Articles | Volume 17, issue 3
Model evaluation paper
08 Feb 2024
Model evaluation paper |  | 08 Feb 2024

Constraining the carbon cycle in JULES-ES-1.0

Douglas McNeall, Eddy Robertson, and Andy Wiltshire

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Cited articles

Al-Taweel, Y.: Diagnostics and Simulation-Based Methods for Validating Gaussian Process Emulators, Ph.D. thesis, University of Sheffield,, 2018. a
Andrianakis, I., Vernon, I. R., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R. N., Goldstein, M., and White, R. G.: Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda, PLoS Comput. Biol., 11, e1003968,, 2015. a
Baker, E., Harper, A. B., Williamson, D., and Challenor, P.: Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES, Geosci. Model Dev., 15, 1913–1929,, 2022. a
Carnell, R.: lhs: Latin Hypercube Samples, r package version 1.1.3, (last access: 8 November 2021), 2021. a
Carslaw, K., Lee, L., Reddington, C., Pringle, K., Rap, A., Forster, P., Mann, G., Spracklen, D., Woodhouse, M., Regayre, L., and Pierce, J. R.: Large contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67–71,, 2013. a, b
Short summary
We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.