•1 min read•from Frontiers in Marine Science | New and Recent Articles
Southern-latitude wind forcing as a predictor of swell energy and coastal wave power in Peru
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The Peruvian coast is significantly shaped by swells originating from the Southern Ocean, making the relationship between southern-latitude wind stress and wave power variability crucial for effective coastal management and renewable energy initiatives. This study analyzes wind stress anomalies at latitudes of 30°S, 40°S, 50°S, and 60°S, utilizing ERA5 reanalysis data from 1979 to 2023. Findings reveal that anomalies at 50°S and 60°S account for 45–55% of wave power variance, with implications for predicting extreme events and
The Peruvian coast is strongly influenced by remotely generated swells from the Southern Ocean. Understanding the relationship between wind stress forcing at southern latitudes and wave power variability in Peru is critical for coastal management, hazard prevention, and renewable energy planning. This study examines wind stress anomalies at 30°S, 40°S, 50°S, and 60°S and their influence on wave power off Paita, Callao, and Ilo using ERA5 reanalysis data (1979–2023). Wave power was computed from significant wave height and energy period, applying cross-correlation, wavelet coherence, and spectral decomposition. Wind stress anomalies at 50°S and 60°S explain 45–55% of the variance in wave power, with lags of 5–7 days depending on location. Interannual variability is linked to ENSO, while decadal oscillations are modulated by the Southern Annular Mode (SAM). These findings highlight the key role of Southern Ocean wind stress in controlling swell energy reaching Peru, providing predictive capacity for extreme events and strategies for coastal management and renewable wave energy.
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Tagged with
#ocean data#interactive ocean maps#ocean circulation#data visualization#wind forcing#wind stress anomalies#swell energy#Southern Ocean#coastal wave power#wave power variability#renewable energy planning#coastal management#significant wave height#hazard prevention#ERA5 reanalysis data#ENSO#interannual variability#predictive capacity#energy period#wavelet coherence