Paleoclimate data provide constraints on climate models’ large-scale response to past CO2 changes
Paleoclimate data provide constraints on climate models’ large-scale response to past CO2 changes
The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.
Lunt, Daniel J.
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Otto-Bliesner, Bette L.
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Brierley, Chris
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Haywood, Alan
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Inglis, Gordon N.
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Izumi, Kenji
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Kageyama, Masa
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Kaufman, Darrell
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Mauritsen, Thorsen
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McClymont, Erin L.
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Salzmann, Ulrich
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Steinig, Sebastian
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Tierney, Jessica E.
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Zhao, Anni
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Zhu, Jiang
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7 August 2024
Lunt, Daniel J.
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Otto-Bliesner, Bette L.
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Brierley, Chris
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Haywood, Alan
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Inglis, Gordon N.
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Izumi, Kenji
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Kageyama, Masa
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Kaufman, Darrell
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Mauritsen, Thorsen
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McClymont, Erin L.
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Salzmann, Ulrich
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Steinig, Sebastian
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Tierney, Jessica E.
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Zhao, Anni
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Zhu, Jiang
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Lunt, Daniel J., Otto-Bliesner, Bette L., Brierley, Chris, Haywood, Alan, Inglis, Gordon N., Izumi, Kenji, Kageyama, Masa, Kaufman, Darrell, Mauritsen, Thorsen, McClymont, Erin L., Salzmann, Ulrich, Steinig, Sebastian, Tierney, Jessica E., Zhao, Anni and Zhu, Jiang
(2024)
Paleoclimate data provide constraints on climate models’ large-scale response to past CO2 changes.
Communications Earth & Environment, 5 (1), [419].
(doi:10.1038/s43247-024-01531-3).
Abstract
The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.
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Paleo_Metrics_paper vn3
- Accepted Manuscript
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s43247-024-01531-3
- Version of Record
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Accepted/In Press date: 26 June 2024
Published date: 7 August 2024
Identifiers
Local EPrints ID: 493889
URI: http://eprints.soton.ac.uk/id/eprint/493889
PURE UUID: 9f028e9d-b6fd-4b61-b0e3-9cc8061348c9
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Date deposited: 17 Sep 2024 16:32
Last modified: 21 Sep 2024 02:02
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Contributors
Author:
Daniel J. Lunt
Author:
Bette L. Otto-Bliesner
Author:
Chris Brierley
Author:
Alan Haywood
Author:
Kenji Izumi
Author:
Masa Kageyama
Author:
Darrell Kaufman
Author:
Thorsen Mauritsen
Author:
Erin L. McClymont
Author:
Ulrich Salzmann
Author:
Sebastian Steinig
Author:
Jessica E. Tierney
Author:
Anni Zhao
Author:
Jiang Zhu
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