Investigating the physical mechanism behind the formation of summer heat-drought weather (HDW) in the Yangtze River Basin (YRB) holds significant importance for predicting summer precipitation and temperature patterns in the region as well as disaster mitigation and prevention. This study focuses on spatiotemporal patterns of July–August (JA) HDW in the YRB from 1979 to 2022, which is linked partially to the preceding May–June (MJ) Antarctic Oscillation (AAO). Key findings are summarized as follows: (1) The MJ AAO displays a marked positive correlation with the JA HDW index (HDWI) in the southern part of upper YRB (UYRB), while showing a negative correlation in the area extending from the Han River to the western lower reaches of the YRB (LYRB); (2) The signal of MJ AAO persists into late JA through a specific pattern of Sea Surface Temperature anomalies in the Southern Ocean (SOSST). This, in turn, modulates the atmospheric circulation over East Asia; (3) The SST anomalies in the South Atlantic initiate Rossby waves that cross the equator, splitting into two branches. One branch propagates from the Somali-Tropical Indian Ocean, maintaining a negative-phased East Asia–Pacific (EAP) teleconnection pattern. This enhances the moisture flow from the Pacific towards the middle and lower reaches of the Yangtze River Basin (MYRB-LYRB). The other branch propagates northward, crossing the Somali region, and induces a positive geopotential height anomaly over Urals-West Asia. This reduces the southwesterlies towards the UYRB, thereby contributing to HDW variabilities in the region. (4) Partial Least Squares Regression (PLSR) demonstrated predictive capability for JA HDW in the YRB for 2022, based on Southern Ocean SST.

A previous study found that the September–October (SO) Antarctic Oscillation (AAO) shows an out‐of‐phase variation of late January–February (JF) wet and cold weather (wet–cold) in southern China. This study explored the underlying mechanism and found that Antarctic sea ice may be responsible partially for such a relationship. The SO AAO stimulates the Antarctic sea‐ice dipole pattern (ADP) anomalies, which induce the anomalous convective precipitation of the Inter‐Tropical Convergence Zone (ITCZ) in Latin America‐Atlantic (LAA) and Western Pacific (WP) in following JF. The anomalous convections over the ITCZ regions trigger planetary wave dispersions from the tropical LAA and WP to adjust the East Asian atmospheric circulation. Through the bridge of anomalous ITCZ, an increased JF ADP decreases the invasion of both northerly cold air flow and southwesterly moist airflow into southern China, resulting in the dissipation of wet–cold and vice versa. Furthermore, the variation of JF ADP is well correlated with the in‐phased polar vortex, connecting to the stratospheric Quasi‐Biennial Oscillation (QBO). The variation of JF QBO induces the anomalous convection over the tropical North Atlantic and ITCZ in LAA and WP, in turn modulating the contemporary invasion of cold and wet airflow into southern China by Rossby wave dispersion, subsequently wet–cold in southern China. To summarize, JF ADP affects the simultaneous wet–cold in southern China through the anomalous ITCZ. The turning phase of QBO modulates such correlation by inducing the anomalous ITCZ correlated with ADP and the anomalous convection due to QBO itself over tropical North Atlantic.

The leading mode of the singular value decomposition (SVD) of geopotential height (GPH) and boundary layer structure index (BLSI).

Based on the Empirical Orthogonal Functions decomposition of the Sea level pressure (SLP) anomalies over the southern extratropic, the second pattern is defined as a southern second mode (SMD2), and its corresponding principal component is named the southern second mode index. The most significant feature of SMD2 is the seesaw pattern of SLP, which indicates the opposite phase of the Amundsen Low and surroundings. The spatial mode of EOF2 varies in different months but EOF2 in March is most similar to SMD2. This study shows that the March SMD2 is negatively associated with the June precipitation in southern China. The mechanisms can be briefly summarized as follows: The March SMD2 is negatively correlated with sea surface temperature (SST) in the midlatitude. Surface wind changes can affect surface heat flux and heat transport, leading to the SST changes. The SST anomaly acts as the “oceanic bridge” to preserve the March SMD2 signal and persists into late summer. The key area of SST is associated with Maritime Continent convective activity anomalies, which can excite and maintain the Pacific‐Japan pattern resulting in precipitation anomalies in south China. Through sea‐air interactions, the SMD2 can affect the precipitation in southern China across seasons. This work is expected to provide a new perspective for forecasting summer precipitation in southern China.

The Antarctic Oscillation (AAO) is the dominant mode of the southern extratropical atmospheric mass variability which has potential influences on the Northern Hemisphere (NH). This study reveals a significantly negative correlation between the September-October (SO) AAO index and the occurrence rate of following January-February (JF) wet and cold weather in the Middle and Lower Reaches of Yangtze River Basin (MLRY) in China. The latter is quantified by a Precipitation-Temperature (PT) Index. JF PT is modulated by both northerly air flow in the lower troposphere and southerly air flow in the lower-middle troposphere. The SO AAO stimulates Southern Ocean Dipole (SOD) pattern-like SST anomalies, which induces a North Atlantic Oscillation (NAO)-like atmospheric response in the following JF through ocean-air interaction. As for the northerly flow, the JF NAO-like pattern triggers an eastward propagating wave train, influencing the intensity of East Asian Winter Monsoon (EAWM) and subsequently the northerly cold flow to MLRY. As for southerly flow, the variation of JF SOD regulates the local meridional cell, in turn modulating the Middle East Jet Stream (MEJS) along with the NAO-like pattern, influencing the intensity of precipitation and the wet and warm flow over Southern China and the adjacent regions. In addition to the tropospheric processes, the stratospheric Quasi Biennial Oscillation (QBO) serves as the ‘bridge’ for linking SOD to NH climate, inducing the JF PT response to SOD SST. To summarize, SO AAO affects the JF PT in MLRY by modulating both cold-dry northerly air flow and warm-wet southerly air flow through ocean-atmosphere interactions and stratospheric pathway.

The Antarctic Oscillation (AAO) is the dominant mode of the southern extratropical atmospheric mass variability which has potential influences on the Northern Hemisphere (NH). This study reveals a significantly negative correlation between the September–October (SO) AAO index (AAOI) and the occurrence rate of following January–February (JF) wet-cold weather (the latter is quantified by a Precipitation-Temperature (PT) Index) in Southern China (SC) especially the Middle and Lower Reaches of Yangtze River Basin (MLRY). JF PT of SC (SCPT) is modulated by both northerly air flow in the lower troposphere and southerly air flow in the middle-lower troposphere. The SO AAO stimulates Southern Ocean Dipole (SOD) pattern-like sea surface temperature anomalies (SST anomalies: SSTA), which induces a Northern Atlantic Oscillation (NAO)-like atmospheric response along with the tropical northern Atlantic (NA) precipitation anomalies in the following JF through ocean-air interaction. As for wet-cold conditions, there exists an eastward propagational wave train from the tropical-subtropical NA to the East Asian atmospheric patterns, in turn influencing the intensity of East Asian Winter Monsoon (EAWM) and Middle East Jet Stream (MEJS), subsequently the penetration of northerly cold flow and southerly wet flow into MLRY. The variation of JF SOD also regulates the local meridional-vertical cells, in turn influencing the intensity of precipitation over the tropical NA and SC, accompanied with the penetration of wet-warm flow into SC and the adjacent regions. In addition to the tropospheric processes, the stratospheric Quasi Biennial Oscillation (QBO) may serve as the stratospheric pathway for the impact of SOD on NH climate, inducing/reducing the JF SCPT response to SOD SST. To summarize, SO AAO affects the JF SCPT by modulating both dry-cold northerly air flow and wet-warm southerly air flow through tropospheric and stratospheric pathway.

利用2013～2019年武汉市生态环境局监测数据、L波段雷达探空资料、NCEP/NCAR逐日再分析资料,对夏季和秋冬季武汉地区污染日的大气污染特征、边界层结构、环流形势、物理量场进行研究,建立了武汉地区大气污染的天气概念模型.主要结论如下:(1)武汉市空气质量具有季节性变化特征,大气污染程度四季分布表现为冬＞秋＞春＞夏.夏季首要污染物是臭氧,冬季首要污染物是PM2.5.(2)比较挑选出的夏季清洁日和污染日的气象要素特征,污染日逆温的平均强度约为清洁日的一倍,逆温底高一般在600 m以下,空气质量一般为轻度-中度污染;静风频率(37.1％)明显高于清洁日的静风频率(2.9％);污染日平均风速小(0.8 m/s),边界层内相对湿度较低.同样比较秋冬季两类天气的气象要素特征,污染日逆温底高低、厚度小,不及清洁日的一半,不利于污染物的扩散,易出现重度污染天气.静风频率(20％)高于清洁日的静风频率(7.5％),风速小(1.6 m/s),污染日边界层内呈明显上千下湿的格局.(3)建立了夏季大气污染的天气概念模型,污染日副高偏弱位置偏东,长江流域易少雨干旱;地面我国东部大范围地区处于均压场中,武汉地区为偏东北异常小风,不利于大气污染物的扩散.(4)建立了秋冬季大气污染的天气概念模型,长江流域环流平直少波动,配合地面弱低压的天气形势和较强的逆温使得大气污染物聚集在近地面.蒙古冷高压强度偏弱,使得入侵我国的冷空气强度偏弱;武汉地区为偏北小风,对雾霾的移除和稀释扩散作用差.该研究结论可供大气污染预测预警研究和环境管理部门大气污染的联防联控参考.

The original version of this article unfortunately contains some mistakes.

Wind, temperature, relative humidity and aerosol mass concentration were monitored simultaneously in Wuhan, China. Several observations were found after analyzing the physical fields of these data. It was obvious that weak pressure and saddle patterns occurred during fog-haze episodes. An inversion layer occurred before heavy fog-haze events and became thicker during fog-haze events. The boundary layer structure index was relatively higher during fog-haze days and had a significant negative correlation with the planetary boundary layer height and turbulence parameters. Wind speeds were generally less than 5 m/s and rarely exceeded this speed on the selected polluted days. Turbulence variation characteristics had special representations, especially before foghaze events. Turbulence intensities always reached abnormal peak values before fog-haze processes, while the intensities remained steady before and during pollution processes with low relative humidity. Both the turbulence kinetic energy and momentum flux decreased to near zero before heavy fog-haze processes. Momentum flux often presented abnormal disturbances before heavy fog-haze processes. These disturbances were often in an active phase before and during pollution processes with low relative humidity, a situation that is not similar to fog-haze events that maintained high relative humidity. There was a feedback mechanism between solar radiation and aerosol mass concentration, and the occurrence of turbulence anomalies may be related to the regulation of atmospheric circulation by wave-flow interaction. The results presented in this study suggest that the turbulence parameters, which display anomalies before the occurrence of heavy fog-haze processes under the background of inversion layers and stable atmospheric patterns, can serve as a means of predicting disastrous weather conditions such as fog-haze pollution.

The original version of this article unfortunately contained an error in the Figure 2 caption.

Ambient air quality monitoring data and radar tracking sonde data were used to study the atmospheric boundary layer structure (ABLS) and its changing characteristics over Wuhan. The boundary layer structure index (BLSI), which can effectively describe the ABLS, was accordingly developed and its ability to describe the near-surface air quality was analyzed. The results can be summarized as follows. (1) An analysis of the ABLS during seriously polluted cases revealed that the ABLS was usually dry and warm with a small ventilation index (VI); meanwhile, the ABLS during clean cases was usually wet and cold with a large VI. (2) The correlation between the air quality and BLSI at 100~300 m was good and passed the confidence level limit at 99%. Moreover, the correlation coefficient increased with the altitude at 10~250 m and showed a downward trend at 250~500 m. The correlation between the BLSI at 250 m and the ground air quality was the most significant (r = 0.312), indicating that the layer ranging from 0 to 250 m is essential for determining the ground air quality. (3) The BLSI considers both the vertical diffusion capability and horizontal removal capability of the atmosphere. Therefore, it is highly capable of describing the ABLS and the ground air quality.

Measurement of PM2.5 concentration, dry and wet deposition of water-soluble inorganic ions (WSII) and their deposition flux was carried out. During sampling, a total number of 31 samples of PM2.5, five wet deposition samples and seven dry deposition samples were collected. The analyses results showed that the average concentration of PM2.5 was 122.95 µg/m3 whilst that of WSII was 51.63 µg/m3, equivalent to 42% of the total mass of PM2.5. The correlation coefficients between WSII in samples of PM2.5 was significant (r = 0.50 and p-value of 0.0019). Ions of SO 4 2 − , NO 3 − , Cl − , and NH 4 + were dominant in the entire samples (PM2.5, dry and wet depositions), nevertheless, the average concentration of both SO 4 2 − and Cl − were below the China environmental quality standard for surface water. The ratio of dominant anions in wet deposition ( SO 4 2 − / NO 3 − ) was 1.59, whilst that for dry deposition ( SO 4 2 − / Cl − ) was 1.4, indicating that acidity was mainly derived from sulphate. In the case of dominant cations, the dry and wet deposition ratios ( Ca 2 + / NH 4 + ) were 1.36 and 1.37, respectively, suggesting the alkaline substances were mainly dominated by calcium salts. Days with higher recorded concentrations of PM2.5 were accompanied by dry and warm boundary layer structure, weak low-level wind and strong inversion layer.

The original version of this article unfortunately contained an error in the Figure 2 caption.

In this study, we investigated six air pollutants from 21 monitoring stations scattered throughout Wuhan city by analyzing meteorological variables in the atmospheric boundary layer (ABL) and air mass backward trajectories from HYSPLIT during the pollution events. Together with this, ground meteorological variables were also used throughout the investigation period: 1 December 2015 to 30 November 2016. Analysis results during this period show that the city was polluted in winter by PM2.5 (particulate matter with aerodynamics of less than 2.5 microns) and in summer by ozone (O3). The most polluted day during the investigation period was 25 December 2015 with an air quality index (AQI) of 330 which indicates ‘severe pollution’, while the cleanest day was 26 August 2016 with an AQI of 27 indicating ‘excellent’ air quality. The average concentration of PM2.5 (O3) on the most polluted day was 265.04 (135.82) µg/m3 and 9.10 (86.40) µg/m3 on the cleanest day. Moreover, the percentage of days which exceeded the daily average limit of NO2, PM10, PM2.5, and O3 for the whole year was 2.46%, 14.48%, 23.50%, and 39.07%, respectively, while SO2 and CO were found to be below the set daily limit. The analysis of ABL during PM2.5 pollution events showed the existence of a strong inversion layer, low relative humidity, and calm wind. These observed conditions are not favorable for horizontal and vertical dispersion of air pollutants and therefore result in pollutant accumulation. Likewise, ozone pollution events were accompanied by extended sunshine hours, high temperature, a calm wind, a strongly suspended inversion layer, and zero recorded rainfall. These general characteristics are favorable for photochemical production of ozone and accumulation of pollutants. Apart from the conditions of ABL, the results from backward trajectories suggest trans-boundary movement of air masses to be one of the important factors which determines the air quality of Wuhan.

利用杭瑞高速公路洞庭湖大桥北岸测风塔的梯度风观测资料、三维超声风温仪资料以及岳阳气象站提供的逐小时气溶胶浓度和能见度观测资料,对湖南岳阳2017年1月28日的一次重度霾天气中的重污染过程的近地层物理量变化特征进行了分析,结果表明:(1)重污染来临前约130 min即28日01:50(北京时间,下同),水平风速、垂直风速、高低层风切变都出现零值,大气处于静稳状态.重污染结束前180 min即28日09:00,上述物理量和高低层温度切变出现零值.(2)湍流强度在重污染来临前有强烈异常信号,其中水平纵向湍流强度异常信号最明显,于重污染发生前130 min出现异常峰值4.15,重污染结束前180 min出现异常峰值3.24.(3)湍流动能和动量通量都在重污染来临前130 min接近0.0 m2/s2,即湍流交换最弱,有利于污染物在近地面的持续堆积和重污染过程的发生.近地层的平均物理量和湍流特征量的异常信号的出现时间有较好的一致性,即出现在重污染来临前的130 min和结束前的180 min.揭示了重度霾污染天气的近地层物理量时间变化规律,着重分析了霾污染的生成、发展、消亡全过程的边界层湍流异常的前期信号,为深入认识霾污染天气进行有益的探索并为这类天气的预测预警提供科学依据.

Using the 3D ultrasound anemometer data from wind measuring tower at Dongting Lake Bridge of Hangfeng highway and hourly precipitation at Yueyang weather station, several conclusions have been reached from the analysis of physical field characteristics in near sur-face layer during three different periods of a heavy rainstorm event from 3 to 5 July 2016. The results show that (1) the physical fields in near surface layer appeared abnormal within 180-230 minutes before the heavy rainfall and within 10-50 minutes before the end of the heavy rain-fall. Comparing with the occurrence of anomalous features before the end of heavy rainfall, the occurrence of anomalous features before the heavy rainfall was earlier. During the heavy rainstorm event, wind speed, turbulence intensity and turbulent kinetic energy had large abnormal values, and at 230 minutes before the heavy rainfall, the wind speed appeared the max value of 4.875 m·s-1. At 50 minutes before the end of heavy rainfall, turbulence intensity showed the max value, turbulence intensity in the direction of horizontal downwind was 1.255, of horizon-tal crosswind was 1.173, of vertical was 0.195. (2) Before the onset and the end of the heavy rainfall, the PSD (Power Spectral Density) in-creased. The frequency of the highest value of PSD shifted to the left in the whole heavy rainfall event. (3) Compared to the scale of eddy dur-ing and after the heavy rainfall, the scale of eddy before the heavy rainfall was the largest one. The scale during the heavy rainfall was less than the one before, and the one after the heavy rainfall was the lowest among the three different periods. The large-scale eddy with low fre-quency had provided energy for the turbulent activity of the heavy rainstorm event.

Air pollution data, air quality index (AQI) data and L-band sounding data of Wuhan City from January 1 to February 28, 2015, were used in this study. Since air quality is mainly determined by the condition of the atmospheric boundary layer structure (ABLS), a detailed analysis was carried out in order to determine the evolution of this layer and its relationship with air quality. During the investigation period, the highest value of AQI was 307 on January 26 and the lowest was 33 on February 28 indicating ‘severe pollution’ and ‘excellent’ air quality, respectively. The concentrations of PM2.5 during the days with the highest and lowest AQI were 142.61 and 9.78 μg/m3, respectively. The percentage of days in which the ratio of PM2.5 in PM10 was more than half was 83.05% which means that the greater portion of pollutants were composed of smaller particles. Moreover, four PM2.5 episodes (three or more consecutive days of PM2.5 ≥ 75 μg/m3) were identified and the average percentage of elementary carbon (EC) in PM2.5 during episode 1 (prior to the episode) was 6.274% (6.276%), episode 2 was 5.634% (7.174%), and episode 4 was 4.067% (7.785%). Higher concentrations of EC prior to episodes suggest biomass burning to be one of the reasons for episodes occurrence. Analysis of the ABLS during polluted days show that the boundary layer was dry and warm and had weak low-level wind and dominance of northerly winds. A different scenario is seen on clean days as the boundary layer is observed to be wet and cool, and there is dominance of strong winds. Back trajectory analysis results show that polluted days were dominated by air mass from north China while on clean days, the dominant air masses were from East China Sea, Mongolia, and west China.