Articles | Volume 18, issue 8
https://doi.org/10.5194/bg-18-2727-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-2727-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Apr 2021
Research article |
| 30 Apr 2021
| 30 Apr 2021
Optimal model complexity for terrestrial carbon cycle prediction
Caroline A. Famiglietti
CORRESPONDING AUTHOR
Department of Earth System Science, Stanford University, Stanford, USA
T. Luke Smallman
School of GeoSciences and National Centre for Earth Observation,
University of Edinburgh, Edinburgh, UK
Paul A. Levine
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, USA
Sophie Flack-Prain
School of GeoSciences and National Centre for Earth Observation,
University of Edinburgh, Edinburgh, UK
Gregory R. Quetin
Department of Earth System Science, Stanford University, Stanford, USA
Victoria Meyer
Department of Liberal Arts, School of the Art Institute of Chicago, Chicago, USA
Nicholas C. Parazoo
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, USA
Stephanie G. Stettz
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, USA
Yan Yang
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, USA
Damien Bonal
Université de Lorraine, AgroParisTech, INRAE, UMR Silva, 54000
Nancy, France
A. Anthony Bloom
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, USA
Mathew Williams
School of GeoSciences and National Centre for Earth Observation,
University of Edinburgh, Edinburgh, UK
Alexandra G. Konings
Department of Earth System Science, Stanford University, Stanford, USA
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Latest update: 24 Apr 2024
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to...
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