Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network
<p>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/clo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-11-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/17/5335/2020/bg-17-5335-2020.pdf |
| _version_ | 1818342594819653632 |
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| author | W.-L. Wang G. Song F. Primeau E. S. Saltzman E. S. Saltzman T. G. Bell T. G. Bell J. K. Moore |
| author_facet | W.-L. Wang G. Song F. Primeau E. S. Saltzman E. S. Saltzman T. G. Bell T. G. Bell J. K. Moore |
| author_sort | W.-L. Wang |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">∼9</span> % and <span class="inline-formula">∼7</span> % of the raw DMS data variance,
respectively.
Multilinear regression can capture more of the raw data variance (<span class="inline-formula">∼39</span> %) but
strongly underestimates DMS in high-concentration regions.
In contrast, the artificial neural network captures <span class="inline-formula">∼66</span> % 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 high-latitude
summertime oceans.
We estimate a lower global sea-to-air DMS flux (<span class="inline-formula">20.12±0.43</span> Tg S yr<span class="inline-formula"><sup>−1</sup></span>)
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.</p> |
| first_indexed | 2024-12-13T16:17:11Z |
| format | Article |
| id | doaj.art-f7807a1342d84f31867d994b2c36623b |
| institution | Directory Open Access Journal |
| issn | 1726-4170 1726-4189 |
| language | English |
| last_indexed | 2024-12-13T16:17:11Z |
| publishDate | 2020-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj.art-f7807a1342d84f31867d994b2c36623b2022-12-21T23:38:49ZengCopernicus PublicationsBiogeosciences1726-41701726-41892020-11-01175335535410.5194/bg-17-5335-2020Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural networkW.-L. Wang0G. Song1F. Primeau2E. S. Saltzman3E. S. Saltzman4T. G. Bell5T. G. Bell6J. K. Moore7Department of Earth System Science, University of California at Irvine, Irvine, California, USASchool of Marine Science and Technology, Tianjin University, Tianjin, 300072, ChinaDepartment of Earth System Science, University of California at Irvine, Irvine, California, USADepartment of Earth System Science, University of California at Irvine, Irvine, California, USADepartment of Chemistry, University of California at Irvine, Irvine, California, USADepartment of Earth System Science, University of California at Irvine, Irvine, California, USAPlymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3DH, UKDepartment of Earth System Science, University of California at Irvine, Irvine, California, USA<p>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 <span class="inline-formula">∼9</span> % and <span class="inline-formula">∼7</span> % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (<span class="inline-formula">∼39</span> %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures <span class="inline-formula">∼66</span> % 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 high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (<span class="inline-formula">20.12±0.43</span> Tg S yr<span class="inline-formula"><sup>−1</sup></span>) 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.</p>https://bg.copernicus.org/articles/17/5335/2020/bg-17-5335-2020.pdf |
| spellingShingle | W.-L. Wang G. Song F. Primeau E. S. Saltzman E. S. Saltzman T. G. Bell T. G. Bell J. K. Moore Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network Biogeosciences |
| title | Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network |
| title_full | Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network |
| title_fullStr | Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network |
| title_full_unstemmed | Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network |
| title_short | Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network |
| title_sort | global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network |
| url | https://bg.copernicus.org/articles/17/5335/2020/bg-17-5335-2020.pdf |
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