Relationships between supermicrometer particle concentrations and cloud water...
This study uses airborne field data fromthe MONterey Aerosol Research Campaign (MONARC: northeast Pacific
– summer 2019) and Aerosol Cloud meTeorology Interactions oVer thewestern 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 mm (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 mm, but with a sharper decline to negligible values at higher minimum
diameters (5–10 mm) 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 mm 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.