Sea State Decadal Variability in the North Atlantic: A Review

Hochet, Antoine; Dodet, Guillaume; Ardhuin, Fabrice; Hemer, Mark; Young, Ian R.

doi:10.3390/cli9120173

Cited by 21 publications

(21 citation statements)

References 117 publications

(203 reference statements)

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“…A limitation of our study concerns the limited representation of the atmospheric multi-decadal variability in climate models. Indeed, a number of recent studies have shown that climate models significantly underestimate the amplitude of multi-decadal NAO, jet stream variability or sea surface temperature (Kim et al, 2018;Kravtsov, 2017;Sévellec & Drijfhout, 2019;Simpson et al, 2018). If the amplitude of the internal multi-decadal variability is in fact larger than simulated by climate models, the DoE for H s would occur even later than what was shown in this study (Figures 4 and 3).…”

Section: Discussionmentioning

confidence: 39%

“…This study focuses on the NA because it is a region of intense maritime traffic, numerous marine energy project and new engineering infrastructures that necessitates an accurate understanding of its wave climate variability. Moreover, numerous studies have demonstrated the link between the NA winter H s variability and the winter Sea Level Pressure (SLP) variability over the same region (Bacon & Carter, 1993; Dodet et al., 2010; Gulev et al., 1998; Hochet et al., 2021; Kushnir et al., 1997; Shimura et al., 2013; Woolf, 2002). In particular, a large part of the H s variability is believed to be correlated with the North Atlantic Oscillation (NAO) winter index (Woolf, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Time of Emergence for Altimetry‐Based Significant Wave Height Changes in the North Atlantic

Hochet¹,

Dodet²,

Sévellec³

et al. 2023

Geophysical Research Letters

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Satellite observations of significant wave height (Hs) have recently reached 30 years of continuous record. Is this length sufficient to detect the effect of anthropogenically forced climate change on wave height trends? Wave height decadal variability is influenced by a combination of internal variability and forced variability caused by both anthropogenic and natural forcing. Using a statistical model to derive Hs from sea level pressure field and exploiting ERA‐5 reanalysis data as well as 80 members of the Community Earth System Model v2 large ensemble, we show that, over the North Atlantic (NA), altimetry‐based Hs trends are mostly caused by internal variability. This suggests that Hs changes computed over the satellite era are not yet controlled by anthropogenic climate change. Starting from 1993, the date of emergence, defined as the date when the forced signal becomes dominant over the internal variability, is later than 2050 for Hs in the NA.

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Section: Discussionmentioning

confidence: 39%

Section: Introductionmentioning

confidence: 99%

Time of Emergence for Altimetry‐Based Significant Wave Height Changes in the North Atlantic

Hochet¹,

Dodet²,

Sévellec³

et al. 2023

Geophysical Research Letters

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“…A number of approaches were used to understand the modulation of climate variability on ocean waves. These methods include linear regression (Kumar et al., 2016; Ramos et al., 2021), composite analysis (Kumar et al., 2019; Odériz et al., 2020; Ramos et al., 2021; Sasaki et al., 2021), EOF analysis (Hemer et al., 2010; Hochet et al., 2021; Izaguirre et al., 2011; Li et al., 2021; Ramos et al., 2021; Semedo et al., 2011; Stopa & Cheung, 2014), and wavelet analysis (Anoop et al., 2015; Castelle et al., 2018; Cromwell, 2006; Sasaki et al., 2021; Scott et al., 2021). Each of these approaches is briefly described below.…”

Section: Methodsmentioning

confidence: 99%

“…The cumulative variances of the first four EOFs account for 46.58% and 45.33% of the variability of H s anomalies in DJF and JJA, respectively. Note that the variances of the EOFs are domain‐dependent (Hochet et al., 2021). Researchers interested in a specific region should downsize the EOF domain and introduce extra significant climate indexes.…”

Section: Eof and Wavelet Analysismentioning

confidence: 99%

“…There are many studies which have been carried out to investigate the impacts of natural climate variability on regional or global ocean waves, based on empirical orthogonal function (EOF) analysis (Cromwell, 2006; Hemer et al., 2010; Hochet et al., 2021; Li et al., 2021; Ramos et al., 2021; Sasaki et al., 2021; Scott et al., 2021; Semedo et al., 2011; Stopa & Cheung, 2014), canonical correlation analysis (Hochet et al., 2021), correlation maps (Hemer et al., 2010; Hochet et al., 2021; J. Liu, Meucci, Liu, et al., 2022; Odériz et al., 2020), linear regression (Hemer et al., 2013; Kumar et al., 2016, 2019), composite analysis (Godoi & Torres Júnior, 2020; Kumar et al., 2016, 2019; Patra et al., 2020; Ramos et al., 2021), or time–frequency analysis (Castelle et al., 2018; Cromwell, 2006; Li et al., 2021; Sasaki et al., 2021; Scott et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Multiple Approaches to Study the Modulation of Ocean Waves Due To Climate Variability

Liu,

Meucci,

Young

2023

JGR Oceans

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Using multiple approaches, the modulation of climate variability on global ocean waves from 1981 to 2020 is investigated based on a wave hindcast model forced by ERA5 winds. These climate variabilities include: El Niño‐Southern Oscillation (ENSO), Antarctic Oscillation (AAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and Indian Ocean Dipole (IOD). Linear regression and composite analysis results indicate that El Niño‐induced low‐pressure systems in December‐January‐February (DJF) generate energetic waves in the Pacific Ocean. Positive AAO events are associated with the southward movement of westerlies in the Southern Hemisphere and thus produce long‐period Southern Ocean swell that impacts the South Pacific Ocean. In the Northern Hemisphere, positive AO is associated with strengthening winds which generate larger waves around the Icelandic region. An empirical orthogonal function (EOF) approach was further undertaken to study interannual variability of significant wave height (Hs) anomalies. In DJF, mode 1 marks the impacts of ENSO and PDO on the Pacific Ocean and AO in the North Atlantic Ocean. Mode 2 is controlled by AO, which is characterized by a dipole pattern in the North Atlantic Ocean. Mode 3 describes the AAO footprints in the Southern Ocean and the eastern Pacific Ocean. In June‐July‐August (JJA), the leading mode is influenced by AAO and AMO. Mode 2 indicates the importance of ENSO and PDO which exhibit opposite patterns in the Pacific Ocean sector of the Southern Ocean. Finally, a wavelet coherence analysis is conducted on the principal components and climate indexes, which shows their respective evolutions over the time‐frequency domain.

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Storm identification for high-energy wave climates as a tool to improve long-term analysis

Kümmerer,

Ferreira,

Fanti

et al. 2023

Clim Dyn

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Coastal storms can cause erosion and flooding of coastal areas, often accompanied by significant social-economic disruption. As such, storm characterisation is crucial for an improved understanding of storm impacts and thus for coastal management. However, storm definitions are commonly different between authors, and storm thresholds are often selected arbitrarily, with the statistical and meteorological independence between storm events frequently being neglected. In this work, a storm identification algorithm based on statistically defined criteria was developed to identify independent storms in time series of significant wave height for high wave energy environments. This approach proposes a minimum duration between storms determined using the extremal index. The performance of the storm identification algorithm was tested against the commonly used peak-over-threshold. Both approaches were applied to 40 and 70-year-long calibrated wave reanalyses datasets for Western Scotland, where the intense and rapid succession of extratropical storms during the winter makes the identification of independent storm events notably challenging. The storm identification algorithm provides results that are consistent with regional meteorological processes and timescales, allowing to separate independent storms during periods of rapid storm succession, enabling an objective and robust storm characterisation. Identifying storms and their characteristics using the proposed algorithm allowed to determine a statistically significant increasing long-term trend in storm duration, which contributes to the increase in storm wave power in the west of Scotland. The coastal storm identification algorithm is found to be particularly suitable for high-energy, storm-dominated coastal environments, such as those located along the main global extratropical storm tracks.

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Sea State Decadal Variability in the North Atlantic: A Review

Cited by 21 publications

References 117 publications

Time of Emergence for Altimetry‐Based Significant Wave Height Changes in the North Atlantic

Time of Emergence for Altimetry‐Based Significant Wave Height Changes in the North Atlantic

A Comparison of Multiple Approaches to Study the Modulation of Ocean Waves Due To Climate Variability

Storm identification for high-energy wave climates as a tool to improve long-term analysis

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