Here, we use 16‐year satellite and reanalysis data in combination with a multivariate regression model to investigate how aerosols affect cloud fraction (CF) over the East Coast of the United States. Cloud droplet number concentrations (N_d), cloud geometrical thickness, lower tropospheric stability, and relative humidity at 950 hPa (RH₉₅₀) are identified as major cloud controlling parameters that explain 97% of the variability in CF. N_d is shown to play an important role in regulating the dependence of CF on RH₉₅₀. The observed annual‐mean CF shows no significant trend due to the cancellation from the opposite trends in N_d and RH₉₅₀. The multivariate regression model revealed that the decline in N_d alone would lead to about a 20% relative decline in CF. Our results indicate the significant aerosol effects on CF and suggest the need to account for pollution‐induced cloud changes in quantifying cloud feedback based on long‐term observations.

Decadal‐scale trends in aerosol and cloud properties provide important ways for understanding aerosol‐cloud interactions. In this paper, by using MODIS products (2003–2017), we analyze synergetic trends in aerosol properties and warm cloud properties over global ocean. Cloud droplet number concentration (CDNC) and aerosol parameters (aerosol optical depth, angstrom exponent, and aerosol index) show consistent decreasing trend over East Coast of the United States (EUS), west coast of Europe (WEU), and east coast of China (EC), and no significant trend in liquid water path is found over these regions during the period 2003–2017. Over regions with significant long‐term trends of aerosol loading and CDNC (e.g., EUS and WEU), the sensitivity of CDNC to aerosol loading based on the long‐term trend is closer to those derived from ground and aircraft observations and larger than those derived from instantaneous satellite observations, providing an alternative way for quantifying aerosol‐cloud interactions. A clear shift in the normalized probability density function of CDNC between the first 5 years (2003–2007) and the last 5 years (2013–2017) is found, with a decrease of around 50% in the occurrence frequency of high CDNC (>400 cm⁻³) over EUS and WEU. The relative variances of cloud droplet effective radius generally decrease with decreasing aerosol loading, providing large‐scale evidence for the effects of anthropogenic aerosols on the dispersion of cloud droplet size distribution. The long‐term satellite data sets provide great opportunities for quantifying aerosol‐cloud interactions and further confronting these interactions in climate models in the future.

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol–cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility S_POP, precipitation intensity susceptibility S_I, and precipitation rate susceptibility S_R in warm marine clouds. We find that S_POP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a −15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that S_POP derived with respect to aerosol index (AI) is about one-third of S_POP derived with respect to cloud droplet number concentration (CDNC). Overall, S_POP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of S_I and S_R are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosol–cloud–precipitation interactions in observations and models.

Large uncertainties remain in the key physical processes associated with aerosol‐cloud interactions (ACI) in models. With the help of A‐Train satellite observations, the Weather Research and Forecasting Model with chemistry (WRF‐Chem) model with two microphysical schemes, Morrison (MOR) and Lin (LIN), is evaluated by quantifying the susceptibilities of cloud properties, precipitation characteristics, and warm rain process to aerosols for marine stratocumulus over the Southeast Pacific. We reduced the meteorological control on clouds by stratifying them using cloud geometric thickness. Our results show that while the cloud fraction increases with increasing cloud droplet number concentration (N_d) in observation and simulations, the susceptibility of cloud fraction to N_d in simulations are only half of that in the observation. The cloud liquid water path increases with N_d in simulations but decreases slightly in the observation. Compared with the observations, the warm rain in WRF‐Chem simulations is generally less suppressed by aerosols, and it initiates at a much smaller cloud droplet effective radius (R_e). The conversion from cloud to rain is substantially faster in simulations compared to satellite observations. The conversion rate accelerates at R_e ≈ 13 μm in observations and at R_e ≈ 9 μm in simulations.