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Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions

Presentation Date
Friday, December 15, 2023 at 8:30am - Friday, December 15, 2023 at 12:50pm
MC - Poster Hall A-C - South



Process-oriented observational constraints for the anthropogenic effective radiative forcing due to aerosol-cloud-interactions (ERFaci) are limited yet highly desirable because the large uncertainty associated with ERFaci poses a significant challenge to climate prediction. The satellite-based Contour Frequency by Optical Depth Diagrams (CFODD) analysis was previously proposed to support evaluation of model representation of cloud liquid to rain conversion processes because the slope of a CFODD, generated from joint MODIS-CloudSat cloud retrievals, provides an estimate of cloud droplet collection efficiency in single-layer warm liquid clouds. Here we present an updated CFODD analysis as an observational constraint for the ERFaci due to warm rain processes and apply it to the U.S. Department of Energy’s Energy Exascale Earth System Model version 2 (E3SMv2). A series of sensitivity experiments shows that E3SMv2 droplet collection efficiencies and ERFaci are highly sensitive to the treatment of a warm rain process (autoconversion), yielding a strong correlation between the CFODD slope and the shortwave component of ERFaci (Pearson’s R = -0.89). We estimate the shortwave component of ERFaci (ERFaciSW), constrained by MODIS-CloudSat, by calculating the intercept of the linear association between E3SMv2 ERFaciSW and the CFODD slopes, using the MODIS-CloudSat CFODD slope as a reference. When E3SMv2’s droplet collection efficiency is constrained to agree with the A-Train retrievals, ERFaciSW is reduced by 21% in magnitude, indicating that correcting bias in the ERFaciSW due to autoconversion would bring E3SMv2’s total ERFaci (-1.50 W m-2) into better agreement with the IPCC AR6 ‘very likely’ range for ERFaci (-1.0 ± 0.7 W m-2). This study provides a new process-oriented observational constraint for ERFaci due to warm rain processes to reduce the uncertainty of climate predictions.

Funding Program Area(s)
Additional Resources:
NERSC (National Energy Research Scientific Computing Center)