Articles | Volume 16, issue 4
https://doi.org/10.5194/gmd-16-1213-2023
https://doi.org/10.5194/gmd-16-1213-2023
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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Latest update: 19 Jul 2024
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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.