Zhipeng Xie

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.

Lakes on the Tibetan Plateau play a crucial role in regional hydrology and climate, yet they are highly sensitive to climate change. Despite their importance, our understanding of lake-atmosphere interactions in this region remains limited, primarily due to lack of multi-scale observations constrained by harsh environmental conditions. To address this data gap, we established a comprehensive hydrometeorological observation network across three lakes representing different climatic zones on the Tibetan Plateau. Since 2019, this network has continuously collected key datasets, including meteorological conditions, turbulent fluxes, water levels, temperature profiles, and salinity measurements. Our study suggests that these lakes significantly influence local climate by alleviating temperature fluctuations, altering wind patterns, and reducing atmospheric stability. The observational network marks a substantial step forward in capturing the lake-region climate system, improving our understanding of lake-atmosphere interactions and their impact on regional climate dynamics. Additionally, it supports the validation of models and refinement of remote sensing products. In the future, we aim to expand the integration of in situ, satellite, and model-based data to better support environmental conservation and water resource management on the Tibetan Plateau.

Land-atmosphere (LA) interaction process, through the turbulent exchange of water, heat and CO₂ flux, significantly influences regional micro-climates, local water cycles, energy budgets, and ecosystem dynamics. The Tibetan Plateau (TP), characterized by vast extent, high elevation, strong solar radiation and convection, as well as extreme weather fluctuations, has been under-explored due to the scarcity of LA interaction stations, particularly in the western and northern regions. To address this gap, this study introduces a newly constructed research and observation platform, which consists of 16 planetary boundary layer towers, spans diverse landscapes and covers dynamic meteorological conditions, with average annual air temperature, wind speed and liquid precipitation ranging from -3.5 to 18.5 °C, 0.6 to 5.6 m s^-1, and 43 mm to 2164 mm. Elevation correlates significantly with all meteorological variables, highlighting a strong spatial heterogeneity distribution patterns of LA coupling. The turbulent flux of water and heat show clear seasonal variations, with highest sensible heat flux (SH) in April–May and largest latent heat flux (LE) in July–August. Further, most stations report negative net ecosystem exchange (NEE) values, ranging from -3.2 g C m^-2 a^-1 at Mangai to -174.3 g C m^-2 a^-1 at NADORS, and function as carbon sinks. However, Medog station, locating in the densely forested Yarlung Zangbo valley, functions as a carbon source which is most probably related to the vegetation destruction and human activities. LE is significantly and closely correlated with SH, NEE and ecosystem respiration, indicating strong coupling between water, heat and carbon fluxes. Precipitation as well as soil water content provide favorable moisture sources and show significance in the water-carbon coupling process. The observation and research platform and the quality-controlled high-temporal resolution data provide valuable in situ measurements for studying water-heat-carbon interactions, validating numerical models and satellite algorithms, and offering ground truth for research on hydrological, meteorological, and ecological responses to global climate change.

Understanding land-atmosphere (LA) interactions through coordinated, multidisciplinary, and multiscale observations is crucial for addressing global challenges such as water resource management, land-use planning, climate change, and ecosystem preservation. In this study, we introduce a comprehensive observation and research platform for LA water, heat, and CO₂ flux exchange over the TP and provide initial insights into the spatial and temporal variations of meteorological conditions, liquid precipitation, and turbulent fluxes over the Tibetan Plateau. Diurnal precipitation patterns reveal three types: peak at night, peak during the day, and bimodal peaks. While liquid precipitation can distinguish between water-limited and energy-limited regions, where soil moisture—both from surface and deeper layers—also plays a key role in surface evapotransporation. Net ecosystem exchange (NEE) fluxes are near zero at bare ground stations, show significant carbon release in forested areas, and function as carbon sinks in most alpine meadows and alpine steppe sites.

The climate of the Tibetan Plateau (TP) has experienced substantial changes in recent decades as a result of the location's susceptibility to global climate change. The changes observed across the TP are closely associated with regional land–atmosphere interactions. Current models and satellites struggle to accurately depict the interactions; therefore, critical field observations on land–atmosphere interactions outlined here provide necessary independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since in situ observations are rarely available under these harsh conditions. However, field observations are currently dispersed among individuals or groups and have not yet been integrated for comprehensive analysis. This has prevented a better understanding of the interactions, the unprecedented changes they generate, and the substantial ecological and environmental consequences they bring about. In this study, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset, derived from 12 field stations covering a variety of typical TP landscapes, provides the most extensive in situ observation data available for studying land–atmosphere interactions on the TP to date in terms of both spatial coverage and duration. Three categories of observations are provided in this dataset: meteorological gradient data (met), soil hydrothermal data (soil), and turbulent flux data (flux). To assure data quality, a set of rigorous data-processing and quality control procedures are implemented for all observation elements (e.g., wind speed and direction at different height) in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly raw data series; the quality-assured data; and supplementary information, including data integrity and the percentage of correct data on a monthly scale, are provided via the National Tibetan Plateau Data Center (https://doi.org/10.11888/Atmos.tpdc.300977, Ma et al., 2023a). With the greatest number of stations covered, the fullest collection of meteorological elements, and the longest duration of observations and recordings to date, this dataset is the most extensive hourly land–atmosphere interaction observation dataset for the TP. It will serve as the benchmark for evaluating and refining land surface models, reanalysis products, and remote sensing retrievals, as well as for characterizing fine-scale land–atmosphere interaction processes of the TP and underlying influence mechanisms.

Climate over the Tibetan Plateau (TP) has undergone substantial changes in recent decades due to its sensitive to global climate change. Regional land-atmosphere interactions are closely linked to the changes that have emerged across the TP. The TP is recognized as an ideal natural laboratory for monitoring ongoing climate changes and examining the mechanisms of land-atmosphere interactions over this high mountain region with diverse landscapes. Current models and satellites are struggle to accurately depict the interactions, critical field observations on land-atmosphere interactions here therefore provide necessitate independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since acquiring critical field observations under this diverse topography and harsh conditions is challenging. However, in-situ observations are currently scattered among individuals or small groups and have yet to be integrated for comprehensive analysis, preventing a better understanding of the interactions, the unprecedented changes they generate, and the substantial massive ecological and environmental consequences they bring about. Here, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset is compiled from 12 field stations covering typical TP landscapes, and represents the most extensive in-situ observation data available for studying land-atmosphere interactions on the TP to date in terms of both spatial coverage and duration. To assure data quality, a set of rigorous data processing and quality control procedures are implemented for all observation elements in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly data series, the quality-assured data, and supplementary information including data integrity and the percentage of correct data on a monthly scale are provided via the National Tibetan Plateau Data Center (https://doi.org/10.11888/Atmos.tpdc.300977, Ma et al., 2023). The current dataset provides the benchmark constraints needed to both evaluate and improve the land surface models, reanalysis products, and remote sensing retrievals. Additionally, it is capable of characterizing fine-scale processes for land-atmosphere interactions of the TP and the underlying influence mechanisms.