# Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models

Alejandro Salimbeni, Walter Dragani, Guadalupe Alonso

► To cite this version:

Alejandro Salimbeni, Walter Dragani, Guadalupe Alonso. Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models. International Journal of Climatology, 2024, 44 (8), pp.2725-2738. 10.1002/joc.8477. insu-04875739

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# Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models

Alejandro Salimbeni<fnref n="1" />,<fnref n="2" />

Walter Dragani<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,<fnref n="4" />

Guadalupe Alonso<fnref n="1" />,<fnref n="3" />

## Correspondence

Walter Dragani, Servicio de Hidrografia Naval, Av. Montes de Oca 2124 (C1270ABV), Ciudad de Buenos Aires, Argentina.

Email: dragani@hidro.gov.ar

## Funding information

Ministerio de Ciencia y Tecnología, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina, Grant/Award Number: 112-202001-01402CO

## Abstract

In situ wave observations are essential for implementing coastal management strategies, designing coastal and offshore structures and understanding many processes related to sedimentology and marine biology. Despite their importance, these observations are rather scarce in the coastal areas of southeastern South America. Moreover, the existing observations are discontinuous, with several gaps and no simultaneity. Today, global wave models have improved their performance by providing reliable wave parameters with high resolution and coverage even at near-shore areas. In this work, wave parameters from five global models were compared with significant wave heights ($H_s$), peak periods ($T$) and propagation direction at nine sites located at the Southwestern South Atlantic Ocean. The global products used were WAVERYS (Global Reanalysis Wav 001 032), IOWAGA (WAVEWATCH III Hindcast - WW3 - Global grid - CFSR), ERA5 (ECMWF reanalysis), NOAA WW3 - 30-year Hindcast Phase 2 and NCEP/EMC Operational global wave model GFS - Wave. Mean arithmetic error (Bias), root mean square deviation, scatter index and the Pearson linear correlation coefficient ($r$) were assessed at each location where wave observations were available. The best agreements for $H_s$ were obtained at the deepest sites located farther from the coast. Lower statistical metrics were achieved at coastal sites where the bathymetry is roughly represented by the global models. The calculated correlation coefficients were found to be lower for $T$ than for $H_s$. Overall, $T$ was slightly underestimated by global models. Wave propagation directions were excellently represented by global models at offshore sites. The WAVERYS model is the global product that best represents the wave parameters in the study area, probably due to its higher spatial resolution (0.2°) and more realistic atmospheric forcing (ERA5). In addition, it satisfactorily represents the temporal variability of $H_s$ in open water as well as swell characteristics at the outermost part of the Río de la Plata.

## KEYWORDS

global wave models, in situ wave observations, Río de la Plata, statistical metrics, swell

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<footnote n="1">¹Servicio de Hidrografia Naval, Ciudad de Buenos Aires, Argentina</footnote>

<footnote n="2">²Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad de Buenos Aires, Argentina</footnote>

<footnote n="3">³Departamento de Ciencias de la Atmósfera y los Océanos, Universidad de Buenos Aires, Ciudad de Buenos Aires, Argentina</footnote>

<footnote n="4">⁴Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI IFAECI/CNRS-CONICET/CIMA/UBA), Ciudad de Buenos Aires, Argentina</footnote>
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# 1 | INTRODUCTION

Wind wave measurements at coastal areas are essential for implementing management strategies, designing and conserving coastal and offshore structures and understanding many processes related to sedimentology and marine biology. The lack of long-term and densely spaced in situ measurements in the Southern Hemisphere in general, and in the South Atlantic Ocean in particular, hinders several investigations due to the lack of detailed met-ocean information (Pereira et al., 2017). Wave parameter series are rather discontinuous, present several gaps and have a very scarce overlapping among them, along the coastal areas of Southeastern South America. Consequently, wave studies are very scarce and mainly based on occasional short-term observations in this region (Pianca et al., 2010). Available observations are insufficient for carrying out regional studies or building a reliable wave climate but have nevertheless been widely used to investigate specific topics, or to validate regional wave models. For instance, Lanfredi et al. (1992) estimated the wave power potential along the coast of Buenos Aires Province, Argentina. Dragani and Romero (2004) presented a statistical analysis of the sea and swell in the outer Río de la Plata (RP) and suggested that the swell and sea would be characterized by periods and heights of around 10 s and 0.8 m, and 5 s and 1.25 m, respectively. Afterwards, wave parameters measured at the mouth of the RP estuary were used to validate the Simulating WAves Near-shore (SWAN) model on the adjacent continental shelf (Dragani et al., 2008, 2010) and for analysing the synoptic patterns associated with the highest wind waves (Dragani et al., 2013). Alonso et al. (2015) assessed the wave energy potential by means of a 31-year wave hindcast performed with the WW3 model (Tolman, 2016), forced by Climate Forecast System Reanalysis (CFSR) winds. The model was calibrated and validated with altimeter data and in situ measurements. Subsequently, a set of wave hindcasts was constructed using two different types of wind calibration, to improve the simulation of extreme wave events in the South Atlantic Ocean. A total of nine wave hindcasts were evaluated against satellite and buoy data (Campos et al., 2018). More recently, the wave climate in the Southern Ocean was investigated using a combined dataset from 33 years of altimeter data and in situ buoy measurements (Young et al., 2020).

Global wave models have improved their performance by providing reliable wave parameters with high resolution and coverage even at near-shore areas, quite close to the coast (Ahn, 2021; Crespo et al., 2022; de Hauteclocque et al., 2020). Therefore, they are useful for describing the wave climate in regional and coastal

projects. Wave parameters produced by global models are usually used without an exhaustive comparison against observations. However, knowing which global model best captures the wave conditions in a given coastal area is the cornerstone of any investigation, especially when observations are very scarce. This paper aims to investigate the performance of five global wave models in the Southwestern Atlantic Ocean, in a region adjacent to the Argentinean and Uruguayan coasts. The performances of WAVERYS model (Global Reanalysis Wav 001 032), IOWAGA model (Integrated Ocean Waves for Geophysical and other Applications), ERA5 model (European Centre for Medium-Range Weather Forecasts – ECMWF – reanalysis) and two versions of WW3 model produced by NOAA (30-year Hindcast Phase 2 and NCEP/EMC Operational global wave model) are described, analysed and compared against in situ wave observations.

# 2 | STUDY AREA

The study area includes the continental shelf waters of Southeastern South America, a vast Southwestern Atlantic Ocean region with an area of around 1 million square kilometres. This area spans from the RP estuary (~36° S) to the southern extreme of Tierra del Fuego Island (~56° S). Shelf width varies between 170 and 850 km. The outer (offshore) shelf edge (shelf break) follows a NE–SW direction between the RP estuary and 44° S, from where it gradually changes to an N–S direction down to 50° S, then acquires an easterly direction towards Malvinas/Falkland Island and, after surrounding them, goes back again to the W and reaches regions near the continent at ~51° S. The depth of the shelf break is variable between 70 and 190 m (Parker et al., 1982). Ocean depths greater than 6000 m can be appreciated eastwards the continental shelf (around 47° S, 51° W) (Figure 1).

In this region, the atmospheric and ocean processes are highly influenced by the Brazil (warm) and Malvinas (cold) ocean currents and, particularly, its confluence zone (Guerrero et al., 2014; Guerrero & Piola, 1997; Matano et al., 2014; Rivas, 1997). The surface atmospheric circulation is dominated by the presence of the South Atlantic Ocean semipermanent high-pressure system, mainly in the northern sector of the study area. The position of this system presents a regular annual cycle and is more intense during the austral winter (Caffera & Berbery, 2003). It is centred around 35° S–45° W during the summer, and it moves northwards (up approximately 30° S) in winter. Such annual spatial variability clearly impacts the intensity and direction of winds (Camilloni, 2005). On the contrary, the surface westerlies
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fundamentally control regional patterns of air temperature, storm tracks and precipitation while also regulating ocean circulation, heat transport and carbon uptake in the southern region of the study area (Goyal et al., 2021). The tidal regime is basically semidiurnal in this area (http://www.hidro.gov.ar/oceanografia/Tmareas/Form_Tmareas.asp). The mean tidal range increases from the RP estuary (around 0.6 m at Buenos Aires city) to the southern extreme of Patagonia (almost 12 m at Puerto Santa Cruz).

<figure><img src="image_6.png" /><figcaption>FIGURE 1 Study area in the Southwestern Atlantic Ocean. Wave measurement locations where wave global models are evaluated are indicated with red circles. Bathymetry is in m. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

3 | DATA

3.1 | Observed wave parameters

Time-series of wave parameters are very scarce in the study area. Moreover, those that do exist are rather short, are discontinuous, have significant gaps and are not simultaneous. In this work, all available historical wave data for the region were analysed. The locations for the time-series of wave parameters are presented in Table 1 and depicted in Figure 1. At the outer RP wave parameters were measured with a directional wave recorder (Datawell Waverider, https://datawell.nl/support/manuals/) between June 1996 and September 2009. Energy density spectra are available only between 1996 and 2001. The instrument was programmed to measure 20 min wave records with a 0.5 s sampling interval, every 2 h 40 min, at a depth of 16 m. At Punta Médanos (PM), wave parameters were obtained with a Datawell Waverider (nondirectional) from March 1981 to March 1982, at 10 m depth (Table 1). The instrument was set for recording 20 min sea-level records every 3 h. Analogous records were digitized with a 0.5 s sampling interval. Close to Quequén Port (QU) wave measurements were obtained with an Interocean S4A model ADW instrument (https://www.bodc.ac.uk/data/documents/nodb/pdf/InterOceanSystems_S4series_07jul2011.pdf) moored 2 m above the bottom (at 13 m depth), from July 2006 to July 2010. Offshore of Bahía Blanca (BB) wave parameters (height and period) were measured with a pressure sensor from July 2006 to March 2018. It was programmed to obtain wave parameters every 30 min, at a depth of around 10 m. Nearby Caleta Paula harbour (CP), wave heights, wave periods, wave and directions were recorded with an Interocean S4A model ADW, moored 1 m above the bottom (with mean depth of 12 m), from June 1994 to

<table><caption>TABLE 1 Location of analysed wave parameters series.</caption><thead><tr><td>Location (code)</td><td>Latitude S (°)</td><td>Longitude W (°)</td><td>Depth (m)</td><td>From (mm/dd/yyyy)</td><td>To (mm/dd/yyyy)</td></tr></thead><tbody><tr><td>Río de la Plata (RP)</td><td>35.66</td><td>55.83</td><td>16</td><td>06/20/1996</td><td>09/30/2009</td></tr><tr><td>Punta Médanos (PM)</td><td>36.93</td><td>56.66</td><td>10</td><td>03/15/1981</td><td>03/29/1982</td></tr><tr><td>Pinamar (PI)</td><td>37.11</td><td>56.83</td><td>2</td><td>08/26/1988</td><td>12/31/2009</td></tr><tr><td>Mar del Plata (MP)</td><td>38.01</td><td>57.53</td><td>5</td><td>10/15/2012</td><td>07/31/2020</td></tr><tr><td>Quequén (QU)</td><td>38.58</td><td>58.66</td><td>13</td><td>07/01/2006</td><td>07/12/2010</td></tr><tr><td>Bahía Blanca (BB)</td><td>38.88</td><td>62.36</td><td>10</td><td>07/13/2006</td><td>03/01/2018</td></tr><tr><td>Caleta Paula (CP)</td><td>46.47</td><td>67.50</td><td>12</td><td>06/18/1994</td><td>02/21/1996</td></tr><tr><td>Tierra del Fuego (TF)</td><td>53.66</td><td>67.30</td><td>66</td><td>01/29/1998</td><td>03/23/1999</td></tr><tr><td>Atlantic Buoy (AB)</td><td>42.92</td><td>42.43</td><td>4500</td><td>10/27/2016</td><td>01/15/2018</td></tr></tbody></table>

Note: Two-digit codes in brackets refer to the locations pointed out in Figure 1. Depths of the measurement sites (m) and data acquisition period are also included.
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February 1996. At Tierra del Fuego continental shelf (TF), a directional wave recorder (Datawell Waverider) was deployed between January 1998 and March 1999 and set to measure 20 min wave records with a 0.5 s sampling interval, every 3 h, where the depth was 66 m. The Ocean Observatories Institute (https://oceanobservatories.org/) deployed a 3-m-diameter buoy (Ocean Observatories Institute Buoy 2: OOI2) in approximately 4500-m water depth, at the Southwestern Atlantic Ocean (AB). Data were analysed for the periods from October 2016 to January 2018. This buoy had a Triaxys directional accelerometer package onboard.

On the other hand, series of visual estimations of wave heights at Pinamar (PI), from August 1988 to December 2009, and Mar del Plata (MP), from October 2012 to July 2020, were used. Visual estimations of the wave direction were not considered for the comparison with global products since this wave parameter is strongly affected by refraction near the coast. As for the period, it is known that observers of wave parameters tend to ignore short-period waves, resulting in overestimates of the wave period. Although wave heights are also severely transformed by refraction and shoaling at the surf zone, visual heights were found to match reasonably well with wave parameters reported by global products. Breaker heights at PI were visually observed from the shore twice a day from August 1988 to December 2009 (without any large gaps), following the guidelines given by the Littoral Environment Observation (LEO) Data Collection Program (Schneider, 1981). In addition, wave parameters were visually observed twice a day from Mar del Plata fisherman's pier, located at an open bay, from October 2012 to July 2020. The mean depth of the observation site was around 5 m, out of the surf zone.

## 3.2 | Global wave products

### 3.2.1 | WAVERYS

This global wave reanalysis is a product from Copernicus Marine Service and describes past sea states from 1993 to 2020 (Law-Chune et al., 2021). The core of WAVERYS is based on the MFWAM model (https://www.mercator-ocean.eu/en/place/meteo-france/), a third-generation wave model that calculates the wave spectrum. Average wave quantities derived from this wave spectrum are delivered on a regular 0.2° grid with a 3-h time step. The atmospheric forcing is ERA5 (Hersbach et al., 2020). WAVERYS provides swell parameters and takes into account oceanic currents from the GLORYS12 physical ocean reanalysis (https://www.mercator-ocean.eu/en/ocean-science/glorys/).

### 3.2.2 | IOWAGA

IOWAGA (https://wwz.ifremer.fr/iowaga/Products/WAVEWATCH-III) produce a sea-states hindcast database generated by WW3 model (https://polar.ncep.noaa.gov/waves/wavewatch/) and forced by Climate Forecast System Reanalysis winds from CFSR (https://climatedataguide.ucar.edu/climate-data/climate-forecast-system-reanalysis-cfsr) on the global grid over 1990-2012 period (Rascle & Ardhuin, 2013). IOWAGA is funded by the European Commission, through a European Research Council. The project aims at providing the best information related to ocean waves for applications in the geosciences (for instance, seismology, coastal dynamics, remote sensing of the ocean and air sea fluxes). Wave parameters are delivered on a regular 0.5° grid with a 3-h time step. This global product is continuously validated with altimeter and buoy data.

### 3.2.3 | ERA5

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past decades (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). The ocean wave model used in ERA5 is WAM (Group, 1988). Data are gridded to a regular grid of 0.5° for the ocean waves with 1-h time step. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions and combines vast amounts of historical observations into global estimates using advanced modelling and data assimilation systems. ERA5 replaces the ERA-Interim reanalysis, which stopped being produced in August 2019. This global product also provides swell parameters.

### 3.2.4 | NOAA-1

A 30-year wave climatology has been generated with the NOAA WW3 model using the Ardhuin et al. (2011) physics package (https://polar.ncep.noaa.gov/waves/hindcasts/nopp-phase2.php) and the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR, https://cfs.ncep.noaa.gov/cfsr/) homogeneous dataset of hourly high-resolution winds. The time period covers from 1979 through 2009. The rectilinear grids were developed using ETOPO-1 bathymetry (Amante & Eakins, 2009), together with v1.10 of the Global Self-Consistent Hierarchical High-Resolution Shoreline (GSHHS) Database. Wave parameters are delivered on a regular 0.5° grid with a 3-h time step.
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### 3.2.5 | NOAA-2

The Global Forecast System (GFS, https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast) is a National Centers for Environmental Prediction (NCEP, https://www.weather.gov/organization/ncep) weather forecast model. The NCEP/EMC global deterministic wave model was unified with the GFS, and the WW3 model is one-way coupled with the atmospheric forecast model (https://polar.ncep.noaa.gov/waves/viewer.shtml?-gfswave-). The computational system produces a post-processed global grid with 0.25° and 3 h of spatial and time resolutions. In addition, ocean currents from the Global Real-Time Ocean Forecast System (RTOFS) model are included as input to the wave model. This global product provides swell parameters. Spatial and temporal resolutions, atmospheric forcings and lapses in which each global ocean model was compared with observed wave parameters are summarized in Table 2.

## 4 | METHODOLOGY

Simulated wave parameters produced by global models are compared with observed wave data measured in the study region. Significant wave heights ($H_s$) and peak periods ($T$) were analysed and compared in this study. All the propagation directions ($D$) mentioned in this paper refer to peak direction, except for the directions obtained from the Atlantic buoy (AB), which represent mean direction. Therefore, when comparing with models, the mean direction was utilized specifically for AB. Mean arithmetic error (Bias), root mean square deviation (RMSD), scatter index (SI) and the Pearson linear correlation coefficient ($r$), for $H_s$, $T$ and $D$, were estimated as follows:

$$ \text{Bias} = \frac{1}{N} \left( \sum z_O - \sum z_M \right) \quad (1) $$

$$ \text{RMSD} = \sqrt{\frac{1}{N} \sum (z_O - z_M)^2} \quad (2) $$

$$ \text{SI} = \frac{\text{RMSD}}{\frac{1}{N} \sum z_O} \quad (3) $$

$$ r = \frac{N \sum z_O z_M - \sum z_O \sum z_M}{\sqrt{N \sum z_O^2 - (\sum z_O)^2} - \sqrt{N \sum z_M^2 - (\sum z_M)^2}} \quad (4) $$

In Equations (1)–(4), $z$ can be $H_s$, $T$ or $D$, the subscript $M$ indicates global model, and $O$ observed data, and all summation operators extend from 1 to $N$, and where $N$ is the number of data pairs. Bias is a measure of the systematic error between the datasets (accuracy). Bias >0 indicates that the mean parameter from observations is greater than from the simulation. RMSD is a metric of the absolute deviation, both systematic and random errors (accuracy and precision). RMSD is one of the most relevant metrics in this type of study. SI represents the dimensionless RMSD difference with respect to mean observation. Finally, $r$ is a measure of the degree or extent to which datasets are linearly related, a metric useful to quantify the relative errors (precision) but not the systematic errors. For each global model, the statistical metrics presented in Equations (1)–(4) were assessed between wave parameters extracted from the nearest model grid node to the wave observation site, and considering temporal differences of up $|\pm 3| h$.

With the growing availability of satellite data acquisition, operational assimilation of wave data has been successfully developed, and the quality of global products significantly enhanced (Toledano et al., 2022). For instance, WAVERYS assimilates wave heights observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards and considers satellite altimeter data from ERS 1 and 2, ENVISAT, Jason 1 and 2, CRYOSAT 2 and SARAL. Consequently, the performances of all the global models were analysed using wave observations without including satellite data in the comparisons.

## 5 | RESULTS

The statistics defined in Equations (1)–(4) were computed between observed wave parameters (Table 1)

<table><caption>TABLE 2 Spatial and temporal resolutions, and atmospheric forcings of the selected global ocean models.</caption><thead><tr><td>Model acronym</td><td>Spatial resolution (°)</td><td>Temporal resolution (h)</td><td>Atmospheric forcing</td><td>From</td><td>To</td></tr></thead><tbody><tr><td>WAVERYS</td><td>0.2</td><td>3</td><td>ERA5</td><td>1993</td><td>2020</td></tr><tr><td>IOWAGA</td><td>0.5</td><td>3</td><td>ECMWF</td><td>1990</td><td>2019</td></tr><tr><td>ERA5</td><td>0.5</td><td>1</td><td>ERA 5</td><td>1959</td><td>Present</td></tr><tr><td>NOAA-1</td><td>0.5</td><td>3</td><td>CFSR</td><td>1979</td><td>2009</td></tr><tr><td>NOAA-2</td><td>0.25</td><td>3</td><td>GFS</td><td>2010</td><td>Present</td></tr></tbody></table>

Note: The lapses in which each global ocean model was compared with observed wave parameters are also indicated.
<table><caption>TABLE 3 Bias, root mean square error (RMSD), scatter index (SI) and Pearson linear correlation coefficient (r), for significant wave heights ($H_S$), wave periods (T) and wave propagation directions (D), computed between wave parameters obtained from WAVERYS and from measurements carried out at locations given in Table 1.</caption><thead><tr><th colspan="2">WAVERYS</th><th colspan="3">H<sub>S</sub></th><th colspan="3">T</th><th colspan="3">D</th></tr><tr><th>Site</th><th>r</th><th>Bias (m)</th><th>RMSD (m)</th><th>SI</th><th>r</th><th>Bias (s)</th><th>RMSD (s)</th><th>SI</th><th>r</th><th>Bias (°)</th><th>RMSD (°)</th><th>SI</th></tr></thead><tbody><tr><td>RP</td><td>0.91</td><td>0.05</td><td>0.22</td><td>0.19</td><td><strong>0.94</strong></td><td>-0.7</td><td>1.4</td><td>0.18</td><td>0.75</td><td>8</td><td>43</td><td>0.30</td></tr><tr><td>PI<sup>a</sup></td><td>0.72</td><td>-0.14</td><td>0.29</td><td>0.35</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>MP<sup>a</sup></td><td>0.68</td><td><strong>-0.44</strong></td><td><strong>0.60</strong></td><td>0.71</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>QU</td><td>0.92</td><td>0.05</td><td>0.29</td><td>0.27</td><td>0.53</td><td>1.1</td><td>2.6</td><td>0.28</td><td>0.63</td><td><strong>15</strong></td><td>41</td><td>0.25</td></tr><tr><td>BB<sup>b</sup></td><td>0.67</td><td>0.04</td><td>0.49</td><td>0.58</td><td>0.44</td><td>0.9</td><td><strong>3.8</strong></td><td><strong>0.50</strong></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CP</td><td>0.57</td><td>-0.31</td><td>0.46</td><td><strong>0.87</strong></td><td>0.36</td><td><strong>1.2</strong></td><td>3.3</td><td>0.37</td><td>0.84</td><td>-13</td><td><strong>46</strong></td><td><strong>0.50</strong></td></tr><tr><td>TF</td><td>0.92</td><td>-0.02</td><td>0.37</td><td>0.21</td><td>0.72</td><td>0.2</td><td>1.5</td><td>0.22</td><td><strong>0.97</strong></td><td>6</td><td>34</td><td>0.15</td></tr><tr><td>AB</td><td><strong>0.93</strong></td><td>-0.18</td><td>0.44</td><td>0.16</td><td>0.63</td><td>-0.3</td><td>2.0</td><td>0.20</td><td><strong>0.97</strong></td><td>8</td><td>22</td><td>0.09</td></tr></tbody></table>

Note: WAVERYS wave parameters are available from 1993. The maximum values (bold) and minimum values (italics) are highlighted (for the Bias, the absolute value was considered).

Abbreviations: AB, Atlantic Buoy; BB, Bahía Blanca; CP, Caleta Paula; MP, Mar del Plata; PI, Pinamar; QU, Quequén; RP, Río de la Plata; TF, Tierra del Fuego.

<sup>a</sup>Only visual wave heights are compared.

<sup>b</sup>Wave sensor is nondirectional.

<table><caption>TABLE 4 Bias, root mean square error (RMSD), scatter index (SI) and Pearson linear correlation coefficient (r), for significant wave heights ($H_S$), wave periods (T) and wave propagation directions (D), computed between wave parameters obtained from IOWAGA model and from measurements carried out at locations given in Table 1.</caption><thead><tr><th colspan="2">IOWAGA</th><th colspan="3">H<sub>S</sub></th><th colspan="3">T</th><th colspan="3">D</th></tr><tr><th>Site</th><th>r</th><th>Bias (m)</th><th>RMSD (m)</th><th>SI</th><th>r</th><th>Bias (s)</th><th>RMSD (s)</th><th>SI</th><th>r</th><th>Bias (°)</th><th>RMSD (°)</th><th>SI</th></tr></thead><tbody><tr><td>RP</td><td>0.86</td><td>-0.02</td><td>0.26</td><td>0.23</td><td><strong>0.90</strong></td><td>-0.7</td><td>1.6</td><td>0.22</td><td>0.76</td><td>5</td><td>43</td><td>0.30</td></tr><tr><td>PI<sup>a</sup></td><td>0.70</td><td><strong>-0.75</strong></td><td><strong>0.88</strong></td><td>1.05</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>MP<sup>a</sup></td><td>0.67</td><td>-0.38</td><td>0.54</td><td>0.62</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>QU</td><td>0.86</td><td>-0.44</td><td>0.56</td><td>0.51</td><td>0.43</td><td>1.0</td><td>2.7</td><td>0.28</td><td>0.56</td><td><strong>21</strong></td><td><strong>55</strong></td><td>0.33</td></tr><tr><td>BB<sup>b</sup></td><td>0.60</td><td>-0.16</td><td>0.54</td><td>0.63</td><td>0.40</td><td>0.1</td><td><strong>3.8</strong></td><td><strong>0.49</strong></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CP</td><td>0.52</td><td>-0.49</td><td>0.62</td><td><strong>1.18</strong></td><td>0.28</td><td><strong>1.8</strong></td><td>3.4</td><td>0.39</td><td>0.83</td><td>-8</td><td>47</td><td><strong>0.52</strong></td></tr><tr><td>TF</td><td>0.90</td><td>-0.27</td><td>0.50</td><td>0.28</td><td>0.64</td><td>0.1</td><td>1.7</td><td>0.25</td><td><strong>0.97</strong></td><td>3</td><td>34</td><td>0.15</td></tr><tr><td>AB</td><td><strong>0.92</strong></td><td>-0.17</td><td>0.48</td><td>0.17</td><td>0.60</td><td>0.0</td><td>1.9</td><td>0.19</td><td>0.91</td><td>11</td><td>35</td><td>0.15</td></tr></tbody></table>

Note: The maximum values (bold) and minimum values (italics) are highlighted (for the Bias, the absolute value was considered).

Abbreviations: AB, Atlantic Buoy; BB, Bahía Blanca; CP, Caleta Paula; MP, Mar del Plata; PI, Pinamar; QU, Quequén; RP, Río de la Plata; TF, Tierra del Fuego.

<sup>a</sup>Only visual wave heights are compared.

<sup>b</sup>Wave sensor is nondirectional.

and those obtained from global models (Table 2). IOWAGA and WAVERYS wave parameters are available from 1990 to 1993 (Table 2), respectively. In consequence, $H_S$ and T at PM (data series from 1981 to 1982) could not be compared with wave parameters produced by these global products. The wave parameter series of the NOAA-1 model go up to 2009, so MP wave heights and AB parameters cannot be compared with this global model either. Finally, NOAA-2 data series started in 2010, consequently, this global product can

only be compared with wave parameters measured at MP (only $H_S$, 2012–2020), BB ($H_S$ and T, 2006–2018) and AB (2016–2018). The statistical metrics assessed from the comparison between WAVERYS, IOWAGA, ERA5, NOAA-1 and NOAA-2 models and wave observations are presented in Tables 3 to 7, respectively. Scatter plots for $H_S$, T and D for observed and simulated wave parameters at RP, TF and AB are presented as Supplementary Material. These three locations effectively span the study region (Figure 1). The linear fit is
<figure><img src="image_9.png" /></figure>

<table><caption>TABLE 5 Bias, root mean square error (RMSD), scatter index (SI) and Pearson linear correlation coefficient (r), for significant wave heights ($H_S$), wave periods (T) and wave propagation directions (D), computed between wave parameters obtained from ERA5 model and from measurements carried out in locations given in Table 1.</caption><thead><tr><th rowspan="2">ERA5<br>Site</th><th colspan="4">H<sub>S</sub></th><th colspan="4">T</th><th colspan="4">D</th></tr><tr><th>r</th><th>Bias (m)</th><th>RMSD (m)</th><th>SI</th><th>r</th><th>Bias (s)</th><th>RMSD (s)</th><th>SI</th><th>r</th><th>Bias (°)</th><th>RMSD (°)</th><th>SI</th></tr></thead><tbody><tr><td>RP</td><td>0.92</td><td>-0.10</td><td>0.23</td><td>0.20</td><td><strong>0.92</strong></td><td>-0.7</td><td>1.5</td><td>0.17</td><td>0.84</td><td>5</td><td>37</td><td>0.26</td></tr><tr><td>PM<sup>a</sup></td><td>0.85</td><td>-0.16</td><td>0.31</td><td>0.31</td><td>0.44</td><td>1.2</td><td>2.9</td><td>0.32</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PI<sup>b</sup></td><td>0.61</td><td><strong>-0.62</strong></td><td><strong>0.77</strong></td><td>0.90</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>MP<sup>b</sup></td><td>0.65</td><td>-0.34</td><td>0.53</td><td>0.64</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>QU</td><td>0.87</td><td>-0.43</td><td>0.54</td><td>0.50</td><td>0.42</td><td>1.0</td><td>2.8</td><td>0.29</td><td>0.58</td><td>4</td><td>41</td><td>0.28</td></tr><tr><td>BB<sup>a</sup></td><td>0.64</td><td>-0.21</td><td>0.54</td><td>0.64</td><td>0.38</td><td>0.9</td><td><strong>3.9</strong></td><td><strong>0.51</strong></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CP</td><td><strong>0.43</strong></td><td><strong>-0.62</strong></td><td><strong>0.77</strong></td><td><strong>1.46</strong></td><td>0.31</td><td><strong>1.5</strong></td><td>3.3</td><td>0.37</td><td>0.76</td><td><strong>-12</strong></td><td><strong>59</strong></td><td><strong>0.64</strong></td></tr><tr><td>TF</td><td>0.90</td><td>-0.30</td><td>0.52</td><td>0.30</td><td>0.68</td><td>0.2</td><td>1.6</td><td>0.24</td><td><strong>0.98</strong></td><td>6</td><td>32</td><td>0.14</td></tr><tr><td>AB</td><td><strong>0.93</strong></td><td>-0.06</td><td>0.44</td><td>0.15</td><td>0.64</td><td>-0.2</td><td>1.9</td><td>0.19</td><td>0.97</td><td>7</td><td>22</td><td>0.09</td></tr></tbody></table>

Note: The maximum values (bold) and minimum values (italics) are highlighted (for the Bias, the absolute value was considered).

Abbreviations: AB, Atlantic Buoy; BB, Bahía Blanca; CP, Caleta Paula; MP, Mar del Plata; PI, Pinamar; PM, Punta Médanos; QU, Quequén; RP, Río de la Plata; TF, Tierra del Fuego.

<sup>a</sup>Wave sensor is nondirectional.

<sup>b</sup>Only visual wave heights are compared.

<table><caption>TABLE 6 Bias, root mean square error (RMSD), scatter index (SI) and Pearson linear correlation coefficient (r), for significant wave heights ($H_S$), wave periods (T) and wave propagation directions (D), computed between wave parameters obtained from NOAA-1 model and from measurements carried out at locations given in Table 1.</caption><thead><tr><th rowspan="2">NOAA-1<br>Site</th><th colspan="4">H<sub>S</sub></th><th colspan="4">T</th><th colspan="4">D</th></tr><tr><th>r</th><th>Bias (m)</th><th>RMSD (m)</th><th>SI</th><th>r</th><th>Bias (s)</th><th>RMSD (s)</th><th>SI</th><th>r</th><th>Bias (°)</th><th>RMSD (°)</th><th>SI</th></tr></thead><tbody><tr><td>RP</td><td><strong>0.90</strong></td><td>-0.01</td><td>0.23</td><td>0.19</td><td><strong>0.90</strong></td><td>-0.7</td><td>1.6</td><td>0.23</td><td>0.79</td><td>2</td><td>40</td><td>0.28</td></tr><tr><td>PM<sup>a</sup></td><td>0.85</td><td>-0.12</td><td>0.29</td><td>0.29</td><td>0.39</td><td>1.3</td><td>3.1</td><td>0.35</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PI<sup>b</sup></td><td>0.69</td><td><strong>-0.77</strong></td><td><strong>0.90</strong></td><td>1.08</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>QU</td><td>0.82</td><td>-0.42</td><td>0.57</td><td>0.50</td><td>0.35</td><td>0.8</td><td><strong>2.8</strong></td><td>0.30</td><td>0.48</td><td><strong>20</strong></td><td><strong>57</strong></td><td>0.34</td></tr><tr><td>CP</td><td>0.52</td><td>-0.52</td><td>0.65</td><td><strong>1.24</strong></td><td>0.24</td><td><strong>1.8</strong></td><td>3.4</td><td><strong>0.39</strong></td><td>0.79</td><td>-6</td><td>52</td><td><strong>0.57</strong></td></tr><tr><td>TF</td><td>0.88</td><td>0.09</td><td>0.46</td><td>0.26</td><td>0.58</td><td>0.3</td><td>1.9</td><td>0.27</td><td><strong>0.97</strong></td><td>1</td><td>33</td><td>0.15</td></tr></tbody></table>

Note: The maximum values (bold) and minimum values (italics) are highlighted (for the Bias, the absolute value was considered).

Abbreviations: CP, Caleta Paula; PI, Pinamar; PM, Punta Médanos; QU, Quequén; RP, Río de la Plata; TF, Tierra del Fuego.

<sup>a</sup>Wave sensor is nondirectional.

<sup>b</sup>Only visual wave heights are only compared.

included in each case where the observed and simulated wave parameters are *x* and *y*, respectively.

The Taylor diagram offers a comprehensive visualization of the agreement between modelled and measured wave parameters, incorporating metrics such as standard deviation, correlation coefficient and RMSD. Taylor diagrams for $H_S$, T and D at AB, RP and TF are presented in Figure 2. Overall, the simulations of $H_S$ and mean periods conducted with the global models exhibit good agreement with the measured wave parameters at AB. However, when it comes to wave directions, ERA5 and WAVERYS perform better representing the observed

data. At RP and TF, the points corresponding to the simulations, are closely clustered for $H_S$, T and D. In general, the points representing WAVERYS are the closest to the observation points, indicating a higher level of agreement between this global model and the actual wave conditions.

General quantile-quantile plots (qq-plots) comparing simulated and observed $H_S$, T and D for AB, RD and TF are presented in Figure 3. If the two datasets share identical distributions, the points in the general qq-plots would align along a straight line. However, some differences can be observed, particularly for high $H_S$ values at AB,
<table><caption>TABLE 7 Bias, root mean square error (RMSD), scatter index (SI) and Pearson linear correlation coefficient (r), for significant wave heights ($H_s$), wave periods (T) and wave propagation directions (D), computed between wave parameters obtained from NOAA-2 model and from measurements carried out at locations given in Table 1.</caption><thead><tr><th colspan="2">NOAA-2</th><th colspan="4">T</th><th colspan="4">D</th></tr><tr><th>Site</th><th>r</th><th>Bias (m)</th><th>RMSD (m)</th><th>SI</th><th>r</th><th>Bias (s)</th><th>RMSD (s)</th><th>SI</th><th>r</th><th>Bias (°)</th><th>RMSD (°)</th><th>SI</th></tr></thead><tbody><tr><td>MP<sup>a</sup></td><td>0.67</td><td>-0.20</td><td>0.44</td><td>0.51</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>BB<sup>b</sup></td><td>0.57</td><td><strong>-0.32</strong></td><td><strong>0.65</strong></td><td><strong>0.76</strong></td><td>0.31</td><td>1.3</td><td>4.1</td><td>0.53</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>AB</td><td><strong>0.92</strong></td><td>-0.04</td><td>0.46</td><td>0.16</td><td>0.59</td><td>-0.2</td><td>2.0</td><td>0.20</td><td>0.90</td><td>11</td><td>38</td><td>0.16</td></tr></tbody></table>

Note: The maximum values (bold) and minimum values (italics) are highlighted (for the Bias, the absolute value was considered).

Abbreviations: AB, Atlantic Buoy; BB, Bahía Blanca; MP, Mar del Plata.

aOnly visual wave heights are compared.

bWave sensor is nondirectional.

<figure><img src="image_10.png" /><figcaption>FIGURE 2 Taylor diagrams for $H_s$, T and D for Atlantic Buoy (AB) (upper panels), Río de la Plata (RP) (middle panels) and Tierra del Fuego (TF) (lower panels). E, ERA-5; I, IOWAGA model; N, NOAA; W, WAVERYS. B are the observed wave parameters. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>FIGURE 3 General quantile-quantile plots (qq-plots) comparing simulated (NOAA, IOWAGA, ERA-5 and WAVERYS models) and observed *H*<sub>S</sub>, *T* and *D*, at Atlantic Buoy (AB) (upper panels), Río de la Plata (RP) (middle panels) and Tierra del Fuego (TF) (lower panels).
[Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

RP and TF. Additionally, differences are evident at low and high periods in AB. Similarly, discrepancies arise at high periods in RP and TF where the global models slightly underestimate the observed values. While the global models perform well in representing the distribution of *D* in AB and RP, there are higher discrepancies between 90° (E) and 240° (SSW) in TF for NOAA, ERA5 and IOWAGA global models.

6 | DISCUSSION

The agreements between the global wave model parame-
ters and observations were found to be strongest at RP,
TF and AB, which can be attributed to their deeper

locations and greater distance from the coast compared
with the other sites analysed. For these sites situated in
open waters, the statistical metrics indicate a high level
of agreement, with *r* around 0.9, Bias within ±0.3 m,
RMSD below 0.5 m, and SI below 0.3 for *H*<sub>S</sub>. The statisti-
cal metrics obtained for these locations closely resemble
the values reported by Reguero et al. (2012) in a compari-
son between a global wave model and buoy measure-
ments. However, the agreements between the simulated
and measured *H*<sub>S</sub> were lower at coastal sites (QU, BB
and CP), where the representation of bathymetry by the
global products was less accurate. Wave transformation
processes, particularly shoaling and refraction, become
more significant in shallow water areas. It can be
observed that the computed *r* for *T* is slightly lower than
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

**FIGURE 4** Observed (red) and WAVERYS (black) wave heights at Río de la Plata (RP) (upper panel), Tierra del Fuego (TF) (central panel) and Atlantic Buoy (AB) (lower panel). Gaps in wave measurements (particularly from March to July, 1998) can be appreciated at Tierra del Fuego (TF). [Colour figure can be viewed at wileyonlinelibrary.com]

that for $H_s$. The maximum *r* value was obtained at RP (0.94) for the WAVERYS model, but it is generally around 0.6 at offshore sites. Regardless of the model used, there is a positive Bias for *T* (around 1–2 s) across most of the study area. This indicates that *T* values are slightly underestimated by global models, except for WAVERYS, ERA5 and NOAA-1 at the RP.

Global products provided the most accurate representation of *D* at TF and AB. All global models produced *r* values greater than 0.90 at both sites, with ERA5 reaching a remarkable 0.98 at TF. In contrast, the poorest performance in *D* was observed in QU and CP. For instance, the highest biases in *D* were observed in QU (21°, IOWAGA model, Table 4) and CP (20°, NOAA-1 model, Table 6), where the observed average propagation directions were 166° (SSE) and 90° (E), respectively. It is worth noting that these slight variations in the modelled directions only resulted in a change in the average simulated direction from SSE to SE in QU and from E to ENE in CP. In the remaining locations, the differences are significantly smaller.

Additionally, it is important to note that WAVERYS wave heights show a very good match ($r = 0.72$, Bias = -0.14 m, RMSD = 0.29 and SI = 0.35) with visually observed wave heights in the surf zone at PI, where the average annual wave height of breaking waves is around 1 m. The agreement was slightly lower at MP compared with PI. These results support the use of global wave models for coastal management in this tourist area of the Buenos Aires coast, at least until instrumental wave measurements become available. Based on the comparison of statistical metrics presented in Tables 3–7, considering the limited spatial and temporal data coverage, it can be concluded that WAVERYS is the global product that best represents the wave parameters in the study area.

Furthermore, WAVERYS adequately captures the temporal variability of $H_s$ at RP, TF and AB (Figure 4), located in the northern, southern and eastern parts of the study area, respectively. However, it should be noted that while WAVERYS satisfactorily captures severe conditions, it slightly underestimates the highest $H_s$ at RP, TF and AB.

Crespo et al. (2022) found that WAVERYS is the dataset that more realistically represented the wave heights observed by buoys and platforms over the southeastern Brazilian basins. On the other hand, wave parameters produced given by ERA5, NOAA, IOWAGA and WAVERYS were compared with buoy and altimeter data in the North Atlantic and Pacific Oceans (de Hauteclocque et al., 2020). It was pointed out that ERA5 and WAVERYS have a better performance due to the satellite data assimilation. In the present study, there would be two possible explanations for the very well performance of WAVERYS: A first explanation could be its higher spatial resolution (Table 2) and a second could also be due to the atmospheric forcing used. Surface wind used for forcing WAVERYS is provided from ERA5. Pescio et al. (2021) evaluated the performance of the zonal and meridional surface wind components, in a similar region to the study area, from seven global reanalyses: NCEP/National Center for Atmospheric Research (NCAR) Reanalysis Version 1, NCEP/Department of Energy (DOE) Reanalysis 2, NCEP/CFSR, ECMWF Interim Reanalysis, Japanese 55-year Reanalysis, the Modern-Era Retrospective Analysis for Research and Application (MERRA), Version 2 and ECMWF ERA5 Reanalysis and concluded that MERRA2 reanalysis is the product that better fitted with satellite data, followed by ERA5 in performance. Also, ERA5 was the database that better compared with the coastal observations measured at the meteorological stations.
**FIGURE 5** Wave spectra at the Río de la Plata mouth. Sea spectrum, 27 February 1998, 8:30 (left), swell spectrum, 6 July 1996, 4:10 (centre) and combined sea and swell spectrum 2 April 1997, 22:40 (right). [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_14.png" /></figure>

In this paper, the seasonal performance of global wave models was analysed at RP, TF and AB; however, limited interannual variability was observed in the study area. The most significant seasonal differences were found in TF. In this location on the southern Argentinean continental shelf, $H_s$ produced by the NOAA-1 global model exhibited the maximum range of variation in $r$, with values between 0.70 (summer) and 0.92 (spring), and the simulated periods by the WAVERYS model showed $r$ ranging from 0.48 (fall) to 0.81 (spring). Pescio et al. (2021) found that $r$ assessed between satellite and reanalysis data from ERA-Interim, JRA-55, CFSR, MERRA-2 and ERA5 for the meridional wind component showed a noticeable decrease towards the coast, especially in the southern zone of the study region (see figs. 8–10 in Pescio et al., 2021). Although the seasonal variability of wind was not investigated, the relatively lower value of $r$ for the meridional wind component in the southern region of South America could potentially explain the seasonal variability observed in $H_s$ and $T$ in TF.

It is evident (Tables 3–6) that the statistical metrics for $T$ at RP are notably better compared with the other locations studied. At the RP mouth, wave spectra can exhibit one or two peaks. In some cases, there is a single peak corresponding to either sea or swell, while in others, there are two simultaneous peaks representing both sea and swell (Figure 5). When the wave spectrum consists of a single peak, its period generally aligns reasonably well with the period provided by the global wave models. However, when the spectrum has two (or occasionally three) peaks, there is a potential for choosing the wrong peak for comparison. In this study, the observed period corresponding to the closest peak to the wave period predicted by the global models was selected. This simple

discrimination among spectral peaks was feasible because wave spectra were available from 1996 to 2001 at RP. A simple test was conducted, and when the statistical metrics were evaluated using the complete measurement period (1996–2009) and considering the period corresponding to the highest spectral peak, the obtained metrics significantly deteriorated at RP.

In the existing scientific peer-reviewed literature, there are no specific studies that focus on the presence of

<figure><img src="image_15.png" /></figure>

**FIGURE 6** Scatter plot for observed and WAVERYS swell heights. The linear fit equation (red line) and the correlation coefficient are presented in the upper left corner of this figure. [Colour figure can be viewed at wileyonlinelibrary.com]
swell in the study area. However, Dragani and Romero (2004) suggested that swell and sea could be characterized by periods of approximately 10 and 5 s, respectively, at the RP mouth. Salimbeni (2019) discovered that swell heights can reach up to 2 m at RP, but the exact areas where the swell originates from remain unknown. Law-Chune et al. (2021) highlighted the good accuracy of WAVERYS in capturing swell propagation, which can be attributed to the assimilation of directional wave spectra.

To evaluate the capability of WAVERYS in simulating swell, a selection of single-peak spectra with peak frequencies lower than 0.0834 Hz (T >12 s) was made at RP. The corresponding $H_S$ values were then compared with swell wave heights provided by the WAVERYS dataset (Figure 6). The comparison yielded a correlation coefficient of 0.86 between observed and simulated swell wave heights, indicating that the WAVERYS dataset is capable of capturing swell in this region. This preliminary result encourages further investigation into swell using the WAVERYS dataset. It appears that this global product is a robust wave dataset for studying the generation areas and travel time of swell recorded at the RP mouth.

7 | CONCLUSIONS

Wave parameters obtained from WAVERYS model, IOWAGA model, ERA5 model and two versions of WW3 model produced by NOAA were compared against in situ wave observations collected in the Southeastern South Atlantic Ocean. For this purpose, a comprehensive collection and analysis of historical wave data in the area was carried out. Bias, RMSD, SI and *r*, for $H_S$, *T* and *D*, were assessed for each one of the global models and at each location where wave observations are available.

The best agreements for $H_S$ were obtained at the deepest sites located farther from the coast. Typical obtained statistical metrics for $H_S$ were *r* around 0.9, Bias <|±0.3 ml, RMSD <0.5 m and SI <0.3 m. Lower values were achieved at coastal sites where the bathymetry is roughly represented by the global models. It was found that the computed *r* for *T* is slight lower than for $H_S$ and, independently of the model, Bias is positive (around 1–2 s) at almost all the study area, which indicates that *T* is slightly underestimated by global models. Wave propagation directions were excellently represented by global models at TF and AB.

WAVERYS model is the global product that better represents the wave parameters in the study area. Likely, it is a direct consequence of a higher spatial resolution (0.2°) and a more realistic atmospheric forcing (ERA5).

WAVERYS is also able to satisfactorily capture the temporal variability of $H_S$ at RP, TF and AB, although, the highest $H_S$ are slightly underestimated during severe conditions. The capability of WAVERYS in simulating swell was also evaluated at the RP mouth. A correlation coefficient of 0.86 was obtained between observed and simulated swell wave heights which indicates its satisfactory skill to represent the swell in this region of the South Atlantic Ocean.

AUTHOR CONTRIBUTIONS

**Walter Dragani:** Conceptualization; methodology; investigation; project administration; resources; writing – original draft; writing – review and editing.

**Alejandro Salimbeni:** Conceptualization; methodology; data curation; investigation; formal analysis; visualization; validation. **Guadalupe Alonso:** Methodology; data curation; investigation; visualization.

ACKNOWLEDGEMENTS

The authors of this paper thank the support to the **Ministerio de Ciencia y Tecnología, Argentina, Projects: A5, A10 and B5 and Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina** 112-202001-01402CO.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

ORCID

Walter Dragani https://orcid.org/0000-0002-0530-2660

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Salimbeni, A., Dragani, W., & Alonso, G. (2024). Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models. *International Journal of Climatology*, 44(8), 2725–2738. https://doi.org/10.1002/joc.8477