A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models
<p>Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically require...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-02-01
|
| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/2123/2025/acp-25-2123-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850084828522741760 |
|---|---|
| author | B. M. Duran C. J. Wall C. J. Wall N. J. Lutsko T. Michibata P.-L. Ma Y. Qin M. L. Duffy M. L. Duffy B. Medeiros M. Debolskiy |
| author_facet | B. M. Duran C. J. Wall C. J. Wall N. J. Lutsko T. Michibata P.-L. Ma Y. Qin M. L. Duffy M. L. Duffy B. Medeiros M. Debolskiy |
| author_sort | B. M. Duran |
| collection | DOAJ |
| description | <p>Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes 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 (<span class="inline-formula"><i>r</i><sub>e</sub></span>) 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 <span class="inline-formula">−</span>0.34 <span class="inline-formula">±</span> 0.23, <span class="inline-formula">−</span>0.22 <span class="inline-formula">±</span> 0.13, and <span class="inline-formula">−</span>0.09 <span class="inline-formula">±</span> 0.11 W m<span class="inline-formula"><sup>−2</sup></span>, 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 <span class="inline-formula"><i>r</i><sub>e</sub></span>–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.</p> |
| format | Article |
| id | doaj-art-dac17c249ef140908a8f34893f398b6c |
| institution | DOAJ |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-dac17c249ef140908a8f34893f398b6c2025-08-20T02:43:54ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-02-01252123214610.5194/acp-25-2123-2025A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate modelsB. M. Duran0C. J. Wall1C. J. Wall2N. J. Lutsko3T. Michibata4P.-L. Ma5Y. Qin6M. L. Duffy7M. L. Duffy8B. Medeiros9M. Debolskiy10Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA, USADepartment of Geosciences, University of Oslo, Oslo, Norwaynow at: Department of Meteorology, Stockholm University, 106 91 Stockholm, SwedenScripps Institution of Oceanography, University of California at San Diego, La Jolla, CA, USAResearch Institute for Applied Mechanics, Kyushu University, Fukuoka, JapanPacific Northwest National Laboratory, Richland, WA, USAPacific Northwest National Laboratory, Richland, WA, USANSF National Center for Atmospheric Research, Boulder, CO, USAnow at: Department of Earth and Planetary Sciences, University of California, Berkeley, CA, USANSF National Center for Atmospheric Research, Boulder, CO, USADepartment of Geosciences, University of Oslo, Oslo, Norway<p>Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes 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 (<span class="inline-formula"><i>r</i><sub>e</sub></span>) 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 <span class="inline-formula">−</span>0.34 <span class="inline-formula">±</span> 0.23, <span class="inline-formula">−</span>0.22 <span class="inline-formula">±</span> 0.13, and <span class="inline-formula">−</span>0.09 <span class="inline-formula">±</span> 0.11 W m<span class="inline-formula"><sup>−2</sup></span>, 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 <span class="inline-formula"><i>r</i><sub>e</sub></span>–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.</p>https://acp.copernicus.org/articles/25/2123/2025/acp-25-2123-2025.pdf |
| spellingShingle | B. M. Duran C. J. Wall C. J. Wall N. J. Lutsko T. Michibata P.-L. Ma Y. Qin M. L. Duffy M. L. Duffy B. Medeiros M. Debolskiy A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models Atmospheric Chemistry and Physics |
| title | A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models |
| title_full | A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models |
| title_fullStr | A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models |
| title_full_unstemmed | A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models |
| title_short | A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models |
| title_sort | new method for diagnosing effective radiative forcing from aerosol cloud interactions in climate models |
| url | https://acp.copernicus.org/articles/25/2123/2025/acp-25-2123-2025.pdf |
| work_keys_str_mv | AT bmduran anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT cjwall anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT cjwall anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT njlutsko anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT tmichibata anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT plma anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT yqin anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT mlduffy anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT mlduffy anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT bmedeiros anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT mdebolskiy anewmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT bmduran newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT cjwall newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT cjwall newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT njlutsko newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT tmichibata newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT plma newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT yqin newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT mlduffy newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT mlduffy newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT bmedeiros newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels AT mdebolskiy newmethodfordiagnosingeffectiveradiativeforcingfromaerosolcloudinteractionsinclimatemodels |