New method for diagnosing ERFACI in global climate models

Emergent constraints on the Twomey effect and LWP adjustment. Each shape represents the estimate of the Twomey effect (purple) and the LWP adjustment (orange) for a specific GCM. The vertical black line indicates the MODIS-reported global annual-mean liquid-cloud fraction over the 2003\textendash 2014 period. The grey shading represents the 68 \% confidence interval (1$\sigma$ range) for the MODIS global-mean LCF. The values in the top-middle section of the plot give the squared coefficient of determination for the ordinary least-squares linear-regression fit (\textit{r}\textsuperscript{2}) for each respective fit.
We use MODIS satellite simulator output and cloud radiative kernels to diagnose ERFACI in 5 CMIP6 models, yielding estimates for the Twomey effect, LWP adjustment, and cloud fraction (CF) adjustment. The magnitude of the global-mean Twomey effect and LWP adjustment are strongly controlled by the model mean-state liquid cloud fraction. Our finding hints at a potential emergent constraint on 2 components of ERFaci, which we hope to explore in a larger ensemble. Models with too few liquid clouds in their base state may be underestimating the strength of the Twomey effect and LWP adjustment. See Duran et al. (2025) for more details.

Aerosol-Ice-Cloud Interactions in a PPE

Aerosol-ice-cloud interactions are relatively understudied and poorly quantified in models. In a CAM6 PPE, an overall cooling effect from ERFaciice is estimated to be -0.43 Wm-2. Strong negative forcing from shifts to smaller ice crystals and increases in ice cloud extent over anthropogenic regions dominates the total response, but loss of high clouds over the tropical Pacific occurs in a pristine aerosol environment. We propose a pathway that relates aerosol-induced subtropical low cloud changes to tropical ice cloud changes via changes in boundary layer moisture. These findings suggest that the modeled ice cloud response to aerosols can be either aerosol or dynamics-driven. Check out our preprint for details!
Two panel figure. Left panel: scatterplot of global-mean MODIS ICF versus LW CRE. PPE members are shown as dots, and observations are shown as pink PDFs. Right panel: PDFs of total SW ERFaci ice. Red shaded histogram is the PPE data, red curve is the PPE prior, and a black posterior PDF is constructed. Posterior is narrow, and 68% confidence intervals are listed in the upper right corner.

Decomposing Cloud Radiative Feedbacks by Cloud-Top Phase

Changes in cloud scattering properties and emissivity that arise from warming cause substantial radiative feedbacks, and the relative importance of the underlying mechanisms is poorly understood. One leading hypothesis is that ice-to-liquid conversions cause clouds to optically thicken, producing a major negative feedback. By developing a method to decompose cloud radiative feedbacks by cloud-top phase, we find that the global mean of the net cloud scattering and emissivity feedback from cloud-phase conversions ranges from −0.17 to −0.01 Wm-2 K-1, while the overall net cloud feedback ranges from 0.02 to 0.91 Wm-2 K-1. The multi-model mean of the cloud scattering and emissivity feedback from cloud-phase conversions is approximately 19% of the magnitude of the multi-model mean of the overall cloud feedback (−0.10 Wm-2 K-1 vs. 0.52 Wm-2 K-1). These results indicate that cloud-phase conversions cause a robust negative feedback by changing cloud scattering and emissivity, but this mechanism makes a modest contribution to the overall cloud feedback at the global scale. See Wall et al. (2025) for more details.