Integrating Top-Down Energetic Constraints With Bottom-Up Process-Based Constraints for More Accurate Projections of Future Warming

Abstract

The quantification of aerosol-induced radiative forcing and cloud feedbacks remains a significant challenge in climate modeling, primarily due to the complex interplay of aerosol and clouds in a warming world. Traditional approaches often rely on either bottom-up process-based models, difficult to constrain against present-day observations, or top-down methods that lack the ability to capture the underlying processes accurately. Here, we present an approach that combines both bottom-up process-based constraints and top-down energetic constraints of aerosol forcing and cloud feedbacks simultaneously to achieve a more comprehensive understanding of aerosol impacts on clouds and the climate. Applying the new method to the Community Atmosphere Model v6, we infer narrower parameter ranges for key process parameters, a reduced effective radiative forcing of −1.08 [−1.29–−0.77] Wm⁻², and hence 66% more precise future projections.