New method for diagnosing ERFACI in global climate models
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. See the manuscript for details!
A simple model for the LWP adjustment in GCMs
In a CAM6 PPE, we find that fractional changes in LWP accurately capture variability in the global mean LWP adjustment. This relationship is supported by the idea of radiative saturation, or the diminishing impact of LWP on cloud albedo as albedo increases. We construct a simple model based on this principle to predict the spatial pattern of the LWP adjustment, and find that a simple model can capture the spatial pattern, but not the magnitude, of cooling from aerosol-induced LWP changes in CAM6. Several biases are responsible for this, including the overestimation of cloud susceptibility when using grid-mean cloud properties. We also find that variations in the warm rain autoconversion parameterization are responsible for the large spread in the LWP adjustment across the PPE.
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.
