QOMS: top of the world observing. Advancing Earth system science on the Tibetan plateau
Ma, Yaoming ; Xie, Zhipeng ; Ma, Weiqiang ; Han, Cunbo ; Sun, Fanglin ; Sun, Genhou ; Liu, Lian ; Lai, Yue ; Wang, Binbin ; Liu, Xin ; Zhao, Wenqing ; Ma, Weiyao ; Wang, Fangfang ; Sun, Lijun ; Ma, Bin ; Han, Yizhe ; Wang, Zhongyan ; Xi, Zhenhua
QOMS: A Comprehensive Observation Station for Climate Change Research on the Top of Earth
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.</p>
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