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Primary Marine Aerosol: Validation of sea spray source functions using observations and transport modeling
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).ORCID iD: 0000-0001-5074-4858
2014 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Sea spray aerosols (SSA) are an important part of the climate system through their effects on the global radiative budget, both directly as scatterers and absorbers of solar and terrestrial radiation, and indirectly as cloud condensation nuclei (CCN) influencing cloud formation, lifetime and precipitation. In terms of their global mass, SSA is the largest source and has the largest uncertainty of all aerosols. In this study I have reviewed 21 SSA source functions from the literature, several of which are used in current climate models, and as a result of this work  a new source function is proposed.

The model FLEXPART was run in backward mode utilizing a large global set of observed SSA concentrations, comprised of several station networks and ship cruise measurement campaigns. FLEXPART backward calculations produce gridded emission sensitivity fields, which can subsequently be multiplied with gridded SSA production fluxes to obtain modeled SSA concentrations. This allows to efficiently evaluate all 21 source functions at the same time. Another advantage of this method is that source-region information on wind speed and sea surface temperatures (SSTs) could be stored and used for evaluating their influence on SSA production.

The main driver of SSA production is wind, and the best fit to the observation data could be obtained when the SSA production is proportional to U103.5. A strong influence of SST on the production could be detected as well, although the underlying physical mechanisms of the SST influence remains unclear. For SST we obtain the best fit to the measurement data when SSA concentration is proportional to 0.031×T+0.39, where T is the source average SST. Based on the model source region average temperature and wind, an empirical fit was made to the data and a new source function obtained. The fit was made by using the model concentrations, observational data, ECMWF winds and the existing source function volume fluxes. Our new source function gives a global SSA production for particles smaller than 10μm of 9Pg yr-1 and is the best fit to the observed concentrations. The existing source functions display the large uncertainties, spanning from a global emitted mass of 1.9 to 100’s of Pg yr-1. Wind dependencies also range strongly and those far from U103.5, have poor correlation with observed values. It is also possible to add temperature dependence to an existing source function to come further towards observed values with the model results.

 Sea spray aerosols (SSA) are an important part of the climate system through their effects on the global radiative budget, both directly as scatterers and absorbers of solar and terrestrial radiation, and indirectly as cloud condensation nuclei (CCN) influencing cloud formation, lifetime and precipitation. In terms of their global mass, SSA is the largest source and has the largest uncertainty of all aerosols. In this study I have reviewed 21 SSA source functions from the literature, several of which are used in current climate models, and as a result of this work  a new source function is proposed.

The model FLEXPART was run in backward mode utilizing a large global set of observed SSA concentrations, comprised of several station networks and ship cruise measurement campaigns. FLEXPART backward calculations produce gridded emission sensitivity fields, which can subsequently be multiplied with gridded SSA production fluxes to obtain modeled SSA concentrations. This allows to efficiently evaluate all 21 source functions at the same time. Another advantage of this method is that source-region information on wind speed and sea surface temperatures (SSTs) could be stored and used for evaluating their influence on SSA production.

The main driver of SSA production is wind, and the best fit to the observation data could be obtained when the SSA production is proportional to U103.5. A strong influence of SST on the production could be detected as well, although the underlying physical mechanisms of the SST influence remains unclear. For SST we obtain the best fit to the measurement data when SSA concentration is proportional to 0.031×T+0.39, where T is the source average SST. Based on the model source region average temperature and wind, an empirical fit was made to the data and a new source function obtained. The fit was made by using the model concentrations, observational data, ECMWF winds and the existing source function volume fluxes. Our new source function gives a global SSA production for particles smaller than 10μm of 9Pg yr-1 and is the best fit to the observed concentrations. The existing source functions display the large uncertainties, spanning from a global emitted mass of 1.9 to 100’s of Pg yr-1. Wind dependencies also range strongly and those far from U103.5, have poor correlation with observed values. It is also possible to add temperature dependence to an existing source function to come further towards observed values with the model results.

Sea spray aerosols (SSA) are an important part of the climate system through their effects on the global radiative budget, both directly as scatterers and absorbers of solar and terrestrial radiation, and indirectly as cloud condensation nuclei (CCN) influencing cloud formation, lifetime and precipitation. In terms of their global mass, SSA is the largest source and has the largest uncertainty of all aerosols. In this study I have reviewed 21 SSA source functions from the literature, several of which are used in current climate models, and as a result of this work  a new source function is proposed.

The model FLEXPART was run in backward mode utilizing a large global set of observed SSA concentrations, comprised of several station networks and ship cruise measurement campaigns. FLEXPART backward calculations produce gridded emission sensitivity fields, which can subsequently be multiplied with gridded SSA production fluxes to obtain modeled SSA concentrations. This allows to efficiently evaluate all 21 source functions at the same time. Another advantage of this method is that source-region information on wind speed and sea surface temperatures (SSTs) could be stored and used for evaluating their influence on SSA production.

The main driver of SSA production is wind, and the best fit to the observation data could be obtained when the SSA production is proportional to U103.5. A strong influence of SST on the production could be detected as well, although the underlying physical mechanisms of the SST influence remains unclear. For SST we obtain the best fit to the measurement data when SSA concentration is proportional to 0.031×T+0.39, where T is the source average SST. Based on the model source region average temperature and wind, an empirical fit was made to the data and a new source function obtained. The fit was made by using the model concentrations, observational data, ECMWF winds and the existing source function volume fluxes. Our new source function gives a global SSA production for particles smaller than 10μm of 9Pg yr-1 and is the best fit to the observed concentrations. The existing source functions display the large uncertainties, spanning from a global emitted mass of 1.9 to 100’s of Pg yr-1. Wind dependencies also range strongly and those far from U103.5, have poor correlation with observed values. It is also possible to add temperature dependence to an existing source function to come further towards observed values with the model results.

 

Place, publisher, year, edition, pages
Stockholm: Department of Applied Environmental Science (ITM), Stockholm University , 2014. , 59 p.
Keyword [en]
marine aerosol
National Category
Other Chemistry Topics
Research subject
Applied Environmental Science
Identifiers
URN: urn:nbn:se:su:diva-98743OAI: oai:DiVA.org:su-98743DiVA: diva2:685418
Presentation
2014-01-17, Ahlmannssalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 15:00 (English)
Opponent
Supervisors
Available from: 2014-01-09 Created: 2014-01-09 Last updated: 2017-02-13Bibliographically approved

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