Characteristics of Surface Current Flow Inferred from a Global Ocean Current Data Set

Since 2011, pelagic Sargassum has experienced extraordinary blooms in the Tropical Atlantic where a system of persistent but seasonally variable currents has retained and consolidated it in large masses. Although beneficial at sea, principally as a unique pelagic habitat, when Sargassum inundates the nearshore environment it can have catastrophic effects on tourism, fisheries, health, and local ecosystems. Providing advanced warning of arrival dates of large masses of Sargassum is critical for enabling preparations and planning for its removal, use, and mitigation. Predictions of arrival time and location involve satellite identification of Sargassum at sea together with ocean current data for forward model tracking. However, forecast ocean current data are generally valid for only 5—7 days. In this study, ocean currents from 2 models (HYCOM and OSCAR) are validated against satellite tracked drifters from the Global Drifter Program with vector correlation and with skill in replicating a drifter pathway. Various wind additions to the models are also tested. Although both models capture the surface current systems in the Tropical Atlantic, they are mediocre in performance along both boundaries. In contrast, a drifter based current data model with 0.5% wind addition had high skill levels. This skill—tested drifter—based model was then used to determine marine connectivity across the Tropical Atlantic and suggests a much broader spread of Sargassum in the eastern Tropical Atlantic than is presently observed by satellites, conforming to earlier hypotheses. This model forms the basis for seasonal scale Sargassum forecasting.

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Estimates of Ocean Currents Using a Labrador Sea Ice Model

Annals of Glaciology ◽

10.3189/s0260305500005371 ◽

1983 ◽

Vol 4 ◽

pp. 141-146

Author(s):

T. E. Keliher ◽

J. S. Foley

Keyword(s):

Current Data ◽

Ocean Current ◽

Labrador Sea ◽

Data Set ◽

Model Simulations ◽

Pack Ice ◽

Ice Drift ◽

Ice Concentration ◽

Fast Ice ◽

Ice Model

Efforts to develop a dynamical model of Labrador pack ice have been hampered by a lack of input data, especially ocean current data. This work reports the results of approaching this problem in another way, where model simulations are used as a basis for adjusting the input currents to obtain agreement with ice drift data. The ice drift data comes from a period in February 1977 when a ship was fast in the northern Labrador pack. Unfortunately, the ship experienced navigation problems and the direct current observations were suspect. The model simulations bear this out indicating that it would have been very unlikely that the observed currents along with the other forcing terms could lead to the observed ice drift. A climatological current data set gave better agreement and was slightly modified to give the best agreement. This improved data set was used for a short sensitivity test of the rheological specifications of the model. It was found that the ice drift tracks were not very sensitive to the linear viscous stress law but were more sensitive to the parameterization used to reduce ice velocities in areas of high ice concentration and fast ice. Further plans are to use a model with a more realistic rheological component in order to assess its importance in estimating currents.

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Improvement of Ocean State Estimation by Assimilating Mapped Argo Drift Data

The Scientific World JOURNAL ◽

10.1155/2014/975618 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6

Author(s):

Shuhei Masuda ◽

Nozomi Sugiura ◽

Satoshi Osafune ◽

Toshimasa Doi

Keyword(s):

State Estimation ◽

Current Data ◽

Global Ocean ◽

Ocean Current ◽

Oceanic Circulation ◽

Synthesis System ◽

Wind Stress Curl ◽

Data Synthesis ◽

Ocean State Estimation ◽

The Impact

We investigated the impact of assimilating a mapped dataset of subsurface ocean currents into an ocean state estimation. We carried out two global ocean state estimations from 2000 to 2007 using the K7 four-dimensional variational data synthesis system, one of which included an additional map of climatological geostrophic currents estimated from the global set of Argo floats. We assessed the representativeness of the volume transport in the two exercises. The assimilation of Argo ocean current data at only one level, 1000 dbar depth, had subtle impacts on the estimated volume transports, which were strongest in the subtropical North Pacific. The corrections at 10°N, where the impact was most notable, arose through the nearly complete offset of wind stress curl by the data synthesis system in conjunction with the first mode baroclinic Rossby wave adjustment. Our results imply that subsurface current data can be effective for improving the estimation of global oceanic circulation by a data synthesis.

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Estimates of Ocean Currents Using a Labrador Sea Ice Model

Annals of Glaciology ◽

10.1017/s0260305500005371 ◽

1983 ◽

Vol 4 ◽

pp. 141-146 ◽

Cited By ~ 3

Author(s):

T. E. Keliher ◽

J. S. Foley

Keyword(s):

Current Data ◽

Ocean Current ◽

Labrador Sea ◽

Data Set ◽

Model Simulations ◽

Pack Ice ◽

Ice Drift ◽

Ice Concentration ◽

Fast Ice ◽

Ice Model

Efforts to develop a dynamical model of Labrador pack ice have been hampered by a lack of input data, especially ocean current data. This work reports the results of approaching this problem in another way, where model simulations are used as a basis for adjusting the input currents to obtain agreement with ice drift data. The ice drift data comes from a period in February 1977 when a ship was fast in the northern Labrador pack. Unfortunately, the ship experienced navigation problems and the direct current observations were suspect. The model simulations bear this out indicating that it would have been very unlikely that the observed currents along with the other forcing terms could lead to the observed ice drift. A climatological current data set gave better agreement and was slightly modified to give the best agreement. This improved data set was used for a short sensitivity test of the rheological specifications of the model. It was found that the ice drift tracks were not very sensitive to the linear viscous stress law but were more sensitive to the parameterization used to reduce ice velocities in areas of high ice concentration and fast ice. Further plans are to use a model with a more realistic rheological component in order to assess its importance in estimating currents.

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Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

International Journal of Navigation and Observation ◽

10.1155/2016/2605198 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Adam Gauci ◽

Aldo Drago ◽

John Abela

Keyword(s):

Oil Spills ◽

Surface Current ◽

Radar Data ◽

Current Data ◽

Hf Radar ◽

Sea Surface ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Spatial Coverage ◽

Essential Components

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

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Oceanic eddy signature on SAR-derived sea ice drift and vorticity

10.5194/egusphere-egu21-2233 ◽

2021 ◽

Author(s):

Angelina Cassianides ◽

Camillie Lique ◽

Anton Korosov

Keyword(s):

Sea Ice ◽

Surface Current ◽

Satellite Observation ◽

The Arctic ◽

Global Ocean ◽

Sar Images ◽

Surface Ocean ◽

Ice Drift ◽

Sea Ice Drift ◽

Oceanic Eddy

In the global ocean, mesoscale eddies are routinely observed from satellite observation. In the Arctic Ocean, however, their observation is impeded by the presence of sea ice, although there is a growing recognition that eddy may be important for the evolution of the sea ice cover. In this talk, we will present a new method of surface ocean eddy detection based on their signature in sea ice vorticity retrieved from Synthetic Aperture Radar (SAR) images. A combination of Feature Tracking and Pattern Matching algorithm is used to compute the sea ice drift from pairs of SAR images. We will mostly focus on the case of one eddy in October 2017 in the marginal ice zone of the Canadian Basin, which was sampled by mooring observations, allowing a detailed description of its characteristics. Although the eddy could not be identified by visual inspection of the SAR images, its signature is revealed as a dipole anomaly in sea ice vorticity, which suggests that the eddy is a dipole composed of a cyclone and an anticyclone, with a horizontal scale of 80-100 km and persisted over a week. We will also discuss the relative contributions of the wind and the surface current to the sea ice vorticity. We anticipate that the robustness of our method will allow us to detect more eddies as more SAR observations become available in the future.

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Interannual response of global ocean hindcasts to a satellite-based correction of precipitation fluxes

Ocean Science Discussions ◽

10.5194/osd-9-611-2012 ◽

2012 ◽

Vol 9 (2) ◽

pp. 611-648 ◽

Cited By ~ 7

Author(s):

A. Storto ◽

I. Russo ◽

S. Masina

Keyword(s):

Ocean Circulation ◽

Surface Current ◽

Global Ocean ◽

Surface Salinity ◽

Near Surface ◽

Convergence Zones ◽

Circumpolar Current ◽

The Tropics ◽

Spatially Varying ◽

The Antarctic

Abstract. We present a methodology to correct precipitation fluxes from the ECMWF atmospheric reanalysis (ERA-Interim) for oceanographic applications. The correction is performed by means of a spatially varying monthly climatological coefficient, computed within the period 1989–2008 by comparison between ERA-Interim and a satellite-based passive microwave precipitation product. ERA-Interim exhibits a systematic over-estimation of precipitation within the inter-tropical convergence zones (up to 3 mm d−1) and under-estimation at mid- and high- latitudes (up to −4 mm d−1). The correction has been validated within eddy-permitting resolution global ocean hindcasts (1989–2009), demonstrating the ability of our strategy in attenuating the 20-yr mean global EMP negative imbalance by 16%, reducing the near-surface salinity fresh bias in the Tropics up to 1 psu and improving the representation of the sea level interannual variability, with an SSH error decrease of 8%. The ocean circulation is also proved to benefit from the correction, especially in correspondence of the Antarctic Circumpolar Current, where the error in the near-surface current speed decreases by a 9%. Finally, we show that the correction leads to volume and freshwater transports that better agree with independent estimates.

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A global high-resolution data set of ice sheet topography, cavity geometry and ocean bathymetry

10.5194/essd-2016-3 ◽

2016 ◽

Cited By ~ 4

Author(s):

Janin Schaffer ◽

Ralph Timmermann ◽

Jan Erik Arndt ◽

Steen Savstrup Kristensen ◽

Christoph Mayer ◽

...

Keyword(s):

High Resolution ◽

Continental Shelf ◽

Ice Sheet ◽

The Arctic ◽

Global Ocean ◽

Data Set ◽

Antarctic Ice Sheet ◽

Ice Shelves ◽

Surface Topographies ◽

The Antarctic

Abstract. The ocean plays an important role in modulating the mass balance of the polar ice sheets by interacting with the ice shelves in Antarctica and with the marine-terminating outlet glaciers in Greenland. Given that the flux of warm water onto the continental shelf and into the sub-ice cavities is steered by complex bathymetry, a detailed topography data set is an essential ingredient for models that address ice-ocean interaction. We followed the spirit of the global RTopo-1 data set and compiled consistent maps of global ocean bathymetry, upper and lower ice surface topographies and global surface height on a spherical grid with now 30-arc seconds resolution. We used the General Bathymetric Chart of the Oceans (GEBCO_2014) as the backbone and added the International Bathymetric Chart of the Arctic Ocean version 3 (IBCAOv3) and the International Bathymetric Chart of the Southern Ocean (IBCSO) version 1. While RTopo-1 primarily aimed at a good and consistent representation of the Antarctic ice sheet, ice shelves and sub-ice cavities, RTopo-2 now also contains ice topographies of the Greenland ice sheet and outlet glaciers. In particular, we aimed at a good representation of the fjord and shelf bathymetry surrounding the Greenland continent. We corrected data from earlier gridded products in the areas of Petermann Glacier, Hagen Bræ and Sermilik Fjord assuming that sub-ice and fjord bathymetries roughly follow plausible Last Glacial Maximum ice flow patterns. For the continental shelf off northeast Greenland and the floating ice tongue of Nioghalvfjerdsfjorden Glacier at about 79° N, we incorporated a high-resolution digital bathymetry model considering original multibeam survey data for the region. Radar data for surface topographies of the floating ice tongues of Nioghalvfjerdsfjorden Glacier and Zachariæ Isstrøm have been obtained from the data centers of Technical University of Denmark (DTU), Operation Icebridge (NASA/NSF) and Alfred Wegener Institute (AWI). For the Antarctic ice sheet/ice shelves, RTopo-2 largely relies on the Bedmap-2 product but applies corrections for the geometry of Getz, Abbot and Fimbul ice shelf cavities. The data set is available in full and in regional subsets in NetCDF format from the PANGAEA database at https://doi.pangaea.de/10.1594/PANGAEA.856844.

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DUACS DT2014: the new multi-mission altimeter data set reprocessed over 20 years

Ocean Science ◽

10.5194/os-12-1067-2016 ◽

2016 ◽

Vol 12 (5) ◽

pp. 1067-1090 ◽

Cited By ~ 180

Author(s):

Marie-Isabelle Pujol ◽

Yannice Faugère ◽

Guillaume Taburet ◽

Stéphanie Dupuy ◽

Camille Pelloquin ◽

...

Keyword(s):

Measurement Errors ◽

Large Scale ◽

Temporal Correlation ◽

Error Reduction ◽

Internal Tides ◽

Global Ocean ◽

Altimeter Data ◽

Data Set ◽

Error Budget ◽

Spatial Coverage

Abstract. The new DUACS DT2014 reprocessed products have been available since April 2014. Numerous innovative changes have been introduced at each step of an extensively revised data processing protocol. The use of a new 20-year altimeter reference period in place of the previous 7-year reference significantly changes the sea level anomaly (SLA) patterns and thus has a strong user impact. The use of up-to-date altimeter standards and geophysical corrections, reduced smoothing of the along-track data, and refined mapping parameters, including spatial and temporal correlation-scale refinement and measurement errors, all contribute to an improved high-quality DT2014 SLA data set. Although all of the DUACS products have been upgraded, this paper focuses on the enhancements to the gridded SLA products over the global ocean. As part of this exercise, 21 years of data have been homogenized, allowing us to retrieve accurate large-scale climate signals such as global and regional MSL trends, interannual signals, and better refined mesoscale features.An extensive assessment exercise has been carried out on this data set, which allows us to establish a consolidated error budget. The errors at mesoscale are about 1.4 cm2 in low-variability areas, increase to an average of 8.9 cm2 in coastal regions, and reach nearly 32.5 cm2 in high mesoscale activity areas. The DT2014 products, compared to the previous DT2010 version, retain signals for wavelengths lower than ∼  250 km, inducing SLA variance and mean EKE increases of, respectively, +5.1 and +15 %. Comparisons with independent measurements highlight the improved mesoscale representation within this new data set. The error reduction at the mesoscale reaches nearly 10 % of the error observed with DT2010. DT2014 also presents an improved coastal signal with a nearly 2 to 4 % mean error reduction. High-latitude areas are also more accurately represented in DT2014, with an improved consistency between spatial coverage and sea ice edge position. An error budget is used to highlight the limitations of the new gridded products, with notable errors in areas with strong internal tides.

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Monitoring the Ocean Heat Content and the Earth Energy imbalance from space altimetry and space gravimetry: the MOHeaCAN product

10.5194/egusphere-egu21-1225 ◽

2021 ◽

Author(s):

Marti Florence ◽

Ablain Michaël ◽

Fraudeau Robin ◽

Jugier Rémi ◽

Meyssignac Benoît ◽

...

Keyword(s):

Time Scales ◽

Radiant Energy ◽

Heat Content ◽

Ocean Heat Content ◽

Global Ocean ◽

Argo Data ◽

Data Set ◽

Energy Imbalance ◽

Temporal Sampling ◽

The Earth

The Earth Energy Imbalance (EEI) is a key indicator to understand climate change. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of <0.3 W.m-2 at decadal time scales is necessary to evaluate the long term mean EEI associated with anthropogenic forcing. Ideally an accuracy of <0.1 W.m-2 at decadal time scales is desirable if we want to monitor future changes in EEI.In the frame of the MOHeaCAN project supported by ESA, the EEI indicator is deduced from the global change in Ocean Heat Content (OHC) which is a very good proxy of the EEI since the ocean stores 93% of the excess of heat&#160; gained by the Earth in response to EEI. The OHC is estimated from space altimetry and gravimetry missions (GRACE). This &#8220;Altimetry-Gravimetry'' approach is promising because it provides consistent spatial and temporal sampling of the ocean, it samples nearly the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean&#8217;s entire depth. Consequently, it complements the OHC estimation from the ARGO network.&#160;The MOHeaCAN product contains monthly time series (between August 2002 and June 2017) of several variables, the main ones being the regional OHC (3&#176;x3&#176; spatial resolution grids), the global OHC and the EEI indicator. Uncertainties are provided for variables at global scale, by propagating errors from sea level measurements (altimetry) and ocean mass content (gravimetry). In order to calculate OHC at regional and global scales, a new estimate of the expansion efficiency of heat at global and regional scales have been performed based on the global ARGO network.&#160;A scientific validation of the MOHeaCAN product has also been carried out performing thorough comparisons against independent estimates based on ARGO data and on the Clouds and the Earth&#8217;s Radiant energy System (CERES) measurements at the top of the atmosphere. The mean EEI derived from MOHeaCAN product is 0.84 W.m-2 over the whole period within an uncertainty of &#177;0.12 W.m-2 (68% confidence level - 0.20 W.m-2 at the 90% CL). This figure is in agreement (within error bars at the 90% CL) with other EEI indicators based on ARGO data (e.g. OHC-OMI from CMEMS) although the best estimate is slightly higher. Differences from annual to inter-annual scales have also been observed with ARGO and CERES data. Investigations have been conducted to improve our understanding of the benefits and limitations of each data set to measure EEI at different time scales.The MOHeaCAN product from &#8220;altimetry-gravimetry&#8221; is now available and can be downloaded at https://doi.org/10.24400/527896/a01-2020.003. Feedback from interested users on this product are welcome.

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