Data assimilation in slow-fast systems using homogenized climate models

TL;DR

This paper demonstrates that reduced stochastic climate models, derived via homogenization for slow-fast systems, can enhance ensemble data assimilation by better detecting regime transitions despite parameter uncertainties.

Contribution

It introduces a homogenized stochastic parametrization for slow-fast climate models and shows its effectiveness in improving forecast analysis and transition detection in data assimilation.

Findings

Stochastic models reproduce slow dynamics statistics with finite scale separation.

They improve ensemble forecast skill over deterministic models.

Stochastic models better detect regime transitions in numerical simulations.

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 parametrization 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 is 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 due to the finite ensemble size; ensembles obtained from the perfect deterministic forecast model lacks 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 parametrizations are attractive for data assimilation despite their sensitivity to uncertainties in the parameters.