2016 MARINE DEBRIS MODELING REPORT 

Modeling 

Modeling Oceanic Transport of 

Floating Marine Debris 

2016 NOAA Marine Debris Program Report 
May 2016 

National Oceanic and Atmospheric Administration 

National Ocean Service 

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NOAA Marine Debris Program 

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Cover photo National Oceanic and Atmospheric Administration. 

For citation purposes, please use: 

National Oceanic and Atmospheric Administration Marine Debris Program. 2016 Report on Modeling Oceanic Transport of Floating Marine Debris. Silver Spring, 

MD. 21 pp 

For more information, please contact: 

NOAA Marine Debris Program 

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www.MarineDebris.noaa.gov 
Acknowledgements 

The National Oceanic and Atmospheric Administration (NOAA) Marine Debris Program would like to acknowledge William L. Balthis (National Centers for Coastal 

Ocean Science – Center for Coastal Environmental Health and Biomolecular Research) for his effort in synthesizing the wide range of modeling research and efforts 

into this paper, and Peter Murphy for providing guidance and support throughout this process. Special thanks go to Dr. Amy MacFadyen, Dr. James Potemra, and 

Laurent Lebreton for reviewing this paper and providing helpful comments. An additional thank you goes to Krista Stegemann for a copy/edit review of this report 

and Catherine Polk for graphic design. Funding for this project was provided by the NOAA Marine Debris Program. 

This publication does not constitute an endorsement of any commercial product or intend to be an opinion beyond scientific or other results obtained by the 

National Oceanic and Atmospheric Administration. No reference shall be made to NOAA, or this publication furnished by NOAA, to any advertising or sales 

promotion which would indicate or imply that NOAA recommends or endorses any proprietary product mentioned herein, or which has as its purpose an interest 

to cause the advertised product to be used or purchased because of this publication. 
 

 

 

 

 

 

 

 

 

 

 

2016 MARINE DEBRIS MODELING REPORT 

Executive Summary 1 

Background 2 

Synthesis of Literature 6 

Surface Currents 6 

Ocean General Circulation Models 8 

Linked Ocean General Circulation Models 

- Particle Tracking Models 10 

Conclusion 13 

State of the Science 13 

Recommendations 14 

Cited References 16 

Glossary 20 

TABLE OF CONTENTS 
2016 MARINE DEBRIS MODELING REPORT 

EXECUTIVE SUMMARY
 

Man-made debris in the ocean is now found from the poles 

to the equator and from shorelines, estuaries, and the sea 

surface to the ocean floor (STAP, 2011). General oceanic 

circulation patterns, particularly surface currents, greatly 

affect the redistribution and accumulation of marine 

debris in the world’s oceans, as do the mass, buoyancy, 

and persistence of the material (Moore, Moore, Leecaster & 

Weisberg, 2001). Because of the relatively sparse amount 

of directly observable data on marine debris relative to 

the immensity of the ocean and the need to predict its 

movement, numerical models are used to simulate the 

transport of floating marine debris. Computer modeling 

simulations, based on data from about 12,000 satellitetracked drifters deployed since the early 1990s as part of 

the Global Drifter Program (GDP, 2011), indicate that debris 

tends to accumulate in a limited number of subtropical 

convergence zones associated with persistent, long-term 

currents in the world’s oceans (Wakata & Sugimori, 1990; 

Kubota, Takayama & Namimoto, 2005; Maximenko & Niiler 

2008). Recent analyses of data on floating debris in the 

Pacific Ocean (Eriksen et al., 2013; Law et al., 2014), Atlantic 

Ocean (Law et al., 2010; Morét-Ferguson et al., 2010), and 

the world’s oceans combined (Eriksen et al., 2014) have 

confirmed these modeling results. Modeling simulations 

can help to understand not only the likely fate of debris 

from known point sources, population centers, or extreme 

events such as hurricanes and tsunamis, but also to identify 

potential sources of debris. This paper reviews and briefly 

describes some of these methods, gives examples of their 

application for modeling the movement and transport of 

marine debris, and attempts to identify gaps in knowledge 

and recommend potential areas of further research. 

The following overview presents some of the numerical 

modeling techniques that have been applied to simulate 

the transport of marine debris. The primary approach to 

modeling the transport of drifting objects, with marine 

debris being one application, typically has involved the use 

of particle tracking models (PTMs) to estimate or simulate 

the movement of ‘particles’ over time. This movement 

depends on transport (advection) by ocean surface currents, 

and some PTMs may incorporate additional factors such 

as movement resulting from the proportion of buoyant 

objects floating high in the water and exposed to the wind 

(windage), or a random motion component (turbulent 

diffusion). 

The surface currents used to drive PTMs have been derived 

using a number of different methods. The main approaches 

identified from this review of the literature include: 

Surface current modeling: focuses only on the surface 

layer, where most floating debris is found. Surface 

currents can be estimated in various ways: 

Currents can be inferred from the paths of drifters, 

either ship drift data or satellite-tracked drifters. 

Currents can be estimated from long-term historical 

measurements of temperature, salinity, depth, and 

sea level pressure. 

Currents can be calculated from satellite-derived 

measurements of sea-surface height and wind stress. 

Ocean general circulation models (OGCMs): solve 

equations of motion in horizontal and vertical 

dimensions and include both physical and 

thermodynamic processes. OGCMs provide the surface 

currents layer used to simulate debris transport. A 

number of systems have been developed to update 

(through a process called data assimilation) these models 

with satellite and in situ measurements in near-real time. 

Many of these model outputs are available online. 

Because some of the terms used in this report may be 

unfamiliar to readers, a glossary of terms is provided at 

the end of this document. Terms or acronyms found in the 

glossary are indicated by an asterisk on first use, unless 

defined explicitly in the text. 
2016 MARINE DEBRIS MODELING REPORT 

BACKGROUND
 

Marine debris is one of the most 

pervasive pollution problems affecting 

the world’s oceans and waterways. 

Debris makes its way into the marine 

environment by way of numerous 

land-based and at-sea sources. Besides 

the negative visual and aesthetic 

impacts of marine debris, there can be 

a number of other detrimental effects 

in terms of potential harm to marine 

life by ingestion or entanglement 

(NOAA MDP, 2014a, 2014b), hazards 

to navigation (Johnson, 2001), acting 

as a pathway or vehicle for invasive 

species (Ruiz, Carlton, Grosholz & 

Hines, 1997; USEPA 2012), or posing 

a chemical hazard due to the sorption 

of organic contaminants to plastic 

debris (Van et al. 2011; Rochman, 

Hoh, Hentschel & Kaye, 2013). Some 

types of debris may sink to the bottom 

or be washed up on beaches and 

shorelines, but a large proportion 

can remain suspended in the water 

column or floating at the surface for 

days or years and can be transported 

over great distances. 

Modeling the transport and 

fate of marine debris must take 

into account winds, ocean surface 

currents, and other factors including 

the type of debris. Lightweight, 

buoyant materials such as some 

plastics tend to float high in the 

water so that they are pushed along 

partially by winds as well as surface 

currents. Such materials are referred 

to as having high “windage.” Other, 

heavier buoyant materials such as 

wooden construction debris float 

just below the surface (low windage) 

and so are influenced primarily by 

surface currents. Still other types 

of debris, such as derelict fishing 

gear, may be suspended lower in 

the water column and drift mainly 

with subsurface currents. Since 

subsurface currents generally move 

at a much slower speed than surface 

currents, the vertical position of 

debris in the water column influences 

how fast it gets transported from a 

point of origin (source) to its final 

destination (receptor). Additionally, 

the characteristics of debris items 

can change over time; encrusting 

organisms can affect the buoyancy of 

objects, large items can be broken into 

smaller fragments, and different types 

of materials can degrade at varying 

rates. 

Numerical models can be utilized 

to simulate the movement of debris 

for a number of different purposes. 

Models can be used to interpolate or 

interpret sparse data in a consistent 

way. Due to the relative immensity 

of the ocean, observations of marine 

debris or other floating/drifting 

objects, and direct measurements 

of ocean surface currents that move 

them, are relatively sparse. Models 

can be used to “fill in the gaps” where 

few observations are available. We 

can also use models to evaluate “what 

if ” scenarios by varying conditions 

or parameters one at a time and 

observing the outcome. It may be 

of interest, for example, to vary the 

amount of windage of simulated 

debris items to estimate its effect on 

travel time. Furthermore, models can 

make predictions, either forecasting 

the likely state of the system at some 

time in the future, or simulating 

the current state based on past 

observations. We often know either 

where debris originates (a spill or 

natural disaster) or where it ends 

up (beached debris). Models can 

be applied to forecast the probable 

trajectories of debris of known origin, 

or to identify potential sources of 

debris by predicting the likely paths 

taken to reach a known destination. 

The equations governing motion 

in the ocean are continuous. Solving 

them on a computer, however, 

requires discretizing, selecting 

distinct points from the range. This 

creates a system of distinct grid 

points on which to solve them. 

Modeling of marine debris transport 

by ocean currents is often carried 

out within an Eulerian-Lagrangian* 

framework, which is related to the 

frame of reference of an observer. 

The Eulerian perspective describes 

the current velocity (speed and 

direction) at a fixed point (or points) 

over time, while the Lagrangian 

approach follows the trajectory, or 

path, of a debris “particle” being 

carried along by currents over time. 

These two points of view can be 

visualized by imagining river flow: 

“...attempting to simulate 

ocean circulation... is much 

more complex since it must 

take into account a large 

number of effects...” 

an observer standing on a rock in 

the middle of the river describes the 

current velocity at that particular 

point (Eulerian perspective), while 

a boater being carried along by the 

current experiences the flow from 

a Lagrangian viewpoint. In the 

context of marine debris modeling, 

current velocities are obtained, by 

various methods, for points or nodes 

on a discrete (Eulerian) grid over a 

series of time steps. The resulting 

current velocities may then be 

used to calculate the trajectory of 

an object over time, in a technique 

sometimes referred to as Lagrangian 

particle tracking. The location of 

a particle at the end of each time 

step becomes the start point for the 

next time step, and the process is 

repeated. Most of the particle tracking 

models presented in this paper 

use Lagrangian particle tracking 

techniques (i.e., describing the 

trajectories of individual particles). 

However, some techniques use an 

Eulerian approach, which describes 

the concentration of passive tracers 

at every (Eulerian) grid point at each 

time step. Both of these particle 

tracking techniques are relatively 

straightforward, in the sense that 

the equations describing transport 

of an object (or concentration of 

tracers) depend only on current 

velocity at each time step (although 

some models also incorporate terms 

for other factors such as windage, 

diffusion, or vertical mixing). In 

contrast, attempting to simulate 

ocean circulation, whether focused 

only on currents in the surface layer 

or circulation throughout the water 

column, is much more complex since 

it must take into account a large 

number of effects, some of which are 

described below. 

2 
2016 MARINE DEBRIS MODELING REPORT 

Oceanic Circulation - A 

Balancing Act 

Ocean dynamics are governed by 

Newton’s laws of motion, expressed 

as a set of equations (i.e., primitive 

equations) that describe relationships 

among a number of variables, 

including pressure, density, wind a. stress (friction between wind and 

ocean), temperature, and salinity. 

The dominant influences that drive 

oceanic circulation are gravity, 

wind stress, and the apparent forces 

resulting from the earth’s rotation 

(Stewart, 2008). Steady ocean currents 

(those that persist over time in a 

prevailing direction) result from a 

balance between these forces (Sudre, 

Maes & Garcon, 2013). Also at play 

are the Sun, moon, and other celestial 

bodies which exert a gravitational 

pull on the ocean. Changes in their 

relative positions with respect to 

the Earth, due to both the Earth’s 

rotation and orbital movements, 

lead to tides, tidal currents, and tidal b.

mixing in the interior of the ocean. 

Gravity also affects the buoyancy of 

parcels of water depending on their 

density, which in turn depends on 

temperature, salinity, and pressure. 

Horizontal pressure gradients are 

caused by differences in water density 

or variations in sea surface elevation. 

Wind stress from prevailing winds 

(westerlies at mid-latitudes, easterly 

trades at lower latitudes), through 

friction on the ocean surface, causes 

currents by transferring horizontal 

momentum to the ocean. All currents 

are subject to the Coriolis effect*, 

which arises from the rotation of the 

earth and causes flow to be deflected. 

Because of the Coriolis effect, the 

shallow layer of surface water set in 

motion by the wind is deflected to c. 

the right of the wind direction in the 

Northern Hemisphere (setting up a 

clockwise rotation pattern) and to 

the left of the wind direction in the 

Southern Hemisphere (causing a 

counter-clockwise rotation) (Figure 

1a). At the surface, the balance 

between frictional wind stress and 

the Coriolis effect results in winddriven, or Ekman*, flow (Figure 1b), 

giving rise to circular current systems 

known as gyres*. The deflection 

towards the center causes water to 

move towards the central region of a 

gyre. This mounding of water towards 

the center of a gyre results in the sea 

Figure 1. Dominant influences driving large-scale 

ocean surface currents: a) Earth’s rotation, b) 

surface winds, c) gravity. 

3 
2016 MARINE DEBRIS MODELING REPORT 

Figure 2. The time-averaged, surface circulation of the ocean during the northern hemi­ sphere’s winter, deduced from a century of oceanographic expeditions. 

surface being higher in elevation (up 

to a meter or so) than the surrounding 

sea surface, which creates a horizontal 

pressure gradient. Since the force of 

gravity causes water to move from a 

high to low pressure area, there is a 

flow outward and down slope from the 

center of the gyre. As the water moves, 

it is deflected by the Coriolis effect. 

Eventually, the outward-directed 

pressure gradient force balances the 

apparent force due to the Coriolis 

effect and water flows around the gyre, 

parallel to contours of elevation of sea 

level. The horizontal movement of 

surface water resulting from a balance 

between the pressure gradient force 

and the Coriolis force is known as 

geostrophic flow* (Figure 1c). Largescale circulation patterns can be 

inferred from sea surface height just as 

meteorologists use sea level pressure 

maps to derive wind.

In the global ocean, these 

influences give rise to large-scale 

circulation patterns constrained by 

“The dominant influences 

that drive oceanic 

circulation are gravity, wind 

stress, and the apparent 

forces resulting from earth’s 

rotation (Stewart, 2008).” 

the continental masses bordering the 

major ocean basins. Major boundary 

currents such as the Kuroshio Current 

and the Gulf Stream link up with 

equatorial and other currents to form 

large oceanic gyres (Figure 2). In the 

North Pacific, the prevailing winds 

form two large oceanic gyres, each 

bounded by major current systems 

(Figure 3). These major currents 

delineate three major oceanographic 

zones: a subtropical zone to the south, 

a subpolar zone to the north, and a 

transition zone between them (Howell, 

Bograd, Morishige, Seki, & Polovina, 

2012).

Oceanic gyres can retain debris, 

since the Ekman flow deflects water 

towards the center of the gyre, as 

described above. However, zones 

of subtropical convergence and 

transition, such as where the North 

Pacific Subtropical and Subpolar 

Gyres collide and interact, have been 

identified as areas important to the 

concentration and accumulation of 

marine debris. Along the North Pacific 

Transition Zone (Figure 3), westerly 

winds force Ekman transport of cool, 

saline water to the south. Since this 

water is more dense, it sinks beneath 

the warmer subtropical waters 

associated with the Subtropical Frontal 

Zone. The horizontal convergence 

causes objects that are sufficiently 

buoyant (to overcome the vertical 

flow of sinking water) to accumulate 

in the Subtropical Convergence 

Zone (Pichel et al., 2007; Howell et 

al., 2012). In addition to this largescale frontal convergence zone, 

other areas of accumulation exist, 

such as the one associated with a 

climatological high pressure zone in 

the oceanic region between California 

and Hawaii (i.e., North Pacific 

Subtropical High). Debris accumulates 

in this convergence zone since the 

wind around high pressure areas in 

the Northern Hemisphere rotates 

clockwise and the Ekman currents of 

the ocean are deflected to the right 

of the downwind direction (Wakata 

& Sugimori, 1990). This corresponds 

to the area shown in Figure 3 as the 

Eastern Garbage Patch.

The circulation patterns presented 

above describe long-term global 

or basin-scale ocean currents. It is 

important to note, however, that ocean 

circulation operates at different scales, 

both temporally and spatially. Current 

velocities also vary with depth. For 

example, the wind-driven surface 

layer can have currents that move 

at velocities of 20 cm/s or more, but 

these velocities decrease with depth. 

Hence, debris objects that change 

buoyancy over time and sink lower 

in the water column will experience 

currents of different velocity and 

direction. Also, instabilities in the 

large-scale circulation, interactions 

between currents and bottom 

topography (bathymetry), and direct 

effects of the wind can create swirling 

currents, or eddies. The size and 

persistence of eddies varies. Some, 

called mesoscale eddies, are tens to 

hundreds of kilometers in diameter, 

and can persist for several days to 

several months. The resolution of an 

ocean model is determined by the 

spacing of grid points and also by the 

“Oceanic gyres can retain 

debris, since the Ekman flow 

deflects water towards the 

center of the gyres...” 

time step (the interval between one set 

of solutions and the next). Hence, a 

model with widely spaced grid points 

and a large time step may be suitable 

only for resolving long-term average 

flow on a global or basin-wide scale. 

Other, higher-resolution models are 

able to represent mesoscale circulation 

4 
2016 MARINE DEBRIS MODELING REPORT 

Figure 3. Schematic representation of the major oceanic currents and zones in the North Pacific. 

(so-called ‘eddy-resolving’ models), 

or model circulation regionally within 

an ocean basin, sea, or gulf and over a 

shorter time period. 

“Different kinds of models 


require various types 


of information, and the 


methods for acquiring the 


data have evolved with 


the development of new 


technologies. “
 

Types and Sources of Data 

Numerical ocean models require 

input of measured data for model 

setup, initialization, and validation. 

Different kinds of models require 

various types of information, and 

the methods for acquiring the data 

have evolved with the development of 

new technologies. Current velocities 

can be measured directly using 

(Lagrangian) drifters, or measured 

at fixed (Eulerian) locations using 

moored current meters. Currents can 

also be calculated from measurements 

of the physical factors that drive them. 

For example, long-term geostrophic 

currents can be obtained by calculating 

variations in sea surface height (and 

resulting pressure gradients) from in 

situ measurements of temperature 

and salinity with depth. Such 

measurements have been compiled 

in the Levitus (1982) Climatological 

Atlas of the World Ocean, which 

provided monthly global temperature 

and salinity climatologies with a 

spatial resolution of 1x1 degree at 19 

depth levels (surface – 1000m). The 

Levitus Atlas has been updated as 

the World Ocean Atlas, and the most 

recent version provides higher vertical 

resolution for all variables, with 102 

vertical levels from the surface to 

5500m depth (WOA, 2013). Other 

products, such as the International 

Comprehensive Ocean-Atmosphere 

Data Set (ICOADS), provide 

observations and monthly summaries 

of sea surface temperature, sea level 

pressure, surface winds, and other 

measurements. 

With the advent of satellite remote 

sensing, many of these quantities 

can be measured directly on a global 

scale and made available in nearreal time. Sea surface height can be 

measured using altimetry, which 

involves sending a microwave pulse 

to the ocean’s surface and recording 

the time it takes to return. Altimetry 

measurements from NASA satellites, 

such as Topex/Poseidon*, Jason-1/2*, 

and the European Space Agency 

(ESA) ERS-1/2*, make it possible to 

measure sea surface height to within 

a few centimeters. Satellites such 

as NASA’s Quikscat/Seawinds* and 

ESA’s ERS-1/2 can also measure ocean 

near-surface wind speed and direction 

using a microwave radar sensor called 

a scatterometer. Such measurements of 

sea surface height and winds are made 

available online. Global surface current 

products calculated directly from 

satellite altimetry and ocean vector 

winds are also provided, for example, 

by OSCAR* (Ocean Surface Current 

Analysis – Real Time), which provides 

long-term, monthly, or 5-day mean 

surface currents from 1992 to the 

present on a 1° grid (OSCAR, 2014). A 

similar global surface current product 

is provided by Mercator/SURCOUF 

(Larnicol et al., 2006) and AVISO* 

(Sudre & Morrow 2008). 

5 
2016 MARINE DEBRIS MODELING REPORT 

SYNTHESIS OF LITERATURE
 

Overview 

Different approaches have been 

used to model the general circulation 

and associated transport of material 

in the ocean. Because marine debris 

modeling techniques make use of 

these approaches, either separately 

or in various combinations, they are 

reviewed briefly below. 

First, we describe a number 

of methods for modeling surface 

currents, which are mainly a 

combination of Ekman currents 

driven by local wind and geostrophic 

currents maintained by the balance 

between pressure gradients and the 

Coriolis force (Maximenko & Niiler, 

2008), as described above. These 

methods include the calculation of 

surface currents from long-term 

historical data, direct estimation of 

surface currents from the paths of 

drifters, and currents derived from 

satellite-based measurements of the 

ocean surface. 

Next, a class of ocean general 

circulation models (OGCMs) that 

simulate motion throughout the 

water column in three dimensions 

are introduced. Brief descriptions of 

these methods along with specific 

applications to the problem of 

modeling marine debris are presented 

below. For more information on 

these approaches, the reader is 

referred to the citations given in the 

text, as well as reviews of techniques 

for application to marine debris by 

Potemra (2012) and overviews of 

satellite-based methods of estimating 

ocean surface currents by Sudre 

and Morrow (2008), Dohan and 

Maximenko (2010), Dohan et al. 

(2010), and Freeman et al. (2010). 

The surface current velocities 

obtained from these various methods 

typically are coupled with a particle 

tracking model (PTM), which 

describes the resulting transport 

of a given “particle” of debris over 

the selected or modeled time step. 

Alternatively, the evolution of tracer 

distribution and density over time 

throughout the model domain can be 

estimated with a statistical model. 

Surface Currents 

Drifters 

For many years, ocean surface 

currents have been estimated directly 

by how they carry drifting objects, 

from the well-known “message in a 

bottle” to glass balls used by Japanese 

fishermen that have washed up on 

beaches in California. An early source 

of ocean current information was 

based on the collection of ship drift 

data, in which currents were estimated 

from the difference between a ship’s 

predicted course and its actual speed 

and direction (Meehl, 1982). With 

the development of satellite tracking 

capability in the 1970s, it became 

possible to track drifting buoys, or 

“drifters”. Many competing drifter 

designs were developed and evaluated, 

and a uniform design was proposed 

in 1992 (Sybrandy & Niiler, 1992; 

see sidebar). The design of satellitetracked drifters was standardized and 

a global array of drifters was deployed 

in the world’s oceans as part of the 

Global Drifter Program (GDP, 2011). 

Drifters provide direct measurements 

of ocean surface currents, which 

are composed mainly of Ekman 

and geostrophic components (see 

glossary). The successive drifter 

position measurements can be used 

to calculate surface layer current 

velocities or to calibrate or validate 

ocean surface current models. While 

these global arrays of drifters describe 

large-scale global and basin-wide 

ocean currents, drifters can also 

provide insight into processes, such 

as ocean eddies, which operate on the 

mesoscale (50-500 km, lasting 10-100 

days, with currents of a few kilometres 

per hour). The paths of drifters also 

suggest the likely paths of floating 

marine debris. 

Various approaches have been used 

to model the movement or dispersion 

of drifters. Wakata and Sugimori 

(1990) used a global ship drift dataset 

of surface current velocities on a 5° 

x 5° latitude-longitude grid (Meehl, 

1982) as input to a Lagrangian PTM 

(based on Newton’s equation of 

motion and conservation laws) to 

investigate the trajectories and density 

“The typical modern drifter 

is a high-tech version of 

the “message in a bottle.” It 

consists of a surface buoy 

and a subsurface drogue 

(sea anchor), attached by a 

long, thin tether. The buoy 

measures temperature and 

other properties, and has a 

transmitter to send the data to 

passing satellites. The drogue 

dominates the total area of the 

instrument and is centered at a 

depth of 15 meters beneath the 

sea surface.” 

distribution of simulated drifters. 

Their overall results agreed well with 

the observed trajectories of satellitetracked drifters in the North Pacific 

described by Kirwan, McNally, Reyna, 

and Merrell (1978). The simulations 

also confirmed areas of debris 

accumulation in the North Pacific, 

reported by Mio, Takehama, and 

Matsumura (1988), as well as other 

areas of accumulation in the center 

of the South Atlantic Ocean and the 

Indian Ocean west of Australia. 

Kirwan et al. (1978) described the 

large-scale near-surface circulation 

of the eastern North Pacific based on 

the trajectories of 23 satellite-tracked 

drifters. Embedded in the main flow 

were numerous mesoscale eddies. In 

subsequent studies, Kirwan, Merrell, 

Lewis, Whitaker, and Legeckis 

(1984) and Kirwan, Lewis, Indest, 

Reinersman, and Quintero (1988) 

developed a model to describe such 

mesoscale features, specifically 

Loop Current eddies in the Gulf 

of Mexico. Modeling of mesoscale 

eddies is more complex than largescale currents, since the model must 

describe not only the movement or 

translation of the eddy center, but also 

6 
2016 MARINE DEBRIS MODELING REPORT 

the circulation characteristics of the 

eddy itself. Additional investigations 

of Loop Current eddies were carried 

out by Glenn and Forristall (1990) 

and Glenn and Ebbesmeyer (1993), 

who also estimated model parameters 

based on satellite-tracked drifter 

trajectories. Eddies may “pinch off ” 

from the main, large-scale current 

flow, and can transport debris within 

mesoscale patches as they propagate 

or migrate over large distances 

(Howell et al., 2012).

More recently, the paths of 

more than 10,000 satellite-tracked 

drifting buoys were combined with 

information from satellite altimetry, 

winds, and gravity measurements 

to create high-resolution maps (1⁄2° 

grid) of the mean dynamic ocean 

topography (sea surface height 

adjusted for variations in the shape of 

the earth due to gravity) and to derive 

the mean geostrophic (resulting 

from pressure gradients) and Ekman 

(wind-driven) circulation in the upper 

ocean (Maximenko et al., 2009). The 

combination of drifter and satellite 

data revealed new features of the 

ocean surface mesoscale circulation 

and yielded ocean dynamic 

topography patterns at a spatial 

resolution higher than that achievable 

from satellite data alone and with 

better accuracy than those obtainable 

from drifter data alone. 

In a subsequent study, Maximenko, 

Hafner, and Niiler (2012) used 

the same satellite-tracked drifter 

trajectories to develop a statistical 

model describing the movement 

of drifters over time. The observed 

displacement of each drifter was 

used to calculate the probability of a 

statistical drifter, or tracer, to move 

into or over 1⁄2° bins surrounding its 

original position. The model was then 

initialized with uniformly-distributed 

“drifters” and the evolution of drifter 

density with time over the (Eulerian) 

grid was tracked for up to 1,000 

years, under the assumption that the 

prevailing winds and currents remain 

steady during the simulation period. 

Similar to the results of Kubota et al. 

(2005), the simulations resulted in 

areas of accumulation corresponding 

roughly to the centers of the five 

subtropical gyres and associated areas 

of convergence.

The (Surface CUrrents from 

Diagnostic) SCUD model utilized 

AVISO satellite altimetry to determine 

the geostrophic current component, 

and used QuikSCAT (satellite 

scatterometry) ocean surface wind 

data to calculate the wind-driven 

Ekman component (Maximenko & 

Hafner, 2010). Maps of total currents 

were produced daily on a 1⁄4° latitude/

longitude grid starting in August 

1999 and running to November 

2009, the time span of QuikSCAT 

data. SCUD coefficients were derived 

such that model estimates matched 

closely with the velocities of surface 

drifters obtained from the Global 

Drifter Program. The goal of the 

project was to diagnose near-surface 

currents consistent with trajectories 

of Lagrangian drifters using satellite 

observations (Maximenko & Hafner, 

2010). Recently, the SCUD model 

has been used to to forecast potential 

trajectories of Japan tsunami marine 

debris (JTMD*) from the March 

2011 Tohoku tsunami (Maximenko 

& Hafner, 2012). In contrast to 

Lagrangian PTMs, the SCUD model 

tracks virtual tracer distributions over 

the Eulerian grid, as in Maximenko 

et al. (2012). The first version of the 

model tracked debris solely using 

ocean currents, while the refined 

model includes tracers sensitive 

to windage. The accuracy of the 

SCUD model deteriorates near 

shore due to higher errors in satellite 

data (due to intrinsic difficulties in 

corrections applied for atmospheric 

effects) and increased complexity of 

oceanographic and tidal dynamics 

(Carson et al., 2013).

The Ocean Surface CURrent 

Simulations (OSCURS) numerical 

model (Ingraham & Miyahara, 

1988) is a research tool originally 

developed to investigate how ocean 

currents might have influenced 

various fish populations in the 

North Pacific Ocean and Bering Sea. 

The model allows oceanographers 

and fisheries scientists to perform 

retrospective analyses of daily ocean 

surface currents anywhere in a 90-km 

North Pacific Ocean-wide grid from 

Baja California to China and from 

“The combination of 

drifter and satellite data 

revealed new features of the 

ocean surface mesoscale 

circulation...” 

10°N to the Bering Strait from 1901 

to the present (Ingraham, 1997). 

OSCURS derives mean geostrophic 

currents from long-term average 

density distributions, computed 

from historical averages of measured 

temperature and salinity versus 

depth. These were combined with 

wind speed and direction calculated 

from daily sea-level pressure data 

to obtain surface current velocities. 

The OSCURS model was developed 

for the North Pacific and Bering 

Sea and calibrated or “tuned” with 

satellite-tracked drifter data for 

the Gulf of Alaska (Ingraham & 

Miyahara, 1989). Incorporating 

drifter data allows the parameters 

of the model to be adjusted so that 

modeled outputs agree more closely 

with actual measurements. Surface 

current velocities calculated on the 

OSCURS grid are used to obtain 

the continuous trajectory of drifting 

objects (i.e., Lagrangian PTM, as 

described previously). OSCURS 

has been used to hindcast* the drift 

trajectories of 1,300 shoes spilled 

from a shipping container lost at sea 

in the North Pacific (Ebbesmeyer & 

Ingraham, 1992) and later used to 

model trajectories of intentionally 

released drift bottles (Ebbesmeyer et 

al., 1993) and accidentally released 

bathtub toy animals (similar to the 

shoe spill), with adjustments for 

increased windage of the plastic toys 

(Ebbesmeyer & Ingraham, 1994). 

The model has also been applied to 

estimate the orbital period (the time 

required for one complete rotation) 

of the Pacific Subpolar Gyre based 

on subsequent sightings of the 

aforementioned toys and other objects 

(Ebbesmeyer, Ingraham, Royer, & 

Grosch, 2007). The OSCURS model 

has been applied to simulate the 

dispersal of marine debris (derelict 

fishing nets) in the North Pacific 

(Ingraham & Ebbesmeyer, 2001) and 

to forecast possible paths of JTMD 

(Ebbesmeyer & Ingraham, 2012). To 

account for the varying amounts of 

windage on the different debris items, 

model parameters for current speed 

and angle were adjusted systematically 

to determine which model trajectories 

ended near reported landfall 

locations. 

Kubota (1994) used the same 

Lagrangian PTM as Wakata and 

Sugimori (1990) and derived surface 

current velocities in an approach 

similar to that used in the OSCURS 

7 
2016 MARINE DEBRIS MODELING REPORT 

model, but used different data 

sets on which to base calculations. 

Geostrophic surface currents were 

calculated on a 1°x 1° latitudelongitude grid (~100km at midlatitudes) from temperature and 

salinity data found in the Levitus 

(1982) atlas. Winds were obtained 

from monthly wind data (1° grid) 

in the Comprehensive OceanAtmosphere Data Set (COADS) 

derived from ships, fixed research 

vessels, buoys, and other devices 

(Slutz et al., 1985). The data were used 

to derive Ekman drift, geostrophic 

currents, and combined currents in 

order to provide a mechanism for the 

observed accumulation of floating 

debris (documented by Mio et al. 

(1988)) in the North Pacific, especially 

north of Hawaii. 

Kubota et al. (2005) carried out a 

similar set of simulations, but using 

satellite-derived measurements. 

Geostrophic currents were calculated 

from TOPEX/POSEIDON altimetery 

and surface winds were derived from 

ERS-1/2 scatterometry. Simulations, 

which were run on a 1° x 1° grid 

(~100km) and initiated with pseudomarine debris distributed uniformly 

over the grid, identified areas of 

accumulation associated with the 

five subtropical gyres and zones of 

convergence, most notably an area of 

accumulation northeast of Hawaii. 

Martinez, Maamaatuaiahutapu, 

and Taillandier (2009) investigated 

the accumulation and convergence 

of floating debris in the easterncentral region of the South Pacific 

subtropical gyre, using a PTM 

coupled to surface currents derived 

in several different ways. In an initial 

experiment, surface currents were 

calculated from satellite altimetry 

(combined TOPEX/POSEIDON 

and ERS-1/2) and sea surface wind 

data (ERS-1/2 scatterometry) on a 

1/3° grid. Comparisons were made 

to results obtained using a lowerresolution product (OSCAR, 1° 

grid), also derived from satellite 

altimetry and scatterometry winds. 

While they noted differences in 

simulation results, the same largescale trends in drift trajectories were 

found: convergence, eastward drift, 

accumulation. 

Ocean General Circulation 

Models (OGCMs) 

Prior to the proliferation of 

numerical ocean simulation models, 

a number of simplified analytic and 

mechanistic models of basin-scale 

ocean circulation were developed to 

describe wind-driven flow (Sverdrup, 

1947; Stommel, 1948; Welander, 

1959), abyssal circulation (Stommel 

& Arons, 1960), and thermohaline 

effects (Wyrtki, 1961). The first 

numerical simulation ocean model 

was developed by Kirk Bryan and 

Michael Cox (Bryan & Cox, 1967, 

1968; Bryan, 1969) at the Geophysical 

Fluid Dynamics Laboratory (GFDL) 

in Princeton and is the predecessor 

to many ocean general circulation 

models (OGCMs) in use today. 

Most ocean simulation models solve 

the basic equations of motion (i.e., 

primitive equations) on a horizontal 

grid (Figure 4a), where the spacing of 

grid points determines the horizontal 

resolution of the model. Vertical 

processes (Figure 4b) are handled 

in various ways, by using several 

horizontal levels (velocities computed 

at specific depths, z-coordinate), 

layers of different density (isopycnal, 

ρ-coordinate), or a fixed number of 

levels regardless of depth (terrainfollowing, σ-coordinate).

 Such models contribute greatly 

to our understanding of oceanic 

circulation, but all numerical 

simulation models are limited in 

the degree to which they are able 

to represent the effects of processes 

that determine how and where water 

will move. The complex dynamics 

of ocean circulation occur on spatial 

scales from millimeters to hundreds 

of kilometers and temporal scales 

from seconds to decades and beyond 

(Potemra, 2012). Limits on computing 

resources require that sub-grid 

scale processes be simplified or 

approximated (i.e., parameterized) to 

estimate their effects. Hence, practical 

models must necessarily be simpler 

than the real ocean. 

Within the context of marine 

debris transport modeling, OGCMs 

provide the surface current velocities 

that can be used to drive PTMs. 

There has been an increase over the 

past 20 – 30 years in the amount 

and availability of satellite-derived 

measurements of sea-surface height 

(SSH), sea-surface temperature (SST), 

surface winds, and satellite-tracked 

drifter trajectories. Systems have been 

developed for assimilating satellite 

data, drifter data, and in situ vertical 

profiles of salinity, temperature, and 

depth obtained from ship-deployed 

instruments. While these in situ 

data are generally too sparse to 

characterize the horizontal variability, 

they provide valuable information 

about the vertical stratification 

(Chassignet et al., 2007). Since 

OGCMs incorporate multiple vertical 

levels, the surface currents they 

provide are connected to and reflect 

the influence of processes or features 

(for example, mixing, stratification, or 

bottom topography) below the surface 

layer. 

A number of ocean modeling 

systems now exist, which can 

assimilate the available data in nearreal time and provide daily outputs 

of model runs. Many of the global 

OGCMs in use today operate at a 

horizontal grid resolution of 1/10° 

or less, which is sufficient to resolve 

mesoscale eddies (see previous 

section for description). One such 

“... all numerical simulation 

models are limited in the 

degree to which they are able 

to represent the effects of 

processes that determine how 

and where water will move.” 

eddy-resolving numerical circulation 

model, the HYbrid Coordinate 

Ocean Model (HYCOM*; Bleck et 

al., 2002), has been applied to global 

ocean simulations at a resolution 

of 1/12° (6.5km on average), with 

higher resolutions in basin-specific 

and regional applications (e.g., 1/25°, 

or ~3.5km, in the Gulf of Mexico). 

High-resolution regional models 

can be nested within basin-scale or 

global models, with the larger scale 

model providing initial conditions or 

boundary conditions for the nested 

model. 

HYCOM uses a hybrid vertical 

coordinate system, where vertical 

coordinates remain isopycnic 

(following surfaces of constant 

density) in the open, stratified ocean. 

However, they transition smoothly 

to z-coordinates in the weakly8 
2016 MARINE DEBRIS MODELING REPORT 

a. 

b. 

stratified upper-ocean mixed layer, to 

terrain-following sigma coordinates 

in shallow-water regions, and back 

to level coordinates in very shallow 

water (Chassignet, Smith, Halliwell, 

& Bleck, 2003; Chassignet et al., 2006; 

Chassignet et al., 2007; Wallcraft, 

Carroll, Kelly, & Rushing, 2003). 

HYCOM is being used by a broad 

partnership of institutions in the 

United States to build global, basinscale, and regional ocean current 

and water properties prediction 

systems (Chassignet et al., 2006; 

Chassignet et al., 2007) in the context 

of a coordinated international effort, 

The Global Ocean Data Assimilation 

Experiment (GODAE). The HYCOM 

system integrates the numerical 

model with information about the 

ocean surface’s space-time variability 

obtained from instruments aboard 

satellites, vertical profiles from 

“A number of ocean modeling 

systems now exist, which can 

assimilate the available data 

in near-real time and provide 

daily outputs of model runs.” 

expendable bathythermographs 

(XBT), conductivity-temperaturedepth (CTD) profilers, and profiling 

floats (e.g., Argo, which measures 

temperature and salinity in the 

upper 2000 m of the ocean), as 

well as from statistics determined 

using past observations in a 

process called data assimilation. 

A technique commonly used in 

weather forecasting and prediction, 

the purpose of data assimilation is to 

determine a best possible ocean state 

using observations and short range 

forecasts. This approach combines 

information from past observations 

brought forward in time by a model 

(background, or first guess) with 

information from new observations. 

This best estimate of the current state 

of the system, called an analysis, is 

then used as the starting point for 

the next analysis cycle. In HYCOM, 

the analysis computes corrections 

to the first-guess fields using all of 

the observations (e.g., temperature, 

salinity, SSH, velocity) that have 

become available since the last 

analysis was made (Cummings, 2005).

Data-assimilative ocean modeling 

systems can provide short-term 

forecasts (typically several days). In 

most of the studies reviewed in this 

paper, these models have been used 

in hindcasting debris transport, over 

the time period for which data are 

available, up to the present. However, 

these types of models can also be 

employed to do statistical probability 

modeling to make predictions much 

in the same way the historical data has 

been used. 

“most of the studies reviewed... 

have been used in hindcasting 

debris transport...” 

Linked Ocean General 

Circulation Models - Particle 

Tracking Models 

Yoon, Kawano, and Igawa (2010) 

simulated marine debris drift and 

beaching in the Japan Sea using a 

Lagrangian PTM coupled to ocean 

current velocities provided by the 

Japan Sea Forecasting System from 

the Research Institute for Applied 

Mechanics ocean circulation model 

(RIAM OCM) and to wind data 

from the Global Spectral Model of 

the Japan Meteorological Agency. 

The RIAM OCM is an example of a 

z-coordinate model, where the model 

operates on multiple horizontal layers 

in the vertical dimension. This is the 

simplest choice of vertical coordinate, 

where z represents the vertical 

distance (i.e., depth) from a resting 

ocean surface at z = 0 (Griffies et al., 

2000). The RIAM OCM operates at a 

Figure 4. Illustrations of a) horizontal (global and nested regional) grids, 

b) approaches for handling vertical processes. 

9 
2016 MARINE DEBRIS MODELING REPORT 

horizontal resolution of 1/12° (~6km 

at mid-latitudes).

Isobe, Kako, Chang, and Matsuno 

(2009) adopted the Princeton Ocean 

Model (POM; Blumberg & Mellor, 

1987), a regional, terrain-following 

model, with a horizontal grid spacing 

of 1/12° (~9km), to compute the 

current field covering the Yellow 

Sea, the East China Sea, and part of 

the North Pacific. Terrain-following 

models are particularly useful for 

studying regional ocean circulation 

in coastal areas since they provide a 

smooth representation of the ocean 

bottom topography (see for example, 

Veneziani, Edwards, Doyle, and 

Foley (2009)), and subtle details in 

ocean bathymetry can make the 

difference between erroneous and 

accurate simulations of the large-scale 

circulation (Gille & Smith, 2003). 

Using surface currents obtained from 

the POM, Isobe et al. (2009) then used 

a two-way PTM to specify statistically 

significant starting locations (sources) 

of drifting objects carried by ocean 

currents. First, a backward-in-time 

PTM was used to identify multiple 

candidate sources for an initial ending 

location (receptor) by releasing virtual 

particles carried by modeled ambient 

currents with direction reversed 

in sign for both horizontal current 

components. The number of particles 

at each source suggests the probability 

of the object sources. Second, using a 

forward-in-time PTM, particles were 

released from all source candidates 

identified in the backward-in-time 

PTM to specify statistically significant 

object sources: after a specified 

amount of time, a candidate source is 

considered statistically significant if 

the true (known) receptor falls within 

a region that is two times the standard 

deviation of the distance between 

simulated particle positions and their 

averaged position (Isobe et al., 2009). 

In a subsequent study, Kako, Isobe, 

Seino, and Kojima (2010a) compared 

model outputs obtained as described 

above to the results of a beach debris 

survey carried out every two months 

from 2007 through 2008. Additionally, 

they applied an inverse method using 

the beach survey results to compute 

the quantity of debris (object outflows) 

originating from each source detected 

in the two-way PTM.

Building on the previous two 

studies described above, Kako et al. 

(2011) and Kako, Isobe, Kataoka, and 

Hinata (2014) validated the source 

quantities (outflows) of marine debris 

estimated by the inverse method 

by hindcasting quantities of debris 

actually observed using the forwardin-time PTM. Particle outflows 

computed using the inverse method 

are given for each source and month, 

and particle motion is governed by 

surface currents computed in an ocean 

circulation model (POM) driven by 

QuikSCAT/SeaWinds data. Quantity 

of beach debris was also forecast using 

the same ocean circulation model, 

but short-term (34-day) forecast wind 

data from the Japan Meteorological 

Business Support Center (JMBSC) 

were used in lieu of driving the model 

by QuikSCAT/Seawinds observed 

data. The hindcast computation was 

conducted in parallel with the forecast 

computations, and the hindcasted 

particle positions at the beginning of 

each forecast computation were used 

for the initial condition in forecasting 

the particle positions. Model hindcast 

performance was validated (that 

is, model predictions compared to 

observations) by estimating the area 

covered by debris on the surveyed 

beach using sequential snapshots taken 

by a webcam (Kako et al., 2010b).

Hardesty and Wilcox (2011) 

investigated potential sources and 

receptors of floating marine debris 

around the Australian continent using 

a 2-way PTM together with an eddyresolving (1/12° in the Australian 

region studied) OGCM based on 

the Modular Ocean Model (MOM4; 

Griffies, Harrison, Pacanowski, & 

Rosati, 2004), which is an example 

of a z-coordinate model as described 

above. Release points of virtual 

particles were based on debris data 

collected by various organizations 

involved in beach cleanups and state 

agencies that have operating marine 

debris monitoring programs. In 

one simulation, the PTM was run 

backward in time from four of the 

beach cleanup sites to identify possible 

sources. Additional experiments ran 

the PTM forward in time to simulate 

releases from locations (sources) 

at the boundary of the Australian 

Exclusive Economic Zone and from 

major population centers along the 

Australian coast. The PTM allowed for 

specifying various values of windage 

(i.e., leeway drift, influence of the drag 

force exerted directly by winds). They 

ran the particle tracking code using 

five different windage values for each 

release area, tracking the released 

particles over 80 days. The model was 

also run twice (January and July) each 

year (1994-2007) to consider potential 

influence of seasonal variation in 

ocean currents on marine debris 

sources and receptors for each release.

The General NOAA Operational 

Modeling Environment (GNOME*) 

is an interactive environmental 

simulation system designed for 

the rapid modeling of pollutant 

trajectories in the marine environment 

(Zelenke, O’Connor, Barker, BeegleKrause, & Eclipse, 2012) that has also 

recently been applied to the modeling 

of JTMD. GNOME is a modular and 

integrated software system that accepts 

input in the form of maps, bathymetry, 

numerical circulation models, 

location and type of spilled substance, 

oceanographic and meteorological 

observations, and other environmental 

data. Spilled substances are modeled in 

GNOME as point masses, or particles, 

whose trajectories depend on “movers”, 

which include winds, currents, and 

horizontal diffusion. 

For the JTMD application, GNOME 

used currents from HYCOM, 

described previously. GNOME 

produced a regular series of hindcasts 

of the probable location of debris 

since the last update cycle. In addition 

to particle trajectories forced by 

HYCOM surface currents, observed 

near real-time winds (NOAA blended 

winds product) were incorporated 

into the model runs and combined 

with estimated debris windage 

based on USCG Search and Rescue 

research data. Simulated particles were 

randomly assigned windage values 

from 1-5%, meaning that they were 

moved not only by ocean currents, 

but were also moved by 1-5% of wind 

speed in the downwind direction 

(NOAA MDP, 2014c).

Lebreton, Greer, and Borrero 

(2012) simulated the transport and 

accumulation of marine debris, 

particularly floating plastic, by 

extracting surface currents from 

the HYCOM system (Cummings, 

2005), and then coupling the velocity 

data extracted from HYCOM to 

the Lagrangian PTM Pol3DD 

(Black, 1996). The Pol3DD model 

tracks virtual particles to simulate 

waterborne dispersion of material 

including neutrally buoyant 

anthropogenic material, larvae, oil 

spills, outfall discharges, and estuarine 

or beach sediment transport (Lebreton 

et al., 2012). While the full HYCOM 

model contains 32 vertical layers, 

only velocities in the surface layer 

were considered as the principal 

10 
2016 MARINE DEBRIS MODELING REPORT 

Model Name SCUD OSCURS GNOME 

Model Full Name Surface CUrrents from 

Diagnostic James Ingraham General NOAA Oceanographic Modeling Environment 

Lead Scientists Nikolai Maximenko, 

Jan Hafner 

NOAA Alaska Fisheries Science 

Center \ Resource Ecology and 

Fisheries Management NOAA Office of Response & 

Restoration Team 

Organization University of Hawaii North Pacific Ocean, 

Bering Sea 

Areas Primarily Used 

Pacific Ocean 

Indian Ocean 

US Navy Fleet Numerical 

Meteorology & Oceanography 

Center sea level pressure data 

Long term average 

distributions/measurements 

of forcing 

Global oceans, though most 

applications in territorial 

waters of United States 

Forcing Data Sources 

Commonly Used 

(in known debris applications) 

AVISO for currents and 

QuikSCAT for winds, 

With debris coefficients tuned 

to velocities from Global 

Drifter Program 

US Navy Fleet Numerical 

Meteorology & Oceanography 

Center sea level pressure data 

Varied: GNOME can integrate 

forcing data from multiple 

sources 

Example Applications 

Japan Tsunami Marine 

Debris (JTMD) 

General N. Pacific debris 

concentration and 

movement patters 

MH370 debris hindcast 

Long term average 

distributions/measurements 

of forcing 

Oil spill modeling 

Incident debris modeling 

Ad-hoc support of response 

agency trajectory requests 

JTMD 

Website http://apdrc.soest.hawaii. 

edu/datadoc/scud.php 

http://www.afsc.noaa.gov/REFM/ 

docs/oscurs/get_to_know.htm 

http://response.restoration. 

noaa.gov/gnome 

Transport model approaches frequently used to estimate debris movement. 

driver of floating particles. Their 

simulation results were in agreement 

with both measured and modeled 

descriptions of concentrations of 

floating debris, showing areas of 

accumulation associated with the 

five major ocean-wide gyres (North 

Atlantic, South Atlantic, North Pacific, 

South Pacific, and Indian Ocean). 

In contrast to models starting from 

initial uniform distribution of floating 

objects (for example, Maximenko et 

al. (2012), described in the previous 

section), Lebreton et al. (2012) 

evaluated scenarios of debris input 

determined by the relative proportion 

of impervious watershed area, coastal 

population density, or global shipping 

routes to produce more realistic 

estimates of the relative concentrations 

of debris in accumulation zones. 

Lebreton and Borrero (2013) used a 

similar modeling approach to simulate 

dispersion and transport of JTMD. 

Their results suggested that some 

isolated debris items would be shed 

from the clockwise-rotating North 

Pacific gyre/accumulation zone and 

pushed towards the west coast of 

North America or the windward 

shores of the Hawaiian Islands. 

However, the bulk of the material 

would eventually accumulate in areas 

of subtropical convergence associated 

with the North Pacific Subtropical and 

Subpolar gyres, adding significantly to 

the total mass of debris contained in 

the “Great Pacific Garbage Patch”. 

The Government of Japan and 

Kyoto University researchers have 

conducted simulations of JTMD 

drift and transport using the Japan 

Meteorological Research Institute 

(MRI) Multivariate Ocean Variational 

Estimation system (MOVE; Usui et 

al., 2006). The OGCM used in the 

MOVE system is the eddy-resolving 

(1/10°) Meteorological Research 

Institute Community Ocean Model 

(MRI COM; Tsujino et al., 2010). Data 

assimilation and forecast experiments 

were performed by using the coupled 

atmosphere-ocean 4D-VAR data 

assimilation system developed by 

the Japan Agency for Marine-Earth 

Science and Technology (JAMSTEC) 

to reproduce the dynamic state of 

the seasonal to interannual timescale global climate and to create 

a comprehensive reanalysis and 

forecast dataset of ocean currents 

and ocean surface winds (MOEJ 

2014). Additionally, particle diffusion 

simulations were carried out using 

a particle random-walk model, the 

SEA-GEARN, developed at the Japan 

Atomic Energy Agency (JAEA). The 

SEA-GEARN uses ocean current data 

calculated by an OGCM as input 

variables. Wind drift current and 

leeway, with parameters determined 

for various kinds of debris by the Japan 

Coast Guard, also were incorporated 

into the experiments (MOEJ 2014). 

Model predictions were validated 

against information on sightings of 

debris reported by ships, as well as 

high-resolution image analysis based 

on synthetic aperture radar (SAR) 

satellite observations. 

11 
2016 MARINE DEBRIS MODELING REPORT 

CONCLUSION
 

State of the Science 

Although knowledge of large-scale 

ocean surface currents has existed 

since the Age of Sail in the 16th to 

19th centuries, the increased accuracy 

and availability of oceanographic 

measurements in recent decades has 

helped to refine and improve surface 

current models. 

Modeling simulations using a 

Lagrangian PTM driven by currents 

derived from ship drift data (Wakata 

& Sugimori, 1990) suggested regions 

of the world’s oceans (5° x 5° global 

grid) where surface currents would 

result in areas of debris accumulation. 

Subsequent studies using in situ 

measurements of temperature, salinity, 

and depth to calculate geostrophic 

currents and surface winds to derive 

Ekman currents together with a PTM 

also suggested zones of accumulation, 

with dense concentrations in the 

area of the North Pacific Subtropical 

High (Eastern Garbage Patch, Figure 

3). Additional simulations revealed 

similar convergence features in 

mid-latitude regions of other ocean 

basins. Currents derived from satellite 

altimetry and scatterometry were also 

used to drive a PTM in simulations of 

debris drift and accumulation in the 

South Pacific Ocean (Martinez et al., 

2009). The simulations were initiated 

with virtual particles homogeneously 

distributed on a 1/3° grid, and also 

with targeted releases from South 

America, Australia, New Zealand, 

and South Pacific Island nations. The 

results suggested that debris would be 

transported and concentrated in the 

eastern portion of the South Pacific 

Subtropical Gyre. Windage of drifting 

objects was not considered in either 

study. 

focusing on the North Pacific 

Ocean basin at a spatial resolution 

of approximately 1° x 1° (Ingraham 

& Miyahara, 1988; Kubota, 1994). 

These latter studies initially did 

not incorporate an adjustment for 

windage of drifting objects, although 

later applications of the OSCURS 

model (Ebbesmeyer & Ingraham, 

1994, 2012; Ebbesmeyer et al., 2007) 

added a windage factor, used to tune 

model parameters so that simulations 

matched closely the known trajectories 

of debris items. Simulated release 

locations of virtual drifters were 

uniformly distributed over the 

model grid (Wakata & Sugimori, 

1990; Kubota, 1994), targeted on 

known debris spill locations (e.g., 

Ebbesmeyer & Ingraham, 1994), or 

evenly spaced along a specific length 

of coastline (e.g., area of the Japanese 

coast impacted by the 2011 tsunami; 

Ebbesmeyer & Ingraham, 2012).

Using altimetry data to calculate 

geostrophic currents and scatterometry 

to derive wind-driven Ekman currents, 

PTM simulations were initialized with 

virtual particles distributed uniformly 

on each 1° x 1° grid in the North 

Pacific Ocean (Kubota et al., 2005). 

The simulations resulted in areas of 

accumulation corresponding roughly 

to the North Pacific Subtropical 

Convergence Zone (see Figure 3), 

“The availability of global 

satellite measurements 

represented a significant 

advance in our ability to 

model ocean surface currents 

and floating debris with 

greater accuracy. “ 

Besides the Lagrangian particletracking approaches described above, 

which describe the trajectories of 

virtual particles, statistical models 

have also been developed to describe 

the evolution of tracer density over 

an Eulerian grid. One such statistical 

model of drifter spread (Maximenko et 

al., 2012) used satellite-tracked drifter 

trajectories to calculate the probability 

of a drifter to move between bins of 

size 1⁄2° x 1⁄2° over a time step of 5 days 

for 10 years, starting from a condition 

of uniform tracer distribution. 

Rather than following individual 

trajectories of many simulated drifters 

over time, these kinds of statistical 

models describe the distribution and 

density of tracers throughout the 

grid domain at each time step. The 

Surface CUrrents from Diagnostic 

model (SCUD model) combines 

satellite altimetry, scatterometry, and 

gravity measurements to calculate 

surface current velocities, but with 

model parameters tuned to reproduce 

velocities derived from trajectories of 

standard drifters drogued at 15 meters 

depth (Maximenko & Hafner, 2010).

The approaches for modeling 

surface currents based on long-term 

average conditions provide a means 

of predicting the likely paths of 

marine debris, with the models often 

tuned, calibrated, or adjusted using 

information on reported sighting 

locations. These models are not able 

to represent small-scale oceanic 

processes, such as mesoscale eddies, 

nor do they model coastal processes, 

such as tides, freshwater inputs, and 

estuarine circulation. Hence, such 

models are not accurate in nearshore 

areas or in areas where mesoscale 

turbulence might be expected to have 

a large or frequent impact on overall 

patterns of water movement (Walter, 

Scandol, & Healey, 1997). The value 

of these models is in their ability to 

forecast or predict the transport of 

debris resulting from long-term mean 

circulation patterns.

In contrast to forecast models 

based on long-term mean currents, 

OGCMs are regularly updated with 

satellite-derived surface measurements 

of SSH, SST, and winds, as well as in 

situ vertical profile measurements of 

temperature, salinity, and depth, and 

simulate three-dimensional dynamics. 

OGCMs simulate the state of the ocean 

at a particular instant in time, based 

on the most recent (near real-time) 

measurements. Hence, in most of 

the examples presented in this paper, 

OGCMs have been used in hindcasting 

applications, although statistical 

methods can be used to produce 

forecasts, as pointed out previously. 

Besides their capacity to model the 

ocean in three dimensions, another 

advantage of OGCMs is their ability 

to resolve finer-scale (mesoscale) 

processes, with some regional models 

capable of 1/25° to 1/36° horizontal 

resolution and 20 to 30 vertical layers. 

Several different OGCMs have 

been used as sources of information 

on surface currents to drive PTMs, 

depending on the specific application. 

12 
2016 MARINE DEBRIS MODELING REPORT 

For the Yellow Sea, East China Sea, 

Japan Sea, and the Asian-Australian 

region studies presented earlier, 

surface currents were derived from the 

z-coordinate RIAM OCM (Yoon et al., 

2010) and MOM (Hardesty & Wilcox, 

2011), or the terrain-following POM 

(Isobe et al., 2009; Kako et al., 2010a; 

Kako et al., 2011; Kako et al., 2014). 

Simulated releases were from uniform 

initial distribution, or targeted releases 

from large cities, major rivers, or other 

known sources of debris (e.g., beach 

debris survey locations). One- and 

two-way PTMs were used in these 

studies, but the ability of the two-way 

PTM approach to identify statisticallysignificant sources provides an 

advantage over one-way PTMs (Isobe 

et al., 2009). Some of these studies 

incorporated adjustments for windage 

or investigated effects of release 

quantities, debris types, and buoyancy 

ratios on movement and beaching 

characteristics of debris. Horizontal 

spatial resolution of these models has 

ranged from 1/6° – 1/12°.

Surface currents provided by 

HYCOM on a 1/12° global grid have 

been used to drive PTMs in other 

studies that investigated different 

debris release quantities (Lebreton et 

al., 2012; Lebreton & Borrero, 2013) 

or windage values for various debris 

types (NOAA MDP, 2014c). The latter 

used HYCOM currents together with 

the GNOME pollution spill model to 

investigate the movement of JTMD. 

In this application, 1,000 simulated 

particles were released from each of 8 

locations on the Japan coastline where 

tsunami wave heights were 3.5 meters 

or greater. In another investigation, 

the Japan Meteorological Institute’s 

MRI COM provided surface currents 

for simulations of JTMD transport 

(MOEJ, 2014). Surface currents from 

an eddy-resolving (1/10° grid) ocean 

model were used together with a PTM 

(which also incorporates windage) 

to provide short-term forecasts. The 

results of these short-term forecasts 

provided initial conditions for longterm forecasts (more than 2 years) 

using surface currents from a coupled 

ocean-atmosphere model (“K7”; 

Sugiura et al., 2008). 

Recommendations 

Future research should focus on 

improvements to existing models, 

particularly nesting of high-resolution 

regional models within basin-scale 

or global models. Global and basinscale models have suggested regions 

of convergence where debris is likely 

to be concentrated. Higher-resolution 

models targeting specific areas 

could be useful in providing insight 

into finer-scale patterns of debris 

accumulation or dispersion. The North 

Pacific Ocean has been the focus of 

many studies, and for good reason: 

it is the largest of the world’s oceans 

and is located in the more highlydeveloped northern hemisphere. 

However, additional studies of other 

ocean basins, particularly those with 

a regional application, could help 

to shed light on local patterns of 

transport and fate. Approaches similar 

to those applied in the Yellow Sea and 

Japan Sea (Isobe et al., 2009; Kako et 

al., 2010a; Kako et al., 2011; Kako et 

al., 2014) using a two-way PTM to 

identify statistically-significant sources 

of debris could be useful in focusing 

efforts to reduce debris loadings.

Long-term historical measurements 

of temperature, salinity, depth, and 

winds from a variety of sources have 

Key Outputs 

Increased accuracy and availability of oceanographic wind and current measurements has provided opportunity 

for significant and relatively rapid improvement in modeling. 

Improved understanding and quantification of windage has improved application of wind data to better estimate the speed and direction of debris movement, and its eventual fate. 

For long term forward-looking estimates, modeling methods that use statistical long term averages of forcing 

(currents & winds) or drifter data can give a good picture of debris movement based on those averages. However, they cannot anticipate fine scale temporal or spatial variations in forcing (e.g. to estimate the weather for 

some time in the future, looking at an almanac can tell you the general trends for a given location and timeframe, 

but not a specific forecast for a given day in the future). 

For short-term trajectories, modeling approaches that use detailed forecasts of currents & winds (forcing) can 

provide fine scale temporal and spatial projections of debris movement, but are limited by the availability and 

duration of those forecasts, typically only 72 hours into the future as of this writing. 

Additional research & data on the behavior and life cycles of debris over time will improve modeling. The effects 

of bio-fouling and degradation and the resulting change in the behavior of an object over time can have a significant impact on the object’s fate and movement. 

Improved communications and information sharing between the modeling community and those collecting realworld observations of debris concentrations and behavior have the potential to help validate and even improve 

modeling processes, products and accuracy. 

13 
2016 MARINE DEBRIS MODELING REPORT 

been used to calculate geostrophic and 

Ekman currents, and have provided 

a means of forecasting large-scale 

movement of debris. However, there 

are now several decades of satellite 

altimetry, scatterometry, gravity 

measurements, and drifter trajectories 

from which to derive surface currents 

and circulation patterns, as well as 

additional in situ measurements 

of temperature, salinity, and other 

parameters. With the development of 

systems to assimilate the data and to 

provide increased accessibility of the 

data online, maps of ocean surface 

currents can be produced with everhigher levels of accuracy. Similarly, 

operational OGCMs provide near-real 

time depictions of ocean circulation 

with high resolution (1/36° for some 

regional models). While most of the 

applications in this paper have used 

such models to hindcast debris, the 

forecasting capability of these models 

should improve as model outputs 

increase and as a long-term record of 

these kinds of data expands. The use 

of coupled atmosphere-ocean models 

to extend forecasting ability (as in 

MOEJ, 2014) should be explored 

further. 

Also needed are experiments that 

provide insight into the life cycle 

of different kinds of debris. Effects 

of windage, encrusting organisms, 

and degradation on debris behavior 

should be explored, and models 

could be refined to include these 

effects on different debris types. 

Investigations into nearshore and 

surf-zone dynamics of debris 

movement, deposition, and refloating 

also could help to inform marine 

debris models. Additionally, increased 

interaction and communication 

between modelers and individuals 

involved in the collection of data 

related to marine debris monitoring 

and detection could help to refine 

modeling efforts. Accurate data on 

observed debris concentrations can 

either validate or improve parameters 

and starting conditions for models, 

while model outputs can be helpful 

in focusing monitoring and detection 

efforts. 

Global circulation debris models 

are all likely to give similar results 

in terms of convergence zones 

and areas of accumulation. A gap 

exists, however, in terms of linking 

with information on real-world 

observations of relative debris 

concentrations and particle densities 

measured in the field that could 

provide a source of model calibration 

data. A number of at-sea field 

data collection efforts have been 

undertaken, involving researchers 

from various organizations and 

institutions. Some examples include: 

Sea Education Association (Law et 

al., 2010; Law et al., 2014; MorétFerguson et al., 2010), Five Gyres 

Institute, Algalita Marine Research 

and Education, and related efforts 

(e.g., Eriksen et al., 2013; Eriksen et 

al., 2014). Establishing a common 

measurement protocol, debris 

definition, and data storage and 

retrieval system to give modelers 

access to systematic, coherent data 

sets on floating marine debris is 

needed. 

14 
2016 MARINE DEBRIS MODELING REPORT 

 

 

 

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GLOSSARY 

AVISO - Archiving, Validation and Interpretation of Satellite Oceanographic data. Aviso distributes satellite altimetry 

data from Topex/Poseidon, Jason-1, ERS-1 and ERS-2, EnviSat, and Doris precise orbit determination and positioning 

products. 

Coriolis effect - Apparent force that must be included if Newton’s laws of motion are to be used in a rotating system. 

First described by Gustave Gaspard Coriolis (1792–1843) in 1835, the force acts to the right of the direction of body 

motion for counterclockwise rotation and to the left for clockwise rotation. On Earth, an object that moves along a 

north-south path, or longitudinal line, will be apparently deflected to the right in the Northern Hemisphere and to 

the left in the Southern Hemisphere. The deflection is related to the motion of the object, the motion of the Earth, and 

latitude. 

Ekman flow - The Ekman spiral, named after Swedish scientist Vagn Walfrid Ekman (1874-1954) who first theorized it 

in 1902, is a consequence of the Coriolis effect. When surface water molecules move by the force of the wind, they drag 

deeper layers of water molecules with them. Each layer of water molecules is moved by friction from the shallower layer, 

and each deeper layer moves more slowly than the layer above it, until the movement ceases at a depth of about 100 

meters (330 feet). Like the surface water, however, the deeper water is deflected by the Coriolis effect—to the right in the 

Northern Hemisphere and to the left in the Southern Hemisphere. As a result, each successively deeper layer of water 

moves more slowly to the right or left, creating a spiral effect. Because the deeper layers of water move more slowly than 

the shallower layers, they tend to “twist around” and flow opposite to the surface current. 

ERS-1/2 - European Remote Sensing satellites (ERS-1, ERS-2). 

Eulerian-Lagrangian - In fluid dynamics the Eulerian-Lagrangian specification of the flow field is a way of looking at 

fluid (or particle) motion. In the Eulerian framework, velocities of the flow are given at fixed points in space as time 

varies (this can be visualized as measuring the flow of a river from a fixed location on the riverbank). In the Lagrangian 

framework, the observer follows an individual fluid parcel as it moves through space and time (this can be visualized as 

sitting in a boat and drifting down a river). 

Geostrophic flow - The horizontal movement of surface water arising from a balance between the pressure gradient 

force and the Coriolis force. 

GNOME - General NOAA Operational Modeling Environment. GNOME is an interactive environmental simulation 

system designed for the rapid modeling of pollutant/particle trajectories in the marine environment. 

Gyre - Component of a large system of circular ocean currents formed by global wind patterns and forces created by 

Earth’s rotation. There are five major ocean gyres: North Atlantic, South Atlantic, Indian, North Pacific, and South 

Pacific. 

Hindcast - A way of testing a mathematical model. Known or closely estimated inputs for past events are entered into 

the model to see how well the output matches the known results. 

HYCOM - HYbrid Coordinate Ocean Model. An eddy-resolving numerical Ocean General Circulation Model 

(OGCM*). 

Jason-1/2 – NASA’s altimetry satellites, used to measure sea surface height. Jason-1 and Jason-2 are the successors to 

TOPEX/POSEIDON. 

JTMD - Japan Tsunami Marine Debris. 

OCGM - Ocean General Circulation Model. 

OSCAR - Ocean Surface Current Analysis - Real time. A collaborative project to derive ocean surface currents from 

satellite altimeter and scatterometer data. 

OSCURS - Ocean Surface CURrent Simulations. 

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PTM - Particle Tracking Model. 

QuikSCAT/SeaWinds - NASA’s Quick Scatterometer satellite. The SeaWinds instrument on the QuikSCAT satellite is a 

specialized microwave radar that measures near-surface wind speed and direction under all weather and cloud conditions 

over Earth’s oceans. 

SCUD - Surface CUrrents from Diagnostic model. A simple diagnostic model that utilizes AVISO satellite altimetry to 

determine geostrophic current component, and QuikSCAT ocean surface wind data to calculate the wind-driven Ekman 

component. 

Stokes drift - The average velocity of a particle floating at the ocean surface, caused by wave motion. A particle on the 

surface experiences a net Stokes drift velocity in the direction of wave propagation. 

TOPEX/POSEIDON - A joint satellite altimetry mission between NASA, the U.S. space agency, and CNES, the French 

space agency, to measure sea surface height (i.e., map the ocean surface topography). 

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Penny Pritzker
 

United States Secretary of Commerce 


Dr. Kathryn D. Sullivan
 

Under Secretary of Commerce for Oceans and Atmosphere
 

Dr. Russell Callendar
 

Assistant Administrator, National Ocean Service