Extreme events: dynamics, statistics and prediction
- 1Environmental Research and Teaching Institute (CERES-ERTI), Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), UMR8539, CNRS-Ecole Normale Supérieure, 75231 Paris Cedex 05, France
- 2Department of Atmospheric & Oceanic Sciences and Institute of Geophysics & Planetary Physics, University of California, Los Angeles, USA
- 3Laboratoire des Sciences du Climat et de l'Environnement, UMR8212, CEA-CNRS-UVSQ, CE-Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette Cedex, France
- 4Centre International pour la Recherche sur l'Environnement et le Développement, Nogent-sur-Marne, France
- 5Météo-France, Toulouse, France
- 6Department of Geography, King's College London, London, UK
- 7International Institute of Earthquake Prediction Theory and Mathematical Geophysics, Russian Academy of Sciences, Russia
- 8Meteorological Institute, University Bonn, Bonn, Germany
- 9Department of Earth & Space Sciences and Institute of Geophysics & Planetary Physics, University of California, Los Angeles, USA
- 10Institut Royal de Météorologie, Brussels, Belgium
- 11Department of Nonlinear Dynamics, Max-Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- 12Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA
Abstract. We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socio-economic events, along with their prediction.
Two important results refer to (i) the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum; and (ii) the need for coupled modeling of natural and socio-economic systems. Both these results have implications for the study and prediction of natural hazards and their human impacts.