Publications

Pepin, N; Deng, HJ; Zhang, HB; Zhang, F; Kang, SC; Yao, TD (2019). An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation-Dependent Warming. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 124(11), 5738-5756.

Abstract
Research has revealed systematic changes in warming rates with elevation (EDW) in mountain regions. However, weather stations on the Tibetan plateau are mostly located at lower elevations (3,000-4,000 m) and are nonexistent above 5,000 m, leaving critical temperature changes unknown. Satellite LST (Land Surface Temperature) can fill this gap but needs calibrating against in situ air temperatures (T-air). We develop a novel statistical model to convert LST to T-air, developed at 87 high-elevation Chinese Meteorological Administration stations. T-air (daily maximum/minimum temperatures) is compared with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua LST (1330 and 0130 local time) for 8-day composites during 2002-2017. Typically, 80-95% of the difference between LST and T-air (Delta T) is explained using predictors including LST diurnal range, morning heating/nighttime cooling rates, the number of cloud free days/nights, and season (solar angle). LST is corrected to more closely represent T-air by subtracting modeled Delta T. We validate the model using an AWS on Zhadang Glacier (5800 m). Trend analysis at the 87 stations (2002-2017) shows corrected LST trends to be similar to original T-air trends. To examine regional contrasts in EDW patterns, elevation profiles of corrected LST trends are derived for three ranges (Qilian Mountains, NyenchenTanglha, and Himalaya). There is limited EDW in the Qilian mountains. Maximum warming is observed around 4,500-5,500 m in NyenchenTanglha, consistent with snowline retreat. In common with other studies, there is stabilization of warming at very high elevations in the Himalaya, including absolute cooling above 6,000 m, but data there are compromised by frequent cloud.

DOI:
10.1029/2018JD029798

ISSN:
2169-897X