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.

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.

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.

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.

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.

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.

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.

The Qinghai-Xizang Plateau Vortex is a mesoscale low-pressure vortex system generated within the boundary layer of the Qinghai-Xizang plateau in summer， which not only has an important influence on the weather patterns and precipitation dynamics across the plateau， but also profoundly impacts the surrounding regions.In this study， the database of the plateau vortex obtained from an objective analysis method， along with ERA5-land reanalysis data， was utilized to conduct a comprehensive statistical and analytical investigation of the vortex's activity from 1950 to 2021.Various analytical methods， including correlation analysis， regression analysis， Bayesian time series analysis algorithm， and probability statistics were used.Furthermore， the intensity and path of the plateau vortex during the years 1950 and 2021 were specifically examined to identify the areas most sensitive to its activity during this time span.Results reveal a noteworthy increasing trend （at a 95% confidence level） in both the annual number and duration of the plateau vortex， with climate tendency rates of 0.16·a^-1 and 1.25 h·a^-1， respectively.However， the growing trend for the total number and duration of the plateau vortex during the active period （May to August） is not statistically significant.The sensitive areas that affect the activity of the plateau vortex are located on the north side of the northern Qinghai-Xizang Plateau and near the Hoh Xil Mountains， corresponding to the main mountains in the central and western Qinghai-Xizang Plateau.Furthermore， the study investigates the relationship between land surface parameters and the vortex's characteristics， showing positive correlations between latent heat， surface longwave radiation， and surface soil moisture （0~7 cm） with the number and duration of the plateau vortex.Conversely， sensible heat exhibits a negative correlation， it is further found that the plateau vortex is relatively consistent with precipitation when the time scale of the study is inter-annual， while on the daily scale， the sensible heat is positively correlated with the number， duration， and intensity of the plateau vortex mainly in the sensitive areas and to the east of the sensitive areas， with the most significant correlation being in the months of May and June.In conclusion， the results derived from this study provide a solid theoretical foundation for further exploration of the land-atmosphere interaction mechanism in the identified sensitive area.Moreover， these findings lay a critical foundation for enhancing numerical simulations and data assimilation studies of the Qinghai-Xizang Plateau Vortex.

Observed soil moisture and precipitation as well as GLDAS and CMFD reanalysis data are used to analyze the spatial and temporal distribution and variation trend in the Loess Plateau region.Regression analysis， Granger causality test and singular value decomposition （SVD） are used to study the relationship between soil moisture and precipitation， and to analyze the temporal scale and spatial range of the influence of initial soil moisture on subsequent precipitation.The results show that the explained variance of the regression analysis of soil moisture and subsequent 1~2 months precipitation on the Loess Plateau is relatively high， with larger values in the summer and fall seasons （July， August， September， and October）.That the correlation between soil moisture and the subsequent 21 days of precipitation in different regions of the Loess Plateau （zones I， II， and III） is more frequent and concentrated than that in the whole region.This indicates that soil moisture on the Loess Plateau is heterogeneous so that a larger lagged precipitation time scale is just suitable for analysis at larger spatial scales.The Granger causality test shows that the initial soil moisture in the fall （October and November） across the Loess Plateau has a significant effect on the precipitation in the following 1 or 2 months， and the soil moisture in August also has a significant effect on the precipitation in October in Area III， which is consistent with the results of the regression analysis.The result of the SVD decomposition shows that from 1979 to 2014， when soils in the central， northern， and eastern parts of the Loess Plateau are wetter in July， the precipitation in the western and northern margins of the plateau is accordingly more in August.A wetter soil in the eastern part of the plateau in September means more precipitation in the western part of the plateau， as well as some parts of the northern and southern parts of the plateau， in October.The significant correlation between soil moisture and precipitation has fewer overlapping regions， suggesting spatial and temporal asymmetry in the influence of soil moisture on precipitation on the Loess Plateau.

The Source Area of the Yellow River is located in the northeastern part of the Qinghai-Xizang Plateau， and the meteorological stations are sparsely distributed in this basin， the study of the applicability of various precipitation data products has an important values in promoting the hydrological modeling in the basin.Based on the China Meteorological Assimilation Datasets for SWAT model Version1.1 （CMADS V1.1）， the Tropical Rainfall Measurement Mission （TRMM） precipitation datasets （3B42 Version7） and the Soil and Water Assessment Tool （SWAT） driven by these precipitation data， respectively， and the SWAT-CUP （SWAT Calibration and Uncertainty Program） and SUFI-2 （Sequential Uncertainty Fitting2） algorithm 27 sensitivity parameters were rate in simulating the variation of multi-year monthly average runoff， the simulated results were compared with the observations to evaluate the accuracy of CMADS and TRMM 3B42 precipitation data products and the applicability of SWAT model were evaluated in the Source Area of the Yellow River source area.The results show that： （1） The distribution of all three precipitation datasets showed an increasing trend from the west to the east， and TRMM 3B42 was in better agreement with the measured precipitation than CMADS data set in terms of annual and monthly variation.（2） The sensitivity analysis of the parameters showed that the sensitivity degree of SCS （Soil Conservation Service） runoff curve number， groundwater lagging coefficient， and soil evaporation compensation coefficient were stronger than that of the others.（3） The simulated runoff by using the CMADS and TRMM 3B42 precipitation datasets had better results than that by using the measured precipitation data， with the correlation coefficients R ² of 0.93， 0.92 and 0.88 for the rate period at the three hydrological stations， respectively， while the results of the TRMM 3B42 simulation were the next best， with the coefficients of correlation （R） of the rate-period and validation-period of above 0.80， and the Nash-Sutcliffe efficiency coefficient （NSE） of the simulations is above 0.50.This research demonstrates the applicability of CMADS datasets and SWAT model for runoff simulation in high-altitude areas with complex landscape types and sensitive to climate change， and provides a replacement solution for improving the hydrological models in areas where there are sparely meteorological stations.

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.

Qinghai Lake is not only the largest lake in China but also an important part of the national ecological security strategy.Under the background of global warming， the water level of Qinghai Lake changes rapidly， which has great effects on the surrounding traffic facilities， residents' safety and the development of animal husbandry， etc.Therefore， it is necessary to study the water level evolution characteristics of Qinghai Lake and its water balance under climate change.Based on the hydrological data of Buha River hydrology station and Xiashe hydrology station， meteorological data of Gangcha meteorological station and CMFD， and water balance equation， this paper first analyzes the inter-annual and intra-year water level evolution characteristics of Qinghai Lake from 1956 to 2020， and the water balance components， such as runoff into the lake （ R_s）， precipitation （P） and evaporation （E）.The second reveals that the changes in water level values calculated in the same months are synchronized with the changes in R_s， P， and E.Finally， the ridge regression method is employed to quantitatively calculate the contribution rates of R_s， P， and E to the water level change of Qinghai Lake based on calculations made for December.The results show that the annual average water level declined at a rate of 0.8 m·（10a）^-1 from 1956 to 2004， of which the main reason for the decrease between 1979 and 2004 was that E exceeded the sum of P and R_s.However， from 2004 to 2020， the water level increased at a rate of 1.9 m·（10a）^-1， of which the main reason for the increase between 2004 and 2018 was the increase of P and R_s.Qinghai Lake exhibits evident intra-annual variations， with the water level starting to rise in May and reaching its peak in September， which aligns with the monthly variations of R_s， P， and E.Furthermore， the impact of the current year's P， R_s， and E changes on the annual water level change for the same months of September to December is greater than that of the previous year.Specifically， the contributions of the current year's P， R_s， and E changes to the water level change calculated based on December data are 10%， 70%， and 20%， respectively.

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.

The daily precipitation data of Kunming Station for nearly 30 years from 1991 to 2020 were used to calculate the beginning and ending periods of Kunming rainy season （May-October）， further determine the length of the rainy season in Kunming.Additionally， statistical yearbook data for Yunnan Province and Kunming City were used， including year-end total population， urban built-up area， urbanization rate， per capita GDP， and other urban development factors， to determine the urban development process in Kunming.This process divided Kunming's urban development into a slow period （1991 -2003） and a fast period （2004 -2020）.The characteristics and differences in the length of the rainy season in Kunming City during these two periods were then analyzed and compared.Various analytical methods， including statistical analysis， wavelet analysis， and Mann-Kendall （M-K） mutation test， were employed to systematically analyze the temporal changes in the length of the rainy season in Kunming City.Additionally， the grey correlation analysis method was used to assess the correlation between the length of the rainy season and urban development in Kunming City.The results indicate that from 1991 to 2020， the start date of Kunming City's rainy season gradually became later， while the end date gradually became earlier， resulting in an overall trend of the rainy season getting shorter.Wavelet coefficient analysis revealed that there was no obvious regularity in the variation of the rainy season's length on time scales below 8 years， but on a 17-year time scale， there was a noticeable cycle of short-long-short-long-short variations， The rainy seasons from 2003 to 2008 and from 2014 to 2017 were relatively long， while the rainy seasons from 1991 to 2002， 2009 to 2012， and 2018 to 2020 were relatively short.The unclosed contour lines from 2018 to 2020 indicate a further trend of shortening.The M-K test showed that the length of the rainy season in Kunming City experienced four abrupt changes between 1991 and 2020， Occurring in 2002， 2008， 2012 and 2017.Regarding the relationship between Kunming's urban development and the length of the rainy season， the variation trend of the rainy season length during the slow urban development period remained relatively stable.However， after 2004， during the rapid urban development period， there was a significant reduction in the length of the rainy season in Kunming City， Extreme volatility became more pronounced as the urban development process accelerated.The SPSS （Statistical Product and Service Solutions） was used to predict the duration of rainy season in the next 10 years in Kunming City， The results show that the rainy season will continue to be shorter in the next 10 years in Kunming.When the grey correlation resolution was set at 0.5， four factors representing the urban development process had varying degrees of influence on the length of the rainy season in Kunming City， with correlation coefficients all exceeding 0.70， the results show that there is a significant correlation between the urban development and the length of rainy season in Kunming.Among these factors， the most influential one was the year-end total population， while the least influential was per capita GDP， with grey correlation coefficients of 0.88 and 0.70， respectively， signifying a high and significant correlation.The order of correlation coefficients for the four factors is as follows： year-end total population > urbanization rate > urban built-up area > per capita GDP.

Clouds play an important role in the Earth-atmosphere system.To deeply analyze the cloud characteristics in the Loess Plateau region （LP）， the macro and micro physical characteristics of clouds were analyzed by using the CloudSat-CALIPSO data from 2007 to 2016 in four regions of the Loess Plateau， namely， semi-humid， semi-arid， arid， and cold arid.The findings indicate that： （1） In the LP， the annual average frequency of clouds exceeds 55%， with the highest frequency in spring and summer， and relatively lower in autumn and winter.The frequency of clouds in semi-humid region is higher than that in other regions.However， the months with the highest frequency of cloud occurrence in the other three regions are earlier than those in the semi-humid region.（2） The frequency of single-layer clouds is the highest in all regions， accounting for over 60% of the total cloud amount， with double-layer clouds being the main type among multi-layer clouds， accounting for about 25% of the total cloud amount.The seasonal variation of cloud height in each region shows that it is greater in spring and summer than in autumn and winter， and that it is greater in semi-humid region than in other regions in all seasons.The seasonal variation of cloud geometric thickness is not significant in all regions， which is between 1 km and 4 km， with mainly thin clouds， and 78.13% of the cloud geometric thickness is less than 2 km.（3） The annual average value of cloud liquid water content in all regions reaches more than 220.5 mg·m^-3， about 6.5 times of the annual average ice water content.It is mainly distributed in the altitude below 8.5 km， and the liquid water content gradually increases as the altitude decreases， in which the cloud liquid water content in the semi-humid region is larger than that in other regions.The ice water content in each region is small throughout the year， mainly distributed in the altitude layer below 16.5 km.（4） The values of the effective radius of liquid droplets in each region are mainly concentrated in the range of 12~16 μm， with a maximum of about 24 μm in the spring in the semi-arid region； the maximum value of the effective radius of ice particles occurs in the summer in the semi-humid region.The values of droplet number concentration in all regions were concentrated at 60~80 cm³， which were smaller than the mean value of ice particle number concentration， with peaks occurring in the summer in all regions， and the peak of ice particle number concentration occurring in the spring in the semi-humid and semi-arid regions.The results of this study can help to understand the cloud characteristics of the Loess Plateau and provide a reference basis for the simulation of cloud characteristics in the Loess Plateau by regional climate models.

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.

Using the precipitable atmospheric water vapor （PWV） data of 17 ground-based GPS remote sensing stations， hourly and daily precipitation data of 14 meteorological stations in Tarim Basin and its surrounding areas from July 2018 to June 2022， this study analyzed the PWV distribution characteristics and its relationship with precipitation in the western （region A） and the eastern （region B） part of Tarim basin.The results show that： （1） The average annual PWV is largest in the northern and southwestern plain areas of the basin， and the average annual PWV is inversely proportional to the altitude at stations with over 1300 m， while that concentrated on 10~12 mm at stations with altitude below 1300 m.The average PWV value in summer is twice than that in spring and autumn at all GPS stations.（2） The monthly distribution of PWV in region A and region B presents a unimodal type， with the peaks occurred in August and in July， respectively.On the rain-day and no-rain day in region A， the both peak value of PWV occurred at 23:00 （Beijing Time， after the same）， While， the peak value of PWV occurred at 11:00 on rain-day and 17:00 on no-rain day in region B， respectively.（3） The peak of ΔPWV （PWV minus monhly mean PWV） at most stations occurred at 0~1 h before precipitation start time in region A， and within 1 h before and after precipitation in region B.In spring， the variation of PWV before the precipitation in region B is more severe than that in region A.In summer， there are more weather processes with σPWV （PWV divide monthly mean PWV） reached 1~1.8 times at 1 h and 5~6 h before the beginning of precipitation in region A and region B.In autumn and winter， The peak of σPWV are concentrated in 1.4~2.0 times and 1.6~2.4 times in region B， respectively.（4） At the end of precipitation in stations with altitude below 1400 m， the PWV value was concentrated in 10~20 mm during May to June and in 15~25 mm during July to August.In stations with altitude over 1400 m， the PWV value is increasing from 15~25 mm to 25~35 mm from May to August at the end of precipitation.

As the main mechanism of extratropical cyclogenesis， moist baroclinic instability plays a central role in the study of cyclone thermodynamics， which can be further divided into four categories： dry baroclinic instability， moist instability， diabatic Rossby wave and Type C cyclogenesis （tropopause intrusion）.The '7·20' heavy rainstorm was caused by the eastward movement of a Yellow River cyclone into North China after its rapid formation on July 18， 2016.Compared with the mature stage of the cyclone， the mechanism of the initial stage is still unclear.This article uses ERA5 reanalysis data and WRF model to study the moist baroclinic instability of the cyclogenesis event numerically.The results show that mid-lower troposphere presented diabatic Rossby wave pattern， that is， the eastward movement of the system was mainly driven by the cycle of vertical motion and diabatic effect.The vertical motion on which the wave relied was more provided by vorticity advection.The PV sink and the ageostrophic wind in the upper layer delayed the eastward movement of the tropopause intrusion PV， maintaining the phase difference between upper and lower layers.Finally， a PV column formed throughout the troposphere in front of dry intrusion.Using piecewise PV inversion， several sensitivity runs are designed to remove unbalanced circulation， tropopause dry intrusion PV and lower-level diabatic-produced PV from the initial field， respectively.Combined with the analysis of generalized omega equation， it shows that the baroclinic wave in this process must be coupled with the diabatic process with the help of sufficient water vapor to develop strongly.The cyclogenesis was suppressed when the latent heat was turned off.Dry baroclinic instability cannot explain this process.The removal of initial unbalanced field did not affect the baroclinic instability but will delay development of the system.Limited by humidity and mesoscale circulation structure， the active area of lower-level unbalanced flow was controlled by dry baroclinic dynamics.In this case， the gradient of {\theta }_{bottom} was too small to organize eastward diabatic Rossby wave by relying only on the initial lower-level PV.Nor can strong lower-level diabatic heating generate as in Type C cyclogenesis by tropopause intrusion.For this Yellow River cyclogenesis case， it is required the initial lower-level PV anomaly to be strong enough to counteract the suppression of the cooling subsidence in front of dry intrusion； on the other hand， it is also required that the dry intrusion， in an appropriate initial phase difference with the lower system， strengthened the ascending motion east of the low-level PV in form of vorticity advection through vertical penetration， so as to promote the eastward momentum of diabatic Rossby wave to enter north China with more saturated environment.None of dry baroclinic instability， diabatic Rossby wave and Type C cyclogenesis could independently explain this cyclogenesis event， which was an initial optimal perturbation growth under the combined effect of diabatic Rossby wave and tropopause dry intrusion.

Using daily 700 hPa geopotential height charts from October 2013 to October 2022， Southwest Vortex （SWV） annual data， European Centre for Medium-Range Weather Forecasts （ECMWF） ERA5 reanalysis data， and precipitation data from stations in Shaanxi Province， a statistical and diagnostic analysis was conducted on individual cases of heavy rain induced by the Southwest Vortex in southern Shaanxi.The results reveal the following insights： （1） Over the course of 10 years， there were a total of 119 days with heavy rain， among which 38 days were associated with heavy rain caused by the Southwest Vortex， accounting for about one-third （32%） of the total heavy rain days.These events were mostly observed from May to September， with the highest frequency in June.Statistically， the precipitation tended to start at night and end during the day.（2） The Southwest Vortex influencing southern Shaanxi originates primarily from the basin vortex， and typically， its movement eastward by 12 to 24 hours can lead to heavy rain in the region.The heavy rain area associated with the Southwest Vortex is mainly located in the northeast quadrant of the 700 hPa vortex center， to the south of the shear line， in the area with a large gradient of potential pseudo-equivalent potential temperature， at the intersection of the 500 hPa westerly trough and the outer southwest flow of the subtropical high， corresponding to the region of strong divergence at the 200 hPa level.（3） The study of the vertical structure of the Southwest Vortex indicates that the strong convergence center at 700 hPa is located to the east of the positive vorticity center.This region corresponds well with the heavy rainfall area.Strong divergence under high-altitude jet streams leads to air quality adjustment， lower-level convergence， and frontal genesis.（4） There are three moisture transport channels： one comes from the warm sea surface of the western Bay of Bengal， the second originates from the warm sea surface in the eastern Bay of Bengal， and the third derives from the South China Sea.During heavy rain periods， the cyclonic-like vorticity， divergence， moisture flux divergence generated by the terrain in the Qinba Mountain region combined with the systematic vorticity， divergence， and moisture flux divergence， enhancing the lower-level convergence.This is also an important factor contributing to the formation of heavy rain induced by the Southwest Vortex in southern Shaanxi.

Based on multi-source data， this article identifies and reconstructs a severe drought event in the modern Tarim Basin.The results show that in the early 20th century， especially in 1917 and 1918， an arc-shaped drought zone formed along the northwest to southwest edge of the basin.The temperature in the Kashgar and Yarkant River basins decreased， precipitation reduced， and the river flow at the north and south edges of the basin reached its lowest level in nearly a hundred years， resulting in severe drought.The drought zone overlapped with the population distribution and irrigation hotspots within the basin， intensifying the conflict between humans and water， and causing severe canalization of water bodies.At the same time， some water systems within the basin disintegrated， water bodies dried up， the desert expanded， endangering rivers， lakes， marshes， and vegetation.Some animal populations became extinct due to the degradation of their habitats.The climatic background of this drought event may be related to the suppression of warm and moist westerlies caused by the North Atlantic Oscillation （NAO）， and the strengthening of the Arctic cold and dry air masses.

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 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.

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.

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.

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.

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.