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. Dimethyl sulfide (DMS) is a volatile biogenic gas with thepotential to influence regional climate as a source of atmospheric aerosolsand cloud condensation nuclei (CCN). The complexity of the oceanic DMS cyclepresents a challenge in accurately predicting sea surface concentrations andsea–air fluxes of this gas. In this study, we applied machine-learningmethods to model the distribution of DMS in the northeast subarctic Pacific(NESAP), a global DMS hot spot. Using nearly two decades of ship-based DMSobservations, combined with satellite-derived oceanographic data, weconstructed ensembles of 1000 machine-learning models using two techniques:random forest regression (RFR) and artificial neural networks (ANN). Ourmodels dramatically improve upon existing statistical DMS models, capturingup to 62 % of observed DMS variability in the NESAP and demonstratingnotable regional patterns that are associated with mesoscale oceanographicvariability. In particular, our results indicate a strong coherence betweenDMS concentrations, sea surface nitrate (SSN) concentrations,photosynthetically active radiation (PAR), and sea surface height anomalies(SSHA), suggesting that NESAP DMS cycling is primarily influenced byheterogenous nutrient availability, light-dependent processes and physicalmixing. Based on our model output, we derive summertime, sea–air fluxestimates of 1.16 ± 1.22 Tg S in the NESAP. Our work demonstrates a newapproach to capturing spatial and temporal patterns in DMS variability,which is likely applicable to other oceanic regions.

Improved prediction of dimethyl sulfide (DMS) distributions in the northeast subarctic Pacific using machine-learning algorithms