The Tibetan Plateau (TP) plays a critical role in influencing regional and global climate, via both thermal and dynamical mechanisms. Meanwhile, as the largest high-elevation part of the cryosphere outside the polar regions, with vast areas of mountain glaciers, permafrost and seasonally frozen ground, the TP is characterized as an area sensitive to global climate change. However, meteorological stations are biased and sparsely distributed over the TP, owing to the harsh environmental conditions, high elevations, complex topography and heterogeneous surfaces. Moreover, due to the weak representation of the stations, atmospheric conditions and the local land–atmosphere coupled system over the TP as well as its effects on surrounding regions are poorly quantified. This paper presents a long-term (2005–2016) in situ observational dataset of hourly land–atmosphere interaction observations from an integrated high-elevation and cold-region observation network, composed of six field stations on the TP. These in situ observations contain both meteorological and micrometeorological measurements including gradient meteorology, surface radiation, eddy covariance (EC), soil temperature and soil water content profiles. Meteorological data were monitored by automatic weather stations (AWSs) or planetary boundary layer (PBL) observation systems. Multilayer soil temperature and moisture were recorded to capture vertical hydrothermal variations and the soil freeze–thaw process. In addition, an EC system consisting of an ultrasonic anemometer and an infrared gas analyzer was installed at each station to capture the high-frequency vertical exchanges of energy, momentum, water vapor and carbon dioxide within the atmospheric boundary layer. The release of these continuous and long-term datasets with hourly resolution represents a leap forward in scientific data sharing across the TP, and it has been partially used in the past to assist in understanding key land surface processes. This dataset is described here comprehensively for facilitating a broader multidisciplinary community by enabling the evaluation and development of existing or new remote sensing algorithms as well as geophysical models for climate research and forecasting. The whole datasets are freely available at the Science Data Bank (https://doi.org/10.11922/sciencedb.00103; Ma et al., 2020) and additionally at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Meteoro.tpdc.270910, Ma 2020).

Significance: Cold regions contain vast stores of permafrost carbon. Rapid warming will cause permafrost to thaw and plant respiration to accelerate, with a resultant loss of CO₂, but could also increase the fixation of CO₂by plants. A network of 32 eddy covariance sites on the Tibetan Plateau, which has the largest store of alpine permafrost carbon on Earth, shows that this region functions as a net CO₂sink. Our sensitivity analyses, experiments, and model simulations consistently showed that the fixation of CO₂by plants outpaces the loss of CO₂from permafrost and accelerates plant respiration. This indicates a plant-dominated CO₂balance on the Tibetan Plateau, which could provide a negative feedback to climate warming.

Lake evaporation can influence basin-wide hydrological cycles and is an important factor in loss of water resources in endorheic lakes of the Tibetan Plateau. Because of the scarcity of data, published lake evaporation values are inconsistent, and their spatial distribution has never been reported. Presenting a plausible hypothesis of energy balance during the ice-free seasons, we explored the multiyear (2003–2016) average ice phenology and evaporation amounts of 75 large dimictic lakes by using a combination of meteorological and satellite data. Evaporation amounts show large variability in spatial distribution, with a pattern of higher values in the south. Lakes with higher elevation, smaller area, and higher latitude are generally associated with a shorter ice-free season and lower evaporation. The total evaporated water amounts have values of approximately 29.4 ± 1.2 km³ year⁻¹ for the 75 studied lakes and 51.7 ± 2.1 km³ year⁻¹ for all plateau lakes included.

Actual terrestrial evapotranspiration (ET_a) is a key parameter controlling land–atmosphere interaction processes and water cycle. However, spatial distribution and temporal changes in ET_a over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ET_a and its spatial distribution on the TP by a combination of meteorological data and satellite products. Validation against data from six eddy-covariance monitoring sites yielded root-mean-square errors ranging from 9.3 to 14.5 mm per month and correlation coefficients exceeding 0.9. The domain mean of annual ET_a on the TP decreased slightly (−1.45 mm yr⁻¹, p<0.05) from 2001 to 2018. The annual ET_a increased significantly at a rate of 2.62 mm yr⁻¹ (p<0.05) in the eastern sector of the TP (long >90 ^∘ E) but decreased significantly at a rate of −5.52 mm yr⁻¹ (p<0.05) in the western sector of the TP (long <90 ^∘ E). In addition, the decreases in annual ET_a were pronounced in the spring and summer seasons, while almost no trends were detected in the autumn and winter seasons. The mean annual ET_a during 2001–2018 and over the whole TP was 496±23 mm. Thus, the total evapotranspiration from the terrestrial surface of the TP was 1238.3±57.6 km³ yr⁻¹. The estimated ET_a product presented in this study is useful for an improved understanding of changes in energy and water cycle on the TP. The dataset is freely available at the Science Data Bank (https://doi.org/10.11922/sciencedb.t00000.00010; Han et al., 2020b) and at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Hydro.tpdc.270995, Han et al., 2020a).

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.