Consistent increase in dimethyl sulfide (DMS) in response to high CO2 in five shipboard bioassays from contrasting NW European waters
- 1Plymouth Marine Laboratory, Plymouth, UK
- *now at: Bigelow Laboratory for Ocean Sciences, Maine, USA
Abstract. The ubiquitous marine trace gas dimethyl sulfide (DMS) comprises the greatest natural source of sulfur to the atmosphere and is a key player in atmospheric chemistry and climate. We explore the short-term response of DMS production and cycling and that of its algal precursor dimethyl sulfoniopropionate (DMSP) to elevated carbon dioxide (CO2) and ocean acidification (OA) in five 96 h shipboard bioassay experiments. Experiments were performed in June and July 2011, using water collected from contrasting sites in NW European waters (Outer Hebrides, Irish Sea, Bay of Biscay, North Sea). Concentrations of DMS and DMSP, alongside rates of DMSP synthesis and DMS production and consumption, were determined during all experiments for ambient CO2 and three high-CO2 treatments (550, 750, 1000 μatm). In general, the response to OA throughout this region showed little variation, despite encompassing a range of biological and biogeochemical conditions. We observed consistent and marked increases in DMS concentrations relative to ambient controls (110% (28–223%) at 550 μatm, 153% (56–295%) at 750 μatm and 225% (79–413%) at 1000 μatm), and decreases in DMSP concentrations (28% (18–40%) at 550 μatm, 44% (18–64%) at 750 μatm and 52% (24–72%) at 1000 μatm). Significant decreases in DMSP synthesis rate constants (μDMSP, d−1) and DMSP production rates (nmol d−1) were observed in two experiments (7–90% decrease), whilst the response under high CO2 from the remaining experiments was generally indistinguishable from ambient controls. Rates of bacterial DMS gross consumption and production gave weak and inconsistent responses to high CO2. The variables and rates we report increase our understanding of the processes behind the response to OA. This could provide the opportunity to improve upon mesocosm-derived empirical modelling relationships and to move towards a mechanistic approach for predicting future DMS concentrations.