Evaluating Uncertainties in Marine Biogeochemical Models: Benchmarking Aerosol Precursors

Ogunro, Oluwaseun O.; Elliott, Scott M.; Wingenter, Oliver W.; Deal, Clara; Fu, Weiwei; Collier, Nathan; Hoffman, Forrest M.

doi:10.3390/atmos9050184

Title: Evaluating Uncertainties in Marine Biogeochemical Models: Benchmarking Aerosol Precursors

Journal Article · Sat May 12 00:00:00 EDT 2018 · Atmosphere (Basel)

DOI:https://doi.org/10.3390/atmos9050184· OSTI ID:1557764

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^[2]; Wingenter, Oliver W. ^[3]; Deal, Clara ^[4];

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
New Mexico Inst. of Mining and Technology, Socorro, NM (United States)
Univ. of Alaska, Fairbanks, AK (United States)
Univ. of California, Irvine, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

The effort to accurately estimate global radiative forcing has long been hampered by a degree of uncertainty in the tropospheric aerosol contribution. Reducing uncertainty in natural aerosol processes, the baseline of the aerosol budget, thus becomes a fundamental task. The appropriate representation of aerosols in the marine boundary layer (MBL) is essential to reduce uncertainty and provide reliable information on offsets to global warming. We developed an International Ocean Model Benchmarking package to assess marine biogeochemical process representations in Earth System Models (ESMs), and the package was employed to evaluate surface ocean concentrations and the sea–air fluxes of dimethylsulfide (DMS). Model performances were scored based on how well they captured the distribution and variability contained in high-quality observational datasets. Results show that model-data biases increased as DMS enters the MBL, but unfortunately over three-quarters of the models participating in the fifth Coupled Model Intercomparison Project (CMIP5) did not have a dynamic representation of DMS. When it is present, models tend to over-predict sea surface concentrations in the productive region of the eastern tropical Pacific by almost a factor of two, and the sea–air fluxes by a factor of three. Systematic model-data benchmarking as described here will help to identify such deficiencies and subsequently lead to improved subgrid-scale parameterizations and ESM development.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC). Biological and Environmental Research (BER); National Science Foundation (NSF); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: 89233218CNA000001; AC05-00OR22725

OSTI ID:: 1557764

Alternate ID(s):: OSTI ID: 1902816

Report Number(s):: LA-UR-19-21266; ATMOCZ

Journal Information:: Atmosphere (Basel), Vol. 9, Issue 5; ISSN 2073-4433

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 4 works

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