Articles | Volume 6, issue 2
22 Dec 2020
 | 22 Dec 2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence

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Cited articles

Anderegg, W. R. L., Wolf, A., Arango-Velez, A., Choat, B., Chmura, D. J., Jansen, S., Kolb, T., Li, S., Meinzer, F. C., Pita, P., Resco de Dios, V., Sperry, J. S., Wolfe, B. T., and Pacala, S.: Woody plants optimise stomatal behaviour relative to hydraulic risk, Ecol. Lett., 21, 968–977,, 2018. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222,, 2020. a
Bauerle, W. L., Daniels, A. B., and Barnard, D. M.: Carbon and water flux responses to physiology by environment interactions: a sensitivity analysis of variation in climate on photosynthetic and stomatal parameters, Clim. Dynam., 42, 2539–2554,, 2014. a
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Bengio, Y.: Practical recommendations for gradient-based training of deep architectures, arXiv [preprint], arXiv:1206.5533, 16 September 2012. a, b, c
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.