ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
<p>Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its po...
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Copernicus Publications
2025-05-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/3065/2025/gmd-18-3065-2025.pdf |
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| author | T. P. Janoski T. P. Janoski T. P. Janoski T. P. Janoski T. P. Janoski I. Mitevski R. J. Kramer M. Previdi L. M. Polvani L. M. Polvani L. M. Polvani |
| author_facet | T. P. Janoski T. P. Janoski T. P. Janoski T. P. Janoski T. P. Janoski I. Mitevski R. J. Kramer M. Previdi L. M. Polvani L. M. Polvani L. M. Polvani |
| author_sort | T. P. Janoski |
| collection | DOAJ |
| description | <p>Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected 11 sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. ClimKern functions contain helpful optional parameters while maintaining standard practices between calculations.</p>
<p>After documenting the kernels and ClimKern package, we test it with sample climate model output from an abrupt <span class="inline-formula">2×CO<sub>2</sub></span> experiment to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread exhibits considerable spatial heterogeneity, with the greatest spread in the surface albedo and cloud feedbacks occurring in the Arctic and Southern Ocean. In the global mean, the Planck and surface albedo feedbacks show the greatest interkernel variability. Our results highlight the importance of using multiple radiative kernels and standardizing feedback calculations in climate feedback, sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it an even greater tool for the radiative feedback community.</p> |
| format | Article |
| id | doaj-art-042ff481b4fb4c7187c74916e54dfa4e |
| institution | OA Journals |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-042ff481b4fb4c7187c74916e54dfa4e2025-08-20T01:56:57ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-05-01183065307910.5194/gmd-18-3065-2025ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacksT. P. Janoski0T. P. Janoski1T. P. Janoski2T. P. Janoski3T. P. Janoski4I. Mitevski5R. J. Kramer6M. Previdi7L. M. Polvani8L. M. Polvani9L. M. Polvani10Department of Earth and Environmental Sciences, Columbia University, New York, NY, USALamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USANOAA Center for Earth System Science and Remote Sensing Technologies (CESSRST-II), City College of New York, New York, NY, USADepartment of Earth and Atmospheric Sciences, City College of New York, New York, NY, USANOAA National Severe Storms Laboratory, Norman, OK, USADepartment of Geosciences, Princeton University, Princeton, NJ, USANOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USALamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USADepartment of Earth and Environmental Sciences, Columbia University, New York, NY, USALamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USADepartment of Applied Physics and Mathematics, Columbia University, New York, NY, USA<p>Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected 11 sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. ClimKern functions contain helpful optional parameters while maintaining standard practices between calculations.</p> <p>After documenting the kernels and ClimKern package, we test it with sample climate model output from an abrupt <span class="inline-formula">2×CO<sub>2</sub></span> experiment to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread exhibits considerable spatial heterogeneity, with the greatest spread in the surface albedo and cloud feedbacks occurring in the Arctic and Southern Ocean. In the global mean, the Planck and surface albedo feedbacks show the greatest interkernel variability. Our results highlight the importance of using multiple radiative kernels and standardizing feedback calculations in climate feedback, sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it an even greater tool for the radiative feedback community.</p>https://gmd.copernicus.org/articles/18/3065/2025/gmd-18-3065-2025.pdf |
| spellingShingle | T. P. Janoski T. P. Janoski T. P. Janoski T. P. Janoski T. P. Janoski I. Mitevski R. J. Kramer M. Previdi L. M. Polvani L. M. Polvani L. M. Polvani ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks Geoscientific Model Development |
| title | ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks |
| title_full | ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks |
| title_fullStr | ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks |
| title_full_unstemmed | ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks |
| title_short | ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks |
| title_sort | climkern v1 2 a new python package and kernel repository for calculating radiative feedbacks |
| url | https://gmd.copernicus.org/articles/18/3065/2025/gmd-18-3065-2025.pdf |
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