Deep-Sea Research I 54 (2007) 792–810 Circumpolar connections between Antarctic krill (Euphausia superba Dana) populations: Investigating the roles of ocean and sea ice transport S.E. Thorpe , E.J. Murphy, J.L. Watkins British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK Received 31 January 2006; received in revised form 22 January 2007; accepted 26 January 2007 Available online 15 February 2007 Abstract Antarctic krill, Euphausia superba Dana, has a heterogeneous circumpolar distribution in the Southern Ocean. Krill have a close association with sea ice which provides access to a critical food source and shelter, particularly in the early life stages. Advective modelling of transport pathways of krill have until now been on regional scales and have not taken explicit account of sea ice. Here we present Lagrangian modelling studies at the circumpolar scale that include interaction with sea ice. The advection scheme uses ocean velocity output from the Ocean Circulation and Climate Advanced Modelling (OCCAM) project model together with satellite-derived sea ice motion vectors to examine the potential roles of the ocean and sea ice in maintaining the observed circumpolar krill distribution. We show that the Antarctic Coastal Current is likely to be important in generating the large-scale distribution and that sea ice motion can substantially modify the ocean transport pathways, enhancing retention or dispersal depending upon location. Within the major krill region of the Scotia Sea, the effect of temporal variability in both the ocean and sea ice velocity fields is examined. Variability in sea ice motion increases variability of influx to South Georgia, at times concentrating the influx into pulses of arrival. This variability has implications for the ecosystem around the island. The inclusion of sea ice motion leads to the identification of source regions for the South Georgia krill populations additional to those identified when only ocean motion is considered. This study indicates that the circumpolar oceanic circulation and interaction with sea ice is important in determining the large-scale distribution of krill and its associated variability. r 2007 Elsevier Ltd. All rights reserved. Keywords: Antarctic krill; Ecosystems; Ocean circulation; Sea ice; Life cycle; Variability; Southern Ocean 1. Introduction Zooplankton are major grazers of phytoplankton and have an important role in determining the fate of carbon fixed in the upper ocean (Banse, 1995). Models of the dynamics of ocean ecosystems are being developed to examine the controls on upper ocean carbon budgets (e.g. Steinberg et al., 2001). However, within ocean ecosystem models developed for analysing carbon budgets, zooplankton are often represented as single compartments with no structure based on the model of Fasham et al. (1990). This may be sufficient to represent small, fast growing microzooplankton (e.g. Popova et al., ARTICLE IN PRESS www.elsevier.com/locate/dsri 0967-0637/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.dsr.2007.01.008 Corresponding author. Tel.: +44 1223221598; fax: +44 1223221259. E-mail address: [email protected] (S.E. Thorpe).
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Deep-Sea Research I 54 (2007) 792–810
www.elsevier.com/locate/dsri
Circumpolar connections between Antarctic krill(Euphausia superba Dana) populations: Investigating
the roles of ocean and sea ice transport
S.E. Thorpe�, E.J. Murphy, J.L. Watkins
British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK
Received 31 January 2006; received in revised form 22 January 2007; accepted 26 January 2007
Available online 15 February 2007
Abstract
Antarctic krill, Euphausia superba Dana, has a heterogeneous circumpolar distribution in the Southern Ocean. Krill have
a close association with sea ice which provides access to a critical food source and shelter, particularly in the early life
stages. Advective modelling of transport pathways of krill have until now been on regional scales and have not taken
explicit account of sea ice. Here we present Lagrangian modelling studies at the circumpolar scale that include interaction
with sea ice. The advection scheme uses ocean velocity output from the Ocean Circulation and Climate Advanced
Modelling (OCCAM) project model together with satellite-derived sea ice motion vectors to examine the potential roles of
the ocean and sea ice in maintaining the observed circumpolar krill distribution. We show that the Antarctic Coastal
Current is likely to be important in generating the large-scale distribution and that sea ice motion can substantially modify
the ocean transport pathways, enhancing retention or dispersal depending upon location. Within the major krill region of
the Scotia Sea, the effect of temporal variability in both the ocean and sea ice velocity fields is examined. Variability in sea
ice motion increases variability of influx to South Georgia, at times concentrating the influx into pulses of arrival. This
variability has implications for the ecosystem around the island. The inclusion of sea ice motion leads to the identification
of source regions for the South Georgia krill populations additional to those identified when only ocean motion is
considered. This study indicates that the circumpolar oceanic circulation and interaction with sea ice is important in
determining the large-scale distribution of krill and its associated variability.
Models of the dynamics of ocean ecosystems arebeing developed to examine the controls on upperocean carbon budgets (e.g. Steinberg et al., 2001).However, within ocean ecosystem models developedfor analysing carbon budgets, zooplankton areoften represented as single compartments with nostructure based on the model of Fasham et al.(1990). This may be sufficient to represent small,fast growing microzooplankton (e.g. Popova et al.,
ARTICLE IN PRESSS.E. Thorpe et al. / Deep-Sea Research I 54 (2007) 792–810 793
2002), but is not a realistic representation forzooplankton that have complex life cycles, live 1, 2or more years and may undergo vertical migrationsof over 1000m and horizontal movements of over1000 km. For such species more complex models arerequired that consider the changes in biological–physical process interactions during development.For some of the well-studied zooplankton species ofthe North Atlantic detailed models of the life cyclesare being developed. For example, populationmodels of Calanus finmarchicus are being coupledwith general circulation models to examine thebiological–physical interactions during their lifecycle (Zakardjian et al., 2003; Heath et al., 2004;Speirs et al., 2005). Developing more realisticmodels for other ocean ecosystems that include thelife cycles of key zooplankton species and theirinteractions with the physical environment is a keygoal of global ocean ecosystem research.
In the Southern Ocean, Antarctic krill (Euphausia
superba Dana) is a key species in the food web beinga major grazer of phytoplankton (Atkinson et al.,2001) and a major prey item of many of the seal andseabird species that occur in such large numbers inthe region (Croxall et al., 1988). However, krill arenot homogeneously distributed in the SouthernOcean and occur in much higher densities acrossthe Scotia Sea and around the continent than inother open ocean regions (Marr, 1962; Atkinsonet al., 2004; Siegel, 2005, Fig. 1). Within theseregions the distribution and abundance of krill showmarked seasonal and interannual variation (e.g.Brierley et al., 1997; Loeb et al., 1997; Siegel et al.,1998), and although there are detailed studies oflocal dynamics (Murphy et al., 1998, 2004a), there islimited understanding of the factors controllingtheir distribution in oceanic regions. Krill grow toapproximately 15mm in length before their firstwinter (see Hofmann and Lascara, 2000, Fig. 1) anddo not mature until they are at least 2 years old(Cuzin-Roudy, 1987a, b; Siegel and Loeb, 1994),during which time they will have overwinteredtwice. During the early summer periods the juvenilekrill will be transported in the ocean currents aslargely passive particles. Larger, adult krill are moremobile and swimming will become more importantas they can maintain speeds of 10215 cm s�1 (Kils,1982). However, in oceanic regions the oceancurrent flows are likely to dominate their distribu-tion during summer. A number of field andmodelling studies have examined the role ofadvection by ocean currents in the distribution of
Antarctic krill (Hofmann et al., 1998; Murphy et al.,1998, 2004a, b; Fach et al., 2002, 2006; Thorpeet al., 2002, 2004; Fach and Klinck, 2006). Themodelling work has focussed on the summer periodin the Scotia Sea and has shown that ocean currentscan connect krill populations at the regional scale.
However, ocean circulation is not the onlyphysical factor that can affect distribution ofAntarctic krill. Sea ice is a key overwinteringhabitat for krill providing both access to a vitalfood source and refuge from predators. Observa-tions of larval and juvenile krill in close associationwith sea ice, both feeding on algae on its undersur-face and taking refuge in channels within the ice, arenumerous (Marr, 1962; Nast, 1982; Daly andMacauley, 1988; Marschall, 1988; Stretch et al.,1988; Daly, 1990; Melnikov and Spiridonov, 1996;Frazer et al., 1997). There is also some evidence,both from direct observations (Marschall, 1988;Daly, 1990; Brierley et al., 2002) and indirectly fromstomach contents of higher predators (Ainley et al.,1987; Daly and Macauley, 1988; Ichii and Kato,1991), of adult krill inhabiting the under-iceenvironment. The seasonal pattern of advance andretreat of Antarctic sea ice results in an areal changefrom 4� 106 km2 in austral summer to 20� 106 km2
in austral winter (Harms et al., 2001). Sea ice driftacts to change the ice distribution. Sea ice movesdifferently to the underlying ocean; ice motion isgenerally determined by a combination of wind,ocean currents and internal stress (Steele et al.,1997), with wind dominating on short timescales(Thorndike and Colony, 1982). Therefore, becauseof the close association of krill with sea ice, theinteraction of krill with the sea ice will be importantfor their subsequent distribution.
Interannual variability in Antarctic sea ice extent(Murphy et al., 1995; White and Peterson, 1996) hasbeen related to population dynamics of krill at theAntarctic Peninsula by affecting recruitment success(Siegel and Loeb, 1995; Quetin and Ross, 2001,2003). The shelf areas around the AntarcticPeninsula are key spawning areas for krill (e.g.Marr, 1962) and are believed to act as sourceregions for krill populations downstream (Marr,1962; Brinton, 1985; Siegel, 1992). The island ofSouth Georgia, lying north of the sea ice zone in thenortheast Scotia Sea, is one such area. Influx toSouth Georgia is particularly important since theisland supports large numbers of breeding highermarine predators whose main food source isAntarctic krill. Although krill are capable of
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reproducing around South Georgia, the eggs andlarvae do not contribute to the South Georgia krillpopulations (Tarling et al., 2006). Instead, the SouthGeorgia krill populations are believed to be main-tained by influx from source regions upstream of theisland such as the Antarctic Peninsula and theWeddell Sea (e.g. Marr, 1962; Everson, 1977;Brinton, 1985; Maslennikov and Solyankin, 1988;Siegel, 1992).
Because of these advective connections betweenkrill populations, sea ice variability has ecologicalimpacts outside the sea ice zone. The fluctuations inrecruitment success at the Antarctic Peninsula,related to sea ice variability, have been noted toaffect the South Georgia krill populations (Murphyet al., 1998; Reid et al., 1999; Murphy and Reid,2001). Biomass estimates fluctuate in concordanceacross the Scotia Sea (Brierley et al., 1999) suggest-ing large-scale connections. In addition, krill trans-port to South Georgia has been linked to winter seaice in modelling studies (Murphy et al., 1998; Wardet al., 2002; Murphy et al., 2004a). Material arrivingat South Georgia originates from under the sea ice afew months prior to arrival at South Georgia. Thissuggests that variability in the extent and drift of thesea ice in the Scotia Sea may affect influx to theSouth Georgia region.
With this research, we advance the earlieradvection modelling by incorporating the effect ofsea ice motion on transport pathways at circumpo-lar and regional scales. A Lagrangian particletracking scheme is used to simulate trajectories invelocity fields with and without sea ice motionincluded to examine the impact of the association ofkrill with sea ice. We use output from a globalnumerical ocean circulation model, OCCAM(Ocean Circulation and Climate Advanced Model-ling project), to provide ocean current informationcombined with satellite-derived sea ice motion data.Differences between the upper level oceanic motionand that of the sea ice are evident in the modelresults. Depending on locality, the sea ice acts aseither a retention or dispersal mechanism whichgenerates alternative connections between the cir-cumpolar krill population centres. Finally, weinvestigate the impact of temporal variability inthe sea ice and ocean datasets on transport to SouthGeorgia. Variability in the sea ice motion enhancesvariability in influx to South Georgia and it isshown that anomalous sea ice conditions open upadditional source regions for the krill populations atSouth Georgia.
2. Data and methods
2.1. Advection scheme
To determine transport pathways, we use theLagrangian particle tracking scheme described byMurphy et al. (2004a). The advection scheme is asecond order Runge–Kutta scheme. It has twodimensions spatially and uses time-varying velocityinput. Horizontal resolution is 1
4
�� 1
4
�, determined
by the velocity fields (described below). Thecomponents of velocity in the longitude and latitudedirections (u, v) at the particle’s position arebilinearly interpolated from the four surroundinggrid points. A no slip condition is applied at landboundaries. To ensure stability of the scheme, atimestep of 2.4 h is used which satisfies theCourant–Friedrich–Lewy stability criterion. Diffu-sion is not treated explicitly within the particletracking scheme.
2.2. Ocean velocity data
The ocean velocity fields used in the particletracking scheme are from the 6-hourly wind forcedrun of the OCCAM project model (Webb et al.,1998; Saunders et al., 1999; Webb and de Cuevas,2003). OCCAM is a global, eddy-permitting z levelprimitive equation model of the Bryan–Cox–Semt-ner type and includes a free surface (Killworth et al.,1991). The version used in this research has ahorizontal resolution of 1
4
�� 1
4
�which gives a long-
itudinal resolution of 21.3 km at the northernmostlatitude of our study region (40�S) decreasing to5.8 km at the southernmost latitude (78�S); latitu-dinal resolution remains constant at 27.8 km.OCCAM has 36 levels in the vertical with thicknessranging from 20m at the surface to 255m at depth.The run of OCCAM that we use has been forced bythe European Centre for Medium-Range WeatherForecasts’ 6-hourly reanalysed wind dataset andrelaxed to climatological temperature and salinitydata (Levitus and Boyer, 1994; Levitus et al., 1994).At the time of our investigations, output fromOCCAM was available over the period January1993–November 2000. OCCAM simulates thecirculation of the Scotia Sea reasonably well(Thorpe et al., 2005) and has been used in othermodelling studies of this kind (Ward et al., 2002;Murphy et al., 2004a).
Our advection investigations use two differentocean velocity datasets derived from OCCAM
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velocity output. To examine circumpolar connec-tions, we use a multi-year monthly mean (climato-logical) velocity dataset, calculated from themonthly mean velocity fields from the 8-year periodof OCCAM output. The second part of ourresearch, an investigation into the effects oftemporal variability in the Scotia Sea, uses time-varying (i.e. not annually repeating) monthly meanOCCAM velocity output. Both studies use a depth-weighted mean velocity of the upper 182m of thewater column (levels 1–7 of OCCAM) followingMurphy et al. (2004a). The majority of krill biomassis found in this depth range in the summer season(Siegel, 1986; Miller and Hampton, 1989; Watkinset al., 2004).
2.3. Sea ice motion data
The version of OCCAM that we use for the oceanvelocity datasets did not incorporate a sea icemodel. Since modelled sea ice motion data werenot available from OCCAM to go with the oceanvelocity output at the time of our study, we insteaduse Polar Pathfinder 25 km sea ice motion vectorsprovided by the National Snow and Ice Data Center(Fowler, 2003). The sea ice motion vectors wereregridded onto the OCCAM 1
4
�� 1
4
�horizontal grid
and are used in two forms. Firstly, multi-yearmonthly means (climatology) were calculated fromthe monthly mean data (available 1978–2003) forthe circumpolar transport study. To avoid aliasingthe high speed motion at the ice-edge into anunrealistically broad zone when calculating the seaice motion climatology, we enforced a cut-off pointof 12 data points (i.e. 50% of the available monthlymeans) in each cell. That is, only cells with 12 ormore data points had a mean calculated; theremaining cells were set to zero ice motion.Secondly, the annually varying, monthly mean(non-climatological) ice motion fields were usedfor the Scotia Sea variability analysis.
2.4. Combined sea ice/ocean velocity data
To incorporate the effects of sea ice motion on thesimulated particle trajectories, we use combined seaice/ocean motion velocity fields in the advectionscheme. In both velocity datasets described above(circumpolar and Scotia Sea), ocean velocities inareas of sea ice cover (defined as those grid cells thathave non-zero sea ice motion) are replaced by thecorresponding sea ice motion data. In this way, the
computational efficiency of the advection scheme isnot compromised; the program need only read inthese combined datasets and does not have to makedecisions about when to use ice motion in pre-ference to ocean motion.
All the monthly velocity fields (whether climato-logical or monthly mean) are modified according toKillworth (1996) before use in the advectionscheme. This avoids errors associated with straight-forward linear interpolation between time-varyingmean velocity fields.
2.5. Model domains and particle releases
The circumpolar transport experiment has acircumpolar model domain with open boundariesin the north at 40�S and in the south at 78�S. Thenorthern boundary was defined to lie generallynorth of the Antarctic Circumpolar Current whilethe southern boundary is defined by the extent ofOCCAM. Particles were released on a regular gridwith horizontal resolution of 2� in latitude and 4� inlongitude to begin with, and then from areas of krillabundance based on the data in Fig. 1(b). Particleswere released on the 1st day of January, April, Julyand October into the climatological (annuallyrepeating) velocity fields and tracked for a max-imum of 3 years in the two different velocitydatasets. Once particles leave the model domainvia either of the open boundaries they do notreenter the simulation. The general patterns fromthe seasonal releases are very similar and wetherefore present only the January release results,the approximate mid-month of the spawning seasonof Antarctic krill around the continent (e.g.,Spiridonov, 1995).
The Scotia Sea variability investigation covers astudy region comprising 50–70�S, 80–20�W. Parti-cles are released on a grid with horizontal resolutionof 0.5� latitude by 1� longitude in the area boundedby 59–69�S, 75–35�W. This release pattern waschosen to cover the source region of particles thatreach South Georgia based on preliminary trajec-tories and as such includes the western AntarcticPeninsula area, the southern Scotia Sea and thenorthwestern Weddell Gyre, areas of known krillspawning activity (e.g. Marr, 1962). Particles thatleave the simulation at an open boundary of themodel domain are assumed lost to the system anddo not reenter. Particles were released on the 1st dayof each month over the period July 1993–May 1999and tracked for a maximum of 1 year. Variability in
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SG
RossSea
WeddellSea
ScotiaSea
DrakePass.
PrydzBay
Am.Sea
Bh.Sea
Cos.Sea
LazarevSea
Dumont
d’UrvilleSea
Fig. 1. (a) Location map. The 2000m isobath is marked. Am.
Plateau; SG, South Georgia. (b) Mean Antarctic krill density
(number krill m�2), reproduced from Atkinson et al. (2004).
Fig. 2. (a) Horizontal circulation of the Southern Ocean as
depicted by passive particles tracked for 10 years in climatological
velocity fields (depth-weighted mean of the upper 182m) from
OCCAM. Particle release locations are shown by black dots.
Particles that exit the study region are coloured blue, those that
remain are coloured red. Arrows indicate direction of flow. (b)
Annual mean sea ice motion (1978–2003). Mean sea ice extent in
January is marked (black line).
S.E. Thorpe et al. / Deep-Sea Research I 54 (2007) 792–810796
the results was quantified by examining transport toSouth Georgia, as defined by the number ofparticles entering a predefined region around theisland.
3. Results
3.1. Mean circumpolar circulation
To examine the mean ocean circulation simulatedby OCCAM, particles were tracked in the climato-
logical velocity fields for 10 years [Fig. 2(a)]. Thetrajectories show the general circulation of theupper �200m of the Southern Ocean in OCCAM.The modelled circulation is in good agreementwith observations (e.g. Gordon et al., 1978; Orsiet al., 1995) with the banded structure of theeastward-flowing ACC, westward Antarctic CoastalCurrent and cyclonic gyres of the Weddell and RossSeas all reproduced.
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To compare, the mean sea ice motion averagedover the available data is shown in Fig. 2(b). Emeryet al. (1997) described the mean motion of Antarcticsea ice based on an average calculated over a shorterperiod of time and the same general circulation canbe noted here. Westward sea ice motion close to thecontinent is connected to eastward motion asso-ciated with the ACC by areas of northward sea icemovement, particularly from the main coastalembayments of the Weddell and Ross Seas andPrydz Bay.
3.2. Differences between circumpolar ocean only and
sea ice/ocean trajectories
To examine the differences in particle advectionthat arise from the inclusion of sea ice motion in theadvection scheme, the distance between end pointsof the simulated trajectories following advection inthe two velocity datasets (with and without sea icemotion) after 1 year was calculated (Fig. 3). 50% ofthe particles were affected by sea ice in the first yearof advection which resulted in different trajectories
Fig. 3. Particle release location shaded according to distance
between end position of the particle following advection in the
ocean velocity fields and advection in the combined sea ice/ocean
velocity fields for 1 year. The mean September sea ice extent as
defined from sea ice motion data is marked (black line).
in the two advection runs [as defined by the particlesbeing more than 1 grid cell apart (0.25�) in eitherlongitude or latitude; Fig. 3]. Particles that do notcome into contact with sea ice have the sametrajectories in both advection runs since in areasnot covered by sea ice the two velocity datasetsare identical. Divergence of the particle trajec-tories in the two simulations, resulting fromadvection by sea ice rather than the ocean, couldbe as great as 2000 km after 1 year of advection(mean for particles affected by sea ice ¼ 387:75 km),and more than 6500 km after 3 years (mean¼ 1003:74 km).
Particles that are affected by sea ice motion intheir first year of advection are generally those thatare released within the area of maximum sea iceextent. However, not all of the particles releasedwithin this area interact with the sea ice (Fig. 3),primarily because of the timing of particle release.The particles were released in January, a month ofminimal sea ice cover in the Southern Ocean, andthe ocean currents [cf. Fig. 2(a)] are sufficient tomove the particles away from the advancing sea iceso that the particle trajectories are the same in bothvelocity datasets. In contrast, in limited releaseareas north of the maximum sea ice extent, theoceanic advection is such as to bring particles intocontact with the advancing sea ice (Fig. 3), discussedlater.
The model results suggest that there are areaswhere the sea ice can cause larger differences to thetransport pathways than in other locations (Fig. 3).Since our interest lies in the relationship betweenthese advection routes and the large-scale distribu-tion of Antarctic krill, we now examine thetrajectories in the two scenarios from particles thatoriginate in areas of krill occurrence as defined byAtkinson et al. (2004) [Fig. 1(b)]. For each grid cellwith krill numbers above 0m�2, 3-year trajectoriesin the two velocity datasets were calculated asbefore, released on 1 January (Fig. 4). Only thetrajectories that are different in the two scenariosare plotted since the mean oceanic circulation isshown in Fig. 2(a).
3.2.1. SE Pacific/SW Atlantic/Weddell Sea:
90�W20�
Particles advected in the ocean only velocity fieldsfrom this region follow three major transportpathways. These are associated with the AntarcticCoastal Current, the ACC and the Weddell Gyre[Fig. 4(a)]. Those particles released from the
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Fig. 4. The 3-year trajectories of particles plotted according to release region: (a,b) 90�W–0� and 90�E–180�W; (c,d) 0�–90�E and
90�W–180�W. (a) and (c) show trajectories derived from the ocean velocity fields, (b) and (d) show those from combined sea ice/ocean
velocity fields. Trajectories are coloured according to time: pink, 0–1 year; blue, 1–2 years; green, 2–3 years. Only those particles that have
different trajectories in the two scenarios after 1 year are plotted. Release sites are marked by black dots.
S.E. Thorpe et al. / Deep-Sea Research I 54 (2007) 792–810798
southwestern Antarctic Peninsula (south of �65�S)and in the Bellingshausen Sea are advected south-wards along the western Antarctic Peninsula andwestward in the coastal current. Some of theseparticles remain in the release region for the 3 yearsof advection while others reach the Ross Sea.Particles released from the northern Antarctic
Peninsula are carried eastwards by the ACC intothe Scotia Sea. Depending on release location,particles from this region can reach the island ofSouth Georgia within 1 year in the model or cantake more than a year to be advected into fastermoving flow, reaching South Georgia in 2 to 3years. Those particles released in the Weddell Sea
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are generally caught in the cyclonic circulation ofthe gyre with some particles crossing into the ScotiaSea. The particles released in the southern WeddellSea between 30�W and 0� take approximately 1 yearto reach the Scotia Sea and 3 years to pass 30�E.Particles released close to the Antarctic continent inthe southern Weddell Sea are generally advectedsouthwards in the model velocity fields and exit themodel domain. Northward flow along the easternAntarctic Peninsula is slow. Advection from thenortheastern tip of the Antartic Peninsula takesover a year to transfer material around the tip of theAntarctic Peninsula and it is more than 2 yearsbefore the material is entrained into the eastwardflow of the ACC.
Incorporating sea ice motion into the advectionscheme affects most of the particles released in thisregion (Fig. 3). The southwestern Antarctic Peninsu-la and Bellingshausen Sea particles are movednorthward out of the coastal current by the sea ice,increasing retention in this region and limitingwestward transport towards the Ross Sea [Fig.4(b)]. The sea ice provides a connection betweenthe Bellingshausen Sea and the South ShetlandIslands (�63�S, 58�W) north of the AntarcticPeninsula that is not present in the ocean velocityfields. The particles released north of the sea iceextent in Drake Passage ð�64�SÞ follow the sameeastward trajectories towards the South OrkneyIslands (�60:5�S, 45�W) for the first 7 months ofadvection in the two velocity datasets. By this time(austral winter), the sea ice is at its maximum extentin this region and moves the particles more rapidlyeastward and via a shorter route than the oceancurrents do so that when the sea ice retreats andleaves the particles in the ocean, they are further eastthan those that were advected without sea icemotion. Particles in the Weddell Sea are movednorthwards by the advancing sea ice and as a resultthere is greater transfer into the eastern Scotia Seafrom this region. Transport across the centralWeddell Sea is much more rapid when sea icemotion is included and particles are no longer lostover the southern boundary of the model from thesouthern Weddell Sea.
3.2.2. Eastern Weddell Gyre/Prydz Bay: 0�290�EThere are two main transport routes for particles
released in this region, associated with the WeddellGyre and the ACC [Fig. 4(c)]. Advection from thesoutheastern Weddell Gyre transports particles tothe Scotia Sea within 2 to 3 years. The northward
limb of the gyre advects material from 0� to 60�E in3 years while the flow of the central Weddell Gyre inthe modelled velocity fields is slow. East of 60�E,particles released north of �65�S are entrained intothe ACC following deflection of the flow fieldaround the Kerguelen Plateau. Some of theseparticles reach 180�W after 3 years of advection.Particles released south of �65�S are advectedwestward in the coastal current to join the cycloniccirculation of the Weddell Gyre.
The majority of particles released in this regionare strongly affected by sea ice motion [Figs. 3,4(d)]. Those particles released further west andsouth are still advected westwards in the coastalcurrent/Weddell Gyre but the northward sea icemotion encountered west of 30�E [Fig. 2(b)] advectsthe particles out of the westward current and northacross the Weddell Gyre. This cuts off the connec-tion to the Scotia Sea from this region. Particlesreleased north of the maximum sea ice extentaround 60�E (north of 60�S) are affected by seaice. The ocean velocity fields take the particlessoutheastward where the particles encounter the seaice in austral winter. As with the Drake Passageparticles, the sea ice motion in this region provides ashorter transport route than the ocean only motionand so the particles in the sea ice/ocean velocityfields are advected further in the same time period.
3.2.3. Dumont d’Urville Sea: 90�E2180�
West of 150�E in this region, oceanic transport isdominated by the westward flow of the AntarcticCoastal Current [Fig. 4(a)]. Most particles areadvected westward to 80�E where the flow diverges;from here some particles continue westward, reach-ing the Cosmonaut Sea in 3 years in the model, andothers are taken northwards to join the ACC bydeflection in the flow caused by the KerguelenPlateau. Recirculation in the Dumont d’Urville Sea,centred at �120�E, is apparent in the particletrajectories (cf. Nicol et al., 2000). East of 150�E,most particles are advected eastward in the ACCwith connection to the Amundsen Sea within the 3years of transport. There is some retention offshoreof the Antarctic continent at �165�E.
When sea ice motion is included [Fig. 4(b)],divergence of the advection pathways from thisregion still occurs at �150�E. West of 150�E,the Antarctic Coastal Current carries particleswestward but all particles are deflected northwardsby the Kerguelen Plateau so that there is no longerany transport to the eastern Weddell Gyre. The gyre
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observed at 120�E in the ocean only velocity fields isstill apparent in the combined sea ice/ocean advec-tion. West of �150�E, sea ice advects all particlesnorthwards into the ACC so that there is increasedeastward transport, reaching as far east as theBellingshausen Sea in 3 years.
3.2.4. Ross Sea, Amundsen Sea: 180�290�WThe dominant oceanic feature in this region is the
Ross Gyre [Fig. 4(c)] which can retain particles forthe 3 years of simulation. Particles released on theeastern side of the cyclonic gyre are advectedsouthwards and then westwards once entrained intothe coastal current. This provides a transportpathway along the Antarctic continent as far as105�E. Particles released west of the Ross Gyre arecarried eastwards by the ACC. Some of the particlesthat originate in the Bellingshausen Sea (east of�100�W) are retained in their source region whileothers are taken westward to the southern Ross Sea.
The inclusion of sea ice motion modifies theseadvection pathways substantially [Fig. 4(d)] and allparticles are affected (Fig. 3). Apart from someretention in the western Amundsen Sea, the sea iceadvects all particles northwards out of the coastalcurrent and across the gyre into the ACC. There isno longer any retention associated with the RossGyre nor westward transport in the AntarcticCoastal Current; instead, transport is eastward withthe ACC. This opens up a connection between theRoss Sea and the Scotia Sea, and from theAmundsen and Bellingshausen Seas to the westernAntarctic Peninsula.
3.3. Scotia Sea variability
So far we have considered advection in climato-logical (annually repeating) monthly mean oceanvelocity and sea ice motion fields. We now go on toexamine the impacts of temporal variability in thetwo datasets on transport pathways across theScotia Sea. We quantify the results by focussingon transport to South Georgia. Thorpe et al. (2004)previously investigated temporal variability inoceanic transport to South Georgia using outputfrom a different global ocean circulation model, theSemtner/Chervin Parallel Ocean Climate Model,and found that changes in regional ocean circula-tion were important. Our investigation extends thisresearch by considering the impacts of sea icevariability.
Incorporating sea ice motion into the advectionscheme provides different source regions for parti-cles that reach South Georgia than do models thatinclude only oceanic transport (Fig. 5). Particles inthe ocean velocity fields that reach South Georgiawithin a year (as defined by the dashed box aroundSouth Georgia marked on Fig. 5) generally origi-nate from a restricted area in the north of the releaseregion that includes the northern Antarctic Penin-sula and the South Scotia Ridge. Particles releasedfurther south along the peninsula and from theWeddell Sea have little if any chance of beingadvected to South Georgia in the modelled oceanvelocity fields at this depth [Fig. 5(a)]. For theparticles advected in the sea ice/ocean velocity fields,the same northern source regions are evident but, inaddition, particles have some chance of reachingSouth Georgia from the Weddell Sea and furthereast along the South Scotia Ridge when the sea icemotion is included [Fig. 5(b)].
During the 6-year period of monthly particlereleases, the probability of particles reaching SouthGeorgia from each monthly release as a whole isvery similar in both the ocean only tracking and thesea ice/ocean tracking [Fig. 6(a)] suggesting thatthe ocean signal dominates at these times. Periodswhen the two series differ show the contribution ofsea ice variability. During the early part of theseries (January 1993–October 1994) there is aslightly higher probability of particles in the seaice/ocean velocity fields reaching South Georgia.Later in the series, the difference is more substantialwith a 50% increase in the probability of reachingSouth Georgia in the sea ice/ocean velocity fieldsfrom releases made in austral autumn 1997. In termsof transport time to South Georgia from the releasesites, we consider only those particles that reachSouth Georgia from each monthly release andcalculate the average time taken by these monthlysubsets of particles [Fig. 6(b)]. The average timetaken ranges from �130 to 250 days in both series.For much of the tracking period, the two seriesare very similar but there are differences whenparticles subjected to sea ice motion take longer toreach South Georgia than those advected in theocean only velocity fields (e.g. austral winter 1995and austral autumn/winter 1997). Despite theseasonal pattern of sea ice advance and retreatwhich might be expected to affect the probabilityof particles reaching South Georgia, there doesnot appear to be a strong seasonal pattern in theresults.
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Fig. 5. Distribution maps of the probability of particles released from each release site reaching South Georgia (defined by the particle
entering the dashed box around the island) within 1 year in (a) the ocean only velocity fields, and (b) the combined sea ice/ocean velocity
fields.
S.E. Thorpe et al. / Deep-Sea Research I 54 (2007) 792–810 801
When the number of particles that arrive at SouthGeorgia in any given month (a function of theprobability of reaching South Georgia and theassociated travel time of each particle) is considered,it is clear that variability is increased with theinclusion of sea ice motion in the advection scheme[Fig. 6(c)]. While influx of particles advected toSouth Georgia in the ocean only velocity fields is
relatively steady with particles arriving each month,including sea ice motion concentrates the particlesinto pulses of arrival. For example, there aremonths of almost zero influx when sea ice motionis included (October–November 1995, October1997, November 1997) together with months ofmuch higher than usual influx (December 1993,October 1994, March 1995, January 1996 and
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Fig. 6. Temporal variability in transport to South Georgia from ocean only velocity fields (black symbols) and combined sea ice/ocean
velocity fields (white symbols). Arrival at South Georgia is determined according to passage into the box marked around South Georgia in
Fig. 5. (a) Probability of each monthly release of particles reaching South Georgia within 1 year. (b) Mean time taken by each monthly
release of particles to reach South Georgia. (c) Total number of particles arriving at South Georgia each month within 1 year of transport.
(d) Mean transport time of the monthly influx of particles to South Georgia. (e) Monthly mean sea ice extent at 40�W [from the OI.v2
Monthly SST Analysis, Reynolds et al. (2002)]. Vertical lines in (a)–(d) mark the start and end of the particle releases.
S.E. Thorpe et al. / Deep-Sea Research I 54 (2007) 792–810802
January–March 1998). These periods of increasedinflux in the sea ice/ocean particle time series tend tocoincide with peaks of increased average ‘age’ of theparticles arriving at South Georgia each month[Fig. 6(d)]—i.e. the average time taken to reach
South Georgia by the ensemble of particles thatreach the island each month. The largest positiveeffect on influx to South Georgia caused by the seaice motion variability occurred in austral summer1998, an anomalous year in terms of summer sea ice
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extent in the Weddell Sea (cf. Ackley et al., 2001)with the sea ice remaining north of the South ScotiaRidge ð�61�SÞ from winter 1997 until summer 1997/1998 [Fig. 6(e)]. The anomalous northward extent ofthe sea ice in November and December provided ameans of transport for particles from further souththan usual and advected the particles that were inassociation with the sea ice further north thannormal. Sea ice retreat then deposited the particlesinto the southern part of the ACC from where theywere transported to South Georgia in the oceancurrents. The particles reached South Georgia thefollowing January–March 1998 [Fig. 6(c)] havingspent some time within the ice and therebyincreasing the average ‘age’ of the particles reachingSouth Georgia during that period of time by morethan two months [Fig. 6(d)].
In a recent modelling study, Fach and Klinck(2006) also examined krill transport to SouthGeorgia. They employed a regional ocean circula-tion model, the Harvard Ocean Prediction System(HOPS), and simulated transport at 50m depth fora run of 10-month duration without interactionwith sea ice. Their results identified five potentialsource regions for krill at South Georgia at thatdepth and within that timeframe: the westernAntarctic Peninsula, Bransfield Strait, Scotia Sea/Elephant Island, the southwestern Antarctic Penin-sula and the Weddell Sea (their Fig. 11). Our study,using a greater depth (average of the upper �200mof the water column), suggests that the South ScotiaRidge is a key source region for South Georgia krillwhen advected in ocean currents only and that theinclusion of sea ice motion adds the northwesternWeddell Sea as a source region and enhances thepotential transport from the area of the SouthShetland Islands (Fig. 5). Results using a shallowerdepth-weighted mean calculated from the OCCAMvelocity fields (the upper 64m of the water column;not shown) indicate the same source regions for krillat South Georgia as the results of Fach and Klinck(2006). This difference in source regions is mostlikely due to the increased northward motionassociated with Ekman transport at the shallowerdepths which quickly moves particles northwardsinto the faster-flowing offshore currents, as noted byHofmann et al. (1998).
In terms of importance of the timing of particlerelease for most successful transport of krill toSouth Georgia, Fach and Klinck (2006) made threereleases of particles in their model in December,January and February. An early release (i.e.
December) increased the number of particles reach-ing South Georgia in their simulation. Our results(Fig. 6), based on monthly releases made over 6years, do not show such a straightforward patternbut instead suggest that interannual variability inthe ocean circulation is important, particularly withrespect to transport time across the Scotia Sea.
4. Discussion
4.1. Physical assumptions of the model
The circumpolar trajectories that we present aredependent on the ability of the ocean model,OCCAM, to simulate a realistic circulation in ourstudy area. Circumpolar validation of OCCAM inthe Southern Ocean has not been carried out butstudies in the Scotia Sea region show that OCCAMreproduces the frontal structure of the southernACC with realistic velocities (albeit slightly in-creased) and transports (Thorpe et al., 2002, 2005).There are discrepancies in the position of thesouthern Antarctic Circumpolar Current front inthe Scotia Sea in OCCAM (Thorpe et al., 2005) butthis does not affect transport times to SouthGeorgia when compared with historical drifter datafor example. Mean transport times in the upperocean of OCCAM from the Antarctic Peninsula toSouth Georgia of �160 days reported by Murphy etal. (2004a) agree with near-surface drifter trajec-tories in the same region (Thorpe, 2001). Thissuggests that those particles advected in the ACCfronts in our study will have realistic transporttimes. Background transport will likely be slower inthe model than in the ocean due to the horizontalresolution of the model and the use of the monthlymean velocity fields in our advection scheme whichreduces mesoscale effects. This should be borne inmind when the transport times in such regions areconsidered.
Use of monthly mean velocity fields (both for theocean circulation and the sea ice motion) reducesthe amount of higher frequency and mesoscalevariability in our results and will somewhat smooththe particle trajectories. Whilst this is acceptable forthe present study, use of higher temporal resolutionvelocity fields where available would be a usefulprogression of the circumpolar transport model andwould in particular allow a more detailed represen-tation of the seasonal sea ice growth and retreatcycle. The lack of a sea ice model in OCCAM ledus to use satellite data for the sea ice motion.
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This ensures realistic movement of the sea ice but useof a coupled sea ice–ocean model will allow investiga-tion of the fate of krill that are beneath the sea ice butnot directly attached to it. As such, our results showthe maximum likely effects of the interaction with seaice but it is likely that observed pathways would besomewhere between the two scenarios.
4.2. Biological assumptions of the model
Our model makes some basic biological assump-tions about the behaviour and life cycle of Antarctickrill. The ontogenesis of krill involves verticalmovement through the water column to a depth of500–800m and generally takes �12–30 days (Hof-mann and Husrevoglu, 2003; Tarling et al., 2006).We have not included this developmental cycle inour model. Krill are active in their environmentfrom an early stage, becoming more mobile andcapable of faster swimming speeds with age (Kils,1982). It is likely that they can respond to ambientfood concentrations, water temperature and preda-tion. We assume the net effect of these smaller-scalemovements is random and do not include them inour model. While directed horizontal migration bykrill has been inferred in coastal regions (Siegel,1988), there is no evidence that krill show directedmigration in oceanic regions. If krill are capable ofthese longer, directed movements, the areas wherethis will have most effect are in regions of slowermoving currents. In the faster ocean jets found inthe Southern Ocean where speeds often exceed themaximum swimming speed of krill, the currents arelikely to dominate over the krill movement (Murphyet al., 2004a, b); however, movements into and outof the jets will be important in determiningsubsequent transport pathways.
Diel vertical migration (DVM) has been well-observed in euphausiids and has been reported forAntarctic krill in the summer season (Miller andHampton, 1989) and more recently over an annualcycle using fishing depth data as a proxy for krilldepth (Taki et al., 2005). Murphy et al. (2004a)investigated the effect of incorporating simple DVMinto model simulations using the same advectionscheme as this study. They found that on the large-scale the DVM scheme had little impact on thetrajectories as compared with using a depth-weightedmean velocity field over the same depth range. Thus,by using the depth-weighted mean velocity fields inour simulations we have to some extent included theeffects of this aspect of krill behaviour. More
complex DVM patterns, where for example the krillspend a larger proportion of time in the surface layerof the ocean than at depth, would affect thetrajectories (c.f. Murphy et al., 2004a) but have notbeen included. The differences noted above betweenour results and those of Fach and Klinck (2006)suggest that the depth range used to simulate krilltransport is important, particularly at a regionalscale. Hence, future models that further examine thismatter should use the appropriate depth for theappropriate stage of the krill life cycle.
A further caveat of our model is that the depthrange that we have chosen for the oceanic advectionis representative of the summer season. Recent datasuggest that Antarctic krill have a seasonallyvarying DVM cycle with deeper vertical distributionand a greater amplitude of DVM in winter (Takiet al., 2005, and see also Siegel, 2005). Our modeldoes not simulate this particular aspect of behaviourbut future runs will examine the impact of theseseasonal changes.
In relation to krill behaviour associated with seaice, our model assumes that when sea ice is presentkrill are moved directly with the sea ice rather thanin the water column below the ice. We have assumedno dispersal under the sea ice; movements by thekrill under the ice could introduce further variabilityand will be worth considering in future studies.There is a lot of evidence that smaller larval andjuvenile krill are found in sea ice habitats on theunder-surface of the ice or in brine channels withinthe ice, feeding on the ice algae (e.g. Quetin andRoss, 2003). Sea ice algae provide an over-winterfood source for krill that appears particularlycrucial for the larvae and juveniles that have littleenergy storage capacity (e.g. Ross and Quetin, 1989;Meyer et al., 2002). For these younger krill that areclosely associated with the sea ice (Frazer et al.,1997), it is likely that they are transported with thesea ice as it is moved. For adult krill, the situationappears more complex. There are reports of adultkrill under the sea ice (Marschall, 1988) but thereare also studies that have suggested that adult krillshow little association with sea ice (Guzman, 1983)and that in some areas the krill are found in thewater column below the ice (Daly and Macauley,1988), at depth below the mixed layer (Holzlohner,1980) or even in association with the sea bed (Guttand Siegel, 1994). In spring, adult krill are found inthe marginal ice zone (e.g. Brierley et al., 2002). Thismay be where the animals overwintered or theresult of physical–biological interaction generating
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Fig. 7. The 3-year trajectories derived from the ocean only
velocity fields to illustrate circumpolar source regions to South
Georgia. Trajectories are coloured according to Fig. 4.
S.E. Thorpe et al. / Deep-Sea Research I 54 (2007) 792–810 805
aggregations of krill. This complexity of apparentresponses of adult krill to sea ice habitats maypartly be explained by the heterogeneity of the seaice environments (Quetin and Ross, 1991), whichmay generate very different over-winter habitats.Our approach thus generates the maximum effectthat association with sea ice may have on thedistribution of krill populations.
Finally, we have presented results for thecircumpolar trajectories from particles released on1st January. The krill spawning season occursbetween November and April, depending on loca-tion (Spiridonov, 1995), and krill are capable ofmultiple spawnings in good conditions (Ross andQuetin, 1983; Cuzin-Roudy, 1987a, b, 2000; Quetinand Ross, 2001; Tarling et al., 2006). Variability inthe timing of spawning will affect interactions withthe changing sea ice distribution so that, althoughon the large scale the main transport routes remainthe same, locally there could be differences in thetimescales of retention and transport pathways asobserved from the Scotia Sea results.
4.3. Circumpolar transport
The traditional view of circumpolar connectionsin the Southern Ocean is dominated by the role ofthe eastward-flowing ACC (e.g. Hofmann andMurphy, 2004). While this research has shown theACC to be important, it has also highlighted theconnections provided by the westward-flowingAntarctic Coastal Current which appears to connectmany of the regions of higher krill density. Forexample, the Antarctic Coastal Current provides aconnection between the regions of increased krilldensity in the Dumont d’Urville Sea to those inPrydz Bay, to the Cosmonaut Sea and ultimately,via the Lazarev and Weddell Seas, to the Scotia Sea.The Coastal Current can transport material asquickly as the ACC in our simulations so that insome places such as the Scotia Sea material canarrive from opposite sides of the Antarctic continent(Ross Sea and Cosmonaut Sea) on the sametimescales (Fig. 7). [Note that the pattern of krillabundance shown in Fig. 1(b) suggests that thewestern Antarctic Peninsula is likely to be thedominant source region for krill at South Georgia.]This suggests that one of the reasons why the ScotiaSea is a consistently high density krill region[Atkinson et al. (2004); Fig. 1(b)] is the existenceof these convergent pathways, where flows fromDrake Passsage and further west meet water from
the Weddell Sea region and further east. Thisconvergence of flows from potential source regionsmay help to explain the asymmetrical krill distribu-tion (Marr, 1962).
Regions of transfer between the ACC and theCoastal Current can create areas of retentionaround the continent, for example along the westernAntarctic Peninsula to the Bellingshausen andAmundsen Seas, and in the Dumont d’Urville Sea.On longer timescales (�10 years; data not shown),advection within the Weddell Gyre forms a closedloop and provides a means of population retentionin that region.
Considering interactions with sea ice increases thecomplexity of the potential connections betweenkrill populations. The model has shown that sea iceinteractions will be important in determining thespatial links that occur in Southern Ocean ecosys-tems. Sea ice-related drift can modify the pathwaysof transport, generating regional connections be-tween areas that are isolated in the mean circulationview. For example, sea ice motion enables north-ward transfer along the western Antarctic Peninsulaconnecting the Bellingshausen Sea to the tip of theAntarctic Peninsula (i.e. the opposite direction tothe mean ocean circulation) and ultimately towardsSouth Georgia, as well as more rapid transfer fromthe southern Weddell Sea into the Scotia Sea. Inother regions, the interaction with sea ice motion
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impedes some of the oceanic connections—primar-ily those associated with the Coastal Current, forexample there is no longer any westward transferfrom the western Antarctic Peninsula to theAmundsen Sea, nor transfer from Prydz Bay tothe Scotia Sea. The potential for enhancement orindeed reduction of regional retention of krill (e.g.the southwestern Antarctic Peninsula and the RossSea, respectively) is generated by the inclusion of seaice-related motion. Clearly, smaller-scale processessuch as mesoscale features and temporal variabilitywill be important in generating transfer between themain pathways of transport illustrated in our resultsand future work will focus on this matter.
Krill spawning can occur throughout much of theSouthern Ocean, but the capacity for successfulspawning, whereby the egg can sink to a requireddepth for hatching and the larva is able to return tothe surface using its energy reserves, will depend onlocal and historical conditions for feeding andtemperature. A modelling study by Hofmann andHusrevoglu (2003) showed that there are specificlocations on the continental shelf around Antarcticawhere the environmental conditions (water tem-perature and depth) are more suited to successfulspawning [in waters deeper than 1000m, Hofmannand Husrevoglu (2003) found no constraints onsuccessful spawning due to the presence of Circum-polar Deep Water]. Key regions for successfulspawning were found to be on the continentalshelves of the western Antarctic Peninsula, theBellingshausen and Amundsen Seas and the Du-mont d’Urville Sea. It is in these localities that theadvective transport will be especially crucial andwhere the accurate representation of the sea ice/ocean contributions to the advection will beimportant. Our modelling study has shown thatthese are regions where including the sea ice motioncan have a large impact on the resultant advection.
We stress that the circumpolar model usesclimatological, monthly mean velocity fields forthese simulations. The use of climatological velocityfields means that while the particle trajectoriesillustrate the mean interactions between the oceancirculation and the sea ice motion, interannualvariability is not considered. Changes from year toyear in both ocean circulation patterns (e.g. frontalpositions, increased mesoscale variability) and seaice motion and extent will affect these generalpathways (as shown in the Scotia Sea investigation).In addition, the monthly mean velocity fieldssmooth out some of the mesoscale variability of
this region and mean that particles are released fromthe sea ice more rapidly than they might be inreality. Furthermore, we have not explicitly in-cluded diffusion. Mesoscale variability and diffu-sion will be important physical processes,potentially increasing retention, transfer from oneoceanic current system to another and contact withsea ice depending on location. There are clearlyregions where these smaller-scale motions (andthose of the krill themselves) could have a bigimpact on the krill distribution. For example,between 0� and 30�W some southward motion,due to sea ice retreat or eddying motion perhaps,could move the krill into the Weddell Gyre ratherthan continuing eastward with the ACC. While weare interested in the mean pathways for thisresearch, it would be useful for future simulationsto include such processes.
4.4. Scotia Sea variability
To investigate interannual variability, we chose asmaller study region focussing on the Scotia Sea, anarea of particularly high krill density. Studies atSouth Georgia have shown interannual variabilityin local krill abundance, related to variability inrecruitment upstream and to variability in transportto the island (Brierley et al., 1997; Murphy et al.,1998; Murphy and Reid, 2001; Trathan et al., 2003;Thorpe et al., 2004). Sea ice motion in the WeddellSea exhibits mesoscale variability that was evidentin the monthly mean motion dataset employed inour model. Speed of the sea ice drift during ourstudy period ranged from almost stationary toapproximately 15 cm s�1, comparable to the swim-ming speed of adult krill.
The model results have shown that variability insea ice motion increases variability in transport toSouth Georgia. For a lot of the time, the variabilityintroduced by the oceanic variability dominates thesignal but in years of extreme sea ice extent, thenthe sea ice introduces additional variability into theScotia Sea transport. These periods when the sea icedoes make a difference will be important for theSouth Georgia krill populations and therefore to thekrill-dependent predators that breed on SouthGeorgia.
Observations of length-frequency distributionmade at South Georgia have suggested differentsource regions for the island’s krill population withvery small krill noted in association with WeddellSea water, suggesting occasional transport from the
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Weddell Sea to South Georgia (Watkins et al., 1999;Siegel et al., 2004). Our model results suggest thatinteraction with sea ice fosters additional sourceregions for krill at South Georgia, especially inyears of anomalous ice extent when more particlesreach South Georgia from the northern WeddellSea. Sea ice extent in the Scotia Sea and circumpo-larly has been observed to have a periodicity ofvariability (Murphy et al., 1995; White and Peter-son, 1996); our results suggest that, while a longertime series is required to fully investigate theimpacts, this has further implications for the SouthGeorgia ecosystem.
5. Conclusions
The modelling studies presented here haveimportant results for the consideration of spatialconnections between the circumpolar populations ofAntarctic krill. The Antarctic Coastal Current canconnect the near-continental regions of higher krilldensity while the ACC provides transport in theopposite direction at lower latitudes. Transferbetween these current systems, either by mesoscaleocean features, movement with sea ice or small-scalekrill movements for example, will affect regionaldispersal or retention which could enhance popula-tion stability.
The interaction with sea ice is a key part of the lifecycle of Antarctic krill and the differences intransport generated by over-winter association withsea ice environments revealed in this study will havea major effect on krill population distribution.Interaction with sea ice can further increase regionalpopulation retention or dispersal. The associationwith sea ice provides more options whereby thesmaller-scale behavioural/development changes ofAntarctic krill can affect the regional and circum-polar distribution. For example, the interactionwith sea ice in winter during early life stagesfollowed by ocean-dominated interaction and trans-port during the adult stages may generate alternatedirections of drift and be an aspect of the life cyclethat gives rise to population retention. Theseinteractions will be particularly important in thehigh krill density regions of the Antarctic Peninsulaand Scotia Sea.
On a regional scale, temporal and spatialvariability in sea ice cover and motion increasesvariability in oceanic transport to South Georgia,both in terms of influx to the island and intimescales of transport. This is likely to have an
impact on the krill populations at South Georgiaand the higher predators that depend on thesepopulations. Variability associated with the sea icewill also be important at other circumpolar loca-tions, particularly in areas of spawning.
Future work will develop these hypotheses. Theavailability of higher resolution coupled ocean-seaice models will allow investigation of more detailedscenarios of krill–sea ice associations, while includ-ing more biological aspects of krill will allow closureof the krill life cycle. Our work has shown that it iscrucial to better resolve these interactions.
Acknowledgements
We thank Beverly de Cuevas at the NationalOceanography Centre, Southampton for providingthe OCCAM output and the National Snow and IceData Center, Boulder, for the sea ice motion data.The OI.v2 Monthly SST Analysis data wereprovided by the Data Support Section of theScientific Computing Division at the NationalCenter for Atmospheric Research. NCAR is sup-ported by grants from the National ScienceFoundation. We are grateful to members of theBritish Antarctic Survey (BAS) DISCOVERY 2010core programme and Prof. Eileen Hofmann foruseful discussions. Angus Atkinson kindly providedthe krill density data for Figure 1(b). We thank thereviewers of this paper and the journal editor fortheir comments which helped us improve theoriginal version. This research forms a contributionto the BAS DISCOVERY 2010 programme.
