Abstract. 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).

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Snow cover in mountainous terrain plays an important role in regional and global water and energy balances, climate change, and ecosystems. Blowing snow is a frequent and important weather phenomenon over the Tibetan Plateau (TP); however, this process is neglected in most current land surface models, despite the consequential role it plays in the land surface and atmospheric water and energy budgets. In this paper, we present a blowing snow model PIEKTUK coupled with the Community Land Model (CLM4.5) to provide a better estimate of the snow dynamics for the consideration of snow redistribution induced by wind. Two simulations with a 0.065° spatial resolution were performed in 2010 over the TP, namely, a sensitivity experiment with the inclusion of blowing snow effects (CLM_BS) and a control run with the original model (CLM). A specific objective of this study was to evaluate the improvements in the simulations of snow dynamics and other key variables in surface energy partitioning provided by the coupled model, such as the surface albedo and land surface temperature (LST). Compared with a variety of remote‐sensing observations, the results show that the surface snow cover, snow depth, and surface albedo can be better reproduced in most of the TP region by CLM_BS than by the original CLM, particularly in the Kunlun Mountains, Hoh Xil area, and the southwestern TP. In areas with reduced bias, variations in the monthly mean snow cover fraction can be reflected particularly well by CLM_BS. For LST, however, a significant decrease in the nighttime LST bias was detected in CLM_BS, while the bias in the daytime LST increases. The results show considerable potential for the inclusion of the blowing snow process to promote the modeling of snow dynamics and land‐atmosphere interactions on the TP.