Variability CO2 parameters in the North Atlantic Subtropical Gyre : a neural network approach

Using artificial neural network to derive the variability of the ocean carbonate system during Spring 2016 over the area of study from monthly satellite-derived wind stress (ASCAT), sea surface salinity (SMOS) and sea surface temperature (OISST) fields over the oceans. The predicted variables were d...

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Bibliographic Details
Main Authors: Galdies, Charles, Garcia-Luque, E., Guerra, R., Ocean Carbon and Biogeochemistry (OCB) Summer Workshop
Format: Conference Object
Language:English
Published: Woods Hole Oceanographic Institution 2018
Subjects:
Waste disposal in the ocean
Ships > Waste disposal
Marine pollution
Marine debris
North Atlantic
Online Access:https://www.um.edu.mt/library/oar/handle/123456789/91395
Description
Summary:Using artificial neural network to derive the variability of the ocean carbonate system during Spring 2016 over the area of study from monthly satellite-derived wind stress (ASCAT), sea surface salinity (SMOS) and sea surface temperature (OISST) fields over the oceans. The predicted variables were dissolved inorganic carbon (DIC), total alkalinity (AT), pHT and partial pressure of ocean surface carbon dioxide (pCO2). Using this approach the components of the seawater carbonate system for springtime 2016 were predicted at high resolution (0.25o x 0.25o) and used to compare against published observations going back since 1988 for the North Atlantic Subtropical Gyre. N/A

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