1173 Evaluating Uncertainties in Marine Biogeochemical Models: The Case of Remote Aerosol

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
O. Ogunro, New Mexico Institute of Mining and Technology, Socorro, NM; and S. M. Elliott, N. O. Collier, C. Deal, O. W. Wingenter, W. Fu, and F. M. Hoffman

Physical and biological processes that control biogeochemical transfer to and from the global ocean are insufficiently represented in most Earth System Models (ESMs). The best-known examples involve the trace gas carbon dioxide, which now requires a new generation of validation to improve our confidence in its computed and globally distributed fluxes. Similarly, surface ocean fluxes of marine aerosol precursors such as dimethylsulfide (DMS) are crucial to the planetary climate state. This is because marine emissions control both primary and secondary aerosol formation in the marine boundary layer (MBL), ultimately exerting control over cloud albedo. The precursor to cloud relationship is even less well constrained, thus, leading to noticeable uncertainty in the representation of ocean climate in ESM results. We have developed new analysis methods and biogeochemistry metrics as part of the International Ocean Model Benchmarking (IOMB) effort which are applicable to carbon and aerosol cycling over the sea. We incorporate long-standing and new observational datasets to evaluate simulation performance and guide future model development, in the hope of reducing marine biogeochemical uncertainty. We use nutrient-driven photosynthesis and carbon fixation as a backdrop to begin making the case for the inclusion of remote aerosol precursors. Emphasis is then placed upon tests for dimethylsulfide and other aerosol emission types, which still tend to be rare in ESM output. Sulfur distribution in the high Arctic is a major consideration in our analysis. We show that uncertainties in the marine aerosol cycling are of comparable importance to the greenhouse gases and discuss ways in which observational data sets could be enhanced and appropriately used to assess and reduce uncertainties in ESMs.
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