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The HadGEM3‐GC3.1 Contribution to the CMIP6 Detection and Attribution Model Intercomparison Project.

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Title: The HadGEM3‐GC3.1 Contribution to the CMIP6 Detection and Attribution Model Intercomparison Project.
Authors: Jones, Gareth S.1 (AUTHOR) gareth.s.jones@metoffice.gov.uk, Andrews, Martin B.1 (AUTHOR), Andrews, Timothy1 (AUTHOR), Blockley, Ed1 (AUTHOR), Ciavarella, Andrew1 (AUTHOR), Christidis, Nikos1 (AUTHOR), Cotterill, Daniel F.1 (AUTHOR), Lott, Fraser C.1 (AUTHOR), Ridley, Jeff1 (AUTHOR), Stott, Peter A.1 (AUTHOR)
Source: Journal of Advances in Modeling Earth Systems. Aug2024, Vol. 16 Issue 8, p1-24. 24p.
Subject Terms: *Climate change detection, *Climate change models, *Atmospheric temperature, Atmospheric models, Atmospheric boundary layer
Abstract: The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary: We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points: The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is describedThe climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other modelsSouthern Ocean temperature variability is unusual and sensitive to climate driver [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The HadGEM3‐GC3.1 Contribution to the CMIP6 Detection and Attribution Model Intercomparison Project.
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  Data: <searchLink fieldCode="AR" term="%22Jones%2C+Gareth+S%2E%22">Jones, Gareth S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gareth.s.jones@metoffice.gov.uk</i><br /><searchLink fieldCode="AR" term="%22Andrews%2C+Martin+B%2E%22">Andrews, Martin B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Andrews%2C+Timothy%22">Andrews, Timothy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Blockley%2C+Ed%22">Blockley, Ed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ciavarella%2C+Andrew%22">Ciavarella, Andrew</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Christidis%2C+Nikos%22">Christidis, Nikos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cotterill%2C+Daniel+F%2E%22">Cotterill, Daniel F.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lott%2C+Fraser+C%2E%22">Lott, Fraser C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ridley%2C+Jeff%22">Ridley, Jeff</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stott%2C+Peter+A%2E%22">Stott, Peter A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advances+in+Modeling+Earth+Systems%22">Journal of Advances in Modeling Earth Systems</searchLink>. Aug2024, Vol. 16 Issue 8, p1-24. 24p.
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  Data: *<searchLink fieldCode="DE" term="%22Climate+change+detection%22">Climate change detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change+models%22">Climate change models</searchLink><br />*<searchLink fieldCode="DE" term="%22Atmospheric+temperature%22">Atmospheric temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+boundary+layer%22">Atmospheric boundary layer</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary: We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points: The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is describedThe climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other modelsSouthern Ocean temperature variability is unusual and sensitive to climate driver [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1029/2023MS004135
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        Text: English
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      – SubjectFull: Climate change detection
        Type: general
      – SubjectFull: Climate change models
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      – SubjectFull: Atmospheric temperature
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              Text: Aug2024
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