Variability of extreme wave heights in the northeast Pacific Ocean based on buoy measurements

Ménendez, Melisa; Méndez, Fernando J.; Losada, Íñigo J.; Graham, Nicholas E.

doi:10.1029/2008gl035394

Cited by 117 publications

(80 citation statements)

References 18 publications

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“…. , X n represent a random sequence of independent variables with the common distribution function F (IID), then the distribution of the block maximum M n 5 max (X i ) can be expressed by Gullfaks C (1999-2009), and Draugen (1996-2009. The 3-hourly NORA10 data are validated against the maximum observed H s (20 min) within 61.5 h of NORA10, the mean observed H s over periods of 20 min and 1, 3, and 6 h, centered at the time of NORA10.…”

Section: A the Am Modelmentioning

confidence: 99%

Wave Extremes in the Northeast Atlantic

2012

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The objective of this study is to compute 100-yr return value estimates of significant wave height using a new hindcast developed by the Norwegian Meteorological Institute. This regional hindcast covers the northeast Atlantic and spans the period 1958-2009.The return value estimates are based upon three different stationary models commonly applied in extreme value statistics: the generalized extreme value (GEV) distribution, the joint GEV distribution for the r largest-order statistic (rLOS), and the generalized Pareto (GP) distribution. Here, the qualitative differences between the models and their corresponding confidence intervals are investigated.

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Section: A the Am Modelmentioning

confidence: 99%

Wave Extremes in the Northeast Atlantic

2012

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“…High-quality wind and wave observations in excess of 20 years are available for climate research. These include voluntary observations along shipping routes [Gulev et al, 2003], point measurements at buoys [e.g., Bromirski et al, 2005;Menendez et al, 2008;Gemmrich et al, 2011], and spatial patterns from altimeters [e.g., Young, 1999;Woolf et al, 2002;Chen et al, 2002;Hemer et al, 2010;Young et al, 2011]. Some of these studies supplemented the measurements with detailed wave data from numerical models to provide space-time connections of the observed processes.…”

Section: Introductionmentioning

confidence: 99%

Periodicity and patterns of ocean wind and wave climate

Stopa

Cheung

2014

JGR Oceans

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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.

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“…The extreme wave height climatology has been documented to be increasing over the last few decades Komar 2000, 2006;Méndez et al 2006;Menéndez et al 2008;Ruggiero et al 2010;Young et al 2011), and this increase has been potentially more responsible than changes in sea level alone for increasing the frequency of extreme events along the PNW coast (Ruggiero 2013). Moreover, wave heights of different magnitudes (i.e., exceedance percentiles) are increasing at different rates such that larger waves are getting bigger faster (Ruggiero et al 2010).…”

Section: Climate Change Scenario Developmentmentioning

confidence: 99%

Incorporating climate change and morphological uncertainty into coastal change hazard assessments

et al. 2014

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Documented and forecasted trends in rising sea levels and changes in storminess patterns have the potential to increase the frequency, magnitude, and spatial extent of coastal change hazards. To develop realistic adaptation strategies, coastal planners need information about coastal change hazards that recognizes the dynamic temporal and spatial scales of beach morphology, the climate controls on coastal change hazards, and the uncertainties surrounding the drivers and impacts of climate change. We present a probabilistic approach for quantifying and mapping coastal change hazards that incorporates the uncertainty associated with both climate change and morphological variability. To demonstrate the approach, coastal change hazard zones of arbitrary confidence levels are developed for the Tillamook County (State of Oregon, USA) coastline using a suite of simple models and a range of possible climate futures related to wave climate, sea-level rise projections, and the frequency of major El Niño events. Extreme total water levels are 123Nat Hazards (2015) 75:2081-2102 DOI 10.1007/s11069-014-1417 more influenced by wave height variability, whereas the magnitude of erosion is more influenced by sea-level rise scenarios. Morphological variability has a stronger influence on the width of coastal hazard zones than the uncertainty associated with the range of climate change scenarios.

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Variability of extreme wave heights in the northeast Pacific Ocean based on buoy measurements

Cited by 117 publications

References 18 publications

Wave Extremes in the Northeast Atlantic

Wave Extremes in the Northeast Atlantic

Periodicity and patterns of ocean wind and wave climate

Incorporating climate change and morphological uncertainty into coastal change hazard assessments

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