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Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum
The Paleocene–Eocene Thermal Maximum (PETM; 56 Ma) is one of our best geological analogs for understanding climate dynamics in a “greenhouse” world. However, proxy data representing the event are only available from select marine and terrestrial sedimentary sequences that are unevenly distributed ac...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586325/ https://www.ncbi.nlm.nih.gov/pubmed/36215472 http://dx.doi.org/10.1073/pnas.2205326119 |
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author | Tierney, Jessica E. Zhu, Jiang Li, Mingsong Ridgwell, Andy Hakim, Gregory J. Poulsen, Christopher J. Whiteford, Ross D. M. Rae, James W. B. Kump, Lee R. |
author_facet | Tierney, Jessica E. Zhu, Jiang Li, Mingsong Ridgwell, Andy Hakim, Gregory J. Poulsen, Christopher J. Whiteford, Ross D. M. Rae, James W. B. Kump, Lee R. |
author_sort | Tierney, Jessica E. |
collection | PubMed |
description | The Paleocene–Eocene Thermal Maximum (PETM; 56 Ma) is one of our best geological analogs for understanding climate dynamics in a “greenhouse” world. However, proxy data representing the event are only available from select marine and terrestrial sedimentary sequences that are unevenly distributed across Earth’s surface, limiting our view of the spatial patterns of climate change. Here, we use paleoclimate data assimilation (DA) to combine climate model and proxy information and create a spatially complete reconstruction of the PETM and the climate state that precedes it (“PETM-DA”). Our data-constrained results support strong polar amplification, which in the absence of an extensive cryosphere, is related to temperature feedbacks and loss of seasonal snow on land. The response of the hydrological cycle to PETM warming consists of a narrowing of the Intertropical Convergence Zone, off-equatorial drying, and an intensification of seasonal monsoons and winter storm tracks. Many of these features are also seen in simulations of future climate change under increasing anthropogenic emissions. Since the PETM-DA yields a spatially complete estimate of surface air temperature, it yields a rigorous estimate of global mean temperature change (5.6 (∘)C; 5.4 (∘)C to 5.9 (∘)C, 95% CI) that can be used to calculate equilibrium climate sensitivity (ECS). We find that PETM ECS was 6.5 (∘)C (5.7 (∘)C to 7.4 (∘)C, 95% CI), which is much higher than the present-day range. This supports the view that climate sensitivity increases substantially when greenhouse gas concentrations are high. |
format | Online Article Text |
id | pubmed-9586325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-95863252023-04-10 Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum Tierney, Jessica E. Zhu, Jiang Li, Mingsong Ridgwell, Andy Hakim, Gregory J. Poulsen, Christopher J. Whiteford, Ross D. M. Rae, James W. B. Kump, Lee R. Proc Natl Acad Sci U S A Physical Sciences The Paleocene–Eocene Thermal Maximum (PETM; 56 Ma) is one of our best geological analogs for understanding climate dynamics in a “greenhouse” world. However, proxy data representing the event are only available from select marine and terrestrial sedimentary sequences that are unevenly distributed across Earth’s surface, limiting our view of the spatial patterns of climate change. Here, we use paleoclimate data assimilation (DA) to combine climate model and proxy information and create a spatially complete reconstruction of the PETM and the climate state that precedes it (“PETM-DA”). Our data-constrained results support strong polar amplification, which in the absence of an extensive cryosphere, is related to temperature feedbacks and loss of seasonal snow on land. The response of the hydrological cycle to PETM warming consists of a narrowing of the Intertropical Convergence Zone, off-equatorial drying, and an intensification of seasonal monsoons and winter storm tracks. Many of these features are also seen in simulations of future climate change under increasing anthropogenic emissions. Since the PETM-DA yields a spatially complete estimate of surface air temperature, it yields a rigorous estimate of global mean temperature change (5.6 (∘)C; 5.4 (∘)C to 5.9 (∘)C, 95% CI) that can be used to calculate equilibrium climate sensitivity (ECS). We find that PETM ECS was 6.5 (∘)C (5.7 (∘)C to 7.4 (∘)C, 95% CI), which is much higher than the present-day range. This supports the view that climate sensitivity increases substantially when greenhouse gas concentrations are high. National Academy of Sciences 2022-10-10 2022-10-18 /pmc/articles/PMC9586325/ /pubmed/36215472 http://dx.doi.org/10.1073/pnas.2205326119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Tierney, Jessica E. Zhu, Jiang Li, Mingsong Ridgwell, Andy Hakim, Gregory J. Poulsen, Christopher J. Whiteford, Ross D. M. Rae, James W. B. Kump, Lee R. Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum |
title | Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum |
title_full | Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum |
title_fullStr | Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum |
title_full_unstemmed | Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum |
title_short | Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum |
title_sort | spatial patterns of climate change across the paleocene–eocene thermal maximum |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586325/ https://www.ncbi.nlm.nih.gov/pubmed/36215472 http://dx.doi.org/10.1073/pnas.2205326119 |
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