Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels
<p>The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased <span class="inline-formula">CO<sub>2</sub></span> concentrations and to partition contributions from changes in cloud amount, altit...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-02-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/1477/2025/acp-25-1477-2025.pdf |
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| author | M. D. Zelinka L.-W. Chao T. A. Myers T. A. Myers Y. Qin S. A. Klein |
| author_facet | M. D. Zelinka L.-W. Chao T. A. Myers T. A. Myers Y. Qin S. A. Klein |
| author_sort | M. D. Zelinka |
| collection | DOAJ |
| description | <p>The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased <span class="inline-formula">CO<sub>2</sub></span> concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.</p> |
| format | Article |
| id | doaj-art-de71c874bb37493bbf041ff233628dd4 |
| institution | Kabale University |
| 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-de71c874bb37493bbf041ff233628dd42025-02-03T14:32:22ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-02-01251477149510.5194/acp-25-1477-2025Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernelsM. D. Zelinka0L.-W. Chao1T. A. Myers2T. A. Myers3Y. Qin4S. A. Klein5Lawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA, USALawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA, USACooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAPacific Northwest National Laboratory, Richland, WA, USALawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA, USA<p>The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased <span class="inline-formula">CO<sub>2</sub></span> concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.</p>https://acp.copernicus.org/articles/25/1477/2025/acp-25-1477-2025.pdf |
| spellingShingle | M. D. Zelinka L.-W. Chao T. A. Myers T. A. Myers Y. Qin S. A. Klein Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels Atmospheric Chemistry and Physics |
| title | Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels |
| title_full | Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels |
| title_fullStr | Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels |
| title_full_unstemmed | Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels |
| title_short | Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels |
| title_sort | technical note recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels |
| url | https://acp.copernicus.org/articles/25/1477/2025/acp-25-1477-2025.pdf |
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