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).

Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Evapotranspiration (ET) is an important component of the surface energy balance and water cycle, especially in arid and semiarid regions. The characteristics of the actual evapotranspiration (ET_a), which was calculated using the eddy covariance method, and the reference evapotranspiration (ET₀), which was estimated using the Food and Agriculture Organisation (FAO) Penman–Monteith method, were analysed. This work focussed on the seasonal variations in evapotranspiration and crop coefficient (K_c) above the heterogeneous canopy of an arid oasis ecosystem in a cornfield of the Zhangye oasis in northwestern China. The results showed that in 2008, the total net radiation (R_n) was 2457.73 MJ∙m⁻² and that the rainfall was 117 mm. The average wind velocity, air temperature, and specific humidity, which were observed 2 m above the ground surface, were 1.23 m∙s⁻¹, 7.07 °C, and 3.66 g∙kg⁻¹, respectively. The total ET_a and ET₀ were 654.69 mm and 1039.92 mm, respectively; thus, the ET₀ was higher than the ET_a. The difference between the ET₀ and ET_a was high in summer and autumn, and low in winter and spring. The ET_a was greatly influenced by irrigation events, whereas the ET₀ was not influenced by irrigation. The ET_a and ET₀ were both greatly influenced by meteorological elements. The K_c values were less than 0.5 outside of the maize-growing stage and greater than 0.5 during the entire maize-growing stage (from 20 April to 22 September 2008). The K_c values were 0.63, 0.75, 0.78, 0.76, 0.61 and 0.71 at the seedling, shooting, heading, filling, and maturity stages and the entire growth stage, respectively.

The local land–atmosphere coupling (LoCo) investigates the interactions between soil conditions, surface fluxes, planetary boundary layer (PBL) growth, and the formations of convective clouds and precipitation. Studying LoCo over the Tibetan Plateau (TP) is of great significance for understanding the TP's role in the Asian water tower. A series of real-case simulations, using the Weather Research and Forecasting (WRF) model with different combinations of land surface model (LSM) schemes and PBL schemes, has been carried out to investigate the LoCo characteristics over a typical underlying surface in the central TP in the rainy season. The LoCo characteristics in the study area are analyzed by applying a mixing diagram to the simulation results. The analysis indicates that the WRF simulations, using the Noah with BouLac, Mellor-Yamada Nakanishi and Niino Level-2.5 PBL (MYNN), and Yonsei University (YSU) produce closer results to the observation in terms of curves of C_p⋅θ and L_v⋅q, surface fluxes (H_sfc and LE_sfc), entrainment fluxes (H_ent, and LE_ent) at site BJ of Nagqu Station (BJ/Nagqu) than those using the Community Land Model (CLM) with BouLac, MYNN, and YSU. The frequency distributions of H_sfc, LE_sfc, H_ent, and LE_ent in the study area confirm this result. The spatial distributions of simulated H_sfc, LE_sfc, H_ent, and LE_ent, using WRF with Noah and BouLac, suggest that the spatial distributions of H_sfc and LE_sfc in the study area are consistent with that of soil moisture, but the spatial distributions of H_ent and LE_ent are quite different from that of soil moisture. A close examination of the relationship between entrainment fluxes and cloud water content (Q_Cloud) reveals that the grids with small H_ent and large LE_ent tend to have high Q_Cloud and H_sfc, suggesting that high H_sfc is conducive to convective cloud formation, which leads to small H_ent and large LE_ent. A sensitivity analysis of LoCo to the soil moisture at site BJ/Nagqu indicates that, on a sunny day, an increase in soil moisture leads to an increase in LE_sfc but decreases in H_sfc, H_ent, and LE_ent. The sensitivity of the relationship between simulated maximum daytime PBL height (PBLH) and mean daytime evapotranspiration (ET) in the study area to soil moisture indicates the rate at which the maximum daytime PBLH decreases with the mean ET increase as the initial soil moisture goes up. The analysis of simulated H_sfc, LE_sfc, H_ent, and LE_ent under different soil moisture conditions reveals that the frequency of H_ent ranging from 80 to 240 W m⁻² and the frequency of LE_ent ranging from −240 to −90 W m⁻² both increase as the initial soil moisture increases. Coupled with the changes in Q_Cloud, the changes in H_ent and LE_ent as the initial soil moisture increases indicate that the rise in soil moisture leads to an increase in the cloud amount but a decrease in Q_Cloud.