Land reanalysis data. We found the central and western Siberian regions to have the highest interannual variability in spring temperature anomaly among all Arctic regions during the 40-year period. Also in this region, we discovered the strength increased for heat extremes and decreased for cold extremes when comparing the first and the last 20 years of our study. Peaks in composited extreme temperature and surface energy budget anomalies were observed to occur concurrently, indicating temperature extremes are not driven by surface energy budget components. Lastly, by utilizing power spectrum analyses, we identified the primary driver of temperature anomaly interannual variability to be operating at a 1- to 2-week frequency. Based on our findings and observations in the recent literature, we hypothesize that the observed interannual variability in springtime temperature can be attributed to increased Arctic sea ice decline and an increase in the frequency and strength of associated atmospheric blocking events.