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Cloud drop number concentrations over the western North Atlantic Ocean:...

Dadashazar, H., D. Painemal, M. Alipanah, M. Brunke, S. Chellappan, A. Corral, E. Crosbie, S. Kirschler, H. Liu, R. H. Moore, C. Robinson, A. J. Scarino, M. Shook, K. Sinclair, K. L. Thornhill, C. Voigt, H. Wang, E. L. Winstead, X. Zeng, L. Ziemba, P. Zuidema, and A. Sorooshian (2021), Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors, Atmos. Chem. Phys., 21, 10499-10526, doi:10.5194/acp-21-10499-2021.

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,

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Radiation Science Program (RSP)
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