Relationships between supermicrometer particle concentrations and cloud water sea salt and dust concentrations: analysis of MONARC and ACTIVATE data

Abstract

This study uses airborne field data from the MONterey Aerosol Research Campaign (MONARC: northeast Pacific – summer 2019) and Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE: northwest Atlantic – winter and summer 2020) to examine relationships between giant cloud condensation nuclei (GCCN) and cloud composition to advance knowledge of poorly characterized GCCN–cloud interactions. The analysis compares cloud water composition data to particle concentration data with different minimum dry diameters between 1 and 10 μm (hereafter referred to as GCCN) collected below and above clouds adjacent to where cloud water samples were collected. The northeast Pacific exhibited higher GCCN number concentrations above 1 μm, but with a sharper decline to negligible values at higher minimum diameters (5–10 μm) as compared to the northwest Atlantic. Vertical profiles of GCCN data revealed the larger influence of sea salt with major reductions above typical boundary layer heights for the two regions. Interrelationships between GCCN and cloud water composition revealed the following major conclusions: (i) sub-cloud GCCN data are better related to cloud water species concentrations in contrast to above-cloud GCCN data owing to overwhelming influence of sea salt relative to dust; (ii) GCCN number concentrations at the lowest (highest) minimum dry diameters were best related to cloud water sea salt concentrations for the northeast Pacific (northwest Atlantic) in part due to hardly any GCCN above 5 μm for the northeast Pacific; (iii) the northwest Atlantic exhibited stronger near-surface winds and turbulence linked to the enhanced levels of larger GCCN and the stronger relationship with cloud water sea salt levels; and (iv) linear regression models have marginal success in predicting cloud water sea salt levels. This study demonstrates feasibility in relating cloud water chemical data with supermicrometer particle data to tease out insights about GCCN–cloud interactions, with results relevant to designing future lab, modeling, and field studies.