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).

The carbon sequestration capacity of alpine grasslands, composed of alpine meadows and steppes, in the Tibetan Plateau has an essential role in regulating the regional carbon cycle. However, inadequate understanding of its spatiotemporal dynamics and regulatory mechanisms restricts our ability to determine potential climate change impacts. We assessed the spatial and temporal patterns and mechanisms of the net ecosystem exchange (NEE) of carbon dioxide in the Tibetan Plateau. The carbon sequestration of the alpine grasslands ranged from 26.39 to 79.19 Tg C year ⁻¹ and had an increasing rate of 1.14 Tg C year ⁻¹ between 1982 and 2018. While alpine meadows were relatively strong carbon sinks, the semiarid and arid alpine steppes were nearly carbon neutral. Alpine meadow areas experienced strong increases in carbon sequestration mainly because of increasing temperatures, while alpine steppe areas had weak increases mainly due to increasing precipitation. Carbon sequestration capacity of alpine grasslands on the plateau has undergone persistent enhancement under a warmer and wetter climate.

Roughness height for heat transfer is a crucial parameter in the estimation of sensible heat flux. In this study, the performance of the Surface Energy Balance System (SEBS) has been tested and evaluated for typical land surfaces on the Tibetan Plateau on the basis of time series of observations at four sites with bare soil, sparse canopy, dense canopy, and snow surface, respectively. Both under- and overestimation at low and high sensible heat fluxes by SEBS was discovered. Through sensitivity analyses, it was identified that these biases are related to the SEBS parameterization of bare soil’s excess resistance to heat transfer (kB⁻¹, where k is the von Kármán constant and B⁻¹ is the Stanton number). The kB⁻¹ of bare soil in SEBS was replaced. The results show that the revised model performs better than the original model.

Surface solar radiation is an important parameter in surface energy balance models and in estimation of evapotranspiration. This study developed a DEM based radiation model to estimate instantaneous clear sky solar radiation for surface energy balance system to obtain accurate energy absorbed by the mountain surface. Efforts to improve spatial accuracy of satellite based surface energy budget in mountainous regions were made in this work. Based on eight scenes of Landsat TM/ETM+ (Thematic Mapper/Enhanced Thematic Mapper+) data and observations around the Qomolangma region of the Tibetan Plateau, the topographical enhanced surface energy balance system (TESEBS) was tested for deriving net radiation, ground heat flux, sensible heat flux and latent heat flux distributions over the heterogeneous land surface. The land surface energy fluxes over the study area showed a wide range in accordance with the surface features and their thermodynamic states. The model was validated by observations at QOMS/CAS site in the research area with a reasonable accuracy. The mean bias of net radiation, sensible heat flux, ground heat flux and latent heat flux is lower than 23.6 W m⁻². The surface solar radiation estimated by the DEM based radiation model developed by this study has a mean bias as low as −9.6 W m⁻². TESEBS has a decreased mean bias of about 5.9 W m⁻² and 3.4 W m⁻² for sensible heat and latent heat flux, respectively, compared to the Surface Energy Balance System (SEBS).

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).