Improved prediction of dimethyl sulfide (DMS) distributions in the northeast subarctic Pacific using machine-learning algorithms
<p>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 se...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-03-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/19/1705/2022/bg-19-1705-2022.pdf |
| Summary: | <p>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 <span class="inline-formula">±</span> 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.</p> |
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| ISSN: | 1726-4170 1726-4189 |