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.

Based on NCEP/NCAR reanalysis data and the daily precipitation data from 105 meteorological stations in the central and eastern Qinghai-Xizang Plateau from 1982 to 2020， we investigate the spatiotemporal anomalous characteristics and major falling areas of warm season extreme precipitation and typical circulation of large-scale extreme precipitation in the central and eastern Qinghai-Xizang Plateau.The results show that： （1） Total precipitation of central and eastern Tibetan Plateau in the warm season shows statistically significant increasing at the rate of 10.7 mm·（10a）^-1 （P<0.05） during 1982 -2020， but there are obvious interdecadal trend shifts in the late 1990s and late 2000s.The increase in extreme precipitation of central and eastern Qinghai-Xizang Plateau is most prominent after 2009， and the climate tendency rate is of 4~5 times greater than that of during 1982 -2020.In terms of the distribution of spatial climate tendency rates， the trend of extreme precipitation in the southern Qinghai-Xizang Plateau is opposite to that in the central and northern Qinghai-Xizang Plateau in the three periods.The increase of extreme precipitation in the northern Qinghai-Xizang Plateau is the most prominent during 1998 -2009， and the extreme precipitation in the southern Qinghai-Xizang Plateau experiences the interdecadal trend shifts of significant increase， significant decrease and significant increase.（2） The small-scale extreme precipitation in central and eastern Qinghai-Xizang Plateau shows statistically significant decreasing trend （P<0.1）， while the large-scale extreme precipitation exhibits statistically significant increasing trend （P<0.05） during 1982 -2020.According to the locations of precipitation center， the level 4 large-scale extreme precipitation can be divided into three types， that is， northeast type （A type）， southern type （B type） and southeastern type （C type）.（3） The difference of location and intensity of the West Pacific Subtropical High is the main factor， which leads to the difference of water vapor sources and the locations of large-scale extreme precipitation falling areas.When the A-type large-scale extreme precipitation occurs， the West Pacific Subtropical High is anomaly weaker and eastward， which leads to the water vapor mainly transported from the Pacific Ocean and the westerlies.When the B-type occurs， the West Pacific Subtropical High is anomaly stronger and extending westward and southward， consequently， the water vapor mainly transported from the Indian Ocean and the Bay of Bengal.When the C-type occurs， the West Pacific Subtropical High is anomaly stronger， extending from the westward and northward， resulting in the water vapor mainly transported from the northwest Pacific Ocean， the South China Sea and the Bay of Bengal.

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.

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.

In order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables， and MCI calculated from meteorological data at the adjacent moments of satellite transit as dependent variables， and uses the Random Forest Regression （RFR） model to construct a integrated remote sensing drought monitoring model.The results show that the accuracy of RFR model is better than that of the Ordinary Least Squares （OLS） model in bothtraining data and test data.The R value of the RFR training data is 0.97， the RMSE is 0.33， the R value of the RFR test data is 0.90， and the RMSE is 0.53.The R value of the OLS training data is 0.78， the RMSE value is 0.73， the R value of the OLS test data is 0.76， and the RMSE value is 0.79.The comparisons of RFR and OLS model in R and RMSE show that the RFR model is superior than the OLS model in the characterization of regional drought.In the application of drought monitoring in Southwest China in 2022， the RFR results are consistent with the spatiotemporal distribution of the MCI index， which can better characterize the spatial and temporal dynamics of the regional drought， reflecting the practicality of the RFR model in the actual drought monitoring process.However， the accuracy of RFR model is related to the number of regional stations and the spatial distribution of stations， and the accuracy of the RFR model is higher in areas with a large number of stations and uniform distribution of stations.

El Ni?o-Southern Oscillation （ENSO） is the most prominent interannual climate mode over the tropical Pacific， which is characterized by a periodic and phase-locked evolution of sea surface temperature anomalies （SSTA）.From the perspective of ENSO cycle， this study objectively classified the SSTA evolution of ENSO from 1961 to 2021 into two results using K-means clustering method （KMA）： 3 or 5 types of ENSO cycle.When it is classified into 3 types， the basic characteristics of ENSO cycle are warm-developing， warm-decaying， and cold-persistence.When it is classified into 5 types， the discrepancies of intensity and zonal distribution of the development and decay processes between super-strong and normal events are highlighted.To further explain these discrepancies， this study employed a KMA considering the Principal Component Analysis （Empirical Orthogonal Function）.Based on the two EOF leading modes which reflects the zonal symmetric and asymmetric development modes， the zonally symmetric and asymmetric development processes of the ENSO cycle are divided.Combined with the KMA clustering analysis， it is further found that zonally asymmetric development mode together with the zonally consistent development mode jointly lead to the zonal asymmetric development speed of ENSO cycle.Reconstruction of the zonally homogeneous and asymmetric evolution modes of ENSO reveals that wind and thermocline thickness anomalies may be key factors controlling the zonal asymmetric evolution of SSTA.This study objectively classified different types of ENSO evolution， providing reference for climate dynamics and impacts of ENSO diversity.

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.

The inland arid zone of northwestern China has two climate regimes， monsoon-dominated and westerly-dominated， and is also an area of monsoon-westerly interaction.The study of climate and environment in this region is of great theoretical and practical significance， and it is also a frontier issue that has attracted much attention in global change research.This paper summarized the research progress on the definition of the monsoon zone， the westerly belt and their boundary zones， the progress on the climatic impacts of these two circulations and their interactions as indicated by instrumental measurements and proxies.Future research needs to focus on the westerly-dominated climate regime and its interaction with the monsoon， spatial definition， and driving mechanisms at high resolution and large spatial and temporal scales.These studies can promote research on the response to global change and its dynamic mechanism， and provide scientific support and theoretical basis for regional desertification management and national ecological security guarantee in arid northwestern China.

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.

As one of the most sensitive natural elements in response to climate change， snow cover has a significant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×10⁶ km² and 98% of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties （high surface albedo） and thermal characteristics （low thermal conductivity）， changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of global warming， the snow cover in the Northern Hemisphere has been decreasing in recent decades， especially in the spring.Therefore， the capabilities of CMIP6 （Coupled Model Intercomparison Project Phase 6） data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center （NOAA/NCDC） as reference data， the Taylor skill scoring， relative deviation， and other methods were applied to evaluate the spring snow cover （SCF） data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6 （CMIP6） during 1982 -2014.The ensemble average of the top three models was further selected to predict the spatiotemporal variation characteristics of SCF under different emission scenarios from 2015 to 2099， providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period （1982 -2014）， SCF was characterized by high coverage at high latitudes and low coverage at low latitudes， with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall， 68.37% of the regions in the Northern Hemisphere showed a decreasing trend in SCF， while 31.63% of the regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition， most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March， April， and May.Various models exhibited differing abilities to simulate SCF， with NorESM2-MM， CESM2， BBC-CSM2-MR， NorESM2-LM， and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean （MME） consistently outperformed individual models， closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution， inter-annual variation trends， and intra-annual variations of SCF in the Northern Hemisphere.At the end of the 21st-century （2067 -2099）， SCF in the Northern Hemisphere exhibited a decreasing trend in most areas， which intensifies with increasing emission intensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF maintains a steady state under the SSP1-2.6 scenario， showed a slight decreasing trend under the SSP2-4.5 scenario， and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040.

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.

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.

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 summer precipitation in eastern China has significant interdecadal variaitons， which can impact the spatiotemporal variability of drought and floods as well as people’s living.Hence， it is important to understand and predict the interdecadal variations of summer precipitation in eastern China.The Coupled Model Intercomparison Project Phase 6（CMIP6） can help to understand the changes in climatic factors and predict their future changes.What is the capability of CMIP6 models in simulating the interdecadal variations of summer precipitation in eastern China？ What are the potential reasons？ In order to understand the above questions， this study evaluated the capability of CMIP6 models in simulating the interdecadal variations of summer precipitation in eastern China， using the CN05.1 observational data， ERA5 reanalysis data， NOAA sea surface temperature （SST） data and the output of historical experiments from 30 CMIP6 models.The results indicate that during 1961 -2014， the summer precpitation over eastern China underwent two notable interdecadal variations， which occurred in the mid-1970s and early-1990s.During these two interdecadal variations， the simutaneously enhanced/weakened Western Pacific Subtropical High （WPSH） and South Asian High（SAH） as well as the interdecadal change in tropical Pacific SSTs induce changes in the winds and divergence at the 850-hPa and 200-hPa pressure levels over southern China （18°N -30°N， 105°E -122°E）.The associated interdecadal changes in water vapor flux， meridional circulation and atmospheric stability in lower troposphere led to interdecadal changes in summer precipitation in eastern China.Although the CMIP6 models can well simulate the climatology of summer precipitation in eastern China， only 5 out of 30 models have relatively good capability in simulating the aforementioned two interdecadal variations in summer precipitation， which have taylor scores larger than 0.7， while the other models have relatively poor skill.In addition， the best multi-model ensemble （BMME） means show better skills in simulating these two interdecadal variations in summer precipitation than individual models.This is because the BMME can well simulate the simultaneous change in WPSH and SAH as well as the change in tropical Pacific SSTs， which leads to a good simulation of the meridional circulation over eastern China， resulting in a good simulation of the summer precipitation anomalies south of the Yangtze River.

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.

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.

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.

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.

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.

South China （SC） is one of the regions with the most annual precipitation in China.Under the background of global warming， there had been significant changes in precipitation at regional and scale levels， the area of dry- and wet-season precipitation was expanding， and regional extreme precipitation events showed a significant upward trend in SC， but the changes and impacts of different types of precipitation were not the same.To better understand how different types of precipitation in SC respond to global warming， this paper investigated the characteristics of convective precipitation （CP） and large-scale precipitation （LSP） in SC based on ERA5 reanalysis precipitation dataset from 1960 to 2022 using linear correlation， trend analysis and wavelet analysis.The results showed that： （1） SC was dominated by LSP in winter while CP in other seasons.（2） CP and LSP in SC showed an increasing trend in winter， but most of CP showed a decreasing trend in other seasons.CP in SC had a relatively significant 2~4 years cycle from the 1980s to the beginning of the 21st century in winter， as well as there was an interdecadal characteristic of shifting from more to less in the 1990s to the beginning of the 21st century in spring， but the significant cyclical variations were mainly found in the period before the 1990s in autumn.In all seasons except winter， the changes in LSP were consistent with CP.（3） The total precipitation （TP） from April to October in Guangdong and Guangxi was mainly CP （CP accounts for about 65% of TP）， with the largest proportion in August （71.8% in Guangdong and 69.0% in Guangxi）.On the other hand， the proportion of CP in the first rainy season （up to 80% in May） was significantly higher than in the second rainy season in Hainan.Additionally， the proportion of CP remained at about 50% even in autumn and winter.（4） The diurnal variation intensity of CP and LSP was strongest in Hainan while the intensity of CP was the weakest in Guangxi.The peak periods of the strongest precipitation in Guangdong， Guangxi， and Hainan occured respectively from 13:00 to 17:00 （Beijing time， the sameas followed）， 15:00 to 17:00， and 14:00 to 16:00.The LSP peaked between 09:00 and 17:00 in Guangdong， and it started to strengthen from 04:00 and weakened in the afternoon， with the strongest period around 10:00 in Guangxi， for Hainan， the strongest period was 12:00 -17:00 before August， but 15:00 -16:00 after late September.In conclusion， the characteristics of precipitation varied with different seasons and types.Therefore， it was necessary to continue considering the impact of different types of precipitation in future research.

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.

Based on the daily minimum temperature station data provided by the National Meteorological Information Centre from 1980 to 2022， the month-by-month reanalysis data of the NCEP/NCAR， and the monthly Sea Surface Temperature （SST） data from the NOAA， by using EOF， simple linear regression and T-N wave flux methods， the main anomalous spatial and temporal characteristics of winter cold wave frequency in the southern China are studied， and the influence mechanisms of atmospheric circulation and winter Atlantic Sea Surface Temperature Anomaly （SSTA） on it are also analyzed.The results show that： （1） The large value areas of winter cold wave frequency are mainly located in the eastern and central of the southern China， with an approximately "inverse C" distribution.There are three main frequency anomalous modes， namely， regionally consistent anomaly， north-south antiphase anomaly and tripole anomaly patterns according to the EOF analysis， among which the regionally consistent anomaly reflects the overall anomalous spatial and temporal characteristics of the winter cold wave frequency in the southern China.（2） The negative phase of the North Atlantic Oscillation （NAO）， the strong Caspian Sea - Tibetan Plateau ridge and the East Asian Trough located to the north and to the east， the weak in the north and strong in the south of the Siberian High， the strong temperate jet and the weak subtropical jet are the key circulation systems affect the winter cold wave frequency in the southern China.The cold air pool is located in the Western Siberia.The high and low level circulation systems cooperate together to make the cold air from Western Siberia move southward to the vicinity of the Caspian Sea， and then transport eastward along the northern side of the Tibetan Plateau， then move southward into the southern China along the eastern side of the Tibetan Plateau， resulting in the increase of the winter cold wave frequency in the whole southern China.（3）In winter， the “+”“-”“+” tripolar SSTA in the North Atlantic can stimulate the -NAO atmospheric circulation anomalies through the exchange of heat fluxes between air and sea and the Rossby wave energy anomalies.The Rossby wave energy propagates from the North Atlantic to East Asia along the south and north two paths， and stimulates the corresponding anomalous waves， which enhance the key circulation systems in the north and south affecting the cold wave frequency in the southern China.When the North Atlantic SSTA exhibits an inverse "C" anomaly in spring， and there is a trend of developing into a “+”“-”“+” tripolar pattern in summer and autumn， the winter cold wave frequency in the southern China can be predicted to more.

In this study， the improvements of lake dynamic module parameters in the literature were added to WRF-Lake （WRF4.4.1） at first， then six microphysical schemes， five cumulus convection schemes and two boundary layer schemes were selected.A total of 60 WRF-Lake simulations with different parameterization schemes were carried out from July 5 to 13， 2008 in the Nam Co Lake area.Sensitivity experiments were conducted to comparatively analyze the effects of different parameterization scheme combinations on atmospheric boundary layer variables.The "ranking method" was employed to comprehensively evaluate the simulation capabilities of different parameterization schemes in the summer atmospheric boundary layer over Nam Co Lake.The results indicated that the model captures the overall spatial and temporal distribution characteristics of the summer average two-meter temperature in Nam Co.However， the simulated values of the two-meter temperature over the lake were higher than the land surface data.Due to the selection of cumulus convection parameterization schemes and the impact of model performance， there was significant differentiation in the simulation effects of precipitation among experimental groups， leading to varying degrees of overestimation of daily precipitation.The daily average variations of latent heat fluxes showed the best correlation with observational values， while sensible heat and wind direction exhibited relatively good performance， and wind speed showed the least satisfactory results.Overall， comprehensive analysis of the simulation capabilities of each experimental group for the summer atmospheric boundary layer over Nam Co Lake revealed that Scheme 58 （SBU-Tiedtke-MYNN3） performed the best in simulations of 2 m temperature， daily precipitation， 10 m wind fields， and surface heat fluxes.The RMSE value for two-meter temperature and daily precipitation was 2.33 °C and 10.48 mm， respectively.The correlation coefficient for the daily average variation of 10 m wind speed was -0.41， and the ratio of standard deviations was 0.94.The correlation coefficient for the daily average variation of 10 m wind direction was 0.59， and the ratio of standard deviations was 0.73.The correlation coefficient for the daily average variation of sensible heat flux was 0.94， and the ratio of standard deviations was 1.89.The correlation coefficient for the daily average variation of latent heat flux was 0.89， and the ratio of standard deviations was 0.91.Therefore， it is recommended to use the aforementioned grid parameterization scheme for simulating the summer atmospheric boundary layer over the region of Lake Nam Co.

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.

Using the observation data of 10 m surface wind speed at 173 meteorological stations in Northwest China from 1979 to 2020 and the ERA-5 reanalysis data， this study investigated the interdecadal variation characteristics of annual and seasonal surface wind speed in Northwest China and displayed the background circulation changes of interdecadal variation in 2003/2004.There are significant regional differences in the trend distribution of surface wind speed during 1979 -2020.The stations of the wind speed with increasing trend were mainly located in central and eastern Gansu， Shaanxi， southwest and northeastern parts of Xinjiang.The others stations generally showed the negative trends of wind speed.The number of the stations with decreasing trends of surface wind speed were obvious larger than that with decreasing trends.The average surface wind speed in the stations with decreasing （increasing） trends were larger （smaller） than that in whole northwest China.The distribution of the annual and four-season leading mode of surface wind speed were generally opposite to the trend distribution from 1979 to 2020.All the PC1s experienced the decadal shift around 2000.The second mode showed positive anomalies over the Ningxia Province and southern Shaanxi Province and negative over others regions.The PC2s both witnessed the decadal shifts in 1987/1988 and 2003/2004.Numerical studies investigated the decadal shift of surface wind speed northwest China in 1987/1988.Therefore， this study focuses on analyzing the possible circulation background of the decadal shift of northwest wind speed in 2003/2004.The composite difference of 500 hPa and 200 hPa geopotential heights between 2004 -2020 and 1988 -2003 presents the meridional dipole mode of Central Asia-Northern Europe in spring， the "Silk Road Pattern" in summer and autumn， the "Scandinavian" mode in winter， and the negative phase of the Arctic Oscillation in annual field.The circulation changes in four seasons and annual averages have their own characteristics.It needs further studies of the physical process of the influences of these circulation factors on the decadal shift of the surface wind speed in northwest China in 2003/2004.

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.

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.