Constraining Aerosol-Cloud-Interaction Forcing in a Perturbed Parameter Ensemble with Multiple Satellite Datasets

Abstract:

We constrain aerosol-cloud interaction forcing uncertainty in a single Earth system model, using satellite microphysical observation data. We densely sample model uncertainty by perturbing around 60 process parameters related to clouds, aerosols, radiation, precipitation and their interactions. Our ensemble of model variants samples regional anthropogenic aerosol emissions uncertainties and includes high-time resolution data for optimal model-measurement comparison. We rule out observationally implausible model variants (parameter combinations) using satellite-derived values of cloud properties such as cloud droplet concentrations, albedo and cloud fraction. The remaining model variants constrain aerosol-cloud interaction forcing uncertainty to observationally plausible values. Additionally, we use statistical methods to identify the processes that cause aerosol-cloud interaction uncertainty and quantify the effect of constraint on parameter values. Processes that cause the remaining uncertainty after constraint, indicate which specific measurement types will be of most value in efforts to further reduce aerosol-cloud interaction forcing uncertainty.