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

The occurrence of lower-than-expected bulk NCCN values over the marginal seas of China - Implications for competitive activation of marine aerosols

In this study, we combined the measured bulk particle number concentration (N_CN), particle number size distribution (PNSD) and bulk cloud condensation nuclei concentration (N_CCN) at various supersaturation (SS) levels to investigate competitive activation of aerosols in the marine atmospheres over the marginal seas of China during two winter campaigns Campaign A (December 9-19, 2019) and Campaign B (December 28, 2019-January 16, 2020). During the two campaigns, we observed various categories of aerosols, i.e., long-range transport continental aerosols, clean marine aerosols, grown new particles ranging from nucleation mode to larger sizes, and grown pre-existing particles ranging from Aitken mode to accumulation mode size, etc. We found that the measured N_CCN increased by only approximately 30 % with increases in SS levels from 0.2 % to 1.0 %, e.g., (1.8 ± 1.4) × 10³ cm^-3 at SS = 0.2 % and (2.4 ± 1.4) × 10³ cm^-3 at SS = 1.0 % during Campaign A. We further calculated the hygroscopicity parameter kappa (κ) by combining simultaneously measured PNSD and bulk N_CCN to explore the causes. The calculated κ values were below 0.1 at SS = 0.4 % during the 72 % (or 88 %) period of Campaign A (or Campaign B). When κ values below 0.1 (or 0.2) were excluded, the remaining κ values were apparently reasonable, with an average of 0.22 (or 0.36) and a standard deviation of 0.10 (or 0.21) at SS = 0.4 % during Campaign A (or Campaign B). The unexpectedly lower κ values were discussed in terms of competitive activation of aerosols in marine atmospheres together with its net contribution to lowering the measured bulk N_CCN below the expected value.

Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations

Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (Nd, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and Nd are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and Nd is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: Nd–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.