ERA-40 Wave Research Articles

Dynamics of Barred Coast at Different Temporal Scales (by the Example of Vistula Spit in the Baltic Sea)

According to fundamental concepts, the morphodynamic system of an accumulative sandy coast with underwater bars exhibits cyclic behavior across various time scales. This raises the question: which factor is more significant for the dynamics of a given coast—individual storms or seasonal changes in wave activity? While observations and studies addressing this issue have primarily been conducted on oceanic coasts, there is a lack of comparable data for fetch-limited areas. Monitoring of the bottom topography along the west coast of Vistula Spit (Baltic Sea) revealed a cyclic behavior in morphology, transitioning from a straightened external bar to its connection with the shore. Analysis of field measurement results indicated that seasonal variations in wave intensity do not significantly impact coastal relief. Furthermore, it was found that the complete cycle of underwater bar evolution lasts approximately two years, during which the coast profile maintains a stable shape at the stage of the straightened external bar. The identification of the primary factor influencing coastal evolution can be characterized by the Dean number (Ω), which combines wave parameters (wave height and period) with sediment fall velocity. Utilizing ERA5 wave reanalysis data, we compared the variability of Ω values on both annual and monthly scales. The analysis revealed that for the section of the coast under consideration, there is no clearly dominant evolutionary factor; rather, the coast is influenced approximately equally by individual storm events and seasonal fluctuations in wave energy. Modeling storm-induced bed profile deformations using the CROSS-PB model demonstrated that the position of the external underwater bar remains nearly constant even during intense and prolonged storms. It is concluded that under specific conditions—determined by a combination of sediment size, coastal slope, and wave regime characteristics—the coast can remain stable, exhibiting minimal response to relatively strong storms and seasonal variations in wave energy. Such coasts are characterized by an absence of a dominant evolutionary factor as indicated by fluctuations in the Dean parameter, allowing their morphodynamic cycles to span several seasons. This type of morphodynamics in coastal accumulative relief appears to be typical for conditions found in fetch-limited areas, such as regional and semi-closed seas.

Mean wave direction and wave height in the ERA5 reanalysis dataset: Comparison with measured data in the coastal waters of India

Information on wave direction and height is an important input to the coastal engineers. The availability of measured data at every location in the ocean makes maritime operations smoother. However, the practical impossibility makes it to look for alternative datasets like ERA5 reanalysis data. In this study, we compare the significant wave height and mean wave direction in the ERA5 with the buoy-measured data available at the nearest locations in the coastal waters of India. Even though the ERA5 overestimates the measured significant wave heights at certain instances, they both are in good agreement at most of the locations. The correlation coefficient varies from 0.82 to 0.99, with the RMSE falling between 0.15 and 0.31 m. However, the ERA5 wave direction deviates significantly from the measured buoy data at certain locations due to the substantial difference between the measured and ERA5 mean direction of wind-seas. The ERA5 dataset matches the measured mean wave direction when swell dominates i.e., during the southwest monsoon for the locations in the Arabian Sea and during post-monsoon season for the locations in the western Bay of Bengal.

Coastal flood susceptibility assessment along the Northern coast of Portugal

Assessing the susceptibility of sandy beaches to flooding during major storms is an important topic, given the implications that these events can have for beach-dependent coastal systems. A flood susceptibility index based on wave run-up, storm surge and beach morphology is applied to three sectors of the northwest coast of Portugal. An extended ERA5 wave dataset is used (1950–2022) to characterize the most frequent storm events, and topographic data from aerial UAV surveys are examined to obtain specific morphological parameters of the sandy beaches – mean face slope and height of the beach. The flood susceptibility index results show that 54 % of the analyzed coast is classified with extreme or very high susceptibility. The most critical sector is the coastal stretch between Cortegaça and Torrão do Lameiro beaches, where mostly natural systems are present, while the urban front of Furadouro is frequently flooded during storm events. Between Miramar beach (south) and Paramos beach, with a susceptibility index classified between medium and extreme, the presence of some urban fronts points to high-risk exposure situations. The sector between São Jacinto and Poço da Cruz beaches presented lower susceptibility levels for the three urban areas. The problem does not end with the threat to urban fronts, which already have several defense structures, but it is also extends to dune systems and sand dykes.

Accuracy Evaluation of Ocean Wave Spectra from Sentinel-1 SAR Based on Buoy Observations and ERA5 Data

Doppler mis-registrations in azimuth can lead to ocean waves shorter than a specific wavelength being undetectable by SAR. In order to evaluate the actual ocean wave observation ability, the accuracy of Sentinel-1 SAR ocean wave spectra from January 2016 to December 2021 is evaluated by comparisons to NDBC buoys, ERA5 wave height, and CMEMS buoys. The results compared with NDBC show that the spectral shape of Sentinel-1 SAR ocean wave spectra is accurate, while the spectral values need to be improved. The wave spectra of Sentinel-1 have the best observations in season autumn. The comparison results of total wave height show the RMSE and bias are 0.91 m and −0.52 m for the comparisons to NDBC buoy wave spectra data, 0.93 m and −0.68 m for the comparison to ERA5 wave height data, and 0.9 m and −0.35 m for the comparisons to CMEMS buoy data. The comparison results of wave height in different wind speeds and areas shows that the accuracy of Sentinel-1 wave mode data is relatively good in the open ocean located in middle and low latitude area under the medium wind speed, while those are relatively poor in high latitude areas or the areas with excessively high or low wind speed.

An 8-model ensemble of CMIP6-derived ocean surface wave climate

We present a global wind wave climate model ensemble composed of eight spectral wave model simulations forced by 3-hourly surface wind speed and daily sea ice concentration from eight different CMIP6 GCMs. The spectral wave model uses ST6 physics parametrizations and a global three-grid structure for efficient Arctic and Antarctic wave modeling. The ensemble performance is evaluated against a reference global multi-mission satellite altimeter database and the recent ECMWF IFS Cy46r1 ERA5 wave hindcast, ERA5H. For each ensemble member three 30-year slices, one historical, and two future emission scenarios (SSP1-2.6 and SSP5-8.5) are available, and cover two distinct periods: 1985–2014 and 2071–2100. Two models extend to 140 years (1961–2100) of continuous wind wave climate simulations. The present ensemble outperforms a previous CMIP5-forced wind wave climate ensemble, showing improved performance across all ocean regions. This dataset is a valuable resource for future wind wave climate research and can find practical applications in offshore and coastal engineering projects, providing crucial insights into the uncertainties connected to wind wave climate future projections.

Global increase in tropical cyclone ocean surface waves

The long-term changes of ocean surface waves associated with tropical cyclones (TCs) are poorly observed and understood. Here, we present the global trend analysis of TC waves for 1979–2022 based on the ERA5 wave reanalysis. The maximum height and the area of the TC wave footprint in the six h reanalysis have increased globally by about 3%/decade and 6%/decade, respectively. The TC wave energy transferred at the interface from the atmosphere to the ocean has increased globally by about 9%/decade, which is three times larger than that reported for all waves. The global energy changes are mostly driven by the growing area of the wave footprint. Our study shows that the TC-associated wave hazard has increased significantly and these changes are larger than those of the TC maximum wind speed. This suggests that the wave hazard should be a concern in the future.

Increasing wave power due to global climate change and intensification of Antarctic Oscillation

In this study, our objective is to evaluate the long-term variability of global wave power and its underlying mechanisms, utilizing the longest wave reanalysis available (ERA5, 1940–2022). We introduce a novel validation of the global wave power of the ERA5 wave reanalysis against measurements from multi-platform satellite altimeters from 1985 to 2022. This validation shows good agreement over the period. Our analysis shows a consistent increase in global wave power throughout the study period. This rise in global wave power predominantly stems from the influences of global climate change and the intensification of Antarctic Oscillation (AAO) events. As low-pressure systems move southward in the Southern Hemisphere over this period, they generate an increase in wave power 2.6 times larger than the global average increase. This energetic wave power propagates towards the mid and low-latitude regions of the Pacific, Indian, and Atlantic Oceans. This conclusion is verified by an examination of wind wave and swell contributions to wave power. Specifically, the wind wave component (swell) is more (less) important in the Southern Ocean than in the low and mid-latitude regions.

Long-term variability analysis of wave energy resources and its impact on wave energy converters along the Chinese coastline

The long-term variability of wave energy resources will affect the practical decadal application of wave energy converters. This paper has evaluated the distributions and long-term trend of wave energy resources in the sea regions along the Chinese coastline for 83 years based on reliable ERA5 wave reanalysis data from 1940 to 2022 after verifying by 39 local buoys, and found that it is of great potential to develop and apply wave energy resources projects. The long-term trend analysis of the wave energy density and its major contributing wave parameters, significant wave height, and wave energy period, is carried out using Theil-Sen estimation showing the general positive tendency. And the impacts of the decadal variation on four wave energy converters are evaluated via computing the decadal mean power production by combining the wave energy density and the wave energy characteristic. It is verified that the long-term wave energy density in this region increases along with the decrease in power production and the capture width. This study shows that long-term variability of wave energy resources and wave energy characteristics should also be considered in the designing of WECs and the identification of energy hotspots.

Analysis of the North Atlantic offshore energy flux from different reanalysis and hindcasts

To date there is a wide range of wave reanalysis and hindcasts available to the scientific and engineering community which are commonly used for different applications, including downscaling or the estimation of the wave energy resource (Morim et al., 2022). These long datasets have been created using different combinations of forcing fields, physical parameterizations, and numerical choices (like spatial and spectral resolution). All these elements have a direct effect on the accuracy of the wave models’ output (e.g., Alday et al., 2021) and thus, they are one of the main reasons for the differences between these products. In the present study we analyze the significant wave heights and peak periods characteristics from a selection of global datasets. We additionally include results from a hindcast created using the WAVEWATCH III model, with adjustments specially aimed to reduce uncertainties of the wave energy resource along the Atlantic coasts of Europe. Models’ output is compared with buoys and altimeter data from the latest ESA (European Space Agency) CCI Sea State V3 product. Preliminary validation of the hindcast we have generated for the North Atlantic already show an important bias reduction for wave heights in the 2.5 to 11.5 range compared to ERA5 wave product. Using the relevant wave parameters, we estimate the power density and quantify the differences between databases. Then, based on scatter diagrams obtained from the joint distributions of significant wave height and peak period, the differences in the power captured by wave energy converters (WEC) related to different wave data sources will be quantified (e.g., Babarit et al., 2011; Henriques et al., 2016).

CMIP6 wave climate simulation in the European North East Atlantic Basin using WaveWatch III

Climate change is expected to have an impact on wind patterns, and therefore the generation of waves. Phase 6 of the Coupled Model Intercomparison Project (CMIP6), provides various realization of outputs integrated global coupled models for different centuries. Wind quality is a cornerstone for wave energy as it is the primary generation driver in any wave model. Therefore, proper quantification of wind wave interactions are key in the evaluation of future wave energy potential. In this study, a wave hindcast for the North-East Atlantic, using the WaveWatchIII model forced by CMIP6 winds is presented. The model uses a grid of 0.25° of spatial resolution, covering a longitude range of -21.0° to 10° (west to east) and a latitude range of 18° to 80° (south to north).The main objective of this work is to assess the quality of historical winds from all the CMIP6 wind data that are available under the first realization criteria (r1i1p1f1) at the time of this study. This leads to understanding limitations and proposing a selection method to choose the optimal wind dataset to force the wave model within the analyzed area.Thus, the optimal CMIP6 historical winds for the North-East Atlantic are used to create a 10 years hindcast(from 2003 to 2012). To further assess the suitability of the selected winds dataset for wave generation, results are compared with the ERA5 wave product. The available CMIP6 models show region-specific variations depending on the Regional Climate models used for their developments. The results show the impact of zonal and, meridional wind intensities, on wave characteristics in different regions over the domain.

Validation of ERA5 Wave Energy Flux through Sailor diagram in Spain (2005-2014)

The aim of this study is to validate the ERA5 Wave Energy Flux (WEF) estimations against observational data. To that end, 0.5° x 0.5° resolution ERA5 reanalisis WEF values and corresponding data from 15 directional REDEXT buoys of Puertos del Estado surrounding the Iberian and Canary Islands’ coast covering a period of ten years (2005-2014) have been used.
 In this study, the Sailor diagram and its methodology [1] have been used to compare the skill of the model for a two-dimensional variable (WEF). The methodology on which the Sailor diagram is built proposes a diagram along with different statistical indices. In this particular case, WEF is a vectorial magnitude, and its zonal and meridional components (WEFx and WEFy) have been assessed at the same time. 
 To compare the observed data with the ERA5 data, well-known indices extended to two dimensions such as RMSE, correlation and bias have been used, as well as variances of each component. Furthermore, to analyse if the WEFx and WEFy components of the ERA5 data are rotated in relation to the observations, their relative rotation angle, eccentricity, and congruence coefficient of the first EOF (Empirical Orthogonal Function) have also been examinated. This way, the analysis is extended to fully cover the dimensionality of the data.
 
 [1] Sáenz J, Carreno-Madinabeitia S, Esnaola G, González-Rojí SJ, Ibarra-Berastegi G, Ulazia A. The Sailor diagram – A new diagram for the verification of two-dimensional vector data from multiple models. Geosci Model Dev. 2020;13(7):3221–40. https://doi.org/10.5194/gmd-13-3221-2020

Long-term trends and wave climate variability in the South Atlantic Ocean: The influence of climate indices

Linking wave climate variability and trends with climate indices is important to better understand and predict large-scale patterns of wave variability down to wave conditions at the coast. This study investigates such links in the South Atlantic Ocean using 72 years of ERA5 wave hindcast. Different wave parameters are computed, including storm wave statistics, and are further analyzed in terms of long-term trends and interannual changes. Our results indicate that, over the last decades, wave height has been significantly increasing across the entire domain, while extreme events statistics are also increasing, although with more complex spatial variability. The variations of these wave properties are primarily correlated, from low to high latitudes, with the Atlantic Multidecadal Oscillation (AMO), Tropical Southern Atlantic Index (TSA) and Southern Annular Mode (SAM), with different preferred timescales. We think that better understanding and predicting the evolution of these climate indices, including under climate change, will be critical to anticipate coastal hazards in this region.

Evaluating the Applicability of Mainstream Wave Energy Converters in the South China Sea

Based on the past ten years of ERA5 wave field data, this study analyzed the distribution of wave energy resources in the coastal waters of each province around the South China Sea. In view of the single resource evaluation method, a regional classification method was established that comprehensively considered the three factors that impact wave energy resource reserves, the suitable water depth of the wave energy conversion device, and the device layout mode that affects energy absorption efficiency. From this information, the locations of key deployment stations around the coastal provinces were determined. Additionally, the CRITIC method was used to build indicators that can comprehensively evaluate the performance of different devices, and the most suitable devices for deployment in the five provinces were identified. The wave energy propagation direction of each key station was analyzed. The results show that the wave energy propagation direction of each station is relatively concentrated, which is convenient since this helps the device absorb and utilize energy more efficiently. Finally, the power generation performance and economic benefits of each key station’s most suitable device were analyzed. This article provides an assessment of the applicability of mainstream wave energy conversion devices in the nearshore area of the South China Sea, which is of great significance for the development and utilization of wave energy resources in the South China Sea’s nearshore waters. It provides a scientific basis for the selection, deployment, operation, and other practical operations of the follow-up devices in the South China Sea, as well as the power generation performance and economic benefits of the devices.

The bivariate region frequency extreme value analysis of significant wave height and mean wave period in the South China Sea

A comprehensive understanding of extreme significant wave height (SWH) and mean wave period (MWP) benefits greatly the design and construction of offshore structures. In this paper, we present the bivariate region frequency analysis method to spatially investigated the extreme sea state. Based on univariate region frequency analysis approach, this bivariate method provides a chance to combine the similar joint characteristics of extreme sea state conditions at various locations with a specific area. The regional assessment of extreme SWH and MWP is conducted in the South China Sea employing ERA5 wave datasets from 1978 to 2021. Based on multivariate L-moments, the SCS is divided into 35 homogeneity regions which experience discordance test and homogeneity test. The Huangyan Island and Nansha Island near area are chosen to verify the model effect. By comparing the theoretical and empirical density, a series of diagnostic figures indicate the goodness of fit between empirical and theoretical distributions.

Use of drifting buoys for wave observation: Effect of current on wave data

Drifting buoys can measure wave data even in areas far from land and with strong currents, such as the western boundary current. We evaluated the dependence of the difference between ERA5 wave data and buoy data on the Lagrangian surface current speeds derived from the drifting buoy positions. The ERA5 wave data does not consider the effects of ocean currents. The difference between the ERA5 wave heights and the buoy wave heights increases as the surface current speed increases. The ERA5 wave height is notably underestimated in the case of the opposing current to the mean wave direction, and the wave height is overestimated in the case of the following current to the mean wave direction. The magnitude of them is up to 20%. The wave periods from ERA5 data and the drifting buoy also have differences in the case of opposing and following currents, mainly due to the Doppler shift. Marked over- and underestimation of the ERA5 wave heights can be observed at higher surface current speeds in the Kuroshio extension area, where currents are influenced by mesoscale eddies. Overestimation and underestimation of the ERA5 wave heights are also frequent in the Kuroshio area.

Evaluating the Accuracy of ERA5 Wave Reanalysis with In Situ Data on the Egyptian Mediterranean Coasts

Evaluating the Accuracy of ERA5 Wave Reanalysis with In Situ Data on the Egyptian Mediterranean Coasts

Long-term trend analysis and wave energy assessment based on ERA5 wave reanalysis along the Chinese coastline

Assessing wave energy is of great importance to estimate the wave energy potential and site selection of wave energy converters. This paper aims to present the distributions of wave energy and long-term wave trend along the Chinese coastline. By using the high-accuracy wave data in ERA5 reanalysis for the 42-year period in 1979–2020, the spatiotemporal distributions of wave variations estimated by Theil-Sen Estimations and mean wave energy resources assessed by the implicit calculation equation are presented. Generally, annually mean wave heights increase in Yellow sea, East China Sea and South China Sea and decrease in Bohai sea. The wave energy assessment shows the great energy potential with annually mean wave energy density of major 6–12 kW/m. There is near Taiwan island and the northern parts of South China Sea that of higher energy density. The seasonal analysis reveals that winter and fall are more energetic with higher values around 20 kW/m. The wave power production in the selected locations was evaluated and provided based on the distribution of various wave states showing the East China Sea and South China Sea are the most suitable sea area for wave energy development.

Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020

Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020

Evaluation of ERA5 Wave Parameters with In Situ Data in the South China Sea

In this paper, the accuracy of wave parameters of the European Centre for Medium-Range Weather Forecasting Reanalysis v5 (ERA5) in the South China Sea (SCS) is systematically examined with field measured data of two buoys at offshore sites and a subsea-based platform at a nearshore site, which has a total observational period of nearly three years. It is suggested that the wave parameters provided by ERA5, such as significant wave height (Hs) and mean wave period (Tm), are in good agreement with the observational data of the three sites. Compared with the in situ data, the correlation coefficient of ERA5 Hs is in the range of 0.87–0.93, and the root mean square error is only in the range of 0.22–0.57 m. The error of standard deviation does not exceed 0.29 m and is even as low as 0.04 m at two sites. The wave propagation directions of the ERA5 and in situ data are also basically the same. However, when the data are applied in engineering, some wave parameters extracted from ERA5 may deviate from the measured statistics. It is possible to significantly overestimate the average duration of a particular Hs. Further analysis shows that the possible errors of ERA5 wave parameters may be due to insufficient description of topographic conditions, which shows that the error value changes obviously with the wave direction. The results preliminarily confirm the validity of ERA5 data in the SCS, but also indicate that it is necessary to calibrate and validate the data adequately when applying the global model and its reanalysis data to specific ocean areas.

Wave climate trends and breakpoints during the Atlantic Multidecadal Oscillation (AMO) in southern Brazil

Understanding how wave climate variability and its trends change over time are crucial analyses required to mitigate potential wave-induced impacts and adapt coastal areas to such effects. The long-term trends and breakpoints of the wave energy flux (WEF) and its relationship with teleconnection patterns in southern Brazil were studied using ERA-5 wave reanalysis with validation using a waverider. We determined that the interannual mean WEF (WEFm) and extreme WEF (WEF98) that reaches the southern Brazil have increased over the past four decades, with a increment of 0.063 and 0.17 kW/m/year, respectively 0.63 and 0.29% per year. By the Muggeo method we determined that subperiods with increasing WEFm trends are related to the SSE and SSW quadrants and that these are also the most energetic ones and with the highest annual increment rates of WEFm and WEF98. Our results also suggest that the positive trends observed in interannual WEF values are likely related to the long term transition of cold to warm Atlantic Multidecadal Oscillation (AMO) phase in the western South Atlantic Ocean. From a coastal risk perspective, it is important to understand the relationship between climatic indices and the wave climate to support long-term coastal management policies.