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Vlaams Instituut voor de Zee

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Open Marien Archief

Het Open Marien Archief van België (OMA) biedt vrije toegang tot de digitale publicaties over de Vlaamse kust en het Belgisch deel van de Noordzee, en alle andere mariene, estuariene en kustgebonden publicaties van Belgische auteurs en wetenschappers en van buitenlandse wetenschappers geaffilieerd aan een Belgische instelling.

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On the use of simple dynamical systems for climate predictions
Crucifix, M.; Rougier, J. (2009). On the use of simple dynamical systems for climate predictions. The European Physical Journal Special Topics 174: 11-31.
In: The European Physical Journal Special Topics. Springer: Berlin. ISSN 1951-6355; e-ISSN 1951-6401, meer
Peer reviewed article  

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  • Crucifix, M., meer
  • Rougier, J.

    Over the last few decades, climate scientists have devoted much effort to the development of large numerical models of the atmosphere and the ocean. While there is no question that such models provide important and useful information on complicated aspects of atmosphere and ocean dynamics, skillful prediction also requires a phenomenological approach, particularly for very slow processes, such as glacial-interglacial cycles. Phenomenological models are often represented as low-order dynamical systems. These are tractable, and a rich source of insights about climate dynamics, but they also ignore large bodies of information on the climate system, and their parameters are generally not operationally defined. Consequently, if they are to be used to predict actual climate system behaviour, then we must take very careful account of the uncertainty introduced by their limitations. In this paper we consider the problem of the timing of the next glacial inception, about which there is on-going debate. Our model is the three-dimensional stochastic system of Saltzman and Maasch (1991), and our inference takes place within a Bayesian framework that allows both for the limitations of the model as a description of the propagation of the climate state vector, and for parametric uncertainty. Our inference takes the form of a data assimilation with unknown static parameters, which we perform with a variant on a Sequential Monte Carlo technique (`particle filter'). Provisional results indicate peak glacial conditions in 60,000 years.

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