Global Ocean Wind‐Wave Model Hindcasts Forced by Different Reanalyzes: A Comparative Assessment

Sharmar, Vitali; Markina, Margarita; Gulev, Sergey

doi:10.1029/2020jc016710

Cited by 25 publications

(22 citation statements)

References 85 publications

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“…These differences are often further complicated and accentuated due to differences among numerical wind-wave modelling methods (e.g., source-term wave parametrizations, numerical resolutions, sea-ice forcing fields and/or bias-correction approaches) available to generate historical wave fields 26 . Despite such differences, most assessments relying on wave characteristics continue to use single pre-selected global wave hindcast or reanalysis products, therefore limiting our confidence in conclusions derived therefrom, as previously discussed 24 , 27 .…”

Section: Introductionmentioning

confidence: 99%

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

et al. 2022

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There are numerous global ocean wave reanalysis and hindcast products currently being distributed and used across different scientific fields. However, there is not a consistent dataset that can sample across all existing products based on a standardized framework. Here, we present and describe the first coordinated multi-product ensemble of present-day global wave fields available to date. This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, includes general and extreme statistics of significant wave height (Hs), mean wave period (Tm) and mean wave direction (θm) computed across 1980–2014, at different frequency resolutions (monthly, seasonally, and annually). This coordinated global ensemble has been derived from fourteen state-of-the-science global wave products obtained from different atmospheric reanalysis forcing and downscaling methods. This data set has been processed, under a specific framework for consistency and quality, following standard Data Reference Syntax, Directory Structures and Metadata specifications. This new comprehensive dataset provides support to future broad-scale analysis of historical wave climatology and variability as well as coastal risk and vulnerability assessments across offshore and coastal engineering applications.

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Section: Introductionmentioning

confidence: 99%

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

et al. 2022

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“…On the real Earth, many of the SGW trends found in the contemporary period and those projected for the twenty-first century are directly related to trends in the zonalmean winds (Fan et al, 2013(Fan et al, , 2014Young and Ribal, 2019;Sharmar et al, 2021). The strongest SGW trends are between 40 and 60°S, i.e.…”

Section: Introductionmentioning

confidence: 97%

“…Many of the most prominent atmospheric phenomena driving SGW variability at the large‐scale, in particular the atmospheric jet streams and trade winds, are features of the zonal‐mean winds (Fan et al., 2013, 2014; Markina et al., 2019; Sharmar et al., 2021). The variability and dynamics of these phenomena have long been studied using the aquaplanet idealization (e.g., Hayashi & Sumi, 1986; Medeiros et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

“…On the real Earth, many of the SGW trends found in the contemporary period (1980–2019) and those projected for the twenty‐first century are directly related to trends in the zonal‐mean winds (Fan et al., 2013, 2014; Sharmar et al., 2021; Young & Ribal, 2019). The strongest SGW trends are between 40° and 60°S, that is, the Southern Ocean region (Sharmar et al., 2021; Young & Ribal, 2019), a region where traditional OGCMs, that neglect SGWs, tend to underestimate oceanic MLD (Huang et al., 2014; Sallee et al., 2013) and where MLD is poorly explained by contributions from shear and buoyancy alone (Panassa et al., 2018). This suggests that SGWs might be significant to the Southern Ocean mixed layer, which is a region where zonal symmetry is a reasonable first‐order simplification (Smith et al., 2006).…”

Section: Introductionmentioning

confidence: 99%

“…The strongest SGW trends are between 40 and 60°S, i.e. the Southern Ocean region (Young and Ribal, 2019;Sharmar et al, 2021), a region where traditional OGCMs, that neglect SGWs, tend to underestimate oceanic MLD (Sallee et al, 2013;Huang et al, 2014) and where MLD is poorly explained by contributions from shear and buoyancy alone (Panassa et al, 2018). This suggests that SGWs might be significant to the Southern Ocean mixed layer, which is a region where zonal symmetry is a reasonable first-order simplification (Smith et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Role of Surface Gravity Waves in Aquaplanet Ocean Climates

Studholme

Markina

Gulev

2021

J Adv Model Earth Syst

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1. Idealized oceanic climates forced by ranging atmospheric regimes following equator-topole thermal gradient perturbations are investigated.2. Wind-forced surface gravity waves deepen the mixed layer, increase mixed layer vertical momentum diffusivity and dampen surface currents.3. The consistency of the effects of waves on ocean dynamics and stratification across cooler/warmer aquaplanet climates is examined.

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A global wave parameter database for geophysical 1 applications. Part 3: improved forcing and spectral 2 resolution

Alday¹,

Accensi²,

Ardhuin³

2020

Preprint

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Numerical wave models are used for a wide range of applications, from the global ocean to coastal scales. Here we report on significant improvements compared to the previous hindcast detailed in Part 2 of the present study by Rascle and Ardhuin (2013). This result was obtained by updating forcing fields, adjusting the spectral discretization and retuning wind wave growth and swell dissipation parameters. Most of the model calibration and performance analysis is done using significant wave heights (Hs) from the recent re-calibrated and denoised satellite altimeter data set provided by the European Space Agency Climate Change Initiative (ESA-CCI), with additional verification using spectral buoy data. We find that, for the year 2011, using wind fields from the recent ERA5 reanalysis provides lower scatter against satellite Hs data compared to historical ECMWF operational analyses, but still yields a low bias on wave heights that can be mitigated by re-scaling wind speeds larger than 20 m/s. Alternative blended wind products can provide more accurate forcing in some regions, but were not retained because of larger errors elsewhere. We use the shape of the probability density function of Hs around 2 m to fine tune the swell dissipation parameterization. The updated model hindcast appears to be generally more accurate than the previous version, and can be more accurate than the ERA5 Hs estimates, in particular in strong current regions and for Hs > 7 m. Highlights► An update on the Rascle and Ardhuin (2013) wave hindcast is presented. ► ERA5 wind forcing gives best results but requires a bias correction for high winds. ► Satellite altimeter data was used to adjust swell dissipation parameters.

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Global Ocean Wind‐Wave Model Hindcasts Forced by Different Reanalyzes: A Comparative Assessment

Abstract: Wind waves are also of great importance for the marine industry. Thus, an accurate assessment of interannual to interdecadal variability in wind waves is crucially important. The global interannual variability in wind waves has been extensively analyzed using satellite altimetry (

Cited by 25 publications

References 85 publications

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

Role of Surface Gravity Waves in Aquaplanet Ocean Climates

A global wave parameter database for geophysical 1 applications. Part 3: improved forcing and spectral 2 resolution

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