Reconciling aerosol-cloud interactions through multiscale observations, modeling, and statistical techniques

Aerosol-cloud interactions remain one of the largest uncertainties in quantifying anthropogenic impacts on climate, particularly through their influence on cloud liquid water path (LWP), cloud droplet number concentration (CDNC), and cloud susceptibility to aerosol perturbations. This presentation synthesizes insights from satellite observations, large-eddy simulations, global climate models, long-term in-situ observations, and advanced statistical analyses to address biases and uncertainties in these interactions. Satellite-based studies often report a decreasing LWP with increasing CDNC, yet retrieval errors and natural spatial variability can mask positive LWP adjustments, leading to an underestimation of the cooling effects of aerosol-cloud interactions. Large-eddy simulations of marine stratocumulus clouds reveal that assumptions of adiabaticity and spatial variability in cloud properties contribute to biases in satellite-derived LWP-CDNC relationships. However, with careful case selection and well-defined meteorological conditions, satellite-based estimations can be improved. Building on these findings, global climate modeling and machine learning analyses highlight the importance of updraft velocity and aerosol size distributions in shaping the CCN-CDNC relationship. Advanced methods such as Elastic Net Regression isolate these confounding factors, refining susceptibility estimates and enhancing consistency with physical expectations. Further, long-term in-situ observations of aerosols and clouds at high-latitude locations reveal that the susceptibility of CDNC to CCN is significantly higher for low-level stratiform clouds than suggested by global oceanic satellite data. This implies stronger aerosol radiative forcing than current satellite-based estimates assume. Comparisons with Earth system models reveal large inter-model variability in susceptibility, driven by differences in sub-grid scale updraft velocities and aerosol size distributions. Even models with relatively accurate susceptibility values exhibit unrealistic underlying physics, highlighting areas for improvement in model representation. Lastly, combining satellite, reanalysis, and in-situ ACTRIS observations, we evaluate the roles of aerosol size distributions and updrafts in warm cloud formation, bridging gaps between microphysical processes and large-scale variability. This comprehensive approach emphasizes the need for integrating multi-platform observations with advanced modeling and statistical methods to reduce biases and improve the fidelity of aerosol-cloud interaction estimates. These advancements are crucial for more accurately quantifying aerosol radiative forcing and its implications for climate prediction.