Articles | Volume 7, issue 4
https://doi.org/10.5194/esd-7-917-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/esd-7-917-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Nov 2016
Research article |
| 24 Nov 2016
The impact of structural error on parameter constraint in a climate model
Doug McNeall
CORRESPONDING AUTHOR
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Jonny Williams
BRIDGE, School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Now at NIWA, 301 Evans Bay Parade, Hataitai, Wellington 6021, New Zealand
Ben Booth
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Richard Betts
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Peter Challenor
University of Exeter, North Park Road, Exeter, EX4 4QE, UK
Andy Wiltshire
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
David Sexton
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
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- Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin K. Bennett et al. 10.1002/2017WR020471
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- Evaluation of Land Surface Subprocesses and Their Impacts on Model Performance With Global Flux Data J. Li et al. 10.1029/2018MS001606
- On the Uncertainty Induced by Pedotransfer Functions in Terrestrial Biosphere Modeling A. Paschalis et al. 10.1029/2021WR031871
- The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble J. Rostron et al. 10.1007/s00382-020-05281-8
- Quantifying the Effects of Climate Change on Aircraft Take-Off Performance at European Airports J. Williams et al. 10.3390/aerospace12030165
- A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5 K. Dagon et al. 10.5194/ascmo-6-223-2020
- Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations Y. Qian et al. 10.1029/2018JD028927
- Toward Efficient Calibration of Higher‐Resolution Earth System Models C. Fletcher et al. 10.1029/2021MS002836
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- Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing M. Yoshioka et al. 10.1029/2019MS001628
- Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model Y. Qian et al. 10.1007/s00382-023-06977-3
- Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing J. Johnson et al. 10.5194/acp-20-9491-2020
- Exploration of diverse solutions for the calibration of imperfect climate models S. Peatier et al. 10.5194/esd-15-987-2024
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- Correcting a bias in a climate model with an augmented emulator D. McNeall et al. 10.5194/gmd-13-2487-2020
- Parametric and Structural Sensitivities of Turbine‐Height Wind Speeds in the Boundary Layer Parameterizations in the Weather Research and Forecasting Model B. Yang et al. 10.1029/2018JD029691
- Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology A. Karmalkar et al. 10.1007/s00382-019-04617-3
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Latest update: 23 Oct 2025
Short summary
We compare simulated with observed forests to constrain uncertain input parameters of the land surface component of a climate model.
We find that the model is unlikely to be able to simulate the Amazon and other major forests simultaneously at any one parameter set, suggesting a bias in the model's representation of the Amazon.
We find we cannot constrain parameters individually, but we can rule out large areas of joint parameter space.
We compare simulated with observed forests to constrain uncertain input parameters of the land...
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