Dynamical proxies of North Atlantic predictability and extremes

• Published in: Scientific Reports
• Main Authors: Faranda, Davide, Messori, Gabriele, Yiou, Pascal
• Other Authors: Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Department of Meteorology Stockholm (MISU), Stockholm University, Bolin Centre for Climate Research, ERC grant No 338965-A2C2
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2017
• Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]; [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis; Statistics and Probability [physics.data-an]; [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph]; North Atlantic
• Online Access: https://hal.science/hal-01340301; https://hal.science/hal-01340301/document; https://hal.science/hal-01340301/file/ExtremeDim_SR.pdf; https://hal.science/hal-01340301/file/ExtendedData.pdf; https://doi.org/10.1038/srep41278

International audience Atmospheric flows are characterized by chaotic dynamics and recurring large-scale patterns . These two characteristics point to the existence of an atmospheric attractor defined by Lorenz as: ``the collection of all states that the system can assume or approach again and again, as opposed to those that it will ultimately avoid". The average dimension $D$ of the attractor corresponds to the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of $D$ has proved challenging . Moreover, $D$ does not provide information on transient atmospheric motions, which lead to weather extremes . Using recent developments in dynamical systems theory , we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at steps of over two weeks. We believe this method provides an efficient and practical way of evaluating and informing operational weather forecasts.