Separating internal and forced contributions to near term SST predictability in the CESM2-LE
Abstract An open question in the study of climate prediction is whether internal variability will continue to contribute to prediction skill in the coming decades, or whether predictable signals will be overwhelmed by rising temperatures driven by anthropogenic forcing. We design a neural network th...
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crioppubl:10.1088/1748-9326/acfdbc 2024-06-02T08:11:26+00:00 Separating internal and forced contributions to near term SST predictability in the CESM2-LE Gordon, E M Barnes, E A Davenport, F V Division of Atmospheric and Geospace Sciences 2023 http://dx.doi.org/10.1088/1748-9326/acfdbc https://iopscience.iop.org/article/10.1088/1748-9326/acfdbc https://iopscience.iop.org/article/10.1088/1748-9326/acfdbc/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters volume 18, issue 10, page 104047 ISSN 1748-9326 journal-article 2023 crioppubl https://doi.org/10.1088/1748-9326/acfdbc 2024-05-07T14:01:15Z Abstract An open question in the study of climate prediction is whether internal variability will continue to contribute to prediction skill in the coming decades, or whether predictable signals will be overwhelmed by rising temperatures driven by anthropogenic forcing. We design a neural network that is interpretable such that its predictions can be decomposed to examine the relative contributions of external forcing and internal variability to future regional sea surface temperature (SST) trend predictions in the near-term climate (2020–2050). We show that there is additional prediction skill to be garnered from internal variability in the Community Earth System Model version 2 Large Ensemble, even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. We further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of decadal variability for the grid points within each region. Article in Journal/Newspaper North Atlantic Southern Ocean IOP Publishing Pacific Southern Ocean Environmental Research Letters 18 10 104047 |
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Abstract An open question in the study of climate prediction is whether internal variability will continue to contribute to prediction skill in the coming decades, or whether predictable signals will be overwhelmed by rising temperatures driven by anthropogenic forcing. We design a neural network that is interpretable such that its predictions can be decomposed to examine the relative contributions of external forcing and internal variability to future regional sea surface temperature (SST) trend predictions in the near-term climate (2020–2050). We show that there is additional prediction skill to be garnered from internal variability in the Community Earth System Model version 2 Large Ensemble, even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. We further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of decadal variability for the grid points within each region. |
author2 |
Division of Atmospheric and Geospace Sciences |
format |
Article in Journal/Newspaper |
author |
Gordon, E M Barnes, E A Davenport, F V |
spellingShingle |
Gordon, E M Barnes, E A Davenport, F V Separating internal and forced contributions to near term SST predictability in the CESM2-LE |
author_facet |
Gordon, E M Barnes, E A Davenport, F V |
author_sort |
Gordon, E M |
title |
Separating internal and forced contributions to near term SST predictability in the CESM2-LE |
title_short |
Separating internal and forced contributions to near term SST predictability in the CESM2-LE |
title_full |
Separating internal and forced contributions to near term SST predictability in the CESM2-LE |
title_fullStr |
Separating internal and forced contributions to near term SST predictability in the CESM2-LE |
title_full_unstemmed |
Separating internal and forced contributions to near term SST predictability in the CESM2-LE |
title_sort |
separating internal and forced contributions to near term sst predictability in the cesm2-le |
publisher |
IOP Publishing |
publishDate |
2023 |
url |
http://dx.doi.org/10.1088/1748-9326/acfdbc https://iopscience.iop.org/article/10.1088/1748-9326/acfdbc https://iopscience.iop.org/article/10.1088/1748-9326/acfdbc/pdf |
geographic |
Pacific Southern Ocean |
geographic_facet |
Pacific Southern Ocean |
genre |
North Atlantic Southern Ocean |
genre_facet |
North Atlantic Southern Ocean |
op_source |
Environmental Research Letters volume 18, issue 10, page 104047 ISSN 1748-9326 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/acfdbc |
container_title |
Environmental Research Letters |
container_volume |
18 |
container_issue |
10 |
container_start_page |
104047 |
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1800757569867743232 |