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Data Assimilation in Slow–Fast Systems Using Homogenized Climate Models

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  • 1 School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
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Abstract

A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare transitions between slow regimes, akin to weather or climate regimes. Using homogenization techniques, a reduced stochastic parameterization model is derived for the slow dynamics. The reliability of this reduced climate model in reproducing the statistics of the slow dynamics of the full deterministic model for finite values of the time-scale separation is numerically established. The statistics, however, are sensitive to uncertainties in the parameters of the stochastic model.

It is investigated whether the stochastic climate model can be beneficial as a forecast model in an ensemble data assimilation setting, in particular in the realistic setting when observations are only available for the slow variables. The main result is that reduced stochastic models can indeed improve the analysis skill when used as forecast models instead of the perfect full deterministic model. The stochastic climate model is far superior at detecting transitions between regimes. The observation intervals for which skill improvement can be obtained are related to the characteristic time scales involved. The reason why stochastic climate models are capable of producing superior skill in an ensemble setting is the finite ensemble size; ensembles obtained from the perfect deterministic forecast model lack sufficient spread even for moderate ensemble sizes. Stochastic climate models provide a natural way to provide sufficient ensemble spread to detect transitions between regimes. This is corroborated with numerical simulations. The conclusion is that stochastic parameterizations are attractive for data assimilation despite their sensitivity to uncertainties in the parameters.

Corresponding author address: Georg A. Gottwald, The University of Sydney, School of Mathematics and Statistics, Sydney 2006, Australia. E-mail: georg.gottwald@sydney.edu.au

Abstract

A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare transitions between slow regimes, akin to weather or climate regimes. Using homogenization techniques, a reduced stochastic parameterization model is derived for the slow dynamics. The reliability of this reduced climate model in reproducing the statistics of the slow dynamics of the full deterministic model for finite values of the time-scale separation is numerically established. The statistics, however, are sensitive to uncertainties in the parameters of the stochastic model.

It is investigated whether the stochastic climate model can be beneficial as a forecast model in an ensemble data assimilation setting, in particular in the realistic setting when observations are only available for the slow variables. The main result is that reduced stochastic models can indeed improve the analysis skill when used as forecast models instead of the perfect full deterministic model. The stochastic climate model is far superior at detecting transitions between regimes. The observation intervals for which skill improvement can be obtained are related to the characteristic time scales involved. The reason why stochastic climate models are capable of producing superior skill in an ensemble setting is the finite ensemble size; ensembles obtained from the perfect deterministic forecast model lack sufficient spread even for moderate ensemble sizes. Stochastic climate models provide a natural way to provide sufficient ensemble spread to detect transitions between regimes. This is corroborated with numerical simulations. The conclusion is that stochastic parameterizations are attractive for data assimilation despite their sensitivity to uncertainties in the parameters.

Corresponding author address: Georg A. Gottwald, The University of Sydney, School of Mathematics and Statistics, Sydney 2006, Australia. E-mail: georg.gottwald@sydney.edu.au
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