Land surface temperature over global deserts: Means, variability, and trends

Author:

Zhou Chunlüe12ORCID,Wang Kaicun12ORCID

Affiliation:

1. College of Global Change and Earth System Science Beijing Normal University Beijing China

2. Joint Center for Global Change Studies Beijing China

Abstract

AbstractLand surface air temperature (LSAT) has been a widely used metric to study climate change. Weather observations of LSAT are the fundamental data for climate change studies and provide key evidence of global warming. However, there are very few meteorological observations over deserts due to their uninhabitable environment. This study fills this gap and provides independent evidence using satellite‐derived land surface temperatures (LSTs), benefiting from their global coverage. The frequency of clear sky from MODerate Resolution Imaging Spectroradiometer (MODIS) LST data over global deserts was found to be greater than 94% for the 2002–2015 period. Our results show that MODIS LST has a bias of 1.36°C compared to ground‐based observations collected at 31 U.S. Climate Reference Network (USCRN) stations, with a standard deviation of 1.83°C. After bias correction, MODIS LST was used to evaluate existing reanalyses, including ERA‐Interim, Japanese 55‐year Reanalysis (JRA‐55), Modern‐Era Retrospective Analysis for Research and Applications (MERRA), MERRA‐land, National Centers for Environmental Prediction (NCEP)‐R1, and NCEP‐R2. The reanalyses accurately reproduce the seasonal cycle and interannual variability of the LSTs, but their multiyear means and trends of LSTs exhibit large uncertainties. The multiyear averaged LST over global deserts is 23.5°C from MODIS and varies from 20.8°C to 24.5°C in different reanalyses. The MODIS LST over global deserts increased by 0.25°C/decade from 2002 to 2015, whereas the reanalyses estimated a trend varying from −0.14 to 0.10°C/decade. The underestimation of the LST trend by the reanalyses occurs for approximately 70% of the global deserts, likely due to the imperfect performance of the reanalyses in reproducing natural climate variability.

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

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