Relationships Between Supermicrometer Sea Salt Aerosol and Marine Boundary Layer Conditions: Insights From Repeated Identical Flight Patterns

Abstract

The MONterey Aerosol Research Campaign (MONARC) in May–June 2019 featured 14 repeated identical flights off the California coast over the open ocean at the same time each flight day. The objective of this study is to use MONARC data along with machine learning analysis to evaluate relationships between both supermicrometer sea salt aerosol number (N>1) and volume (V>1) concentrations and wind speed, wind direction, sea surface temperature (SST), ambient temperature (Tamb), turbulent kinetic energy (TKE), relative humidity (RH), marine boundary layer (MBL) depth, and drizzle rate. Selected findings from this study include the following: (i) Near surface (<60 m) N>1 and V>1 concentration ranges were 0.1–4.6 cm−3 and 0.3–28.2 μm3 cm−3, respectively; (ii) four meteorological regimes were identified during MONARC with each resulting in different N>1 and V>1 concentrations and also varying horizontal and vertical profiles; (iii) the relative predictive strength of the MBL properties varies depending on predicting N>1 or V>1, with MBL depth being more highly ranked for predicting N>1 and with TKE being higher for predicting V>1; (iv) MBL depths >400 m (<200 m) often correspond to lower (higher) N>1 and V>1 concentrations; (v) enhanced drizzle rates coincide with reduced N>1 and V>1 concentrations; (vi) N>1 and V>1 concentrations exhibit an overall negative relationship with SST and RH and an overall positive relationship with Tamb; and (vii) wind speed and direction were relatively weak predictors of N>1 and V>1.

An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast – Part 1: Analysis of Aerosols, Gases, and Wet Deposition Chemistry

“…As expected, sea salt is mostly confined in the boundary layer and close to its chief source (i.e., the ocean surface), in agreement with airborne measurements (e.g., Murphy et al., 2019; Reid et al., 2001; Schlosser et al., 2020). Similar to sea salt, dust generally decreased with altitude but was different in that significantly high levels remained up to higher altitudes especially in MAM and JJA with some degree of layering.…”

On the relationship between cloud water composition and cloud droplet number concentration

“…Previous studies have used back-trajectory analysis to show that air in the MBL in the study region is predominantly influenced by air mass transport from the north and northwest ( Schlosser et al, 2020 ; Wang et al, 2016 ; Wonaschütz et al, 2013 ). Thus, the cloud water in this study was influenced by a variety of local and long-range sources such as ship exhaust ( Chen et al, 2012 ; Coggon et al, 2012 ), biomass burning ( Prabhakar et al, 2014 ; Mardi et al, 2018 ), ocean emissions ( Dadashazar et al, 2017 ; MacDonald et al, 2018 ), continental pollution ( Ma et al, 2019 ; Wang et al, 2016 ), and dust ( Mardi et al, 2019 ; Wang et al, 2014 ).…”

“…The limit of detection (LOD) for each ion and element measured is shown in Table S1 in the Supplement. The concentration of non-sea-salt (NSS) species was calculated using the relative abundance of a NSS species to Na + in natural sea salt (Seinfeld and Pandis, 2016). Cloud water sample acidity was quantified by measuring pH (the aqueous concentration of hydrogen ions, H + ) using a Thermo Scientific Orion 9110DJWP Combination Semi-Micro pH electrode for E-PEACE, NiCE, and BOAS and a Thermo Scientific Orion 8103BNUWP Ross Ultra Semi-Micro pH probe for FASE.…”

“…These species have known sources as follows. MSA: ocean biogenic (Sorooshian et al, 2009); NH + 4 : agriculture (Bauer et al, 2016), marine emissions (Bouwman et al, 1997), and wildfires (Reid et al, 1998); NO − 3 and Ox: fire (Prabhakar et al, 2014; Maudlin et al, 2015); Tot-SO 2− 4 : sea salt (Seinfeld and Pandis, 2016), ocean biogenic (Charlson et al, 1987), and shipping (Coggon et al, 2012), with NSS-SO 2− 4 missing the sea salt contribution; Fe: dust (Jickells et al, 2005) and fire (Maudlin et al, 2015); Na: sea salt (Seinfeld and Pandis, 2016); and V: shipping (Wang et al, 2014). Note that we retained both Tot-SO 2− 4 and NSS-SO 2− 4 ; this is to eval- Leaitch et al, 1992;Saxena and Menon, 1999), whereas others have used NSS-SO 2− 4 (Novakov et al, 1994; Boucher and Lohmann, 1995).…”

Demystifying the complex nature of stratocumulus clouds with machine learning

“…A general tutorial on the mathematics and implemention of Gaussian process emulation in any field is presented in O'Hagan (2006). Two studies comparing machine learning regression techniques found Gaussian process regression to give the best results (Salter and Williamson, 2016; Schlosser et al ., 2020). Several studies use various machine learning techniques to train parameterisations, either to speed up an existing one or to approximate a relationship that is unknown (O'Gorman and Dwyer, 2018; Seifert and Rasp, 2020; Chiu et al ., 2021).…”