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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Copernicus Publications
2018
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00005530 2023-05-15T17:35:49+02:00 Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales Galí, Martí Levasseur, Maurice Devred, Emmanuel Simó, Rafel Babin, Marcel 2018-06 electronic https://doi.org/10.5194/bg-15-3497-2018 https://noa.gwlb.de/receive/cop_mods_00005530 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00005487/bg-15-3497-2018.pdf https://bg.copernicus.org/articles/15/3497/2018/bg-15-3497-2018.pdf eng eng Copernicus Publications Biogeosciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2158181 -- http://www.copernicus.org/EGU/bg/bg.html -- 1726-4189 https://doi.org/10.5194/bg-15-3497-2018 https://noa.gwlb.de/receive/cop_mods_00005530 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00005487/bg-15-3497-2018.pdf https://bg.copernicus.org/articles/15/3497/2018/bg-15-3497-2018.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2018 ftnonlinearchiv https://doi.org/10.5194/bg-15-3497-2018 2022-02-08T22:59:29Z 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. Article in Journal/Newspaper North Atlantic Southern Ocean Niedersächsisches Online-Archiv NOA Southern Ocean Biogeosciences 15 11 3497 3519 |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Galí, Martí Levasseur, Maurice Devred, Emmanuel Simó, Rafel Babin, Marcel Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales |
topic_facet |
article Verlagsveröffentlichung |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Galí, Martí Levasseur, Maurice Devred, Emmanuel Simó, Rafel Babin, Marcel |
author_facet |
Galí, Martí Levasseur, Maurice Devred, Emmanuel Simó, Rafel Babin, Marcel |
author_sort |
Galí, Martí |
title |
Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales |
title_short |
Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales |
title_full |
Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales |
title_fullStr |
Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales |
title_full_unstemmed |
Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales |
title_sort |
sea-surface dimethylsulfide (dms) concentration from satellite data at global and regional scales |
publisher |
Copernicus Publications |
publishDate |
2018 |
url |
https://doi.org/10.5194/bg-15-3497-2018 https://noa.gwlb.de/receive/cop_mods_00005530 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00005487/bg-15-3497-2018.pdf https://bg.copernicus.org/articles/15/3497/2018/bg-15-3497-2018.pdf |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
North Atlantic Southern Ocean |
genre_facet |
North Atlantic Southern Ocean |
op_relation |
Biogeosciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2158181 -- http://www.copernicus.org/EGU/bg/bg.html -- 1726-4189 https://doi.org/10.5194/bg-15-3497-2018 https://noa.gwlb.de/receive/cop_mods_00005530 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00005487/bg-15-3497-2018.pdf https://bg.copernicus.org/articles/15/3497/2018/bg-15-3497-2018.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/bg-15-3497-2018 |
container_title |
Biogeosciences |
container_volume |
15 |
container_issue |
11 |
container_start_page |
3497 |
op_container_end_page |
3519 |
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1766135099243364352 |