Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Preprints
https://doi.org/10.5194/bg-2020-72
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-72
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 11 Mar 2020

Submitted as: research article | 11 Mar 2020

Review status
A revised version of this preprint is currently under review for the journal BG.

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Wei-Lei Wang1, Guisheng Song2, François Primeau1, Eric S. Saltzman1,3, Thomas G. Bell1,4, and J. Keith Moore1 Wei-Lei Wang et al.
  • 1Department of Earth System Science, University of California at Irvine, Irvine, California, USA
  • 2School of Marine Science and Technology, Tianjin University, Tianjin, 300072, China
  • 3Department of Chemistry, University of California at Irvine, Irvine, California, USA
  • 4Plymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3DH, UK

Abstract. Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. However, a knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to understand the factors controlling surface ocean DMS and its impact on climate. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 57 810 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of lat-lon coordinates, time-of-day, time-of-year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, silicate, and oxygen. Linear regressions of DMS against the environmental parameters show that on a global scale mixed layer depth and solar radiation are the strongest predictors of DMS, however, they capture 14 % and 12 % of the raw DMS data variance, respectively. The multi-linear regression can capture more (∼29 %) of the raw data variance, but strongly underestimates high DMS concentrations. In contrast, the ANN captures ~61 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in DMS concentration and sea-to-air flux. The highest concentrations (fluxes) occur in the high-latitude oceans during the summer. We estimate a lower global sea-to-air DMS flux (17.90 ± 0.34 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used.

Wei-Lei Wang et al.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Wei-Lei Wang et al.

Wei-Lei Wang et al.

Viewed

Total article views: 285 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
208 77 0 285 0 2
  • HTML: 208
  • PDF: 77
  • XML: 0
  • Total: 285
  • BibTeX: 0
  • EndNote: 2
Views and downloads (calculated since 11 Mar 2020)
Cumulative views and downloads (calculated since 11 Mar 2020)

Viewed (geographical distribution)

Total article views: 183 (including HTML, PDF, and XML) Thereof 181 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 07 Jul 2020
Publications Copernicus
Download
Short summary
Dimethyl sulfide, a volatile compound produced as a byproduct of marine phytoplankton activity, can be emitted to the atmosphere via gas exchange. In the atmosphere, DMS is oxidized to cloud condensation nuclei thus contributing to cloud formation. Therefore, oceanic DMS plays an important role in regulating the planet’s climate by influencing the radiation budget. In this study, we use an artificial neural network model to update the global DMS climatology and estimate the sea-to-air flux.
Dimethyl sulfide, a volatile compound produced as a byproduct of marine phytoplankton activity,...
Citation