Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors
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Published:2021-07-13
Issue:13
Volume:21
Page:10499-10526
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Dadashazar Hossein, Painemal David, Alipanah MajidORCID, Brunke Michael, Chellappan SeethalaORCID, Corral Andrea F., Crosbie Ewan, Kirschler Simon, Liu HongyuORCID, Moore Richard H.ORCID, Robinson Claire, Scarino Amy Jo, Shook MichaelORCID, Sinclair KennethORCID, Thornhill K. Lee, Voigt ChristianeORCID, Wang HailongORCID, Winstead Edward, Zeng Xubin, Ziemba Luke, Zuidema PaquitaORCID, Sorooshian Armin
Abstract
Abstract. Cloud drop number concentrations (Nd) over the western North
Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and
lowest in summer (JJA), in contrast to aerosol proxy variables (aerosol
optical depth, aerosol index, surface aerosol mass concentrations, surface
cloud condensation nuclei (CCN) concentrations) that generally peak in
spring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote
sensing, ground-based in situ measurement data, and reanalysis data,
we characterize factors explaining the divergent seasonal cycles and
furthermore probe into factors influencing Nd on seasonal timescales.
The results can be summarized well by features most pronounced in DJF,
including features associated with cold-air outbreak (CAO) conditions such
as enhanced values of CAO index, planetary boundary layer height (PBLH),
low-level liquid cloud fraction, and cloud-top height, in addition to winds
aligned with continental outflow. Data sorted into high- and low-Nd days
in each season, especially in DJF, revealed that all of these conditions
were enhanced on the high-Nd days, including reduced sea level pressure
and stronger wind speeds. Although aerosols may be more abundant in MAM and
JJA, the conditions needed to activate those particles into cloud droplets
are weaker than in colder months, which is demonstrated by calculations of the
strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing Nd to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration
of sulfate). We used three machine learning models and up to 14 input
variables to infer about most influential factors related to Nd for DJF and JJA, with the best performance obtained with gradient-boosted regression
tree (GBRT) analysis. The model results indicated that cloud fraction was
the most important input variable, followed by some combination (depending
on season) of CAO index and surface mass concentrations of sulfate and
organic carbon. Future work is recommended to further understand aspects
uncovered here such as impacts of free tropospheric aerosol entrainment on
clouds, degree of boundary layer coupling, wet scavenging, and giant CCN
effects on aerosol–Nd relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite
estimation of Nd.
Funder
National Aeronautics and Space Administration
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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