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Gennady K. Korotaev, Sergey M. Sakerin, Aleksandr M. Ignatov, Larry L. Stowe, and E. Paul McClain

,respectively); Canary--6; and Mediterranean Sea--7. Statistical analysis was performed using measurements of r~ at the wavelength 0.552 #m only. Characteristics of measurements in this channel are presented in Table 1 1. Figures 6a-g, respectively, provide frequency of occurrence information about within each of theabove-mentioned regions. The main conclusions areas follows.TABLE I 1. Statistics of r~.~ in different regions.Region I 2 3 4

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Douglas Vandemark, James B. Edson, and Bertrand Chapron

wind speed and significant wave height and their implications on buoy-altimeter comparisons. J. Geophys. Res., 93, 2285–2302. 10.1029/JC093iC03p02285 Queffeulou, P., T. Elfouhaily, V. Kerbaol, P. Le Borgne, and J. M. Lefevre, 1996: Remote sensing of wind and waves over the western Mediterranean Sea. Proc. Second ERS Pilot Project Workshop, London, United Kingdom, European Space Agency, 47–52. Smith, S. D., 1988: Coefficients for sea surface wind stress. J. Geophys. Res., 93, 15 467

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Eunjeong Lee, Yign Noh, and Naoki Hirose

-diagonal components of matrices are all zeros, and the errors of the measured and simulated SSTs can be estimated by R and Q , respectively. REFERENCES Artale V. , D’Ortenzio F. , Iudicone D. , Marulloa S. , Rupolo V. , and Santolerib R. , 1998 : Assimilation of satellite AVHRR SST in an OGCM of the Mediterranean Sea: Data processing, new parametrizations, and physical results. Earth Surface Remote Sensing II, G. Cecchi and E. Zilioli, Eds., International Society for Optical Engineering (SPIE

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Albena D. Veltcheva and C. Guedes Soares

and the Rayleigh model for the probability distributions of wave heights, he concluded that in cases where the wave height was greater than twice the significant wave height, the wave would be considered abnormal. The value of this ratio, which was later called the abnormality or amplification index (AI), is thus related to the duration of a sea state and a probability level, although there is no strong reason why 2.0 was chosen instead, for example, of 1.9, 2.1, or a similar number. In fact

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Christopher G. Piecuch, Ichiro Fukumori, Rui M. Ponte, and Ou Wang

the Mediterranean Sea . Geophys. Res. Lett. , 40 , 553 – 557 , doi: 10.1002/grl.50140 . Large, W. , and Yeager S. , 2004 : Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR Tech. Note NCAR/TN-460+STR, 112 pp. [Available online at .] Makowski, J. K. , 2013 : Understanding transport variability of the Antarctic Circumpolar Current using ocean bottom

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S. Y. Erofeeva, Laurie Padman, and Gary Egbert

are weak, for example, the western Mediterranean Sea ( Garcia-Gorriz et al. 2003 ). However, to allow for the complex patterns of current variations frequently encountered in shallow seas, higher-order polynomials or a large number of other spatial basis functions are required. A poor choice of complex basis functions can result in an ill-conditioned least squares problem, causing spurious oscillations in areas where data coverage is poor and making accurate separation of two or more tidal

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Haifeng Zhang, Helen Beggs, Alexander Ignatov, and Alexander V. Babanin

and cool skin corrections for bulk water temperature measurements for air-sea interaction studies . J. Geophys. Res. Oceans , 122 , 6470 – 6481 , . 10.1002/2017JC012688 Artale , V. , D. Iudicone , R. Santoleri , V. Rupolo , S. Marullo , and F. D’Ortenzio , 2002 : Role of surface fluxes in ocean general circulation models using satellite sea surface temperature: Validation of and sensitivity to the forcing frequency of the Mediterranean

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R. Mínguez, A. Espejo, A. Tomás, F. J. Méndez, and I. J. Losada

. This dataset contains model data and simultaneous observations and it is applied to the significant wave height dataset of the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). Cavaleri and Sclavo (2006) made use of the overall information on models, buoys, and satellite to obtain calibrated decadal time series at a large number of points, distributed at 0.5° intervals in the Mediterranean Sea. These two approaches are applied on a point-to-point basis without

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Haidong Pan, Zheng Guo, Yingying Wang, and Xianqing Lv

. Larnicol , 2016 : Sea-level variability in the Mediterranean Sea from altimetry and tide gauges . Climate Dyn. , 47 , 2851 – 2866 , . 10.1007/s00382-016-3001-2 Cheng , Y. , T. Ezer , and B. Hamlington , 2016 : Sea level acceleration in the China Seas . Water , 8 , 293 , . 10.3390/w8070293 Cheng , Y. , T. Ezer , L. P. Atkinson , and Q. Xu , 2017 : Analysis of tidal amplitude changes using the EMD

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Claudia Schmid

for the relatively large temperature differences in the North Atlantic, and that regional differences may play a role (see above). It is noted that the region where the largest temperature differences occur coincides quite well with the region where “meddies” (Mediterranean salt lenses), carrying the relatively salty and warm water originating in the Mediterranean Sea, are found (e.g., Armi and Zenk 1984 ; Käse and Zenk 1987 ; Sparrow et al. 2002 ). This gives rise to relatively large

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