Articles | Volume 18, issue 8
Biogeosciences, 18, 2727–2754, 2021
https://doi.org/10.5194/bg-18-2727-2021
Biogeosciences, 18, 2727–2754, 2021
https://doi.org/10.5194/bg-18-2727-2021
Research article
30 Apr 2021
Research article | 30 Apr 2021

Optimal model complexity for terrestrial carbon cycle prediction

Caroline A. Famiglietti et al.

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Cited articles

Aguilos, M., Herault, B., Burban, B., Wagner, F., and Bonal, D.: What drives long-term variations in carbon flux and balance in a tropical rainforest in French Guiana?, Agr. Forest Meteorol., 253–254, 114–123, https://doi.org/10.1016/j.agrformet.2018.02.009, 2018. 
Atkin, O. K., Bahar, N., Bloomfield, K., Griffin, K. L., Heskel, M. A., Huntingford, C., and de la Torre, A. M.: Plant Respiration: Metabolic Fluxes and Carbon Balance, edited by: Tcherkez, G. and Ghashghaie, J., Springer, Cham, Switzerland, 302 pp., 2017. 
Bacour, C., Peylin, P., MacBean, N., Rayner, P. J., Delage, F., Chevallier, F., Weiss, M., Demarty, J., Santaren, D., Baret, F., Berveiller, D., Dufrêne, E., and Prunet, P.: Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process-oriented biosphere model, J. Geophys. Res.-Biogeo., 120, 1839–1857, https://doi.org/10.1002/2015JG002966, 2015. 
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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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