Preprints
https://doi.org/10.5194/bg-2021-189
https://doi.org/10.5194/bg-2021-189

  13 Aug 2021

13 Aug 2021

Review status: this preprint is currently under review for the journal BG.

Improved Prediction of Dimethyl Sulfide (DMS) Distributions in the NE Subarctic Pacific using Machine Learning Algorithms

Brandon McNabb1 and Philippe Tortell1,2 Brandon McNabb and Philippe Tortell
  • 1Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
  • 2Department of Botany, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

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.

Brandon McNabb and Philippe Tortell

Status: open (until 20 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-189', Anonymous Referee #1, 07 Sep 2021 reply

Brandon McNabb and Philippe Tortell

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Short summary
The trace gas dimethyl sulfide (DMS) plays an important role in the ocean sulfur cycle, and can also influence Earth’s climate. Our study used two statistical methods to predict surface ocean concentrations and rates of sea-air exchange of DMS in the NE Subarctic Pacific. Our results show improved predictive power over previous approaches and suggest that nutrient availability, light-dependent processes, and physical mixing may be important controls on DMS in this region.
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