Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES)

Abstract

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.

Evaluation of ocean dimethylsulfide concentration and emission in CMIP6 models

“…In CNRM-ESM2-1, DMS concentration is computed by the biogeochemical model PISCES (Aumont and Bopp, 2006), embedded within the global general ocean circulation model NEMO. A prognostic DMS scheme was introduced in PISCES by Bopp et al (2008) and updated by Belviso et al (2012) based on the PlankTOM5 model of Vogt et al (2010). The version of PISCES used in CNRM-ESM2-1 for CMIP6 is PISCESv2-gas, based on PISCES-v2 (Aumont et al, 2015) with the addition of a specific module to compute the cycle of gases relevant to climate.…”

“…Three more processes describe the sinks for DMS in seawater: bacterial and photochemical degradation and ventilation to the atmosphere. A more thorough description of PISCES can be found in Belviso et al (2012), with some adjustments further listed in Masotti et al (2016). An additional stress factor accounting for the change in pH is also included in this version following the study of Six et al (2013).…”

Non-volatile marine and non-refractory continental sources of particle-phase amine during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES)

“…Seawater and atmospheric dimethylsulfide (DMS) concentrations were measured continuously during NAAMES (Bell et al, 2021). A Single Particle Soot Photometer (SP2, DMT, Boulder, CO) measured the mass concentrations of refractory black carbon (BC) in particles with diameters of 60 to 700 nm.…”

US SOLAS Science Report

“…For example, many models rely on observation-based climatologies to estimate emissions (DMS: Lana et al, 2011;halocarbons: Ziska et al, 2013). The databases and techniques used to generate the climatologies themselves may need to be updated in light of recent work (Bell et al, 2021), new measurements (e.g., satellite proxies for dimethyl sulfoxide), and processbased observations. As two examples of ways in which models can advance US SOLAS priorities, PMAs and nutrient cycling are discussed in more detail below.…”