Articles | Volume 23, issue 15
https://doi.org/10.5194/acp-23-8749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-8749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Aug 2023
Research article |
| 08 Aug 2023
| 08 Aug 2023
Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing
Leighton A. Regayre
CORRESPONDING AUTHOR
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Centre for Environmental Modelling and Computation, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Lucia Deaconu
Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, OX1 3PU, UK
Faculty of Environmental Science and Engineering, Babes-Bolyai University, Cluj, 400294, Romania
Daniel P. Grosvenor
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Centre for Environmental Modelling and Computation, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
David M. H. Sexton
Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Christopher Symonds
Centre for Environmental Modelling and Computation, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Tom Langton
Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, OX1 3PU, UK
Duncan Watson-Paris
Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, OX1 3PU, UK
Scripps Institution of Oceanography and Halicioğlu Data Science Institute, University of California San Diego, La Jolla, CA 92093, USA
Jane P. Mulcahy
Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Kirsty J. Pringle
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Centre for Environmental Modelling and Computation, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Edinburgh Parallel Computing Centre, Bayes Centre, University of Edinburgh, EH8 9BT, UK
Mark Richardson
Centre for Environmental Modelling and Computation, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Jill S. Johnson
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH, UK
John W. Rostron
Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Hamish Gordon
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Department of Chemical Engineering and Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Grenville Lister
Department of Meteorology, University of Reading, RG6 6AH, Reading, UK
National Centre for Atmospheric Science, Reading, RG6 6AH, UK
Philip Stier
Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, OX1 3PU, UK
Ken S. Carslaw
Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
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Cited
2 citations as recorded by crossref.
- The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics H. Ahsan et al. 10.5194/acp-23-14779-2023
- A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model S. Fiddes et al. 10.5194/gmd-17-2641-2024
2 citations as recorded by crossref.
- The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics H. Ahsan et al. 10.5194/acp-23-14779-2023
- A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model S. Fiddes et al. 10.5194/gmd-17-2641-2024
Latest update: 08 May 2024
Short summary
Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in climate simulations over generations of climate model development. We show that structural deficiencies in a climate model are exposed by comprehensively exploring parametric uncertainty and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. This provides a future pathway towards building models with greater physical realism and lower uncertainty.
Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in...
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