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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.

Known as “Water Tower of Asia”, the Tibetan Plateau (TP) is widely distributed with numerous inflow lakes. Lakes on the TP are less affected by human actives and can be considered as a sensitive indicator of climate change, changes of lakes can well reflect the change of regional climate. However, due to the harsh environment, data acquisition is not easy, studies on the response of lake change to climate mainly focus on several typical lakes (Nam Co, Selin Co Ngoring lake, etc.), but less attention is paid to variation characteristics of mesoscale lake (~100km2). To compensate for this deficiency, we selected three typical mesoscale lakes (Bamu Co, Langa Co and Longmu Co) in different climate zones and studied the lake changes and their responses to climate change using in-situ observations data and remote sensing data. By using multisource remote sensing and water level observation data, this study systematically analyzed inter-annual changes from 1970 to 2021 and monthly changes from 2019 to 2021. The main conclusions are as follows: (1) The changes to lakes in different climatic regions are different: lakes in the monsoon-dominated region showed a significant trend of expansion from 2000 to 2014, but the trend slowed down and stabilized after 2014; lakes in the westerlies-dominated region showed a small expansion trend; lakes in the region affected by both westerlies and the monsoon showed an overall shrinking trend. (2) The monthly variation of lake water volume showed a periodical trend of first increasing and then decreasing, with the largest relative change of lake water volume in August and September. (3) Temperature and precipitation are dominant meteorological elements affecting the variation of lakes, and with the warming of the TP, temperature plays an increasingly important role. Combining observational data and remote sensing data, the study of mesoscale lakes changes can increase the understanding of relationship between lake change and climate change, provide help for further study of lake - atmosphere interaction and climate effect and climate change in the TP.Key words: Tibetan Plateau; mesoscale lakes; change of lake water volume; multisource altimetry data; in-situ observation; climate zones

Tibetan Plateau Observation Platform (TORP), Third Pole Environment (TPE) observation and research Platform (TPEORP) and new stations were set up in recently years.Firstly, based on the difference of model and in-situ observations, a serious of sensitive experiments were done by using numerical model, Such as Noah-MP and WRF. Better schemes in ground temperature and soil water content at shallow layer simulation were selected by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and Soil Moisture Active Passive (SMAP) soil moisture data. In order to use remote sensing products, a land-atmosphere model was initialized by ingesting land surface parameters, such as AMSR-E RS products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations. Secondly, we analyzed the spatiotemporal variation characteristics of the heating field in the Tibetan Plateau through the observation data and reanalysis data and then revealed the influencing factors and potential effects of the changes of atmospheric heat sources on the Tibetan Plateau in summer and winter. Finally, the relationship between the change of spring AHS in the Tibetan Plateau and the change of summer precipitation in Northeast China was also analyzed from the level of mathematical statistics.Thirdly, based on historical observations daily data for 1981-2016 from 130 meteorological stations over and around the Tibetan Plateau , the trends of sensible heat flux (SH) and their elevation-dependence were investigated. Results indicate that the SH over and around the Tibetan Plateau experienced apparent trends’ shift in approximate 2000, demonstrating noticeable reductions during 1981-2000 and pronounced recovery during 2001-2016 for the four seasons.All of the different methods will clarify the water and energy parameters in complex plateau, it also can affect atmospheric cycle over the Tibetan Plateau even all of the global atmospheric cycle pattern.