the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Meteorological Drivers of Extreme Swells on the Peruvian Coast
Abstract. In this study, we analyze the meteorological configurations leading to extreme ocean swells along the Peruvian coast, which are frequently produced by remote Pacific storms from both hemispheres. Using extreme-swell warnings from the Peruvian Navy and ERA5 reanalysis, we examine five austral-winter Southern Hemisphere (SH) events (very strong, south-westerly) and six boreal-winter Northern Hemisphere (NH) events (strong, north-westerly). Event-centred composites are computed over lead windows guided by estimated swell travel times (3–4 days in the SH; 8–11 days in the NH). In both hemispheres, a deep extratropical cyclone becomes vertically aligned from sea-level pressure through 500 hPa to 250 hPa before coastal peak swell, while an upper-level jet core strengthens and organizes a persistent corridor of enhanced surface westerlies over the swell-generation region. In the SH, coherent surrounding ridging tightens the meridional pressure gradient and co-occurs with a strengthened, sharper polar-front jet. In the NH, preconditioning is dominated by a deep central–western North Pacific low with comparatively weak, localized ridging and a markedly intensified, more zonally extended subtropical jet, while the polar-front jet weakens. A flow-analogue framework suggests a recent strengthening of SH event-related surface winds consistent with increased large-scale pressure contrasts and a shift toward more positive Southern Annular Mode conditions, whereas NH events show no robust trend and attribution is obscured by strong interannual-to-decadal variability. These results can support earlier recognition of remote swell hazards affecting Peru and, consequently, can lead to an improvement of early warning systems.
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Status: open (until 07 May 2026)
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CC1: 'Comment on egusphere-2026-408', Momme Hell, 24 Feb 2026
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CC2: 'Reply on CC1', Soledad Maribel Collazo Inglesini, 24 Feb 2026
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Thank you for pointing this out. Upon careful inspection, we confirm that the co-authors in the Hell et al. (2021) citation were indeed listed incorrectly, while the title, journal, and DOI were correct. We apologize for this oversight, which occurred during the reformatting of references to comply with the journal's style requirements.
We have thoroughly reviewed all references in the manuscript. The corrected citation reads:
Hell, M. C., Ayet, A., & Chapron, B. Swell generation under extra-tropical storms. Journal of Geophysical Research: Oceans, 126, e2021JC017637. https://doi.org/10.1029/2021JC017637, 2021
During this verification process, we also identified and corrected an error in the citation of Semedo et al. (2011). All remaining references have been thoroughly checked and confirmed to be accurate.
Citation: https://doi.org/10.5194/egusphere-2026-408-CC2
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CC2: 'Reply on CC1', Soledad Maribel Collazo Inglesini, 24 Feb 2026
reply
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RC1: 'Comment on egusphere-2026-408', Anonymous Referee #1, 19 Apr 2026
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This study uses extreme swell warnings issued by the Peruvian Navy and ERA5 reanalysis data to analyze the atmospheric circulation precursors of extreme swell events in the Southern Hemisphere (5 events) and Northern Hemisphere (6 events). It reveals the hemispherically differentiated configuration of the polar-front jet and the subtropical jet, and employs a flow-analogue approach to assess the impact of climate change on event intensity. The research topic has clear practical value, the methodology is relatively advanced, and the results are directly relevant to swell early warning along the Peruvian coast. The overall scientific quality is good; however, there are several methodological limitations and deficiencies in result interpretation. It is recommended that the manuscript be revised before acceptance. The specific suggestions are as follows:
1. Only five SH events and six NH events are included, spanning 17 years. The conclusions may be affected by interdecadal bias, raising doubts about their generalizability. It is recommended to extend the event selection period back to beyond 1980 (using earlier wave reanalysis or historical documentary records) to increase the number of events.
2. The study uses a fixed great-circle distance and a constant wave period (16–20 s), without considering nonlinear dissipation, refraction, diffraction, or background currents (e.g., equatorial currents) during wave propagation. The 8‑ to 11‑day composite window for NH events may incorporate substantial unrelated meteorological noise. It is recommended to use at least one wave hindcast model (e.g., WAVEWATCH III) to simulate one or two representative events (one from each hemisphere), validating the wave height, period, and travel time from the generation region to the Peruvian coast. If such modelling is not feasible, a sensitivity analysis should be added: recompute the travel-time window using different wave periods (14–22 s) and great-circle distances (±10%), and assess the sensitivity of the composite fields to window shifts.
3. For NH events, wind speeds weaken in E6–E8 but strengthen in E9–E11. Although modes such as PDO, ENSO, PNA, AO, and WP are listed, their influences are inconsistent across events, and no unified physical explanation or statistical model is provided. It is recommended to divide the NH events into two groups (E6–E8 vs. E9–E11) and perform separate composite and flow-analogue analyses for each group, comparing the phase distributions of modes such as PDO and ENSO between the two groups. In addition, multiple linear regression or random forest methods should be used to quantify the relative contributions of each climate mode to the 10‑m wind speed anomalies and to identify the dominant factors.
4. The flow-analogue method uses only SLP, Z500, and Z250, without including boundary conditions such as sea surface temperature (SST) or sea ice. This may mistakenly classify weather patterns that are dynamically similar but thermodynamically different as the same type, thereby introducing spurious climate change signals. It is recommended to add the SST field (e.g., monthly SST anomalies) as a fourth variable in the analogue similarity calculation, or at least to perform a sensitivity test: keep the existing three variables unchanged, but use the SST anomalies corresponding to the analogue dates as a posteriori diagnostic variable to test whether SST differs significantly between the earlier and later periods.
5. The study uses only the significant height of wind waves (SHWW) in the generation region as an indicator of swell generation, without providing measured wave height, period, or energy spectral data at the Peruvian coast. The causal chain between the source region and coastal impacts is therefore incomplete. It is recommended to use measured wave data from at least one coastal site in Peru (e.g., Callao, Miraflores, or Paita), which may come from wave buoys, tide gauge inversion, or ERA5 nearshore points, and to produce time-series plots for one or two typical events (e.g., E1 and E9) showing the evolution from SHWW anomalies in the source region to nearshore wave heights, thereby directly illustrating the wave evolution during propagation. If measured data are unavailable, significant wave height time series should be extracted from ERA5 nearshore points (within 50 km of the coast) and subjected to lagged correlation analysis with the SHWW anomalies in the generation region (with the lag time set to the estimated travel time) to quantitatively verify the causal relationship.
Citation: https://doi.org/10.5194/egusphere-2026-408-RC1
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At least one citation in the bibliography is incorrect (Hell et al., 2021) and lists the wrong co-authors.
This suggests the use of an LLM for citation generation.