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

Type Article
Date 2020-11
Language English
Author(s) Wang Wei-Lei1, Song Guisheng2, Primeau Francois1, Saltzman Eric S.1, 3, Bell Thomas G.1, 4, Moore J. Keith1
Affiliation(s) 1 : Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA.
2 : Tianjin Univ, Sch Marine Sci & Technol, Tianjin 300072, Peoples R China.
3 : Univ Calif Irvine, Dept Chem, Irvine, CA 92717 USA.
4 : Plymouth Marine Lab, Prospect Pl, Plymouth PL1 3DH, Devon, England.
Source Biogeosciences (1726-4170) (Copernicus Gesellschaft Mbh), 2020-11 , Vol. 17 , N. 21 , P. 5335-5354
DOI 10.5194/bg-17-5335-2020
Abstract

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. 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 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude-longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. 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. These parameters capture similar to 9 % and similar to 7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (similar to 39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures similar to 66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the highlatitude summertime oceans. We estimate a lower global seato-air DMS flux (20.12 +/- 0.43 Tg S yr(-1)) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

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