We bias-corrected and assessed summer precipitation data over the Qinghai-Xizang Plateau （QXP） based on 18 models from the Coupled Model Intercomparison Project Phase 6 （CMIP6）.Our assessment of CMIP6 data， conducted for the period 1979-2014， centered on the performance of both the ensemble and individual models.We evaluated the CMIP6 data before and after bias correction， according to considering mean precipitation and extreme precipitation.The results highlight the correction method's dependence on ERA5 reanalysis data quality over the QXP.Although corrected mean summer precipitation over the QXP shows improvement in bias and bias rate， it exhibits inferior interannual time-varying characteristics compared to pre-corrected data.Most of the models were able to better simulate the spatial variability characteristics of mean precipitation over the QXP， gradually increasing from northwest to southeast from 1979 to 2014.Pre-correction precipitation data overestimates precipitation over the QXP with a bias rate of 60.4%， while corrected data is relatively underestimated with a deviation rate of -13.9%.The mean bias of the corrected data from ERA5 is only 0.003 mm·d^-1， with a spatial correlation as high as 0.999.Spatial trend analysis of observed data indicates a slight increase in summer precipitation over most of the TP from 1979 to 2014， with a significant decreasing trend only along the eastern edge.Both pre- and post-corrected data generally capture this spatial distribution， though pattern correlation coefficients of most individual uncorrected CMIP6 models do not exceed 0.5.Comparing with the interannual variability of the precipitation data obtained from observations， the pre-corrected data overestimate the precipitation on the QXP， while the post-corrected data are underestimated in comparison with the observation results.Extreme precipitation is selected by determining the 95% thresholds， a revealing a spatial distribution similar to the mean annual precipitation， increasing from northwest to southeast.This feature is well captured by some models， such as MRI-ESM2-0 （The Meteorological Research Institute Earth System Model version 2.0） and ACCESS-CM2 （Australian Community Climate and Earth System Simulator Climate Model Version 2.0）.Earth System Simulator Climate Model Version 2）， the spatial correlation coefficients are 0.851 and 0.821， respectively， compared with the observations， but the spatial correlation of the corrected data decreases from 0.861 to 0.730， failing to accurately characterize the stepwise increase of extreme precipitation on the QXP.The deviation distribution of the corrected extreme precipitation data is similar to pre-correction data， with lower areas concentrated in the southern hinterland and eastern part of the QXP.The analysis of extreme precipitation contribution shows that both the observation results and the CMIP6 precipitation data indicate that the trend of extreme precipitation contribution is not obvious during 1979-2014.Among individual models， EC-Earth3-Veg （European Community Earth-Vegetation model version 3） and EC-Earth3 （European Community Earth Model version 3） and CanESM5 （The Canadian Earth System Model version 5） ranked high in several parameters， showing better simulation capability， while IPSL-CM6A-LR （Institute Pierre-Simon Laplace Climate Model 6A Low Resolution） ranked high in the mean precipitation deviation and extreme precipitation deviation.

Frozen soil is the essential component of terrestrial cryosphere.Soil freeze-thaw process （SFT） affects soil structure， soil hydrothermal transfer， and biogeochemical processes， thereby influencing local and global weather and climate through land-atmosphere interaction.Therefore， it is of importance to explore SFT for human activities in frozen soil regions and for studying weather and climate change for local and remote regions.This paper reviews the effects and physical mechanisms of gravel and soil organic matter （SOM） on soil thermal and hydrological parameters and SFT， and summaries achievements in parameterizations of SFT， with focuses on soil thermal conductivity， hydraulic parameters， water-heat coupled parameterization， and freeze-thaw fronts.Gravel （SOM） has higher （lower） thermal conductivity and lower （higher） heat capacity， and thus they have different effects on the soil heat transfer and vertical distribution of soil temperature.Additionally， the existences of gravel and SOM change soil porosity， matrix capillary and adsorption， thereby affecting the transfer and vertical distribution of soil water content.Previous studies show that： （1） the Johansen scheme and its derivatives are widely incorporated into land models to calculate soil thermal conductivity.In consideration of the effect of gravel and SOM on soil thermal conductivity， the Balland-Arp scheme， a derivative of the Johansen scheme， better describes soil thermal conductivity during SFT.The thermal-hydro-deformation interaction thermal conductivity scheme comprehensively describes the water-heat coupling and frost heave impacts， resulting in more accurate simulation of characteristics of soil thermal conductivity in the drastic phase transition.（2） Supercooled water parameterization scheme can depict the existence of liquid water below 0 °C in soil.Variable freezing threshold parameterization depicts that water phase transition to ice happens below 0 °C.Taking account of the impedance of soil ice to liquid water infiltration improves model performance in simulating the hydrological process in frozen soil.（3） The water-heat coupled scheme is proposed to capture the synergistic changes of both thermal and hydraulic processes in soil， especially the interaction between water and heat.These schemes describe complex physical mechanisms during SFT in detail， and therefore can reduce model biases in simulating the transfer and vertical distribution of heat and water in soil.（4） Most numerical models with an isothermal framework assume that phase change of soil water/ice occurs in the middle of each soil layer and the entire model layer is either frozen or thawed， resulting in serious misestimates of the freeze-thaw depth in soil.To solve this problem， the freeze-thaw front parameterization scheme is developed and incorporated into models.Despite great progress in simulating SFT， there are still some deficiencies.Saline soil lowers freezing point of soil water， but this has not been considered in most current numerical models； although the impact of SOM on soil thermal and hydraulic conductivities has been taken into account， the content of SOM and its vertical distribution is not realistically associated with the growth of vegetation roots； the entire soil depth is not sufficient deep and the assumption of zero heat flux through bottom of soil in numerical models is not the case in the reality.Therefore developments of parameterization schemes to simulate the transfer and distribution of soil salt， to depict the root growth and vertical distribution of SOM， to take account of the influence of deep soil layers and real bottom boundary conditions are among the possible improvements in the future land models to improve the simulation of SFT.

The China Meteorological Forcing Dataset（0.1°×0.1°） from 1979 -2018 was used as atmospheric forcing data to drive CLM5.0 （Community Land Model version 5.0） to simulate soil temperature and moisture changes in the Qinghai-Xizang Plateau region from 1979 to 2018.Divide the soil freeze-thaw process into two stages： freezing period and thawing period.By comparing and validating CLM5.0 simulation with site observation data， assimilation data （GLDAS-Noah）， and satellite remote sensing data （MODIS soil temperature data and ESA CCI-COMBINED soil moisture data） in two stages， this study explores the applicability of CLM5.0 simulation of soil temperature and moisture in the Qinghai-Xizang Plateau.The results indicate that： （1） CLM5.0 can accurately describe the dynamic changes in soil temperature and moisture at stations on the Qinghai-Xizang Plateau.The soil temperature and moisture simulated by CLM5.0 have consistent variation characteristics with the observed data and are numerically close.The accuracy of CLM5.0 simulation is higher than that of GLDAS Noah.CLM5.0 provides a more accurate description of soil temperature at the stations.（2） CLM5.0 can accurately describe the soil temperature and moisture characteristics during the freeze-thaw process in the Qinghai-Xizang Plateau.CLM5.0 simulated soil temperature and moisture show a significant positive correlation with MODIS and ESA CCI-COMBINED remote sensing data on the Qinghai-Xizang Plateau， with correlation coefficients mostly above 0.9.CLM5.0 has relatively better simulation ability for soil temperature in Qinghai-Xizang Plateau areas.CLM5.0 has better simulation ability for soil moisture during thawing periods than during freezing periods.CLM5.0 overestimates the soil temperature of the Qinghai-Xizang Plateau as a whole， with an average deviation mostly between 0~4 ℃.The average deviation of soil moisture simulated by CLM5.0 is mostly between -0.1~0.1 m³·m^-3， and the average deviation of soil moisture during thawing period is relatively small.（3） The soil temperature and moisture data from CLM5.0 simulation， GLDAS-Noah， MODIS， and ESA CCI-COMBINED remote sensing all have similar spatial distribution characteristics， with higher similarity in the spatial distribution characteristics of soil temperature.CLM5.0 has higher spatial resolution and more precise soil stratification， which can better describe the details of soil temperature and moisture.（4） The CLM5.0 simulation data shows an overall warming and drying trend in the Qinghai-Xizang Plateau， while the MODIS and ESA CCI-COMBINED remote sensing data show an overall warming and moistening trend.The trend of soil temperature changes simulated by CLM5.0 is relatively accurate， while there is a greater deviation in the trend of soil moisture changes.

Purposes Methods Extreme weather and climate events have been exhibiting an intensification under global warming.This intensified extremity thus augments the damaging impacts on both society and the economy.In the Northern Drought-prone Belt （NDPB）， extreme drought events are becoming more frequent and more intense with a broader distribution.In this study， by using statistical analysis and composite analysis， characteristics of the main factors affecting prolonged spring-summer extreme drought events in NDPB are analyzed based on the meteorological drought composite index， precipitation and near-surface air temperature data observed by meteorological stations， reanalysis dataset， sea surface temperature， snow cover， and the sea ice concentration data.Findings Conclusions Results show that the main circulation factors are as follows： the eastward propagating wave trains from Baffin Bay in March， a “positive-negative-positive-negative-positive-negative” geopotential height anomaly from the south of Hudson Bay to the east of Lake Baikal in May， an eastward wave train near the 60°N latitude， the Silk Road wave train and the eastward， weak Western Pacific Subtropical High in June； the main external forcing factors are listed below： the phase transition from La Ni?a to El Ni?o， warmer sea surface temperature over the Indian Ocean basin and the central North Atlantic； the shrinking snow cover in the mid-to-high latitudes of Eurasia and between 40°N and 60°N of North America， a snow cover reversal from abnormally high to abnormally low over the Qinghai-Tibet Plateau， a “negative-positive” sea ice concentration anomaly with less near Baffin Bay and Davis Strait but more near Greenland Sea， and a “positive-negative-positive” sea ice concentration anomaly from the Barents Sea to the Kara Sea.

From 22:00 on September 6， 2023 to 04:00 （Beijing Time） on September 7， Xiahe County in Gansu Province experienced severe convective weather， with short-term heavy rainfall in some areas， causing flash floods in Guoning Village， Xiahe County， resulting in casualties.In this study， the characteristics of Radar Quantitative Precipitation Estimation （Radar-QPE）， FengYun 4B Quantitative Precipitation Estimation （FY4B-QPE）， and CMA Multi-source Precipitation Analysis （CMPA） precipitation products were contrastive analyzed based on meteorological station observations.These precipitation data were used to drive the hydrodynamic hydrological model and evaluate the effect of different precipitation data in the flash flood simulation.The results showed that： （1） Among the 12-hour cumulative precipitation amounts， CMPA demonstrated higher accuracy in terms of the position of large value areas and differences in local precipitation levels； Radar-QPE was closer to AWS （Automatic Weather Station） in terms of cumulative precipitation level but showed significant differences in spatial distribution； FY4B-QPE overestimated the cumulative precipitation level by 33.8%.（2） In terms of hourly distribution， CMPA was most similar to AWS in terms of temporal evolution， spatial distribution， and precipitation level； Radar-QPE's peak values were smaller， and the peak times were lagged， with negative deviations in precipitation being dominant； FY4B-QPE's peak values and peak times were consistent with reality， but there were deviations in the start and end times of precipitation， with positive deviations in precipitation being dominant.（3） In the hydrological simulation study， CMPA， Radar-QPE， and FY4B-QPE all overestimated water levels， but the timing of water level peaks was more consistent with AWS.CMPA performed best in terms of RMSE （Root Mean Square Error）， NSE （Nash Efficiency Coefficient）， and Bias （Relative Deviation）， followed by Radar-QPE， and FY4B-QPE performed relatively poorly.Although existing site-observed precipitation cannot fully meet the needs of research and early warning for small and medium scale mountain floods， the high precision of CMPA data could effectively supplement the deficiencies of traditional meteorological observation stations to some extent.Meanwhile， the algorithms and accuracy of Radar-QPE and FY4B-QPE needed to be further improved and enhanced.

In recent years， rapid urbanization and global warming have led to frequent and severe rainstorm and flood disasters in the Sichuan-Chongqing region.This change will not only have a serious impact on the ecological environment and socio-economic development of the area， but also significantly increase the pressure on urban infrastructure and threaten the safety of people's lives and property.Therefore， it is particularly important to scientifically and accurately analyze the disaster risk of rainstorm and flood in Sichuan-Chongqing region in the past and future.This paper utilized daily precipitation data from 50 selected meteorological stations in the Sichuan-Chongqing region， precipitation data from 5 CMIP6 models， gridded population and economic data under Shared Socioeconomic Pathways （SSPs）， as well as DEM and land use remote sensing data.Firstly， using Taylor diagrams， quantitative indices （S）， and standardized anomaly sequences， the study evaluated the simulation performance of 5 individual CMIP6 models， an equal-weighted aggregation of 5 models （EWA-5）， and unequally-weighted aggregations of 5 models （UEWA-5） for five selected extreme precipitation indices.Then， by building a comprehensive risk assessment model of rainstorm and flood disaster based on disaster risk and vulnerability of disaster bearing body， the study conducted risk assessments， future projections， and comparative analyses of rainstorm and flood disasters during baseline （1995-2014） and future near-term （2025-2044） and long-term （2045-2064） periods under three different climate change scenarios （SSP1-2.6， SSP2-4.5， SSP5-8.5）.Results indicated： （1） The EC-Earth3 model performed best in simulating the five extreme precipitation indices， with correlation coefficients between simulated and observed values of 0.78 for R95p， 0.90 for RX1day， and 0.77 for RX5day.Overall， the simulation performance of UEWA-5 exceeded that of EWA-5.（2） During the baseline period， central Sichuan exhibited high values for the five extreme precipitation indices， followed by eastern Sichuan and Chongqing， while western Sichuan showed lower values.The year 1998 recorded peak values for all five indices， with a maximum single-day precipitation of 86 mm for RX1day and an intensity （SDII） value of 11.3 mm·d^-1.（3） In future periods， the five extreme precipitation indices display a spatial distribution characterized by higher values in central regions and lower values around the periphery.Higher levels of social vulnerability and radiative forcing correlate with larger values of extreme precipitation indices.Comparing the two future periods， values of the indices are larger in the long term， notably with R95p averaging 846.8 mm， an increase of 169.2 mm compared to the near term.（4） During historical periods， areas with higher comprehensive risk of rainstorm and flood disasters were concentrated in central Sichuan and downtown Chongqing.In the two future periods， the high and moderately high-risk areas in central Sichuan are expected to expand， while the moderate-risk areas will shrink.The range of low-risk areas in the western Sichuan Plateau will also decrease， and the risk levels in southern Sichuan and eastern Sichuan-Chongqing border areas will respectively decrease to moderate-low and low-risk zones.Comparing the two future periods， the range of moderately high and moderate-risk areas in central Sichuan is expected to expand， while southwestern Chongqing will transition to a moderate-risk area in the long term.Other regions will generally maintain their original risk levels.Changes in disaster risk levels in the Sichuan-Chongqing region are less pronounced with increasing social vulnerability and radiative forcing， especially in the western Sichuan Plateau and northeastern Sichuan， where changes in disaster risk levels are minimal.The study results can provide important references for reducing disaster risks， enhancing emergency response capabilities， and making scientifically informed decisions for disaster prevention in the Sichuan-Chongqing region.

Atmospheric rivers significantly impact the ocean-land-ice-atmosphere interaction around Antarctica.However， the shortage of in situ observations limits people’s understanding， bringing considerable uncertainty in numerical simulation results and products.This study utilized ship-borne radiosonde data collected during the 37^th Chinese Antarctic Expedition to evaluate four kinds of state-of-the-art atmospheric reanalysis datasets （ERA5， CFSv2， JRA-55， and MERRA-2） during an atmospheric river event in the Southern Ocean.All reanalysis provide acceptable descriptions of integrated water vapor transport （IVT） compared with the observation， even during the atmospheric river events.However， all reanalyses overestimated the humidity and underestimated the wind speed across the entire atmospheric column （from surface to 300 hPa）.Moreover， all reanalyses， except for ERA5， failed to capture the variation in the covariance term between humidity and wind speed in the vertical direction； the latter contributes to a considerable bias in the IVT of reanalyses.The ERA5 demonstrates superior performance during the observation period， especially in humidity and low-level jet profiles when the atmospheric river arrives at the observation site.In this study， ERA5 seems to be the best atmospheric reanalysis for studying atmospheric rivers in the Southern Ocean.

Plant diversity significantly affects the structure and function of ecosystems and plays a crucial role in soil organic carbon sequestration.In the past， the effects of plant diversity on soil organic carbon were mostly carried out under artificial plant diversity control， indicating that high plant diversity significantly promoted soil organic carbon accumulation.However， in natural grassland ecosystem， the research on the effect of plant diversity on soil organic carbon is relatively weak.In this study， 15 typical alpine meadows in the northeastern part of the Qinghai-Xizang Plateau were selected as sample sites.By measuring plant above-ground and subsurface biomass， soil pH value， soil microbial biomass carbon and nitrogen， soil organic carbon， granular organic carbon， mineral-bound organic carbon， total nitrogen and total phosphorus， etc.， the effects of plant diversity on soil organic carbon sequestration under natural conditions were explored.It provides theoretical basis for the change of soil carbon storage and scientific management of grassland.The results showed that plant diversity significantly increased plant coverage and aboveground biomass （P < 0.01）， but had no significant effect on underground biomass in different soil layers （0~20 cm and 20~40 cm）.In 0~20 cm and 20~40 cm soil layers， the increase of plant diversity significantly increased soil microbial biomass carbon and organic carbon contents （P<0.05）， but had no effect on microbial biomass nitrogen in different soil layers.According to the classification of soil organic carbon， there was a significant positive correlation between plant diversity and soil mineral bound organic carbon content （P<0.01）， but no correlation with soil particulate organic carbon content.In conclusion， in the alpine meadow of the Qinghai-Xizang Plateau， higher plant diversity under natural conditions has a significant promoting effect on soil organic carbon content， which is mainly reflected in the increase of mineral binding organic carbon content.This study provides new insights and theoretical basis for the relationship between plant diversity and soil carbon pool in grassland ecosystem.

Drought represents a significant contributing factor to global climate-related disasters.It not only endangers the stability of global ecosystems and biodiversity but also has far-reaching implications for socio-economic development.As global climate change intensifies， so too does the frequency and intensity of droughts.Drought events in ecologically fragile regions not only threaten the availability of water resources but also increase the risk of food insecurity， ecological degradation and social conflict.Nevertheless， despite the growing body of research in this area， there remain significant gaps in our understanding of the spatial and temporal characteristics of drought occurrences over the past four decades， as well as its evolutionary trends under different climate scenarios in the future.This study employs the Standardized Precipitation Evapotranspiration Index （SPEI） and CMIP6 climate change scenarios to analyze the spatial and temporal characteristics of global droughts over the past four decades and to predict the evolution of global droughts under different climate scenarios （SSP1-2.6、 SSP2-4.5、 SSP5-8.5） over the next 80 years.The findings of the study indicate that： （1） During the period between 1980 and 2022， there were notable variations in the spatial and temporal characteristics of global drought across different regions.Globally， approximately 57% of the land area does not exhibit a significant drought trend.However， about 33% of the land shows a persistent aridification trend， particularly in some already arid regions， where the intensity of drought has increased.Conversely， only 10% of the area is becoming wetter， indicating that the regions of the globe that are becoming drier are significantly larger than those that are becoming wetter.This suggests that the aridification process is spreading globally； （2） Over the past four decades， the globe has experienced an arid trend with no significant seasonal differences.However， the arid regions in winter are expanding， accounting for 33.2% of the global land area； （3） Different vegetation cover types exhibit varying responses to drought.Sparsely vegetated areas are more susceptible to drought， while densely vegetated areas tend to be wetter.Furthermore， arid climate zones situated within diverse climatic contexts are confronted with more pronounced drought-related challenges.The largest proportion of severe drought is observed in extreme arid zones， which account for up to 67% of the global land area， this indicates a higher frequency of drought events in drylands； （4） The probability of drought events is predicted to increase significantly in Africa， South America， southeastern Asia， and southern North America， particularly in tropical or warm climatic zones， extreme arid zones， and evergreen broadleaf climate zones.Furthermore， droughts are expected to become more frequent and severe， especially in tropical or very warm climate zones， arid zones， and broadleaf evergreen forest regions.The SSP5-8.5 scenario is projected to have the highest probability and intensity of drought events over the next 80 years， and will be challenged by more frequent and severe droughts.The findings of this study underscore the pervasive and severe nature of the global aridification trend， particularly in the context of climate change， where the frequency and intensity of droughts are projected to increase significantly.This trend not only enhances our comprehension of the risk of drought， but also furnishes an essential point of reference for policymakers， water managers， and the general public.In order to mitigate the potential intensification of droughts in the future， it is imperative that all sectors of society implement more proactive and efficacious measures to promote adaptation and mitigate the challenges posed by droughts.The rational management of water resources， improvements in agricultural irrigation techniques， enhanced ecosystem resilience and the strengthening of monitoring and early warning systems for climate change and droughts will ensure global ecological security and facilitate sustainable socio-economic development.

Global warming has led to rapid changes in the sea ice of the Antarctic and Arctic， triggering a number of climate feedbacks.Turbulent heat exchange between the polar seas and the air plays an important role in these feedbacks.Solar radiation， as the main energy source at the polar sea surface， is mainly used for sea ice melting and air-sea heat exchange， but the higher albedo of sea ice results in low radiation absorption.Air-sea heat exchange is influenced by temperature and humidity gradients between the sea surface and the atmosphere， with sensible heat dominating at the sea-ice edge and latent heat farther from the sea ice.In the Arctic， air-sea heat exchange is dominated by sensible heat， whereas in the Antarctic it is dominated by latent heat.The air-sea heat fluxes at the north and south polar seas vary seasonally.Sea ice can also inhibit air-sea heat exchange to some extent.Accurate parameterization of the turbulent heat exchange between the sea and the atmosphere in the sea ice regions is crucial for simulating air-sea interactions， however， in situ observations remain extremely rare due to limitations of the polar environment， and accurately representing the turbulent air-sea exchange in polar oceans remains a challenge.In the future， the network of air-sea flux measurements in polar seas should be strengthened， especially in the marginal ice zone， which are necessary and crucial for a deep understanding of the role of air-sea interactions in polar regions on global climate change， and for reducing the uncertainty in climate models.Secondly， the dynamics and thermal properties of ice must be fully considered to optimize the parameterization scheme or develop new models to improve the simulation accuracy.Furthermore， the influence of waves on the air-sea heat exchange in the sea ice region should be clarified to fill the research gaps.Finally， the contribution of the air-sea heat exchange to climate change in the polar regions should be evaluated further to improve the understanding of the role of polar oceans in climate change.

The freeze-thaw cycle of near-surface soil in the perennial permafrost region of the Qinghai-Xizang （Tibetan） Plateau plays a crucial role in regulating water and energy exchange between the soil and the atmosphere.Investigating its spatiotemporal characteristics and response to climate change is essential for understanding the mechanisms driving climate change on the plateau.In this study， we calculated near-surface freeze-thaw parameters-including the start and end times of soil freezing， thawing duration， and freezing duration-across the perennial permafrost region of the plateau from 1980 to 2017 using the Common Land Model 5.0 （CLM5.0）.We further analyzed their spatiotemporal variations and correlations with temperature， precipitation， snow depth， and vegetation index.The results show that： （1） The onset of near-surface soil freezing in the plateau’s permafrost region occurs between September and mid-to-late October， while the thawing period ends between February and May.Semi-humid regions have the longest thawing duration， whereas semi-arid regions have the shortest， with an average difference of 15 days.The freeze-thaw status of permafrost soil on the plateau exhibits significant changes.Except for areas near the Karakoram Mountains， most permafrost regions show a decreasing trend in freezing duration and an increasing trend in thawing duration.The average growth rate of soil thawing duration across the plateau is 2 d·（10a）⁻¹， with the most significant increase observed in semi-humid regions， reaching 4 d·（10a）⁻¹.（2） The freeze-thaw parameters of the plateau's permafrost are associated with geographical factors.In the latitude range of 29°N -36°N and longitude range of 82.5°E -103°E， the thawing duration shows an increasing trend； however， the rate of change decreases in some areas while increasing in others.Additionally， as elevation increases， the growth rate of thawing duration declines.（3） The duration of permafrost thawing is significantly correlated with snow depth， near-surface temperature， precipitation， and vegetation index， though these relationships vary across different climatic regions.Near-surface temperature exhibits a strong positive correlation across all regions， making it the primary driver of freeze-thaw changes.Precipitation and snow depth show positive and negative correlations， respectively， with particularly strong correlations in semi-humid areas.The vegetation index is positively correlated with thaw duration in all regions， with the strongest correlation observed in semi-arid areas.（4） The relationship between thawing duration and seasonal climatic factors varies.Near-surface air temperature exerts a significant influence on the freeze-thaw process at seasonal scales， with the most pronounced impact occurring in spring.Precipitation is positively correlated in summer but negatively correlated in winter.Both snow depth and vegetation index are significantly correlated with thawing duration in semi-arid and semi-humid regions during spring， exhibiting negative and positive correlations， respectively.（5） Near-surface temperature influences the freeze-thaw cycle in the plateau’s perennial permafrost region during both dry and wet seasons.However， the effects of snow depth， precipitation， and vegetation index are more pronounced during the wet season.

Photosynthetically Active Radiation （PAR） spectrum， in visible light， is the wavelength range sensitive to plants and can be absorbed by them for photosynthesis.The characteristics of ground PAR spectrum directly affect the growth， development， morphology， physiological metabolism， yield， and adaptability of plants.In order to further understand the distribution characteristics of PAR in high-altitude areas of Xizang， this study utilized the International High-Precision Solar Spectroradiometer to conduct field observations of the PAR spectrum characteristics in the Mt.Everest， Shigatse， Lhasa， and Nyingchi regions of the Qinghai-Xizang Plateau from 2021 to 2022.The observations found that during the winter and summer solstices on the Qinghai-Xizang Plateau， the variation in PAR was significant.The peak monochromatic radiation illuminance of PAR at Mt.Everest during the summer solstice ［1251 mW·（m²·nm）^-1］ to the winter solstice ［1935 mW·（m²·nm）^-1］ fluctuated by up to 684 mW·（m²·nm）^-1.The winter solstice integrated value of PAR spectrum at Mt.Everest （309.86 W·m^-2） was 41.61% lower than the AM0 standard spectrum integrated value of PAR （530.67 W·m^-2）， and 28% lower than the AM1.5 standard spectrum integrated value of PAR （429.83 W·m^-2）.During the summer solstice， the PAR spectra at Mt.Everest， Shigatse， and Lhasa in Xizang all exceeded the AM1.5 standard spectrum at noon and were close to the AM0 standard spectrum.In Shigatse， Xizang， during the spring equinox and autumn equinox， the peak PAR spectra were 1699 mW·（m²·nm）^-1 and 1696 mW·（m²·nm）^-1 respectively， with peak values being nearly identical.This similarity is due to the same local solar altitude angle at noon （e.g.， 59.84 radians in Shigatse） during the equinoxes at the same observation point on the Tibetan Plateau， assuming other factors affecting the spectrum are the same.Comparison of observations between the Qinghai-Xizang Plateau and low-altitude areas such as Beijing， Anhui's Lu'an， and Henan's Puyang revealed that on a clear day near the winter solstice （November 20， 2021）， the integrated value of PAR spectrum at high-altitude Mt.Everest （309.86 W·m^-2 was 17.19% higher than that in low-altitude Lu'an， Anhui （264.41 W·m^-2）； on a clear day near the summer solstice （June 3， 2021）， the integrated value of PAR spectrum at high-altitude Mt.Everest （487.41 W·m^-2） was 23.66% higher than that in low-altitude Beijing （394.15 W·m^-2）； near the autumn equinox （September 19， 2021）， the integrated value of PAR spectrum in low-altitude Beijing （315.23 W·m^-2） was only 71.24% of that at high-altitude Mt.Everest （442.49 W·m^-2）； near the spring equinox （March 19， 2021）， the integrated value of PAR spectrum in high-altitude Shigatse （413.34 W·m^-2） was 64.75% higher than that in low-altitude Puyang， Henan （261.82 W·m^-2）.The results indicate that the integrated value of PAR spectrum is positively correlated with altitude， with higher altitudes corresponding to larger integrated values.Additionally， through observations of PAR spectra on clear days throughout the year， it was found that there are certain temporal variations in spectral radiation illuminance.Specifically， the spectral radiation illuminance is lowest at the winter solstice， then increases daily until reaching its peak the following year after the spring equinox， decreases daily after the summer solstice， reaches its lowest point again at the winter solstice after the autumn equinox， with the spectral radiation illuminance characteristics being basically the same during the spring equinox and autumn equinox.

In an effort to elevate the precision of low-level wind shear forecasting， this paper amalgamates European Centre for Medium-Range Weather Forecasts （ECMWF） fifth-generation reanalysis data （ERA5） and National Centers for Environmental Prediction Final Operational Global Analysis （FNL） reanalysis data， high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model （ASTER GDEM） terrain data， and real-time observational data from Lanzhou Zhongchuan Airport.It employs the Weather Research and Forecasting Model （WRF）， WRF integrated with Computational Fluid Dynamics （CFD）， and Long Short-Term Memory （LSTM） neural network methods to simulate and analyze two wind shear events at Lanzhou Zhongchuan Airport on April 15-16， 2021.The findings reveal that： （1） within grids smaller than 1 kilometer utilizing Large Eddy Simulation （LES）， the WRF model demonstrates superior performance in wind speed simulation for individual stations， yet it falls short when compared to the WRF model combined with Computational Fluid Dynamics （CFD） models in simulating near-surface horizontal wind field wind speeds； （2） concerning the simulation of two low-level wind shears encountered during aircraft landing， both Weather Research and Forecasting Model - Large Eddy Simulation （WRF-LES） and Weather Research and Forecasting Model - Computational Fluid Dynamics （WRF-CFD） models are capable of simulating the first wind shear， however， the second appears to be influenced by the potentially lower wind speed data input into the models， with neither model achieving the threshold for wind speed difference， necessitating further validation in future work； （3） under low wind speed conditions （6 meters per second）， the LSTM-based single-variable wind speed prediction model maintains an average absolute error of approximately 0.59 meters per second， effectively capturing the nonlinear relationship of wind speed changes under various terrain and circulation background conditions.Despite being constrained by WRF errors and incomplete observational elements， multi-variable wind speed prediction can achieve wind speed forecasting with higher computational efficiency and generalization capabilities while ensuring that the average absolute percentage error is less than 6.60%.This paper not only verifies the differences between WRF-CFD and WRF-LES coupling schemes in wind field and low-level wind shear forecasting but also explores the feasibility and accuracy of LSTM-based wind speed prediction， aspiring to offer new perspectives and methods for enhancing wind field simulation accuracy and reducing the time required for detailed wind field simulation.

Using effective bias correction methods and transforming non-stationary data to stationary can enhance the scientific accuracy of temperature analysis， allowing for a deeper understanding of its temporal and spatial distribution characteristics and evolution patterns.This study utilizes the ERA5_Land near-surface （2 m） monthly mean temperature observation dataset covering the period from 1970 to 2014.Initially， it employs the Taylor diagram， Taylor index， interannual variability skill score， and rank scoring method to evaluate and select among six climate models from the International Coupled Model Intercomparison Project Phase 6 （CMIP6） and the multi-model ensemble （MME） average models.Subsequently， the superior models are refined using the Delta bias correction method and the Normal distribution matching method.Finally， the study analyzes the temporal and spatial temperature variation characteristics of the Qinghai-Xizang Plateau from 2015 to 2100 under the SSP1-2.6， SSP2-4.5， and SSP5-8.5 scenarios.The results indicate that： （1） Among the six CMIP6 models and the multi-model ensemble （MME） average models analyzed in this study， the EC-Earth3 model demonstrates the most effective performance in simulating temperature.（2） When comparing the Delta bias correction results of the EC-Earth3 model with observational data， the regional averages of the coefficient of determination （R²） and the Nash-Sutcliffe efficiency coefficient （NSE） are 0.992 and 0.983， respectively.After applying the Normal distribution matching method for correction， the regional average values of R² and NSE are 0.990 and 0.978， respectively.This comparison reveals that the Delta bias correction method exhibits superior correction efficacy for the model's monthly temperature.（3） According to the combination of EOF-EEMD， the annual temperature of the first typical field of the three scenarios changes uniformly in the whole region， and there is a common sensitive area of temperature change under SSP1-2.6 and SSP2-4.5 scenarios， that is， the central region of the Qiangtang Plateau.The temperature dynamics in the second typical field reveal a gradual reverse-phase change from the upper reaches of the Zhaqu River to surrounding areas.Under the SSP1-2.6 scenario， the plateau experiences overall cooling in the east and warming in the west.Conversely， under the SSP2-4.5 and SSP5-8.5 scenarios， the plateau initially warms in the east and cools in the west， followed by a subsequent cooling in the east and warming in the west.This study provides a reference for bias correction methods that enhance the accurate application of climate model data in the Qinghai-Xizang Plateau region and offers essential foundational information for a comprehensive assessment of the impacts of temperature changes on water resources， ecosystems， and the environment in this area.

Utilizing the Anomaly Numerical-correction with Observations （ANO） based on historical observation data and anomaly integration， in conjunction with the ERA5 reanalysis data， a rectification test was conducted on the forecasting products of the Southwest Center WRF-ADAS Real-time Modeling System （SWMS in short）.This study evaluated the efficiency of the ANO method in enhancing short- to medium-term weather forecasts for meteorological quantities during a catastrophic regional heavy rainfall event over the complex topography from June 20 to 25， 2019.Results revealed that the SWMS model exhibited commendable predictability in the middle and upper troposphere， although its accuracy gradually reduced in the lower layers.After post-processing correction using the ANO method， all the predicted variables showed obvious improvements.The average Anomaly Correlation Coefficient （ACC） for the 500 hPa and 700 hPa geopotential height fields within the 72-hour integration increased by a range of 0.1 to 0.2， reaching approximately 0.8， while the 850 hPa geopotential height ACC showed a maximum enhancement of 0.6.Concurrently， the Root Mean Square Error （RMSE） for the corrected 700 hPa and 850 hPa geopotential heights exhibited significant reductions with an average decrease of 24% and 66%， respectively.The correction outcomes for temperature， specific humidity， and horizontal wind also displayed positive effects， which reveal the efficient correcting performance of the ANO method based on historical observation data in rectifying short- to medium-term numerical forecasts of the SWMS model over complex topography.

This study， through comprehensive observations of the radiation field in the Everest National Nature Reserve （hereinafter referred to as "Everest"）， reveals the spatiotemporal variation patterns and potential impacts on the ecological environment and climate.By establishing a meteorological gradient observation network covering diverse ecological environments， including alpine shrubs， alpine wetlands， and alpine desert grasslands， and utilizing advanced radiation observation equipment combined with data quality control and automated processing， the study collected multi-year， continuous， high-precision measurements of four-component radiation fluxes.The results show the following： （1） The net radiation flux at Everest and alpine shrub sites exhibits an annual increase of 0.7 W·m^-2 on an interannual scale； （2） In terms of multi-year monthly average net radiation flux， the values at Everest and alpine shrub sites increase from 15 W·m^-2 in January， peak at 110 W·m^-2 in August， and then decrease to 14 W·m^-2 by December.The multi-year monthly average downward shortwave radiation flux rises from 210 W·m^-2 in January to a maximum of 375 W·m^-2 in June， followed by a significant drop to 230 W·m^-2 in July， remains relatively stable from July to October， then sharply declines from October， reaching a minimum of 200 W·m^-2 in December； （3） The multi-year summer daily average net radiation flux at Everest and alpine shrub sites rises from 0 W·m^-2 at 07:00 （Beijing Time， the same as after）， peaks at 530 W·m^-2 at 13:00， and declines to -110 W·m^-2 at 20:00.The multi-year summer daily average downward shortwave radiation flux increases from 0 W·m^-2 at 07:00， peaks at 860 W·m^-2 at 13:00， and drops back to 0 W·m^-2 at 21:00.In winter， the daily average net radiation flux rises from -120 W·m^-2 at 08:00， reaches a maximum of 370 W·m^-2 at 14:00， then decreases， reaching -120 W·m^-2 again at 20:00.The daily average downward shortwave radiation flux in winter rises from 0 W·m^-2 at 08:00， peaks at 840 W·m^-2 at 14:00， and falls to 0 W·m^-2 by 20:00； （4） There are significant differences in radiation flux among the stations， with the contrast being particularly prominent between the alpine wetland station and the other two stations.The annual average net radiation， annual average downward shortwave radiation， multi-year monthly average net radiation， multi-year monthly average downward shortwave radiation， and the daily average net and downward shortwave radiation in both summer and winter are all higher at the alpine wetland station than at the Everest and alpine shrub stations.The results of this study provide new insights into understanding climate change in high-altitude regions and offer essential data support for developing remote sensing monitoring technology， improving global climate models， and formulating environmental protection strategies in plateau areas.

Considering the spatiotemporal variability of raindrop spectra is an effective way to improve radar quantitative precipitation estimation （QPE）.When using radar to estimate precipitation， the difference of raindrop spectrum is mainly manifested by the formulas of Z-R relation.Using the method of tile partitioning QPE （QPE_TP）， the precipitation estimation area is divided into tile partitions， the Z-R relationship is dynamically fitted using radar and automatic station data to carry out QPE within each tile.The QPE_TP effect was evaluated by utilizing six weather cases.From the evaluation indexes of QPE， the capability of QPE is significantly improved compared with the traditional fixed Z-R relationship and the global dynamic Z-R relationship.The QPE results are basically consistent with the heavy precipitation center， and the bias evaluation indexes are the least.The results show that the QPE_TP method is an effective way to improve radar QPE.

Based on the China snow depth time series data set and high resolution ground meteorological element driven dataset， this study analyzes the spatial and temporal variation of snow depth on the Qinghai-Xizang （Tibetan） Plateau by watershed and elevation gradient during the 1980 -2020 snow season considering different river basins and elevation gradients.Additionally， the study investigates the response of snow depth to climate change in the context of hydrothermal factors.The results show that： （1） Spatial difference in snow depth on the Qinghai-Xizang （Tibetan） Plateau was obvious， showing a distribution pattern of high in the west and low in the east， and greater in the high-altitude mountain areas than in the basin plains， with the average snow depth in the high-altitude mountain areas generally greater than 10 cm.The average snow depth decreased at a rate of 0.25 cm/decade， 64.74% of the regions showed a declining trend， with statistically significant decreases in 29.09% on the Qinghai-Xizang （Tibetan） Plateau during the snow season from 1980 to 2020.（2） There is a clear vertical zonation of snow depth and its trend as influenced by altitude.Below an altitude of 4.2 km， average snow depth increased with elevation.Between 4.2 km and 4.8 km， average snow depth decreased as elevation rises.Above 4.8 km， average snow depth again increased with elevation.A decreasing trend in snow depth was observed across all elevation bands， with the rate of decrease initially increasing and then decreasing with elevation， exhibiting a threshold at approximately 5.0 km.The most rapid decrease in mean snow depth ［3.36 cm·（10a）^-1］occurred in the 5.0~5.2 km elevation band.The interannual variation of mean snow depth exhibited a pronounced altitude-dependent pattern， the rate of snow depth reduction was significantly higher at higher elevations than at lower elevations， especially at 4.8~5.5 km.（3） Climate change on the Qinghai-Xizang （Tibetan） Plateau is ‘warmer and wetter’ overall， but ‘warmer and drier’ in the north-west and south during the snow season from 1980 to 2020.However， there are watershed differences and elevation differences in the response of snow depth to climate change.Specifically， in the Nujiang， Ganges， Amu Darya， and Indus River basins， the warming and aridification of climate conditions have contributed to a reduction in snow depth.Conversely， temperature has a more pronounced effect on snow depth in the Yarlung Tsangpo River， the interior plateau， as well as the Yangtze River basins， the Qaidam Basin， and the Tarim Basin.Additionally， precipitation plays a more significant role in influencing snow depth in the Yellow River， Heihe River basin.In regions with altitudes below 3.5 km， climate conditions characterized by warming and aridification have led to a reduction in snow depth.However， in areas with altitudes above 3.5 km， temperature has a more pronounced influence on snow depth.The altitude-dependent warming of temperature accounts for the altitude-dependent reduction in snow depth.

On August 14， 2018， Typhoon Yagi （2018） moved northward and impacted Shandong Province of China， resulting in widespread rainstorm and heavy rainstorm.The total rainfall caused by the typhoon in Shandong presents a round-shaped distribution.Specifically， on August 14， an outer spiral rainband appeared on the typhoon periphery in southeastern Shandong， bringing short-term heavy rainfall and local heavy rainstorms.Due to the relatively small scale of this rainband， both numerical forecasting models and forecasters face challenges in predicting its rainfall accurately.To study the mechanisms of the outer spiral rainbands of Typhoon Yagi， the characteristics and causes of the spiral rainbands are investigated in this study by using radar data and the observations from ground-based stations， radiosonde stations and aircraft.Numerical experiments are also conducted based on the Advanced Research WRF （Weather Research and Forecasting） model and its Hybrid-3DVAR （three-dimensional variational） data assimilation system.The model adopts 12 km and 4 km one-way nested grids， with 44 vertical layers.The initial ensemble perturbation fields are generated by using a stochastic perturbation method， and the Ensemble Transform Kalman Filter （ETKF） method is used for the bias correction of ensemble forecast， providing flow dependent background errors for the Hybrid-3DVAR assimilation module.Comparative experiments with and without the Aircraft Meteorological Data Relay （AMDAR） data assimilation are conducted by adopting 100% flow-dependent error covariance and by using a 45-minute assimilation time window.The results indicate that the outer spiral rainbands are formed by the merging and development of several linear mesoscale convective systems （MCSs）.The outer spiral rainbands exhibit distinct characteristics of the linear MCSs with leading stratiform precipitation， i.e.， the linear MCSs consist of several convective cells with back-building convection.There are several stronger linear MCSs merging laterally into other linear MCSs.Broad stratiform echoes appear in the front （eastern part） of the linear MCS in its maturity stage， and the convection develops up to 10 km or more.There is a weak-echo transition zone between the strong convective line and the sub-strong stratiform echo region.Short-term heavy rainfall occurs along the linear MCS at the maturity stage.The water vapor of heavy rainfall mainly comes from the near-surface layer （below 850 hPa） around the typhoon， and the water vapor flux convergence is mainly concentrated near the wind field convergence line.Before convection initiation， the middle and lower levels over Shandong are thermally unstable with high temperature and high humidity， and the wind rotates clockwise with height， which favor the development of convective systems.As the typhoon slowly moves northward， downward intrusion of cold air appears at 500 hPa.Below 900 hPa， on the southeast of the typhoon over central Shandong there are local convergence between southwesterly wind and southerly wind， and between southerly wind and southeasterly wind.The convergence-induced dynamic uplift triggers the release of unstable energy， stimulating several local linear MCSs.The MCSs develop northward along the steering flow.The linear MCSs merge and strengthen for several times， and finally the elongated spiral rainbands occur.During the convection lifetime， the updrafts are noticeably stronger than the downdrafts.At the mature stage of the convective systems， dry and cold downdrafts appear in the lower levels in the front of the MCS.Convective systems at the heights above 600 hPa move rapidly eastward with the upper-air steering flow， leading to the gradual weakening and dissipation of the linear MCS.Assimilation of AMDAR can improve the typhoon track and wind field forecasts of the WRF model， as well as the dynamical triggering mechanism of convective systems.Thus， the occurrence of spiral rainbands in the typhoon periphery could be accurately forecasted.Furthermore， central Shandong is a mountainous region， so how does the topography influence the triggering and developing of convective systems？ What are the differences between typhoon outer spiral rainbands and the main body spiral rainbands？ What are the differences between outer spiral rainbands？ These issues deserve further studies.

The Coupled Model Intercomparison Project （CMIP） provides reliable scientific data for predicting ecology， hydrology and climate under the backdrop of global change.However， there are large biases in current climate models， especially on the Qinghai-Xizang Plateau （QXP）.In this study， we employed Detrended Quantile Mapping （DQM） and Quantile Delta Mapping （QDM） methods to correct and evaluate the precipitation and temperature data of eight CMIP6 models with better simulation performance， utilizing the China Meteorological Forcing Dataset （CMFD）.The results showed that Both methods had corrected the simulation biases of the models， and the correction effects for temperature and precipitation data over the QXP were relatively consistent between the two methods.Then， based on the corrected multi-model ensemble mean （MME） results from QDM method， we analyzed the spatial and temporal variation characteristics of extreme high temperature events， low temperature events， atmospheric dryness and precipitation over the QXP in the early 21^st century （2015 -2057） and later 21^st century （2058-2100）.Under different emission scenarios in the future， extreme high temperature events strengthen， especially in the southeast of the QXP.Extreme high temperature events enhance with the increase of radiation.Extreme low temperature events decrease， with no occurrence in the later 21^st century under high emission scenarios （SSP370 and SSP585）.Under different emission scenarios， precipitation and saturated vapor pressure difference both exhibit a significant increasing trend on the QXP.With global warming， the increase of precipitation does not mitigate atmospheric drought.The atmospheric dryness increases significantly under the future scenarios， especially in summer， at 1.3 to 2 times compared to annual average.

Based on the hourly precipitation and 10 m wind speed data from the European Centre for Medium-Range Weather Forecasts （ECMWF） Fifth Generation Atmospheric Reanalysis dataset from 1979 to 2023， spatiotemporal changes and its corresponding clustering characteristics of compound precipitation and wind speed extremes （PWEs）， and the circulation characteristics in different periods in China were studied by using compound extreme events definition， trend analysis， spatial statistical analysis， and composite analysis.The results showed that PWEs in China were generally more frequent in the east than in the west.Among the PWEs in each subregion， the highest value was found in East China， where the mean value of the frequency and the days were the most in the range of 4~8 times and 4~8 d， and the corresponding area share reached 78.9% and 71.5%， respectively.The overall trend of PWEs from 1979 to 2023 had been decreasing， with the rate of change from 2011 to 2023 being 2.3 times and 3.4 times that of 1979 to 2010.The trend of PWEs from 1979 to 2010 showed an increasing trend in the central and eastern region of Eastern China， the central region of Southwest， and the northern region of Northwest， and the fastest decrease in Central China.From 2011 to 2023， positive trend values were mainly concentrated in the central region of China， and the Eastern China was the region of the fastest growth with rates of 0.96 times and 1.12 d per decade.In contrast， Southern China exhibited a decrease at rates of 0.81 times·（10a）^-1 and -0.77 d·（10a）^-1.The hot spot areas were concentrated on the west side of the Hu Line and coast region from 1979 to 2010， and the distribution of hot spot areas from 2011 to 2023 were consistent with the positive distribution of trend change.In addition， PWEs are the result of the combined effects of the high， middle， and low-level atmospheric layers.The enhancement of atmospheric high-level divergence and the weakening of the jet belt promote the upward movement of the atmosphere and the westward extension of the west Pacific subtropical high.The anomalous easterly wind in the middle atmosphere is conducive to the entry of water vapor from the periphery of the Northwest Pacific subtropical high into the inland regionof China， and the anomalous southeast wind in the low-level atmosphere further promotes the transport of water vapor to the inland region of China.The atmospheric circulation characteristics after 2010 also showed the development of PWEs events towards inland region of China.

The seasonal vegetation greening in the Three River Source Region （TRSR） has a profound impact on the regional ecological environment and water resource security.In this study， the moisture drivers of seasonal vegetation greening in the TRSR and their responses to climate change were investigated using multi-source data from 2003 to 2021， through the application of trend analysis， correlation analysis and partial information decomposition （PID） analysis.The results showed that： （1） From 2003 to 2021， the linear trend of the Leaf Area Index （LAI） generally increased in spring， summer and autumn in TRSR， although the environmental conditions varied significantly between seasons.Linear trends in precipitation， soil moisture （SM） and snow cover （SC） all showed an increasing trend in spring and autumn， with insignificant changes in temperature.In summer， linear trends of temperature were slightly increased， but precipitation and SM slightly decreased， as well as insignificant changes in SC.（2） In terms of the effects of moisture driving factors on LAI： correlation analyses results showed that LAI was significantly positively correlated with SM in spring and summer， but not in autumn.The correlation between LAI and SC was weak in all seasons.By introducing the PID analysis method， the nonlinear and synergistic effects of SM and SC on LAI were effectively revealed.The independent information contribution of SC to LAI changes was higher in spring and autumn， making it the main moisture driver in these seasons， while SM contributed more in summer.At the same time， the synergistic effects of SM and SC played an important role in the changes of LAI in all seasons， with the synergistic information contribution exceeding 30% in all seasons.（3） Response of moisture drivers to climate change： correlation analyses results showed that SM was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in spring； SC was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in both spring and autumn.PID analyses also indicated that precipitation was the main meteorological factor influencing changes in SM and SC across the three seasons， with a higher independent contribution than temperature.However， the synergistic effects of temperature and precipitation on SM and SC in all seasons should not be overlooked.

Based on the hourly precipitation data of 3454 stations with dense space in Sichuan Province and the high-precision grid elevation data， the characteristics of precipitation in flood season in 7 regions of Sichuan Province in recent 10 years were analyzed.The results showed that： （1） There were 3 maximum centers of rainfall in flood season in Sichuan Province： Ya 'an in the southwest of the basin， Anxian in the northwest of the basin and Yanbian in the south of Panxi area.Anxian was the center of heavy rainstorm， and the rainfall in flood season was mainly contributed by the weather process of R ₂₄ ≥100 mm.（2） Affected by the trend of the mountains and the steepness of the terrain， the morphology and isoline gradient of the large value area around the basin had obvious differences.And the larger the accumulated rainfall in flood season， the more the sites were concentrated on the windward slope of the mountains.（3） The degree of night rain gradually weakened from southwest to northeast， among which Panzhihua was the most significant area of night rain in flood season.（4）The daily distribution of R ₂₄ ≥25 mm heavy rainfall was closely related to topography， and the heavy rainfall stations were only distributed in the steep transition zone between the western basin and the plateau.In addition， the percentage of stations with hourly rain intensity ≥50 mm·h^-1 in the rainstorm days in the northwest of the basin was the highest.（5） Compared with persistent heavy rain， the site distribution of persistent heavy rain was more significantly affected by the windward slope topography.

Tropospheric ozone is an important air pollutant and greenhouse gas.It is harmful to human health and seriously harm the ecological environment.In this study， we use ozonesondes data from WOUDC （World Ozone and Ultraviolet Radiation Data Centre） during 2007 -2018 to evaluate tropospheric ozone column products from GOME-2A （Global Ozone Monitoring Experiment 2 aboard METOP-A） and Ozone Monitoring Instrument （OMI） satellite， as well as tropospheric ozone products from Updated Tropospheric Chemistry Reanalysis （TCR-2）.The results of the analysis show that in the equatorial American， subtropical， western European and Canadian regions， the correlation coefficients between GOME-2A and ozonesondes observations are up to 0.56， and the absolute values of the relative percentage deviations do not exceed 15%； in the eastern US.and western European regions， the correlation coefficients between OMI and ozonesondes observations are 0.65~0.72， and the standardized root-mean-square errors are 0.47~0.56； for the whole Northern Hemisphere region， the correlation coefficients between the TCR-2 tropospheric ozone column content and ozonesondes observations are 0.41~0.95， with standardized root-mean-square errors （RMSEs） of 0.18~0.48， which are better than the other two satellite data.Furthermore， the results indicate that the TCR-2 tropospheric ozone column trend is consistent with the trend direction of the ozonesondes observations.Through a more robust data assessment， it is evident that tropospheric ozone columns have increased in the equatorial Americas， Western Europe and China.Conversely， there has been a decrease in tropospheric ozone columns in the Arctic， Canada and the eastern United States.

Research on the characteristics of carbon and water fluxes and water use efficiency （WUE） in the alpine meadow ecosystem of the Qinghai-Xizang Plateau is of crucial significance for accurately assessing the carbon balance， water cycle， and carbon-water coupling of the alpine grassland ecosystem under the background of climate change.In this study， based on the observation data made using eddy correlation in the alpine meadow on the eastern Qinghai-Xizang Plateau from 2012 to 2017， from the Maqu observation point of the Zoige Wetland Ecosystem Research Station of the Northwest Institute of Eco-Environment and Resources， Chinese Academy of Sciences， we analyzed the changes in carbon and water fluxes and WUE during the growing season.By combining multiple stepwise regression and structural equation modeling， we explored the main driving factors of carbon and water fluxes and WUE during the growing season.The results indicate that： （1） The average annual net ecosystem CO₂ exchange （NEE）， ecosystem respiration （Re）， and total gross primary productivity （GPP） of the Maqu alpine meadow ecosystem over 6 years were -109.7， 798.6， and 908.3 gC·m^-2·a^-1， respectively， showing an overall carbon sink； the annual average evapotranspiration （ET） is 446.5 kg·m^-2·a^-1 the 6-year average water use efficiency （WUE） was 2.0 gC·kg^-1.（2） The daily variations of NEE and GPP during the growing season showed a distinct unimodal pattern， peaking around 14:00 （Beijing Time， the same as after）， while Re showed a relatively flat diurnal pattern， lower slightly at night than during the day； the daily variation of ET exhibited a unimodal pattern， with peak monthly and monthly accumulative values in July； WUE displayed an asymmetric "U" curve with the minimum value at 13:00 -14:00， showing significant daily and monthly variations in July and August.（3） During the growing season， both multiple stepwise regression and structural equation models confirmed the dominant role of temperature in controlling carbon fluxes and radiation in controlling ET.Temperature and radiation were identified as the main influencing factors for WUE during the growing season.

Precipitation plays a critical role in the Earth's hydrological and energy cycles， significantly influencing the biogeochemical cycles and energy exchanges on the land surface.In the ecologically fragile region of the Loess Plateau， the spatial and temporal variability of precipitation has profound implications for both the ecological environment and socioeconomic development.Therefore， study on the spatial and temporal variations of precipitation in the Loess Plateau holds substantial theoretical and practical significance.This study utilizes daily precipitation data from 115 meteorological stations across the Loess Plateau and its surrounding areas， covering the period from 1959 to 2018.By employing methods such as Inverse Distance Weighting （IDW） interpolation and wavelet analysis， the study provides a comprehensive analysis of the spatial and temporal characteristics of precipitation over the past 60 years in the Loess Plateau.The results showed that： （1） The spatial distribution of precipitation in the Loess Plateau exhibits a clear "stepped" pattern， gradually decreasing from southeast to northwest.This distribution highlights a significant gradient where the southeastern regions receive more precipitation than the northwestern regions， with a similar trend of more rainfall in the south compared to the north.Furthermore， localized topography plays a crucial role in modulating precipitation， with higher elevations generally receiving more rainfall.（2） Under the influence of changes in the East Asian monsoon and atmospheric circulation patterns， the spatial distribution of precipitation from 1989 to 2018 differs significantly from that of 1959 to 1988.Specifically， the 200mm and 400mm isohyets have shifted northward， with a notable decrease in precipitation in the southeastern monsoon-dominated areas， while precipitation has increased in the non-monsoon northwestern areas.The monsoon marginal zone of the Loess Plateau is particularly sensitive to monsoon variability.The continuous weakening of the East Asian summer monsoon has diminished the capacity for moisture transport， further exacerbated by El Niño-Southern Oscillation （ENSO） warm events， both of which have contributed to reduced precipitation in the southeast.Conversely， changes in atmospheric circulation have led to increased precipitation in the northwest， resulting in a slight expansion of the semi-humid regions in the area.（3） Over the study period， precipitation in the Loess Plateau exhibits a fluctuating upward trend， indicative of a general tendency towards increased wetness in the region.This suggests a long-term shift towards more humid conditions， which could have significant implications for the region's ecological restoration and water resource management.（4） The interannual variability of precipitation in the Loess Plateau is characterized by oscillations on multiple time scales， specifically at 5-year， 7-year， 11-year， and 43~45-year intervals， with the 5-year cycle identified as the dominant periodicity.

On August 13-14， 2022， an extreme rainstorm event occurred in Yuzhong region of Gansu Province， northeast of the Qinghai-Xizang Plateau.Accumulated daily precipitation reached 130.6 mm and the maximum hourly precipitation was 36.6 mm， breaking the heaviest daily precipitation records of the region and causing serious social impact and economic losses.Based on the data of surface minute observation and high altitude observation， Lanzhou Doppler radar and ECMWF Reanalysis v5（ERA5）， by analyzing the observation characteristics， environmental conditions， topographic effects and instability mechanism of the two heavy precipitation stages in this extreme rainstorm， the results show that： （1） The rainstorm was caused by the convergence of weak cold air brought by the shortwave trough in the westerlies and warm and humid air outside the subtropical high in the Longzhong area.The 700 hPa shear line provided the dynamic lifting conditions， and the surface cold front provided the triggering conditions.（2） The radar reflectance factor in the rainstorm process was characterized by persistent strong echoes accompanied by "backward propagation"， low-level jets and obvious convergence.In the second stage， the echo top height behind the cold front was similar to that in the first stage， but the scope was larger and the structure was more compact， and the convective cloud development was more vigorous.（3） The water vapor conditions of the rainstorm were abundant.In the first stage， there was significant convective instability due to strong convergence and upward movement at the lower level and high convective effective potential energy.In the second stage， the upward movement was weakened， the convective effective potential energy was 0， and the dynamic and convective instability conditions were weak.（4） The release of unstable energy triggered by cold front baroclinic frontogenesis was the main triggering mechanism of precipitation in the first stage.After the transit of the cold front， the precipitation in the second stage was formed by the combination of terrain， frontogenic secondary circulation and instability.Since the heavy precipitation after the summer cold front was not common in the northeast part of the Qinghai-Xizang Plateau， forecasters tended to ignore such kind of rainstorms.Therefore， we need to strengthen monitoring and early warning of such rainstorm events.

The Qinghai-Xizang Plateau summer monsoon is an important component of the Asian monsoon system， significantly influencing the energy and moisture cycles in the plateau and its surrounding regions.This study uses JRA-55 monthly reanalysis data from 1980 to 2020 and GPCC monthly precipitation data， combined with the Qinghai-Xizang Plateau Monsoon Index.Various statistical methods， including correlation analysis， regression analysis， composite analysis， and dynamic diagnostics， are used in this study.This paper focuses on the impact of the summer monsoon over the Qinghai-Xizang Plateau on water transport， such as precipitation， atmospheric circulation， and water budget.The results show that： （1） When the Qinghai-Xizang Plateau summer monsoon is strong （weak）， precipitation in the central and eastern parts of the plateau increases （decreases）.（2） From the perspective of water vapor transport， when the summer monsoon over the plateau is stronger， there is an anomalous anticyclonic circulation over central India， an anomalous westerly airflow to the south of the plateau， and the water vapor transport over the plateau is primarily dominated by the westerly water vapor transport channel.（3） Analysed in terms of moisture budget， when the Qinghai-Xizang Plateau summer monsoon is strong （weak）， moisture inflow at the southern and western boundaries of the plateau increases （decreases）， while moisture inflow at the northern boundary decreases （increases）， resulting in an increase （decrease） in regional net moisture budget.（4） The impact of the Qinghai-Xizang Plateau summer monsoon on moisture convergence/divergence is mainly driven by the contribution of the wind’s dynamic component， while the thermal component from moisture advection is relatively small.

Climate and land-use change are important drivers of variation in carbon storage within terrestrial ecosystems.Investigating the effects of climate and land-use change on carbon storage has practical implications for proposing adaptive management strategies for carbon sequestration in a changing environment.In this study， the InVEST model and the PLUS model were used to evaluate the spatial and temporal dynamics of carbon storage in the water conservation area of the Yellow River under the dual influence of climate and land-use change.The results showed that the land-use in the water conservation area of the Yellow River was dominated by grassland and forest from 1980 to 2020， accounting for 80 % of the total area of the basin， with an increasing trend in the area of forest land， grassland， watershed and construction land， and a decreasing trend in the area of other land-uses.The types of land-use transfer include unused land to grassland， grassland to forest land and cultivated land.From 1980 to 2020， the carbon storage in the water conservation area of the Yellow River showed an overall growth trend.The growth area of carbon storage was mainly located in the western and central regions， increasing by 573.5×10⁶ t， which was closely related to climate warming and humidification and ecological restoration.The urban expansion areas in the central and northern regions are the main areas of carbon storage reduction.In the future， under different land-use scenarios， the area of forest and grassland in the ecological protection scenario will increase significantly.From 2030 to 2050， under SSP119 and SSP245 scenarios， carbon storage will increase by 294.83×10⁶ t and 79.56×10⁶ t， respectively， under natural development scenarios， and carbon storage will increase by 364.8×10⁶ t and 151.95×10⁶ t， respectively， under ecological protection scenarios.Low emission and ecological protection scenarios are favorable for carbon storage increase.In the future， the increase in carbon storage will mainly come from grassland， conversion of unused land into forest and cropland， and conversion of unused land into grassland.The decrease in carbon storage is mainly related to the conversion of forest land into grassland and cropland.It can be seen that protecting forest and grass is an important measure to improve the carbon storage of regional ecosystem.The results can provide a scientific basis for adjusting the land-use structure and carbon sequestration of the ecosystem in the water conservation area of the Yellow River.

Climate comfortableness is the key factor that has impact in many fields， such as residents’ life quality， tourism development， and urban planning layout.The Universal Thermal Climate Index （UTCI is currently the most important and effective way to evaluate climate comfortableness at the international level.In-depth research on the climate comfortableness of the Yellow River Basin can not only fill the gap in the study of climate comfortableness in the Yellow River Basin area but also supplement a comprehensive understanding.Based on the results of climate zoning， the Yellow River Basin is divided into six sub-regions Using the reanalyzing data of ERA 5， the spatial distribution and temporal change of climate comfortableness in the Yellow River Basin from 1979 to 2022 were analyzed and discussed with the adoption of UTCI.The results show as follows： （1） From an overall perspective， the annual average UTCI value of the Yellow River Basin is 2.8 ℃， with a comfortable grade of coolness.The UTCI value is mostly in the cold zone and comfortable zone.The distribution of hot zone is relatively less.There is a large difference in UTCI distribution among the internal regions.Region I has a relatively longer duration of low temperature， and the area， with mild cold stress （coolness） and stronger cold stress （uncomfortable coldness）， is larger.Region II is mostly in the cold zone.Region III and Region IV are relatively close， and are dominated by “comfortableness” and “coolness”.The UTCI values in region V and region VI are at a higher level， but most areas are still in the comfortable zone.（2） In terms of the seasons， the four regions of III， IV， V， and VI have relatively extensive comfortable zones in spring and autumn， the overall comfortable area will be expanded in summer across the entire Yellow River basin， and the cold and uncomfortable zone will become dominant in winter， while the overall comfortable zone will be shrinked across the entire Yellow River basin.（3） The average UTCI in China as a whole has shown an overall upward trend from 1979 to 2022， with a change rate of 0.4 ℃·（10a）^-1.The range of change in sub-regions is 0.14~0.85 ℃·（10a）^-1.The annual UTCI change in the Yellow River basic shows a significant feature of west-high-east-low and north-high-south-low in the spatial distribution.（4） The level of climate comfortableness， taken as a whole， is mainly in the comfortable and slightly uncomfortable categories.The number of days in each of the six comfortable levels is as follows： 24 days （cold discomfort）， 126 days （slightly cold， discomfort）， 59 days （cool）， 131 days （comfort）， 19 days （slightly hot discomfort）， and 6 days （hot discomfort）.Region I and Region II have not been affected by the discomfort caused by heat.However， regions in the Yellow River basin， and regions of III， IV， V， and VI， are affected by slightly hot discomfort and the average duration in the slightly hot discomfort zone throughout the year is 19 days， 23 days， 24 days， 46 days， and 60 days respectively.