23 May 2023
 | 23 May 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Evaluation of Liquid Cloud Albedo Susceptibility in E3SM Using Coupled Eastern North Atlantic Surface and Satellite Retrievals

Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast

Abstract. The impact of aerosol number concentration on cloud albedo is a persistent source of spread in global climate predictions due to multi-scale, interactive atmospheric processes that remain difficult to quantify. We use 5 years of geostationary satellite and surface retrievals at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site in the Azores to evaluate the representation of liquid cloud albedo susceptibility for overcast cloud scenes in DOE Energy Exascale Earth System Model version 1 (E3SMv1) and provide possible reasons for model-observation discrepancies.

The overall distribution of surface 0.2 % CCN concentration values is reasonably simulated but simulated liquid water path (LWP) is lower than observed and layer-mean droplet concentration (Nd) comparisons are highly variable depending on the Nd retrieval technique. E3SMv1’s cloud albedo is greater than observed for given LWP and Nd values due to a lesser cloud effective radius than observed. However, the simulated albedo response to Nd is suppressed due to a solar zenith angle (SZA)-Nd correlation created by the seasonal cycle that is not observed. Controlling for this effect by examining the cloud optical depth (COD) shows that E3SMv1’s COD response to CCN concentration is greater than observed. For surface-based retrievals, this is only true after controlling for cloud adiabaticity because E3SMv1’s adiabaticities are much lower than observed. Assuming a constant adiabaticity in surface retrievals as done in TOA retrievals narrows the retrieved lnNd distribution, which increases the cloud albedo sensitivity to lnNd to match the TOA sensitivity.

The greater sensitivity of COD to CCN is caused by a greater Twomey effect in which the sensitivity of Nd to CCN is greater than observed for TOA-retrieved Nd, and once model-observation adiabaticity differences are removed, this is also true for surface-retrieved Nd. The LWP response to Nd in E3SMv1 is overall negative as observed. Despite reproducing the observed LWP-Nd relationship, observed clouds become much more adiabatic as Nd increases while E3SMv1 clouds do not, associated with more heavily precipitating clouds that are partially but not completely caused by deeper clouds and weaker inversions in E3SMv1. These cloud property differences indicate that the negative LWP-Nd relationship is likely not caused by the same mechanisms in E3SMv1 and observations. The negative simulated LWP response also fails to mute the excessively strong Twomey effect, highlighting potentially important confounding factor effects that likely render the LWP-Nd relationship non-causal. Nd retrieval scales and assumptions, particularly related to cloud adiabaticity, contribute to substantial spreads in the model-observation comparisons, though enough consistency exists to suggest that aerosol activation and convective drizzle processes are critical areas to focus E3SMv1 development for improving the fidelity of aerosol-cloud interactions in E3SM.

Adam C. Varble et al.

Status: open (until 04 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Adam C. Varble et al.


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Short summary
We evaluate how clouds change in response to changing atmospheric particle (aerosol) concentrations in a climate model and find that the model predicted cloud brightness increases too much as aerosols increase because the cloud drop number increases too much. Excessive drizzle in the model mutes this difference. Many differences between observational and model estimates are explained by varying assumptions of how much liquid has been lost in clouds, which impacts the estimated cloud drop number.