Ocean fronts construct spatial zonation in microfossil assemblages

This paper provides a concise review of the remote sensing of ocean fronts in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms and techniques, including those proposed by Canny, Cayula and Cornillon, Miller, Shimada et al., Belkin and O’Reilly, and Nieto et al.. A case is made for a feature-based approach that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

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Coherent pathways for subduction from the surface mixed layer at ocean fronts

10.1002/essoar.10504909.1 ◽

2020 ◽

Author(s):

Mara Freilich ◽

Amala Mahadevan

Keyword(s):

Mixed Layer ◽

Surface Mixed Layer ◽

Ocean Fronts

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Destruction of Potential Vorticity by Winds

Journal of Physical Oceanography ◽

10.1175/jpo2830.1 ◽

2005 ◽

Vol 35 (12) ◽

pp. 2457-2466 ◽

Cited By ~ 145

Author(s):

Leif N. Thomas

Keyword(s):

Boundary Layer ◽

Wind Stress ◽

Potential Vorticity ◽

Ekman Layer ◽

Buoyancy Flux ◽

Coriolis Parameter ◽

Mode Water ◽

Ocean Fronts ◽

Secondary Circulations ◽

Frictional Forces

Abstract The destruction of potential vorticity (PV) at ocean fronts by wind stress–driven frictional forces is examined using PV flux formalism and numerical simulations. When a front is forced by “downfront” winds, that is, winds blowing in the direction of the frontal jet, a nonadvective frictional PV flux that is upward at the sea surface is induced. The flux extracts PV out of the ocean, leading to the formation of a boundary layer thicker than the Ekman layer, with nearly zero PV and nonzero stratification. The PV reduction is not only active in the Ekman layer but is transmitted through the boundary layer via secondary circulations that exchange low PV from the Ekman layer with high PV from the pycnocline. Extraction of PV from the pycnocline by the secondary circulations results in an upward advective PV flux at the base of the boundary layer that scales with the surface, nonadvective, frictional PV flux and that leads to the deepening of the layer. At fronts forced by both downfront winds and a destabilizing atmospheric buoyancy flux FBatm, the critical parameter that determines whether the wind or the buoyancy flux is the dominant cause for PV destruction is (H/δe)(FBwind/FBatm), where H and δe are the mixed layer and Ekman layer depths, FBwind = S2τo/(ρof ), S2 is the magnitude of the lateral buoyancy gradient of the front, τo is the downfront component of the wind stress, ρo is a reference density, and f is the Coriolis parameter. When this parameter is greater than 1, PV destruction by winds dominates and may play an important role in the formation of mode water.

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The structure of mesoscale turbulence and horizontal spreading at ocean fronts

Deep Sea Research Part A Oceanographic Research Papers ◽

10.1016/0198-0149(84)90104-3 ◽

1984 ◽

Vol 31 (3) ◽

pp. 245-269 ◽

Cited By ~ 36

Author(s):

R.W. Griffiths ◽

E.J. Hopfinger

Keyword(s):

Ocean Fronts

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The Seasonal Cycle of Blocking and Associated Physical Mechanisms in the Australian Region and Relationship with Rainfall

Monthly Weather Review ◽

10.1175/mwr-d-13-00040.1 ◽

2013 ◽

Vol 141 (12) ◽

pp. 4534-4553 ◽

Cited By ~ 27

Author(s):

M. J. Pook ◽

J. S. Risbey ◽

P. C. McIntosh ◽

C. C. Ummenhofer ◽

A. G. Marshall ◽

...

Keyword(s):

Southern Ocean ◽

Seasonal Cycle ◽

Seasonal Temperature ◽

Ocean Fronts ◽

Temperature Changes ◽

Physical Mechanisms ◽

Australian Region ◽

Continental Antarctica ◽

Positive Correlations ◽

Blocking Index

Abstract The seasonal cycle of blocking in the Australian region is shown to be associated with major seasonal temperature changes over continental Antarctica (approximately 15°–35°C) and Australia (about 8°–17°C) and with minor changes over the surrounding oceans (below 5°C). These changes are superimposed on a favorable background state for blocking in the region resulting from a conjunction of physical influences. These include the geographical configuration and topography of the Australian and Antarctic continents and the positive west to east gradient of sea surface temperature in the Indo-Australian sector of the Southern Ocean. Blocking is represented by a blocking index (BI) developed by the Australian Bureau of Meteorology. The BI has a marked seasonal cycle that reflects seasonal changes in the strength of the westerly winds in the midtroposphere at selected latitudes. Significant correlations between the BI at Australian longitudes and rainfall have been demonstrated in southern and central Australia for the austral autumn, winter, and spring. Patchy positive correlations are evident in the south during summer but significant negative correlations are apparent in the central tropical north. By decomposing the rainfall into its contributions from identifiable synoptic types during the April–October growing season, it is shown that the high correlation between blocking and rainfall in southern Australia is explained by the component of rainfall associated with cutoff lows. These systems form the cyclonic components of blocking dipoles. In contrast, there is no significant correlation between the BI and rainfall from Southern Ocean fronts.

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Storms drive outgassing of CO2 in the subpolar Southern Ocean

Nature Communications ◽

10.1038/s41467-021-27780-w ◽

2022 ◽

Vol 13 (1) ◽

Author(s):

Sarah-Anne Nicholson ◽

Daniel B. Whitt ◽

Ilker Fer ◽

Marcel D. du Plessis ◽

Alice D. Lebéhot ◽

...

Keyword(s):

Southern Ocean ◽

Critical Region ◽

Process Model ◽

Ekman Transport ◽

Ocean Turbulence ◽

Ocean Fronts ◽

Ocean Variability ◽

Synoptic Variability ◽

Ocean Physics ◽

Global Mean

AbstractThe subpolar Southern Ocean is a critical region where CO2 outgassing influences the global mean air-sea CO2 flux (FCO2). However, the processes controlling the outgassing remain elusive. We show, using a multi-glider dataset combining FCO2 and ocean turbulence, that the air-sea gradient of CO2 (∆pCO2) is modulated by synoptic storm-driven ocean variability (20 µatm, 1–10 days) through two processes. Ekman transport explains 60% of the variability, and entrainment drives strong episodic CO2 outgassing events of 2–4 mol m−2 yr−1. Extrapolation across the subpolar Southern Ocean using a process model shows how ocean fronts spatially modulate synoptic variability in ∆pCO2 (6 µatm2 average) and how spatial variations in stratification influence synoptic entrainment of deeper carbon into the mixed layer (3.5 mol m−2 yr−1 average). These results not only constrain aliased-driven uncertainties in FCO2 but also the effects of synoptic variability on slower seasonal or longer ocean physics-carbon dynamics.

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An Efficient Algorithm for Ocean-Front Evolution Trend Recognition

Remote Sensing ◽

10.3390/rs14020259 ◽

2022 ◽

Vol 14 (2) ◽

pp. 259

Author(s):

Yuting Yang ◽

Kenneth Kin-Man Lam ◽

Xin Sun ◽

Junyu Dong ◽

Redouane Lguensat

Keyword(s):

Classification Accuracy ◽

State Of The Art ◽

Classification Algorithm ◽

Final Decision ◽

Ocean Fronts ◽

Ocean Front ◽

Novel Method ◽

Evolution Laws ◽

The Relationship ◽

Evolution Trend

Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the evolution laws of marine hydrological elements are urgently needed. In this paper, a novel method, named Evolution Trend Recognition (ETR), is proposed to recognize the trend of ocean fronts, being the most important information in the ocean dynamic process. Therefore, in this paper, we focus on the task of ocean-front trend classification. A novel classification algorithm is first proposed for recognizing the ocean-front trend, in terms of the ocean-front scale and strength. Then, the GoogLeNet Inception network is trained to classify the ocean-front trend, i.e., enhancing or attenuating. The ocean-front trend is classified using the deep neural network, as well as a physics-informed classification algorithm. The two classification results are combined to make the final decision on the trend classification. Furthermore, two novel databases were created for this research, and their generation method is described, to foster research in this direction. These two databases are called the Ocean-Front Tracking Dataset (OFTraD) and the Ocean-Front Trend Dataset (OFTreD). Moreover, experiment results show that our proposed method on OFTreD achieves a higher classification accuracy, which is 97.5%, than state-of-the-art networks. This demonstrates that the proposed ETR algorithm is highly promising for trend classification.

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Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

Remote Sensing ◽

10.3390/rs11070844 ◽

2019 ◽

Vol 11 (7) ◽

pp. 844 ◽

Cited By ~ 1

Author(s):

Fan Wu ◽

Peter Cornillon ◽

Lei Guan ◽

Katherine Kilpatrick

Keyword(s):

Quality Data ◽

Retrieval Algorithm ◽

Ocean Fronts ◽

Noaa Avhrr ◽

Data Record ◽

Along Track ◽

Level 2 ◽

Very High ◽

Significant Effort

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.

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