Mechanistic prediction of coastal CO2 from satellite observations
Burke
Hales, Oregon State University, bhales@coas.oregonstate.edu
(Presenting)
Ricardo
Letelier, Oregon State University, letelier@coas.oregonstate.edu
Pete
Strutton, Oregon State University, strutton@coas.oregonstate.edu
Chris
Sabine, NOAA-PMEL, chris.sabine@noaa.gov
Dick
Feely, NOAA-PMEL, richard.a.feely@noaa.gov
Constraint of pCO2 distributions in the coastal oceans is complicated by the wide range of spatial and temporal scales of large-amplitude variability, by the diversity of biogeochemical regimes, and by the nonlinear response of the carbonate system to biological and physical forcing. The coastal waters of the central North American Pacific coast, while abundantly sampled, epitomize this situation, with a pCO2 dynamic range of over 700 µatm that defies simple empirical characterization. We developed a robust method for generating predicted surface-ocean pCO2 based on remotely-sensed parameters that relied on two key components: First, satellite based SST, Chlorophyll, and wind stress were used with a self-organizing map approach to objectively categorize the region into 13 distinct biogeochemical sub-regions and second, a mechanistic meta-model of the pCO2 dependence on SST and chlorophyll was empirically optimized to describe the pCO2 distributions in each region. This yielded significant improvements in pCO2 predictability over non-mechanistic empirical approaches, and helped to identify regions in need of improved data coverage.
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