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Atmospheric flow Analogues and Climate Change (A2C2)
Date du début: 1 mars 2014, Date de fin: 28 févr. 2019 PROJET  TERMINÉ 

"The A2C2 project treats two major challenges in climate and atmospheric research: the time dependence of the climate attractor to external forcings (solar, volcanic eruptions and anthropogenic), and the attribution of extreme climate events occurring in the northern extra-tropics. The main difficulties are the limited climate information, the computer cost of model simulations, and mathematical assumptions that are hardly verified and often overlooked in the literature.A2C2 proposes a practical framework to overcome those three difficulties, linking the theory of dynamical systems and statistics. We will generalize the methodology of flow analogues to multiple databases in order to obtain probabilistic descriptions of analogue decompositions.The project is divided into three workpackages (WP). WP1 embeds the analogue method in the theory of dynamical systems in order to provide a metric of an attractor deformation in time. The important methodological step is to detect trends or persisting outliers in the dates and scores of analogues when the system yields time-varying forcings. This is done from idealized models and full size climate models in which the forcings (anthropogenic and natural) are known.A2C2 creates an open source toolkit to compute flow analogues from a wide array of databases (WP2). WP3 treats the two scientific challenges with the analogue method and multiple model ensembles, hence allowing uncertainty estimates under realistic mathematical hypotheses. The flow analogue methodology allows a systematic and quasi real-time analysis of extreme events, which is currently out of the reach of conventional climate modeling approaches.The major breakthrough of A2C2 is to bridge the gap between operational needs (the immediate analysis of climate events) and the understanding long-term climate changes. A2C2 opens new research horizons for the exploitation of ensembles of simulations and reliable estimates of uncertainty."