# A drifter-derived monthly climatology of global near-surface currents

Version number:Version number: 3.2. Created on: 30 April 2018. Most recent data included: 28 February 2018. Documentation: Laurindo et al. (2017). The following changes have been made from what is described in Laurindo et al.: (1) in Laurindo et al., velocities were lowpassed at 5 days. Here, the velocities were lowpassed at 1.5 times the local inertial period, or at 5 days if that is smaller, with a floor of 1 day to guarantee removing diurnal tides. This results in stronger mean velocities and much more eddy kinetic energy at higher latitudes; (2) more recent data were used; (3) mean values of SST are now calculated, using the same methodology as with currents; (4) a refined check is performed to see if a grid point lies within the data coverage of each bin. We now use a rectangular area rotated by the angle of the minimum-variance 1-D polynomial, with 100 km height, as wide as the 1-D polynomial. This new scheme results in the exclusion of a smaller number of estimates, and is more compatible with the assumption that the mean flow varies solely as a function of the distance across the time-mean velocity isolines in the bin.

On this page, you will find a climatology of near-surface currents and temperatures for the world, at monthly and one-quarter degree resolution, derived from satellite-tracked surface drifting buoy observations. Animations of these currents are available here. Maps of these currents in various regions can be seen on the Ocean Surface Currents web site.

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.2017.04.009This climatology is an update of older climatologies, archived at ftp://ftp.aoml.noaa.gov/phod/pub/lumpkin/drifter_climatology.

For more information about the drifters, see the Global Drifter Program information page.

The climatology is available in three formats (Matlab, ASCII and NetCDF), and three versions are available. The first version ( Matlab binary format/ ASCII/ NetCDF ) contains annual mean values of the near-surface currents. The Matlab file contains the following variables:

**Lon (1x1440): longitude (degrees), negative=West.**

**Lat (1x632): latitude (degrees), negative=South.**

**U (632x1440): eastward speed (m/s) vs. Lat/Lon.**

**V (632x1440): northward speed (m/s).**

**SST (632x1440): bulk sea surface temperature (degC).**

**eU, eV, eSST (632x1440): standard errors for U, V (m/s), and SST (degC).**

**N (632x1440): number of drifter-days per square degree.**The columns of the ASCII dataset are: Lat, Lon, U, V, SST, eU, eV, eSST, N.

The second version ( Matlab binary format/ ASCII/ NetCDF ) contains monthly mean values of surface currents and SST. The variable names of the Matlab file are the same as above. The third index of matrix U, V, etc. corresponds to the month: 1=January, 2=February, etc. In the ASCII dataset, the first column is the month; the rest of the columns are the same as in the annual climatology file described above.

The third version ( Matlab binary format/ ASCII/ NetCDF ) contains information about current variance (eddy kinetic energy) derived from residuals with respect to the time-mean, seasonal, spatial gradient, and SOI-related currents. The Matlab file contains the following variables:

**Lon (1x720): longitude (degrees), negative=West.**

**Lat (1x317): latitude (degrees), 73S to 85N.**

**Up2bar (317x720): Zonal Velocity Variance, mean(u' ^2) (m^2/s^2).**

**Vp2bar (317x720): Meridional Velocity Variance, mean(v' ^2) (m^2/s^2).**

**rA (317x720): Variance ellipse semimajor axis (m^2/s^2).**

**rB (317x720): Variance ellipse semiminor axis (m^2/s^2).**

**angle (317x720): Orientation angle of variance ellipse (degrees, 0=east/west).**

**N (317x720): Number of drifter-days per square degree.**Note that Up2bar+Vp2bar=rA+rB, which is twice the eddy kinetic energy.

** Description**: 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).

*Credits:* This climatology was developed
by Rick Lumpkin (NOAA/AOML) and Lucas Laurindo (Univ. Miami), in collaboration
with Arthur Mariano (Univ. Miami), Mayra Pazos (NOAA/AOML), and Erik Valdes (CIMAS/AOML).
Previous versions were developed with
Gregory Johnson (NOAA/PMEL), Silvia Garzoli (NOAA/AOML),
Jessica Redman (CIMAS), and Zulema Garraffo(Univ. Miami).

*References*

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Labrador Sea boundary currents and the fate of the Irminger Sea Water. J.
Phys. Oceanogr. 32, 627-647.
**

**Hansen, D. and A. Herman, 1989: Temporal sampling
requirements for surface drifting buoys in the tropical Pacific. J.
Atmos. Oceanic Technol. 6, 599-607.
**

**Hansen, D. and P.-M. Poulain, 1996: Quality control and
interpolations of WOCE-TOGA drifter data. J. Atmos. Oceanic Technol.
13, 900-909.
**

**Johnson, G.C., 2001: The Pacific Ocean Subtropical Cell Surface Limb. Geophys. Res. Lett., 28, 1771-1774.
**

**Laurindo, L. C., A. Mariano, and R. Lumpkin, 2017: An improved near-surface velocity climatology
for the global ocean from drifter observations. Deep-Sea Res. I, 124, 73-92.
**

**Lumpkin, R., 2003: Decomposition of surface drifter
observations in the Atlantic Ocean. Geophys. Res. Lett. 30(14),
1753, 10.1029/2003GL017519.
**

**Lumpkin, R. and S. L. Garzoli, 2005: Near-surface
Circulation in the Tropical Atlantic Ocean. Deep-Sea Res.I 52
(3), 495-518, 10.1016/j.dsr.2004.09.001.
**

**Lumpkin, R. and Z. Garraffo, 2005: Evaluating the
Decomposition of Tropical Atlantic Drifter Observations. J. Atm.
Oceanic Technol. 22, 1403-1415.
**

**Lumpkin, R. and G. C. Johnson, 2013: Global Ocean Surface Velocities from Drifters: Mean, Variance, ENSO Response, and Seasonal Cycle. J. Geophys. Res.-Oceans, 118, pp 2992-3006, doi:10.1002/jgrc.20210.
**

**Lumpkin, R. and P. Flament, 2013: On the extent and energetics of the Hawaiian Lee Countercurrent. Oceanography, 26(1), 58-65.
**

**Niiler, P. P., 2001: The world ocean surface circulation. In
Ocean Circulation and Climate, G. Siedler, J. Church and J.
Gould, eds., Academic Press, Volume 77 of International Geophysics
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**

**Niiler, P. P., R. Davis and H. White, 1987: Water-following
characteristics of a mixed-layer drifter. Deep-Sea Res. 34,
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**

**Niiler, P. P. and J. D. Paduan, 1995: Wind-driven motions in
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Oceanogr. 25, 2819-2830.
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**Niiler, P. P., A. Sybrandy, K. Bi, P. Poulain and D.
Bitterman, 1995: Measurements of the water-following capability of
holey-sock and TRISTAR drifters. Deep-Sea Res. 42,
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**Pazan, S. E. and P. P. Niiler, 2000: Recovery of
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**Sybrandy, A. L. and P. P. Niiler, 1991: WOCE/TOGA Lagrangian
drifter construction manual. WOCE Rep. 63, SOI Ref. 91/6, 58pp, Scripps
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**