BEDMAP: A new ice thickness and subglacial topographic model of

BEDMAP: A new ice thickness and subglacial topographic model of

JOURNAL OF GEOPHYSICALRESEARCH, VOL. 106,NO. B6, PAGES 11,335-11,351,
JUNE 10, 2001
BEDMAP- A new ice thicknessand subglacialtopographic
model
of Antarctica
MatthewB. Lythe,• David G. Vaughan,• andthe BEDMAP Consortium
BritishAntarcticSurvey,NaturalEnvironmentResearchCouncil,Cambridge,UnitedKingdom
Abstract. Measurements
of ice thicknesson the Antarcticice sheetcollectedduringsurveysundertakenoverthe past50 yearshavebeenbroughttogetherintoa singledatabase.
Fromthesedata,a seamless
suiteof digitaltopographic
modelshavebeencompiledfor
Antarcticaand its surroundingocean. This includesgridsof ice sheetthicknessoverthe
groundedice sheetandice shelves,watercolumnthickness
beneaththe floatingice shelves,
bedelevationbeneaththe groundedice sheet,andbathymetryto 60øS,includingthe subice-shelfcavities. Thesegridsare consistent
with a recenthigh-resolution
surfaceelevation
modelof Antarctica.While the digitalmodelshavea nominalspatialresolutionof 5 km,
suchhighresolutionisjustifiedby the originaldatadensityonly overa few partsof the ice
sheet. The suitedoes,however,providean unparalleledvisionof the geosphere
beneaththe
ice sheetanda morereliablebasisfor ice sheetmodelingthanearliermaps. The total volume of the Antarcticice sheetcalculatedfrom the BEDMAP grid is 25.4 million km-• and
thetotalsealevelequivalent,
derived
fromtheamount
oficecontained
withinthegrc•unded
ice sheet,is 57 m, comprising52 rn from the EastAntarcticice sheetand 5 rn from the West
Antarcticice sheet,slightlylessthanearlierestimates.The griddeddatasetscanbe obtained from the authors.
1. Introduction
mentsin manyareas.The SPRIFolio Serieswaslaterdigitized,
on a 20-km grid, for large-scale
modelingstudies[Buddet al.,
Mappingthe topographyof the Earth'ssurfacehasbeena
1984],andversionsof thisarestill widelyusedtoday[vonFrese
major preoccupation
of geoscientists,
surveyors,hydrographers,
et al.,1999;Barnbetet al., 2000;Huybrechts
et al., 2000].With
and cartographersfor severalhundredyears [Canadian Hydrographic Office, 1981;National GeophysicalData Center, 1988],
but in recentdecades,satelliteremotesensingand imagingdepth
soundershave addedto their tools, and the majority of the surface of the geosphereis now mappedat high resolution[Smith
and Sandwell, 1994; Barnheret al., 1998; Liu et al., 1999]. One
part of the geosphere'ssurfacehas, however, remainedbeyond
the reach of conventional
or satellite methods: the bed beneath
the great ice sheetsof Antarcticaand Greenland.Antarcticahas
been the subject of many local topographic compilations
[•ankowskiand Drewry, 1981;Shahtaleet al., 1987; Vaughanet
al., 1994; Steinhageet al., 2000], but there has been no coordinatedeffort to integrateall the existingdata into a singletopographicmodel of the entirecontinent.
Prior to this study,the most widely cited topographicrepresentation of the bed beneath the Antarctic
ice sheet was a folio of
mapspublishedby ScottPolar ResearchInstitute(SPRI) [Drewry
and Jordan, 1983; Drewry, 1983a, 1983b]. The SPRI Folio Series wasproducedby usingdataduringsurfacetraversesand airbornesurveys,notablythe campaignmountedby SPRI, the National ScienceFoundation(NSF), and the TechnicalUniversity
of Denmark (hereinafter SPRI/NSF/TUD) between 1967 and
1979. This campaigncoveredaboutonethird of the continentat
100-kin line spacing. Although contoursof bed elevationwere
drawnover the entirecontinentwith the exceptionof the Antarctic Peninsula, there were more than 500 km between measure-
Copyright2001 by theAmericanGeophysical
Union.
Papernumber2000JB900449.
0148-0227/01/2000JB900449509.00
the exceptionof updatesover limitedareas,thesedatasetsdo not
incorporate
thelargequantityof datacollected
overthepasttwo
decades.
In this paper,we presenta suiteof integrateddigitaltopographicalmodelsfor the Antarcticcontinentand its surrounding
ocean,which incorporateall the availabledata from Antarctica.
Our aim was to producea self-consistent
model,at sufficiently
highresolution
to providea basisfor meaningful,time-dependent
ice sheetmodeling.To thisend,we designed
an automated
gridding schemewhich relies on direct ice thicknessmeasurements
and otherindirectsupportingdata.We aimedto honorthe meas-
urementswherethey existandto producea plausiblerepresentation where no data exist.
To facilitatemodelingof the ice sheetthrougha complete
glacial cycle, we extendedthe subglacialtopographyto 60øS,
beyondthe continentalshelfedge,the probablelimit of the ice
sheetduringrecentglaciations.We seamlessly
matchedsubglacial, bathymetric,andsub-ice-shelf
topography.
The processingstepsinvolvedin the constructionof the final
digital elevationmodel (DEM) are shownin Figure 1. In summary, we producedan ice sheetthicknessgrid from the ice thickness measurementsand several supportingderived data sets.
This grid was then subtractedfrom the best available surface
DEM [Liu et al., 1999] to determine the bed beneath the
groundedice sheet. The resultinggrid was then matchedat the
groundinglineswith a compilationof the bathymetry.In the interveningregion coveredby ice shelveswe used seismicdata,
whereavailable,to controlthe configuration
of bedtopography.
Integrationof all the sourcedata requireda consistentprojection, geoid model and geographicframework. In accordance
with the recommendationsof the Scientific Committeeon Ant-
11,335
11,336
LYTHE ET AL.' BEDMAP SUBGLACIAL TOPOGRAPHY OF ANTARCTICA
Direct
Bathymetric
Compilation
Ice Thickness
Measurements
Indirect
Sub-ice
Ice Thickness
information
shelf
depth data
Geographic
Geographic
Constraints
Constraints
MARINE AND SUB-ICE SHELF
BED ELEVATION
DEM
GROUNDED
ICE SHEET
BED ELEVATION
DEM
(MERGE)
COMPOSITE
ANTARCTIC
BED ELEVATION
DEM
Figure
1. Processing
steps
involved
intheconstruction
offinal
bedelevation
DEM.
arctic Research Working Group on Geodesyand Cartography
[1961], we usedthe polar stereographic
projection,with 71øSas
the latitudeof true scaleand 0øE as the centralmeridian.A geopotentialmodel, OSU91A [Rapp et al., 1991], waschosenasthe
vertical referencesystemfor integrationof all the sourcedata.
2.1.1.
Sources
of ice thickness
data.
Data were obtained
both from the literatureand from unpublished surveyscontributed to the BEDMAP Project by a consortiumof institutesand
investigators(Table 1). We includeddata from more than 150
independentsurveys,conductedby 15 nations,over the past 50
The minor differences between mean sea level elevation and
years. The compilationincludesover 1,400,000km of airborne
rageoid orthometricheight are expectedto be 1.5 m on average radarsoundingprofiles,around250,000 km of ground-based
dar and seismic traverse, and more than 5000 seismic reflection
[Barnbetand Bindschadler,1997] and so were ignored.
To delimit the continentand its physiographicelements(e.g., stations.
While considerable new data are available, the overall coverice sheet, ice shelf, ice-free ground) (Figure 2a), we used the
1:1,000,000 scale data set from the Antarctic Digital Database age is still patchy,as the surveyswere undertakenin supportof
(ADD) [BASet al., 1993]. This is the highest-resolutiondataset otherscientificactivities(Figure 2b). Althougha few areashave
with coverageof the entirecontinent,and it providesdescriptions densedata coveragewith track-spacingof around 10 km (e.g.,
of rock outcroppolygons,groundinglines, ice shelves,and ice Amery Ice Shelf, Ronne-FilchnerIce Shelf, Siple Coast), the
rises. The ADD has a maximum error in horizontalpositionat distance between tracks in other areas is over 50 km. There are
severalareasfor which even reconnaissance
level data have yet
this scaleof around300 rn [Fox and Cooper, 1994].
The integration,manipulation,and generationof all products to be collected, (e.g., Queen Mary Land, Wilhelm II Land,
wascarriedout by usingthe ARC/INFO GeographicInformation coastalMarie Byrd Land, and the area of the polar plateaubeSystem (GIS), supplementedwith customizeddata transforma- tweenthe SouthPole and 80øSand between45øW and 10øE).
2.1.2. Ice thickness calculation. The reliability of ice thicktion, error correction,and interpolationfunctions.
nessmeasurements
determinedby radar,seismic,and gravimetric
techniquesis contingenton assumptionsspecificto the method.
For radardata,the greatestuncertaintyresultsfrom the speedof
2. Ice Thickness
Model
radio wavesin ice [Drewry, 1975]. No singlevalue hasbeenuni2.1. Ice Thickness Data Compilation
formly appliedto all measurements;
differentworkershave asIn the 1950s and 1960s, only seismicand gravimetricmeth- sumedmean speedsof between168 and 171 m/laS. Additional
ods were used to measure ice thickness. Collection of these data
sourcesof error arisethroughuncertaintiesin correctingfor the
was laboriousand could only be done at infrequentintervalson low-densityfirn layer, wherethe speedof radio wavesis considground-basedtraverses. Gravity measurements
are more rapid erablyhigher; correctionsin the rangeof 10-15 m of ice thickthan seismicshooting,but to be useful, they must to be con- nesshave beenroutinelyapplied. The methodsof recordingand
trolled by seismicor otherdata [Robin, 1971]. Nowadays,radio digitizing field recordscan also introduceuncertainties.The asechosoundingenablesfasteracquisitionof databy producinga sembledradar data have an estimatedtotal measurementprecicontinuousrecord of the bottom profile beneaththe aircrafttra- sion of between 1% and 5% of ice thickness.
For seismic measurements,the principal source of error
jectory [Robin et al., 1973].
The data assembled within the BEDMAP
database derive
arisesfrom uncertaintyin the wave propagationvelocity,which
from five types of measurement;airborne radar sounding, is affectedby variationsin density,crystalsize and orientation,
ground-based
radar sounding,seismicrefractionand reflection, and temperature[Rothlisberger,1972]. Althoughthe velocity
gravimetricmeasurement,
and drilling, with the majoritygath- does vary with depth, most researchershave used a constant
eredduringice-sounding
radarprogrames.Sincethe datahave
beencollectedby differentresearchers
usingdifferenttechniques
which have evolvedconsiderably,assessment
of dataqualityand
the harmonizationof all datasetsrequiredconsiderable
effort.
valuearound
3.915km s'1. Bentley
[1964]suggested
anaccuracy of 3% for ice thicknessvalues from seismicshootingin
Antarctica;however, misinterpretationof seismograms
and mistakesin digitizationcan leadto largererrors.
LYTHE ET AL.' BEDMAP SUBGLACIAL TOPOGRAPHY OF ANTARCTICA
11,337
SOUTHERN OCEAN
DRONNING MAUD
LAND
Weddell Sea
ENDERBY LAND
COATS
LAND
LARSEN ICE SHELF
//
ß
.
ß
'\
KEMPLAND
FILCHNER
ANTARCTIC
",ICE SHELF
PENINSULA
SHELF
/
,
ELLSWORTH
LAND
WEST ANTARCTICA
•'•-....
/
..
A•,oxirnate
nositkm
'BYRD
LAND
of oontinentaishelf edge
QUEEN
•RY •D
/
'\.,,.,
--" MARIE
/
•
'• .
/
ß
/
,.
,,
/
Sea
/
Figure 2a. Antarcticlocationmap.
Gravity observations
differ from othersin that they yield an GPS suchas celestialnavigation,inertialavionics(INS), Doppler
averagethicknessovera regionratherthana pointmeasurement. avionics,traditionalsurvey,and dead reckoninghave far larger
Uncertaintyarisesfrom instrumentaldrift and calibrationbut is uncertainties.For example,the positionalerror for traversedata
primarilydueto theassumptions
regarding
geological
properties navigatedby using astronomicaland solar observationsare ap-
andterraincorrection
factors.KapitsaandSorokhtin[1963]sug- proximately2-3 km, while the SPRI/NSF/TUD radar soundings
gestan averageaccuracyof 7-10% for gravimetricice thickness using inertial navigation systemsare only accurateto •-5 km
measurements,
but errorsof 15-20% are likely in areasof com- [Drewry, 1983a]. Of the assembleddata, around50% are posiplex bedtopography.
tionedwith GPS, 25% with INS, 11% with traditionalsurveyand
In compilingthe BEDMAP databasewe acceptedthe values about8% with Doppler avionics.
of icethickness
supplied
by contributors
andfromthepublished In additionto the navigationaluncertainty,severalof the data
databut storedwhatinformationis availableconcerning
the cor- sets were fixed to maps which had inaccuratelylocated georectionsalongsidethe data for each mission. We have not at- graphicfeatures. Our analysishas shown(see"Crossoveranalytemptedto recalculatevaluesby usingstandardassumptions,
as sis") that while refixing of the navigationaldata might be possithe raw datawere not alwaysavailable.
ble, usingknown positionalfixes or even using a randomwalk
2.1.3. Navigation. Sincethe late 1980smostgeophysical technique,this is not requiredfor the presentgenerationof contisurveyshave beennavigatedby usingGlobalPositioningSys- nent-scaletopographicmodels and so is not attemptedin this
tems(GPS)andarethuswell fixedwith respectto an ellipsoidal study.
referenceframework. Navigationmethodsemployedprior to
2.1.4.
The
BEDMAP
database.
The BEDMAP
database
11,338
LYTHE ET AL ß BEDMAP SUBGLACIAL TOPOGRAPHY OF ANTARCTICA
180'
---- AirborneRESflightline
ß Seismic
stations
..........Overland RES traverse
x Borehole sites
= Gravity/Seismic
traverse
o
500
lOOO
ß Icefreeground
fromADD
15oo
L•oookm
Figure2b. Distribution
oficethickness
dataintheBEDMAPdatabase.
currently
includes
around2,500,000directmeasurements
of ice
basisthattheyremainunderthe controlof the contributing
in-
thickness,an estimated90% of all datacollectedoverthe Ant-
stitutes;
thusaccess
restrictions
remainon a numberof datasets.
arcticice sheetto date. The outstandingdataincludesurveysun-
2.2.
Construction
of Ice Thickness
Model
dertaken
duringthepast2-3 seasons,
anda smallquantity
of old
The primarydatausedin the construction
of the ice thickness
datawhichare,to ourpurposes,
lost. The dataaredividedinto
missions,
whichrepresent
datacollectedduringoneor several model were direct ice thicknessmeasurements,but we also used
consecutive
field seasons
by onescientificpartyusingconsistent rock outcroppolygons,which approximateto isoplethsof zero
techniques.The datawereconsolidated,
with accompanyingmean ice thickness,and ice shelf thicknessdeterminedby hydrostaticconversionof surfacealtimetry.
Even with these additionsthe total coveragewas, however,
For the majorityof datasetswe havecompiled
onlythe de- too sparseto generatea reasonablemodel over the entire contirivedparameters,
surfaceelevation,icethickness,
bedelevation, nent. For this reason,we designedotherfields to help controlthe
and water column thickness. Data have been contributed on the
griddingin areaswherethe datawere sparseand in mountainous
documentationfor each mission,in a relationaldatabasehoused
at the BritishAntarcticSurvey(BAS), Cambridge,UK.
LYTHE
•
-4
-4
ET AL.:
o
<
BEDMAP
<
<
SUBGLACIAL
-4 o
TOPOGRAPHY
•
-4 o
OF ANTARCTICA
-4
<
-4 <
11,$$9
o
<
•
11,340
LYTHE
ET AL ß BEDMAP
SUBGLACIAL
regions,Thesefieldsrely on a priori assumptions
andare derived
from regressionmodels,andwhile they do not representthe true
ice thickness,they producea plausiblesurfaceandhelp to reduce
the appearanceof artefactscausedby data sparsity. We added
one further control:by definingthe longitudinalprofile of the
large outlet glaciersin East Antarcticawhich are importantas
TOPOGRAPHY
OF ANTARCTICA
1.0
0.8
outlets for ice from the interior ice sheet,we used a linear inter-
polationof ice thicknessat the groundingline to the nearestupstreammeasurement
to ensurethat they were represented
in the
final model.
Finally, the combineddata sourceswere usedas inputinto a
specificallydesignedspatialinterpolationalgorithmwhich is described below.
N = 16084
0.4
RMS = 156.2 m
2.2.1. Data preprocessing. The ice thicknessmeasurements
include inherent uncertainties,and to extract a reliable set of ob-
servationsfor the gridding,we carriedout somedata preprocessing. The goal was to flag erroneousvalueswithin the databaseand reducethe data setbeforeinterpolation.
The datapreprocessing
wascarriedout in threesteps.
2.2.1.1. Crossoveranalysis: Crossoveranalysisprovidesan
estimationof the overall integrity and precisionof the data set.
Here a crossingpoint was defined where two observationsfrom
separatemissionsor separateflights within the same mission
were separatedhorizontally by 500 m or less. From around
16,000 crossingpointswe found a root-mean-square
(RMS) differencein ice thicknessof 156.2 m and a medianabsolute(MA)
difference
of 21 m.
The distribution
MA=21
m
0.2
0.0
0
200
400
600
800
1000
Absolute Difference (rn)
Figure3. Cumulative
distribution
of crossover
errorsin ice
thickness measurements.
of crossover errors has a
sharperpeak and longertail than a normaldistribution.
The distributionof the absolutecrossovererrors(Figure3) re-
used insteadof the mean value to avoid unnecessarysmoothing
and to ensurethat the value assignedwas an actual observed
veals that around 58% of crossover errors are less than 20 m, value. Where data was highly anisotropic,with track spacing
73% are less than 50 m, and 84% are less than 100 m. This indi- greaterthan 50 km, we filteredthe databy usinga 5000-m samcates that the majority of crossovererrors fall within typical ple spacing.
navigational and measurementuncertainties. The long-tailed
While this procedureremovedlegitimatehigh-frequency
indistributionsuggeststhere is a small percentageof large cross- formationalongthe track,particularlyon the very denselyspaced
over errors,but neighborhoodvariancechecks(describedbelow) data, the resultingprofiles depictthe variationin ice thickness
suggestthat a programof extensiveflight line refixing was not over a wavelengthnearerthe resolutionof the final grid. This
required. Refixing was, however, carried out on a few flight reducedthe problemsassociatedwith anisotropicdata distribulineswhereclearerrorsin track positionexisted.
tion. Approximately2 million datapointsfrom both anisotropic
2.2.1.2. Data reduction: The majority of ice thicknessdata airborneand ground-based
track line data were filtered by using
havebeencollectedby usingairborneradioechosounding(RES) this procedure,leavinga reduceddatasetof around160,000data
techniques,a methodthat resultsin highly anisotropicdata dis- points,significantlyreducingprocessingtime requiredfor the
tribution;i.e., the data are denselysampledalongtrack while the spatialinterpolation.
flight tracksthemselvesare widely spaced. The SPRI/NSF/TUD
2.2.1.3. Neighborhoodvariance check: In general,ice sheet
data, for example,have a typical along-trackspacingbetween thicknessis a parameterexhibitinga high degreeof spatialautosamplesof 1-2 km and an across-trackseparationof 50 km. Such correlation. The correlation length indicates the distance or
a distribution poses serious difficulties for most interpolation rangeof spatialdependence
and variesacrossthe ice sheet.The
techniquesand resultsin a directionalbias in the grid. To coun- semivariogramexpressesmathematicallychangesin variance
ter this, we useda filter to reducethe high-densitytrack line data, with distanceand directionbetweenany two points [Cressieand
but beforethis, we preprocessed
the datato identify and remove Hawkins, 1980]. Semivariograms
indicatethe natureof autocorisolatederroneousdatapoints.
relationin differentpartsof the ice sheet.
Threeparameters
definethe semivariogram:
the nugget,the
In the preprocessing
step, a seven-pointfilter was appliedto
each track-line. The mean ice thickness,standarddeviation, and measurementerror and natural variance for nearby measurealong-trackspreadof the seven data points were calculated. If ments;the sill, the varianceof the datasetbeyondthe rangeof
the along-track spread was greater than 6 times the mean ice spatial correlation;and the range, the distancebeyond which
thickness,the data were regardedas insufficientlydensefor fil- spatialcorrelationis insignificant. The relative nuggeteffect
tering. If the spreadwas lessthan 6 timesthe meanice thickness (ratioof nuggetto sill) givesan indicationof the level of spatial
describedby the semivariogram
model.
and the centralobservationwithin the window differedby more dependence
The semivariograms
(Figure4) showthat spatialautocorrethan twice the standarddeviation, that observationwas removed.
This procedureprovedeffectivein removingaround1500 spikes lation is large over both the interior ice sheetand the RossIce
from the track line data.
Shelf. In mountainousregions,large changesin ice thickness
In the data reductionphase,new data pointswere established occur over short distances, and the autocorrelation is much
alongthe profile with a spacingof 2500 m. Thesenew sample lower. The high nuggetvalue and large relativenuggeteffect
nodeswere then assignedthe median ice thicknessof observa- (0.86) in the Trans-AntarcticMountains show that over distance
tions within 2500 m along that profile. The medianvalue was greaterthan around40 km, ice thicknessis not a regionalized
LYTHE ET AL.' BEDMAP SUBGLACIAL TOPOGRAPHY OF ANTARCTICA
•200
11,341
neighborhood
mean,it was flaggedas unreliableand removed
a) East Antarctica
fromtheinterpolation
dataset. Of thepoints
flagged
aspotentiallyanomalous,
justover1000(---7%)
wereremoved
by using
•000
this procedure.After the removalof theseobservations,
the
overallRMS errorof all remaining
crossover
pointsdropped
from156.6to 134.2,justifying
thepreprocessing
step.
The preprocessing
describedaboveyieldeda final set of
--200,000ice thickness
datapoints,comprising
approximately
800
600
160,000datapointsfrom the reducedtrack line dataand around
40,000 other ice thicknessmeasurements.
400
Nugget= 180m2
200
Range = 140 km
Sill = 420 m2
0
200
400
600
2.2.2. Rock outcrops.Approximately
0.3% of theAntarctic
continent
is free of ice for at leastpartof the year [Foxand,
Cooper,1994]. Theseareasof permanent
rockoutcroparea
valuablesourceof informationfor the ice thicknessmodel. Here
800
iooo
Distance lag (km)
we usedrockoutcroppolygonsfromthe 1'1,000,000scaleADD:
a totalareaof approximately
50,000km2. over75% havean area
of lessthan1 km2 andonly250 haveanareaof greater
than5
600
E
b)Ross
IceShelf
km•. Thusmostof the outcrops
aremuchsmallerthanthe reso-
Nugget= 22 rn
lutionof the final gridwe choseto depictice-freegroundas
•
4oo
Range -- 110 km
pointdata,allowingus to usethem in the samemanneras other
•
Sill
=106
m
2
measurements of ice thickness.
200
._•
•
.--
The conversion
of thepolygondatato pointobservations
was
carriedout by usinga two-stageprocedure.First,we extracted
._•. _ ...... :::
E
u)
fromall outcrops
thepolygoncentroids.Theseprovidean ade-
o
0
200
400
600
800
1000
1200
c) Trans-Antarctic Mountains
looo
•
t-.
soo
not disproportionately
represented.
2.2.3. Hydrostaticconversionfor floatingice. Over distances
greater
thana fewtimestheicethickness,
icecannotsupportsignificant
verticalshearstresses
for morethana fewyears
[Casassa
and14/hillaris,
1994];consequently,
floatingiceshelves
õoo
._•
E
quaterepresentation
of the small nunataks. Second,we selected
outcrops
with an areagreaterthan25 km• (thesizeof a single
gridcellin finalmodel)andextracted
a setof vertices
spaced
at2
km around
thepolygonperimeter.
Theresulting
nodesprovided
a reasonable
description
of theextentof thelargerice-freeareas.
Thisprocedure
ensures
thatextensive
outcrops
are adequately
represented,
whilenunataks
smallerthanthe grid spacing
were
Distance lag (km)
½oo
are generallyin regionalhydrostaticequilibrium. The total ice
Nugget= 415 m2
200
Range = 50 km
Sill = 480 m2
-
thickness
or draftis relatedto the surfaceelevationby the followingrelation[Jenkins
andDoake,1991]'
,
0
200
400
600
800
1000
h=
pw - pi
pi
H +•d
+ e,
(1)
Distance lag (km)
whereh is the freeboardwith respectto the ellipsoid;H is the
of theiceshelf;PwandPI, arethemeandensities
of
(a) East Antarctica,(b) Ross Ice Shelf, and (c) Trans- thickness
Figure 4. Geostatisticalpropertiesof the ice thicknessdata in
Antarctic
Mountains.
the seawaterand ice, respectively;d is the thicknessof the airfractioncontainedin the low-densityupperlayers;and e is the
geoid/ellipsoidseparation.
This relationship is linear if constantdensitiesare used.
variable. Over the ice shelvesand interior ice sheet,relative Whilethedensityof waterbeneath
theshelfis relativelyconstant
nuggeteffectvaluesof 0.19 and0.23, respectively,
showgood (1028-1032kg m-3),the densityof ice may vary owingto (1)
spatialautocorrelation.
This principleprovidesthe basisfor our difference
in thedensification
rateof thefirn layer,(2) freeze-on
final qualitycontrolprocedure,
a neighborhood
variancecheck.
of marineice to the baseof the shelf,or (3) crevassing.This reWe identified crossingpoints defined where two observa- lationshiphas been widely used [Budd, 1966; Thyssenand
tionsfrom separate
flightswereseparated
by lessthan 1000m. Grosfeld,1988]. The slopevarieswith densitycharacteristics
of
Of these,crossovers
with an absolute
valuegreaterthan 100 m the ice, while the interceptvalue represents
a correctionfor the
wereflaggedaspotentially
in error.Around15,000datapoints low-densityfirn andoffsetsin the geoidmodelfrom sealevel.
wereidentifiedfrom thisprocedure.
Analysisof the regression
modelsdevelopedfor differentice
For each of these data points we establisheda localized shelvesshowsthat the total variationbetweenmodelsis apneighborhood,
definedas all pointswithin a radial distanceof proximately 10 m, mostly arising from the different densities
5000 m. The variancecharacteristics
(meanandstandard
devia- used. Thiserrorenvelopesuggests
thatthisconversion
provides
tion) of all otherobservations
withinthis neighborhood
were a usefuladditionalsourceof ice thickness
dataoverunsurveyed
then determined.Providingthe neighborhood
containedat least or data-sparseice shelves. The value of the method is increased
five measurements,if the value of the central observationwas becauseradarsoundingis unableto measuretotal ice thicknessin
greater than twice the standard deviation different from the areaswhich containa marine ice layer or exhibit crevassing
11,342
LYTHE ET AL'
BEDMAP SUBGLACIAL
TOPOGRAPHY
OF ANTARCTICA
12oo
1 ooo
•
800
ß
* *
y=223.98Ln(x)
- 1108.4
ß--- 600
._u
=
400
2OO
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Distance to nearest outcrop (m)
Figure5. Thiniceregression
models
developed
fromdatain PrinceCharles
Mountains (diamonds)
andDronning
MaudLand(squares).
Thelineindicates
thethinicemodelderviedfromall
datawithin 10 km of ice- freegroundin bothareas.
[Vaughan et al., 1995] such as the Filchner-RonneIce Shelf
2.2.5. Outlet glaciers. Fast-flowingglaciers,which drain
(FRIS) and the Amery Ice Shelf, which are known to contain much of the Antarctic ice sheet, must be resolved if we are to
significantmarine ice layers [Englehardtand Determann,1987; producereliablemodelsof the ice sheet[Payne, 1997, 1999].
Morgan, 1972].
Many suchglaciers,particularlythoseborderedby rockoutcrop
We have determinedtotal ice thicknessfor data-sparse
ice by (outlet glaciers),which have not been sounded,would not natuusinga hydrostaticconversionmodelappliedto a high-resolution rally appearin our grid. We haveensuredthat somerepresentaAntarcticDEM [Liu et al., 1999]. Over FRIS and other ice tion of thesefeaturesremains,by including"manufactured"ice
shelveswheresurfacecrevassingis apparent,dataderivedin this thicknessdataalongtheirlength. This methodwasappliedto the
way were usedin preferenceto radarmeasurements.
Pointswere major outlet glacierswhich did not have measuredprofiles(Tadeterminedon a regular2-km grid. A singlemodel,
ble 2). For thesefeatures,we derivedice thicknessdata along
h = 0.108H + 16,
(2) their centerlineby linear interpolationfrom the ice thicknessat
in the upperbasin
was usedwith a slopebasedupon a meandensityfor ice of 917 the groundingline to the closestmeasurement
kg m-3 and water of 1028 kg m-3. A constantinterceptvalueof of the glacier.
16 m was usedfor all regionsto accountfor the lower firn density of the upper ice layer and geoid/ellipsoidseparation.Data
pointswithin 10 km of groundedice were not included.
To validate this conversion, we determined the cross-
correlation
coefficient(r2= 0.91) betweenmeasured
icethickness
and thicknessderivedfrom hydrostaticconversion,[for all seismic datamorethan 10 km from groundingice.]
2.2.4. Thin ice model. In some mountainousareas(e.g.,
AntarcticPeninsula,Victoria Land, and DronningMaud Land),
there are few measurements
of ice thickness. Many glaciers,
Table 2.
East Antarctica Outlet Glaciers for Which
"Manufactured"
Data Are Included in the Ice
Thickness Model
Glacier
Grounding
LinePosition ProfileLength,
km
Beardmore
171.604øE, 83.594øS
152.6
Nimrod
162.512øE, 82.374øS
159.2
Mulock
160.818øE, 79.138øS
146.1
Skelton
162.289øE, 78.969øS
101.8
Ferrar
163.530øE, 77.688øS
106.4
Mackay
162.296øE, 76.973øS
51.8
Mawson
162.422øE, 76.204øS
104.9
Priestly
163.453øE, 74.485øS
114.7
Campbell
164.334øE, 74.530øS
121.9
Aviator
165.096øE, 73.923øS
106.8
Mariner
166.714øE, 73.011 øS
84.1
Lillie
163.923øE, 70.765øS
202.4
Rennick
161.802øE, 70.657øS
256.0
n6v6s, and basins have not been sounded at all. Here rock out-
cropsare the only sourceof ice thicknessdata,andthe measurementsare thusheavily biasedtowardzero ice thickness.Without
somefurthercontrol,we would producea grid in whichmostof
the mountainousareaswere represented
asentirelyice free.
To producea more plausiblemodel in thesedata-sparse
regions, we developedan empiricalregressionmodel where ice
thicknessis related to the distanceto the nearestexposedrock
(Figure5). The resultsshowthat within 10 km of outcropsthere
is a goodcorrelationbetweenice thicknessandoutcropdistance.
This model provides a more plausible solutionfor mean ice
thicknessin data-sparseregionscontainingexposedrock, although this thin ice model still underestimates
ice thicknessin
these areas.
The thin ice modelwasusedto generateice thicknessdataon
a regular 10-km grid in regionswhere exposedrock outcrops
were the principal sourceof data. These includethe Antarctic
Peninsula, Trans-Antarctic Mountains, Coats Land (Theron
Mountains),DronningMaud Land,andMarie Byrd Land.
LYTHE ET AL.: BEDMAP
SUBGLACIAL
2.2.6. Continentality model. Finally, to producea continuous surfacefor the entire ice sheet,we generateda set of manufacturedice thicknessdatapointson a regular50-km grid in the
large data-sparseareasin Wilhelm II Land, Queen Mary Land,
Coats Land, and Marie Byrd Land. To provide some plausible
controlin theseregions,we useda continentalitymodel derived
by Vaughanand Barnbet [1998]. This model relatesthe ice surface elevation/icethicknessto the distancefrom the grounding
line.
2.2.7. Choice of grid spacing. We choseto presentthe gridded ice thicknessand bed elevation on a regular grid at 5-km
resolutionfor two principalreasons.First, most applicationsrequire a regulargrid of basedata, and so presentingthe data in
any form otherthan a grid would invite usersto grid the databy
whateveralgorithmwas availableto them. Second,5 km is sufficient to resolveice streamsand major outletglaciers,and those
featuresmuchbe reproducedby any realisticice sheetmodel.Finally, there are someareaswhere the data are denseenoughto
supportthis resolution,althoughwe do emphasizethe proviso
that there are wide areaswithin the grid where the data available
arenot strictlysufficientto justify evena 100-kmgrid.
2.2.8. Spatial interpolation. Most spatialinterpolationalgorithmsembodya model of continuousspatialchangethat can be
describedby a mathematicallydefinedsurface. The optimumalgorithm for an application should be chosen according to
whetherinputdatapointsareto be honoredexactly,how smooth
and continuousthe outputsurfaceis to be, and how weightings
shouldbe applied.
For the purposesof ice sheetmodelingit is importantthat the
final bed DEM incorporateslocal-scalevariation where it is
known, while also providing a smoothcontinuoussurface. We
consideredseveral algorithms including triangulation, bicubic
splines,moving averages,and geostatisticalmethods. While all
of these methodsperformedreasonablywell in areasof welldistributeddata, their performancewas poor where there were
substantialgapsbetweenobservationsor anisotropicdata distribution.
The estimationprocedureembodiedin geostatistics,
knownas
kriging, showedpromise,but it is heavilyreliant on the choiceof
semivariogramwhich expresses
mathematicallythe way variance
changeswith distancebetweenobservations.We were unableto
adequatelyfit a singlesemivariogramto suit the entire interpolation area(Figure 4). We couldhave usedseveralsemivariogram
models for different regions,but this would have requiredthe
mosaicingof regionalgridsto producea final continentalgrid.
To copewith the variabledata density,irregulardistribution
and anisotropy,we developed an inverse distanceweighting
(IDW) algorithmthat determinesestimatesby usingan octagonal
searchneighborhoodsimilar to the quadrantsearchusedby Liu
et al. [1999]. The IDW algorithmusesthe nearesttwo observations in each octant(a total of 16 data points) to estimatethe
value at eachgrid node. This avoidedthe directionalbiasthat resultsfrom a nearest-neighbor
search,while mitigatingthe effects
of data clusteringand anisotropyby assigningequal weightsto
TOPOGRAPHY
OF ANTARCTICA
11,343
2.3. Uncertainty in the Ice Thickness Grid
Ideally, validation of the model requirescomparisonwith an
independent"true" surface,but in the absenceof sucha data set,
we adopteda "jackknife" procedure.In jackknifing, truncated
data setsare createdby selectivelyremovingsamplesfrom the
original data. The statisticsare then recalculatedfor each truncateddata set and the variability amongthe original samplesis
usedto describethe variabilityof the model[Tichelaarand Ruff,
19891.
We removeda randomly selectedsetof 5% of the data and regeneratedthe grid by using the remainingdata. The deleted5%
(around 10,000 points) were then usedto test the output grid.
The medianabsolutedifferencebetweenobservedand predicted
ice thicknesswas 41 m, while the RMS errorof thejackknife resamplewas 152 m. As with the crossovererrors,the RMS error
is exaggeratedbecauseof occasionallarge errors. The cumulative distribution
of absolute differences revealed that 70% of er-
rors are less than 50 m and around 86% are less than 150 m.
This statistic,however,provideso,.fiya singlemeasureof dispersion;it tells us nothing about the spatial variation in error
acrossthe grid. To quantifyerror in differentpartsof the model
we determinedthe RMS error of thejackknife samplein different
regions(Table 3). The resultsshowthat the magnitudeof error
reflectsthe frequencyand amplitudeof topographicvariation.
Over the ice shelvesthe fi'equencyand magnitudeof ice thickness variation are relatively low, comparedwith the Antarctic
Peninsulaor the Trans-AntarcticMountains,wherelargechanges
in ice thickness occur over short horizontal
distances.
The errors
in these areasreflect the inability of the model to incorporate
subgrid-scalevariation. It should be noted that the octagonal
IDW interpolationprocedureis by definition a smoothingtechnique, designedto estimatethe mean ice thicknessin a local
neighborhood. The model therefore is representativeonly of
topographicvariationsgreaterthan 5 km.
3. Bed DEM
The constructionof the bed DEM was completedin three
stageswith a differentprocedurebeing usedfor the groundedice
sheet, the sub-ice-shelf seabed, and the continental
shelf/SouthernOcean seabed(Figure 1). In summary,the ice
sheetthicknessgrid was subtractedfrom the best availablesurface DEM andthen matchedat the groundingline with a compilation of the adjacent ocean floor. Where seismic data were
available,they were used to determinethe bed topographybeneath ice shelves.
3.1. Subglacial Bed DEM
While surfaceelevation was routinely measuredat the same
time as ice thickness,e.g., by barometricleveling, those measurementswere of variable, often poor, quality and were sometimes subjectto unrepeatablepostprocessing.Thus we discarded
the original surfaceelevationmeasurements
in favor of usinga
singleconsistentsurfaceDEM for the determinationof bed eleeach sector. Observations within a search radius of 100 km were
vation over the groundedice sheet. Using separatelyconstructed
usedin the estimationof eachgrid node. This distancewas cho- surfaceDEM and ice thicknessgrids, we can ensureconsistency
senas a meancorrelationlengthin the semivariogramderivedfor
betweenthe threeparameterssurfaceelevation,ice thickness,and
several parts of the ice sheet. After considerabletesting we
bed elevation. This has the added advantageof enabling easy
foundthat inverse-cubed
weightingproducedthe mostplausible updateof the bed DEM when future improvementsare made to
surfacegiven the data distribution. This weighting reducesthe the surface DEM's.
influenceof observations
furtherawayfrom the estimationpoint.
We choseto use a recenthigh-resolutionsurfaceDEM of the
The grid of ice sheetthicknessis shownin Plate 1.
Antarctic [Liu et al., 1999], hereinafterthe Liu-DEM, which in-
11,344
LYTHE ET AL.: BEDMAP SUBGLACIAL TOPOGRAPHY OF ANTARCTICA
Table3. Regional
Variation
inConfidence
LimitsofIceThickness
Model
Resulting
FromtheJackknifing
Procedure
Region
MedianAbsolute
RMSError,rn
Difference, rn
East Antarctica interior ice sheet
59
153
WestAntarctica
43
136
RossIce Shelf
4
18
AmeryIceShelf
21
101
AntarcticPeninsula
57
163
Trans-Antarctic
138
332
111
204
Mountains
Prince Charles Mountains
We haveusedthe predictedseafloortopographyby Smithand
tegratessatellitealtimetry,airbornealtimetry,digitalcartographic
data, and groundsurveydata referencedto the OSU91A geoid. Sandwell [1997] in this compilation. In the generationof this
Absolutevertical accuracyvaries acrossthe Liu-DEM depending satellite gravity field and available depth measurementswere
on the source data used in its construction. For the ice shelves it
used to determinethe correlationbetween gravity and the seais around2 m, over the interior ice sheetit is better than 15 m, in floor topography.The verticalaccuracyof thisinferreddatasetis
the steeperice sheetperimeterit is 35 m, and over the rugged around250 rn [Smithand Sandwell, 1997]. Althoughthe satelmountainsit is approximately 100-130 m [Liu et al., 1999]. It lite altimetry data extendedto 81.5øS this solutionwas carried
should be noted that in some of the mountainousregions, e.g., only to 72øS becauseof concernsabout ice problems(W. H.
1999). The grid spacingis 3
Antarctic Peninsula, the vertical accuracy of the Liu-DEM is Smith,personalcommunication,
more than 250 m, as it is basedon estimatedform lines (A. Coo- min of longitudeby 1.5 min of latitude.
3.2.2. Bathymetricchart of the Weddell Sea. Themostacper and J. Thomson,personal communication,1999).
3.2. Bathymetry
cuaretebathymetric
datasetfor theWeddellSeais theBathymetric Chartof the WeddellSea(BCWS) [Schenke
et al., 1998] The
chartscoverthe regionfrom 66øSto 78øSand from 68øWto
The probablelimit of groundedice duringthe Quaternarywas
0øE, andbecauseof the lack of observations
for ice coveredarthe edgeof the continentalshelf. We choseto extendthe subglaeas,supplementary
geophysical
and geographical
information
cial topographyto 60øS to includeall of the Antarcticcontinenwasalsoincluded.Over the majorityof the modelthe depthsare
tal shelf, so that our bed DEM would be useful for ice sheet
modelingthroughglacial cycles.
Bathymetricdata coveragein the Antarctic region is far from
satisfactory,and many of the available chartsare inconsistent
[Angrisano,1995]. In some,particularlysouthof 60øS,thereare
hundreds of kilometers between shipboard depth-sounding
tracks,and much of this was collectedby usingcelestialnavigation; discrepancies
in depthsreportedat crossoverpointsexceed
100-250 rn at half the track intersections [Smith, 1993]. While
there is a needfor a new model that integratesimprovedcoastline
data and all new soundings,it was not within the scopeof this
programto generatesucha product. We have thus developeda
bathymetricmodel south of 60øS which integratesseveralimportantbathymetriccompilations.This is not intendedto be a
definitive bathymetry;rather,one that is consistentand integrates
with the subglacialtopographypresentedhere.
3.2.1. Satellite altimetry and ship measurements. Radar
altimetersaboardthe ERS1 and GEOSAT spacecrafthave been
usedto surveythe marine gravity field over most of the world's
oceansto a high accuracyand moderatespatialresolution(track
spacing of 2-4 km). Over intermediatewavelengths(15-200
km), variationsin gravity anomalyare highly correlatedwith seafloor topographyif sedimentcover on the oceanfloor is thin
[Smithand Sandwell, 1994]. Long-wavelength(greaterthan 160
km) topographyis isostaticallycompensated
and is not correlated
with the gravity field. There are ongoingeffortsto combineship
and satellite data to form a uniform resolutiongrid of seafloor
topography[Sichoixand Bonneville,1996; Smithand Sandwell,
1994, 1997].
accurate to within 50 m. Matched to this data set, west of the
AntarcticPeninsulais a griddeddata set compiledfrom publishedand unpublished
soundings
(P. Morris, personalcommunication, 1999).
3.2.3. Earth Topography5. Southof 72øS(excludingthe
WeddellSea)wherecompilation
derivedfromsatellite-altimetry
arenot available,we integratedthe EarthTopography5 arcminutegrid(ETOPO-5)[NationalGeophysical
Data Center,1988].
3.2.4. Model generation. The derivedseafloortopography,
BCWS, and westernAntarctic Peninsuladata setswere resam-
pledontoa 5-kmgridby usinga cubicconvolution
filter. These
datasetswerethenmergedwith the ETOPO-5 datasetby usinga
Hermitecubicmethodin overlappingareas. The Hermitecubic
is a proximityanalysisalgorithmthattakesthe inputgridsand
calculates
an outputvalueareabasedon the normalizeddistance
of the width of the overlappingarea.
3.3. Sub-ice-shelfTopography
The sub-ice-shelf
regionof the seabedis inaccessible
to both
ship-sounding
or airborneradar. Generally,only seismic
soundings
from the ice shelfsurfacecan providethe seabed
depthbeneath
an ice shelf,andseismic
measurements
areonly
available over a few ice shelves. Where no seismic data were
available,
we haveuseda bicubicsplineto interpolate
theseabed
surfacebetweenthe groundingline and the seabedat the ice
front. Where seismicdata were present,principallyover the
Ross,Filchner-Ronne,
Larsen,andAmeryIce Shelves,we used
the octagonal
IDW algorithmdescribed
previously
(with a re-
11,345
30'VV
60øE
90NV
90'E
120øE
180'E
0
500 1000 1500 20C•0
km
A- Bentley Subglacial Trench
B- Byrd Subglacial Basin
C - Wilkes Subglacial Basin
D- Astrolable Subglacial Basin
E - Adventure SubglacialTrench
F- Aurora Subglacial Basin
G - Gamburtsev SubglacialMountains
F
Ice Thickness (metres)
0
1000
2000
3000
4000
Plate 1. BEDMAP grid of Antarcticice sheetthickness
with coastline,andice shelffro•ts fromthe
ADD.
11,346
LYTHE
ET AL.:
BEDMAP
SUBGLACIAL
TOPOGRAPHY
OF ANTARCTICA
ducedmaximum searchdistanceof 50 km) to estimatethe seabed matethe accuracyof the groundedice sheetmodelto be genertopography.
ally between150 and 300 m. In somemountainous
regions,
where the bed DEM uncertaintyis greater,the verticalerror is
probablycloserto 400 m. The verticalaccuracyof the bathyme3.4. MergingtheBedDEMS
try
datarangesfrom approximately50 m for the BCWS to 250 m
The three bed DEMs (subglacialelevation, open-seabathymetry, and sub-ice-shelfbathymetry)were mergedinto a com- (satellite-derivedgravity data) to around500 m for ETOPO-5.
positegrid by usingthe Hermite cubic function. The resulting For the majority of the sub-ice-shelfregionit is difficult to estimate uncertainty,as the bed in theseareasis only speculative.
composite
modelof bedtopography
for theregionsouthof 60øS
Where the model is controlledbelow the ice shelves,we estimate
is a 1334 x 1334 data array with a grid spacingof 5 km; the
the
vertical accuracyto be approximately200 m. Overall, the
model hasa total elevationrangeof approximately11,000 m. In
vertical
accuracyof the compositebed DEM is between50 and
addition to the orthometric DEM, which is referenced to the
500 m.
OSU91A geopotentialmodel,we also constructed
an ellipsoidal
heightDEM relativeto the WGS84 ellipsoidby addingthe geoidal-ellipsoidseparationwhichrangesfrom-67 to +42 m [Rappet 4. Discussion
al., 1991].
4.1. Principal Features of Ice Thickness Model
3.5. Estimated Uncertainty in Final Bed DEM
The estimateduncertaintyvariesacrossthe bed DEM according to the distributionof the originalsourcedataandthe procedureusedin its construction.We have evaluatedbothplanimetric
andverticalaccuracyconfidencelimits for thebedDEM.
3.5.1. Horizontal accuracy. For the groundedice sheetthe
horizonatalaccuracydependson both the Liu-DEM and the ice
thicknessgrid. The Liu-DEM has an absolutepositionalaccuracyof between100 and300 m [Liu et al., 1999] exceptin some
areas,suchasthe inlandplateau,wherethe accuracyis around10
km. As was outlined previously (see "Navigation"), the
positionalaccuracyof the ice thicknessdatavariesconsiderably
accordingto the navigation methodsused. While the GPSnavigateddata are accurateto 100 m, much of the of older data
collectedhave confidencelimits of up to 5 km.
The positionalaccuracyof the bathymetricand sub-ice-shelf
data is also variable. The satellite-derivedgravity data have a
horizontal resolutionlimit of 5-10 km in position [Smith and
Sandwell, 1997]; the BCWS, around 100 m [Schenkeet al.,
1998]; andthe ETOPO-5 data,approximately10 km. Wherethe
sub-ice-shelfbathymetry is controlled by seismic data, the
positionalaccuracyis similarto the ice thickness
grid. We estimate that the horizontal accuracyof our bed DEM is between
200 m and 10 kin.
The qualityanddetailof the ice thickness
model(Plate1) reflect the densityand distributionof the input data. In areasof
closelyspacedRES data (e.g., West Antarctica,EnderbyLand,
Kemp Land, and coastalDronning Maud Land) the model resolvesvariation at spatialscalesof around 15-20 km, while in
more data-sparseregionsthe true horizontalresolutionis more
than 100 km. In severalareas,where only a few measurements
are available, the model contains isolated nodes of thick or thin
ice. While thesefeaturesmay not reflect the regionalice thickness,they have been retained, as we have no reasonto doubt
their accuracy.
The thickestice is locatedin the major subglacialbasins.In
WestAntarctica,the Byrd SubglacialBasinandthe BentleySub-
glacialTrencharewell defined(containing
over3000 m of ice).
In East Antarctica, prominentfeaturesinclude the Wilkes Subglacial Basin, the Astrolabe SubglacialBasin, the Adventure
SubglacialTrench,and the Aurora SubglacialBasin. The thickestice observation
(4776 m) is locatedin the AstrolabeSubglacial Basin. Relativelythick ice is alsoapparentin EnderbyLand
(between75øSand 80øSand between30øE and 60øE), an area
which is far better delineatedthan in the SPRI Folio Series,because of the inclusion of considerable new Soviet and Russian
data.
The frequencydistributionof ice thicknessvalueswithin the
3.5.2. Vertical accuracy. The absolutevertical accuracyof
the bed DEM also varies acrossthe model. By combiningthe
confidencelimits of the Liu-DEM (2-130 m) and the ice thicknessgrid (RMS error of 152 m for jackknife resample)we esti-
model (Figure 6) revealsa bimodal distributionwith a peak
around400 m, corresponding
to the ice shelves,and a secondary
peak at-•2800 m, corresponding
to the interiorof the ice sheet.
The minor, but significant,peak at -•20 m represents
the moun-
6000
5OO0
4000
3000
2000
1ooo
o
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Ice Thickness (m)
Figure 6. Ice thicknessfrequencydistributionin the BEDMAP grid of Antarcticice sheetthickness.
LYTHE ET AL.: BEDMAP SUBGLACIAL TOPOGRAPHY OF ANTARCTICA
Table 4.
11,347
MeasuredPropertiesof the AntarcticIce SheetFrom the 5-km Ice ThicknessGrid•
Parameter
East Antarctica
West Antarctica
Total
Mean ice thickness,m
2146
1048
1856
Totalvolumeincluding
iceshelves,
km3
Grounded
icesheetvolume,km3
21.8 x 106
21.7 x 106
3.6 x 106
3.0 x 106
25.4 x 106
24.7 x 106
GIS volumeabovemeansealevel,km3
20.5 x 106
2.1 x 106
22.6 x 106
GIS volumebelowmeansealevel,km3
1.1 x 106
1.0x 106
2.1 X 106
Sealevel equivalent,m
52
5
57
•'GIS, groundedice sheet.
tainousregionsand areasof exposedrock. The mean ice thicknessacrossthe entire grid is 1856 m, •-10% lower than the SPRI
Folio Seriesestimateof 2160 m [Drewry, 1983b]but comparable
to a recent estimate of 1903 m [Huybrechtset al., 2000]. The
meanice thicknessof the groundedice sheetis 2034 m, while for
Tabaccoet al., 1998; Blankenshipet al., 2000; Steinhageet al.,
2000; Damm, 1996]. The present model, however, contains
greaterdetail, is morejustifiable and will be more easilyupdated
thanany previouslypublishedcontinentalbedDEM.
the ice shelves we determine a mean of 440 m.
morphologicallydistinct.East Antarcticais a largelycontiguous
The BEDMAP grids were usedto determinethe total volume
of iceresidentin theAntarcticice sheet(Table4). We computea
Plate 2 confirms
that East Antarctica
and West Antarctica
are
landmasswith a few broad basins,while West Antarctica is com-
prisedof archipelagoesin discretetopographicunits (Antarctic
totalvolume
of 25.4x 106km3 fortheentireicesheet
(icesheet Peninsula, Ellsworth Mountains, Executive Committee and
andiceshelves),
whichiscloseto earlyestimates
of 24-26x 106 Flood Ranges, Whitmore Mountains, and the Ellsworth Land
km3 [Thiel, 1962; Vinnik et al., 1976]. The total volume from
this compilationis, however, 15% lessthan the widely quoted
30.1 x 106 km3 estimatedfrom the SPRI Folio Series[Drewry,
1983b]. This offsetwas expected,aspreviousworkers[Bamber
and Huybrechts,1996; Huybrechts,et al., 2000] have foundconsiderableuncertaintyin the SPRI Folio Seriesestimate. Much of
the uncertaintystemsfrom the significantdifferencesbetween
the surfaceelevationdistributionof the SPRI Folio map and improvedelevationmodelsderivedfrom satellitealtimetry.
Our derived volume for the Antarctic ice sheet
is 4% lower
coast), which may representdistinct tectonic blocks. These
blocks are separatedby deep trenches,sometimesoccupiedby
ice streamsand outlet glaciers,that are more than 3000 m below
sea level in places. This contrastswith the topographyinland
from Siple Coast,which is very smoothwith modestridgesand
troughsmarkingthe locationof the ice streamsdraininginto the
Ross Ice Shelf.
The topographicboundarybetweenEast Antarcticaand West
Antarcticawhich seemedclearin SPRI Folio Sheet3 is no longer
so obviousin Plate 2. There appearsto be no directconnection
thanthemostrecent
estimate
of 26.4x 106km3 [Huybrechts
et between
ThielTroughandthetroughson the SipleCoastadjaal., 2000] determinedby usinga 20-km ice thicknessgrid based
on an updatedversionof the digitizedSPRI Folio map. The reductionin meanice thicknessand ice volumeis principallydue
to the higher topographyin EnderbyLand and Kemp Land,
mappedfor the first time by usingSovietand Russiandata.
To determinethe sea level equivalentof the ice volume, we
calculatedthe total massof the groundedice sheet(assuminga
cent to the Trans-AntarcticMountains. In East Antarctica,the
elongated
Wilkes SubglacialBasin,whichextendsalong145øE,
is a dominant feature west of the Trans-Antarctic Mountains and
extendsoffshorebeyondthe grounding
line. The topography
in
QueenMary Land and Wilhelm II Land (southof 80øS and between120øEand80øE)remainspoorlymapped.
With the incorporationof previouslyunpublished
Russian
meandensity
of 917kgm-3) andthemass
of seawater
requiredand SovietRES data,the GamburtsevSubglacialMountainsare
to fill the regionbelow sealevel if the ice sheetwasremoved(as- more extensivethan shown by SPRI Folio Sheet 3. Unfortusuming
a meanseawaterdensity
of 1028kg m-3). Giventhat nately,the widelyspaced
flight lines(•-50 km) still preventus
360 Gt of water is requiredto raise global sea level by 1 mm frombeingableto delineate
individualrangesin thisregion.The
[Jacobset al., 1992], the total Antarcticice sheetrepresents highestelevationin the Gamburtsev
Subglacial
Mountains(2980
around57 m of sealevel rise, comprising52 m for the EastAnt- m) is •-1000 m below the ice sheet.Plate 2 showsmore extensive
arctic ice sheet and 5 m for the West Antarctic ice sheet.
rollingtopographyinlandof EnderbyLand comparedwith the
Althoughthis calculationprovidesa substantialre-estimateof SPRI Folio Sheet 3 and improvedtopographicdefinition in
a widely quotedfigure, around73 m [Robin, 1986], and is lower DronningMaud Land, in particularnorth of 80øS, describedin
thanthe mostrecentestimate,around61 m [Huybrechts
et al., more detail by Steinhageet al. [2000]. Within the continental
2000] we cannotsimplyassumethatthis represents
a realistic boundaryitselfthe bedDEM hasa totalelevationrangeof over
potential
contribution
to sealevelrise. The calculation
currently 7000 m with the highestpointbeingVinsonMassif(4678 m) and
ignoressecond-order
effectsrelatedto historicaldeglaciationthe lowestin the BentleySubglacialTrench (-2496 m).
(postglacial
rebound,andalteration
of thegeoidsurface
afterdeglaciation),and steric effects of increasedfreshwatercontentof
theworld'soceans,etc.).
5. Summary
4.2. Principal Features of the Bed DEM
ness observations of the Antarctic ice sheet which will be of sub-
We have establisheda databasefor the majorityof ice thick-
The morphology
of the bedDEM (Plate2) largelyconfirms stantialutility to many branchesof geoscience.The suite of 5that presentedin the SPRI Folio Sheet 3 and from more recent km grid digital topographicmodels for the Antarctic continent
published
localsurveys
[Allisonet al., 1982;Retzlaffet al., 1993; andits surrounds,
producedfrom this database,
providea reliable
11,348
LYTHE ET AL.' BEDMAP SUBGLACIAL
TOPOGRAPHY
OF ANTARCTICA
0•
'•,30
øE
30 øW
/.
"•60'E
60 øW
80'S
90'VV
"
120'W
',,
'
120 øE
150 øw
150øE
180 øE
5OO
1000
A- Executive Committee Range
B- Flood Range
C- Whitmore
Mountains
D- Thiel Trough
E- Wilkes Subgacial Basin
F- Aurora Subglacial Basin
G - GamburtsevSubglacialMountains
H- Belgica Subglacial Highlands
I- Vostok Subglacial Highlands
Elevation(m)
Verticaldatum:OSU91A geopotentialmodel
-6000 -5000 -4000 -3000 -2000 -1000
0
1000
2000
3000 4000
1500 20(•0
km
90øE
LYTHE
ET AL.: BEDMAP
SUBGLACIAL
TOPOGRAPHY
OF ANTARCTICA
11,349
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forassistance
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of a snowmobile-based
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system
at
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