Preprints
https://doi.org/10.5194/egusphere-2024-3063
https://doi.org/10.5194/egusphere-2024-3063
02 Oct 2024
 | 02 Oct 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

A new method for diagnosing effective radiative forcing from aerosol-cloud interactions in climate models

Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy

Abstract. Aerosol-cloud interactions (ACI) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty of estimating the ERF from aerosol-cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code and cannot disentangle the contributions from different process to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly-averaged cloud fraction partitioned by cloud droplet effective radius (re) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud-fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud, and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed −0.34 ± 0.23, −0.22 ± 0.13, and −0.09 ± 0.11 Wm−2, respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS re– LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

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Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy

Status: open (until 13 Nov 2024)

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Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy

Interactive computing environment

modis_cloud_radiative_kernels Brandon Duran https://doi.org/10.5281/zenodo.13839355

Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy

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Short summary
We use satellite simulator data generated by global climate models to investigate how aerosol particles impact the radiative properties of liquid clouds. Specifically, we quantify the radiative perturbations arising from aerosol-driven changes in the number density of cloud droplets, the vertically integrated cloud water mass, and the cloud amount. Our results show that in models, aerosol effects on the number density of cloud droplets contributes the most to anthropogenic climate forcing.