EXTREME WAVE CLIMATE CHANGE PROJECTION AT THE END OF 21STCENTURY

2011 ◽  
pp. 341-348 ◽  
Author(s):  
TOMOYA SHIMURA ◽  
NOBUHITO MORI ◽  
SOTA NAKAJO ◽  
TOMOHRO YASUDA ◽  
HAJIME MASE
Keyword(s):  
Climate Change ◽  
Wave Climate ◽  
Extreme Wave ◽  
Climate Change Projection

scholarly journals Future behavior of wind wave extremes due to climate change

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hector Lobeto ◽  
Melisa Menendez ◽  
Iñigo J. Losada
Keyword(s):  
Climate Change ◽  
Wave Climate ◽  
Climate Change Scenarios ◽  
Return Periods ◽  
Extreme Wave ◽  
Future Behavior ◽  
Climate Simulations ◽  
Wave Conditions ◽  
Ne Pacific ◽  
Change In Mean

AbstractExtreme waves will undergo changes in the future when exposed to different climate change scenarios. These changes are evaluated through the analysis of significant wave height (Hs) return values and are also compared with annual mean Hs projections. Hourly time series are analyzed through a seven-member ensemble of wave climate simulations and changes are estimated in Hs for return periods from 5 to 100 years by the end of the century under RCP4.5 and RCP8.5 scenarios. Despite the underlying uncertainty that characterizes extremes, we obtain robust changes in extreme Hs over more than approximately 25% of the ocean surface. The results obtained conclude that increases cover wider areas and are larger in magnitude than decreases for higher return periods. The Southern Ocean is the region where the most robust increase in extreme Hs is projected, showing local increases of over 2 m regardless the analyzed return period under RCP8.5 scenario. On the contrary, the tropical north Pacific shows the most robust decrease in extreme Hs, with local decreases of over 1.5 m. Relevant divergences are found in several ocean regions between the projected behavior of mean and extreme wave conditions. For example, an increase in Hs return values and a decrease in annual mean Hs is found in the SE Indian, NW Atlantic and NE Pacific. Therefore, an extrapolation of the expected change in mean wave conditions to extremes in regions presenting such divergences should be adopted with caution, since it may lead to misinterpretation when used for the design of marine structures or in the evaluation of coastal flooding and erosion.

Impacts of Climate Change on Extreme Wave Climate Along the Western Coast of Sri Lanka

2016 ◽  
Author(s):  
R. M. J. Bamunawala ◽  
S. S. L. Hettiarachchi ◽  
S. P. Samarawickrama ◽  
P. N. Wikramanayake ◽  
Roshanka Ranasinghe
Keyword(s):  
Climate Change ◽  
Sri Lanka ◽  
Wave Climate ◽  
Western Coast ◽  
Extreme Wave ◽  
Impacts Of Climate Change

scholarly journals Projection of Extreme Wave Climate Change under Global Warming

2010 ◽  
Vol 4 (0) ◽  
pp. 15-19 ◽  
Author(s):  
Nobuhito Mori ◽  
Tomohiro Yasuda ◽  
Hajime Mase ◽  
Tracey Tom ◽  
Yuichiro Oku
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Wave Climate ◽  
Extreme Wave

Statistical Analysis on the Extreme Events of Big Waves Under Wave Climate Change Around Taiwan Waters

2011 ◽  
Author(s):  
Ching-Her Hwang ◽  
Wen-Ching Lee ◽  
Wen-Fang Hsieh ◽  
Ching-Piao Tsai ◽  
Hwa Chien
Keyword(s):  
Climate Change ◽  
Wave Field ◽  
Extreme Events ◽  
Wind Field ◽  
Wave Climate ◽  
Model Parameters ◽  
Climate Change Research ◽  
Weather Bureau ◽  
Central Weather Bureau ◽  
Typhoon Wind

This study aimed to analyze the statistical characteristics of wave heights, wave energy and wave steepness, in order to investigate the wave climate changes around Taiwan Waters, especially for extreme events of big waves. The operational observation of Taiwan sea waves was initiated by the Central Weather Bureau in 1998; however, due to insufficient data length and low data space coverage, the data are unable to serve as references for long-term wave climate change research. Hence, this study adopted the SWAN (Simulation of Wave in Nearshore) Numerical Wave Hindcasting Method, which is a common method used in many studies, to hindcast the history of a wave field. The re-analysis on wind field data of the last 60 years (1948∼2008), published by the National Centers for Environmental Prediction (NCEP), was employed to make the wind field grid consistent with the hindcast wave field grid. Moreover, the Typhoon Wind Field Grid Down Scaling technique proposed by Winter & Chiou (2007) was applied to interpolate a U10 analysis field that better fits an actual typhoon wind field. The hindcast wave data were compared and validated with directional spectra, which were observed by the meteorological/oceanographic data buoys set up by the Central Weather Bureau and Water Resources Agency since 1997. Longdong, Hualien and Hsinchu Stations were chosen to represent the wave characteristics of sea areas around the island of Taiwan. According to observation data, model parameters were adjusted so that the hindcast results could be closer to observed data in Taiwan sea areas.

scholarly journals Automated classification of the atmospheric circulation patterns that drive regional wave climates

2014 ◽  
Vol 14 (8) ◽  
pp. 2145-2155 ◽  
Author(s):  
J. Pringle ◽  
D. D. Stretch ◽  
A. Bárdossy
Keyword(s):  
Atmospheric Circulation ◽  
Low Pressure ◽  
Wave Climate ◽  
Automated Classification ◽  
Coastal Vulnerability ◽  
Extreme Wave ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Spatially Distributed ◽  
Wave Heights

Abstract. Wave climates are fundamental drivers of coastal vulnerability; changing trends in wave heights, periods and directions can severely impact a coastline. In a diverse storm environment, the changes in these parameters are difficult to detect and quantify. Since wave climates are linked to atmospheric circulation patterns, an automated and objective classification scheme was developed to explore links between synoptic-scale circulation patterns and wave climate variables, specifically wave heights. The algorithm uses a set of objective functions based on wave heights to guide the classification and find atmospheric classes with strong links to wave behaviour. Spatially distributed fuzzy numbers define the classes and are used to detect locally high- and low-pressure anomalies. Classes are derived through a process of simulated annealing. The optimized classification focuses on extreme wave events. The east coast of South Africa was used as a case study. The results show that three dominant patterns drive extreme wave events. The circulation patterns exhibit some seasonality with one pattern present throughout the year. Some 50–80% of the extreme wave events are explained by these three patterns. It is evident that strong low-pressure anomalies east of the country drive a wind towards the KwaZulu-Natal coastline which results in extreme wave conditions. We conclude that the methodology can be used to link circulation patterns to wave heights within a diverse storm environment. The circulation patterns agree with qualitative observations of wave climate drivers. There are applications to the assessment of coastal vulnerability and the management of coastlines worldwide.

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