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 DMSSAT, a new r...

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Published in:Biogeosciences Vol. 15; no. 11; pp. 3497 - 3519
Main Authors: Galí, Martí, Levasseur, Maurice, Devred, Emmanuel, Simó, Rafel, Babin, Marcel
Format: Journal Article
Language:English
Published: Katlenburg-Lindau Copernicus GmbH 14.06.2018
Copernicus Publications
Subjects:
Aerosol-cloud interactions
Aerosols
Algorithms
Annual variations
Atmospheric chemistry
Atmospheric models
Chemical properties
Chlorophyll
Chlorophyll a
Climate
Climate effects
Climate models
Climatology
Cloud formation
Cloud seeding
Cloud-climate relationships
Dimethyl sulfide
Dimethylsulphoniopropionate
Emission analysis
Emissions
Environmental aspects
Interactions
Interannual variability
Light penetration
Light scattering
Mathematical models
Measurement
Organic chemistry
Phytoplankton
Plankton
Radiation
Ratios
Regional analysis
Remote sensing
Satellite data
Satellites
Sea surface
Seasonal variations
Seasonality
Temperature (air-sea)
Time series
Water pollution
ISSN:1726-4189, 1726-4170, 1726-4189
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Abstract 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 DMSSAT, 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 a 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: log10DMS=α+βlog10DMSPt+γPAR. The two-step DMSSAT algorithm is computationally light and can be optimized for global and regional scales. Validation at the global scale indicates that DMSSAT 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, DMSSAT can leverage atmospheric chemistry and climate models and advance our understanding of plankton–aerosol–cloud interactions in the context of global change.
AbstractList 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 DMSSAT, 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 a 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: log10DMS = α + βlog10DMSPt + γPAR. The two-step DMSSAT algorithm is computationally light and can be optimized for global and regional scales. Validation at the global scale indicates that DMSSAT 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, DMSSAT can leverage atmospheric chemistry and climate models and advance our understanding of plankton–aerosol–cloud interactions in the context of global change.
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 presentDMSSAT, 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 a 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: log10DMS=α+βlog10DMSPt+γPAR. The two-stepDMSSAT algorithm is computationally light and can be optimized for global and regional scales. Validation at the global scale indicates that DMSSAT 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, DMSSAT can leverage atmospheric chemistry and climate models and advance our understanding of plankton–aerosol–cloud interactions in the context of global change.
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, 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 a 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 DMS = α + βlog.sub.10 DMSPt + γPAR. The two-step DMS.sub.SAT algorithm is computationally light and can be optimized for global and regional scales. Validation at the global scale indicates that DMS.sub.SAT 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 can leverage atmospheric chemistry and climate models and advance our understanding of plankton-aerosol-cloud interactions in the context of global change.
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 DMSSAT, 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 a 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: log10DMS=α+βlog10DMSPt+γPAR. The two-step DMSSAT algorithm is computationally light and can be optimized for global and regional scales. Validation at the global scale indicates that DMSSAT 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, DMSSAT can leverage atmospheric chemistry and climate models and advance our understanding of plankton–aerosol–cloud interactions in the context of global change.
Audience Academic
Author Devred, Emmanuel
Simó, Rafel
Levasseur, Maurice
Galí, Martí
Babin, Marcel
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  givenname: Rafel
  surname: Simó
  fullname: Simó, Rafel
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  givenname: Marcel
  surname: Babin
  fullname: Babin, Marcel
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Snippet The marine biogenic gas dimethylsulfide (DMS) modulates climate by enhancing aerosol light scattering and seeding cloud formation. However, the lack of time-...
The marine biogenic gas dimethylsulfide (DMS) modulates climate by enhancing aerosol light scattering and seeding cloud formation. However, the lack of time-...
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SubjectTerms Aerosol-cloud interactions
Aerosols
Algorithms
Annual variations
Atmospheric chemistry
Atmospheric models
Chemical properties
Chlorophyll
Chlorophyll a
Climate
Climate effects
Climate models
Climatology
Cloud formation
Cloud seeding
Cloud-climate relationships
Dimethyl sulfide
Dimethylsulphoniopropionate
Emission analysis
Emissions
Environmental aspects
Interactions
Interannual variability
Light penetration
Light scattering
Mathematical models
Measurement
Organic chemistry
Phytoplankton
Plankton
Radiation
Ratios
Regional analysis
Remote sensing
Satellite data
Satellites
Sea surface
Seasonal variations
Seasonality
Temperature (air-sea)
Time series
Water pollution
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Title Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales
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