1Department of Applied Environmental Science (ITM), Atmospheric Science Unit, Stockholm University, 106 91 Stockholm, Sweden
2Norwegian Institute for Air Research (NILU), P.O. Box 100, 2027 Kjeller, Norway
3Finnish Meteorological Institute (FMI), Air Quality Research, Erik Palmenin aukio 1, P.O. Box 503, 00101 Helsinki, Finland
4Division of Atmospheric Sciences, Department of Physics, University of Helsinki, P.O. Box 64 (Gustaf Hällströmin katu 2a) 00014 University of Helsinki, Helsinki, Finland
5NOAA Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, 98115, WA, USA
Abstract. Sea-spray aerosols (SSA) are an important part of the climate system because of 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 have the largest uncertainty of all aerosols. In this study we review 21 SSA source functions from the literature, several of which are used in current climate models. In addition, we propose a~new function. Even excluding outliers, the global annual SSA mass produced spans roughly 3–70 Pg yr−1 for the different source functions, for particles with dry diameter Dp < 10 μm, with relatively little interannual variability for a given function. The FLEXPART Lagrangian particle dispersion model was run in backward mode for 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 in order to obtain modeled SSA concentrations. This allowed us to efficiently and simultaneously evaluate all 21 source functions against the measurements. Another advantage of this method is that source-region information on wind speed and sea surface temperatures (SSTs) could be stored and used for improving the SSA source function parameterizations. The best source functions reproduced as much as 70% of the observed SSA concentration variability at several stations, which is comparable with "state of the art" aerosol models. The main driver of SSA production is wind, and we found that the best fit to the observation data could be obtained when the SSA production is proportional to U103.5, where U10 is the source region averaged 10 m wind speed. A strong influence of SST on SSA production, with higher temperatures leading to higher production, could be detected as well, although the underlying physical mechanisms of the SST influence remains unclear. Our new source function with wind speed and temperature dependence gives a global SSA production for particles smaller than Dp < 10 μm of 9 Pg yr−1, and is the best fit to the observed concentrations.