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

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

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1 digital object.

Cite This Work

Duran, Brandon M.; Wall, Casey J.; Lutsko, Nicholas J.; Michibata, Takuro; Ma, Po-Lun; Qin, Yi; Duffy, Margaret L.; Debolskiy, Matvey; Medeiros, Brian (2024). Data from: A new method for diagnosing effective radiative forcing from aerosol-cloud interactions in climate models. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0P26ZF1

Description

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.

Creation Date
  • 2024
Date Issued
  • 2024
Authors
Funding

National Science Foundation Graduate Research Fellowship, Grant No. DGE-2038238.

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Language
  • English
Identifier

Identifier: Brandon M. Duran: https://orcid.org/0000-0001-5427-8700

Identifier: Brian Mederios: https://orcid.org/0000-0003-2188-4784

Identifier: Casey J. Wall: https://orcid.org/0000-0001-7682-5576

Identifier: Margaret L. Duffy: https://orcid.org/0000-0003-2206-2424

Identifier: Matvey Debolskiy: https://orcid.org/0000-0002-9634-3627

Identifier: Nicholas J. Lutsko: https://orcid.org/0000-0003-2733-7810

Identifier: Po-Lun Ma: https://orcid.org/0000-0003-3109-5316

Identifier: Takuro Michibata: https://orcid.org/0000-0002-1491-0297

Identifier: Yi Qin: https://orcid.org/0000-0001-9045-8688

Related Resources

    Primary associated publication

    • Duran, B. M., Wall, C. J., Lutsko, N. J., Michibata, T., Ma, P.-L., Qin, Y., Duffy, M. L., Medeiros, B., and Debolskiy, M.: A new method for diagnosing effective radiative forcing from aerosol-cloud interactions in climate models, EGUsphere [preprint], 2024. https://doi.org/10.5194/egusphere-2024-3063

    Software

    Collection image

    • Image credit: Brandon Duran. "Ensemble results for the new method for diagnosing SW ERFaci."

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