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

The quantification of aerosol-induced radiative forcing 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. We generate one million samples of parametric uncertainty of aerosol forcing and cloud feedback and estimate the historic temperatures they would have produced using an impulse-response model. Both the temperature trajectories and the associated microphysical properties (such as the hemispheric contrast in cloud droplet number concentration) can then be compared to observations simultaneously.

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.