Articles | Volume 16, issue 4
Methods for assessment of models
21 Feb 2023
Methods for assessment of models |  | 21 Feb 2023

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang

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Cited articles

Addcock, B., Brugiapaglia, S., and Webster, C.: Sparse Polynomial Approximation of High-Dimensional Functions, SIAM, 1–310,, 2022. a
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Blatter, H.: Velocity and stress fields in grounded glaciers: A simple algorithm for including deviatoric stress gradients, J. Glaciol., 41, 333–344,, 1995. a, b
Bleck, R. and Boudra, D.: Wind-driven spin-up in eddy-resolving ocean models formulated in isopycnic and isobaric coordinates, J. Geophys. Res.-Oceans, 91, 7611–7621,, 1986. a
Clare, M. C. A., Leijnse, T. W. B., McCall, R. T., Diermanse, F. L. M., Cotter, C. J., and Piggott, M. D.: Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding, Nat. Hazards Earth Syst. Sci., 22, 2491–2515,, 2022. a
Short summary
This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.