Abstract:The article reviews the progress made in 2024 under China's National Key R＆D Program for Earth System and Global Change. It highlights key research findings across five areas: observational facts and circulation characteristics of extreme event changes, key oceanic processes and air-sea interaction mechanisms, the influence of oceanic processes on extreme weather and climate, land surface processes and their impacts, and the simulation and prediction of extreme events. Significant advances in 2024 include the following:1)Our study examined the impact of long-term warming trends on record-breaking high-temperature in China events over the past 40 years. Using the Universal Thermal Climate Index (UTCI), we characterized the spatial distribution and long-term trends of extreme high-temperature events. Observational evidence was provided for changes in hourly and daily extreme precipitation across eastern China during different warming periods. Additionally, regional differences in the duration of summer heatwaves and their associated large-scale circulation anomalies were identified. The dynamic and thermal characteristics of extreme heatwaves in the middle and lower reaches of the Yangtze River were analyzed, and attribution studies assessed the influence of human activities on extreme temperature events in Asian hotspots.2)We quantified the role of the winter North Pacific Oscillation (NPO) in shaping subsequent ENSO events, emphasizing the contribution of tropical Pacific interannual-decadal variability to prolonged La Niña events. Our research also proposed a modulation mechanism whereby La Niña's zonal position influences the Indian Ocean Dipole (IOD) and identified seasonal reversals in ENSO's impact on sea surface temperature (SST) in the East China Sea-Kuroshio Region. Furthermore, we investigated the seasonal predictability of SST anomalies and marine heatwaves in the Kuroshio Extension Region, highlighting the role of nonlinear processes in the amplitude evolution of the Madden-Julian Oscillation (MJO) and extreme MJO formation. Additionally, pathways linking anthropogenic forcing, natural variability, and internal climate fluctuations to multidecadal changes in the North Atlantic were explored.3)Our findings revealed asymmetric ENSO influences on the late-winter “Warm Arctic-Cold Eurasia” pattern and identified links between ENSO and precipitation patterns in eastern China during early and late winter. The connection between ENSO and the Kuroshio anticyclone was also examined. Seasonal mechanisms linking ENSO to SST variability in the East China Sea-Kuroshio Region and its influence on East Asian precipitation were clarified. We found a strong association between Mega-ENSO and the poleward shift of typhoon genesis locations in the western North Pacific, while trans-basin tropical air-sea interactions were shown to affect typhoon formation frequency. Furthermore, we identified Antarctic warming-induced South Atlantic warming as a key driver to the 2022 East Asian heatwaves and demonstrated that tropical North Atlantic variability modulates forest wildfire activity in Northeast China.4)We investigated the role of Eurasian soil moisture variability in triggering clustered extreme precipitation events in northern China and evaluated the influence of snow cover on subseasonal temperature variability and predictability. Attribution and projection studies examined drought patterns influenced by land surface factors and land-atmosphere coupling. The physical mechanisms linking land surface processes and land-sea interactions to summer precipitation, extreme Meiyu events, and heavy rainfall in China were further elucidated. Additionally, we assessed the impact of summer irrigation in North China on the diurnal cycle of precipitation and regional water cycles, as well as the effects of urbanization on mesoscale convective systems and heavy rainfall in the Pearl River Delta during warm seasons.5)The simulation capabilities of CMIP6 models in representing the subseasonal reversal of the “Warm Arctic-Cold Eurasia” pattern were systematically evaluated. A seasonal prediction model for compound extreme heat-humidity events in southeastern China was developed. Using an interannual increment approach, a physical-statistical prediction model for summer heavy precipitation days (HPDs) in North China was established. Additionally, deep learning techniques were employed to enhance dynamical model predictions of summer extreme precipitation in the middle and lower Yangtze River basin. Further advancements in dynamical model development and their predictive applications were explored. Finally, this article outlines key challenges and research priorities for future studies, aiming to advance understanding of extreme weather and climate in China.

Abstract:In the context of global warming, extreme weather and climate events have become more frequent, particularly since the 21st century, exhibiting significant clustering, persistence, complexity and extremity. Under the influence of the same weather system, extreme temperature events of the same type often display spatiotemporal clustering, manifested by spatial correlation and temporal persistence. Previous studies based on single-stations analyses have been unable to effectively capture the evolution and movement of weather systems, thus limiting the ability to assess the clustering characteristics of extreme temperature events. In our previous works, we proposed a simplified method for identifying clustered extreme weather and climate events, which has been demonstrated to be effective. Using daily observed temperature records from 1961 to 2020, this study established a dataset of five types of clustered extreme temperature events in China by incorporating spatiotemporal correlations. These include hot weather, scorching weather, extreme high temperature, extreme low temperature, and severe cooling events. The results indicate that hot and scorching weather events primarily occur in summer, with high-frequency located in Xinjiang region and the Changjiang-Huaihe River Basin. Notably, seven of the ten most intense summer clustered scorching weather events since 1961 occurred after 2000, showing significantly prolonged duration and expanded affected areas. Clustered severe cooling events predominantly occur in seasons other than summer, with high frequency in Nei Monggol and the southern part of Northeast China. While the duration, affected area, and intensity of severe cooling events have remained relatively stable, their frequency has exhibited a slight decline. Clustered extreme high and extreme low temperature events occur throughout the year across various regions. In response to global warming, extreme high-temperature events show an increasing trend, whereas extreme low-temperature events demonstrate a decline in multiple indices. This dataset developed in this study, spanning from 1961 to 2020, provides a comprehensive foundation for understanding the characteristics and long-term trends of clustered extreme temperature events. Further supplement and update will incorporate the latest observational data to refine and expand the dataset. In addition, methodology employed can be extended to identify other extreme events, such as extreme precipitation events, compound extreme events, and high-impact weather and climate events. The establishment of this dataset lays a crucial foundation for in-depth study on the mechanisms, impacts, and predictability of extreme temperature events in a changing climate.

Abstract:The Qinghai-Xizang Plateau plays a significant role in influencing China's climate and weather patterns.However,the regional observation density is considerably lower than that of eastern China.Despite this sparsity,observations from the Qinghai-Xizang Plateau can significantly affect weather forecasts for downstream areas,such as the middle and lower reaches of the Yangtze River.With advancements in the resolution of regional models,many operational forecasting systems in eastern China exclude the Qinghai-Xizang Plateau,leaving the impact of its observations on downstream forecast quality unclear.This study uses the CMA-MESO rapid updating cycle system to simulate the Meiyu weather event that occurred in early July 2020 in the middle and lower Yangtze River region.The study evaluates the influence of missing observations from the Qinghai-Xizang Plateau on the accuracy of forecasts for downstream weather systems.Results indicate that the absence of Qinghai-Xizang Plateau observations significantly degrades the 24—72-hour forecasts for surface 2-meter temperature and 10-meter wind fields in the middle and lower Yangtze River.It also negatively affects precipitation forecasts during the first three days.Analysis of the observational impact highlights that Qinghai-Xizang Plateau observations primarily influence the middle and lower Yangtze River region,with the largest variance in the 24—72-hour forecast signal occurring in these areas.Thus,improving forecast quality in the middle and lower Yangtze River requires accounting for upstream observational data from the Qinghai-Xizang Plateau and the spatiotemporal propagation of this.To enhance severe weather forecasting in eastern China,it is crucial to not only increase local observation infrastructure,such as radars and weather stations,but also to deploy additional radars and satellites in the Qinghai-Xizang Plateau.Incorporating three-dimensional observational profiles,such as soundings,and deploying mobile observation stations in regions prone to sudden localized heavy precipitation can significantly improve forecast quality.Additionally,to mitigate the limitations of finite-region models,approaches such as high-low resolution nesting,variable-resolution modeling,or blending analyses methods should be adopted to incorporate upstream information.

Abstract:Dust weather has significant socioeconomic and environmental impacts and pose serious threats to human health.Understanding its interdecadal variability and future changes is of great importance.In this study,station observations and reanalysis datasets are used to investigate the key atmospheric circulation patterns governing the interdecadal variability of dust days in North China from March to April during the period 1961—2020.Additionally,two model datasets,CMIP6 and CESM-LE,are employed to project future changes in dust days.Results show a significant shift in the number of dust days in North China at the transition between the late 1980s and early 1990s.During the high-incidence period (1961 to 1989,P1),the average number of dust days was approximately 3.5 times as high as that in the low-incidence period (1992 to 2020,P2).This variation is closely linked to an atmospheric circulation pattern associated with a wave train extending eastward from the western European Plain to the Ural Mountains and the Mongolian Plateau (EUM).Compared to P2,the wave train was more pronounced during P1,with an increased meridional extent of atmospheric circulation over mid-and high-latitude Eurasia.At the 500 hPa,the geopotential height field exhibited significant negative,positive,and negative anomalies over western Europe,the Ural Mountains,and the Mongolian Plateau,respectively.The intensified blocking high over the Ural Mountains,along with the deepened Mongolian low,strengthened the northerly winds east of the Ural Mountains and enhanced the pressure gradient force at mid-latitudes near the surface,facilitating the southward transport of cold air.Simultaneously,the intensified Mongolian cyclone,characterized by strong winds and unstable stratification,promoted dust uplift and transport.Moreover,water vapor divergence over the dust source regions suppressed precipitation,further favoring dust generation.The EUM index exhibits interdecadal variability highly consistent with the number of dust days in North China,making it a valuable predictor for future dust activity in the region.However,significant differences exist among models in simulating the interdecadal changes in the intensity of atmospheric circulation at the wave train center.The model performance is closely related to the spatial correlation coefficient and the standard deviation ratio between the simulated and observed 500 hPa geopotential height anomalies over the study area.To identify reliable models,selection criteria are set as follows:the spatial correlation coefficient must exceed 0.35,and the standard deviation ratio between the model and observations must be greater than 0.3.Models meeting these criteria successfully reproduce the interdecadal weakening of the EUM in both spatial and temporal domains are referred to as the “optimal ensemble”.Projections based on the optimal ensemble indicate that under a high-emission scenario,the EUM is expected to strengthen significantly in the near future (2021 to 2050),leading to an increase in dust events in North China.

Abstract:The eastern slope of the Qinghai-Xizang Plateau (ESQP) features complex topography and substantial elevation gradients,making it a region of intricate climate dynamics.This study assesses the ability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) models to simulate precipitation over the EQSP,with a focus on comparing high-resolution (HR) and low-resolution (LR) models in capturing both annual and extreme precipitation events.Using data from 1985 to 2014,including historical simulations and observational datasets,the study evaluates extreme precipitation indices such as R10mm (heavy precipitation days) and Rx1day (maximum 1-day precipitation).Results show that both HR and LR models successfully capture the general spatial distribution of annual precipitation,which increases from northwest to southeast across the region.However,HR models exhibit significant improvements over LR models in reducing biases and improving simulation accuracy.Specifically,the annual precipitation bias is reduced from 1.05 mm·d^-1 in LR models to 0.96 mm·d^-1 in HR models,demonstrating the benefits of increased resolution in minimizing simulation errors.For extreme precipitation events,HR models outperform LR models in both occurrence and intensity representation.The R10mm index shows a 6％ reduction in relative error for HR models compared to LR models,while the Rx1day index exhibits a 5％ improvement in HR model performance.These improvements are particularly notable in the Sichuan Basin,a region historically challenging to simulate due to its complex terrain and variable moisture conditions.Further analysis investigates the thermodynamic and dynamic contributions to differences between HR and LR model simulations using a physical scaling diagnostic equation.Results indicate that dynamic effects account for 91％ of the improvements observed in HR models,while thermodynamic effects contribute only 8％.This suggests that higher resolution enhances the representation of atmospheric circulation,vertical motion,and moisture transport-key processes in extreme precipitation simulation.HR models demonstrate improved simulations of cyclonic flows and moisture convergence in the Sichuan Basin,leading to reduced wet biases in high-altitude areas and better overall accuracy in extreme precipitation simulations.Despite these advancements,certain biases persist,particularly over the Yunnan-Guizhou Plateau,where precipitation remains difficult to simulate accurately.This highlights the need for continued improvements in model physics and resolution to better capture the complex atmospheric and topographic influences on precipitation patterns.The findings of this study have important implications for climate risk assessments in the ESQP,particularly in relation to extreme precipitation events and associated hazards such as floods and landslides.The enhanced accuracy of HR models is crucial for understanding future precipitation changes under global warming.As extreme precipitation events are expected to intensify with climate change,further improving climate model simulations is essential for effective risk management and policy development.In conclusion,this research demonstrates that increasing model resolution significantly enhances precipitation simulation accuracy,particularly for extreme events,over the eastern Qinghai-Xizang Plateau.The improvements in HR models,particularly in capturing dynamic atmospheric processes,provide valuable insights into the regional climate dynamics of this complex terrain.However,challenges remain in accurately simulating precipitation over specific areas,underscoring the need for further advancements in model development.Future research should focus on refining the representation of atmospheric circulation and moisture processes in regions with complex topography and exploring multi-model ensemble approaches to reduce uncertainties in climate projections.

Abstract:The Atlantic ocean spans a wide range of latitudes and exhibit numerous important climate phenomena across multiple time scales.Regarding sea surface temperature anomalies (SSTA) in the Atlantic,most previous studies have focused on the North Atlantic and tropical Atlantic,with relatively less attention given to the South Atlantic.Research has demonstrated inverse-phase sea surface temperature patterns in the eastern and southwestern parts of the Indian Ocean,a phenomenon defined as the Southern Indian Ocean Dipole (SIOD).Subsequently,studies analyzing sea surface temperature variability in the South Atlantic using empirical orthogonal function (EOF) and singular value decomposition (SVD) identified dipole-like spatial structures similar to those observed in the South Indian Ocean and South Pacific Ocean.The El Niño-Southern Oscillation (ENSO),the most prominent mode of interannual climate variability,is closely related to the sea surface temperature variations across the entire South Atlantic.Exploring the South Atlantic Subtropical Dipole (SASD) and its specific correlation with ENSO can enhance our understanding of climate variability and improve the prediction of both ENSO and SASD events.Using monthly sea surface temperature data from the Hadley Center,as well as monthly sea surface wind data,sea level pressure data and latent heat flux data from the NCEP/NCAR reanalysis for the period 1960 to 2022.This study examine the characteristics of subtropical sea surface temperature variability in the South Atlantic and its relationship with ENSO.The results show that:1) During the Northern Hemisphere winter,EOF analysis of the subtropical SSTA in the South Atlantic Ocean reveals a northeast-southwest dipole like pattern,explaining 27.82％ of the total variance.This phenomenon,termed the SASD,primarily arises due to the variability in sea surface wind associated with fluctuations in the South Atlantic subtropical high.These wind anomalies induce a northeast-southwest dipole pattern in latent heat flux anomalies,which subsequently drives sea surface temperature (SST) variability.2) Composite analysis of SST anomalies indicates that SASD exhibits strong seasonal phase-locking.It typically develops from September to November (SON),and reaches its peak during December to February (DJF),and weakens from March to May (MAM).3) Wavelet analysis shows that SASD variability is dominated by periodic fluctuations on a 4—6 years timescale over the period 1960 to 2022.4) SASD is significantly correlation with ENSO,with a correlation coefficient of 0.55.Positive (negative) SASD events are generally correspond to El Niño (La Niña) events.Furthermore,the co-occurrence of positive SASD and El Niño enhances the intensity and spatial extent of El Niño events.Additionally,when positive SASD and El Niño occurs simultaneously,they tend to trigger La Niña events the following year.Conversely,La Niña also can enhance the intensity of the negative SASD the subsequent year.This study analyses a strong correlation between ENSO and SASD,however,further investigation is needed to determine whether ENSO have an impact on the evolution of SASD and whether different ENSO types (i.e.,East-Pacific ENSO vs.Central-Pacific ENSO) exert distinct effects on SASD development.

Abstract:May precipitation plays a crucial role in the transition from the dry to the wet season in Yunnan,marking the onset of the region's rainy season.Understanding the mechanisms driving its anomalies has been a key focus and challenge in climate change research.Before the 1990s,May precipitation in Yunnan was primarily characterized by interannual variability.Therefore,meteorological studies predominantly examined precipitation anomalies in specific years.However,over the past 25 years,interannual variability has weakened,while interdecadal fluctuations have become more pronounced.A distinct shift from a wetter to drier phase occurred between the periods 1999—2008 and 2009—2023.This study analyzes the characteristics and underlying causes of these variations.
The results indicate the following:1)The average precipitation anomaly percentage in May was 37.9％ during 1999—2008 and -19.7％ during 2009—2023.The precipitation difference between the two periods exceeded 20 mm across most regions and reached 60 mm in central and western Yunnan.These differences were statistically significant across most of the province.2)Atmospheric circulation patterns during 1999—2008 (2009—2023) were (were not) conducive to the southward penetration of cold air into Yunnan.At 500 hPa,the Arabian Sea and Bay of Bengal exhibited low (high) geopotential heights,while at 700 hPa,anomalous cyclonic (anticyclonic) circulation prevailed.These conditions facilitated (hindered) more the transport of moisture into Yunnan,leading to increased (decreased) precipitation.3)The most pronounced circulation differences between the two periods occurred in the mid-and low-latitude regions.During 1999—2008,both the Arabian Sea monsoon and the Indo-Burma trough were strong,and May precipitation in Yunnan was primarily influenced by the intensity of the Arabian Sea monsoon.In contrast,during 2009—2023,the Arabian Sea monsoon did not coincide with the active phases of the Indo-Burma trough,while the western Pacific subtropical high was anomalously strong.The enhanced easterly winds along its southern flank obstructed moisture transport from the Indian Ocean,while the strengthened westerly winds on its northern side extended over Yunnan and South China,suppressing the intrusion of cold air.4)Water vapor transport flux characteristics were analyzed using both Eulerian and Lagrangian methods.Eulerian analysis revealed moisture flux convergence over Yunnan in May during 1999—2008 but divergence during 2009—2023.The influx of moisture from Yunnan’s southwestern border was greater during 1999—2008 than in 2009—2023,while moisture flux from the western border was smaller during 1999—2008 than in 2009—2023.Lagrangian tracking of moisture transport pathways at 700 hPa and 500 hPa showed that the number of water vapor transport trajectories originating from the Indian Ocean was 21％ and 25％ higher during 1999—2008 than in 2009—2023,respectively.In contrast,the proportion of moisture transport from the westerlies was 2％ and 41％ lower during 1999—2008 compared to 2009—2023.
The findings of this study indicate that the persistent May precipitation anomalies in Yunnan over the past 25 years have been closely linked to variations in moisture transport from the Indian Ocean.The interdecadal differences in precipitation during 1999—2008 and 2009—2023 can largely be attributed to shifts in Indian Ocean moisture.However,these decadal-scale variations may also be influenced by external forcing factors,such as sea surface temperature anomalies.The relationship between May precipitation in Yunnan and sea surface temperature anomalies remains a critical avenue for future research.

Abstract:With the ongoing intensification of global warming,extreme precipitation events have become more frequent,exerting dramatic impact on socio-economic development and poses a severe risks to lives and property in the Haihe River basin.This study analyzes the spatiotemporal variability and statistical characteristics of extreme precipitation in the basin under climate change,utilizing daily precipitation observation from 1961 to 2022.Extreme precipitation events are defined using the annual maximum precipitation (AM) series and peak-over-threshold (POT) series.The results show that the spatial distribution patterns of the multi-year average AM and POT series are similar,with maximum daily precipitation primarily concentrated west of the Taihang Mountains and south of the Yanshan Mountains.Variability analysis reveals that annual maximum daily precipitation at most meteorological stations ranged between 0 and 50 mm,with the highest variability along the Taihang and Yanshan Mountains ranges,where the standard deviation reaches approximately 40 to 50 mm.Furthermore,trends in the annual maximum precipitation show spatial heterogeneity:among the 133 meteorological stations analyzed,54 exhibit increasing trend,whereas 79 stations showed a decreasing trend over the study periods (1961—2022).On a station-specific scale,the majority of extreme daily precipitation events occurred during the flood season (July to August) in the 1960s and 1970s.
To model extreme precipitation,various extreme value distribution function,including the Generalized Extreme Value (GEV),Generalized Pareto (GP),and Gamma distributions,were evaluated using the L-moment method and the Kolmogorov-Smirnov (K-S) test.The findings demonstrated that the GEV distribution effectively models the AM series,while the GP distributions provides an optimal fit for POT series.Furthermore,comparative analysis of precipitation estimates across different return periods suggests that the AM series,in conjunction with the GEV distribution provide a better representation of extreme precipitation in the Haihe River basin.To validate the model performance,three historical extreme rainfall events,each independently assessed as exceeding a 100-year return period threshold,were selected.The GEV based AM extreme exhibited smaller relative error (ranging from 6％ and 11％) compared to the GP based POT estimates,further confirming the superior performance of the GEV distribution in simulating extreme precipitation.These findings have important implications for disaster risk assessment,flood mitigation strategies,and sustainable socio-economic development in the Haihe River basin.

Abstract:Understanding the spatial distribution of rainfall within a river basin is fundamental for effective flood control and drainage management.Using the Haihe River basin in China as a case study,this research examines the impact of how extreme rainfall events on the spatial distribution of precipitation,which is critical for enhancing flood mitigation efforts and ensuring regional safety.To achieve this,daily rainfall data from 252 national meteorological stations in the Haihe River basin from 1961 to 2023 were collected and processed to identify individual rainfall events.Four key rainfall characteristics were selected for quantitative analysis:total rainfall days (I_day),cumulative rainfall (I_pre),average daily rainfall (I_24pre),and longest continuous rainfall duration (I_dur).These variables capture essential aspects of precipitation,including intensity,duration,and total accumulation.First,a comprehensive analysis of historical rainfall patterns in the Haihe River basin was conducted to establish a long-term spatial distribution baseline.Special focus was then placed on two highly representative extreme rainfall events,the “23.7” and “63.8” rainstorms,to examine their distinct spatial distribution characteristics.To further quantify their influence on overall rainfall distribution,a K-means clustering analysis was employed to explore internal data relationships.Finally,by comparing changes in rainfall characteristic after removing individual extreme rainfall events,the contribution of these events to the basin-wide spatial distribution of precipitation was quantitatively assessed.
The results reveal that along a southwest-to-northeast axis in front of the Taihang Mountains,the historical dataset shows that although meteorological stations in this region experience relatively fewer total rainfall days (I_day),the cumulative rainfall (I_pre) is significantly high,indicating a concentration of extreme precipitation events.Comparing the two extreme rainstorms,the “23.7” event had a notably larger spatial coverage.During this event,over half of the meteorological stations recorded at least 3 days of rainfall,with the longest continuous duration reaching 6 days.The maximum cumulative rainfall (I_pre) at a single station reached 1 009.7 mm,and the highest recorded average daily rainfall (I_24pre) was 452.7 mm/d.Furthermore,the study finds that in the absence of the “23.7” and “63·8” extreme rainfall events,the spatial distribution of precipitation in the Haihe River basin would become more uniform.The originally complex distribution pattern of “mountainous area-piedmont plain-plain” would simplify to a “mountainous area-piedmont and plain” pattern.Notably,the contribution of a single extreme rainfall event to the overall basin-wide precipitation characteristics can exceed 10％,highlighting the significant role of extreme events in altering rainfall distribution patterns.The findings of this study provide valuable insights for updating flood control and drainage design standards.They also offer a scientific basis for policymakers and relevant authorities in formulating and adjusting flood management and disaster mitigation strategies.

Abstract:Soil temperature (ST) anomaly memory describes the persistence of ST anomalies and their influence on subsequent ST and atmospheric anomalies.Due to the limited availability of long-term ST observational data,reanalysis datasets are widely used in numerical simulations and statistical diagnostic analyses.However,the extent to which reanalysis data affect the persistence of ST anomalies remains uncertain,directly impacting the reliability of conclusions drawn from numerical and statistical studies on the influence of antecedent ST anomalies on atmospheric processes.Therefore,evaluating the memory of ST anomalies in reanalysis datasets is essential.This study evaluates the memory of ST anomalies at a monthly timescale using ERA-Interim,ERA5,and GLDAS reanalysis datasets from 1979 to 2019,alongside observational data from China.The ST anomaly memory is quantified using the autocorrelation method while considering the vertical propagation of ST anomaly signals across different soil layers.Results indicate that all three reanalysis datasets effectively reproduce the spatial distribution of multi-year average shallow ST.However,notable differences exist in ST memory.High-memory regions in ERA5 and ERA-Interim are primarily located in areas with an average annual precipitation of 400 to 800 mm,where ST anomalies persist for approximately 8—10 months.In contrast,GLDAS exhibits a distinctly different spatial pattern,with ST memory values significantly higher in the western part of China than in the eastern part of China.The intermonthly variations of ST memory show strong spatial consistency across different months in all datasets.Additionally,ST anomalies tend to propagate into deeper soil layers over time.ERA-Interim and ERA5 indicate longer ST anomaly persistence in the first soil layer in Shanxi,Shaanxi,and Henan Provinces,while in GLDAS,ST anomalies persist longer in western China.Compared with observational data,GLDAS and ERA5 better represent the persistence characteristics of ST anomalies in the first soil layer but fail to accurately capture the persistence characteristics of ST anomalies across the entire soil column.The ability of reanalysis datasets to reproduce ST anomaly persistence varies significantly by region and season.These findings highlight differences in ST anomaly persistence among reanalysis datasets,contributing to a better understanding of the climatic effects of antecedent ST anomalies.Moreover,discrepancies in soil stratification among different reanalysis datasets complicate direct comparisons of ST memory.Therefore,when using reanalysis datasets to study the influence of antecedent ST anomalies on subsequent climate variability,it is crucial to evaluate ST anomaly persistence to ensure the reliability of research conclusions.This study enhances the understanding of uncertainties in land surface-atmosphere interactions and the climatic impacts of antecedent land surface anomalies.

Abstract:Atmospheric blocking is a significant large-scale anticyclonic circulation system in midlatitude,characterized by prolonged duration beyond synoptic timescales and strong nonlinearity.Blocking events often occur at the exit of jet stream,exerting substantial meteorological impacts such as cold surges,heat waves,and flooding,and pollutants stagnation,both locally and downstream.Understanding blocking dynamics is thus of critical scientific and practical importance.Since the 1950s,extensive observational and statistical studies have been conducted to characterized blocking patterns,leading to the development of multiple detection indices.These indices generally fall into two main types:one capturing the meridional gradient reversal of the zonal wind anomalies at latitude of the eddy-driven jet,and the other quantifying the anomalous amplitude of the large scale circulation.However,discrepancies in blocking frequency,location,and intensity due to index-specific sensitivities pose challenges for both observational and simulation studies.In this study,we introduce the instantaneous blocking index (I_BA) formulated within the anti-cyclonic local finite-amplitude wave activity (ALWA) framework.As a nonlinear diagnostic based on quasi-geostrophic potential vorticity (QGPV),I_BA effectively quantify the polar displacement of QGPV relative to the eddy-free reference state,while maintain as inverse correlation with the spatial-temporal variability of the eddy-driven jet.Consequently,I_BA integrates the key attributes of conventional blocking indices,capturing both westerly flow reversals and wave amplitude anomaly.Furthermore,it addresses limitations of existing indices by accurately characterizing jet variability,and prevents misidentification of unrelated large-scale systems,such as cut-off low,weak trough,and subtropical or subpolar highs.Most importantly,I_BA possesses intrinsic dynamical significance which can be used to quantify the blocking variability directly through the LWA budget.Statistical analysis of I_BA for winter blocking over North Atlantic and Urals reveals a significant increase in Ural blocking frequency and duration since 1990.On interannual timescale,North Atlantic blocking exhibits a significantly negatively correlated with the NAO (North Atlantic Oscillation),with increased frequency,intensity,and lifespan during the negative NAO period.Similarly,Ural blocking shows negatively correlated with EA/WR (East Atlantic/West Russia) and ENSO (El Niño-Southern Oscillation),with higher frequency during negative EA/WR and negative La Niña phases,and extended lifespan during negative EA/WR phase.These teleconnections correspond to more intense evolution of trough/ridges and intensified cold air activity over East Asia.
This study represents a fundamental application of ALWA in describing the strongly nonlinear atmospheric system such as blocking.This nonlinear dynamical framework offers significant advantages for quantifying blocking dynamics and variability.Furthermore,by utilizing its budget,it allows for quantifying the internal dynamical processes,diabatic heating effects,wave-breaking dissipation,and other nonconservative processes in the further research.

Abstract:This study investigates the spatiotemporal characteristics of convective available potential energy (CAPE) in the Yangtze-Huaihe region during summer(2018—2022) using IGRA sounding data and ERA5 reanalysis data.A linear regression method was applied to develop a correction formula for ERA5 CAPE based on IGRA sounding observations,yielding a corrected dataset (CAPE_COR) for 08:00 BST and 20:00 BST on a daily scale.Correlation analysis shows that CAPE_ERA5 and CAPE_IGRA exhibit relatively strong agreement,with correlation coefficients of 0.58 at 08:00 BST and 0.59 at 20:00 BST.The scatter plot indicates that data points are densely clustered along the diagonal,confirming the accuracy of the correction.After applying the adjustment,the correlation coefficients between CAPE_COR and CAPE_IGRA slightly decrease to 0.57 and 0.58,respectively,demonstrating the reliability of the correction formula and its improved representation of the actual convective environment.
Spatially,summer CAPE_COR in the Yangtze-Huaihe region exhibits a distinct geographical pattern,increasing from southeast to northwest and from river valleys to mountain peaks.High CAPE_COR values are primarily concentrated in the Yangtze River valley,particularly in areas enclosed by three major mountain ranges,while lower values are observed over higher elevations.This distribution highlights a strong correlation between CAPE and topographic features,with higher CAPE_COR values more frequently occurring in the Yangtze River valley and weaker values over elevated terrains.Further analysis reveals that CAPE_COR at both 08:00 BST and 20:00 BST is negatively correlated with terrain height and positively correlated with the terrain index,suggesting that areas with lower elevations and steeper slopes exhibit stronger convective energy.These findings emphasize the influence of topography on local convective conditions,where low-altitude valleys with pronounced terrain gradients are more conducive to the accumulation of convective energy.Temporally,CAPE_COR in the Yangtze-Huaihe region exhibits significant intra-seasonal and diurnal variations.The seasonal fluctuation exceeds 1 400 J/kg,peaking at approximately 1 800 J/kg in late July.The diurnal cycle follows a distinct pattern:CAPE_COR decreases in the early morning (00:00 BST—05:00 BST),rises sharply from morning to mid-afternoon (05:00 BST—15:00 BST),and then declines again in the evening and night (15:00 BST—24:00 BST).During the daytime (08:00 BST—20:00 BST),CAPE_COR consistently exceeds 1 000 J/kg,while at night (20:00 BST—08:00 BST),it remains below 1 000 J/kg.The daily minimum occurs around 05:00 BST (approximately 850 J/kg),while the maximum appears at 15:00 BST (around 1 200 J/kg).This diurnal cycle indicates that convective activity peaks in the afternoon,coinciding with maximum solar heating,and weakens during the early morning hours.
Overall,this study underscores the critical role of geographic and topographic features in shaping the spatial and temporal distribution of convective energy in the Yangtze-Huaihe region.These findings enhance the understanding of CAPE characteristics and provide a valuable scientific basis for improving severe weather forecasting and advancing knowledge of convective processes in complex terrain.

Abstract:General aviation (GA) has experienced significant growth in China since its formal definition in 1986.The enactment of the Civil Aviation Law of the People's Republic of China in 1995 further clarified GA as civil aviation activities outside public air transport,encompassing industrial,agricultural,humanitarian,and emergency services.This expansion has been driven by improvements in industry regulations,infrastructure development,and ongoing airspace reforms.However,meteorological services for GA remain a critical area for improvement.
The 2008 Wenchuan earthquake underscored the vital role of GA in disaster relief,despite limitations in communication and meteorological support.Since then,GA has expanded into various sectors,including tourism,mapping,and logistics,making substantial contributions to economic and social development.By the end of 2022,China had 399 registered GA airports and 3 186 aircraft,with projections indicating an increase to 500 airports and 4.5 million flight hours by 2025.This rapid growth necessitates enhanced meteorological services tailored to GA operations.
Unlike commercial aviation,GA represents unique meteorological challenges due to diverse aircraft types,variable flight altitudes,and a heavy reliance on low-altitude weather conditions.These factors require highly localized,precise meteorological forecasts.However,China's current GA meteorological infrastructure is limited by insufficient observational equipment,incomplete data coverage,and inadequate forecasting capabilities——particularly for low-altitude operations.
Internationally,significant advancements have been made in GA meteorological services.Numerical Weather Prediction (NWP) systems have improved through higher-resolution models and advanced data assimilation techniques.Observational technologies,such as Doppler radar and lidar,provide high-resolution data,enhancing forecast accuracy.Studies have demonstrated that advanced NWP systems and data assimilation techniques can significantly improve forecasts of small-scale weather phenomena,such as thunderstorms (Shun et al.,2009;Joe et al.,2022).Additionally,international efforts have focused on integrating multiple observational datasets and forecast models to provide comprehensive meteorological support for GA operations.
In contrast,China's GA meteorological services remain underdeveloped.The current system lacks comprehensive observational networks,advanced forecasting capabilities,and efficient data-sharing platforms.Most GA airports have limited meteorological infrastructure,and available meteorological data are often insufficient for low-altitude operations.To address these challenges,it is essential to develop high-resolution meteorological products,enhance observational capabilities,and establish a unified meteorological information-sharing platform for GA.
This paper reviews the current state and future trends of GA meteorological services,both domestically and internationally.It identifies key meteorological hazards affecting GA safety,including low-altitude wind shear,turbulence,typhoons,low visibility,thunderstorms,icing,and precipitation.Furthermore,it discusses the development of a GA meteorological warning and forecasting system,emphasizing real-time data integration,multi-source data fusion,and user-friendly meteorological products.
The proposed GA meteorological service system aims to provide real-time meteorological data,integrate observational and forecasting capabilities,and offer personalized meteorological services for GA operations.This system will leverage modern technologies,including artificial intelligence,big data,and mobile applications,to enhance meteorological support.Future efforts will focus on improving the spatial and temporal resolution of meteorological products,developing region-specific meteorological services,and enhancing data-sharing mechanisms through integrated platforms.
In conclusion,developing advanced GA meteorological services is essential for ensuring the safe and efficient operation of GA in China.By learning from international experiences and adopting advanced technologies,China can significantly improve its GA meteorological capabilities,thereby enhancing industry development and mitigating meteorological risks associated with GA operations.