Preprints
https://doi.org/10.5194/gmd-2021-434
https://doi.org/10.5194/gmd-2021-434
Submitted as: methods for assessment of models
10 Feb 2022
Submitted as: methods for assessment of models | 10 Feb 2022
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Analog Data Assimilation for the Selection of Suitable General Circulation Models

Juan Ruiz1, Pierre Ailliot2, Thi Tuyet Trang Chau3, Pierre Le Bras4,5, Valérie Monbet6, Florian Sévellec4,7, and Pierre Tandeo5 Juan Ruiz et al.
  • 1Centro de Investigaciones del Mar y la Atmósfera, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET-UBA, Buenos Aires, Argentina, UMI-IFAECI (CNRS-CONICET-UBA), Buenos Aires, Argentina
  • 2Univ Brest, CNRS UMR 6205, Laboratoire de Mathematiques de Bretagne Atlantique, France
  • 3LSCE, IPSL-CEA Saclay, 91191 Gif-sur-Yvette cedex, France
  • 4Laboratoire d’Océanographie Physique et Spatiale, IUEM, Univ. Brest, CNRS, IRD, Ifremer, Brest, France
  • 5IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, France
  • 6INRIA & Univ Rennes, CNRS, IRMAR - UMR 6625, F-35000 Rennes, France
  • 7Ocean and Earth Science, University of Southampton, Southampton, United Kingdom

Abstract. Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the performance of model selection based on model evidence computed using data-driven data assimilation, where dynamical models are emulated using machine learning methods. In this work, the methodology is tested with the three-variable Lorenz' model and with an intermediate complexity atmospheric general circulation model (a.k.a. the SPEEDY model). Numerical experiments show that the data-driven implementation of the model selection algorithm performs as well as the one that uses the dynamical model. The technique is able of selecting the best model among a set of possible models and also to characterize the spatio-temporal variability of the model sensitivity. Moreover, the technique is sensitive to differences in the model dynamics which are not reflected in the moments of the climatological probability distribution of the state variables. This suggests the implementation of this technique using available long-term observations and model simulations.

Juan Ruiz et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-434', Anonymous Referee #1, 11 Mar 2022
    • AC1: 'Reply on RC1', Juan Ruiz, 16 Jun 2022
  • RC2: 'Comment on gmd-2021-434', Anonymous Referee #2, 23 May 2022
    • AC2: 'Reply on RC2', Juan Ruiz, 16 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-434', Anonymous Referee #1, 11 Mar 2022
    • AC1: 'Reply on RC1', Juan Ruiz, 16 Jun 2022
  • RC2: 'Comment on gmd-2021-434', Anonymous Referee #2, 23 May 2022
    • AC2: 'Reply on RC2', Juan Ruiz, 16 Jun 2022

Juan Ruiz et al.

Juan Ruiz et al.

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Short summary
We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.