New Insights on Aerosol Indirect Effects and Forcing from Ship Emissions and Their Impacts on Marine Low Clouds (Invited)

Ship-tracks provide good opportunistic experiments for studying aerosol-cloud interactions. We have developed machine learning algorithms to automatically detect and analyze them during both day and night. Here we utilize a large number of ship-track samples over the global ocean to better understand behaviors of aerosol-cloud interactions under different background cloud and environmental conditions. We show robust and systematic changes in the response of liquid water path, precipitation, and cloud fraction to aerosols with a suite of important background variables such as cloud droplet number concentration and above cloud relative humidity. We can derive robust statistical relationship between sensitivity of LWP and CF to aerosols and such background variables. We then apply these relationships and derive an observation-based estimate of global aerosol indirect forcing and highlight the important role of cloud fraction changes due to aerosols. The CF effect can be as large as the Twomey, if not larger. Furthermore, we compare forcing estimates from ship emissions in the Southeast Atlantic shipping lane using top-down and bottom-up approaches. Our observation-based estimates and constraints provided by two types of approaches also help to elucidate the radiative forcing of recent changes in emissions from maritime shipping. Results from these investigations are beneficial for studies on aerosol-cloud interactions, aerosol indirect forcing and marine cloud brightening.