Preprints
https://doi.org/10.5194/bg-2020-478
https://doi.org/10.5194/bg-2020-478

  29 Dec 2020

29 Dec 2020

Review status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

Optimal model complexity for terrestrial carbon cycle prediction

Caroline A. Famiglietti1, T. Luke Smallman2, Paul A. Levine3, Sophie Flack-Prain2, Gregory R. Quetin1, Victoria Meyer4, Nicholas C. Parazoo3, Stephanie G. Stettz3, Yan Yang3, Damien Bonal5, A. Anthony Bloom3, Mathew Williams2, and Alexandra G. Konings1 Caroline A. Famiglietti et al.
  • 1Department of Earth System Science, Stanford University, Stanford, USA
  • 2School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
  • 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA
  • 4School of the Art Institute of Chicago, Chicago, USA
  • 5Université de Lorraine, AgroParisTech, INRAE, UMR Silva, 54000 Nancy, France

Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly-determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at 6 globally-distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust, observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.

Caroline A. Famiglietti et al.

 
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Caroline A. Famiglietti et al.

Data sets

NEE and LAI prediction metrics for DALEC model suite (COMPLEX experiment) Caroline A. Famiglietti et al. https://doi.org/10.6084/m9.figshare.13409096

Caroline A. Famiglietti et al.

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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.
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