Wave Energy Flux Variability in the Caribbean Sea

Wave energy flux (WEF) is assessed in the Caribbean Sea from a 60-year (1958--2017) wave hindcast. We use a novel approach, based on neural networks, to identify coherent regions of similar WEF and their association with different climate patterns. This method allows for a better evaluation of the underlying dynamics behind seasonal and inter-annual WEF variability, including the effect induced by the latitudinal migration of the Intertropical Convergence Zone (ITCZ), and the influence of El Ni\~no-Southern Oscillation events. Results show clear regional differences of the WEF variability likely due to both a clear regionalization of the WEF due to both the intensification and migration of the ITCZ. WEF exhibits a strong semiseasonal signal in areas of the continental shelf, with maximums in January and June, in agreement with the sea surface temperature and sea level pressure variability. At larger scales, WEF shows a significant correlation with the Oceanic Ni\~no Index depicting positive values in the central and western basin and negative ones at the eastern side.

Wind Waves in the Mediterranean Sea: An ERA5 Reanalysis Wind-Based Climatology

A climatology of the wind waves in the Mediterranean Sea is presented. The climate patterns, their spatio-temporal variability and change are based on a 40-year (1980–2019) wave hindcast, obtained by combining the ERA5 reanalysis wind forcing with the state-of-the-art WAVEWATCH III spectral wave model and verified against satellite altimetry. Results are presented for the typical (50th percentile) and extreme (99th percentile) significant wave height and, for the first time at the regional Mediterranean Sea scale, for the typical and extreme expected maximum individual wave height of sea states. The climate variability of wind waves is evaluated at seasonal scale by proposing and adopting a definition of seasons for the Mediterranean Sea states that is based on the satellite altimetry wave observations of stormy (winter) and calm (summer) months. The results, initially presented for the four seasons and then for winter and summer only, show the regions of the basin where largest waves occur and those with the largest temporal variability. A possible relationship with the atmospheric parameter anomalies and with teleconnection patterns (through climate indices) that motivates such variability is investigated, with results suggesting that the Scandinavian index variability is the most correlated to the Mediterranean Sea wind-wave variability, especially for typical winter sea states. Finally, a trend analysis shows that the Mediterranean Sea typical and extreme significant and maximum individual wave heights are decreasing during summer and increasing during winter.

Wave Energy Resource Harnessing Assessment in a Subtropical Coastal Region of the Pacific

Most wave energy converters (WECs) are designed to operate in high-latitude energetic seas, limiting their performance in regions usually dominated by milder conditions. The present study assesses the performance of complete test-stage WECs in farms that satisfy a decentralized energy scheme (DES) on the coast of Baja California, which is considered one of the most energetic regions along the Mexican Pacific. A high-resolution 11-year nearshore wave hindcast was performed and validated with Acoustic Doppler Current Profilers (ADCPs) data to characterize the wave energy resource in the study area. Two hotspots were identified from the wave power climatology. In these sites, the extractive capacities of seven well-known WEC technologies were determined based on their power matrices. Finally, the power extracted by small WEC farms, with the minimum number of devices required to satisfy a DES, was estimated. The studied region has moderate wave power availability with marked seasonality and low inter-annual variability. Out of all the evaluated devices, WaveDragon extracts the highest wave power; however, Pelamis has the best performance, with maximum monthly mean capacity factors up to 40%. Coupling WEC farms with storage modules or hybrid renewable systems are recommended to satisfy a continuous DES during the less energetic summer months.

How deep ocean-land coupling controls the generation of secondary microseism Love waves

Wind driven ocean wave-wave interactions produce continuous Earth vibrations at the seafloor called secondary microseisms. While the origin of associated Rayleigh waves is well understood, there is currently no quantified explanation for the existence of Love waves in the most energetic region of the microseism spectrum (3–10 s). Here, using terrestrial seismic arrays and 3D synthetic acoustic-elastic simulations combined with ocean wave hindcast data, we demonstrate that, observed from land, our general understanding of Rayleigh and Love wave microseism sources is significantly impacted by 3D propagation path effects. We show that while Rayleigh to Love wave conversions occur along the microseism path, Love waves predominantly originate from steep subsurface geological interfaces and bathymetry, directly below the ocean source that couples to the solid Earth. We conclude that, in contrast to Rayleigh waves, microseism Love waves observed on land do not directly relate to the ocean wave climate but are significantly modulated by continental margin morphologies, with a first order effect from sedimentary basins. Hence, they yield rich spatio-temporal information about ocean-land coupling in deep water.

Sea state trends and variability: consistency between the ESA Sea State Climate Change Inititative dataset, ERA5 winds and microseisms

<p>Abstract: Wave hindcasts of long time series ( > 30 years) have been instrumental in understanding the wave climate. However, it is still difficult to have a consistent reanalysis suitable for study of trends and interannual variability. Here we explore the consistency of wave hindcast with independent observations from moored buoys, satellite altimeters, and  microseism data. We use the ECMWF 5th generation re-analysis (ERA5) winds to drive two wave models, using either ECMWF WAM (Bidlot et al. 2019) or WAVEWATCH III (The WAVEWATCH III Develoment Group 2019, Alday et al. 2020). We also use seismic data in the dominant double-frequency band, around 5 s period, that are generated by opposing waves of equal frequencies and compare these to modeled microseims. We find that the inter-platform corrections in the ESA CCI Version 1.1 dataset (Dodet et al. 2020) introduced a trend that differs from the microseism trends. However, the results converge when using a revised correction of this dataset. We also look at the microseism spectral signature of large storms in the North Atlantic and discuss how we may compare the severity of different storms that move over different ocean bathymetry with different wave to microseism conversions. </p><p>References: Stopa, J. E., Ardhuin, F., Stutzmann, E., & Lecocq, T. (2019).  Sea state trends and variability: Consistency between models, altimeters, buoys, and seismic data (1979–2016). Journal of Geophysical Research: Oceans, 124. https://doi.org/10.1029/2018JC014607 </p><p>Dodet, G., Piolle, J.-F., Quilfen, Y., Abdalla, S., Accensi, M., Ardhuin, F., Ash, E., Bidlot, J.-R., Gommenginger, C., Marechal, G., Passaro, M., Quartly, G., Stopa, J., Timmermans, B., Young, I., Cipollini, P., and Donlon, C.: The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations, Earth Syst. Sci. Data, 12, 1929–1951, https://doi.org/10.5194/essd-12-1929-2020 , 2020.</p>