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Global wave hindcasts are developed using the third generation spectral wave model WAVEWATCH III with the observation‐based source terms (ST6) and a hybrid rectilinear‐curvilinear, irregular‐regular‐irregular grid system (approximately at 0.25°×0.25°). Three distinct global hindcasts are produced: (a) a long‐term hindcast (1979–2019) forced by the ERA5 conventional winds U10 and (b) two short‐term hindcasts (2011–2019) driven by the NCEP climate forecast system (CFS)v2 U10 and the ERA5 neutral winds U10,neu, respectively. The input field for ice is sourced from the Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea‐ice concentration climate data records. These wave simulations, together with the driving wind forcing, are validated against extensive in‐situ observations and satellite altimeter records. The performance of the ST6 wave hindcasts shows promising results across multiple wave parameters, including the conventional wave characteristics (e.g., wave height Hs and wave period) and high‐order spectral moments (e.g., the surface Stokes drift and mean square slope). The ERA5‐based simulations generally present lower random errors, but the CFS‐based run represents extreme sea states (e.g., Hs>10 m) considerably better. Novel wave parameters available in our hindcasts, namely the dominant wave breaking probability, wave‐induced mixed layer depth, freak wave indexes and wave‐spreading factor, are further described and briefly discussed. Inter‐comparisons of Hs from the long‐term (41 years) wave hindcast, buoy measurements and two different calibrated altimeter data sets highlight the inconsistency in these altimeter records arising from different calibration methodology. Significant errors in the low‐frequency bins (period T>15 s) for both wave energy and directionality call for further model development. Plain Language Summary: Ocean surface waves are fundamentally important for ocean engineering design, ship navigation, air‐sea exchange of gas, heat, momentum and energy, upper ocean dynamics, and remote sensing of the ocean. Spectral wave modeling is an indispensable tool to estimate sea state information. In this study, we present new global wave hindcasts developed using the state‐of‐the‐art model physics and numerics and the modern reanalysis winds and satellite sea ice records. It is demonstrated through validation against in‐situ observations and altimeter records that the global wave hindcasts perform well across multiple parameters. Meanwhile, intercomparisons of wave height from the long‐term hindcast, buoys, and altimeters reveal inconsistency and potential inhomogeneity in these different data sets. The wave hindcasts we developed, in combination with global wave databases published previously, will form a large ensemble of realizations of historical evolution of sea states simulated with distinct wave physics and wind forcing, which will help quantify sea states in real oceans more accurately. Key Points: Global wave hindcasts using the observation‐based source terms are developed and validated against extensive observationsThe wave hindcasts show promising performance across multiple parameters, including wave height, period, and high‐order spectral momentsIntercomparisons of wave height from the hindcast, buoys, and altimeters highlight the inconsistency and inhomogeneity in these data sets [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Australia is widely recognised as having an abundant wave energy resource which could contribute to the country's future energy mix. Prior assessments have provided general broad scale information on the resource magnitude, but detail needed to support next level site assessments has been deficient. Aiming to support all stakeholders in Australia's emerging wave energy industry, this study presents a revised assessment of Australia's national wave energy resource. The assessment is based on a state-of-the-art global wave hindcast, with higher resolution in the Australian region. Validation of the hindcast relative to in-situ wave buoy and satellite altimeter observations show better comparison than prior assessments. The total nationally available resource is similar in magnitude to earlier studies, but regional differences are evident. The total integrated energy flux across the 200 m contour is approximately 2730 TWh/yr, with estimates of resource along the north and eastern coasts being less than previously estimated. This revised pre-competitive resource information is delivered coincidently with marine management and alternative use (constraint layers), and energy infrastructure, spatial information via the open-access Australian Wave Energy Atlas (AWavEA), served through the Australian Renewable Energy Infrastructure (AREMI). The Atlas serves to reduce barriers to emergence of an Australian wave energy industry. [ABSTRACT FROM AUTHOR]

Indonesia is an archipelago country with great potential for marine renewable energy, particularly for wave energy. This study will provide a wave energy assessment of Indonesia over a 6.5-year period (2011–2017) with resolution about 5.5 km. This assessment is based on data generated with a two-way nested high-resolution wave model WAVEWATCH III with observation-based physics (ST6). Three grids have been generated, namely a 'high resolution' of 3 arc-minute (0.05°) grid is nested inside a 12 arc-minute (0.2°) grid which is nested within a 0.5° global grid. Validations against altimeters and buoys show good agreement with the model. Mean wave energy has been classified based on meteorological seasons and it is found that the most energetic months are June, July, August for all areas of south, southwest and west of Indonesia where it can exceed 30 kW/m. In some locations wave energy is available throughout the entire year, that is in the south of Jawa island, Bali island and West Nusa Tenggara while in the region of west Sumatera, promising wave energy is available during the time from March to November. In addition, within the Indonesian Archipelago the monthly variations are about 5 kW/m and relatively small in terms of absolute values but this region is large relative to the mean wave power energy. • Wave energy assessment for Indonesian archipelago with spatial resolution of 0.05°. • Based on a dynamically downscaled wave model with observation-based physics (ST6). • Mean wave power in excess of 30 kW/m was found for season June to August. • 10% of mean wave power exceeds 40 kW/m with the top-one percent up to 60 kW/m. [ABSTRACT FROM AUTHOR]