Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales
The marine biogenic gas dimethylsulfide (DMS) modulates climate by enhancing aerosol light scattering and seeding cloud formation. However, the lack of time- and space-resolved estimates of DMS concentration and emission hampers the assessment of its climatic effects. Here we present DMS<sub&...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-06-01
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| Series: | Biogeosciences |
| Online Access: | https://www.biogeosciences.net/15/3497/2018/bg-15-3497-2018.pdf |
| Summary: | The marine biogenic gas dimethylsulfide (DMS) modulates climate by enhancing
aerosol light scattering and seeding cloud formation. However, the lack of
time- and space-resolved estimates of DMS concentration and emission hampers
the assessment of its climatic effects. Here we present
DMS<sub>SAT</sub>, a new remote sensing algorithm that relies on
macroecological relationships between DMS, its phytoplanktonic precursor
dimethylsulfoniopropionate (DMSPt) and plankton light exposure. In the first
step, planktonic DMSPt is estimated from satellite-retrieved chlorophyll <i>a</i>
and the light penetration regime as described in a previous study (Galí
et al., 2015). In the second step, DMS is estimated as a function of DMSPt
and photosynthetically available radiation (PAR) at the sea surface with an
equation of the form: log<sub>10</sub>DMS = <i>α</i> + <i>β</i>log<sub>10</sub>DMSPt + <i>γ</i>PAR. The two-step
DMS<sub>SAT</sub> algorithm is computationally light and can be
optimized for global and regional scales. Validation at the global scale
indicates that DMS<sub>SAT</sub> has better skill than previous
algorithms and reproduces the main climatological features of DMS seasonality
across contrasting biomes. The main shortcomings of the global-scale
optimized algorithm are related to (i) regional biases in remotely sensed
chlorophyll (which cause underestimation of DMS in the Southern Ocean) and
(ii) the inability to reproduce high DMS ∕ DMSPt ratios in late summer
and fall in specific regions (which suggests the need to account for
additional DMS drivers). Our work also highlights the shortcomings of
interpolated DMS climatologies, caused by sparse and biased in situ sampling.
Time series derived from MODIS-Aqua in the subpolar North Atlantic between
2003 and 2016 show wide interannual variability in the magnitude and timing
of the annual DMS peak(s), demonstrating the need to move beyond the
classical climatological view. By providing synoptic time series of DMS
emission, DMS<sub>SAT</sub> can leverage atmospheric chemistry and
climate models and advance our understanding of plankton–aerosol–cloud
interactions in the context of global change. |
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| ISSN: | 1726-4170 1726-4189 |