Abstract:Estimation of the long‐term behavior of wave climate is crucial for harnessing wave energy in a cost‐effective way. Previous studies have linked wave heights to the north‐south atmospheric pressure anomalies in the North Atlantic, suggesting that the wave climate fluctuates as a response to changes in zonal circulation in the atmosphere. We identify changes in wave power in the North‐East Atlantic that are strongly correlated to the dominant pressure anomalies, the North Atlantic Oscillation (NAO), and other m… Show more
“…For the extreme wave height analysis, H s and T p are used. Previous analysis of mean wave power variability using the same data had investigated the comparisons of hindcast/buoys in terms of the annual mean values at Haltenbanken and Forties, and in general, the agreement is reasonable [ 12 ]. Figure 1.…”
Section: Methodsmentioning
confidence: 91%
“…Hence, a link between pressure anomalies and extreme wave climate is likely. The NAO and other modes are found to correlate well with mean wave climate [ 12 ], so we extend this approach to extreme waves. The temporal pattern of each mode is characterized by a climate index, values of which are obtainable from National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC; ), and are based on the rotated empirical orthogonal function analysis by Barnston & Livezey [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…This study is a continuation of previous work by Taylor and co-workers [ 14 , 15 ] using earlier hindcast wave models, and Santo et al . [ 12 ], who looked at annual mean wave power variability using the same hindcast wave model.…”
Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958–2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.
“…For the extreme wave height analysis, H s and T p are used. Previous analysis of mean wave power variability using the same data had investigated the comparisons of hindcast/buoys in terms of the annual mean values at Haltenbanken and Forties, and in general, the agreement is reasonable [ 12 ]. Figure 1.…”
Section: Methodsmentioning
confidence: 91%
“…Hence, a link between pressure anomalies and extreme wave climate is likely. The NAO and other modes are found to correlate well with mean wave climate [ 12 ], so we extend this approach to extreme waves. The temporal pattern of each mode is characterized by a climate index, values of which are obtainable from National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC; ), and are based on the rotated empirical orthogonal function analysis by Barnston & Livezey [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…This study is a continuation of previous work by Taylor and co-workers [ 14 , 15 ] using earlier hindcast wave models, and Santo et al . [ 12 ], who looked at annual mean wave power variability using the same hindcast wave model.…”
Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958–2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.
“…However, this very high variability could be explained by more global phenomena like the North Atlantic Oscillation (NAO). Good correlations were obtained between wave energy resource and climate indices [20]. Understanding and better quantifying the link between wave energy resource and the pressure anomalies, together with improving the ability of predicting accurately those pressure anomalies, would benefit the ability to predict future wave energy resources.…”
Wave resource assessment is the first step toward the installation of a wave energy converter (WEC). To support initiatives for wave energy development in the southwest of France, a coastal wave database is built from a 44-year hindcast simulation with the spectral wave model SWAN (Simulating WAve Nearshore) run on a high-resolution unstructured grid. The simulation includes shallow-water processes such as refraction, shoaling, and breaking. The model is validated against a five-year coastal wave buoy recording. The study shows that most of the resource is provided by sea states with wave heights ranging from 2 to 5 m, with wave periods from 10 and 15 s, and coming from a very narrow angular sector. The long hindcast duration and the refined unstructured grid used for the simulation allow assessment of the spatiotemporal distribution of wave energy across the coastal area. On the one hand, large longshore variations of the resource caused by steep bathymetric gradients such as the Capbreton submarine canyon are underlined. On the other hand, the study highlights that no specific long-term trend can be extracted regarding the coastal wave energy resource evolution. The provided downscaled local wave resource information may be used to optimize the location and design of a future WEC that could be deployed in the region.
“…The use of a relation including peak spectral wave period instead of the actual wave energy period should not impact significantly the results of a spatio-temporal analysis of wave energy. Regarding the computation of WEF, in Santo et al [42] and Varlas et al [43], the T p was used instead of T e , while in Sasaki [44], the significant wave period (T s ) was employed. In Goddijn-Murphy et al [45], the zero-crossing period (T z ) was used to estimate the energy period (T e = 1.18T z , adapted by [41]).…”
The necessity to reduce C O 2 emissions in combination with the rising energy demand worldwide makes the extensive use of renewable energy sources increasingly important. To that end, countries with long coastlines, such as Norway, can exploit ocean wave energy to produce large amounts of power. In order to facilitate these efforts as well as to provide quantitative data on the wave energy potential of a specific area, it is essential to analyze the weather and climatic conditions detecting any variabilities. The complex physical processes and the atmosphere-wave synergetic effects make the investigation of temporal variability of wave energy a challenging issue. This work aims to shed new light on potential wave energy mapping, presenting a spatio-temporal assessment of swell- and wind-sea-induced energy flux in the Nordic Seas with a focus on the Norwegian coastline using the NORA10 hindcast for the period 1958–2017 (59 years). The results indicate high spatial and seasonal variability of the wave energy flux along the coast. The maximum wave energy flux is observed during winter, while the minimum is observed during summer. The highest coastal wave energy flux is observed in the Norwegian Sea. The majority of areas with dominant swell conditions (i.e., in the Norwegian Sea) are characterized by the highest coastal wave energy flux. The maximum values of wave energy flux in the North Sea are denoted in its northern parts in the intersection with the Norwegian Sea. In contrast to the Norwegian Sea, areas located in the North Sea and the Barents Sea show that wind sea is contributing more than swell to the total wave energy flux.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
