Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations

Abstract

We combine in situ measurements of sea salt aerosols (SS) from open ocean cruises and ground-based stations together with aerosol optical depth (AOD) observations from MODIS and AERONET, and the GEOS-Chem global chemical transport model to provide new constraints on SS emissions over the world's oceans. We find that the GEOS-Chem model using the Gong (2003) source function overestimates cruise observations of coarse mode SS mass concentrations by factors of 2–3 at high wind speeds over the cold waters of the Southern, North Pacific and North Atlantic Oceans. Furthermore, the model systematically underestimates SS over the warm tropical waters of the Central Pacific, Atlantic, and Indian Oceans. This pattern is confirmed by SS measurements from a global network of 15 island and coastal stations. The model discrepancy at high wind speeds (>6 m s<sup> −1</sup>) has a clear dependence on sea surface temperature (SST). We use the cruise observations to derive an empirical SS source function depending on both wind speed and SST. Implementing this new source function in GEOS-Chem results in improved agreement with in situ observations, with a decrease in the model bias from +64% to +33% for the cruises and from +32% to −5% for the ground-based sites. We also show that the wind speed-SST source function significantly improves agreement with MODIS and AERONET AOD, and provides an explanation for the high AOD observed over the tropical oceans. With the wind speed-SST formulation, global SS emissions show a small decrease from 5200 Mg yr<sup>−1</sup> to 4600 Mg yr<sup>−1</sup>, while the SS burden decreases from 9.1 to 8.5 mg m<sup>−2</sup>. The spatial distribution of SS, however, is greatly affected, with the SS burden increasing by 50% in the tropics and decreasing by 40% at mid- and high-latitudes. Our results imply a stronger than expected halogen source from SS in the tropical marine boundary layer. They also imply stronger radiative forcing of SS in the tropics and a larger response of SS emissions to climate change than previously thought

Climate Feedback on Aerosol Emission and Atmospheric Concentrations

“…While phytoplankton productivity and thus, e.g., DMS production depend on the water temperatures (together with light and nutrient supplies), the temperature dependence of primary aerosol emissions was recognized only in the recent years. The temperature influence was detected in laboratory experiments and confirmed by field measurements [60][61][62]. At constant wind speeds, warmer temperatures lead to higher emissions and to larger particle sizes.…”

Development towards a global operational aerosol consensus: basic climatological characteristics of the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME)

“…The BSC-Dust module is fully embedded into the Non-hydrostatic Multi-scale Model NMMB developed at NCEP Janjic and Gall, 2012). It includes a physically based dust emission scheme, which explicitly takes account of saltation and sandblasting processes (White, 1979;Marticorena and Bergametti, 1995;Marticorena et al, 1997) and assumes a viscous sublayer between the smooth desert surface and the lowest model layer (Janjic, 1994;Nickovic et al, 2001). For the source function, the model uses the topographic preferential source approach after Ginoux et al (2001) and the NESDIS (National Environmental Satellite, Data, and Information Service) vegetation fraction climatology (Ignatov and Gutman, 1998).…”

“…BSC-CNS maintains global and regional dust and sea salt aerosol forecasts based on NMMB/BSC-CTM. The BSC-Dust module is fully embedded into the Non-hydrostatic Multi-scale Model NMMB developed at NCEP Janjic and Gall, 2012). It includes a physically based dust emission scheme, which explicitly takes account of saltation and sandblasting processes (White, 1979;Marticorena and Bergametti, 1995;Marticorena et al, 1997) and assumes a viscous sublayer between the smooth desert surface and the lowest model layer (Janjic, 1994;Nickovic et al, 2001).…”

Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

“…We have constrained the sea salt mass concentration to only include submicron sea-spray in an attempt to consider only the most CCN-relevant aerosol. However, the MERRA2 reanalysis simply uses wind speed and SST to predict sea spray flux based on a parameterization (Gong, 2003;Jaeglé et al, 2011) and in the context of the analysis presented in this paper the relation between submicron sea salt mass and CDNC is at some level the relation between near-surface wind speed and CDNC. The precise values of the coefficient should change if a different size distribution is used in the parameterization, 10 but it is likely that the qualitative dependence of CDNC on sea salt would remain the same.…”