Éditeur(s) : Wiley-Blackwell
Résumé : The Climate Forecast System Reanalysis (CFSR) provides a wealth of information spanning 1979–2009 for investigation of ocean wind and wave climate. Preprocessing of the data is necessary to remove the dominant seasonal signals and to create time series of semimonthly averaged wind speed and significant wave height over a 0.5° global grid. We perform an empirical orthogonal function (EOF) analysis to extract the dominant space-time patterns. The results for the three major ocean basins show strong zonal structures in the winds and saturation of the swells corroborating prior works with various data sets. We reexamine the CFSR data in the frequency domain to identify periodic signals associated with published climate indices. The Fourier transform of the preprocessed time series generates spectra ranging from 1 month to 15 years period for an EOF analysis. The results demonstrate the spatial structures and periods of climate phenomena. The Arctic Oscillation dominates the Atlantic basin with a broad range of intra-annual signals off the European coasts. The Indian and Pacific Oceans are strongly influenced by inter-annual cycles of the El Niño Southern Oscillation (ENSO) and the Antarctica Oscillation. The Indian Ocean also has strong intra-annual components ranging from 50 to 80 days period. The ENSO proves to be a ubiquitous signal around the globe, and in particular, saturates the Pacific with strong influences in the equatorial region and the Southern Hemisphere Westerlies. A commonality of all basins is that the magnitude and the spatial structure of the intra-annual and inter-annual signals are similar suggesting a wide range of periods in each of the climate cycles examined.
Journal of Geophysical Research: Oceans (21699275) (Wiley-Blackwell), 2014-08 , Vol. 119 , N. 8 , P. 5563-5584

Droits : 2014. American Geophysical Union. All Rights Reserved.
http://archimer.ifremer.fr/doc/00210/32149/30606.pdf
http://archimer.ifremer.fr/doc/00210/32149/30607.pdf
DOI:10.1002/2013JC009729
http://archimer.ifremer.fr/doc/00210/32149/

Éditeur(s) : Amer Geophysical Union
Résumé : Seismic noise is an indirect source of information on ocean waves. Using a model of noise generation and propagation, seismic stations can be separated into those that are mostly sensitive to local sea states, and those that integrate sources from a large oceanic area. The model also provides a classification of noise-generating sea states into three classes. The analysis of Central California seismic noise data, well correlated with local waves, reveals that class I events dominate in summer, caused by a single wind-sea system, and for which ocean wave spectral levels are proportional to seismic spectral levels to an exponent b similar or equal to 0.9. In winter, noise is dominated by class II generation, for which coastal reflection is important, with a wave spectral density roughly proportional to the seismic spectral density to an exponent b similar or equal to 0.7. Sporadic events of class III probably produce some of the strongest noise events in Central California and need to be properly screened. These events are caused by opposed wave systems that are usually the wind-sea and a swell. This noise classification can be used to improve on the correlation between measured and estimated wave heights(up to r = 0.93 for daily averages). For other locations, where remote oceanic sources are recorded, a significant wave height estimated from the seismic noise compares well with area-averaged satellite data or wave model results(r > 0.85 for daily averages). These analyses pave the way for quantitative uses of seismic records, including the reconstruction of past wave climates, and the calibration of wave hindcasts.
Journal Of Geophysical Research-oceans (0148-0227) (Amer Geophysical Union), 2012-05 , Vol. 117 , N. C05002 , P. 19 pp.

Droits : 2012 AGU
http://archimer.ifremer.fr/doc/00083/19443/17051.pdf
DOI:10.1029/2011JC007449
http://archimer.ifremer.fr/doc/00083/19443/

Éditeur(s) : Elsevier Sci Ltd
Résumé : A multi-scale global hindcast of ocean waves is presented that covers the years 1994-2012, based on recently published parameterizations for wind sea and swell dissipation [Ardhuin, F., Rogers, E., Babanin, A., Filipot, J.-F., Magne, R., Roland, A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., Collard, F., 2010. Semi-empirical dissipation source functions for wind-wave models: Part I. Definition, calibration and validation. J. Phys. Oceanogr. 40 (9), 1917-1941]. Results from this hindcast include traditional wave parameters, like the significant wave height and mean periods, and we particularly consider the accuracy of the results for phenomenal sea states, with significant heights above 14 m. Using unbiased winds, there is no evidence of a bias in wave heights even for this very high range. Various spectral moments were also validated, including the surface Stokes drift and mean square slopes that are relevant for wave-current interactions modelling and remote sensing, and also spectra of seismic noise sources. The estimation of these parameters is made more accurate by the new wave growth and dissipation parameterizations. Associated air-sea fluxes of momentum and energy are significantly different from what is obtained with the WAM-Cycle 4 parameterization, with a roughness that is practically a function of wind speed only. That particular output of the model does not appear very realistic and will require future adjustments of the generation and dissipation parameterizations.
Ocean Modelling (1463-5003) (Elsevier Sci Ltd), 2013-10 , Vol. 70 , P. 174-188

Droits : 2012 Elsevier Ltd. All rights reserved.
http://archimer.ifremer.fr/doc/00155/26582/25477.pdf
DOI:10.1016/j.ocemod.2012.12.001
http://archimer.ifremer.fr/doc/00155/26582/

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Forecasting of sea-state characteristics, with warning time of a few hours, appears a necessity in Operational Oceanography. linking sophisticated marine monitoring systems with forecasting modeling tools. In this paper, instead of using conventional numerical models, a Takagi-Sugeno-rule-based Fuzzy Inference System (FIS) was developed aiming at forecasting wave parameters based on the wind speed and direction, and the lagged-wave characteristics. Initial and final antecedent fuzzy membership functions were identified using the subtractive clustering method. The model was applied on the wind and wave dataset recorded in years 2000-2006 by an oceanographic buoy deployed in the Aegean Sea. The model showed perfect fit for the training period (2000-2005; 12,274 data points), and expanded its hindcasting ability during 2006 (1044 data points), as the verification part of the series. Model results, for a lead time of 3 h, showed good agreement between the predicted and the observed significant wave height (RMSE=0.216) and zero-up-crossing period (RMSE=0.315). According to other model performance criteria, the fuzzy model slightly underpredicted both wave characteristics (the linear regression slope was 0.911 for wave height and 0.788 for wave period), and reduced its forecasting ability at higher prediction intervals (+6 to + 12 h). Overall, model results illustrated that the developed FIS could serve as a valuable tool for the operational prediction of wave characteristics in Northern Aegean Sea, through the utilization of the POSEIDON network.
ISSN: 0029-8018

hal-00456020
https://hal.archives-ouvertes.fr/hal-00456020
DOI : 10.1016/j.oceaneng.2009.08.016

Éditeur(s) : HAL CCSD
Résumé : International audience
In the analysis of coastal hazards, the features of extreme waves are determining information to question the impact of storms to the coast. The spatial behaviour of extreme waves is even more valuable especially since it is sparsely provided. Regarding recent applications in other contexts, a kind of statistical models called max-stable processes is relevant for modelling spatial extreme events. Max-stable processes are extensions of the well-known Generalised Extreme Value (GEV) distribution. Unlike univariate approaches, max-stable processes consider spatial dependence of a phenomenon. Such a modelling also overtakes a standard multivariate approach by providing information continuously over the area studied, even where no observation is available. Relying on such a stochastic modelling, the aim of this study is to discuss the extreme waves hazards in the Gulf of Lions, focusing on their spatial behaviour.
34th Conference on Coastal Engineering
Seoul, South Korea

lirmm-01382568
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01382568
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01382568/document
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01382568/file/7553-31212-1-PB.pdf
DOI : 10.9753/icce.v34.management.17

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : International audience
Seasonal climate forecasts mainly rely on the atmospheric sensitivity to its lower boundary conditions and on their own predictability. Besides sea surface temperature (SST), soil moisture (SM) may be an additional source of climate predictability particularly during boreal summer in the mid-latitudes. In this work, we investigate the role of SM initial conditions on near-surface climate predictability during ten boreal summer seasons using three complementary ensembles of AMIP-type simulations performed with the Arpège-Climat atmospheric general circulation model. First we have conducted an assessment of the SM predictability itself through a comparison of simple empirical SM models with Arpège-Climat. The statistical and dynamical models reveal similar SM prediction skill patterns but the Arpège-Climat reaches higher scores suggesting that it is at least suitable to explore the influence of SM initialization on atmospheric predictability. Then we evaluate the relationships between SM predictability and some near surface atmospheric predictability. While SM initialization obviously improves the predictability of land surface evaporation, it has no systematic influence on the precipitation and near surface temperature skills. Nevertheless, the summer hindcast skill is clearly improved during specific years and over certain regions (mainly north America and eastern Europe in the Arpège-Climat model), when and where the SM forcing is sufficiently widespread and strong. In this case, a significant impact is also found on the occurrence of heat waves and heavy rains, whose predictability at the seasonal timescale is a crucial challenge for years to come.
ISSN: 0930-7575

meteo-00346948
https://hal-meteofrance.archives-ouvertes.fr/meteo-00346948
DOI : 10.1007/s00382-008-0375-9