Seasonal and interannual changes of significant wave height in shelf seas around India during 1998–2012 based on wave hindcast

Kumar, V. Sanil; Joseph, Jossia; Amrutha, M. M.; Jena, Basanta Kumar; Sivakholundu, K. M.; Dubhashi, K. K.

doi:10.1016/j.oceaneng.2018.01.022

Cited by 20 publications

(8 citation statements)

References 28 publications

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“…Anoop et al () analyzed the climatology and variability of the wave climate in the north IO with the European Centre for Medium‐Range Weather Forecast (ECMWF) global atmospheric re‐analysis data (ERA‐Interim) for the period 1979–2012. Recently, seasonal and interannual variations in significant wave height (SWH) were examined in shelf seas around India by using wave hindcast data obtained from a numerical model over a 15‐year period (1998–2012; Kumar et al, ). These studies are conducted based on observational data to analyze the trends of wind and wave climate in the IO from recent decades (15 to 20 years).…”

Section: Introductionmentioning

confidence: 99%

Influence of Natural Climate Variability on the Extreme Ocean Surface Wave Heights Over the Indian Ocean

et al. 2019

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Extreme ocean waves are part of the climate system but responsible for significant impacts on coastal and offshore environments, structures, and populations. In the Indian Ocean (IO), the wind and wave climate can be significantly influenced by natural climate variability, such as the El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM), yet our understanding on their regional influence is limited, particularly for seasonal extremes. Here, seasonal extreme significant wave heights (SWHs) and winds in the IO are examined over the period 1957–2010 utilizing ERA‐20C reanalysis data and the nonstationary generalized extreme value distribution to understand climatic extremes, by considering climate indices as covariates. ENSO influence on extreme SWHs include increases in the Bay of Bengal, southwest tropical IO (TIO), southern IO (SIO; a broad extension south of Australia), and South China and Philippine (SCP) Seas, and decreases in the Arabian Sea in boreal summer during El Niño. Extreme SWH responses to the IOD include increases in the eastern TIO, southwest TIO, and SIO in boreal autumn during its positive phase. Lastly, Southern Annular Mode not only significantly affects the SIO year round but has a weak influence in the northern and tropical IO. Composite analysis of ENSO and IOD events further highlight in phase combinations display less significant influence than out of phase combinations during summer, but not autumn. Mean and extreme wind responses are consistent with SWH responses to natural climate variability, and climate mode teleconnection patterns help explain the seasonal variations.

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Section: Introductionmentioning

confidence: 99%

Influence of Natural Climate Variability on the Extreme Ocean Surface Wave Heights Over the Indian Ocean

et al. 2019

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“…CC BY 4.0 License. major extreme weather in the SCS, which always drives the storm wave (Anoop et al, 2015;Hithin et al, 2015; Sanil Ojeda et al, 2017;Wang et al, 2017;Mortlock et al, 2018;Sanil Kumar et al, 2018). In addition, the number of tropical cyclones is counted in the SCS, which shows that the tropical cyclone wave is feasible for studying extreme significant wave heights.…”

Section: Introductionmentioning

confidence: 99%

Study of the threshold for the POT method based on hindcasted significant wave heights of tropical cyclone waves in the South China Sea

Shao

Liang

et al. 2019

Preprint

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Abstract. An assessment of extreme significant wave heights is performed in the South China Sea (SCS), which is crucial for the coastal and offshore engineering in this area. Two significant factors influencing the assessment are the initial database and the assessing method. The initial database is a basic for assessment, and the assessing method is used to extrapolate appropriate return significant wave heights based on this database during a period. In this study, a 40-year (1975–2014) hindcasted significant wave height of tropical cyclone waves is adopted as the initial database. Based on this database, the peak significant wave height of every tropical cyclone wave is directly extracted as the initial sample; the independent and identically distributed assumption is satisfied; and the interference for the selection of the sample is avoided. The peak over threshold (POT) method with the generalized Pareto distribution (GPD) model is employed to extract the sufficiently large and high sample for model estimation. The peak excesses over a sufficiently high value (i.e., threshold) are fitted; thus, the return significant wave heights are highly dependent on the threshold. To determine the unique threshold for the POT method, characteristics of tropical cyclone waves are researched. The research results reveal that the separation value shown in the distribution of the initial sample is suitable for sampling in the SCS. Because the separation value is within the stable threshold range and the asymptotic tail approximation and estimation uncertainty are reasonable, the selected threshold is suitable and the corresponding return significant wave height is reliable.

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“…The global energy demand has been predicted to rise by 30% by 2040, which is the equivalent to adding another China and India into the present worldwide demand [2]. At the same time, the pressure from climate change and fossil fuel consumption has also reignited interest and excitement in marine renewable resources around the world [3,4], as the ocean has always been considered one of the main sources of renewable energy on our planet [5][6][7]. For example, Cape Verde Islands have been relying on oil imports for the past few decades, leading to high power costs and greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

10-Year Wind and Wave Energy Assessment in the North Indian Ocean

et al. 2019

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With increasing energy shortages and global warming, clean and renewable energy sources, such as wind and wave energy, have gained widespread attention. In this study, the third-generation wave model WAVEWATCH-III (WW3) is used to simulate wave height in the North Indian Ocean (NIO), from 2008 to 2017, using the wind data from the European Centre for Medium-Range Weather Forecasts Renalysis datasets. The simulated results show good correlation with data obtained from altimetry. Analysis of wind and wave energy resources in the NIO is carried out considering energy density, the exploitable energy, the energy density stability, and monthly and seasonal variability indices. The results show that most areas of the NIO have abundant wind energy and at the Somali Waters are rich in wave energy resources, with wind energy densities above 200 W/m2 and wave energy densities above 15 KW/m. The most energy-rich areas are the Somali Waters, the Arabian Sea, and the southern part of the NIO (wind energy density 350–650 W/m2, wave energy density 9–24 KW/m), followed by the Laccadive sea (wind energy density 150–350 W/m2, wave energy density 6–9 KW/m), while the central part of the NIO is relatively poor (wind energy density less than 150 W/m2, wave energy density below 6 KW/m).

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Seasonal and interannual changes of significant wave height in shelf seas around India during 1998–2012 based on wave hindcast

Cited by 20 publications

References 28 publications

Influence of Natural Climate Variability on the Extreme Ocean Surface Wave Heights Over the Indian Ocean

Influence of Natural Climate Variability on the Extreme Ocean Surface Wave Heights Over the Indian Ocean

Study of the threshold for the POT method based on hindcasted significant wave heights of tropical cyclone waves in the South China Sea

10-Year Wind and Wave Energy Assessment in the North Indian Ocean

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