To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?

Abstract It depends. The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called ‘model democracy’ by discounting climate models that are too warm over the historical period (i.e., models that ‘run hot’) when making projections of globa...

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Main Authors: Abigail McDonnell, Adam Michael Bauer, Cristian Proistosescu
Format: Article
Language:English
Published: Wiley 2024-10-01
Series:Earth's Future
Subjects:
Online Access:https://doi.org/10.1029/2024EF004844
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author Abigail McDonnell
Adam Michael Bauer
Cristian Proistosescu
author_facet Abigail McDonnell
Adam Michael Bauer
Cristian Proistosescu
author_sort Abigail McDonnell
collection DOAJ
description Abstract It depends. The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called ‘model democracy’ by discounting climate models that are too warm over the historical period (i.e., models that ‘run hot’) when making projections of global temperature change. However, the IPCC did not address whether this procedure is reliable for other quantities. Here, we explore the implications of weighting climate models according to their skill in reproducing historical global‐mean surface temperature using three other climate variables of interest: global average precipitation change, regional average temperature change, and regional average precipitation change. We find that the temperature‐based weighting scheme leads to an improved prediction of global average precipitation, though we show that this prediction could be overconfident. On regional scales, we find a heterogeneous pattern of error reduction in future regional precipitation. This stands in sharp contrast with the broad regional pattern of error reduction in future temperature projections, though we do find regions where error is not significantly reduced. Our results demonstrate that practitioners using weighted climate model ensembles for climate projections must take care when weighting by temperature alone, lest they produce unreliable climate projections that result from an inappropriate weighting procedure.
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publishDate 2024-10-01
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spelling doaj-art-86b10a9bf652485e82e2b7387f3e582f2025-08-20T02:12:11ZengWileyEarth's Future2328-42772024-10-011210n/an/a10.1029/2024EF004844To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?Abigail McDonnell0Adam Michael Bauer1Cristian Proistosescu2Department of Climate, Meteorology, and Atmospheric Sciences University of Illinois Urbana‐Champaign Urbana IL USADepartment of Physics University of Illinois Urbana‐Champaign Loomis Laboratory Urbana IL USADepartment of Climate, Meteorology, and Atmospheric Sciences University of Illinois Urbana‐Champaign Urbana IL USAAbstract It depends. The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called ‘model democracy’ by discounting climate models that are too warm over the historical period (i.e., models that ‘run hot’) when making projections of global temperature change. However, the IPCC did not address whether this procedure is reliable for other quantities. Here, we explore the implications of weighting climate models according to their skill in reproducing historical global‐mean surface temperature using three other climate variables of interest: global average precipitation change, regional average temperature change, and regional average precipitation change. We find that the temperature‐based weighting scheme leads to an improved prediction of global average precipitation, though we show that this prediction could be overconfident. On regional scales, we find a heterogeneous pattern of error reduction in future regional precipitation. This stands in sharp contrast with the broad regional pattern of error reduction in future temperature projections, though we do find regions where error is not significantly reduced. Our results demonstrate that practitioners using weighted climate model ensembles for climate projections must take care when weighting by temperature alone, lest they produce unreliable climate projections that result from an inappropriate weighting procedure.https://doi.org/10.1029/2024EF004844climate changeclimate projectionsCMIP6model democracy
spellingShingle Abigail McDonnell
Adam Michael Bauer
Cristian Proistosescu
To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?
Earth's Future
climate change
climate projections
CMIP6
model democracy
title To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?
title_full To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?
title_fullStr To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?
title_full_unstemmed To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?
title_short To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?
title_sort to what extent does discounting hot climate models improve the predictive skill of climate model ensembles
topic climate change
climate projections
CMIP6
model democracy
url https://doi.org/10.1029/2024EF004844
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