scholarly journals Projection and Analysis of Extreme Wave Climate

2006 ◽  
Vol 19 (21) ◽  
pp. 5581-5605 ◽  
Author(s):  
Sofia Caires ◽  
Val R. Swail ◽  
Xiaolan L. Wang
Keyword(s):  
Poisson Process ◽  
Sea Level ◽  
Wave Height ◽  
Significant Wave Height ◽  
Climate Model ◽  
Extreme Values ◽  
Nonhomogeneous Poisson Process ◽  
Sea Level Pressure ◽  
Significant Wave ◽  
Level Pressure

Abstract The nonhomogeneous Poisson process is used to model extreme values of the 40-yr ECMWF Re-Analysis (ERA-40) significant wave height. The parameters of the model are expressed as functions of the seasonal mean sea level pressure anomaly and seasonal squared sea level pressure gradient index. Using projections of the sea level pressure under three different forcing scenarios by the Canadian coupled climate model, projections of the parameters of the nonhomogeneous Poisson process are made, trends in these projections are determined, return-value estimates of significant wave height up to the end of the twenty-first century are projected, and their uncertainties are assessed. The uncertainty of estimates associated with the nonhomogeneous Poisson process estimates is studied and compared with the homologous estimates obtained using a nonstationary generalized extreme value model.

scholarly journals Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

2015 ◽  
Vol 12 (6) ◽  
pp. 2955-3001
Author(s):  
H. Cannaby ◽  
M. D. Palmer ◽  
T. Howard ◽  
L. Bricheno ◽  
D. Calvert ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Wave Height ◽  
21St Century ◽  
Significant Wave Height ◽  
Quantitative Assessment ◽  
Climate Model ◽  
Mean Sea Level ◽  
Significant Wave

Abstract. Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea-level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time mean sea level were evaluated using the process-based climate model data and methods presented in the IPCC AR5. Regional surge and wave solutions extending from 1980 to 2100 were generated using ~ 12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled (~ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980–2010, enabling a quantitative assessment of model skill. Simulated historical sea surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the RCP 4.5 (8.5) scenarios respectively. Trends in surge and significant wave height 2 year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ~ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.

scholarly journals Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

Ocean Science ◽  
2016 ◽  
Vol 12 (3) ◽  
pp. 613-632 ◽  
Author(s):  
Heather Cannaby ◽  
Matthew D. Palmer ◽  
Tom Howard ◽  
Lucy Bricheno ◽  
Daley Calvert ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Wave Height ◽  
21St Century ◽  
Significant Wave Height ◽  
Quantitative Assessment ◽  
Climate Model ◽  
Mean Sea Level ◽  
Significant Wave

Abstract. Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time-mean sea level were evaluated using the process-based climate model data and methods presented in the United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Regional surge and wave solutions extending from 1980 to 2100 were generated using  ∼  12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled ( ∼  12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980 to 2010, enabling a quantitative assessment of model skill. Simulated historical sea-surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data, respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. Trends in surge and significant wave height 2-year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of  ∼  84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.

scholarly journals SAMPLING BIAS IN THE ESTIMATION OF SIGNIFICANT WAVE HEIGHT EXTREME VALUES

2017 ◽  
pp. 33
Author(s):  
Fabio Dentale ◽  
Ferdinando Reale ◽  
Felice D'Alessandro ◽  
Leonardo Damiani ◽  
Angela Di Leo ◽  
...  
Keyword(s):  
Wave Height ◽  
Time Resolution ◽  
Significant Wave Height ◽  
Extreme Values ◽  
Sampling Bias ◽  
Threshold Value ◽  
Return Time ◽  
Sampling Interval ◽  
Significant Wave ◽  
Wave Models

It has been shown before, and it is intuitively evident, that in a Significant Wave Height (SWH) time series, the longer the sampling interval, the lower is the number of events which are above a given threshold value. As a consequence, the use of data with a low time resolution (such as a 3 h sampling, for instance) causes a considerable undervaluation of the extreme SWH values for a given return time RT. In this paper an example of such a bias is provided, and a method is suggested to estimate it on a regional basis. Results may help to improve the use of historical wave meters data which were often collected with a low time resolution, and may also provide a tool to improve the application of Numerical Meteo-Wave models to the evaluation of extremes.

scholarly journals Reconstruction of high resolution atmospheric fields for Northern Europe using analog-upscaling

2012 ◽  
Vol 8 (5) ◽  
pp. 1681-1703 ◽  
Author(s):  
F. Schenk ◽  
E. Zorita
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
Climate Model ◽  
Regional Climate ◽  
Low Frequency ◽  
Climate Simulation ◽  
Northern Europe ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Realistic Situation

Abstract. The analog method (AM) has found application to reconstruct gridded climate fields from the information provided by proxy data and climate model simulations. Here, we test the skill of different setups of the AM, in a controlled but realistic situation, by analysing several statistical properties of reconstructed daily high-resolution atmospheric fields for Northern Europe for a 50-yr period. In this application, station observations of sea-level pressure and air temperature are combined with atmospheric fields from a 50-yr high-resolution regional climate simulation. This reconstruction aims at providing homogeneous and physically consistent atmospheric fields with daily resolution suitable to drive high resolution ocean and ecosystem models. Different settings of the AM are evaluated in this study for the period 1958–2007 to estimate the robustness of the reconstruction and its ability to replicate high and low-frequency variability, realistic probability distributions and extremes of different meteorological variables. It is shown that the AM can realistically reconstruct variables with a strong physical link to daily sea-level pressure on both a daily and monthly scale. However, to reconstruct low-frequency decadal and longer temperature variations, additional monthly mean station temperature as predictor is required. Our results suggest that the AM is a suitable upscaling tool to predict daily fields taken from regional climate simulations based on sparse historical station data.

scholarly journals EXTREMAL PREDICTION OF SIGNIFICANT WAVE HEIGHT

1978 ◽  
Vol 1 (16) ◽  
pp. 14 ◽  
Author(s):  
Enrique Copeiro
Keyword(s):  
Distribution Function ◽  
Wave Height ◽  
Significant Wave Height ◽  
Goodness Of Fit ◽  
Theoretical Foundation ◽  
Extreme Values ◽  
Point Of View ◽  
Significant Wave ◽  
Wide Range ◽  
Empirical Point

The most generally used procedure for estimating the extremal distribution of ge_o physical variates consists in obtaining a sample of extreme values (for instance a number of annual maxima) and fitting to them a distribution function. One of the main problems - involved in this procedure is the choice of the type of distribution adequate in each case. No general agreement exists, to date, for any geophysical variate. This means a serio us trouble because of the wide range of extrapolations which can usually be obtained by - using different functions. Some of the authors who have tackled this problem have adopted a strictly empirical point of view, going as far in it as to advise to make a choice for each particular case, according to the goodness-of-fit obtained when several types of dis tribution functions are fitted to the sample. Others have instead tried to base the choices on some theoretical foundation, placing less emphasis in the goodness of the fits and generally suggesting the use of one or other of the three well known Asymptotic Extremal Distributions.

scholarly journals Study of Directional Declustering for Estimating Extreme Wave Heights in the Yellow Sea

2020 ◽  
Vol 8 (4) ◽  
pp. 236 ◽  
Author(s):  
Huijun Gao ◽  
Zhuxiao Shao ◽  
Guoxiang Wu ◽  
Ping Li
Keyword(s):  
Wave Height ◽  
Yellow Sea ◽  
Significant Wave Height ◽  
Extreme Values ◽  
The Yellow Sea ◽  
Distribution Model ◽  
Significant Wave ◽  
Window Method ◽  
Wave Heights ◽  
Significant Wave Heights

The study of extreme waves is important for the protection of coastal and ocean structures. In this work, a 22-year (1990–2011) wave hindcast in the Yellow Sea is employed to perform the assessment of extreme significant wave heights in this area. To extract the independent sample from this database, the fixed window method is used, which takes the peak significant wave height within five d. With the selected samples, directional declustering is studied to extract the homogenous sample. The results show that most of the independent samples (especially large samples) are observed in the North. In this direction, the peak over threshold (POT) method is used to extract the extreme sample from the homogenous sample, and then the generalized Pareto distribution model is used to extrapolate the extreme significant wave height. In addition to this combination, the annual maxima method with the Gumbel model is also used for estimating extreme values. The comparisons show that the return significant wave heights of the first combination are reliable, resulting from a flexible sampling window in the POT method. With this conclusion, the extreme significant wave height is extrapolated from the Yellow Sea, which can be used to protect the structure in the main directional bin.

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

2005 ◽  
Vol 18 (7) ◽  
pp. 1032-1048 ◽  
Author(s):  
S. Caires ◽  
A. Sterl
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Values ◽  
Domain Of Attraction ◽  
Altimeter Data ◽  
Time Variability ◽  
Threshold Method ◽  
Significant Wave ◽  
Ocean Wind

Abstract In this article global estimates of 100-yr return values of wind speed and significant wave height are presented. These estimates are based on the ECMWF 40-yr Re-Analysis (ERA-40) data and are linearly corrected using estimates based on buoy data. This correction is supported by global Topographic Ocean Experiment (TOPEX) altimeter data estimates. The calculation of return values is based on the peaks-over-threshold method. The large amount of data used in this study provides evidence that the distributions of significant wave height and wind speed data belong to the domain of attraction of the exponential. Further, the effect of the space and time variability of significant wave height and wind speed on the prediction of their extreme values is assessed. This is done by performing detailed global extreme value analyses using different decadal subperiods of the 45-yr-long ERA-40 dataset.

scholarly journals Global Estimates of Extreme Wind Speed and Wave Height

2011 ◽  
Vol 24 (6) ◽  
pp. 1647-1665 ◽  
Author(s):  
J. Vinoth ◽  
I. R. Young
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Return Period ◽  
Significant Wave Height ◽  
Extreme Values ◽  
Significant Wave ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Global Estimates ◽  
Buoy Measurements

Abstract A long-term dataset of satellite altimeter measurements of significant wave height and wind speed, spanning 23 years, is analyzed to determine extreme values corresponding to a 100-yr return period. The analysis considers the suitability of both the initial distribution method (IDM) and peaks-over-threshold (POT) approaches and concludes that for wave height both IDM and POT methods can yield reliable results. For the first time, the global POT results for wave height show spatial consistency, a feature afforded by the larger dataset. The analyses also show that the POT approach is sensitive to spatial resolution. Since wind speed has greater spatial and temporal variability than wave height, the POT approach yields unreliable results for wind speed as a result of undersampling of peak events. The IDM approach does, however, generate extreme wind speed values in reasonable agreement with buoy estimates. The results show that the altimeter database can estimate 100-yr return period significant wave height to within 5% of buoy measurements and the 100-yr wind speed to within 10% of buoy measurements when using the IDM approach. Owing to the long dataset and global coverage, global estimates of extreme values can be developed on a 1° × 1° grid when using the IDM and a coarser 2° × 2° for the POT approach. The high-resolution 1° × 1° grid together with the long duration of the dataset means that finescale features not previously identified using altimeter data are clearly apparent in the IDM results. Goodness-of-fit tests show that the observed data conform to a Fisher–Tippett Type 1 (FT-1) distribution. Even in regions such as the Gulf of Mexico where extreme forcing is produced by small-scale hurricanes, the altimeter results are consistent with buoy data.

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