Abstract. Dimethyl sulfide (DMS) is a volatile biogenic gas with the potential to influence regional climate as a source of atmospheric aerosols and cloud condensation nuclei (CCN). The complexity of the oceanic DMS cycle presents a challenge in accurately predicting sea surface concentrations and sea–air fluxes of this gas. In this study, we applied machine-learning methods to model the distribution of DMS in the northeast subarctic Pacific (NESAP), a global DMS hot spot. Using nearly two decades of ship-based DMS observations, combined with satellite-derived oceanographic data, we constructed ensembles of 1000 machine-learning models using two techniques: random forest regression (RFR) and artificial neural networks (ANN). Our models dramatically improve upon existing statistical DMS models, capturing up to 62 % of observed DMS variability in the NESAP and demonstrating notable regional patterns that are associated with mesoscale oceanographic variability. In particular, our results indicate a strong coherence between DMS concentrations, sea surface nitrate (SSN) concentrations, photosynthetically active radiation (PAR), and sea surface height anomalies (SSHA), suggesting that NESAP DMS cycling is primarily influenced by heterogenous nutrient availability, light-dependent processes and physical mixing. Based on our model output, we derive summertime, sea–air flux estimates of 1.16 ± 1.22 Tg S in the NESAP. Our work demonstrates a new approach to capturing spatial and temporal patterns in DMS variability, which is likely applicable to other oceanic regions.

We developed two machine learning models to map the Southern Ocean distribution of the climate-active gas dimethyl sulfide (DMS) at 20 km resolution. Results obtained from ensembles of random forest regressions and artificial neural networks reproduce observed DMS distributions with significantly higher accuracy than traditional statistical techniques, and are less prone to biases and spatial distortions than existing interpolationbased climatologies. Both models predict persistently low offshore DMS concentrations associated with the Antarctic circumpolar current, suggesting that wind-driven overturning mixing is the dominant regional control on DMS distributions. In addition, 60% of the variance in DMS seasonality is explained by changes in mixed layer depth and sea surface temperature, with a significant correlation between DMS concentrations and sea-ice cover in coastal waters. We further identify the tracer Si* (defined as Si OH ð Þ 4 Â Ã À NO À 3

Phytoplankton are the base of nearly all marine food webs and mediate the interactions of biotic and abiotic components in marine systems. Understanding the spatial and temporal changes in phytoplankton growth requires comprehensive biological, physical, and chemical information. Long-term datasets are an invaluable tool to study these changes, but they are rare and often include only a small set of measurements. Here, we present biological, physical and chemical oceanographic data measured periodically between March 2010 and November 2017 from the euphotic zone of Saanich Inlet, a temperate fjord on the west coast of British Columbia, Canada. The dataset includes measurements of dissolved macronutrients, total and size-fractionated chlorophyll-a, particulate carbon, nitrogen and biogenic silica, and carbon and nitrate uptake rates. This collection describes phytoplankton dynamics and the distribution of biologically-available macronutrients over time in the upper water column of Saanich Inlet. We establish a baseline for future investigations in Saanich Inlet and provide a data collection protocol that can be applied to similar productive coastal regions.

Abstract. Dimethyl sulfide (DMS) is a volatile biogenic gas with the potential to influence regional climate as a source of atmospheric aerosols and cloud condensation nuclei (CCN). The complexity of the oceanic DMS cycle presents a challenge in accurately predicting sea-surface concentrations and sea-air fluxes of this gas. In this study, we applied machine learning methods to model the distribution of DMS in the NE Subarctic Pacific (NESAP), a global DMS hot-spot. Using nearly two decades of ship-based DMS observations, combined with satellite-derived oceanographic data, we constructed ensembles of 1000 machine-learning models using two techniques, random forest regression (RFR) and artificial neural networks (ANN). Our models dramatically improve upon existing statistical DMS models, capturing up to 62 % of observed DMS variability in the NESAP and demonstrate notable regional patterns that are associated with mesoscale oceanographic variability. In particular, our results indicate a strong coherence between DMS concentrations, sea surface nitrate (SSN) concentrations, photosynthetically active radiation (PAR) and sea surface height anomalies (SSHA), suggesting that NESAP DMS cycling is primarily influenced by heterogenous nutrient availability, light-dependent processes and physical mixing. Based on our model output, we derive summertime, sea-air flux estimates ranging between 0.5–2.0 Tg S yr−1 in the NESAP. Our work demonstrates a new approach to capturing spatial and temporal patterns in DMS variability, which is likely applicable to other oceanic regions.

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