81

Drift Simulation of Tsunami Debris in the North Pacific
Hideyuki KAWAMURA1*, Takuya KOBAYASHI1, Shiro NISHIKAWA2,
Yoichi ISHIKAWA2, Norihisa USUI3, Masafumi KAMACHI3, Noriko ASO4,
Yusuke TANAKA2 and Toshiyuki AWAJI2,5
1

Nuclear Science and Engineering Center, Japan Atomic Energy Agency
2-4, Shirakata-Shirane, Tokai-mura, Naka-gun, Ibaraki, 319-1195 Japan
2
Data Research Center for Marine-Earth Scicences,
Japan Agency for Marine-Earth Science and Technology
3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 236-0001 Japan
3
Meteorological Research Institute, Japan Meteorological Agency
1-1, Nagamine, Tsukuba, Ibaraki, 305-0052 Japan
4
Satellite Applications Mission Directorate 1, Japan Aerospace Exploration Agency
4-6 Kandasurugadai Chiyoda-ku, Tokyo, 101-8008 Japan
5
Headquarters, Kyoto University
Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501 Japan*e-mail: kawamura.hideyuki@jaea.go.jp

Abstract
We conducted a drift simulation of tsunami debris flushed out from the Tohoku district, Japan into theNorth Pacific in the wake of the catastrophic tsunami on March 11, 2011, to monitor and forecast the drift pathover the entire North Pacific. We performed a hindcast run of the drift path to identify and characterize the
distribution of tsunami debris from March 2011 to September 2013 using ocean current and ocean surface
wind time-series data obtained by a three-dimensional variational data assimilation system for the ocean andatmosphere, respectively. We further performed a forecast run from October 2013 to June 2016 in which theforecasted data with a coupled ocean-atmosphere four-dimensional variational data assimilation system wasused. The results showed that the tsunami debris was first transported eastward by both the intense Kuroshio
Extension and westerly winds, spreading northward and southward under the influence of both an energetic
ocean eddy and a storm track over the ocean. Tsunami debris with larger windage was transported over the
North Pacific by ocean surface winds rather than ocean currents and arrived at the west coast of North
America in the fall of 2011. Tsunami debris located near the North American Continent migrated in association with a basin-scale seasonal change in the atmospheric pressure pattern. Our forecast run suggested that atsunami debris belt will be formed, stretching from the North American Continent in the east to thePhilippines in the west.
Key words: data assimilation, drift simulation, Great East Japan Earthquake, North Pacific, tsunami debris

1. Introduction
The tsunami resulting from the 9.0 magnitude Tohoku
earthquake that occurred in Japan on March 11, 2011,
flushed out a tremendous amount of debris from the coastof the Tohoku district into the North Pacific. In fact, theSecretariat of the Headquarters for Ocean Policy, theCabinet Secretariat and the Ministry of the Environment
in Japan have shown that tsunami debris, estimated atroughly 1.5 million tons, was flushed out into the North
Pacific in the wake of this disaster. Part of the tsunami
debris is now recognized to have dispersed across theNorth Pacific owing to ocean currents and ocean surface
winds, and will drift within the North Pacific for several
Global Environmental Research
18/2014: 81-96
printed in Japan

years. The floating wreckage could damage navigatingships, and the fishery industries of many countries will beharmed if tsunami debris reaches their coastal zones.
Countries surrounding the North Pacific are also concerned about ecological preservation. Therefore, scientific efforts are critical for obtaining appropriate information on the tsunami debris path, and for improving
forecast precision using promising state-of-the-art technology.
We have conducted a drift simulation of tsunami
debris in collaboration with the Japan Atomic Energy
Agency (JAEA), Japan Agency for Marine-Earth Scienceand Technology (JAMSTEC), Meteorological ResearchInstitute (MRI) of the Japan Meteorological Agency©2014 AIRIES

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(JMA), Japan Aerospace Exploration Agency (JAXA),
and Kyoto University towards emergency response.
Studies of the distributions and drift characteristics offloating marine debris, garbage and contaminant agglomerations represent significant contributions towards
successful management of the marine environment.
Accurate forecasting of the motion of such unwelcome
material has important societal implications and nowheremore so than in coastal regions, where incidences ofbeach contamination are on the increase and demands on
emergency services to respond to oil-spill events widen.
With these issues in mind, a number of research groups
have been active in advancing the technique of computer-based drift prediction. For example, attempts todetermine both the ultimate destinations of marine debris
patches generated by human activities and the original
source regions of beached litter have been investigatednumerically in the Japan Sea (Yoon et al., 2010), Australian coastal region (Hardesty & Wilcox, 2011), andHawaii (Carson et al., 2013). Furthermore, the Centre forEnvironment, Fisheries and Aquaculture Science (Cefas)of the U.K. government has developed a model-based
monitoring and short-range forecast system that has been
an effective tool in practical oil spill response (Kirby &Law, 2010), marine litter drift tracking, and environmental monitoring.
On the other hand, research is being carried out into
what may be termed the “climatological perspective” ofthe marine debris issue, in which the focus now shifts tothe larger scales of entire oceanic basins. One major
concern here is the impact of microplastics on marine
ecosystems as a whole. Particular unease in this regard
has been voiced over the accumulation of debris in the
famous “Great Pacific Garbage Patch”, to the northeastof the Hawaiian Islands (Mio & Takehama, 1988;
Matsumura & Nasu, 1997). The formation process of thepatch, or belt, has been investigated by a numericalparticle-tracking method using climatological geostrophic ocean currents combined with ocean surface
wind data (Wakata & Sugimori, 1990; Kubota, 1994;
Kubota et al., 2005; Maximenko et al., 2012). In related
work, Lebreton & Borrero (2013) attempted to assess thetransport of tsunami debris using hindcast data. Overall,these studies have revealed the importance of ocean surface winds in determining debris drift through the surface
Ekman current, Stokes drift, and windage, in addition to
the underlying ocean currents.
Although such fundamental research is essential forthe security of ship transportation, development offisheries, and conservation of the marine environment,
our understanding of the time-varying processes governing marine drift over periods of a few years within an
entire oceanic basin remains poor. Furthermore, our attempts to improve forecasting techniques by decreasing
the uncertainty through more effective use of observational data are still in their infancy.
In this study, we firstly performed a hindcast run inorder to obtain debris cloud movements up to September
30, 2013, and thus to identify and characterize the debris

drift process throughout the entire North Pacific following the Great East Japan Earthquake of March 2011. To
meet these goals, we used realistic time series data for
ocean currents and ocean surface winds that wereobtained by a three-dimensional variational (3DVAR)
data assimilation technique. Using the final state of thehindcast run as the initial condition, we attempted athree-year forecasting of the tsunami debris drift over theentire North Pacific (hereafter called “forecast run”)
using a coupled ocean-atmosphere four-dimensional data
assimilation system. Thus, the present study can offer aconsiderably more reliable representation of the drift
behavior of tsunami debris, and particularly of the place
and time of its landing. Such information is required forenvironmental monitoring and the adaptation of policies
to meet this crisis. Moreover, in advancing our scientificunderstanding of debris movements, it is in line with theaims of the important collaboration with the National
Oceanic and Atmospheric Administration (NOAA) in the
U.S.

2. Methodology
We implemented the following investigation tounderstand the drift characteristics of tsunami debris.
Currently, observation of floating tsunami debris is acomplicated task. In particular, when floating tsunami
debris has already diffused widely over the sea surface,
both satellite-based detection and ship-born observationsof tsunami debris are quite difficult. Therefore, a numerical simulation of the tsunami debris path is likely to bepositive, while satellite and ship observations might provide opportunities to assess numerical results. Therefore,
this study is based on a numerical simulation approach,as illustrated in Fig. 1.

Fig. 1 Numerical simulation approach to conducting the drift
simulation of tsunami debris in this study. The abbreviations of the organizations are as follows: JAEA: Japan
Atomic Energy Agency, JAMSTEC: Japan Agency for
Marine-Earth Science and Technology, MRI: Meteorological Research Institute, JAXA: Japan Aerospace
Exploration Agency. MOVE is the ocean data assimilation system developed at MRI of the Japan Meteorological
Agency (JMA) and K7 is the atmosphere-ocean coupling
data assimilation system developed at JAMSTEC andKyoto University. JCDAS is the climate data assimilation
system developed at JMA.

Drift Simulation of Tsunami Debris in the North Pacific

When attempting a robust simulation of a floating
debris path over the entire North Pacific, a realistic timeseries data on oceanic and atmospheric circulations(particularly ocean surface currents and ocean surfacewind) is a key factor (Kubota, 1994). This is essential forthe identification and characterization of temporal variations in tsunami debris movement and for better forecasting of the future route. Recent studies have shownthat using a variational data assimilation technique is a
useful approach for the optimal synthesis of observations
and models (Stammer et al. 2002; Masuda et al., 2003;
Sugiura et al., 2008).
To obtain realistic oceanic and atmospheric data, weconducted a 3DVAR ocean data assimilation experimentusing a Multivariate Ocean Variational Estimation system (hereafter called the MOVE system) developed at
MRI, JMA (Usui et al., 2006; Tsujino et al., 2010). The
MOVE system adopts a nesting method: MOVE-NP is
the entire North Pacific version covering the region northof 15°S with a horizontal resolution of 1/2° while thenested sub-model called MOVE-WNP is embedded in
the northwestern North Pacific region from 15°N to 65°N
and 117°E to 160°W with smaller horizontal resolutions;
1/10° from 15°N to 50°N and 1/6° from 50°N to 65°N,
and 1/10° from 117°E to 160°E and 1/6° from 160°E to
160°W (Fig. 2). Thus, the output of MOVE is beneficial
in tracking the actual movement of tsunami debris floating in the energetic Kuroshio Extension region where
meso-scale meandering and eddy-like disturbances areactive.
The ocean surface wind data necessary for both thedrift calculation and the forcing of the MOVE systemwere supplied by the JMA Climate Data Assimilation
System (JCDAS). These high-resolution reanalysis data
have been constructed based on output from weather
forecasts made by JMA and from observational data.
The forecast run of the tsunami debris drift path needs
reliable forecasted data representing the future states ofthe ocean and atmosphere. Hence, we conducted ourcoupled ocean-atmosphere four-dimensional variational

83(4DVAR) data assimilation experiment with a global
4DVAR data assimilation system called the K7 system,
which was developed at JAMSTEC and Kyoto University. Since the K7 system has adopted relatively coarse
ocean and atmospheric general circulation models(horizontal resolutions of approximately 1° and 3° forthe oceanic and atmospheric models), both models
are unfortunately incapable of resolving meso-scale
phenomena.
In addition, JAXA analyzed Synthetic Aperture
Radar (SAR) images obtained by the phased array
L-band SAR (PALSAR) mounted on the Advanced Land
Observing Satellite (ALOS) called “Daichi.” Fortunately,tsunami debris was successfully detected near the coastline of the Tohoku district, which enabled us to set
reasonable initial release points and duration in conducting drift simulation.
Using these ocean current and ocean surface winddata and initial conditions of tsunami debris, we performed a hindcast run of tsunami debris until September
2013 and a subsequent three-year forecast run with aparticle advection-diffusion model developed at JAEA
called SEA-GEARN (Kobayashi et al., 2006, 2007). We
used drift coefficients from the Japan Coast Guard for
floating marine debris of various shapes, since they havea long history of forecasting for search and rescue missions, and tracking drifting oil released in large-scale
spillages. Because marine debris drift is influenced byocean surface winds depending on the volume of themarine debris above the sea surface, we classified
tsunami debris into four types according to the ratio ofthe volume above the sea surface to that below the sea
surface.
The time series of the tsunami debris distribution
obtained in the hindcast run was validated by observational reports, whereas the quality of the present forecast
approach for tsunami debris drift was assessed bycomparison between the hindcast-sensed ensemble forecast run and the hindcast run using the reanalysis datafrom the MOVE system.

Fig. 2 Sea bottom topography (m) in the North Pacific. The Northwest Pacific,
enclosed by dashed lines, is the domain of MOVE-WNP.

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3. Configuration of the Drift Simulation
3.1 Determination of the initial state of tsunami
debrisIn general, a realistic initial deployment of marine
debris is critical for numerical simulation of marine
debris drift paths. In this study, the initial conditions of
the drift simulations were determined using SAR images
analyzed by JAXA as mentioned in Section 2. As a result,particles were released during March 2011 at three points(39°N, 143°E; 38°N, 142°E; 37°N, 142°E) off the coastof Iwate, Miyagi and Fukushima Prefectures, where alarge amount of tsunami debris was detected in SAR
images. The Ministry of the Environment, Japan, hasannounced that debris in the amounts of 699,000,
443,000, and 394,000 tons was estimated to have been
flushed out from Iwate, Miyagi and Fukushima Prefectures, respectively, into the ocean. We released thecorresponding number of particles at offshore points nearthese prefectures during March 2011, assuming that one
particle represented ten tons of tsunami debris.
3.2 Form types of modeled tsunami debris
Tsunami debris may have consisted of various items
including shattered lumber, floats, boats, and plastic
products used by aquaculture and set-net fisheries. Since
the influence of leeway on tsunami debris drift depends
on the portion of the upper body above the sea surface,
we divided tsunami debris according to the ratio of thevolume above the sea surface to that below the sea surface as follows:• Entire volume of tsunami debris below the sea surface(DEBRIS1): This type, DEBRIS1, is fully submerged,
and it is not affected by leeway at all. Representativedebris is wet, deteriorated lumber.
• Standard tsunami debris (DEBRIS2): The ratio of thevolume above the sea surface to that below the sea
surface is 1:1. Actually, a large amount of tsunami
debris is considered DEBRIS2, and is represented bylumber, sunken boats, etc.
• Large volume of tsunami debris above the sea surface(DEBRIS3): The ratio of the volume above the sea
surface to that below the sea surface is 2:1. The drift of
DEBRIS3 is strongly affected by ocean surface winds.
• Enormous volume of tsunami debris above the sea
surface (DEBRIS4): The ratio of the volume above thesea surface to that below the sea surface is 4:1. The
drift of DEBRIS4 is affected by ocean surface winds
more strongly than DEBRIS3. Representative examples are floats, buoys used by aquaculture andset-net fisheries, and undamaged boats.
3.3 Drift simulation system
The domain of SEA-GEARN used for tracking
tsunami debris covers the North Pacific, extending
approximately from 15°S to 65°N and 100°E to 75°W
(Fig. 2). The horizontal grid resolutions were set at 1/10°
in the hindcast run of the tsunami debris drift path fromMarch 2011 to September 2013, and 1° in the forecast

run from October 2013 to June 2016. The time intervalsof SEA-GEARN were set at 360 seconds in the hindcast
run and 3600 seconds in the forecast run, respectively.
The random walk of particles, the magnitude of which isexpressed as 24 KΔt (0.5 − R ) during time interval Δt,corresponding to horizontal turbulent diffusion in theocean was introduced, where K indicates horizontal diffusion coefficients (1.0 × 106 cm2s-1 in the hindcast run
and 1.0 × 107 cm2s-1 in the forecast run) and R indicatesrandom numbers between 0 and 1. Particles reaching thecoasts were stopped afterwards in the drift simulation.
This boundary condition hardly had any effect on theoverall distribution of the tsunami debris. Verticalmovement of tsunami debris was neglected, so that thedrift simulation in this study was conducted only in thelatitudinal and longitudinal directions. Tables 1 and 2
describe the SEA-GEARN configurations in the hindcast
and forecast runs.
The tsunami debris was driven by the ocean currents,
wind-driven currents, and leeway in the drift simulation.
There was no difference in the influence of the ocean
current and wind-driven
current among the four types,rwhile the leeway vl depended on the tsunami debris type
and was calculated using the following equation:r
A r
vl = k× W10 ,
B
where k is a wind pressure coefficient, A and B are thevolumes ofrtsunami debris above and below the sea surface, and W10 is the ocean surface wind at 10 m height.
The wind pressure coefficient was set at 2.5 × 10-2
based on information provided by the Japan Coast Guard.
Table 1 SEA-GEARN configuration in the hindcast run.
Simulation period
Time step
Model domain
Horizontal grid number
Horizontal grid resolution
Horizontal diffusion
coefficient
Duration of the initial release
Ocean currents
Ocean surface winds

March 12, 2011 to September
30, 2013
360 s
180° × 80° in the PacificOcean
1800 × 800
1/10°
1.0 × 106 cm2s-1
March 12 to March 31, 2011
MOVE (1-day interval)
JCDAS (6-hour interval)

Table 2 SEA-GEARN configuration in the forecast run.
Simulation period
Time step
Model domain
Horizontal grid number
Horizontal grid resolution
Horizontal diffusion
coefficient
Ocean currents
Ocean surface winds

October 1, 2013 to June 30,
20163600 s180° × 75° in the PacificOcean
180 × 75
1°
1.0 × 107 cm2s-1
K7 (approximately 10-day
interval)
K7 (1-day interval)

Drift Simulation of Tsunami Debris in the North Pacific

The actual ocean current in coastal regions is affectedby complicated shorelines, steeply-inclined seafloors,tide dynamics and river discharge. These physical factors
were not considered in this study because the spatial
resolutions of our simulation models were insufficient todescribe oceanographic and meteorological phenomena
in coastal regions. Thus, this should be noted when considering the present results with regard to tsunami debris
movement in coastal regions.
3.4 Oceanic and atmospheric data used forthe hindcast and forecast runs
For the hindcast run of the tsunami debris drift fromMarch 2011 to September 2013, we employed the dataset
obtained by MOVE for ocean currents and by JCDAS forthe ocean surface winds. In addition to the description of
the horizontal setting of MOVE in Section 2, both
MOVE-NP and MOVE-WNP have 54 vertical levels
with varying thickness, ranging from 1 m at the sea surface to 600 m near the deepest sea bottom. The MOVE
system assimilates satellite data on sea surface height(SSH) and sea surface temperature (SST) as well as

85

in-situ temperature and salinity profiles. The verticallyaveraged horizontal velocity from the first to the fifthlayer of MOVE was used as the ocean current in the drift
simulation. In addition, JCDAS’s ocean surface winds
were used for calculation of the leeway.
The tsunami debris distribution after September 2013
was estimated using forecast data supplied from the K7
system. This coupled ocean-atmosphere data assimilation system can create a comprehensive forecast datasetof ocean currents and ocean surface winds, whose quality
has been already assessed, for example, through the successful prediction of the Asian Monsoon and 1.5-year
lead time prediction of the 1997-1998 EL Nino event(Sugiura et al., 2008). The ten-day averaged ocean current data were used in the forecast run, while thedaily-mean ocean surface winds were incorporated toaccount for short-term wind fluctuations.
Figure 3 compares daily SST maps of MOVE output
with observed SST images on March 13 and April 30,
2011. The observed SST on March 13, 2011, immediately following the Great East Japan Earthquake, exhibits
a warm-core eddy off the Tohoku coast. This eddy is wellFig. 3 Sea surface temperature (SST) (°C) near the main islands of Japan on March 13 and April 30, 2011, obtained from MOVE (leftpanels) and actually-observed NOAA images (Ibaraki Prefectural Fisheries Experimental Station, 2011) (right panels). The
temperature at 0.5 m depth reanalyzed using MOVE is substituted for SST, where the ocean current (ms-1) at 0.5 m depth is plotted.

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reproduced in the MOVE SST map, while the ocean
surface current field of the MOVE output presents ananticyclonic circulation associated with this eddy. In
April 2011, the anticyclonic circulation still remained,
although this eddy structure was not as clear in either the
MOVE output or the observed SST image. Looking at theKuroshio Extension region, we find features that are
frequently observed, such as the latitude of the Kuroshio
Extension and position of troughs and ridges of its
meanders. The oceanic and atmospheric forecast fields
after September 2013 according to the K7 system arepresented in Section 4 together with the results of thetsunami debris drift forecast.

4. Results and Discussion of the Drift
Simulation
4.1 Hindcast run
Satellite-derived SST images showed that the warm
eddy remained off the Tohoku district in March and April
2011 after the Great East Japan Earthquake. This suggests that the warm eddy may have played an importantrole in carrying tsunami debris out to the open ocean.
Figure 4 shows the tsunami debris distribution, ocean
currents, ocean surface winds, and SSH on April 30,
2011. It was discovered that the warm eddy off the
Tohoku district captured a large amount of tsunami
debris, and that the tsunami debris was mainly transported southeastward after the northward excursion via
the warm eddy and under the influence of southwesterly

winds off the Tohoku district. Subsequently, the tsunami
debris was largely transported to the east by the Kuroshio
Extension at about 35°N and storm track propagating
eastward over the ocean. The ocean current and SSH
distributions clearly show active anticyclonic andcyclonic meso-scale eddies around the Kuroshio Extension. These meso-scale eddies efficiently spread tsunami
debris in the northward and southward directions in the
Kuroshio Extension region.
It was quite difficult to ascertain the actual location oftsunami debris. However, NAVAREA XI (World-Wide
Navigational Warning Service Area XI, south of 45°N
and west of 180°E in the Northwest Pacific) navigational
warnings available on the website of the Headquarters for
Ocean Policy, Cabinet Secretariat (2011) provided useful
shipboard sighting information on tsunami debris basedon the reports of navigating ships, fishery training ships,
etc. Figure 5 shows a comparison between the results of
the hindcast run and NAVAREA XI navigational
warnings from April to November 2011. Here, DEBRIS1,
DEBRIS2, DEBRIS3, and DEBRIS4 were merged in the
results of the hindcast run, since the NAVAREA XI
navigational warnings included various items. Overall,
the hindcast results agree well with the NAVAREA XI
navigational warnings, confirming the accuracy of theocean current data used in this study. In fact, the MOVE
output shows that the Kuroshio Extension flowed
eastward from 30°N to 40°N and 140°E to 160°W andmeandered in accordance with the anticyclonic andcyclonic meso-scale eddies throughout the year (Fig. 6).
Tsunami debris reached the central North Pacific region,

Fig. 4 Tsunami debris distribution, ocean currents (ms-1) at 0.5 m depth, ocean surface winds (ms-1), and sea
surface height (SSH) (cm) near the main islands of Japan on April 30, 2011. Tsunami debris includes
DEBRIS1, DEBRIS2, DEBRIS3 and DEBRIS4, where particles are plotted at 30-particle intervals.

Drift Simulation of Tsunami Debris in the North Pacific87

Fig. 5 Tsunami debris distribution in the Northwest Pacific from April to November 2011. The black circles with the crosses indicate the positionsof tsunami debris identified by the NAVAREA XI navigational warnings. Here, tsunami debris in the drift simulation includes DEBRIS1,
DEBRIS2, DEBRIS3 and DEBRIS4. The simulation result is the tsunami debris distribution in the middle of each month, while tsunami
debris identified by the NAVAREA XI navigational warnings was tsunami debris actually found during each month.

Fig. 6 MOVE’s ocean currents (ms-1) at 0.5 m depth and JCDAS’s ocean surface winds (ms-1) averaged in February (2012 - 2013) and August (2011 - 2013).

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crossing the date line in July 2011. Moreover, thehindcast results demonstrated that a part of the tsunami
debris reached the southeastern coast of Hokkaido in thesummer of 2011, corresponding to the fact that tsunami
debris was also identified on the coast of Hokkaido in thesummer of 2011. Many countries surrounding the North
Pacific have concerns about the issue of tsunami debris.
Thus, information about tsunami debris, such as a fishingboat found off the coast of Canada in March 2012 or a
floating pier found on a beach in Oregon, U.S., in June
2012, have frequently been reported by the news evenafter one year had passed since the Great East Japan
Earthquake.
Figures 7-10 show the distribution of DEBRIS1,
DEBRIS2, DEBRIS3, and DEBRIS4 from February
2012 to August 2013 in the hindcast run. Here, the colorsof tsunami debris represent the ratio of particle numbers
existing within each model grid against the total numberof released particles. Areas with blue indicate a low
possibility that tsunami debris existed there, while thosewith red indicate a high possibility. Differences in the
distribution among four types were clearly identifiable
about one year after the Great East Japan Earthquake.
DEBRIS3 and DEBRIS4, which were driven strongly by
the ocean surface winds, tended to migrate in a northeastward direction (Figs. 9 and 10). According to
JCDAS’s ocean surface wind data, southwesterly winds
predominated over the Kuroshio Extension from 30°N to
40°N and 140°E to 160°W (Fig. 6).
Such drift behaviors, particularly in the case of
DEBRIS4, reveal the important role of ocean surface
winds in moving tsunami debris across the ocean

Fig. 7 DEBRIS1 distribution in the North Pacific from February 2012 to August 2013 in the hindcast run.
The colors of DEBRIS1 indicate the percentage of particle numbers within the computational cell
(1/10° × 1/10°) to the total number of released particles.

Drift Simulation of Tsunami Debris in the North Pacific89

Fig. 8 Same as Fig. 7, but the DEBRIS2 distribution.

(Fig. 11). The basin-scale distribution of tsunami debris
with larger windage was influenced strongly by seasonalchanges in the atmospheric sea surface level pressure(SLP). DEBRIS4 located in the eastern North Pacific
moved northward in the early fall due to the strengthening of the Aleutian Low (AL) but then subsequently
changed its course to the south owing to the strengthening of the North Pacific High (NPH) from the late spring.
It is notable that a part of DEBRIS4 representing
floats, buoys, boats and so on approached the Canadiancoast in February 2012. This corresponds to reports of aJapanese fishing boat, washed out by the Great East
Japan Earthquake, being identified off the Canadiancoast approximately 1,500 km north of Vancouver inMarch 2012. Moreover, DEBRIS2, DEBRIS3 and
DEBRIS4 approached the western coast of the U.S. in
May 2012, corresponding to observations of a floatingpier that actually landed on a beach in Oregon in June
2012. In the hindcast run, DEBRIS2, DEBRIS3 and
DEBRIS4 washed ashore on the western coast of theNorth American Continent until September 2013,
although DEBRIS1, without the effect of leeway, had notbeached yet.
DEBRIS2, DEBRIS3 and DEBRIS4 were largely
transported in a southwestward direction after separatingfrom the vicinity of the North American Continent. They
approached the Hawaiian Islands in August 2012, while
an actual floating pier and large plastic container wereidentified off Maui and Oahu Islands, respectively, in
September 2012. The clockwise wind circulation
centered at 35°N and 140°W off the North American
Continent may have been responsible for the southwestward transportation of tsunami debris (Fig. 6).
The representative trajectories of DEBRIS1,

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Fig. 9 Same as Fig. 7, but the DEBRIS3 distribution.

DEBRIS2, DEBRIS3, and DEBRIS4 clearly show thedifferences in their excursions, depending on the influence of the ocean surface winds (Fig. 12). DEBRIS1
migrated slowly in an eastward direction, while
DEBRIS2, DEBRIS3 and DEBRIS4 were widely spreadat higher speeds in the North Pacific. It is noteworthy thatsome of them re-entered the Kuroshio Extension region
after circulating in a clockwise direction between 10°N
and 40°N.
Accordingly, our examination of the different drift
patterns among these four types in conjunction with thetime series of ocean currents and ocean surface winds
indicates that some of the tsunami debris arriving in theregion near the North American Continent moved innorthward and southward directions due to seasonalchanges in the basin-scale SLP pattern, and then was
subsequently directed into the well-known debris belt at

approximately 20°N in the North Pacific.
4.2 Forecast run
Figure 13 shows the tsunami debris distribution from
November 2013 to May 2016 in the forecast run.
DEBRIS4, which was driven primarily by the ocean
surface winds, is predicted to spread widely, so that alarge portion of it will disappear in the North Pacific by
November 2013. Furthermore, most DEBRIS4 will havealready landed on a coast or traveled out of the North
Pacific. It is suggested that DEBRIS1 will eventuallywash ashore on the western coast of the North American
Continent in 2014. DEBRIS1 near the North American
Continent will be transported in the southwestward
direction, in an identical manner to DEBRIS2, DEBRIS3
and DEBRIS4. Consequently, tsunami debris will bechiefly distributed in the debris belt at approximately

Drift Simulation of Tsunami Debris in the North Pacific91

Fig. 10 Same as Fig. 7, but the DEBRIS4 distribution.

20°N from the North American Continent in the east to
the Philippines in the west. The North Equatorial Current
and northeasterly trade wind between 10°N and 20°N
forecasted using K7 are probably important factors informing the debris belt (Fig. 14). It is especially remarkable that a relatively large amount of tsunami debris
will be concentrated around the Hawaiian Islands.
4.3 Assessment of the quality of the forecast
approach
In general, there is uncertainty in any forecast dataarising from model parameters, model resolutions, initialand boundary conditions, and so on. Therefore, we performed a hindcast-sensed ensemble prediction from
April to November 2012 with eleven members of the K7
forecasting system and compared it with the correspond-

ing results obtained using MOVE and JCDAS data. We
adopted the lagged average forecast approach; each run
was performed in a fashion similar to the time series run,aside from slight unevenness due to day-by-day shiftingof the atmospheric field in the initial condition.
Figure 15 shows the results of the ensemble prediction and hindcast run in November 2012. The results arefor the distribution of DEBRIS2. Although differences intsunami debris distribution were found among the elevenmembers in the hindcast-sensed ensemble prediction, theaveraged distribution of tsunami debris fundamentallyagreed with that obtained in the hindcast run using
MOVE and JCDAS data. The results suggest that theensemble prediction of tsunami debris distribution is
likely to provide reliable future patterns.

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Fig. 11 DEBRIS4 distribution in the North Pacific in the hindcast run (left panels) and JCDAS’s ocean surface winds(ms-1) in March and September 2012 (right panels). The colors of DEBRIS4 indicate the percentage of particle
numbers within the computational cell (1/10° × 1/10°) to the total number of released particles. AL and NPH are
the abbreviations for the Aleutian Low and North Pacific High, respectively.

Fig. 12 Representative trajectories of DEBRIS1, DEBRIS2, DEBRIS3 and DEBRIS4 in the North
Pacific from April 2011 to September 2013 in the hindcast run.

Drift Simulation of Tsunami Debris in the North PacificFig. 13 Tsunami debris distribution in the North Pacific from November 2013 to May 2016 in the forecast run.
Tsunami debris includes DEBRIS1, DEBRIS2, DEBRIS3 and DEBRIS4.

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Fig. 14 The K7 ocean currents (ms-1) at 5 m depth and K7 ocean surface winds (ms-1) averaged in
February (2014 - 2016) and August (2014 - 2015).

Fig. 15 DEBRIS2 distribution in November 2012 in the ensemble prediction (left panel) and hindcast run (right panel). The result of
the ensemble prediction is the percentage of particle numbers to the total number of released particles, which was averaged
from the results of each of the 11 members. The result of the hindcast run is that within the computational cell (1/10° × 1/10°).

5. Summary
The drift simulation suggests that tsunami debris waslargely transported eastward by the Kuroshio Extension
and westerly, with northward and southward spreading,by oceanic meso-scale eddies and storm tracks over theocean, although there were differences in the movementof tsunami debris depending on the type. The results
showed that the portion of the tsunami debris that arrived
near the North American Continent first migrated innorthward and southward directions, due in part to theseasonal changes in the basin-scale SLP pattern, and wasthen transported in a southwestward direction. The forecast run suggests that a debris belt will be formed fromthe North American Continent in the east to the Philippines in the west in the North Pacific. In addition, it issuggested that a relatively large amount of tsunami debris
will be concentrated around the Hawaiian Islands in the
future.
The tsunami debris distribution in the drift simulation

Drift Simulation of Tsunami Debris in the North Pacificis probably overestimated, because deterioration andsinking of tsunami debris were not considered in thisstudy. Furthermore, the horizontal grid resolutions of thedrift simulation in this study were insufficient for closely
examining the behavior of tsunami debris in coastal areas,
because tsunami debris is affected by tidal currents,
coastal winds, wind waves and complicated sea bottom
topography. Therefore, intensive research with finer
numerical models will be necessary in the future. The
accuracy of the forecast run in this study remains
controversial. Ensemble forecast runs will be indispensable in order to reduce the uncertainty of forecast
accuracy with respect to the movement of tsunami debris,as exhibited by the hindcast-sensed ensemble prediction.
The countries surrounding the North Pacific havebeen concerned about the issue of tsunami debris, so
numerous kinds of drift simulation have been conductedso far. We used sophisticated drift simulation in collaboration with NOAA in the U.S. We expect that thisstudy will provide the countries surrounding the North
Pacific with useful information on tsunami debris.

Acknowledgements
The authors are grateful to Dr. Wakamatsu, Dr.
Igarashi, Dr. Sasaki, and Dr. Hiyoshi of JAMSTEC for
their valuable support in providing K7 forecast data anddiscussions. We thank Dr. Ishizaki and Dr. Fujii of MRI,JMA for their work on producing MOVE reanalysis data.
We are grateful to Mr. Fujii of JAEA for his support for
conducting the drift simulation. We also appreciate theuseful information on drift simulation provided by Dr.
Watabayashi and Dr. MacFadyen of NOAA. Thanks areextended to Dr. Matthews for his critical reading. We
express our gratitude to the Cabinet Secretariat andMinistry of the Environment for their support for thisstudy.

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Hideyuki KAWAMURA

Yusuke TANAKA

Hideyuki KAWAMURA is an assistant principal researcher at the Japan
Atomic Energy Agency. His specialty is marine environment studies basedon numerical simulation. He has studied the movement of materials in theocean according to ocean circulation. He is currently working on
numerical simulation of the movement of radio-active materials in theocean.

Yusuke TANAKA is a project engineer at the Japan Agency for MarineEarth Science and Technology. He majored in physical oceanography andgot his M.-S. degree from Kyoto University. He is currently engaged indeveloping an ocean data assimilation system.

Takuya KOBAYASHI

Toshiyuki AWAJI has been executive vice- president in charge of education
at Kyoto University from 2010. He previously worked for many years at
the Graduate School of Science of Kyoto University as a professor and theJapan Agency for Marine-Earth Science and Technology as a seniorresearcher. His speciality is ocean science and climate prediction based on
numerical modeling and data assimilation. He is an international committee member of GODAE OceanView.

Takuya KOBAYASHI is a principal researcher at the Japan Atomic Energy
Agency. His specialty is environmental radioactivity in the ocean. He hasdeveloped an oceanic dispersion model capable of simulating oceanic
dispersion of pollutants. He is currently working on numerical simulationof the movement of radioactive materials in the ocean.

Shiro NISHIKAWA
Shiro NISHIKAWA is a research scientist in the Data Synthesis ResearchGroup of the Geoinformatics Research Department, Center for Earth
Information Science and Technology (CEIST), Japan Agency for
Marine-Earth Science and Technology (JAMSTEC). His research interestis physical oceanography, and he has focused on the formation, distribution, and circulation of surface water masses in the North Pacific using
ocean models.

Yoichi ISHIKAWA
Yoichi ISHIKAWA received his B.E. and Ph.D. degrees in Science from
Kyoto University, Kyoto, Japan, in 1994 and 1999, respectively. He
became an assistant professor at the Graduate School of Science, Kyoto
University in 2001. Currently, he is a principal research scientist at theJapan Agency for Marine-Earth Science and Technology, and has been agroup leader in the Data Synthesis Research Group, Geoinformatics Research Department, Center for Earth Information Science and Technology
since 2012. His research interests are physical oceanography and climate
physics, especially for numeric modeling and data assimilation systems.

Norihisa USUINorihisa USUI is a senior researcher at the Oceanography andGeochemistry Research Department of the Meteorological ResearchInstitute, Japan Meteorological Agency (JMA). He received his Ph.D.
from Kyoto University. His research interests include physical oceanography and data assimilation. He has developed a high- resolution
data-assimilative model in the western North Pacific, which has been used
as an operational ocean monitoring and forecasting model by the JMA.

Masafumi KAMACHI
Dr. Masafumi KAMACHI is director of the Oceanography andGeochemistry Research Department, Meteorological Research Institute,Japan Meteorological Agency. He received his Dr. of Eng. in Civil
Engineering from Kyushu University. He is an expert on physicaloceanography and data assimilation. He has been a member of the Global
Ocean Data Assimilation Experiment (JCOMM/ GODAE Ocean View)
and of the Variability and Predictability of the Ocean-Atmosphere
System/Global Synthesis and Observations Panel (CLIVAR/GSOP).

Noriko ASO
Noriko ASO is a senior engineer at the Japan Aerospace Exploration
Agency (JAXA), Japan, in the Disaster Management Support Systems
Office (DMSSO). She has been in charge of research on utilization of earth
observation satellites for disaster mitigation. She also coordinates several
disaster mitigation working groups with disaster management organization in Japan.

Toshiyuki AWAJI(Received 14 January 2014, Accepted 4 April 2014)