This work analyzed carbon dioxide exchange and its controlling factors over an alpine grassland on the eastern Qinghai-Tibetan Plateau. The main results show that air temperature and photosynthetically active radiation are two dominant factors controlling daily gross primary production. Soil temperature and soil water content are the main factors controlling ecosystem respiration. Canopy photosynthetic activity is also responsible for the variation of daily ecosystem respiration other than environmental factors. No clear correlation between net ecosystem exchange and environmental factors was observed at daily scale. Temperature sensitive coefficient was observed to increase with larger soil water content. High values of temperature sensitive coefficient occurred during the periods when soil water content was high and grass was active. Annual integrated net ecosystem exchange, gross primary production and ecosystem respiration were -191, 1145 and 954 g C m^-2 for 2010, and -250, 975 and 725 g C m^-2 for 2011, respectively. Thus, this alpine grassland was a moderate carbon sink in both of the two years. Compared to alpine grasslands on the Qinghai-Tibetan Plateau, this alpine grassland demonstrated a much greater potential for carbon sequestration than others. Annual precipitation is a dominant factor controlling the variation of annual net ecosystem exchange over this grassland. The difference in gross primary production between the two years was not caused by the variation in annual precipitation. Instead, air temperature and the length of growing season had an important impact on annual gross primary production. Variation of annual ecosystem respiration was closely related to annual gross primary production and soil water content during the growing season.

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.

Surface albedo is a crucial parameter in accurately and quantitatively estimating energy and water budget on the Tibetan Plateau (TP) and is also one of the largest radiative uncertainties in land surface modelling attempts. Based on an 8-year ground-based observation of the surface albedo over typical alpine meadows at Maqu and Maduo sites in the eastern TP, the performance of surface albedo products of Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) in describing albedo variations at daily, 8-day, seasonal timescales, and during different special weather conditions were analyzed. Compared with the ground-based observation in Maqu, the 8-day albedo products from GLASS and MCD43B3 present maximum negative biases of −0.030 and −0.027 at Maqu, respectively. The black-sky albedo (BSA) of GLASS product coincides well with the ground-based observation in Maduo, with root mean square error (RMSE) of 0.092 and correlation coefficient (R) of 0.833, whereas that of MCD43B3 had an RMSE of 0.072 and R of 0.752. However, they are underestimated when the albedo is greater than 0.4. At the seasonal timescale, the BSA of GLASS and MCD43B3 underestimated the ground-based observation of Maqu by 0.015 in summer, while their white-sky albedo (WSA) are slightly overestimated and closer to the ground-based observation. In daily timescale, the response of surface albedo to soil moisture is different in semihumid and semiarid areas in summer. For both sites, the blue-sky-albedo of MCD43A3 has better agreement with the ground-based observation than GLASS and MCD43B3, as it improves the temporal resolution and calculates the albedo by weighting multiple observations within 16 days to be closer to the actual surface. However, even MCD43A3 could not capture the slowdown processes of albedo changes resulted by small snowfall processes or the snow aging due to cloud cover and inversion algorithms.

The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area, primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period (2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography, and ecological changes like the encroachment of weeds and scrubland.

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m⁻²·s⁻¹, 0.315 μmol·m⁻²·s⁻¹, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m⁻²·s⁻¹, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m⁻²·s⁻¹, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO₂ concentrations from the eastern to the central and western TP and the influx of low-concentration CO₂ inhibits biospheric CO₂ uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget.

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is a critical headwater area with complex alpine terrain and significant climate variability. Accurately simulating 2 m air temperature (T₂) in this region remains challenging for models such as the Weather Research and Forecasting (WRF) model. This study integrated remote sensing data into the WRF model to improve T₂ simulations over the TRSR. Two simulations were conducted for 2020: a control simulation with default static vegetation parameters (EXP_control) and a sensitivity simulation with realistic vegetation and associated surface albedo of 2020 from the Global Land Surface Satellite (GLASS) datasets (EXP_glass). Results showed that incorporating the GLASS-derived datasets significantly alleviated the cold bias in simulated T₂ during winter and spring, while maintaining comparable performance in summer and autumn. The EXP_glass run achieved better agreement with observations (R = 0.98, p < 0.01) and reduced root-mean-square error (RMSE) by 36.4% compared to EXP_control. Energy balance analysis indicated that the GLASS-derived datasets enhanced solar energy absorption and increased net radiation. Consequently, EXP_glass produced greater turbulent heat fluxes and warmer surface skin temperature (TSK) and T₂. This study underscores the importance of accurate land surface characterization and highlights the utility of remote sensing data for improving regional climate model performance in high-altitude regions.