Advances in Atmospheric Sciences最新文献

A cold vortex occurred over the northeastern Tibetan Plateau (TP) on 27 April 2018 and subsequently brought excessive rainfall to the spring farming area in southern China when moving eastward. This study investigates the genesis mechanism of the cold TP vortex (TPV) by diagnosing reanalysis data and conducting numerical experiments. Results demonstrate that the cold TPV was generated in a highly baroclinic environment with significant contributions of positive potential vorticity (PV) forcing from the tropopause and diurnal thermodynamic impact from the surface. As a positive PV anomaly in the lower stratosphere moved towards the TP, the PV forcing at the tropopause pushed the tropospheric isentropic surfaces upward, forming isentropic-isplacement ascent and reducing static stability over the TP. The descent of the tropopause over the TP also produced a tropopause folding over the northeastern TP associated with a narrow high-PV column intruding downwards over the TPV genesis site, resulting in ascending air in the free atmosphere. This, in conjunction with the descending air in the valley area during the night, produced air stretching just at the TPV genesis site. Because the surface cooling at night increased the surface static stability, the aforementioned vertical air-stretching thus converted the produced static stability to vertical vorticity. Consequently, the cold TPV was generated over the valley at night.

Here, a nonhydrostatic alternative scheme (NAS) is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core. The NAS is designed to replace this solver, which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time. Recent advances in machine learning (ML) provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship. In this study, an ML approach called a neural network (NN) was adopted to select leading input features and develop the NAS. The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting (WRF) model. The forward time difference of the nonhydrostatic tendency was used as the target variable, and the five selected features were the nonhydrostatic tendency at the last time step, and four hydrostatic variables at the current step including geopotential height, pressure in two different forms, and potential temperature, respectively. Finally, a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution, which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency. Corrected by the NN-based NAS, the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias, anomaly root-mean-square error, and the error of the wave spatial pattern, which proves the feasibility and superiority of this scheme.

In relatively coarse-resolution atmospheric models, cumulus parameterization helps account for the effect of subgrid-scale convection, which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation. In this study, the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model, i.e., the Yin-Yang-grid Unified Model for the Atmosphere. Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing. Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger. By reducing the occurrence of convection during peak solar radiation hours, the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak. In addition, the revised scheme enhanced the simulation capability of the precipitation probability density function, such as increasing the extremely low- and high-intensity precipitation events and decreasing small and moderate rainfall events, which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.

The Pearl River Delta (PRD), a tornado hotspot, forms a distinct trumpet-shaped coastline that concaves toward the South China Sea. During the summer monsoon season, low-level southwesterlies over the PRD’s sea surface tend to be turned toward the west coast, constituting a convergent wind field along with the landward-side southwesterlies, which influences regional convective weather. This two-part study explores the roles of this unique land–sea contrast of the trumpet-shaped coastline in the formation of a tornadic mesovortex within monsoonal flows in this region. Part I primarily presents observational analyses of pre-storm environments and storm evolutions. The rotating storm developed in a low-shear environment (not ideal for a supercell) under the interactions of three air masses under the influence of the land–sea contrast, monsoon, and storm cold outflows. This intersection zone (or “triple point”) is typically characterized by local enhancements of ambient vertical vorticity and convergence. Based on a rapid-scan X-band phased-array radar, finger-like echoes were recognized shortly after the gust front intruded on the triple point. Developed over the triple point, they rapidly wrapped up with a well-defined low-level mesovortex. It is thus presumed that the triple point may have played roles in the mesovortex genesis, which will be demonstrated in Part II with multiple sensitivity numerical simulations. The findings also suggest that when storms pass over the boundary intersection zone in the PRD, the expected possibility of a rotating storm occurring is relatively high, even in a low-shear environment. Improved knowledge of such environments provides additional guidance to assess the regional tornado risk.

Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation patterns exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter remarkably changes, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. During the early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entirety of eastern China, thus contributing to more precipitation there. During the late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in differing water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly the opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central-eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China.

Air pollution in China covers a large area with complex sources and formation mechanisms, making it a unique place to conduct air pollution and atmospheric chemistry research. The National Natural Science Foundation of China’s Major Research Plan entitled “Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China” (or the Plan) has funded 76 research projects to explore the causes of air pollution in China, and the key processes of air pollution in atmospheric physics and atmospheric chemistry. In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally, an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan. This project has classified and integrated these data, forming eight categories containing 258 datasets and 15 technical reports in total. The integration project has led to the successful establishment of the China Air Pollution Data Center (CAPDC) platform, providing storage, retrieval, and download services for the eight categories. This platform has distinct features including data visualization, related project information querying, and bilingual services in both English and Chinese, which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research. Air pollution control in China, especially in the past decade, is undeniably a global exemplar, and this data center is the first in China to focus on research into the country’s air pollution complex.

Globally, 2023 was the warmest observed year on record since at least 1850 and, according to proxy evidence, possibly of the past 100 000 years. As in recent years, the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world. Here, we provide an overview of those of 2023, with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change. We also highlight emerging features associated with some of these extreme events. Hot extremes are occurring earlier in the year, and increasingly simultaneously in differing parts of the world (e.g., the concurrent hot extremes in the Northern Hemisphere in July 2023). Intense cyclones are exacerbating precipitation extremes (e.g., the North China flooding in July and the Libya flooding in September). Droughts in some regions (e.g., California and the Horn of Africa) have transitioned into flood conditions. Climate extremes also show increasing interactions with ecosystems via wildfires (e.g., those in Hawaii in August and in Canada from spring to autumn 2023) and sandstorms (e.g., those in Mongolia in April 2023). Finally, we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

We investigate the characteristics and mechanisms of persistent wet-cold events (PWCEs) with different types of cold-air paths. Results show that the cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part. The pattern of single-station frequency of the PWCEs are “Yangtze River (YR) uniform” and “east–west inverse”. The YR uniform pattern is the dominant mode, so we focus on this pattern. The cold-air paths for PWCEs of the YR uniform pattern are divided into three types—namely, the west, northwest and north types—among which the west type accounts for the largest proportion. The differences in atmospheric circulation of the PWCEs under the three types of paths are obvious. The thermal inversion layer in the lower troposphere is favorable for precipitation during the PWCEs. The positive water vapor budget for the three types of PWCEs mainly appears at the southern boundary.

This study investigated the effects of upper-level descending inflow (ULDI) associated with inner-eyewall convection on the formation of the moat in tropical cyclones (TCs) with secondary eyewall formation (SEF). In our numerical experiments, a clear moat with SEF occurred in TCs with a significant ULDI, while no SEF occurred in TCs without a significant ULDI. The eyewall convection developed more vigorously in the control run. A ULDI occurred outside the inner-eyewall convection, where it was symmetrically unstable. The ULDI was initially triggered by the diabatic warming released by the inner eyewall and later enhanced by the cooling below the anvil cloud. The ULDI penetrated the outer edge of the inner eyewall with relatively dry air and prevented excessive solid-phase hydrometeors from being advected further outward. It produced extensive sublimation cooling of falling hydrometeors between the eyewall and the outer convection. The sublimation cooling resulted in negative buoyancy and further induced strong subsidence between the eyewall and the outer convection. As a result, a clear moat was generated. Development of the moat in the ongoing SEF prevented the outer rainband from moving farther inward, helping the outer rainband to symmetrize into an outer eyewall. In the sensitivity experiment, no significant ULDI formed since the eyewall convection was weaker, and the eyewall anvil developed relatively lower, meaning the formation of a moat and thus an outer eyewall was less likely. This study suggests that a better-represented simulation of inner-eyewall convective structures and distribution of the solid-phase hydrometeors is important to the prediction of SEF.

Artificial intelligence (AI) models have significantly impacted various areas of the atmospheric sciences, reshaping our approach to climate-related challenges. Amid this AI-driven transformation, the foundational role of physics in climate science has occasionally been overlooked. Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics, rather than an “either/or” scenario. Scrutinizing controversies around current physical inconsistencies in large AI models, we stress the critical need for detailed dynamic diagnostics and physical constraints. Furthermore, we provide illustrative examples to guide future assessments and constraints for AI models. Regarding AI integration with numerical models, we argue that offline AI parameterization schemes may fall short of achieving global optimality, emphasizing the importance of constructing online schemes. Additionally, we highlight the significance of fostering a community culture and propose the OCR (Open, Comparable, Reproducible) principles. Through a better community culture and a deep integration of physics and AI, we contend that developing a learnable climate model, balancing AI and physics, is an achievable goal.