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 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.

Mount Everest (Qomolangma), the highest mountain on Earth, is an unrivaled natural research platform for understanding multispheric interactions over heterogeneous landscapes. The land–atmosphere interactions in this iconic mountain region have paramount importance for weather and climate predictions at both regional and global scales; however, observing and modeling these interactions is inherently challenging due to the extreme environment. The scarcity of multiscale observations hinders progress in this field. Thus, establishing a comprehensive network to systematically observe the land–atmosphere interactions across multiscales in this unrivaled region, is the basis for gaining a better understanding of weather, climate, and climate change. As one of the 69 national observation and research stations in China, the Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS) observation network of land–atmosphere interactions has been established over the northern slope of Mount Everest since 2005. This network consists of six sites with different underlying surfaces, which significantly improves the observational capabilities for the climate system. These observations have promoted the understanding of land–atmosphere interactions and their impacts on multiscale weather patterns, atmospheric circulations, and climate and have provided data support for informing and guiding model development and remote sensing monitoring. Facing an unprecedented opportunity with enormous development possibilities, we emphasize the considerable potential of these observations for understanding and predicting weather and climate in the Himalayas and beyond. Additionally, we expect to extend the future focus to model–data fusion and to societally relevant applications, such as natural disaster prevention and climate change mitigation and adaptation.

In this paper, the reliability of the wind speed, temperature, humidity, pressure, and precipitation values of three surface meteorological forcing products [China Meteorological Administration Land Data Assimilation System, version 2 (CLDAS-2); China Meteorological Forcing Dataset (CMFD); and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)] in the Tibetan Plateau (TP) region was investigated from 2008 to 2014. Compared with the China Meteorological Administration (CMA) observations, CLDAS-2 exhibited the highest correlation coefficient for wind speed, CMFD displayed the best coefficients for temperature and specific humidity, and MERRA-2 best reflected pressure variations. Based on the biases, CLDAS-2 exhibited the best overall performance for temperature, specific humidity, and pressure, while CMFD displayed the best performance for wind speed. The high overall accuracy and false alarm ratio of precipitation based on MERRA-2 both stem from its continuous overestimation of the precipitation frequency. Both CLDAS-2 and CMFD overestimated the nonprecipitation frequency in comparisons with CMA observations, and a significant positive bias exists in MERRA-2 based on the analysis of daily precipitation. The results obtained from the comparisons with field observations over the TP and CMA observations are similar, except for the temperature and humidity biases of CLDAS-2. The meteorological effects on the coupled land–blowing snow modeling discussed in this paper suggest that the occurrence of blowing snow and snowdrift sublimation are projected to be reduced by CLDAS-2 due to the underestimation of wind speed, continual lack of snowfall events, and the positive biases in low temperatures and humidity, while simulations of blowing processes by MERRA-2 are likely to be much more severe than they actually are. These results may contribute to identifying deficiencies associated with the development of land surface models coupled with a blowing snow model.

The Tibetan Plateau (TP) features unique and highly heterogeneous soils, terrains, vegetation, and climate. Accurately modeling complex freeze‐thaw processes and their hydrothermal impacts remains a great challenge. This study focused on deciphering the spatiotemporal variability of diverse parameterization schemes in the soil hydrothermal simulations using the Noah‐MP land surface model. We first discussed the spin‐up time required by the model to reach the equilibrium state, and then performed a sensitivity analysis of these schemes. The Moderate Resolution Imaging Spectroradiometer land surface temperature and Soil Moisture Active Passive remote sensing products were used as benchmarks to evaluate the schemes' performance. Results show that longer spin‐up times are required in permafrost regions owing to water phase changes. Ground temperature and soil temperature are mainly sensitive to energy‐related schemes. Vegetation‐related schemes play an important role after the growing season begins on the southeastern TP. Soil water content shows strong sensitivity to schemes related to both water and energy transport. However, the sensitivity of these energy‐related schemes is weakened when simulating total soil moisture, including the total amount of water and ice, indicating that these schemes have marked impacts on soil freeze‐thaw processes. These results reveal the different spatial (both regional and depth‐related) and temporal effects of parameterization schemes; we also provided a preliminary selection of these schemes at a regional scale that could facilitate the further improvement of the soil hydrothermal simulations on the TP.