Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-3017-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-3017-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
17 Jul 2019
Development and technical paper |
| 17 Jul 2019
| 17 Jul 2019
Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Oxford e-Research Centre, University of Oxford, Oxford, UK
David E. Rupp
Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA
Linnia Hawkins
Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA
College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA
Philip W. Mote
Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA
College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA
Doug McNeall
Met Office Hadley Centre, FitzRoy Road, Exeter, UK
Sarah N. Sparrow
Oxford e-Research Centre, University of Oxford, Oxford, UK
David C. H. Wallom
Oxford e-Research Centre, University of Oxford, Oxford, UK
Richard A. Betts
Met Office Hadley Centre, FitzRoy Road, Exeter, UK
College of Life and Environmental Sciences, University of Exeter, Exeter, UK
Justin J. Wettstein
College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Change Research, Bergen, Norway
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Cited
11 citations as recorded by crossref.
- Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests L. Hawkins et al. 10.1029/2018MS001577
- The making of the New European Wind Atlas – Part 1: Model sensitivity A. Hahmann et al. 10.5194/gmd-13-5053-2020
- 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
- Performances of climatic indicators from seasonal forecasts for ecosystem management: The case of Central Europe and the Mediterranean J. Costa-Saura et al. 10.1016/j.agrformet.2022.108921
- Exploration of diverse solutions for the calibration of imperfect climate models S. Peatier et al. 10.5194/esd-15-987-2024
- Systematic Calibration of a Convection‐Resolving Model: Application Over Tropical Atlantic S. Liu et al. 10.1029/2022JD037303
- What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models R. Sheikholeslami et al. 10.5194/gmd-12-4275-2019
- OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting S. Sparrow et al. 10.5194/gmd-14-3473-2021
- Spatial patterns of extreme precipitation and their changes under ~ 2 °C global warming: a large-ensemble study of the western USA D. Rupp et al. 10.1007/s00382-022-06214-3
- Forecast-based attribution of a winter heatwave within the limit of predictability N. Leach et al. 10.1073/pnas.2112087118
- Anthropogenic Influence on Recent Severe Autumn Fire Weather in the West Coast of the United States L. Hawkins et al. 10.1029/2021GL095496
11 citations as recorded by crossref.
- Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests L. Hawkins et al. 10.1029/2018MS001577
- The making of the New European Wind Atlas – Part 1: Model sensitivity A. Hahmann et al. 10.5194/gmd-13-5053-2020
- 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
- Performances of climatic indicators from seasonal forecasts for ecosystem management: The case of Central Europe and the Mediterranean J. Costa-Saura et al. 10.1016/j.agrformet.2022.108921
- Exploration of diverse solutions for the calibration of imperfect climate models S. Peatier et al. 10.5194/esd-15-987-2024
- Systematic Calibration of a Convection‐Resolving Model: Application Over Tropical Atlantic S. Liu et al. 10.1029/2022JD037303
- What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models R. Sheikholeslami et al. 10.5194/gmd-12-4275-2019
- OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting S. Sparrow et al. 10.5194/gmd-14-3473-2021
- Spatial patterns of extreme precipitation and their changes under ~ 2 °C global warming: a large-ensemble study of the western USA D. Rupp et al. 10.1007/s00382-022-06214-3
- Forecast-based attribution of a winter heatwave within the limit of predictability N. Leach et al. 10.1073/pnas.2112087118
- Anthropogenic Influence on Recent Severe Autumn Fire Weather in the West Coast of the United States L. Hawkins et al. 10.1029/2021GL095496
Latest update: 14 Dec 2024
Short summary
Understanding the unfolding challenges of climate change relies on climate models, many of which have regional biases larger than the expected climate signal over the next half-century. This work shows the potential for improving climate model simulations through a multiphased parameter refinement approach. Regional warm biases are substantially reduced, suggesting this iterative approach is one path to improving climate models and simulations of present and future climate.
Understanding the unfolding challenges of climate change relies on climate models, many of which...
