Microphysical fingerprints in anvil cloud albedo

Improved understanding of anvil cloud radiative effect (CRE) and feedback is critical for reducing uncertainty in climate projections, with recent research highlighting cloud microphysics and anvil albedo as requiring further investigation. In this study, we use nine observation-informed model experiments to simulate a 24 d period from the Deep Convective Microphysics Experiment (DCMEX), with our analysis quantifying the influence of cloud microphysics on high-cloud albedo. We find that increasing cloud droplet number (2×) or ice-nucleating particles (INPs) (∼ 10×), within the range of observed variability, significantly increased high-cloud albedo by 1 %–3 % (p value <0.05). To isolate the microphysical drivers of albedo changes, we introduce fingerprint metrics based on an ice water path (IWP) threshold, distinguishing between thick and thin high clouds. We find that increased droplet number enhances albedo in both thick and thin clouds, while higher INP concentrations primarily affect thick cloud albedo. These fingerprints offer a novel approach for elucidating causes of variability in high-cloud albedo in both models and observations. Future work should explore how the fingerprints translate across different high-cloud regimes and global climate context. Beyond direct microphysical influences, we also identify strong correlations between albedo and large-scale environmental factors such as relative humidity, thereby motivating future investigation of anvil albedo feedback using cloud controlling factor analysis. Our study highlights both the large-scale environment and microphysical processes as important for accurate prediction of CREs and feedbacks in climate models.