External forcings and predictability in Lorenz model: An analysis via neural network modelling

Pasini, Antonello (2008) External forcings and predictability in Lorenz model: An analysis via neural network modelling. Il nuovo cimento C, 31 (3). pp. 357-370. ISSN 1826-9885

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What’s about predictability in future climate scenarios? At present, we have no answer to this question in realistic climate models, due to the need of a difficult and time-consuming analysis. So, in the present paper an investigation of this situation has been performed through low-dimensional models, by considering unforced and forced Lorenz systems as toy-models. By coupling dynamical and neural network analyses, some clear results are achieved: for instance, an increase of mean predictability in forced situations (which simply mimic the actual increase of anthropogenic forcings in the real system) is discovered. In particular, the application of neural network modelling to this problem supplies us with some “surplus” information and opens new prospects as far as the operational assessment of predictability is concerned.

Item Type: Article
Uncontrolled Keywords: Low-dimensional chaos ; Climate dynamics ; Climatology, climate change and variability ; Neural networks, fuzzy logic, artificial intelligence
Subjects: 500 Scienze naturali e Matematica > 550 Scienze della Terra > 551.6 Climatologia e tempo atmosferico (Classificare qui i lo studio dei Cambiamenti climatici)
Depositing User: Marina Spanti
Date Deposited: 23 Mar 2020 16:17
Last Modified: 23 Mar 2020 16:17
URI: http://eprints.bice.rm.cnr.it/id/eprint/16364

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