Global near-surface currents (annual mean)

A drifter-derived annualy climatology of global near-surface currents. Satellite-tracked SVP drifting buoys provide observations of near-surface circulation at unprecedented resolution.

http://www.aoml.noaa.gov/phod/dac/dac_meanvel.php

Satellite-tracked SVP drifting buoys (Sybrandy and Niiler, 1991; Niiler, 2001) provide observations of near-surface circulation at unprecedented resolution. In September 2005, the Global Drifter Array became the first fully realized component of the Global Ocean Observing System when it reached an array size of 1250 drifters. A drifter is composed of a surface float which includes a transmitter to relay data, a thermometer that reads temperature a few centimeters below the air/sea interface, and a submergence sensor used to detect when/if the drogue is lost. The surface float is tethered to a holey sock drogue, centered at 15 m depth. The drifter follows the flow integrated over the drogue depth, although some slip with respect to this motion is associated with direct wind forcing (Niiler and Paduan, 1995). This slip is greatly enhanced in drifters that have lost their drogues (Pazan and Niiler, 2000). Drifter velocities are derived from finite differences of their position fixes. These velocities, and the concurrent SST measurements, are archived at AOML's Drifting Buoy Data Assembly Center where the data are quality controlled and interpolated to 1/4-day intervals (Hansen and Herman, 1989; Hansen and Poulain, 1996).

In this study, 6h winds were interpolated onto the drifter positions and used to estimate and remove the slip (Niiler and Paduan, 1995; Pazan and Niiler, 2000; Laurindo et al., 2017). All velocities and SSTs were lowpassed at 1.5 times the local inertial period, or five days if that is smaller, to remove high frequency variability (diurnal, tidal, inertial). If 1.5 times the local inertial period is shorter than one day, the lowpass is done at one day.

Drifters sample regions of the ocean inhomogeneously, which can cause aliased time-mean values if strong seasonal or interannual variations are neglected. To address the seasonal cycle, Lumpkin (2003) developed a methodology to simultaneously decompose the drifter observations into time-mean, seasonal and eddy components using a Gauss-Markov approach that produces formal error bars on all components. Lumpkin showed that this methodology produces significantly different results than standard bin averaging. This methodology was further developed and evaluated using SST observations and products, and simulated drifters in the MICOM model (Lumpkin and Garraffo, 2005). The method produces significantly improved estimates of the mean currents and SST, and simultaneously provides the annual and semiannual amplitudes and phases at a nominal resolution of one degree squared.

To address inhomogeneous interannual sampling associated with ENSO, Johnson (2001) added a component proportional to a five-month lowpassed Southern Oscillation Index, and estimated components in elliptical bins with axes scaled and oriented using the residual variability (i.e., the eddy fluctuations).

In Lumpkin and Johnson (2013), the methodologies of Lumpkin (2003) and Johnson (2001) are combined. The observations are projected onto a time mean, annual and semiannual, SOI, and spatial gradient components within elliptical bins scaled and oriented using eddy fluctuations, and error bars are estimated for all terms.

In Laurindo et al. (2017), slip correction using a spatially-varying coefficient was introduced in order to exploit velocity information from undrogued drifters. This approximately doubles the number of available observations, which allowed the bin sizes to be considerably reduced (one degree radius circles). In addition, 1D spatial fitting was done in the direction that maximizes the explained variance, allowing for greater resolution of cross-stream velocity gradient. These two changes result in significantly better spatial resolution of ocean current structure. In addition, Laurindo et al. examined how the formal standard errors compare to actual errors when using a "toy" dataset of surface currents derived from AVISO and subsampled at the drifter observation locations and times; they found that formals errors underestimate actual errors by approximately a factor of 2.

When these more advanced methodologies are applied to the modern data set of tropical Atlantic drifter observations, many features of the near-surface circulation become apparent which were not resolved by older ship-drift-based climatologies or by SEQUAL/FOCAL drifter trajectories (Lumpkin and Garzoli, 2005). In the Hawaiian Island region, the thousand-kilometer long island wake is revealed at unprecedented detail (Lumpkin and Flament, 2013) Zonally elongated mid-ocean striations are resolved in the zonal currents of all major ocean basins (Laurindo et al., 2017).

Datasets available for download

Additional Info

Field Value
Last Updated July 9, 2026, 06:51 (UTC)
Created March 24, 2026, 21:03 (UTC)
Domain / Topic
Domain or topic of the dataset being cataloged.
Title
Title for the Dataset.
Global near-surface currents (annual mean)
Description
A description of the dataset.

A drifter-derived annualy climatology of global near-surface currents. Satellite-tracked SVP drifting buoys provide observations of near-surface circulation at unprecedented resolution.

http://www.aoml.noaa.gov/phod/dac/dac_meanvel.php

Satellite-tracked SVP drifting buoys (Sybrandy and Niiler, 1991; Niiler, 2001) provide observations of near-surface circulation at unprecedented resolution. In September 2005, the Global Drifter Array became the first fully realized component of the Global Ocean Observing System when it reached an array size of 1250 drifters. A drifter is composed of a surface float which includes a transmitter to relay data, a thermometer that reads temperature a few centimeters below the air/sea interface, and a submergence sensor used to detect when/if the drogue is lost. The surface float is tethered to a holey sock drogue, centered at 15 m depth. The drifter follows the flow integrated over the drogue depth, although some slip with respect to this motion is associated with direct wind forcing (Niiler and Paduan, 1995). This slip is greatly enhanced in drifters that have lost their drogues (Pazan and Niiler, 2000). Drifter velocities are derived from finite differences of their position fixes. These velocities, and the concurrent SST measurements, are archived at AOML's Drifting Buoy Data Assembly Center where the data are quality controlled and interpolated to 1/4-day intervals (Hansen and Herman, 1989; Hansen and Poulain, 1996).

In this study, 6h winds were interpolated onto the drifter positions and used to estimate and remove the slip (Niiler and Paduan, 1995; Pazan and Niiler, 2000; Laurindo et al., 2017). All velocities and SSTs were lowpassed at 1.5 times the local inertial period, or five days if that is smaller, to remove high frequency variability (diurnal, tidal, inertial). If 1.5 times the local inertial period is shorter than one day, the lowpass is done at one day.

Drifters sample regions of the ocean inhomogeneously, which can cause aliased time-mean values if strong seasonal or interannual variations are neglected. To address the seasonal cycle, Lumpkin (2003) developed a methodology to simultaneously decompose the drifter observations into time-mean, seasonal and eddy components using a Gauss-Markov approach that produces formal error bars on all components. Lumpkin showed that this methodology produces significantly different results than standard bin averaging. This methodology was further developed and evaluated using SST observations and products, and simulated drifters in the MICOM model (Lumpkin and Garraffo, 2005). The method produces significantly improved estimates of the mean currents and SST, and simultaneously provides the annual and semiannual amplitudes and phases at a nominal resolution of one degree squared.

To address inhomogeneous interannual sampling associated with ENSO, Johnson (2001) added a component proportional to a five-month lowpassed Southern Oscillation Index, and estimated components in elliptical bins with axes scaled and oriented using the residual variability (i.e., the eddy fluctuations).

In Lumpkin and Johnson (2013), the methodologies of Lumpkin (2003) and Johnson (2001) are combined. The observations are projected onto a time mean, annual and semiannual, SOI, and spatial gradient components within elliptical bins scaled and oriented using eddy fluctuations, and error bars are estimated for all terms.

In Laurindo et al. (2017), slip correction using a spatially-varying coefficient was introduced in order to exploit velocity information from undrogued drifters. This approximately doubles the number of available observations, which allowed the bin sizes to be considerably reduced (one degree radius circles). In addition, 1D spatial fitting was done in the direction that maximizes the explained variance, allowing for greater resolution of cross-stream velocity gradient. These two changes result in significantly better spatial resolution of ocean current structure. In addition, Laurindo et al. examined how the formal standard errors compare to actual errors when using a "toy" dataset of surface currents derived from AVISO and subsampled at the drifter observation locations and times; they found that formals errors underestimate actual errors by approximately a factor of 2.

When these more advanced methodologies are applied to the modern data set of tropical Atlantic drifter observations, many features of the near-surface circulation become apparent which were not resolved by older ship-drift-based climatologies or by SEQUAL/FOCAL drifter trajectories (Lumpkin and Garzoli, 2005). In the Hawaiian Island region, the thousand-kilometer long island wake is revealed at unprecedented detail (Lumpkin and Flament, 2013) Zonally elongated mid-ocean striations are resolved in the zonal currents of all major ocean basins (Laurindo et al., 2017).
Tags / Keywords
Keywords/tags categorizing the dataset.
Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
Dataset Size
Dataset size in megabytes.
73760.0
Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
Published Date
Published date of the dataset.
2017-08-02
Time Period Data Span (start date)
Start date of the data in the dataset.
Time Period Data Span (end date)
End date of time data in the dataset.
GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
Field Value
Access category
Type of access granted for the dataset (open, closed, service, etc).
public
License
License used to access the dataset.
Please cite as reference:Laurindo, L., A. Mariano, and R. Lumpkin, 2017: An improved near-surface velocity climatology for the global ocean from drifter observations Deep-Sea Res. I, 124, pp.73-92, doi:10.1016/j.dsr.2017.04.009
Limits on use
Limits on use of data.
Location
Location of the dataset.
https://climat.esri.ca/datasets/esrifrance::global-near-surface-currents-annual-mean
Data Service
Data service for accessing a dataset.
Owner
Owner of the dataset.
rgarnier_esrifrance
Contact Point
Who to contact regarding access?
Esri France
Contact Point Email
The email to contact regarding access?
[email protected]
Publisher
Publisher of the dataset.
Esri France
Publisher Email
Email of the publisher.
Author
Author of the dataset.
rgarnier_esrifrance
Author Email
Email of the author.
Accessed At
Date the data and metadata was accessed.
2025-07-01
Field Value
Identifier
Unique identifier for the dataset.
ef5342d164ba4e32af439796d5c9e9b7
Language
Language(s) of the dataset
English
Link to dataset description
A URL to an external document describing the dataset.
https://climat.esri.ca/datasets/esrifrance::global-near-surface-currents-annual-mean
Persistent Identifier
Data is identified by a persistent identifier.
Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
Contains data about individuals
Does the data hold data about individuals?
Contains data about identifiable individuals
Does the data hold identifiable data about individual?
Contains Indigenous Data
Does the data hold data about Indigenous communities?
Portal Type
Platform type of the source portal.
Field Value
Version
Version of the datatset
None
Source
Source of the dataset.
None
Version notes
Version notes about the dataset.
Is version of another dataset
Link to dataset that it is a version of.
Other versions
Link to datasets that are versions of it.
Provenance Text
Provenance Text of the data.
Esri France
Provenance URL
Provenance URL of the data.
Temporal resolution
Describes how granular the date/time data in the dataset is.
GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
Field Value
Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
Community Permission
Community permission (who gave permission).
The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
Field Value
Number of data rows
If tabular dataset, total number of rows.
504736
Number of data columns
If tabular dataset, total number of unique columns.
7
Number of data cells
If tabular dataset, total number of cells with data.
3533152
Number of data relations
If RDF dataset, total number of triples.
Number of entities
If RDF dataset, total number of entities.
Number of data properties
If RDF dataset, total number of unique properties used by the triples.
Data quality
Describes the quality of the data in the dataset.
Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.

0 Comments

Please login or register to comment.