Articles | Volume 15, issue 19
https://doi.org/10.5194/bg-15-5801-2018
https://doi.org/10.5194/bg-15-5801-2018
Research article
 | 
04 Oct 2018
Research article |  | 04 Oct 2018

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Istem Fer, Ryan Kelly, Paul R. Moorcroft, Andrew D. Richardson, Elizabeth M. Cowdery, and Michael C. Dietze

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Cited articles

Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal. Process., 50, 174–188, https://doi.org/10.1109/78.978374, 2002.
Aslanyan, G., Easther, R., and Price, L. C.: Learn-as-you-go acceleration of cosmological parameter estimates, J. Cosmol. Astropart. P., 2015, 005, 2015.
Bradford, J. B., Weishampel, P., Smith, M.-L., Kolka, R., Birdsey, R. A., Ollinger, S. V., and Ryan, M. G.: Carbon pools and fluxes in small temperate forest landscapes: Variability and implications for sampling design, Forest Ecol. Manag., 259, 1245–1254, https://doi.org/10.1016/j.foreco.2009.04.009, 2010.
Braswell, B. H., Sacks, W. J., Linder, E., and Schimel, D. S.: Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations, Global Change Biol., 11, 335–355, https://doi.org/10.1111/j.1365-2486.2005.00897.x, 2005.
Brynjarsdóttir, J. and O'Hagan, A.: Learning about physical parameters: the importance of model discrepancy, Inverse Problems, 30, 114007, 2014.
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The computer models we use to understand and forecast the ecosystem changes have multiple components that determine their outcomes. Due to our limited observation capacities, these components bear uncertainties that in return affect our predictions. While there are techniques for reducing these uncertainties, they are not applicable to every model due to computational and statistical barriers. This research presents a method that lowers those barriers and allows us to improve model predictions.
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