Altimetry-Based Diagnosis of Deep-Reaching Sub-Mesoscale Ocean Fronts

Siegelman, Lia; Klein, Patrice; Thompson, Andrew F.; Torres, Héctor; Menemenlis, Dimitris

doi:10.3390/fluids5030145

Cited by 22 publications

(20 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the ocean surface has been observed at high temporal and spatial resolution with satellites since the early 1990s. Remote sensing allows us to observe surface signa-E. Pauthenet et al: Ocean Stratification network ture of mesoscale to submesoscale features (Siegelman et al, 2020b) and to track climatic trends of sea surface height (Nerem et al, 2018), temperature (Merchant et al, 2019) and salinity (Reul et al, 2020). It is therefore highly valuable for combining sparse in situ profiles and high-resolution remote sensing observations in order to predict the ocean stratification at higher resolution and frequency.…”

Section: Introductionmentioning

confidence: 99%

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

et al. 2022

View full text Add to dashboard Cite

Abstract. Despite the ever-growing number of ocean data, the interior of the ocean remains undersampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T−S) profiles down to 1000 m and the mixed-layer depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T−S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4∘ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The lowest confidence in the prediction is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-Argo years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean stratification, it is also a tool to study how the ocean stratification relates to surface data. We can compute the relative importance of each input for each T−S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results demonstrate the potential of machine learning methods to improve predictions of ocean interior properties from observations of the ocean surface.

show abstract

Section: Introductionmentioning

confidence: 99%

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The FSLE from AVISO product are derived with δ 0 = 0.02° and δ f = 0.6°, which is half of one grid point and about the local first Rossby radius, respectively. As such, the FSLE from AVISO have been employed to delineate submesoscale structures (Meunier & LaCasce, 2021;Siegelman et al, 2020). Previous studies have suggested that regions with large FSLE mainly correspond to the regions where submesoscale transports are strong while regions with small FSLE are located in the regions of weak currents (Calil & Richards, 2010;Guo et al, 2019).…”

Section: Satellite Productsmentioning

confidence: 99%

Oceanic Fronts Structure Phytoplankton Distributions in the Central South Indian Ocean

2021

View full text Add to dashboard Cite

In the South Indian Ocean (SIO), high surface chlorophyll concentrations are often observed in mesoscale anticyclonic eddy cores. Yet the role of submesoscale dynamics in modulating phytoplankton distributions remains largely unknown in the region. By using a biogeochemical‐Argo float and a high‐resolution model, we show that elevated chlorophyll concentrations were evident in a frontal region, and the depth‐integrated chlorophyll in this frontal region can be higher than that in the anticyclonic eddy core. Submesoscale frontogenesis was shown to inject nutrients vertically and enhance phytoplankton growth in the frontal region. Frontal dynamics tend to facilitate large phytoplankton such as diatom growth, which consequently increases diatom fraction. Over the entire central SIO, we found a significant correlation between the phytoplankton composition index and frontogenesis. However, the correlation between total phytoplankton biomass and frontogenesis was not significant. These results demonstrate the complex role of frontal dynamics in structuring phytoplankton distributions in the central SIO.

show abstract

“…Ferrari and Wunsch 2009;McWilliams, 2016;Rocha et al, 2016a). Additionally, they play a critical role in connecting the surface ocean with the interior, through the modulation of the mixed layer seasonality and heat transfer (Capet et al, 2008;Klein et al, 2008;Thomas et al, 2008;Su et al, 2020;Siegelman, 2020). on the location and season, the energy and SSH signature associated with unbalanced motions (including near-inertial flows, internal tides, and inertia-gravity waves) can overcome that of the balanced motions at smaller scales (Rocha et al, 2016b;Qiu et al, 2018, Chereskin et al, 50 2019), imposing a wavelength boundary beyond which SSH measurements provided by satellite altimetry can no longer be used to infer upper ocean geostrophic flows.…”

Section: Introductionmentioning

confidence: 99%

Global submesoscale diagnosis using alongtrack satellite altimetry

Vergara

Morrow²,

Pujol³

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. The ocean’s sea surface height (SSH) field is a complex mix of motions in geostrophic balance and unbalanced motions including high-frequency tides, internal tides and internal gravity waves. Barotropic tides are well estimated for altimetric SSH in the open ocean, but the SSH signals of internal tides remains. The transition scale, Lt, at which these unbalanced ageostrophic motions dominate balanced geostrophic motions, is estimated for the first-time using satellite altimetry. Lt is critical to define the spatial scales above which surface geostrophic currents can be inferred from SSH gradients. We use a statistical approach based on the analysis of 1 Hz altimetric SSH wavenumber spectra to obtain four geophysical parameters that vary regionally and seasonally: the background error, the spectral slope in the mesoscale range, a second spectral slope at smaller scales, and Lt. The mesoscale slope and error levels are similar to previous studies based on satellite altimetry. The break in the wavenumber spectra to a flatter spectral slope can only be estimated in mid-latitude regions where the signal exceeds the altimetric noise level. Small values of Lt are observed in regions of energetic mesoscale activity, while larger values are observed towards low latitudes and regions of lower mesoscale activity. These results are consistent with recent analyses of in situ observations and high-resolution models. Limitations of our results and implications for reprocessed nadir and future swath altimetric missions are discussed.

show abstract

Altimetry-Based Diagnosis of Deep-Reaching Sub-Mesoscale Ocean Fronts

Cited by 22 publications

References 52 publications

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

Oceanic Fronts Structure Phytoplankton Distributions in the Central South Indian Ocean

Global submesoscale diagnosis using alongtrack satellite altimetry

Contact Info

Product

Resources

About