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 (T(amb)), 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 μm(3) 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 T(amb); and (vii) wind speed and direction were relatively weak predictors of N(>1) and V(>1).