Collaborative Research: Advancing Understanding of Aerosol-Cloud Feedback Using the World's First Global Climate Model with Explicit Boundary Layer Turbulence

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Aerosols, meaning tiny particles suspended in the atmosphere, play a key role in cloud formation, as cloud droplets and ice particles are produced when water vapor condenses onto aerosols. When more aerosols are present clouds tend to have a larger number of smaller droplets, making them brighter and more effective in reflecting sunlight back to space. Thus increases in aerosol amount due to industrial activity can increase the brightness of clouds, resulting in a cooling effect on climate. The extent to which the global temperature increase from greenhouse warming has been offset by human-induced radiative forcing from aerosol-cloud-interactions (RFaci) is an important and unsolved problem in climate science.One obstacle to progress on RFaci is the difficulty of performing computer simulations which explicitly represent cloud properties yet cover the whole earth, so that global climatic effects can be assessed. Cloud motions are turbulent and require models with grid points spaced a fraction of a kilometer apart, while global model grid spacing is typically tens to hundreds of kilometers. To bridge this scale gap the PIs have developed an ultraparameterized (UP) model, meaning a global model with coarse grid spacing in which each grid box contains a fine-scale cloud resolving model with a domain size much smaller than the grid box. The model is challenging both scientifically and computationally, and the project includes a concerted effort to improve computational efficiency to make simulations practical.The research addresses several specific questions regarding RFaci. One question is why climate models tend to overestimate RFaci compared to estimates from satellites, in some cases by a factor of two. Comparisons between the UP model and satellite observations will be facilitated by a nudging methodology, in which external forcing is used to constrain the simulated weather patterns to match the days when the satellite observations were taken. The nudging minimizes differences between simulated and satellite-estimated RFaci due to incorrect simulation of large-scale circulation features, allowing attribution of differences to aerosol-cloud interactions.The work has broader impacts due to the societal implications of high versus low RFaci: if the cooling effect of industrially-driven RFaci is large, the strength of greenhouse warming must be at the high end of current estimates in order to explain the warming seen over the past century. Likewise, if industrial RFaci cooling was small over the last century, the sensitivity of global temperature to greenhouse gas increase is likely to be on the lower end of its estimated range. RFaci is thus among the largest uncertainties in determining climate sensitivity and the severity of climate change impacts. In addition, software developed under the project is made available to the research community, in part through a version of the Community Earth System Model. The project provides support and training for a postdoctoral research scholar, thereby providing workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.