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Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty.

The observation-based source terms available in the third-generation wave model WAVEWATCH III (i.e., the ST6 package for parameterizations of wind input, wave breaking, and swell dissipation terms) are recalibrated and verified against a series of academic and realistic simulations, including the fetch/duration-limited test, a Lake Michigan hindcast, and a 1-yr global hindcast. The updated ST6 not only performs well in predicting commonly used bulk wave parameters (e.g., significant wave height and wave period) but also yields a clearly improved estimation of high-frequency energy level (in terms of saturation spectrum and mean square slope). In the duration-limited test, we investigate the modeled wave spectrum in a detailed way by introducing spectral metrics for the tail and the peak of the omnidirectional wave spectrum and for the directionality of the two-dimensional frequency–direction spectrum. The omnidirectional frequency spectrum E ( f ) from the recalibrated ST6 shows a clear transition behavior from a power law of approximately f −4 to a power law of about f −5 , comparable to previous field studies. Different solvers for nonlinear wave interactions are applied with ST6, including the Discrete Interaction Approximation (DIA), the more expensive Generalized Multiple DIA (GMD), and the very expensive exact solutions [using the Webb–Resio–Tracy method (WRT)]. The GMD-simulated E ( f ) is in excellent agreement with that from WRT. Nonetheless, we find the peak of E ( f ) modeled by the GMD and WRT appears too narrow. It is also shown that in the 1-yr global hindcast, the DIA-based model overestimates the low-frequency wave energy (wave period T > 16 s) by 90%. Such model errors are reduced significantly by the GMD to ~20%.

The present study examines the impact of the Pacific-Japan (PJ) pattern on tropical Indo–Western Pacific Ocean significant wave heights during the boreal summer season (June through August, JJA) for the first time. The PJ pattern is a dominant teleconnection pattern characterized by meridional propagating Rossby waves over the Western North Pacific (WNP) and East Asia. The strong southwesterly monsoon winds prevail over the North Indian Ocean (NIO) during JJA inducing strong wave heights in the mean state. The regression analysis of significant wave height anomalies on the PJ index exhibits strong negative wave height anomalies over the Bay of Bengal (BoB), the tropical WNP region and slightly weaker negative anomalies over the Arabian Sea (AS) due to a reduction in the wind-wave growth. The weakening of wave heights in the BoB and WNP regions during PJ is attributed to the anomalous low-level anticyclonic circulation accompanied by high sea-level pressure anomalies over the BoB and WNP regions. The anomalous anticyclonic circulation opposes the mean south-westerlies and reduces the wave heights over the NIO and WNP. Further, the composite analysis of positive and negative PJ patterns display significant asymmetries in their signature on the wind and wave parameters. Thus, our findings suggest that the WNP region’s climate conditions strongly modulate the NIO’s surface waves.

Freak or rogue waves are so called because of their unexpectedly large size relative to the population of smaller waves in which they occur. The 25.6 m high Draupner wave, observed in a sea state with a significant wave height of 12 m, was one of the first confirmed field measurements of a freak wave. The physical mechanisms that give rise to freak waves such as the Draupner wave are still contentious. Through physical experiments carried out in a circular wave tank, we attempt to recreate the freak wave measured at the Draupner platform and gain an understanding of the directional conditions capable of supporting such a large and steep wave. Herein, we recreate the full scaled crest amplitude and profile of the Draupner wave, including bound set-up. We find that the onset and type of wave breaking play a significant role and differ significantly for crossing and non-crossing waves. Crucially, breaking becomes less crest-amplitude limiting for sufficiently large crossing angles and involves the formation of near-vertical jets. In our experiments, we were only able to reproduce the scaled crest and total wave height of the wave measured at the Draupner platform for conditions where two wave systems cross at a large angle.

As an emerging remote sensing technology, GNSS reflectometry (GNSS-R) has been widely investigated for retrieving ocean parameters including ocean significant wave height (SWH). Ocean SWH consists of contribution from swell and wind waves, which are commonly modeled separately in the field of marine science and engineering to facilitate practical application. In this study, we present a deep convolutional neural network (DCNN) model for retrieving swell and wind wave SWHs. The DCNN model makes use of auxiliary data and effective DDM features extracted in the convolution layer, and it is trained by using the ERA5 data and CYGNSS observations. The proposed DCNN model and seven existing models [i.e., random forest, extremely randomized trees, bagging tree (BT), decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network] were extensively tested using the ERA5 and WaveWatch III (WW3) data. The results show that when ERA5 is used as reference data, the proposed DCNN model performs best among the eight models, with the root mean square errors (RMSEs) of retrieving swell and wind wave SWH being better than 0.394 m and 0.397 m, respectively, and the correlation coefficient ( R ) being 0.90. Compared with the SVM model, RMSEs are improved by 28.82% and 31.92%, respectively. When WW3 is employed as reference, the RMSEs of retrieving swell and wind wave SWH are better than 0.497 m and 0.502 m, respectively, with R of 0.89 and 0.90. Compared with the BT model, RMSEs are improved by 26.74% and 27.41%, respectively. The research also found that the auxiliary variables are important for swell and wind wave SWH retrieval. Furthermore, the retrieval of SWH for swells and wind waves using spaceborne GNSS-R technology is affected by rainfall, resulting in about 6% increase in RMSE. This method provides a new idea for studying global ocean swell and wind waves using CYGNSS data.

This study investigates the contribution of external forcings on global and regional ocean wave height change during 1961–2020. Historical significant wave height (Hs) produced for different CMIP6 external forcings and preindustrial control conditions following the Detection and Attribution Model Intercomparison Project (DAMIP) are employed. The internal variability ranges are compared with different external forcing scenario. Statistically significant linear trends in Hs computed over regional ocean basins are found to be mostly associated with anthropogenic forcings: greenhouse gas‐only (GHG) and aerosol‐only (AER) forcing. For Hs, GHG signals are robustly detected and dominant for most of the global ocean, except over North pacific and South Atlantic, where AER signals are dominant. These results are supported by multi‐model analysis for wind speed. The remarkable increase in Hs over the Arctic (22.3%) and Southern (8.2%) Ocean can be attributed to GHG induced sea‐ice depletion and larger effective fetch along with wind speed increase. Plain Language Summary We quantify the influence of anthropogenic forcings (greenhouse gas‐only and aerosol‐only forcing) and natural forcing to the significant wave height trends during 1961–2020 using CMIP6 individual forcing experiments. It is shown that anthropogenic influence is majorly responsible for the significant wave height changes and natural (solar and volcanic activities) forcings show limited influence. The human‐induced greenhouse gas increases are found to be the dominating factor for most of the global ocean, whereas anthropogenic aerosols are the dominating forcing for a few ocean basins, such as North Pacific and South Atlantic. The multimodel analysis for wind speed corroborates the relative dominance of signals in wave height change. In the polar ocean (Arctic and Southern Ocean), we see exceptional wave height increase compared to other regions. Sea‐ice decline associated with greenhouse gas forcing provides larger fetch for the waves to grow in polar region. Moreover, the contrasting influence of greenhouse gas and aerosol forcing to sea‐ice area and wind speed changes are shown to drive the total wave height changes. Key Points CMIP6/DAMIP simulations show that anthropogenic signals are robustly detected for the significant wave height (Hs) trends during 1961–2020 Greenhouse gases are the major contributor for Hs trends over the global ocean, but aerosols dominance is seen for a few regional basins High increase in Hs over the Polar oceans is due to greenhouse gas induced sea‐ice decline, fetch enlargement and wind speed increase

A set of realistic coastal simulations in California allows for the exploration of surface gravity wave effects on currents (WEC) in an active submesoscale current regime. We use a new method that takes into account the full surface gravity wave spectrum and produces larger Stokes drift than the monochromatic peak-wave approximation. We investigate two high-wave events lasting several days—one from a remotely generated swell and another associated with local wind-generated waves—and perform a systematic comparison between solutions with and without WEC at two submesoscale-resolving horizontal grid resolutions ( dx = 270 and 100 m). WEC results in the enhancement of open-ocean surface density and velocity gradients when the averaged significant wave height H s is relatively large (>4.2 m). For smaller waves, WEC is a minor effect overall. For the remote swell (strong waves and weak winds), WEC maintains submesoscale structures and accentuates the cyclonic vorticity and horizontal convergence skewness of submesoscale fronts and filaments. The vertical enstrophy ζ 2 budget in cyclonic regions ( ζ / f > 2) reveals enhanced vertical shear and enstrophy production via vortex tilting and stretching. Wind-forced waves also enhance surface gradients, up to the point where they generate a small-submesoscale roll-cell pattern with high vorticity and divergence that extends vertically through the entire mixed layer. The emergence of these roll cells results in a buoyancy gradient sink near the surface that causes a modest reduction in the typically large submesoscale density gradients.

The Black Sea coasts from the northwest of Turkey through Crimea to Georgia were strongly affected by severe storms in Autumn, 2023. The aim of the work is to compare the performance of different wave model approaches and wind datasets in extreme weather conditions in the Black Sea. The study covers the continuous period from the 1st to the 30th of November including two strong storms with wave heights up to 9–10 m. Wave simulations are performed using WAM and the 2D parametric model for surface wave development suggested in Kudryavtsev et al. (2021a). The wave models are forced by hourly wind fields from four datasets: ECMWF Reanalysis (ERA5), ECMWF Level-4 bias-corrected operational model, NCEP (CFSv2), and the regional WRF-ARW model with 6-hour NCEP/NCAR atmospheric forecast as input. The high-resolution Level-4 wave analysis for the Black Sea produced by CMEMS (also using WAM Cycle 6) is also considered. Simulation results are validated against along-track altimeter measurements of significant wave height, CFOSAT SWIM information on dominant wavelength and wave direction, and in-situ data from an oceanographic platform near Crimea. All models demonstrate their overall good performance, though third-generation wave spectral models give an expectedly higher correlation between simulations and observed data, while the parametric model is less accurate. Some recommendations to combine wind and wave models for the most accurate predictions are further given. As known, the wind speed fields produced by ECMWF are underestimated at winds higher than 15–20 m/s. While the wind correction is crucial when using the parametric model, WAM better reproduces the observed extreme waves without it. As also obtained, WAM simulations forced by NCEP and WRF winds lead to an overestimation of the largest storm waves. Increased resolution of the wind fields does not lead to significant improvement in the quality of wave predictions, which can be explained by the wind accumulation effect during wave development.

The Spotter is a low-cost, real-time, solar-powered wave measurement buoy that was recently developed by Spoondrift Technologies, Inc. (Spoondrift). To evaluate the data quality of the Spotter device, we performed a series of validation experiments that included comparisons between Spotter-derived motions and prescribed wave motions (monochromatic and random waves) on a custom-built, motion-controlled validation stand and simultaneous in-water measurements using a conventional wave measurement buoy, the Datawell DWR-G4 (Datawell). Spotter evaluations included time-domain validation (i.e., wave by wave) and comparisons of wave spectra, directional moments, and bulk statistical parameters such as significant wave height, peak period, mean wave direction, and directional spread. Spotter wave measurements show excellent fidelity and lend a high degree of confidence in data quality. Overall, Spotter-derived bulk statistical parameters were within 10% of respective Datawell-derived quantities. The Spotter’s low cost and compact form factor enabled unique field deployments of multiple wave measurement buoys for direct measurements of wave characteristics such as ocean wave decorrelation length scales, wave speed, and directional spread. Wave decorrelation lengths were found to be inversely proportional to the width of the spectrum, and wave speeds compared well against linear wave theory.

The ocean component and coastal impacts of Storm Gloria, which hit the western Mediterranean between 20 and 23 January 2020, are investigated with a numerical simulation of the storm surges and wind waves. Storm Gloria caused severe damages and beat several historical records, such as significant wave height or 24 h accumulated precipitation. The storm surge that developed along the eastern coasts of the Iberian Peninsula, reaching values of up to 1 m, was accompanied by wind waves with a significant wave height of up to 8 m. Along the coasts of the Balearic Islands, the storm footprint was characterised by a negligible storm surge and the impacts were caused by large waves. The comparison to historical records reveals that Storm Gloria is one of the most intense among the events in the region during the last decades and that the waves' direction was particularly unusual. Our simulation permits quantification of the role of the different forcings in generating the storm surge. Also, the high spatial grid resolution down to 30 m over the Ebro Delta allows determination of the extent of the flooding caused by the storm surge. We also simulate the overtopping caused by high wind waves that affected a rocky coast of high cliffs (∼15 m) on the eastern coast of Mallorca.