Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1789-2017
© 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-10-1789-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
27 Apr 2017
Methods for assessment of models |
| 27 Apr 2017
Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model
Daniel B. Williamson
CORRESPONDING AUTHOR
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
Adam T. Blaker
National Oceanography Centre, Southampton, SO14 3ZH, UK
Bablu Sinha
National Oceanography Centre, Southampton, SO14 3ZH, UK
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- Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data A. Wernecke et al. 10.5194/tc-14-1459-2020
- 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
- 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
- The Atlantic Meridional Overturning Circulation in High‐Resolution Models J. Hirschi et al. 10.1029/2019JC015522
- Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks S. Sharma et al. 10.1029/2022EF003050
- The CNRM Global Atmosphere Model ARPEGE‐Climat 6.3: Description and Evaluation R. Roehrig et al. 10.1029/2020MS002075
- Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures V. Volodina & D. Williamson 10.1137/19M124438X
- Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation S. Li et al. 10.1002/2017MS001222
- Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation S. Li et al. 10.5194/gmd-12-3017-2019
- The importance of uncertainty quantification in model reproducibility V. Volodina & P. Challenor 10.1098/rsta.2020.0071
- Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
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- Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections F. Hourdin et al. 10.1126/sciadv.adf2758
- Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available E. Baker et al. 10.1080/10618600.2020.1750416
- Revisiting Antarctic ice loss due to marine ice-cliff instability T. Edwards et al. 10.1038/s41586-019-0901-4
- A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions S. Lienert & F. Joos 10.5194/bg-15-2909-2018
- Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases J. Salter et al. 10.1080/01621459.2018.1514306
- A review on computer model calibration C. Sung & R. Tuo 10.1002/wics.1645
- An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 S. Phipps et al. 10.5194/gmd-14-5107-2021
- Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators R. Oughton et al. 10.1137/20M1370902
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- Differentiable programming for Earth system modeling M. Gelbrecht et al. 10.5194/gmd-16-3123-2023
- Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts L. Mansfield et al. 10.1029/2023MS003715
- Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement F. Couvreux et al. 10.1029/2020MS002217
- Process‐Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global F. Hourdin et al. 10.1029/2020MS002225
- On Constraining the Mesoscale Eddy Energy Dissipation Time‐Scale J. Mak et al. 10.1029/2022MS003223
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Latest update: 18 Apr 2024
Short summary
We present a method from the statistical science literature to assist in the tuning of global climate models submitted to CMIP. We apply the method to the NEMO ocean model and find choices of its free parameters that lead to improved representations of depth integrated global mean temperature and salinity. We argue against automatic tuning procedures that involve optimising certain outputs of a model and explain why our method avoids common difficulties with/arguments against automatic tuning.
We present a method from the statistical science literature to assist in the tuning of global...
