Presentation_1_Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES).pdf

• Main Authors: Thomas G. Bell, Jack G. Porter, Wei-Lei Wang, Michael J. Lawler, Emmanuel Boss, Michael J. Behrenfeld, Eric S. Saltzman
• Format: Conference Object
• Language: unknown
• Published: 2021
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; dimethylsulfide; North Atlantic; marine aerosol; DMS; artificial neural network
• Online Access: https://doi.org/10.3389/fmars.2020.596763.s001; https://figshare.com/articles/presentation/Presentation_1_Predictability_of_Seawater_DMS_During_the_North_Atlantic_Aerosol_and_Marine_Ecosystem_Study_NAAMES_pdf/13603316

This work presents an overview of a unique set of surface ocean dimethylsulfide (DMS) measurements from four shipboard field campaigns conducted during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) project. Variations in surface seawater DMS are discussed in relation to biological and physical observations. Results are considered at a range of timescales (seasons to days) and spatial scales (regional to sub-mesoscale). Elevated DMS concentrations are generally associated with greater biological productivity, although chlorophyll a (Chl) only explains a small fraction of the DMS variability (15%). Physical factors that determine the location of oceanic temperature fronts and depth of vertical mixing have an important influence on seawater DMS concentrations during all seasons. The interplay of biomass and physics influences DMS concentrations at regional/seasonal scales and at smaller spatial and shorter temporal scales. Seawater DMS measurements are compared with the global seawater DMS climatology and predictions made using a recently published algorithm and by a neural network model. The climatology is successful at capturing the seasonal progression in average seawater DMS, but does not reproduce the shorter spatial/temporal scale variability. The input terms common to the algorithm and neural network approaches are biological (Chl) and physical (mixed layer depth, photosynthetically active radiation, seawater temperature). Both models predict the seasonal North Atlantic average seawater DMS trends better than the climatology. However, DMS concentrations tend to be under-predicted and the episodic occurrence of higher DMS concentrations is poorly predicted. The choice of climatological seawater DMS product makes a substantial impact on the estimated DMS flux into the North Atlantic atmosphere. These results suggest that additional input terms are needed to improve the predictive capability of current state-of-the-art approaches to estimating seawater DMS.