Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States

Al-Yaari, A.; Ducharne, A.; Cheruy, F.; Crow, W. T.; Wigneron, J. -P.

doi:10.1038/s41598-018-38309-5

Title: Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States

Journal Article · 07 February 2019 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-018-38309-5 · OSTI ID:1959801

^[1]; Ducharne, A. ^[2]; Cheruy, F. ^[3]; Crow, W. T. ^[4];

^[1]

French National Research Institute for Agriculture, Food and Environment (INRAE), Villenave d’Ornon (France); Centre National de la Recherche Scientifique-Mixed Organizations (CNRS-UMR), Paris (France)
Sorbonne Univ., Paris (France); Centre National de la Recherche Scientifique-Mixed Organizations (CNRS-UMR), Paris (France)
Sorbonne Univ., Paris (France); Centre National de la Recherche Scientifique (CNRS) (France)
US Dept. of Agriculture (USDA), Beltsville, MD (United States). Agricultural Research Service (ARS)

Past studies have shown that climate simulations have substantial warm and dry biases during the summer in the conterminous United States (CONUS), particularly in the central Great Plains (CGP). These biases have critical implications for the interpretation of climate change projections, but the complex overlap of multiple land-atmosphere feedback processes make them difficult to explain (and therefore correct). Even though surface soil moisture (SM) is often cited as a key control variable in these processes, there are still knowledge gaps about its specific role. Here, we use recently developed remotely sensed SM products to analyse the link between spatial patterns of summertime SM, precipitation and air temperature biases over CONUS in 20 different CMIP5 simulations. We identify three main types of bias combinations: (i) a dry/warm bias over the CGP region, with a significant inter-model correlation between SM and air temperature biases (R = -0.65), (ii) a wet/cold bias in NW CONUS, and (iii) a dry/cold bias in SW CONUS. Combined with irrigation patterns, these results suggest that land-atmosphere feedbacks over the CGP are not only local but have a regional dimension, and demonstrate the added-value of large-scale SM observations for resolving the full feed-back loop between precipitation and temperature.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center

Sponsoring Organization:: European Space Agency; National Centre for Space Studies; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Contributing Organization:: Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1959801

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 9; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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