Lakes are sensitive indicators of climate change and exhibit distinct responses to climatic variability. Using in situ eddy covariance and meteorological observations from Nam Co ("large lake") and a small lake ("small lake") adjacent to Nam Co, we evaluate the performance of the FLake model in simulating lake processes. The model generally reproduces the seasonal variations in mixed-layer depth and surface water temperature, although diurnal amplitudes are underestimated. Simulated sensible and latent heat fluxes agree well with observations when appropriate lake depth and light extinction coefficients are applied, with RMSEs of similar to 1 degrees C, 8 W m(-2), and 22 W m(-2) for lake surface temperature, sensible heat flux, and latent heat flux, respectively. For the "large lake", latent heat flux simulations differ markedly between land-based and lake-based forcing, primarily due to differences in wind speed and air temperature. Long-term simulations (1981-2024) suggest progressive warming of lake surface waters, strengthened thermal stratification, and increasing surface heat fluxes, with downward longwave and shortwave radiation and near-surface air temperature identified as the dominant climatic drivers.

The three most intense snowstorm processes in winter (January), influenced by the southern branch trough in the southern edge of Tibet, since 2015 were chosen for this study. Based on the conventional observation data, NECP_FNL reanalysis data, and FY-2G cloud top blackbody brightness temperature data, the large-scale circulation background during the snowstorm was examined, and the moist potential vorticity and helicity were analyzed. This study examined the influence of the southern trough on the snowstorm weather process and presented a conceptual model of winter snowstorm weather in the region under the influence of the southern trough. The results demonstrated that: (1) the southern trough to the west of 80E was the primary system affecting the winter snowstorm along the southern margin of Tibet. The geopotential height gradient between the southern branch trough and the western Pacific subtropical high determines the intensity of the southwest jet stream ahead of the trough. The deep southern trough facilitated the ascent of the strong southwest jet stream ahead of the trough over the Himalayas and transported warm and humid water vapor to the snowstorm area, which occurred in the water vapor convergence area and the active convective cloud area. The snowstorm area is located on the left (north) side of the exit region of the upper-level jet stream and on the left front side of the entrance region of the low-level jet stream. The coupling of high and low air jet streams provided favorable dynamic conditions for heavy snowfall. (2) Under the conservation of moist potential vorticity (MPV)(,) heavy snow occurred near the steep dense zone of iso-theta(se). The positive pressure term of the moist potential vorticity (MPV1) < 0 and the barotropic term of the moist potential vorticity (MPV2) > 0 in the middle troposphere can be important indicators of heavy snowfall. (3) The positive center of the 500 hPa moist helicity coincided with the center of the snowstorm, with the positive center corresponding to the period of a snowstorm. During the period of heavy precipitation, the moist helicity increased significantly.

Land-atmosphere (LA) interactions, through the turbulent exchange of water, heat and CO2 fluxes, strongly influence regional micro-climates, water cycles, energy budgets, and ecosystem dynamics. The Tibetan Plateau (TP), characterized by its vast extent, high elevation, strong solar radiation and extreme weather variability, remains underexplored due to the scarcity of LA observation sites, particularly in its western and northern regions. This study introduces a newly established research and observation platform, comprising 16 planetary boundary layer towers that span diverse landscapes and dynamic meteorological conditions. Across these sites, mean annual air temperature, wind speed, and liquid precipitation range from -3.5 to 18.5 degrees C, 0.6 to 5.6 ms-1, and 43 to 2164 mm, respectively. Elevation exhibits significant correlations with all meteorological variables, highlighting the pronounced spatial heterogeneity of land-atmosphere coupling across the region. The turbulent fluxes of water and heat exhibit distinct seasonal patterns, with maximum sensible heat flux (SH) in April-May and latent heat flux (LE) in July-August. Most stations act as carbon sinks, with net ecosystem exchange (NEE; the net CO2 exchange between the ecosystem and the atmosphere, where negative values indicate net ecosystem CO2 uptake) ranging from -3.2 to -174.3 gCm-2a-1, except the Medog station, which behaves as a carbon source likely linked to vegetation disturbance and human activity. LE is significantly correlated with SH, NEE and ecosystem respiration, revealing a strong coupling among water, heat and carbon fluxes. This high-resolution, quality-controlled dataset provides critical in situ observations for studying water-heat-carbon coupling, validating models and satellite algorithms, and improving understanding of climate-ecosystem interactions over the TP. The whole datasets are freely available at the National Tibetan Plateau Data Center (10.11888/Atmos.tpdc.302428; Wang and Ma, 2025).

Lakes affect regional climate by influencing heat and moisture exchange and near-surface wind. However, their global impact on the atmospheric boundary layer, a key layer of land–atmosphere interaction, remains unclear. Here, we combine satellite-based atmospheric profiles with the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis to assess the impacts of large inland lakes (area greater than 500 square kilometers). Lakes enhance heat (1.6 degrees Celsius) and moisture (0.4 grams per kilogram) transport toward surrounding land, shifting low-level instability center to within 25 kilograms of the shoreline and strengthening turbulent mixing and convective development. Consequently, atmospheric boundary layer height over these lake-adjacent lands increases by 0.3 to 0.6 kilometers, while it remains lower over lakes because of stronger stratification. The dominant processes of lake effects vary with season, latitude, elevation and lake size. These findings underscore the importance of incorporating lake–atmosphere coupling into weather and climate models. Large inland lakes tend to increase the atmospheric boundary layer height over nearby land areas and reduce it over the lake itself, with the dominant mechanisms (thermal, moisture, and dynamic processes) varying with season, lake elevation, size, and latitude.

This study investigates technical principles and advancements in the application of ground-based microwave radiometers for the observation of atmospheric temperature and humidity profiles,explores critical pathways that can enhance measurement accuracy and stability,and provides theoretical support for meteorological monitoring and climate research.Based on physical foundations including the thermal radiation theory,radiative transfer equations,and brightness temperature,this work systematically reviews microwave radiation measurement principles and calibration technology evolution as well as data quality control methods.By comparing performance parameters of typical domestic and international devices,this study focuses on analyzing the direction of retrieval algorithms optimization and the potential of integrating machine learning approaches.Ground-based microwave radiometers enable continuous detection of atmospheric temperature and humidity profiles within 0-10 km through multi-frequency(22-59 GHz)observations.Liquid nitrogen cold calibration and tilt-curve calibration techniques improve brightness temperature accuracy up to 0.2-0.5 K.Neural network algorithms reduce the Root Mean Square Error(RMSE)of temperature inversion to 1.48℃,while physically constrained models decrease the Mean Absolute Error(MAE)of high-altitude(＞8 km)temperature inversion by 0.19℃.However,challenges persist,including radio frequency interference in complex weather(10%error in K-band),insufficient long-term calibration stability(annual drift＞0.2 K),and increased humidity inversion error under cloudy/rainy conditions(RMSE up to 25.21%).Ground-based microwave radiometers,enhanced by anti-interference hardware design and dynamic real-time calibration and machine learning-physical model fusion algorithms,can significantly improve the retrieval accuracy of atmospheric parameters.This research lays theoretical and technical foundations for their widespread application in meteorological monitoring and climate studies.Future efforts should prioritize breakthroughs in multi-frequency collaborative observations,nonlinear error correction,and extreme weather adaptability.Through in-depth exploration of environmental noise suppression,calibration error tracking,and retrieval robustness enhancement,this work provides a critical support for advancing atmospheric detection technologies under global climate change,facilitating the development of high-precision,all-weather atmospheric profile observation systems.

The Afro-Asian summer monsoon (AfroA-SM) provides essential rainfall for billions of people. However, in recent decades, unusually wet conditions have persisted into autumn, creating unprecedented extremes. Using multidecadal observations, reanalysis datasets, and Polar Amplification Model Intercomparison Project (PAMIP) simulations, we show that Arctic warming significantly postpones the retreat of the AfroA-SM. This delay shifts the northernmost summer monsoon boundary by 5.6°–12° and accounts for 33%–69% of September precipitation across North Africa, South Asia, the Tibetan Plateau, and East Asia. The mechanism is anchored in a northward displacement of the subtropical jet stream, which is driven by reduced meridional temperature gradients resulting from sea‐ice loss and Arctic amplification. The resulting Eurasian anticyclone weakens the tropospheric dipole of the polar vortex, promoting negative North Atlantic Oscillation (NAO)-like anomalies and a pronounced Eurasian wave train. These reinforce the northward shift of the subtropical high and enhance warm‐moisture advection into northernmost margin summer monsoon belts. PAMIP ensemble experiments further indicate that future Arctic amplification will intensify this autumn monsoon persistence and its associated precipitation. Our findings are critical for understanding the intensification of late-season hydroclimatic extremes, underscoring the urgent need for improved seasonal forecasting, adaptive agricultural strategies, and flood-risk management in a warming climate.

Abstract The atmospheric diabatic heating over the Tibetan Plateau (TP) acts as a pivotal heat engine driving the Asian summer monsoon. However, its precise numerical intensity across the plateau remains elusive. Temporal aliasing in polar‐orbiting satellite observations and the coarse resolution of reanalysis data collectively obscure the thermodynamic hysteresis between radiative forcing and land‐atmosphere responses, hindering high‐frequency insights into the TP heat engine. To bridge this gap, a seamless, hourly atmospheric heat source (AHS) data set for 2020 was constructed by integrating Fengyun 4A geostationary observations with a direct calculation method. Validation against ground benchmarks demonstrates the data set's reliability, with a sensible heat Root Mean Square Error (RMSE) of 74.3 W m −2 and a relative bias of −2.1%, as well as a precipitation RMSE of 5.0 mm d −1 and a median relative bias of 0.9%. Crucially, these hourly records resolve the spatiotemporal heterogeneity of thermodynamic hysteresis. In the western arid zone, a sustained Sensible Heat Air Pump effect is identified. Soil thermal inertia maintains high heating rates past sunset, creating a counterclockwise hysteresis loop. Conversely, the eastern humid zone exhibits a significant phase lag of 2–3 hr between the radiation peak and latent heat release. These spatiotemporal phase shifts create a dynamic relay between western surface heating and eastern convective maturation. These results provide critical observational constraints for correcting both phase biases and energy intensity in convective parameterization schemes. The findings highlight the necessity of resolving sub‐daily perturbations to accurately capture the climate dynamics of the TP.

Study region: Longmu Co, a deep hypersaline lake in the western Tibetan Plateau, located in a cold, arid region dominated by the westerlies. Study focus: The annual mixing regime, under-ice thermal evolution, and relative roles of salinity, thermal forcing, and meteorological variability in deep hypersaline Tibetan Plateau lakes remain poorly understood. We investigated the thermodynamic regime of Longmu Co using 451 days of high-frequency water-temperature observations, a vertical salinity profile, concurrent meteorological measurements, bathymetric data, and satellite-derived ice phenology. TEOS-10-based density calculations and decomposition of squared buoyancy frequency showed that salinity provided the dominant background stability, separating a seasonally active mixolimnion from a persistently isolated monimolimnion. The mixolimnion exhibited cold monomictic circulation, mixing once annually from mid-November to early February, whereas the deep water column remained density-stratified. During complete ice cover, upper-water temperatures increased after early February, indicating a distinct under-ice thermal transition before ice breakup. Thermal forcing governed seasonal upper-layer stratification, while wind produced rapid but shallow perturbations. New hydrological insights for the region: In deep hypersaline lakes of the high-altitude western Tibetan Plateau, persistent haline stratification can prevent full-depth overturn despite strong westerly winds and severe winter cooling. Meteorological forcing acts mainly within the mixolimnion. This buffering effect explains why such lakes may remain meromictic and should be represented explicitly in regional lake models and assessments of climate-driven hydrological change.

Climate warming and moistening are enhancing the frequency and intensity of Tibetan Plateau Vortices (TPVs), altering precipitation patterns across the Tibetan Plateau (TP) and adjacent regions. Although TPVs are the primary weather systems driving heavy precipitation over northern China, how their impacts vary across different geographic environments remains poorly understood. Reanalysis data and potential vorticity (PV) diagnostics are used to examine dynamical and thermodynamic processes associated with the TPV-induced crossregional precipitation event (8-13 July 2021) across the Eastern Slope of the TP (ESTP) and the North China Plain (NCP). In the ESTP, heavy precipitation results from a dynamical-thermodynamic coupled mechanism, where low-level jets transporting moisture from the Indian Ocean and South China Sea interact with orographic lifting to trigger warm and moist advection, further enhanced by PV advection and diabatic heating. In the NCP, a dual-channel moisture transport pathway emerges as the TPV interacts with warm-moist flow along the western flank of the Western Pacific Subtropical High (WPSH), with precipitation peaks preceding PV maxima, indicating that dynamical forcing associated with PV advection dominates the heavy precipitation. These findings show that TPV-driven heavy precipitation results from spatiotemporal coupling between dynamical triggering and thermodynamic feedback of moisture. Latent heating rapidly amplifies nascent convection, while thermal processes enhance low-level instability, compensating for weakening dynamical forcing and sustaining precipitation. These findings highlight that regional disparities in TPV-induced heavy precipitation stem from asymmetrical dynamical-thermodynamic coupling, underscoring the need to elucidate the physical mechanisms by which TPVs trigger cross-regional precipitation, in order to improve forecasts of TPV-related weather and associated hazards.

Winter mean and extreme temperature indices obscure the cumulative sub-zero temperature deficit relevant to freeze–thaw processes. However, systematic assessment of the variability and drivers of these processes remains lacking. In this study, using station observations, reanalysis fields and CMIP6 multi-model simulations, we assess the spatiotemporal variability, atmospheric drivers and future trajectory of the winter accumulated negative temperature (WANT), defined as the cumulative daily mean temperature below 0 °C, over Northeast China during 1960–2020. The WANT increases significantly at a rate of 32.86 °C d per decade (p < 0.01), with the most pronounced increases at low elevations. The winter mean temperature increases at 0.38 °C per decade (p < 0.01), whereas the winter mean sub-zero daily temperature increases at 0.33 °C per decade (p < 0.01), indicating that winter mean warming may lead to overestimation of the decline in below-freezing temperature conditions. The long-term variation of the WANT shows no systematic northward increase, whereas the interannual anomaly patterns during the cold and warm periods differ in both magnitude and latitudinal organisation. Beyond sea ice alone, cold and warm WANT periods are individually associated with distinct combinations of autumn snow cover, sea ice and sea surface temperature anomalies that jointly precondition contrasting winter waveguide structures and downstream wave energy convergence over Northeast China. The CMIP6 ensemble-mean trends reproduce the observed variations in the WANT, with less than 5% error. Using SSP5-8.5, the WANT is projected to increase by approximately 600 °C d by 2100, exceeding the full range of historical interannual variability, whereas SSP1-2.6 indicates a change limited to approximately 110 °C d. These results underscore the importance of incorporating WANT into climate assessments for cold regions and adaptation strategies for freeze–thaw sensitive ecosystems and infrastructure.

The Tibetan Plateau alpine grasslands are highly sensitive to climate change and play a key role in regional and global carbon cycling. Accurate quantification of net ecosystem CO2 exchange (NEE) and its climatic controls is therefore essential for understanding carbon dynamics and improving projections of carbon-climate feedbacks. However, robust regional assessments of NEE have been hindered by the sparse spatial coverage and uneven distribution of eddy-covariance flux observations across the Tibetan Plateau, resulting in substantial uncertainties in regional carbon flux estimates. Here, we produced spatially explicit, high-resolution (1/30 degrees) monthly NEE estimates for Tibetan Plateau alpine grasslands from 1982 to 2023 by upscaling flux measurements from 38 eddy-covariance sites with satellite and meteorological predictors. Results revealed a substantial strengthening of the regional alpine grassland carbon sink, with mean annual NEE decreasing from -16.7 to -50.1 g C m(-)& sup2; yr(-)& sup1;. Humid eastern meadows acted as strong carbon sinks (mostly < -150 g C m(-)& sup2; yr(-)& sup1;), whereas arid western steppes remained weak carbon sources (0-60 g C m(-)& sup2; yr(-)& sup1;). Seasonal patterns showed that carbon uptake peaked in summer and accounted for most of the interannual variability, while the other seasons were generally characterized by net carbon release. Sensitivity experiments showed that warming and wetting enhanced carbon uptake, whereas drought reduced carbon sink strength, particularly in the drier alpine steppe regions. These findings provide critical insights into alpine grassland carbon dynamics and underscore the vulnerability of the Tibetan Plateau to future climate change.

Abstract A coherent Doppler wind LiDAR (Wind3D 6000) has been operating in the northern Mount Qomolangma region since 2023. We retrieved planetary boundary layer height (PBLH) from LiDAR observations collected from October 2023 to September 2025 using a hybrid algorithm combining signal‐to‐noise ratio (SNR)‐based thresholding and wavelet covariance transform (WCT). The method agrees well with the available daytime radiosonde measurements, although these radiosonde observations are limited to a short monsoon‐season period and therefore cannot fully validate all seasons. Case studies of a clear‐sky day, a cloudy day, and a strong‐wind episode show that the LiDAR‐derived PBLH captures rapid convective growth under clear skies, abrupt collapse under cloud‐limited conditions, and mechanically sustained deep layers during high winds. In contrast, ERA5 and MERRA‐2 show systematic differences in the timing and magnitude of these diurnal transitions. Composite statistics reveal a robust diurnal cycle, with a shallow nocturnal layer (∼300–400 m), rapid growth after sunrise, and an afternoon maximum of 1.5–1.8 km. Seasonally, daytime PBLH is lowest in winter, increases during spring, reaches its largest monthly mean in June, and becomes slightly lower during July and August when monsoon clouds and moisture are more frequent. ERA5 generally underestimates daytime PBLH, especially during the warm season, whereas MERRA‐2 and the LiDAR's default product overestimate it throughout the year. This 2‐year PBLH record provides an observational basis for understanding boundary layer development and evaluating atmospheric reanalysis products over complex high‐mountain terrain, despite limited nocturnal validation and weather‐related data gaps.

This study leverages a comprehensive set of CMIP6 GCM outputs, downscaled via a convolutional neural network (CNN), to examine Beijing's temperature data spanning from 1961 to 2100. The ensemble of downscaled CMIP6 results indicates a consistent rise in Beijing's annual mean maximum (Tmax) and minimum (Tmin) temperatures from 1961 to 2100. Additionally, the potential impacts on local temperature-related extreme events are assessed using 8 indices of extreme climate events. The findings show that hot days (SU30) are projected to see a significant increase in both intensity (CSU30) and frequency (CSU30 frequency) over time and across scenarios, surpassing historical levels through a gradual step-up increase, a pattern similarly observed in warm nights (TR). The frequency and intensity of extreme temperatures associated with cold events (FD, ID) in Beijing are expected to decrease markedly, particularly in the central southern part of the study area. Regarding the temporal variation of urban-rural contrast, Tmin is on a downward trend under the SSP585 scenario, while that of Tmax is likely to remain stable under both the SSP245 and SSP585 scenarios in the future. The difference in the intensity of extreme weather events (SU30, CSU30, ID, FD, CFD, TR) between urban and suburban areas is growing, yet the frequency of extreme events (CSU30 frequency, CFD frequency) is on the decline.

In recent decades, the climate of Tibetan Plateau has undergone notable changes, which has a strong influence on global climate systems and human activities, making it a research hotspot. However, due to its extreme elevation, harsh environment, and complex underlying surface, long-term observations of the central Plateau's atmospheric vertical profiles have been challenging and scientific data sharing is crucial and in urgent need. This paper presents a 9-year observational dataset (2014-2022) with hourly temporal resolution from the Nagqu region of northern Tibet. The dataset is a combination of four field stations covering the central Tibetan Plateau, which recorded near-surface meteorological data, radiation budget, turbulent fluxes, and soil hydrothermal characteristics. All observational items in this dataset underwent data processing and quality control to ensure data quality. This dataset represents the most detailed raw observational data on the central Tibetan Plateau's spatial coverage and recent changes. It holds significant value in revealing energy and water exchanges between the land surface and the atmosphere on the Tibetan Plateau. Main datasets are freely available at the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Meteoro.tpdc.270010, Hu et al., 2019 and https://cstr.cn/18406.11.Meteoro.tpdc.270010, last access: 5 November 2025) and additionally at National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Atmos.tpdc.300325, Wang et al., 2023a and https://cstr.cn/18406.11.Atmos.tpdc.300325, last access: 5 November 2025).

The land surface heat flux is a crucial parameter that plays a significant role in the transformation and cycling of energy and matter between the atmospheric and land surface layers. This parameter serves as an essential input for various numerical models. Most land surface schemes deduce soil heat flux by amalgamating the heat conduction equation and residual method of energy balance. However, substantial discrepancies could be observed in soil heat flux simulations. These occurred among different Numerical Weather Prediction and offline Land Surface models, even though they were driven by the same atmospheric processes. The presence of discrepancies in models necessitated the accurate calculation of soil heat flux in order to reduce uncertainty in the allocation of sensible and latent heat flux at the surface. By reducing this uncertainty, we could decrease uncertainties in surface energy partitioning, achieved through diminishing the bias in simulated precipitation. However, in the Tibet plateau, soil heat flux observations were sparsely distributed, and the coverage period was different and limited, primarily used for model and remote sensing validation. There was a notable gap in research on the precise variations in soil heat flux in the Tibet plateau, particularly in studies employing sampled soil observations to accurately calculate soil heat flux. This study addressed these aforementioned deficiencies by focusing on soil attributes in the Tibet plateau to accurately calculate soil heat flux. In calculating soil heat flux precisely, factors like topography, land use, and vegetation type were considered. To ensure stability, representative soil cores were carefully observed and selected, obtaining samples through a layer-by-layer sampling approach. All sampling work had currently been completed. Utilizing comprehensive, synchronous, and continuous soil heat flux observations at the BJ site, in conjunction with long-term observational data and soil samples, we employed sampled soil attributes and soil heat flux plate observations to ascertain the requisite parameters for accurate soil heat flux calculation. These parameters, including the physical properties and porosity of soil profiles, enabled us to precisely determine the surface soil heat flux fluctuations at the BJ site. As a result of global warming, the Nagqu region had experienced elevated temperature, augmented precipitation, and amplified soil heat flux. In summary, accurately calculating soil heat flux is vitally important for allocating sensible and latent heat flux at the surface, which in turn diminishes uncertainty in the surface energy balance within models. This reduction in uncertainty is crucial for establishing a foundation to mitigate biases in local precipitation simulations within existing models.

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM2.5, SO42−, NO3−, NH4+, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), NO3− + NH4+ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones.

The Tibetan Plateau, a critical regulator of the global water cycle and climate system, represents a highly sensitive region to environmental changes, with significant implications for sustainable development. This study focuses on Nam Co Lake, the third largest lake on the Tibetan Plateau, and investigates the hydrochemical evolution of the lake and the driving mechanisms in regard to the lake–river–groundwater system within the Nam Co Basin over the last 20 years. Our findings provide critical insights for sustainable water resource management in regard to fragile alpine lake ecosystems. The hydrochemical analyses revealed distinct temporal patterns in the total dissolved solids, showing an increasing trend during the 2000s, followed by a decrease in the 2010s. Piper diagrams demonstrated a gradual change in the anion composition from the Cl type to the HCO3 type over the study period. The ion ratio analyses identified rock weathering (particularly silicate, halite, sulfate, and carbonate weathering), ion exchange, and evaporation processes as primary controlling processes, with notable differences between water bodies: while all four weathering processes contributed to the lake’s water chemistry, only halite and carbonate weathering influenced river and groundwater compositions. The comparative analysis revealed more pronounced ion exchange processes in lake water than in river and groundwater systems. Climate change impacts were manifested through two primary mechanisms: (1) enhanced evaporation, leading to elevated ion concentrations and isotopic enrichment; and (2) temperature-related effects on the water chemistry through increased dilution from precipitation and glacial meltwater. Understanding these mechanisms is essential for developing adaptive strategies to maintain water security and ecosystem sustainability. The relationships established between climate drivers and hydrochemical responses provide a scientific basis for predicting future changes and informing sustainable management practices for inland lake systems across the Tibetan Plateau.

Frozen soil, covering most of the Tibetan Plateau (TP), critically influences land surface and climate simulations. Although some studies have made advancements in simulations, further investigation into the distinct mechanisms underlying relevant parameterization schemes remains essential. This study compares two frozen soil permeability schemes in Noah-MP (NY06: high-permeability; Koren99: low-permeability) to elucidate their distinct hydrological mechanisms. Although significant disparities exist in the simulation of soil water and ice content between the two schemes in permafrost regions, the simulated soil water content in the shallow layer exhibits similarity. Their underlying physical processes behind this similarity differ fundamentally: Koren99 relies on cross-seasonal ice melt recharge, whereas NY06 depends more on current-season precipitation and snowmelt. With greater soil depth, soil water differences progressively propagate downward, amplifying variations in hydraulic conductivity, and soil memory effects become increasingly dominant. Meanwhile, the Koren99 scheme more effectively impedes bottom-up melting water transport than top-down effect. However, the aforementioned disparities are not apparent in seasonally frozen soil. Notable disparities also exist in simulated evapotranspiration and surface runoff over permafrost regions, particularly during the summer months. This research investigates the differences in water transport within frozen soil over the TP, elucidates the distinct hydrological mechanisms underlying different frozen soil permeability schemes, and highlights that similar soil hydrothermal simulations are associated with different physical processes, leading to varying degrees of effectiveness in soil memory. Furthermore, this research elucidates the dual role of soil ice (permeability restriction and water storage) in hydrological processes, providing a theoretical basis for improving frozen soil parameterization.

Accurate daily surface net radiation (Rn) estimation over the Tibetan Plateau’s complex and highly heterogeneous terrain is essential for advancing the understanding of land–atmosphere exchanges and regional climate processes. This study developed an optimized deep learning framework that systematically evaluates 19 CNN architectures using a per-pixel multivariate regression design (1 × 1 × 21). The channel-rich representation incorporates engineered neighborhood descriptors to statistically embed spatial context while fully avoiding the mosaic and boundary artifacts common in patch-based approaches. Among all tested networks, Xception delivered the best combination of accuracy (R2 > 0.94), computational efficiency, and physical consistency. Its depthwise separable convolutions and skip connections enable hierarchical nonlinear cross-channel feature learning, effectively capturing the complex dependencies between surface variables and Rn. Independent validation confirmed stable performance under diverse weather conditions and substantially better skill than GLASS, especially across rugged terrain and high-albedo surfaces. SHAP analysis further highlights physically meaningful behavior, with astronomical and topographic factors contributing ~70% and surface properties ~25% to predictions. Remaining challenges include dependence on continuous high-quality multi-source inputs and scale effects from mixed pixels. Future work will enhance operational deployment through automated daily preprocessing, improved sub-diurnal characterization via multi-scale data fusion, and stronger physical constraints to increase reliability.