Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

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.

Seasonal updraft speeds change cloud droplet number concentrations in low-level clouds over the western North Atlantic

“…The pairs in summer exhibit a bimodal distribution of either very clear or with high N CCN 0.43 % and are similar within a day, while the conditions with high aerosol loading occur within a higher frequency. The N C mean ranges from 103 up to 739 cm −3 , which is 25 % lower in terms of all pairs average of 400 cm −3 compared to the wintertime 535 cm −3 average and in good agreement with the findings of Dadashazar et al (2021b). A similar trend is observed in the w measurements, where the mean w is from 0.35 to 0.95 m s −1 and thus 33 % lower in terms of all pairs average 0.68 m s −1 in comparison with the wintertime 1.02 m s −1 average.…”

“…This work focuses on the western North Atlantic Ocean (WNAO) (Sorooshian et al, 2020), which provides ideal conditions for studying aerosol cloud interactions due to influ-ence from the polluted east coast of North America. Dadashazar et al (2021b) find an anti-correlation in the seasonal cycle of AOD and N C for this area, which is in contrast to findings in other regions (e.g., Penner et al, 2006Penner et al, , 2011Quaas et al, 2008;Gryspeerdt et al, 2016). Braga et al (2017a) use a statistical approach (Haddad and Rosenfeld, 1997) to quantify the relationship of w to N C at cloud bases of convective clouds over the Amazon basin.…”

Dilution of Boundary Layer Cloud Condensation Nucleus Concentrations by Free Tropospheric Entrainment During Marine Cold Air Outbreaks

“…Such N d gradients are particularly strong during CAOs (Dadashazar et al, 2021), coincident with greater than usual growth in cloud top height. Dadashazar et al (2021) furthermore suggest a similar FT-MBL CCN difference from aerosol extinction retrievals. Our findings are also consistent with CAO simulations (Tornow et al, 2021), which yield comparable entrainment rates and relative roles of FT entrainment and hydrometeor collisional loss upwind of intense precipitation.…”

“…Our analysis points to CCN dilution via FT entrainment as a plausible leading explanation for satellite‐observed N d gradients close to the US East Coast during winter (Painemal et al., 2021). Such N d gradients are particularly strong during CAOs (Dadashazar et al., 2021), coincident with greater than usual growth in cloud top height. Dadashazar et al.…”

Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors

“…To compare the traditionally used ordinary least squares regression (OLS) (Cesana & Del Genio, 2021; Klein et al., 2017; McCoy et al., 2017; Myers & Norris, 2016; Myers et al., 2021; Scott et al., 2020) and machine learning techniques that have gained relevance in the field lately, artificial neural networks (ANNs; Andersen et al., 2017) and extreme gradient boosting (XGB; Chen & Guestrin, 2016) are used. XGB is a gradient tree boosting method similar to the popular gradient boosting regression trees (GBRTs) that have been used in many aerosol and cloud related studies recently (Andersen et al., 2021; Dadashazar et al., 2020, 2021; Fuchs et al., 2018; Stirnberg et al., 2020, 2021), with the advantages of a built‐in regularization techniques and much shorter run times (Chen & Guestrin, 2016).…”