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Volume 25, issue 2 | Copyright

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 413-427, 2018
https://doi.org/10.5194/npg-25-413-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Jun 2018

Research article | 19 Jun 2018

Evaluating a stochastic parametrization for a fast–slow system using the Wasserstein distance

Gabriele Vissio1,2 and Valerio Lucarini2,3,4 Gabriele Vissio and Valerio Lucarini
  • 1International Max Planck Research School on Earth System Modelling, Hamburg, Germany
  • 2CEN, Meteorological Institute, University of Hamburg, Hamburg, Germany
  • 3Department of Mathematics and Statistics, University of Reading, Reading, UK
  • 4Walker Institute for Climate System Research, University of Reading, Reading, UK

Abstract. Constructing accurate, flexible, and efficient parametrizations is one of the great challenges in the numerical modeling of geophysical fluids. We consider here the simple yet paradigmatic case of a Lorenz 84 model forced by a Lorenz 63 model and derive a parametrization using a recently developed statistical mechanical methodology based on the Ruelle response theory. We derive an expression for the deterministic and the stochastic component of the parametrization and we show that the approach allows for dealing seamlessly with the case of the Lorenz 63 being a fast as well as a slow forcing compared to the characteristic timescales of the Lorenz 84 model. We test our results using both standard metrics based on the moments of the variables of interest as well as Wasserstein distance between the projected measure of the original system on the Lorenz 84 model variables and the measure of the parametrized one. By testing our methods on reduced-phase spaces obtained by projection, we find support for the idea that comparisons based on the Wasserstein distance might be of relevance in many applications despite the curse of dimensionality.

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Constructing good parametrizations is key when studying multi-scale systems. We consider a low-order model and derive a parametrization via a recently developed statistical mechanical approach. We show how the method allows for seamlessly treating the case when the unresolved dynamics is both faster and slower than the resolved one. We test the skill of the parametrization by using the formalism of the Wasserstein distance, which allows for measuring how different two probability measures are.
Constructing good parametrizations is key when studying multi-scale systems. We consider a...
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