Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3567-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-3567-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
28 Sep 2017
Development and technical paper |
| 28 Sep 2017
Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3
School of Geosciences, University of Edinburgh, Crew Building, Alexander Crum Brown Road, The King's Buildings, Edinburgh
EH9 3FF, UK
Kuniko Yamazaki
School of Geosciences, University of Edinburgh, Crew Building, Alexander Crum Brown Road, The King's Buildings, Edinburgh
EH9 3FF, UK
Met Office, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Michael J. Mineter
School of Geosciences, University of Edinburgh, Crew Building, Alexander Crum Brown Road, The King's Buildings, Edinburgh
EH9 3FF, UK
Coralia Cartis
Mathematical Institute, University of Oxford, Andrew Wiles Building,
Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
Nathan Eizenberg
Mathematical Institute, University of Oxford, Andrew Wiles Building,
Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
Bureau of Meteorology, G.P.O. Box 1289, Melbourne, VIC 3001, Australia
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- The potential for structural errors in emergent constraints B. Sanderson et al. 10.5194/esd-12-899-2021
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- Automated model optimisation using the Cylc workflow engine (Cyclops v1.0) R. Gorman & H. Oliver 10.5194/gmd-11-2153-2018
- Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
- Long-window tandem variational data assimilation methods for chaotic climate models tested with the Lorenz 63 system P. Kennedy et al. 10.5194/npg-32-353-2025
1 citations as recorded by crossref.
Latest update: 08 Oct 2025
Short summary
The paper shows it is possible to automatically calibrate the parameters in the atmospheric component of two climate models. The resulting atmosphere–ocean models are often, but not always, stable and realistic. The computational cost to do this is feasible. The implications are that it is possible to generate multiple configurations of a single model with different parameter values but which all look similar to the standard model and that the techniques could be used to calibrate other models.
The paper shows it is possible to automatically calibrate the parameters in the atmospheric...
