Accurately identifying stratospheric wave reflection is crucial for understanding wintertime extremes. Using ERA5 (fifth generation ECMWF atmospheric reanalysis) data from 1979 to 2023, this study establishes a novel objective index for regional planetary wave reflection based on the leading EOF (empirical orthogonal function) mode of 100-hPa eddy heat flux over North Pacific–North America. This mode reveals a significant “Aleutian–North America” dipole structure. Diagnostic analyses demonstrate that reflection events are triggered by the deceleration of background westerlies and the reversal of vertical wind shear in the upper stratosphere, which cause the refractive index to become negative. This creates a critical layer that forces planetary waves to refract downward. Upon entering the troposphere, the reflected waves amplify Alaskan blocking and the North American trough through linear interference, inducing a robust positive PNA (Pacific–North American) pattern. This circulation configuration steers polar cold air southward, resulting in widespread and persistent cold anomalies across North America. The proposed index effectively captures these dynamical processes, overcoming the limitations of previous fixed-region metrics.

Variations of sea ice in the Barents-Kara seas attracts global attention because of its both local and remote climate impacts.The accurate prediction of Barents-Kara Seas sea ice con-centration anomalies(BKSICA)is critically important for science and economics.This study em-ploys four machine learning(ML)models,including extreme learning machine(ELM),nonlinear au-toregressive exogenous model(NARX),long short-term memory(LSTM),and extreme gradient boosting(XGBoost),combined with empirical orthogonal function(EOF)methods to predict season-al BKSICA.The ML models are trained based on the 1979-2014 oceanic and meteorological data,and are then used to predict the BKSICA for 2015-2022.Results indicate that the ML models pro-vide reliable prediction up to 6 months,achieving a PCC over 0.6.Such prediction skill outperforms the state-of-the-art dynamical model at 2-6 months'prediction,although it is slightly less accurate at 1 month lead time.Among them,the ELM exhibits the optimal performance,attaining a regional average Pearson correlation coefficient(PCC)of 0.18 higher than the ECMWF at a 6-month lead time.The physical interpretability of the ML models is also analyzed,showing that subsurface ocean heat content anomalies to be a critical new predictor for BKSICA.These results highlight the effectiveness of the ML models in seasonal sea ice prediction.

Cascading compound extreme weather events, characterized by sequential occurrences of distinct extremes such as heatwaves, floods or droughts, pose increasing risks in a warming climate. However, existing approaches for identifying such events focus either on temporal persistence or spatial coherence alone, and are thus unable to identify the most severe events with both characteristics. Here, we propose a new approach based on dynamical systems theory that treats variables as coupled systems, to enable a mechanistic understanding of their interactions. We illustrate the application of the method to temperature and relative humidity data during the period 1979–2020, identifying cascading hot-dry extremes over the Mississippi Region, southeastern China and France. While these events are controlled by different large-scale climate modes and blocking patterns, 10 of 26 events occurred during rapid transitions (< 12 months) from El Niño to La Niña. In China, these transitional events were consistently preceded by heavy rainfall approximately two weeks earlier. Key drivers include the prolonged presence of the western north Pacific subtropical high and land-atmosphere feedbacks. Our findings uncover the speed and severity of wet-to-dry transitions within as little as two weeks during El Niño transition years, and the need for a greater understanding of their driving mechanisms.

Mesoscale Convective Complexes (MCCs) are major convective weather systems occurring in midlatitude regions, typically associated with significant weather phenomena such as heavy rainfall, thunderstorms, strong winds, and hail. Based on the cloud-top temperature (CTT) data of the FY-2G satellite, and through multi-threshold screening combined with morphological analysis, an automated algorithm for MCC identification and tracking was developed. The algorithm is then applied to generate an hourly dataset of MCC variables over mainland China from June 2015 to December 2024. The dataset encompasses variables describing the spatial extent of the cold-core region (CTT < - 52 degrees C) of MCCs, the minimum cloud-top temperature within the cold cloud shields, and the geographic coordinates (longitude and latitude) of the centroids of the cold cloud shields. This work also conducts a preliminary analysis of the spatial and temporal distribution characteristics of MCCs over mainland China based on the dataset. Results indicate that MCCs occur more frequently in Southwest China than in other regions of the country, and over 70 % of MCC events occur in summer both in Southwest China and mainland China as a whole. Moreover, MCC frequency in Southwest China exhibits significant interannual variability. The dataset is publicly available at 10.5281/zenodo.17349888 (Xu, 2025).

Climate change and the intensification of extreme weather events are exerting increasing pressure on electricity systems worldwide. In Southeastern China, where power systems experience pronounced temperature and humidity fluctuations, understanding meteorological influences on electricity generation is essential for climate-resilient energy planning. This study develops a machine learning framework based on the eXtreme Gradient Boosting algorithm to capture nonlinear relationships between meteorological variables, socioeconomic factors, and daily electricity output. Model interpretability, enhanced through Shapley Additive Explanations and partial dependence plot analyses, reveals key drivers and sensitivity thresholds, including a pronounced increase in electricity demand when temperatures exceed approximately 25 degrees C. Composite analyses indicate that concurrent heat and humidity extremes in the warm season amplify electricity generation anomalies, whereas cold-season meteorological variability has a weaker effect. Provinces with less-industrialized economies exhibit stronger correlations between meteorological fluctuations and electricity generation. These findings highlight that daily meteorological variability is a dominant driver of electricity generation and provide a robust framework for forecasting electricity demand, assessing climate risks, and supporting adaptive energy planning under variable meteorological conditions.

The tropopause plays a critical role in stratosphere-troposphere exchange and climate change. Its height is widely defined based on the World Meteorological Organization (WMO) threshold temperature gradient. High-resolution (5–10 m) soundings, therefore, are expected to substantially minimize uncertainties of tropopause height (TH) arising from limited vertical resolution and imprecise temperature measurements. The high-resolution radiosonde data, accumulated from 2000 to 2023 from a globally distributed, sparse network (about 1.5 million profiles from 222 stations), offers valuable insights into climatological tropopause variability. While radiosonde observations are limited by spatiotemporal coverage, European Centre for Medium–Range Weather Forecasts Reanalysis v5 (ERA5) reanalysis datasets offer globally complete tropopause representations. To leverage both the high resolution of radiosonde measurements and the global coverage of ERA5, this study compares their TH estimates and analyzes long-term trends across different latitude zones and seasons. The results indicate that the mean and absolute differences (radiosonde minus ERA5) in TH were 32 and 336 m, respectively, with larger discrepancies observed during the spring season in the tropics (±20°). Overall, point-to-point comparisons indicate that ERA5 effectively captures climatological TH variations in both time and space. Long-term trend analyses revealed increases of +9 m yr−1 (radiosonde) and +7 m yr−1 (ERA5) based on point-to-point comparisons. However, these site-specific trends may differ substantially from the long-term trends observed in ERA5 with complete spatiotemporal resolution, even showing opposite trends. Therefore, continued accumulation of high-resolution radiosonde profile data is crucial to further characterize tropopause changes in a warming climate.

In recent years, the southwestern region of China has experienced frequent drought disasters, causing severe impacts on the local economy and environment. The Gravity Recovery and Climate Experiment (GRACE)/GRACE-follow-on (FO) gravity satellites can invert terrestrial water storage anomalies (TWSAs), providing a new technological approach for drought monitoring. However, the data gap between the GRACE and GRACE-FO has affected the completeness and continuity of TWSA data, posing challenges for long-term drought monitoring and analysis. This study employs four machine learning (ML) models: extreme learning machine (ELM), nonlinear autoregressive with external input (NARX), long short-term memory (LSTM), and extreme gradient boosting (XGBoost), combined with the empirical orthogonal function (EOF) method. The key innovation of this study lies in integrating EOF decomposition with ML models to enhance the accuracy and reliability of TWSA gap filling and drought monitoring under limited monthly scale data conditions. The approach effectively bridges the data gap between GRACE and GRACE-FO and compares the results with reanalyzed/simulated TWSA and recently generated TWSA prediction products. The results indicate that all machine learning (ML) models can effectively reconstruct the interannual variations in TWSA, and the LSTM model performs the best. Compared with TWSA prediction products provided by recent studies, the LSTM model offers more accurate TWSA predictions. In addition, this study constructs the GRACE water storage deficit index (WSDI) based on the reconstructed TWSA data, and by comparing it with the standardized precipitation evapotranspiration index (SPEI), finds a correlation coefficient of 0.80 at a six-month timescale, indicating that GRACE WSDI can effectively reflect the long-term cumulative effects of drought. The reconstruction of GRACE WSDI successfully detected the extreme drought event in the southwestern region of China in 2022, further confirming the effectiveness of GRACE WSDI in identifying and assessing drought severity.

Abstract. The tropopause plays a critical role in stratosphere–troposphere exchange and climate change. Its height is conventionally defined based on the World Meteorological Organization (WMO) threshold temperature gradient, yet this gradient is intrinsically linked to vertical resolution. Data with higher vertical resolution inevitably reveal finer gradient structures. While in situ radiosonde temperature measurements are considered the most reliable source for tropopause structure, high-resolution (5–10 m) soundings would be expected to yield more precise height estimates. The near-global coverage of high-resolution radiosondes, accumulated over even decades, promises valuable insights into long-term tropopause variability. However, our analysis demonstrates that the original WMO definition can lead to an underestimation of the tropopause height when using high-resolution soundings, potentially misidentifying the tropopause within thin inversions or temperature gradient discontinuities below tropopause. To address this, we leverage ERA5 tropopause heights as a reference to develop a high-resolution-optimized method. We evaluate three methods: original WMO method, Moving average method, and Coarse–Fine method. The results reveal that the mean differences between the three methods and ERA5 were 800 m, 280 m, and 180 m, respectively. Notably, ERA5 systematically overestimated the tropopause height compared to all methods, with this discrepancy particularly pronounced in the edges of the Hadley circulation. The proposed Coarse–Fine method, by effectively bypassing thin inversions and gradient extrema while preserving the fine–scale structure of the tropopause height, presents a promising tool for future investigations into long-term tropopause trends.

AbstractAs Arctic surface temperatures exhibit amplified warming, a warming center in the upper troposphere and lower stratosphere (UTLS) is also notable, which potentially plays a crucial role in polar‐to‐mid‐latitude interactions. While the surface warming has accelerated recently, UTLS temperatures, however, have declined since 2001, as demonstrated by multiple reanalysis data sets. This shift in trends provides a unique opportunity to understand the factors influencing Arctic UTLS temperature changes. Further analysis indicates that the descending component of the Brewer‐Dobson (BD) circulation over the Arctic was strengthened before 2000 but weakened thereafter. Simultaneously, Arctic lower stratospheric water vapor showed a decreasing trend before 2000, followed by an increasing trend. The weakening of the BD circulation led to reduced dynamical heating, and the increased water vapor resulted in enhanced radiative cooling, which together contributed to the cooling of the Arctic UTLS after 2000.

Tropospheric ozone pollution poses a major environmental challenge in China. As its primary natural source, Stratosphere-to-Troposphere Transport (STT) has been recognized as a significant contributor to tropospheric ozone in western, northeastern, and eastern China. However, the extent of STT’s influence on southeastern China has been less studied due to data limitations. Using a recently available one-year dataset of ozonesonde observations from a regional background station, we find that STT contributes significantly to tropospheric and surface ozone elevation in southeastern China. Our results show that STT plays a more substantial role in shaping tropospheric ozone during spring than previously believed, accounting for over 30% of ozone concentrations above 4 km. Without the stratospheric contribution, the spring seasonal peak almost disappears. STT can also significantly influence ozone concentrations at the surface. For example, a distinct ozone profile was observed on 4 May 2022, with a notable increase in tropospheric ozone. This tropospheric ozone increase was caused by a STT event triggered by a robust horizontal trough and subsequent southward movement of subtropical jets in the upper troposphere. According to a stratospheric tracer derived from an atmospheric chemistry model, this STT event contributed to 25%–30% of the surface ozone increase. Overall, this study highlights the important role of STT in driving tropospheric ozone variations, even in regions with comparatively lower ozone levels in southeastern China.

Skilled seasonal forecasting will effectively reduce the economic losses caused by droughts and floods. Because of the powerful data mining capability of deep learning networks, it is increasingly applied in studies of seasonal rainfall prediction. However, there remain two prominent issues in the modeling process: the lack of enough training samples and the effect of a small number of extreme values on the model optimization. To tackle these deficiencies, we combine strategies such as principal component analysis, reduction of model hidden layers, and early-stopping with Attention U-Net to construct a rainfall classification forecasting model. These steps reduced the model outfitting and improved the model generalization. The results show that the prediction accuracy of this network with leads of 1-3 months is obviously better than that of the numerical model. Further analysis also supports that the spatial features of precipitation predicted by the network are very close to the observations. Accurate summer precipitation forecasts can effectively reduce economic losses and casualties caused by heavy precipitation and flooding in China. Nowadays, seasonal forecasting still mainly relies on numerical models based on physical knowledge. However, the predictors of rainfall are so broad and complex that some physical processes are not yet fully understood. As a result, the improvement of the model skills encountered a bottleneck. Recently, deep learning (DL) network can achieve complex function approximations of data by automatically extracting and learning data features. Yet the existing DL seasonal prediction models suffer from over-fitting and interference from noise and extreme values. Here, we use Attention U-Net to predict rainfall levels and combine strategies such as principal component analysis, reduced model depth, and early-stopping. This network not only solves the two drawbacks mentioned above. It also shows the superiority in the prediction results. First, the rainfall level prediction of this network is more accurate than the numerical model at 1-3 months ahead. Second, the network captures the spatial features with excellent performance. The predicted distributions are largely consistent with the true values. The Attention U-Net model outperforms the numerical model in predicting summer precipitation levels with a forecast lead of 1 monthConverting a regression task into a classification task can eliminate the bad effects of noises and extremesThe principal component analysis and the early-stopping strategy facilitate the model's generalization

This paper analyses the intraseasonal evolution of the winter haze pollution over the Beijing‐Tianjin‐Hebei (BTH) region in China between 1979 and 2013, and the influences of the atmospheric intraseasonal oscillations (ISOs) in mid–high latitudes. Two significant ISO signals of 10–20‐day and 20–40‐day are identified from the winter haze pollution over the BTH region, and their superposition notably contributes to the occurrence of haze pollution. On the 10–20‐day timescale, the haze pollution is intimately associated with a mid–high‐level southeastward propagating wave train that originates from the North Atlantic. With the wave evolution, an anomalous anticyclone reaches the Korean Peninsula–Japan, leading to strong descents and anomalous southerly winds over the BTH region, favourable for haze pollution. On the 20–40‐day timescale, the atmospheric wave train originates from the eastern North America, propagates eastward to the northwestern Lake Baikal, and then turns southward to the northeastern China–Japan. The peak stage of the haze pollution is related to the resultant zonal dipole, with the anomalous cyclone over the northwestern Lake Baikal and the anomalous anticyclone over the northeastern China–Japan. The dominance of the mid–upper tropospheric anticyclone over the BTH region suppresses the development of convection, thus hindering the vertical diffusion and wet deposition of pollutants. Moreover, the anomalous westerly winds between the dipole weaken the incursion of cold mass from higher latitudes, thus further contributing to the peak of haze pollution.

. Kelvin Helmholtz instability (KHI) is most likely to be the primary source 29 for clear-air turbulence that is of importance in pollution transfer and diffusion and 30 aircraft safety. It is exemplarily indicated by the critical value of Richardson ( Ri ) 31 number, which is typically taken as 1/4. However, Ri is fairly sensitive to the vertical 32 resolution of the dataset: a higher resolution systematically leads to a finer structure. 33 The study aims to evaluate the performance of ERA5 reanalysis (137 model levels) in 34 determining KHI spatial-temporal variabilities, by comparing it against a near-global 35 high-resolution (10-m) radiosonde dataset during years 2017 to 2022, and to further 36 highlight the global climatology and dynamical environment of KHIs. Overall, the 37 occurrence frequency of Ri <1/4 in the free atmosphere is inevitably underestimated 38 by the ERA5 reanalysis over all climate zones, compared to radiosonde, due largely to 39 the severe underestimation in wind shears. Otherwise, the occurrence frequency of 40 KHI indicated by Ri <1 in ERA5 is climatologically consistent with that from 41 radiosondes in the free troposphere, especially over the midlatitude and subtropics in 42 the Northern/Southern Hemisphere. Therefore, we infer that the threshold value of Ri 43 should be approximated as 1, rather than 1/4, when using ERA5 for the KHI 44 estimation. KHI occurrence frequencies revealed by both datasets exhibit significant 45 seasonal cycles over polar, midlatitude, and subtropics regions, and they are 46 consistently strong at heights of 10–15 km in the tropic region. In addition, the 47 frequency at low-levels is positively correlated with the standard derivation of 48 orography, and it is exceptionally strong over the Niño 3 region at heights of 6–13 km. 49 Furthermore, the dynamical environment of KHI favors strong wind shears probably 50 induced by the mean flows and the propagation of orographic or non-orographic 51 gravity waves. 52

In summertime, eastern China experiences severe ozone pollution. Stratosphere-to-troposphere transport (STT), as the primary natural source of tropospheric ozone, may have a non-negligible contribution to ground-level ozone. Rossby wave breaking (RWB) is a leading mechanism that triggers STT, which can be categorized as anticyclonic wave breakings (AWBs) and cyclonic wave breakings (CWBs). This study uses an objective method to diagnose AWBs and CWBs and to investigate their influence on the surface ozone in eastern China using ground-based ozone observations, satellite ozone data from AIRS, a stratospheric ozone tracer simulated by CAM-chem, and meteorological fields from MERRA-2. The results indicate that AWBs occur mainly and frequently over northeast China, while CWBs occur mostly over the northern Sea of Japan. STTs triggered by AWBs mainly have sinking areas over the North China Plain, increasing the ground-level ozone concentrations by 5–10 ppbv in eastern China. The downwelling zones in the CWBs extend from Mongolia to the East China Sea, potentially causing an elevation of 5–10 ppbv of ozone in both central and eastern China. This study gives an overview of the impacts of AWBs and CWBs on surface ozone in eastern China and helps to improve our understanding of summertime ozone pollution in eastern China.

The impacts of the Arctic stratospheric polar vortex (SPV) changes on wintertime frontogenesis in the northern middle latitudes are analyzed. Both composite analysis and model simulations reveal that the intensity and frequency of frontogenesis over West Russia, the Mongolian Plateau, the Mediterranean and the southern North Atlantic during weak SPV years are significantly stronger and larger than those during strong SPV years, while the frontogenesis over the northern parts of the North Atlantic and North Pacific Oceans are weaker and less occur during weak SPV years. These features are more noticeable in middle January and February. The contributions of resultant deformation changes to frontogenesis intensity changes over most regions of the middle latitudes are larger than those of horizontal divergence changes, and the contribution of stretching deformation is slightly larger than that of shearing deformation. The changes in frontogenesis intensity are attributed to changes in the tropospheric circulation and temperature gradient associated with the SPV changes. Potential vorticity (PV) anomalies in the upper troposphere and lower stratosphere (UTLS) caused by the weakened and shifted SPV towards Eurasia lead to tropospheric cyclonic flows, favoring more cold-air mass transported towards mid-latitude Eurasia. Meanwhile, more high-PV air towards Eurasia results in steeper tropospheric isentropes during weak SPV years. Consequently, both temperature gradient and frontogenesis over Russia are enhanced. More southward transport of cold-air mass due to the equatorward shift of the polar jet stream induced by the weak SPV enhances the frontogenesis over the southern North Atlantic. Furthermore, the angle between dilatation axis and the isentropes over West Russia, the Mongolian Plateau and the southern North Atlantic is more likely to occur between 0° and 45°, which promotes stronger frontogenesis over these regions during weak SPV years. By contrast, opposite processes occur over the northern North Atlantic, causing weaker frontogenesis there.

Meridional shifts of the intertropical convergence zone (ITCZ) in response to tropical or extratropical forcings have been investigated widely within an emerging energy framework. While few studies concerned the forcings of different spatial patterns. This work explores the dependence of the ITCZ response on the patterns of thermal forcings, which are hemispherically antisymmetric and zero in global mean, using the Community Earth System Model coupled with a slab ocean. Results show that the magnitudes of ITCZ displacements caused by mid-latitude perturbations are larger than their low-latitude counterparts, though the perturbation amounts are the same in hemispherically average. It is found mid-latitude perturbations cause more cross-equatorial atmospheric energy transport (AET) and moisture transport, the upper and lower branches of an anomalous Hadley cell. The reason lies further in the different mechanisms of precipitation response to low- and mid-latitude thermal perturbations. That is when perturbations are added in tropics, evaporation and clouds share the responses, limiting the magnitude of either one; while when perturbations are added in mid-latitudes, baroclinicity change allows local evaporation and tropical clouds to respond fully. Results also show that the zonal mean precipitation responses have asymmetric components even though the warming and cooling are symmetric; that is the increase of precipitation in the warmed hemisphere is more poleward than the decrease of precipitation in the cooled hemisphere. This is especially true when the mid-latitudes of the southern hemisphere (SH) are warmed. The phenomenon is a manifestation of the positive feedback of precipitation to the anomalous Hadley circulation. And that the asymmetric components of precipitation response are greater in the case of warming SH than those in the case of warming the northern hemisphere is attributable to the offset of AET by transient eddy in the latter case.

The double Intertropical Convergence Zone (ITCZ) bias is an outstanding bias in many climate models. This work assesses the annual‐mean double‐ITCZ problem in the models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on several quantitative indices. Within the 46 CMIP6 models, 9 models from mainland China are evaluated as a group to verify the effort of model development from one perspective. The double‐ITCZ bias and its large intermodel spread still exist in CMIP6 models. The overall performance of the models from Chinese mainland is similar with all CMIP6 models. It is found that the top‐five models with relatively low double‐ITCZ biases can effectively restrain the frequency of deep convection and related sea surface temperature (SST) bias in the southeastern Pacific dry subsidence region, which highlights the necessity of improving convective physics in climate models. Impacts of model resolution on the double‐ITCZ problem are examined by comparing the high‐ and low‐resolution groups in CMIP6 and High Resolution Model Intercomparison Project (HighResMIP) historical experiments, respectively. Increased resolution in atmospheric models is found to be able to reduce the positive precipitation bias over the tropical southern Atlantic and improve the simulation of deep convection frequency and convective precipitation ratio there. However, the double‐ITCZ bias over the Pacific is not improved significantly by increased resolution.

The middle atmosphere plays an important role in the research of various dynamical and energy processes. Microwave Limb Sounder (MLS), reanalyses and model simulations with NCAR’s Whole Atmosphere Community Climate Model (WACCM) data in the range between 100 and 0.1 hPa from 2005 to 2020 have been analyzed with a focus on the temperature semi-annual oscillations (SAO). Significant SAO of temperature is prominent in the tropical region (20°S–20°N) around 1–3 hPa, which is consistent with previous studies. We also found significant SAO in the northern hemisphere (NH) high latitudes between 8 and 0.3 hPa and southern hemisphere (SH) high latitudes between 0.5 and 0.1 hPa, which has been of less concern in previous studies. The thermal budget based on MERRA2 and simulations is used to explain the mechanism of SAO in the middle atmosphere. In the tropics, the two temperature peaks are mainly determined by radiative processes. In the NH high latitudes of the stratosphere, the temperature peak in January is mainly related to dynamical processes, while the temperature peak in July is determined by a combination of dynamical and radiative processes. In the NH high latitudes of the lower mesosphere, the first peak in June is primarily associated with dynamical and radiative processes, while the second peak in December is primarily associated with the dynamical processes. In the SH high latitudes of the lower mesosphere, the first temperature peak in July is mainly due to dynamical processes while the second temperature peak in December is mainly due to radiative processes. Various features are present in the SH and NH high latitude SAO in the lower mesosphere. Furthermore, we performed model simulations with and without SAO in sea surface temperatures (SST-SAO) to study the connection between SST and temperature SAO. WACCM6 results indicate that the SAO in the middle atmosphere is partially affected by the existence of an SST-SAO. By removing SAO in SST, the PSD magnitude of the SAO decreases in the tropical region and increases in the polar region. The amplitudes of total heating rates are also modified. The WACCM experiment confirms the relationship between SST-SAO and temperature SAO in the middle atmosphere.

利用再分析的陆地降水、环流和辐射数据,以及表征大气波动的指数,对比了1951-2020年期间El Ni?o春季快速衰减年和缓慢衰减年的东亚环流和华北夏季降水异常情况,并从大气波动强度的角度探讨了为何El Nino在一些年份的春季会发生快速衰减.结果表明,相较于其他不发生El Nino衰减的年份,El Nino春季快速衰减年华北7、8月的降水量显著偏多,尤其是8月;而El Nino缓慢衰减年夏季,华北降水相较其他年份偏多不明显.El Ni?o春季快速衰减年6-8月850 hPa上菲律宾到南海存在异常反气旋,其强度强于El Ni?o缓慢衰减年;El Ni?o春季快速衰减年8月500 hPa西太平洋副热带高压(西太副高)显著偏北,而缓慢衰减年西太副高偏北的特征不明显,而是以偏西为主;200 hPa副热带西风急流在El Nino春季快速衰减年8月显著偏北,而在El Nino缓慢衰减年中反而略偏南;El Nino春季快速衰减年6-8月沃克环流显著偏强,相比之下El Ni?o缓慢衰减年沃克环流偏强的特征要弱很多.上述环流异常特征为El Ni?o春季快速衰减年华北7-8月降水异常偏多提供了有利条件.通过近地面风场合成分析发现,春季El Nino快速衰减月前后,赤道中西太平洋异常东风爆发非常明显,而El Ni?o缓慢衰减年的异常东风信号较弱.春季El Nino快速衰减前印度洋对流活动非常强盛,并向海洋性大陆传播,这种对流可能通过不断激发大气波动,继而引发近地面东风爆发,最终导致El Ni?o出现快速衰减.

Abstract The Hunga Tonga‐Hunga Ha'apai (HTHH) eruption on 15 January 2022 was one of the most explosive volcanic events of the 21st century so far. According to satellite‐based measurements, 0.4 Tg of sulfur dioxide (SO2) was injected into the stratosphere during the eruption. By using observations and model simulations, here we investigate changes in the chemical compositions of the stratosphere 1 year after the HTHH eruption and examine the key physical and chemical processes that influence the ozone (O3) concentrations. Injected SO2 was oxidized into sulfate during the first 2 months, and transported from the tropics to the Antarctic by the Brewer‐Dobson circulation within 1 year. In mid‐to‐low latitudes, enhanced sulfate aerosol increased O3 concentrations in the middle stratosphere but declined in the lower stratosphere. In addition to the chemical processes, sulfate aerosols also reduced polar low‐stratospheric O3 concentrations through enhanced Antarctic upwelling anomalies.