100-Year Return Value Estimates for Ocean Wind Speed and Significant Wave Height from the ERA-40 Data

Caires, S.; Sterl, Andreas

doi:10.1175/jcli-3312.1

Cited by 243 publications

(200 citation statements)

References 27 publications

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“…The ERA-40 wave data consist of 6-hourly global fields with a 1.58 3 1.58 grid resolution. Sterl and Caires (2005) produced a statistically corrected dataset of the ERA-40 significant wave height H s (CERA-40). The PC20 H s global fields are compared to the CERA-40.…”

Section: Validation Data: Reanalyses and Buoy Datamentioning

confidence: 99%

“…Analyses of recent and ongoing changes rely mostly on observations from voluntary observing ships (VOS) with known biases (e.g., Gulev et al 2003;Gulev and Grigorieva 2004) or on global wave datasets produced within wave reanalysis (e.g., Caires et al 2004;Sterl and Caires 2005;Caires and Sterl 2005;Caires et al 2005;Semedo et al 2011;Chawla et al 2011Chawla et al , 2013. Satellite altimetry combined with wave model hindcasts (e.g., Young 1999;Chen et al 2002) or only remote sensing data (Young et al 2011) have also been used for present-day global wave climate studies.…”

Section: Introductionmentioning

confidence: 99%

“…The twentiethcentury period will be referred to as the control run. The wave fields from the control run are compared to the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al 2005;Sterl and Caires 2005) and Interim ECMWF Re-Analysis (ERA-Interim; Dee et al 2011) wave reanalyses and validated against long-term quality controlled in situ buoy wave height observations. The dynamically based global wave climate projections in this study are part of the COWCLIP ensemble effort (Hemer et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Projection of Global Wave Climate Change toward the End of the Twenty-First Century

et al. 2012

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Wind-generated waves at the sea surface are of outstanding importance for both their practical relevance in many aspects, such as coastal erosion, protection, or safety of navigation, and for their scientific relevance in modifying fluxes at the air-sea interface. So far, long-term changes in ocean wave climate have been studied mostly from a regional perspective with global dynamical studies emerging only recently. Here a global wave climate study is presented, in which a global wave model [Wave Ocean Model (WAM)] is driven by atmospheric forcing from a global climate model (ECHAM5) for present-day and potential future climate conditions represented by the Intergovernmental Panel for Climate Change (IPCC) A1B emission scenario. It is found that changes in mean and extreme wave climate toward the end of the twenty-first century are small to moderate, with the largest signals being a poleward shift in the annual mean and extreme significant wave heights in the midlatitudes of both hemispheres, more pronounced in the Southern Hemisphere and most likely associated with a corresponding shift in midlatitude storm tracks. These changes are broadly consistent with results from the few studies available so far. The projected changes in the mean wave periods, associated with the changes in the wave climate in the middle to high latitudes, are also shown, revealing a moderate increase in the equatorial eastern side of the ocean basins. This study presents a step forward toward a larger ensemble of global wave climate projections required to better assess robustness and uncertainty of potential future wave climate change.

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Section: Validation Data: Reanalyses and Buoy Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Projection of Global Wave Climate Change toward the End of the Twenty-First Century

et al. 2012

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“…The rationale, under these selections, demands the run length to be large enough to cover the time period required for the phenomenon to be fully developed. In previous offshore studies [14,15,24,[26][27][28][29][30], these values were found to vary from 24 to 60h.…”

Section: Repeat Steps 2-4 For All the Available (Annual) Time Seriesmentioning

confidence: 99%

The Effect of Declustering in the r-Largest Maxima Model for the Estimation of HS-Design Values

Soukissian¹

2011

TOOEJ

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A major problem often encountered in design sea-state prediction is the limited amount of available extremetype wave data. The Annual Maxima model is consistent with the conditions of the mathematical background of Extreme Value Theory, yet its application raises statistical uncertainties in cases where the initial data population is limited. Due to this, alternative models of similar theoretical background have been developed to describe extreme values, including the "r-largest maxima method". A main problem in applying this model refers to the appropriate selection of a sample comprising the r independent maxima within each year of the available time series: since in nature environmental extremes tend to appear in clusters, the native time series under examination should be appropriately "de-clustered" to satisfy the independency assumption. Some established declustering procedures refer to: a) the selection of a "Standard Storm Length", b) the combination of a run length k and a relatively high threshold value u (Runs declustering), c) the estimation of wave energy reductions between consecutive sea-state systems (DeClustering Algorithm) and d) the selection of the three largest monthly maxima of each year of the initial significant wave height time series (triple annual maximum series). The aim of this paper is to assess the effect of the aforementioned declustering procedures on the numerical results obtained by the r-largest model.

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“…Recent applications of the POT approach to analyse the extremes of ocean waves are presented in e.g. Caires and Sterl (2005) and Thevasiyani and Perera (2014).…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in extreme value modelling of wave data in a climate change perspective

Vanem

2015

J. Ocean Eng. Mar. Energy

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The extreme values of wave climate data are of great interest in a number of different applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. Typically, the return values of certain met-ocean parameters such as significant wave height are of particular importance. In a climate change perspective, projections of such return values to a future climate are of great importance for risk management and adaptation purposes. However, there are various ways of estimating the required return values, which introduce additional uncertainties in extreme weather and climate variables pertaining to both current and future climates. Many of these approaches are investigated in this paper by applying different methods to particular data sets of significant wave height, corresponding to the historic climate and two future projections of the climate assuming different forcing scenarios. In this way, the uncertainty due to the extreme value analysis can also be compared to the uncertainty due to a changing climate. The different approaches that are considered in this paper are the initial distribution approach, the block maxima approach, the peak over threshold approach and the average conditional exceedance rate method. Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained. This exercise reveals that the uncertainty due to the extreme value analysis method is notable and, as expected, the variability of the estimates increases for higher return periods. Moreover, even though the variability due to the extreme value analysis is greater than the climate variability, a shift towards higher extremes in a future wave climate

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100-Year Return Value Estimates for Ocean Wind Speed and Significant Wave Height from the ERA-40 Data

Cited by 243 publications

References 27 publications

Projection of Global Wave Climate Change toward the End of the Twenty-First Century

Projection of Global Wave Climate Change toward the End of the Twenty-First Century

The Effect of Declustering in the r-Largest Maxima Model for the Estimation of HS-Design Values

Uncertainties in extreme value modelling of wave data in a climate change perspective

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