Abstract. In an experimental assessment of the potential impact of Arctic Ocean acidification on seasonal phytoplankton blooms and associated dimethyl sulfide (DMS) dynamics, we incubated water from Baffin Bay under conditions representing an acidified Arctic Ocean. Using two light regimes simulating under-ice or subsurface chlorophyll maxima (low light; low PAR and no UVB) and ice-free (high light; high PAR + UVA + UVB) conditions, water collected at 38 m was exposed over 9 days to 6 levels of decreasing pH from 8.1 to 7.2. A phytoplankton bloom dominated by the centric diatoms Chaetoceros spp. reaching up to 7.5 µg chlorophyll a L −1 took place in all experimental bags. Total dimethylsulfoniopropionate (DMSP T ) and DMS concentrations reached 155 and 19 nmol L −1 , respectively. The sharp increase in DMSP T and DMS concentrations coincided with the exhaustion of NO − 3 in most microcosms, suggesting that nutrient stress stimulated DMS(P) synthesis by the diatom community. Under both light regimes, chlorophyll a and DMS concentrations decreased linearly with increasing proton concentration at all pH levels tested. Concentrations of DMSP T also decreased but only under high light and over a smaller pH range (from 8.1 to 7.6). In contrast to nanophytoplankton (2-20 µm), pico-phytoplankton (≤ 2 µm) was stimulated by the decreasing pH. We furthermore observed no significant difference between the two light regimes tested in term of chlorophyll a, phytoplankton abundance and taxonomy, and DMSP and DMS net concentrations. These results show that ocean acidification could significantly decrease the algal biomass and inhibit DMS production during the seasonal phytoplankton bloom in the Arctic, with possible consequences for the regional climate.

Dimethyl sulfide (DMS), dimethylsulfoniopropionate (DMSP), and dimethyl sulfoxide (DMSO) are key components of the marine sulfur cycle. The concentrations of these compounds exhibit large spatial and temporal variability in the surface ocean, creating a need for high resolution sampling. Existing automated underway measurement systems for DMS do not measure DMSP or DMSO, so their spatial variability is less well-characterized. We present an accurate and robust method for the automated, high throughput sampling and measurement of DMS, DMSO, and DMSP (DMS/O/P) in a single water sample. The method is based on a three-step sequence of purge and trap gas chromatography, where DMS analysis is followed by the enzymatic reduction of DMSO to DMS and the alkaline hydrolysis of DMSP to DMS. The system, which we call the Organic Sulfur Sequential Chemical Analysis Robot (OSSCAR), includes automated calibrations and blank determinations. OSSCAR can be used as a front-end system for any sulfur detector and is suited for continuous underway analysis or the measurement of discrete water samples. The system described here has a minimum detection limit of 0.

Environmental context The trace gas dimethylsulfide (DMS) is emitted from surface ocean waters to the overlying atmosphere, where it forms aerosols that promote cloud formation and influence Earth’s climate. We present an updated climatology of DMS emissions from the vast Southern Ocean, demonstrating how the inclusion of new data yields higher regional sources compared with previously derived values. Our work provides an important step towards better quantifying the oceanic emissions of an important climate-active gas. Abstract The Southern Ocean is a dominant source of the climate-active gas dimethylsulfide (DMS) to the atmosphere. Despite significant improvements in data coverage over the past decade, the most recent global DMS climatology does not include a growing number of high-resolution surface measurements in Southern Ocean waters. Here, we incorporate these high resolution data (~700000 measurements) into an updated Southern Ocean climatology of summertime DMS concentrations and sea–air fluxes. Owing to sparse monthly data coverage, we derive a single summertime climatology based on December through February means. DMS frequency distributions and oceanographic properties (mixed-layer depth and chlorophyll-a) show good general coherence across these months, providing justification for the use of summertime mean values. The revised climatology shows notable differences with the existing global climatology. In particular, we find increased DMS concentrations and sea–air fluxes south of the Polar Frontal zone (between ~60 and 70°S), and increased sea–air fluxes in mid-latitude waters (40–50°S). These changes are attributable to both the inclusion of new data and the use of region-specific parameters (e.g. data cut-off thresholds and interpolation radius) in our objective analysis. DMS concentrations in the Southern Ocean exhibit weak though statistically significant correlations with several oceanographic variables, including ice cover, mixed-layer depth and chlorophyll-a, but no apparent relationship with satellite-derived measures of phytoplankton photophysiology or taxonomic group abundance. Our analysis highlights the importance of using regional parameters in constructing climatological DMS fields, and identifies regions where additional observations are most needed.

Abstract. We present seawater concentrations of dimethyl sulfide (DMS) and dimethylsulfoniopropionate (DMSP) measured across a transect from the Labrador Sea to the Canadian Arctic Archipelago during summer 2015. Using an automated ship-board gas chromatography system and a membrane-inlet mass spectrometer, we measured a wide range of DMS (∼ 1 to 18 nM) and DMSP (∼ 1 to 150 nM) concentrations. The highest DMS and DMSP concentrations occurred in a localized region of Baffin Bay, where surface waters were characterized by high chlorophyll a (chl a) fluorescence, indicative of elevated phytoplankton biomass. Across the full sampling transect, there were only weak relationships between DMS(P), chl a fluorescence and other measured variables, including positive relationships between DMSP : chl a ratios and several taxonomic marker pigments, and elevated DMS(P) concentrations in partially ice-covered areas. Our high spatial resolution measurements allowed us to examine DMS variability over small scales (< 1 km), documenting strong DMS concentration gradients across surface hydrographic frontal features. Our new observations fill in an important observational gap in the Arctic Ocean and provide additional information on sea–air DMS fluxes from this ocean region. In addition, this study constitutes a significant contribution to the existing Arctic DMS(P) dataset and provides a baseline for future measurements in the region.

Abstract. The balance between ocean mixing and stratification influences primary productivity through light limitation and nutrient supply in the euphotic ocean. Here, we apply a hierarchical clustering algorithm (Ward's method) to four factors relating to stratification (wind energy, freshwater index, water-column-averaged vertical eddy diffusivity, and halocline depth), as well as to depth-integrated phytoplankton biomass, extracted from a biophysical ocean model of the Salish Sea. Running the clustering algorithm on 4 years of model output, we identify distinct regions of the model domain that exhibit contrasting wind and freshwater input dynamics, as well as regions of varying water-column-averaged vertical eddy diffusivity and halocline depth regimes. The spatial regionalizations in physical variables are similar in all 4 analyzed years. We also find distinct interannually consistent biological zones. In the northern Strait of Georgia and Juan de Fuca Strait, a deeper winter halocline and episodic summer mixing coincide with higher summer diatom abundance, while in the Fraser River stratified central Strait of Georgia, shallower haloclines and stronger summer stratification coincide with summer flagellate abundance. Cluster-based model results and evaluation suggest that the Juan de Fuca Strait supports more biomass than previously thought. Our approach elucidates probable physical mechanisms controlling phytoplankton abundance and composition. It also demonstrates a simple, powerful technique for finding structure in large datasets and determining boundaries of biophysical provinces.