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.
Highlights
IntroductionDimethyl sulfide (DMS), a volatile biogenic gas, is an important component of the marine sulfur cycle
Dimethyl sulfide (DMS), a volatile biogenic gas, is an important component of the marine sulfur cycle.This molecule contributes the largest fraction of bulk non-sea salt (NSS) sulfate emissions to the atmosphere (Bates et al, 1992), where it is rapidly oxidized to form aerosols that act as cloud condensation nuclei (CCN; Charlson et al, 1987; Hegg et al, 1991; Korhonen et al, 2008), potentially influencing regional albedo and climate (Charlson et al, 1987; Ayers and Cainey, 2007)
To benchmark the performance of our random forest regression (RFR) and Artificial Neural Network (ANN) models, we first evaluated the predictive skill of four existing empirical DMS algorithms (SD02, W07, VS07, & G18), in addition to simple and multiple linear regression models
Summary
Dimethyl sulfide (DMS), a volatile biogenic gas, is an important component of the marine sulfur cycle. This molecule contributes the largest fraction of bulk non-sea salt (NSS) sulfate emissions to the atmosphere (Bates et al, 1992), where it is rapidly oxidized to form aerosols that act as cloud condensation nuclei (CCN; Charlson et al, 1987; Hegg et al, 1991; Korhonen et al, 2008), potentially influencing regional albedo and climate (Charlson et al, 1987; Ayers and Cainey, 2007). Oxidative stress generated by other variables such as temperature (Kirst et al, 1991), salinity (Dickson and Kirst, 1987), UV radiation (Kinsey et al, 2016), and nutrient limitation (Bucciarelli et al., 2013; Spiese & Tatarkov, 2014) may enhance the cycling of DMSP and DMSO, which may regulate DMS concentrations through cascading oxidative pathways (Sunda et al, 2002). Variability in surface wind fields can modulate the rates of DMS sea-air exchange, providing a significant source of heterogeneity in surface water
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