A Multi-Model Analysis of Aerosol Effects on Clouds Simulated By Global Climate Models

Steven John Ghan, Pacific Northwest National Lab, Richland, WA, United States, Minghuai Wang, Nanjing University, School of Atmospheric Sciences, Nanjing, China, David Neubauer, ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland, Ulrike Lohmann, ETH Zurich, Institute for Atmospheric and Climate Science, Zurich, Switzerland, Sylvaine Ferrachat, ETH Swiss Federal Institute of Technology Zurich, Institute for Atmospheric and Climate Science, Zurich, Switzerland, Toshihiko Takemura, Kyushu University, Research Institute for Applied Mechanics, Fukuoka, Japan, Daniel Partridge, University of Oxford, Oxford, United Kingdom, Yunha Lee, NASA GISS, New York, United States, Andrew Gettelman, Pacific Northwest National Laboratory, Richland, WA, United States, Hugh Morrison, NSF NCAR, MMM Laboratory, Boulder, United States and Hailong Wang, Pacific Northwest National Laboratory, Richland, United States
Abstract:
Estimates of effective radiative forcing by aerosol-cloud interactions (ERFaci) have varied widely. These differences arise from differences in the simulation of multiple factors, including differences in emissions of aerosols and precursor gases, the efficiency of the production of cloud condensation nuclei (CCN) from the emissions, the sensitivity of cloud droplet number concentration to increases in CCN concentration, the sensitivity of cloud optical depth to increases in droplet number concentration, and the sensitivity of the planetary energy balance to changes in the cloud optical depth. The relative contributions of each of these factors to uncertainty in the estimated ERFaci have not been quantified. We have decomposed the ERFaci simulated by a variety of global aerosol models into these factors. We find that much of the uncertainty ERFaci is due to uncertainty in the response of droplet number to anthropogenic changes in CCN concentration and in the response of cloud liquid water path to changes in droplet number concentration. We will show examples of how observations can be used to constrain simulated values of each of the factors.
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