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Vous êtes ici : Accueil / Actualités / Séminaires / Seminaires Septembre 2017-Aout 2018 / Jeudi 22 février - Alberto Carrassi - Attribution of climatic events using a data assimilation–based formulation of model evidence

Jeudi 22 février - Alberto Carrassi - Attribution of climatic events using a data assimilation–based formulation of model evidence

Par SEMSOU Dernière modification 31/01/2018 09:34
Quand ? Le 22/02/2018,
de 11:00 à 12:00
Où ? salle Lyot
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Alberto Carrassi

Nansen Environmental and Remote Sensing Center, Bergen, Norway

 

Titre: Attribution of climatic events using a data assimilation-based formulation of model evidence

 

Abstract: Data assimilation (DA) methods were oroginally designed for state estimation, but are starting to be increasingly applied to the model selection and attribution problems as well.

Probabilistic event attribution is the problem of assessing the probability of occurrence of an observed episode under different hypotheses: a notable example is the causal assessements about episodes of extreme weather or unusual climate conditions. Two quantities are computed: (i) the probability of occurence, pi, referred to as factual, which represents the probability in the real world; and (ii) the probability, po, counterfactual, in an alternative world that might have occured had the forcing of interest been absent. The so-called fraction of attribuable risk (FAR) is then defined as the change in likelyhood of an event that is attribuable to the external forcing. 

The approach widely used to compute the FAR is very costly as it uses a large ensemble of model simulations, unconstrained from the observations, and is difficult to implement in a timely, systematic way in the aftermath of a climatic episode. We will show, as a proof of concept, that these obstacles are removed or mitigatedby using the FAR using DA, leading to an efficient DA-based approach to the attribution of climate related events.

Carrassi et al., 2017 have introduced a contextual formulation of model evidence (CME) that allows for estimating the two concurrent probabilities, po and pi needed to compute the FAR. In particular, these authors have shown that the CME can be efficiently computed usin an ensemble Kalman filter with localization - a requirement for ensemble-based DA with high dimensional models - Metref et al., 2018 developped a new formulation of the CME using domain localization, the domain-localized CME (DL-CME). In this talk we will first define the CME and shows that it can be computed using state-of-the-art DA methods. We will later provides examples of its application to the model selection and to the attribution problems using low dimensional numerical models and the complexity global atmospheric SPEEDY model.

 

This presentation will be given in english.

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