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).

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).

Lakes are an important part of the landscape on the Tibetan Plateau. Most of the Plateau lakes' area has been expanding in recent years, but lake‐atmosphere energy and water interaction is poorly understood because of a lack of observational data and adequate modeling systems. Based on the eddy covariance observation over a high‐altitude shallow and small lake (the small Nam Co Lake) during an ice‐free period from 10 April to 30 August 2012, this study analyzes the lake‐air heat and water vapor turbulent transfer processes and evaluates two popular lake‐air exchange models: a bulk aerodynamic transfer model (B model) and a multilayer model (M model). Our main results are as follows: (1) observations show that the bulk transfer coefficient (C_E) and roughness length (z_oq) for water are higher than those for heat (C_H and z_0h), especially under low wind speed; (2) both models underestimate turbulent fluxes due to inaccurate values of the Charnock coefficient (α) and the roughness Reynolds number (R_r) which are both important parameters for calculating the roughness length for momentum (z_0m) over water; (3) α within a reasonable range of 0.013–0.035 for rough flow and R_r for smooth flow (R_r = 0.11) are 0.031 and 0.54, respectively, by our observation. The wave pattern of shorter wavelength gives a larger z_0m in the small and shallow lake; and (4) the B model and the M model gave consistent results, and both models are more suitable for simulation of turbulent flux exchange after z_0m optimization.