the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Oct 2024
A new method for diagnosing effective radiative forcing from aerosol-cloud interactions in climate models
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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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(6307 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6307 KB) - Metadata XML
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
- RC1: 'Comment on egusphere-2024-3063', Anonymous Referee #1, 29 Oct 2024
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RC2: 'Comment on egusphere-2024-3063', Christopher Smith, 28 Nov 2024
Duran et al propose a method for separating out the contributions to effective radiative forcing from aerosol-cloud interactions (ERFaci) into the Twomey effect and adjustments due to liquid water path and cloud fraction change. This is achieved using an extension to the set of standard monthly diagnostics from CMIP experiments, and is a valuable addition to the list of methods currently available such as the rough and ready attempt I made at this separation in 2020. I hope that many modelling groups will implement the MODIS simulator to allow this decomposition to be calculated in several models in CMIP7. As the authors point out, the individual terms of the decomposition may lend themselves better to observational constraints than the overall ERFaci, and if well-founded, can contribute important knowledge towards narrowing uncertainty in warming projections. In RFMIP we have requested that it be added to the data request. It's a neat method, and the paper is well written – one of those rare papers where it’s difficult to find any fault, which is why this review is comparatively short.
My only real comment is that the method doesn’t quite decompose linearly. Summing the components from each model in Table 1 underestimates the ERFaci for the three non-MIROC models by more than 10%. I suggest that the residual term is given the same level of prominence in this table and the text. In the opinion of the authors, is this a non-linearity due to second order cross-terms, or could there be other aspects of the ERFaci that we don’t or can’t evaluate?
Minor comments:
Line 32: just clarify that this is the instantaneous forcing due to aerosol-cloud interactions.
Lines 128-132: could you clarify? >70% liquid means classified as liquid, <30% liquid means classified as ice, and 30-70% liquid means undetermined. Did you also mean 0.3 to 0.7 optical thickness in line 131, or is this a coincidence?
Citation: https://doi.org/10.5194/egusphere-2024-3063-RC2 - AC1: 'Comment on egusphere-2024-3063', Brandon Duran, 18 Dec 2024
Interactive discussion
Status: closed
- RC1: 'Comment on egusphere-2024-3063', Anonymous Referee #1, 29 Oct 2024
-
RC2: 'Comment on egusphere-2024-3063', Christopher Smith, 28 Nov 2024
Duran et al propose a method for separating out the contributions to effective radiative forcing from aerosol-cloud interactions (ERFaci) into the Twomey effect and adjustments due to liquid water path and cloud fraction change. This is achieved using an extension to the set of standard monthly diagnostics from CMIP experiments, and is a valuable addition to the list of methods currently available such as the rough and ready attempt I made at this separation in 2020. I hope that many modelling groups will implement the MODIS simulator to allow this decomposition to be calculated in several models in CMIP7. As the authors point out, the individual terms of the decomposition may lend themselves better to observational constraints than the overall ERFaci, and if well-founded, can contribute important knowledge towards narrowing uncertainty in warming projections. In RFMIP we have requested that it be added to the data request. It's a neat method, and the paper is well written – one of those rare papers where it’s difficult to find any fault, which is why this review is comparatively short.
My only real comment is that the method doesn’t quite decompose linearly. Summing the components from each model in Table 1 underestimates the ERFaci for the three non-MIROC models by more than 10%. I suggest that the residual term is given the same level of prominence in this table and the text. In the opinion of the authors, is this a non-linearity due to second order cross-terms, or could there be other aspects of the ERFaci that we don’t or can’t evaluate?
Minor comments:
Line 32: just clarify that this is the instantaneous forcing due to aerosol-cloud interactions.
Lines 128-132: could you clarify? >70% liquid means classified as liquid, <30% liquid means classified as ice, and 30-70% liquid means undetermined. Did you also mean 0.3 to 0.7 optical thickness in line 131, or is this a coincidence?
Citation: https://doi.org/10.5194/egusphere-2024-3063-RC2 - AC1: 'Comment on egusphere-2024-3063', Brandon Duran, 18 Dec 2024
Peer review completion
Journal article(s) based on this preprint
Interactive computing environment
modis_cloud_radiative_kernels Brandon Duran https://doi.org/10.5281/zenodo.13839355
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Cited
1 citations as recorded by crossref.
Casey J. Wall
Nicholas J. Lutsko
Takuro Michibata
Po-Lun Ma
Margaret L. Duffy
Brian Medeiros
Matvey Debolskiy
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6307 KB) - Metadata XML