Regional Greenland accumulation variability from Operation IceBridge airborne accumulation radar

16 Pages·2017·10.77 MB·English

TheCryosphere,11,773–788,2017
www.the-cryosphere.net/11/773/2017/
doi:10.5194/tc-11-773-2017
©Author(s)2017.CCAttribution3.0License.
Regional Greenland accumulation variability from Operation
IceBridge airborne accumulation radar
GabrielLewis1,ErichOsterberg1,RobertHawley1,BrianWhitmore1,HansPeterMarshall2,andJasonBox3
1DepartmentofEarthSciences,DartmouthCollege,Hanover,NH,USA
2GeosciencesDepartment,BoiseStateUniversity,Boise,ID,USA
3GeologicalSurveyofDenmarkandGreenland(GEUS),Copenhagen,Denmark
Correspondenceto:GabrielLewis([email protected])
Received:22October2016–Discussionstarted:14November2016
Revised:10February2017–Accepted:13February2017–Published:23March2017
Abstract. The mass balance of the Greenland Ice Sheet additional in situ measurements to verify model uncertain-
(GrIS) in a warming climate is of critical interest to sci- ties.
entists and the general public in the context of future sea-
levelrise.Animprovedunderstandingoftemporalandspa-
tial variability of snow accumulation will reduce uncer-
tainties in GrIS mass balance models and improve pro- 1 Introduction
jections of Greenland’s contribution to sea-level rise, cur-
rently estimated at 0.089±0.03m by 2100. Here we an- AssessingthestabilityoftheGreenlandIceSheet(GrIS)in
alyze 25 NASA Operation IceBridge accumulation radar awarmingworldiscrucialforpredicatingfutureglobalsea-
flights totaling >17700km from 2013 to 2014 to deter- levelriseanditssocietalandeconomicimpacts(Dumontet
mine snow accumulation in the GrIS dry snow and perco- al., 2014; IPCC, 2014). The mass balance of the GrIS de-
lation zones over the past 100–300 years. IceBridge accu- creased over the 1988–2016 period, with a conservative es-
mulation rates are calculated and used to validate accumu- timate of ice sheet mass loss of 272±24Gta−1 (van den
lation rates from three regional climate models. Averaged Broekeetal.,2016;Enderlinetal.,2014;Hannaetal.,2013a;
over all 25 flights, the RMS difference between the models Khanetal.,2015;Sasgenetal.,2012;Shepherdetal.,2012),
and IceBridge accumulation is between 0.023±0.019 and oranequivalentglobalsea-levelriseof∼0.7±0.2mma−1
0.043±0.029mw.e.a−1,althougheachmodelshowssignif- (Ettemaetal.,2009;Helmetal.,2014).Thedominantmass
icantlylargerdifferencesfromIceBridgeaccumulationona loss process for the GrIS has changed from ice discharge
regionalbasis.Inthesoutheastregion,forexample,theMod- (i.e.,calving)tosurfacemassbalance(SMB)sincethemid-
èleAtmosphériqueRégional(MARv3.5.2)overestimatesby 1990s(vandenBroekeetal.,2009,2016).SMBisoneofthe
anaverageof20.89±6.75%acrossthedrainagebasin.Our largest sources of error in estimates of the ice sheet’s total
resultsindicatethattheseregionaldifferencesbetweenmodel mass balance (van den Broeke et al., 2009) due to complex
andIceBridgeaccumulationarelargeenoughtosignificantly relationships between accumulation variability and surface
alterGrISsurfacemassbalanceestimates.Empiricalorthog- melt runoff (Dumont et al., 2014; Hanna et al., 2005; Mc-
onal function analysis suggests that the first two principal Connell et al., 2000). GrIS snow accumulation varies spa-
componentsaccountfor33and19%ofthevariance,andcor- tiallyinresponsetosurfacetopography(e.g.,Hawleyetal.,
relatewiththeAtlanticMultidecadalOscillation(AMO)and 2014), wind redistribution (Déry and Yau, 2002), and pre-
wintertime North Atlantic Oscillation (NAO), respectively. ferred modes of climate variability like the North Atlantic
Regionsthatdisagreestrongestwithclimatemodelsarethose Oscillation (NAO; e.g., Wong et al., 2015), Atlantic Multi-
inwhichwehavethefewestIceBridgedatapoints,requiring decadal Oscillation (AMO; e.g., Mernild et al., 2014), and
Greenland Blocking Index (GBI; e.g., Hanna et al., 2016).
Accumulation also varies through time largely in response
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
774 G.Lewisetal.:RegionalGreenlandaccumulationvariability
to temporal changes in these climate modes (Mernild et al., sphériqueRégional(MARv3.5.2;Fettweisetal.,2016).We
2014). Ice cores accurately record temporal accumulation also compare accumulation results with a gridded land-ice
changesatpointlocations(BantaandMcConnell,2007;Mc- accumulationdataset(Boxetal.,2013;hereafter“Box13”)
Connell et al., 2000; Mosley-Thompson et al., 2001), and and a krigged ice core accumulation record (Bales et al.,
havebeenusedwithsnowpitsandcoastalprecipitationdata 2009; hereafter “Bales09”). We further use principal com-
to determine large-scale accumulation patterns over the en- ponentandcorrelationanalysestoevaluatethedominantcli-
tire ice sheet (Bales et al., 2009). However, ice cores and mateforcingmechanismsdrivingregionalGrISprecipitation
snowpitsaretoosparsetocapturethefullspatialvariability trends.
of GrIS accumulation, especially in the high-accumulation
southeastregionwherelittleinsitudataexist.Further,many
2 Methods
Greenlandicecoreswerecollectedduringthe1990sorear-
lier,priortotherecentaccelerationofGrISmassloss(Boxet
2.1 Accumulationradar
al.,2013).Anupdated,morespatiallydistributed,andrepre-
sentativeGrISaccumulationdatasetisneededtoevaluatere-
We calculate a spatially continuous record of accumulation
centprecipitationtrendsandtovalidateGrISSMBestimates
along 17730km of NASA Operation IceBridge accumula-
fromregionalclimatemodels(RCMs)overrecentdecadesof
tion radar flights (hereafter “IceBridge accumulation”). Op-
increasedmassloss.
eration IceBridge was designed to bridge the gap in polar
HerewedeveloparecordofGrISsnowaccumulationover
observations between the Ice, Cloud, and Land Elevation
a large portion of the GrIS interior from AD 1712 to 2014
Satellite(ICESat;2003–2009)andICESat-2,whichissched-
usingtheairborneNASAOperationIceBridgeaccumulation
uled to launch in 2017. Laser altimeters, four to five differ-
radar (Leuschen et al., 2011). Airborne and ground-based
ent frequency radars, a gravimeter, and a magnetometer are
radars have been used to map spatial patterns of accumula-
mounted on NASA’s P-3B and DC-8 airplanes, which con-
tioninGreenlandoverdecadal(Hawleyetal.,2014;Miègeet
duct airborne surveys in both the Arctic and Antarctic each
al.,2013)andannualresolutions(Koenigetal.,2016;Med-
spring.
ley et al., 2013). Operation IceBridge collected accumula-
The IceBridge accumulation radar captures a continuous
tion radar data from 2009 to 2014, and it has been used in
electromagnetic profile of the top few hundred meters of
several studies (Karlsson et al., 2016; Forster et al., 2014;
the ice sheet, displaying distinct internal reflecting hori-
Leuschenetal.,2011;Medleyetal.,2013)tocalculatelocal
zons (IRHs) that can be traced for hundreds of kilometers
accumulation.Weexamineaccumulationradardatafromev-
(Leuschenetal.,2011).Theaccumulationradaroperatesin
eryIceBridgeflightacrosstheGreenlandinteriorduringthe
the 600–900MHz range and has an average vertical resolu-
2013 and 2014 seasons to measure accumulation rates over
tion of 0.28m in snow/firn, which is fine enough to resolve
themajorityofthedryandupperpercolationzones.
IRHsthathavebeenshowntorepresentisochrones(Medley
Regional climate models and reanalysis products provide
et al., 2013; Rodriguez-Morales et al., 2014; Spikes et al.,
spatially and temporally comprehensive estimates of accu-
2004; Hawley et al., 2014). The average distance between
mulation at ice-sheet scales. The magnitude of mesoscale
radartracesis16m,whichwethenaverageover10adjacent
model uncertainty can be as large as the natural variability,
traces to increase the signal-to-noise ratio. The position of
or larger in areas with sparse in situ measurements like ice
eachtraceisknownfromdifferentialGPSreceiversmounted
cores, potentially obscuring climate fluctuations with ran-
on the aircraft. We do not perform any time variable gain
dom error (Burgess et al., 2010; Box et al., 2006). A 2013
or additional filtering on the IceBridge accumulation data.
study (Vernon et al., 2013) determined that 1960–2008 cli-
Dependingonsignalattenuationwithinthesnowpack,IRHs
mate model SMBs differ by as much as 130Gta−1 across
canbetracedtoadepthof50–150mandprovideaccumula-
the ice sheet, with especially large differences in the south-
tion records over the past 100–300 years (Fig. 1). For areas
ern (80.1Gta−1) and northwestern (40.4Gta−1) drainage
with high attenuation (i.e., shallow penetration of the radar
basins. Many of the variables included in these models are
signal),suchasthoseatrelativelylowerelevations(e.g.,be-
validated with snow pits and ice cores, such as the 1997– low∼2500m),wecalculateaccumulationresultsfor1921–
1998 Program for Regional Climate Assessment (PARCA)
2014. Where the signal is less attenuated higher on the ice
ice core campaign (Mosley-Thompson et al., 2001), which
sheet, we calculate accumulation over the 1712–2014 time
predate the recent period of accelerated surface melting in
period(seeFig.2).
Greenland(McGrathetal.,2013).WecompareourIceBridge
accumulation data with outputs from three RCMs to pin- 2.2 Depth–agescalesanddensityprofiles
pointtheirareasofhighestuncertainty:(1)thePennsylvania
StateUniversity–NationalCenterforAtmosphericResearch To calculate accumulation rates using ice penetrating radar,
Fifth-GenerationMesoscaleModel(PolarMM5;Burgesset onemustknowtheamountofsnowmassbetweenIRHsand
al., 2010), (2) the Regional Atmospheric Climate MOdel their relative ages. The mass between IRHs is a function of
(RACMO2.3; Noël et al., 2016), and (3) the Modèle Atmo- the depth–age scale, travel time–depth conversion rate, and
TheCryosphere,11,773–788,2017 www.the-cryosphere.net/11/773/2017/
G.Lewisetal.:RegionalGreenlandaccumulationvariability 775
(cid:48)
Figure1.(a)RadargramshowingflightA–A (seeFig.2forlocation).(b)Nineteentracedinternalreflectinghorizonsfromtwodatedice
coresatSummitStationthroughEGIGT-31andthePARCA6943icecore.
firn or ice density. We obtain both the density profile and permittivity, ε (dimensionless), and the speed of light in a
r
depth–agescalefromtwodatedicecorescollectedatSum- vacuum,c(3×108ms−1),from
mit Station (Mary Albert, personal communication, 2015;
c
Cole-Dai et al., 2009). These ice core sites are 3 and 7km v= √ . (1)
fromtheclosestIceBridgeradartrace,andweassumesimi- (cid:15)r
laraccumulationratesacrossthissmalldistance.Wecorrect
In turn, the dielectric permittivity is calculated from the
forthe7-yeardifferencebetweenicecorecollectionandIce-
density,ρ (gcm−3),ofsnowandiceatdepthforeachradar
Bridgeradarflightsbyextrapolatingthedepth–agecurve.
trace(followingKovacsetal.,1995)by
WecalibrateaHerronandLangway(1980)depth–density
modelatSummitusingdatafrombothicecores,thenusethe
(cid:15) =(1.0+0.845×ρ)2. (2)
calibratedmodelparameterstoestimatedensityprofileselse- r
whereinourstudyregion.Inputparametersforthismodelin-
The snow surface reflection is readily identified in each
cludesatellite-derivedmeanannualtemperature(Halletal.,
radarprofilefromthelargesignalamplitude.Wethencalcu-
2012),modeledaccumulation(Burgessetal.,2010),andan
latethedepthforeachsubsequentradarsampleintheprofile
estimate of surface snow density from field measurements
usingtheradartraveltimeandvelocityprofilefromEqs.(1)
alonggroundtraverses,shallowfirncores,andMARmodel
and(2),followingHawleyetal.(2014).
output. Since we are using the density profile to calculate
accumulation based, in part, on modeled accumulation, the
2.4 Internalreflectinghorizons
resultscouldbeseentobecircular.However,ourresultsare
largely insensitive to changes in this modeled accumulation
We manually select 19 clear, strong IRHs to consistently
inputbecauseaccumulationestimatesareminimallyaffected
tracefromSummitStationtowardstheNNWandSWalong
by input variations to the Herron–Langway model. For ex-
twomainflightpaths(5Apriland2May,2014,respectively;
ample, adjusting input accumulation and surface density by
seeFig.1).Whenalayerappearstobifurcateduetochanges
±5%resultsin<1%changeinthecalculatedaccumulation
inaccumulation,wecontinuetotracethelayerbasedonthe
rates.
trajectory of surrounding IRHs. Horizons are not traced in
areaswherethesignal-to-noiseratiomadethemtoodifficult
2.3 Travel-time-to-depthconversion todiscern.
Internalreflectinghorizonsfortheother23flightsinthis
Weconverttheradartraveltimetodepthbyiterativelymul- studyaretracedfromcrossoverlocationswiththetwomain
tiplyingthevelocityoftheelectromagneticwavebythesig- flight paths. Wherever possible, we trace IRHs outwards
nal’straveltimetoeachIRH.Theelectromagneticspeedof from crossover locations along the two main flight paths to
the radar wave, v (ms−1), is calculated from the dielectric locationswherethosetracedlayerscrossanotherflightpath.
www.the-cryosphere.net/11/773/2017/ TheCryosphere,11,773–788,2017
776 G.Lewisetal.:RegionalGreenlandaccumulationvariability
each epoch, average age of the epoch, a (year), and water-
equivalentthicknessoftheGrIS,H (m),fromMorlighemet
al.(2014):
λ(z)=e−Hb˙a. (4)
Uncertainty in accumulation can arise from independent
errorsintracingIRHs,errorsfromincorrectlydatingtheice
core,and/orerrorsinthedensitiesusedforconvertingfrom
separationdistancetowater-equivalentaccumulation.
To reduce tracing errors, two authors separately retraced
each IRH along the two main flights paths four times each.
CloseinspectionoftheIRHsrevealsthatthepeaksdefining
IRHs are within ±2 radar samples (within ±0.557m), and
incorrectly jumping to the next layer would result in an er-
ror of at most ±5 samples (at most ±1.39m). Our average
epoch between IRHs is 16.7 years, which corresponds to a
maximumerrorof∼±0.083ma−1.
Figure2.Dateofoldestresolvableinternalreflectinghorizon(IRH)
We take uncertainty in dating the Summit ice cores to
along 25 IceBridge accumulation radar flights totaling 17730km.
(cid:48) (cid:48) (cid:48) (cid:48)(cid:48) be ±1% for the top 100 years, ±2% for 100–200 years
Locations are shown for A–A (Fig. 1) and B–B–B (Fig. 7) as
ago,and±3%for200–300yearsago.Theoldestisochrones
well as EGIG-T31 and D3, D4, D5, NEEM, NGRIP, NASA-U,
CampCentury,andPARCAicecores(seeFig.5andTable1). tracedinthisstudyaredatedto1712,whichsuggestsamax-
imum error of 3% using a 2007 Summit Station ice core.
At the lowest accumulation locations, the smallest distance
Wheneverwehaveaccumulationdifferencesatcrossoverlo-
between layers is 0.26mw.e. over an epoch of 5.18 years.
cationslargerthanouracceptederror,wereviewIRHstode-
This gives an uncertainty in accumulation due to dating of
terminewhichlayersareincorrectlytraced. ∼±0.03mw.e.a−1.
The error associated with measuring density using simi-
2.5 Accumulationcalculationsanduncertainty
lartechniqueshasbeenestimatedtobe1.4%(Karlöfetal.,
2005).However,followingHawleyetal.(2014)weconser-
Finally, we calculate snow accumulation using the ice core
vativelyassumethatourmeasurementshaveanerrorofupto
depth–agescales,modeleddepth–densityprofiles,andtraced
twicethislarge,correspondingtoamaximumaccumulation
IRHs.Wecalculateaccumulationbetweeneachpairofadja-
errorof±0.014mw.e.a−1.
cent IRHs for every radar trace along the flight lines. Spa-
The three error sources are all random, non-systematic,
tialchangesinaccumulationareevidentfromvaryingverti-
andthuscanbeassumedtobenon-additive(followingHaw-
caldistancesbetweenIRHsalongeachflightline.Temporal
leyetal.,2014).Overtheextentofthedatasetwecanassume
changes in accumulation are evident from examining accu-
thattheerrorsarenotcorrelated,andthusweestimateaccu-
mulationduringdifferentepochsatoneradartrace.Wecal-
culatethewater-equivalentaccumulation,b˙(mw.e.a−1),be- mulation uncertainty from all sources at ±0.127mw.e.a−1
foranysingleepoch.Duetotherandomandnon-systematic
tweenadjacentIRHsfromthedepth,z(m),andage,t (year),
of each layer, the mean density, ρ (kgm−3), of each layer, nature of these errors, we can assume that they are unlikely
andthedensityofwater,ρ (1000kgm−3): to contribute to regional or temporal accumulation bias. To
w
calculate uncertainty for accumulation averaged over multi-
1 Zz2 ρ(z) ple epochs, we divide our uncertainty by the square root of
b˙= ∂z. (3) thenumberoftracedlayersatthatlocation.
t −t ρ
2 1 w
z1 2.6 Modelcomparison
Wedonotcorrectforiceflowduetoadvectionoftheice
sheetsincenearlyalloftheradartracesoccurinareaswith WecompareourIceBridgeaccumulationresultswithannual
surfacevelocities<50ma−1.Theonlyareaswithhigherve- outputsfromPolarMM5(1958–2008;Burgessetal.,2010),
locitiesareacrosstheNortheastGreenlandIceStreamanda MARv3.5.2(1948–2015;Fettweisetal.,2016),RACMO2.3
highvelocityregioninthesouthwest.Velocitiesinthesear- (1958–2015;Noëletal.,2016),andBox13(1840–1999;Box
eas are ∼60–100ma−1 over the time domain of this study etal.,2013).Gridcellsizesforthesemodeloutputsare24,
and do not significantly affect accumulation results. How- 5,1,and5km,respectively.Sinceaccumulationcanbebilin-
ever, we do correct for layer thinning using a Nye (1963) earlyinterpolatedoverthedistanceofthesegridcellswithout
model.Foreachradartrace,thethinningfactor,λ(z),iscal- significantlossofdetail(BoxandRinke,2003),wechooseto
culated from the average accumulation, b˙ (mw.e.a−1) of compareIceBridgeaccumulationwithbilinearlyinterpolated
TheCryosphere,11,773–788,2017 www.the-cryosphere.net/11/773/2017/
G.Lewisetal.:RegionalGreenlandaccumulationvariability 777
Figure4.ComparisonofIceBridgeaccumulationratesdetermined
Figure3.Averageaccumulationoverthetemporaldomainofeach at 87 crossover locations for each epoch, totaling 1241 measure-
radartracecalculatedfromIceBridgeaccumulationradaroverall25 ments.Therearenotemporalorspatialpatternsincrossoverloca-
flights. IceBridge accumulation matches large-scale accumulation tionaccumulationdifferences.Shadedregionisthecalculatedun-
patternsfromicecoresandsnowpitsfromBalesetal.(2009). certaintyof±0.127mw.e.a−1.
modelgridoutputtocompareaccumulationatcorresponding
meltattenuatestheradarsignalandreducesthedensitygra-
spatiallocations.
dientsthatproduceIRHs(Fig.2).
TheBox13datasetiscorrectedusingacorrectionmulti-
We assess the internal consistency of IceBridge accumu-
pliergrid,whichisestimatedusingatriangularirregularnet-
lationbycomparingtheaccumulationat87locationswhere
work interpolation of the ratio between 1961–1990 average
IceBridgeflightpathscrossoneanother(hereafter“crossover
Box13 ice core accumulation rates and RACMO2.1 output.
points”).Differencesatcrossoverpointsaremostlikelydue
Themultipliershaverespectiveminimumandmaximumval-
toerrorsinlayerpickingwhereisochronesbecomedifficult
uesof0.605and1.891.Weassumethatthecalibrationcoef-
todetectordistinguish.Therearenospatialortemporalpat-
ficientsarestationaryinbothtimeandspace,sinceFettweis
ternsinaccumulationdifferencesatcrossoverpointsoverthe
et al. (2016) show that MAR accumulation reconstructions
data set. Moreover, the differences are normally distributed
aresimilartothosefromBox13after1930.
withameanof0.017±0.022mw.e.a−1 (n=1241),andall
Additionally, we compare our IceBridge accumulation
butfivecrossoverpointaccumulationdifferencesfallwithin
with an accumulation map krigged from 295 snow pits and
ourcalculateduncertaintyof0.127mw.e.a−1(Fig.4).
icecoresand20coastalweatherstations(Balesetal.,2009).
Whilethismapestimatesaccumulationoverthetimedomain
3.2 Validationwithinsitumeasurements
oftheoldesticecores,wechoosetocompareIceBridgeac-
cumulationwiththehighestaccuracyaccumulationestimates
AccumulationratesderivedfromicecorescollectedatCamp
from1950to2000,whichincludeweatherstationsandrecent
Century, D3, and D4 (see Fig. 2 for locations) correspond
icecores.
closely with our IceBridge accumulation rates, matching
their long-term mean and tracking their decadal variability
(Fig. 5). Additionally, we compare IceBridge accumulation
3 Resultsanddiscussion
rates and trends to the NASA-U, NEEM, D5, B26, B29,
3.1 IceBridgeaccumulationrates NGRIP,andPARCAicecoresovercorrespondingtemporal
domains (Table 1). IceBridge accumulation rates and accu-
IceBridge accumulation patterns are consistent with ob- mulationtrendsarestatisticallyindistinguishablefromeach
served large-scale spatial patterns from ice cores and snow of these cores at a p<0.05 confidence level using a Stu-
pits(Balesetal.,2009),withhighaccumulationratesinthe dent’st test.
southeastandsouthwestandloweraccumulationratesinthe In Fig. 6 we compare IceBridge accumulation to
northeast and at higher elevations of the ice sheet interior snow pit measurements at station T-31 on the Expédition
(Fig. 3). The number of traceable layers is highest towards Glaciologique Internationale au Groenland (EGIG) traverse
the interior of the ice sheet and lowest in warmer areas to- (Fischeretal.,1995;HurbertusFischer,personalcommuni-
wards the coast and in the south, where enhanced surface cation, 2015), and to accumulation rates calculated at this
www.the-cryosphere.net/11/773/2017/ TheCryosphere,11,773–788,2017
778 G.Lewisetal.:RegionalGreenlandaccumulationvariability
Table1.AveragedicecoreaccumulationcomparedwithIceBridge(IB)accumulationaveragedovertheoverlappingtimedomainofeach
ice core. Uncertainty figures represent 1 standard deviation of ice core accumulation and average IceBridge accumulation at the closest
radartracetoeachcore,respectively.TrendsandtheirstandarddeviationarereportedforbothicecoreaccumulationandnearestIceBridge
accumulation.
Icecore Averageicecore AverageIceBridge Timeperiodof Trend(core) Trend(IB)
accumulation accumulation comparison (mmw.e.a−2) (mmw.e.a−2)
(mw.e.a−1) (mw.e.a−1)
NASA-U 0.35±0.07 0.36±0.07 1921–1991 −0.09±0.38 −0.38±0.95
NEEM 0.19±0.04 0.21±0.06 1855–2004 −0.06±0.08 −0.08±0.41
CampCentury 0.35±0.10 0.35±0.03 1817–2004 −0.24±0.13 −0.39±0.15
D3 0.45±0.10 0.49±0.07 1836–1999 −0.08±0.09 −0.16±0.40
D4 0.42±0.07 0.43±0.06 1746–2002 0.11±0.06 0.14±0.15
D5 0.38±0.07 0.38±0.07 1941–2002 0.12±0.51 0.10±0.92
B26 0.18±0.03 0.19±0.04 1712–1991 −0.03±0.03 0.01±0.12
B29 0.16±0.03 0.18±0.04 1712–1991 0.00±0.02 −0.03±0.12
NGRIP 0.19±0.03 0.19±0.03 1712–1997 −0.02±0.02 −0.04±0.11
P-6839 0.39±0.15 0.39±0.08 1987–1997
P-6841 0.48±0.16 0.45±0.03 1987–1997
P-6938 0.36±0.07 0.34±0.05 1987–1997
P-6941 0.40±0.10 0.40±0.03 1987–1997
P-6943 0.39±0.10 0.40±0.07 1976–1997
P-7345 0.28±0.07 0.32±0.07 1976–1997
P-7347 0.29±0.09 0.33±0.09 1976–1997
P-7551 0.32±0.09 0.30±0.08 1962–1997
P-7653 0.35±0.09 0.40±0.09 1976–1997
Figure5.IceBridgeaccumulation(blue)withuncertainty(bluecircles)comparedwithCampCentury,D3,andD4(seeFig.2forlocations)
icecoreannualaccumulation(thinredlines)andicecoreaccumulationaveragedovercorrespondingepochs(thickredlines).Aredsquare
denotes1standarddeviationoficecoreannualaccumulationovereachepoch.NotethelongertimescalefortheD4icecore.Thereisno
statisticallysignificantdifferencebetweenIceBridgeandicecoreaccumulationforanyoftheseicecores.
TheCryosphere,11,773–788,2017 www.the-cryosphere.net/11/773/2017/
G.Lewisetal.:RegionalGreenlandaccumulationvariability 779
2008 compared to IceBridge accumulation averaged from
1957 to 2004. The difference between IceBridge averaged
over 1957–2014 and IceBridge averaged over 1957–2004
alongthisflightis0.00096±0.0021mw.e.a−1,wellwithin
calculatederror.
Next, we compute the magnitude and percent differ-
ences between RCM output and IceBridge accumulation
over the entire domain of this data set. Averaged over
all 25 flights, the RMS difference between the models
and IceBridge accumulation is 0.036±0.022mw.e.a−1
for Polar MM5, 0.023±0.019mw.e.a−1 for
RACMO2, 0.043±0.029mw.e.a−1 for MAR, and
0.033±0.026mw.e.a−1 for Box13. These average RMS
errors are remarkably small, but Fig. 8 shows considerably
largermodel-specificregionaldifferencesbetweenIceBridge
Figure6.IceBridgeaccumulationresultsatEGIGT-31(seeFig.2
accumulationandRCMoutput.Itisworthnotingthesedif-
for location) from 1957 to 2014 are statistically indistinguish-
ferences are a significant improvement from previous
able from Airborne SAR/Interferometric Radar Altimeter System
(ASIRAS) accumulation Overly et al., 2016) and field measure- versionsoftheregionalclimatemodeloutput.Forexample,
ments(H.Fischer,personalcommunication,2015).Errorbarsare1 the RMS difference between model and IceBridge accumu-
standarddeviationofASIRASaccumulationoverdatapointsfrom lation for MARv3.2 (∼2013) is 0.064±0.033mw.e.a−1
thattimeperiod. and for RACMO2.1 (∼2014) is 0.043±0.018mw.e.a−1.
TheseresultshighlighttheimportanceofupdatedRCMsand
additional in situ data to continually validate model results
location from the Airborne SAR/Interferometric Radar Al- forimprovedGreenlandSMBcalculations.
timeterSystem(ASIRAS;Overlyetal.,2016;seeFig.2for We divide the GrIS into six major drainage basins (see
location). IceBridge accumulation rates are statistically in- Fig. 8) following Vernon et al. (2013) to evaluate and dis-
distinguishable(p<0.05)frombothsnowpitmeasurements cussthespatialdifferencesbetweenmodelandIceBridgeac-
andASIRASaccumulationresults(Fig.6). cumulation. Table 2 shows both percent and magnitude dif-
ferences between the models and 1957–2014 averaged Ice-
3.3 Comparisontomodeledaccumulation Bridgeaccumulationineachofthesixdrainagebasins.Sta-
tisticallysignificantdifferences(p<0.05)arehighlightedin
WecompareIceBridgeaccumulationtoRCMaccumulation bold.
results along the length of each flight. IceBridge accumula- Averaged across basin A, the northern basin with gener-
tion is averaged over 1957–2014 to compare with averaged allylowaccumulationrates,therearenostatisticallysignif-
Polar MM5 (1958–2008), MAR (1948–2015), RACMO2 icant differences between IceBridge accumulation and any
(1958–2015), and Box13 (1840–1999). An example of this of the RCMs used in this study. Although the models dis-
comparison along a single flight (B–B(cid:48)–B(cid:48)(cid:48) in Fig. 2) is agreewitheachotherinthisbasin,assuggestedbyVernonet
shown in Fig. 7. Differences between the IceBridge accu- al. (2013), the differences from the IceBridge accumulation
mulationandRCMoutputarespatiallyheterogeneousalong are neither large nor statistically significant. Basin B in the
theflightpath,varyinginbothlocationandmagnitude.Aver- northeasthassomeofthelargestdifferencesbetweenmodels
agedovertheentirelengthoftheflight,PolarMM5underes- andIceBridgeaccumulation.Averagedacrossall815points
timatesaccumulationby0.001±0.010mw.e.a−1,MARun- inbasinB,MARandBox13underestimateby18.68±9.29
derestimatesby0.006±0.012mw.e.a−1,RACMO2overes- and17.29±6.30%,respectively.BasinCintheeastalsohas
timates by 0.008±0.011mw.e.a−1, Box13 underestimates significant differences between model and IceBridge accu-
by 0.028±0.017mw.e.a−1, and Bales09 overestimates by mulation; Polar MM5 underestimates by 9.45±3.80% and
0.007±0.014mw.e.a−1.Inaddition,thehighspatialresolu- MAR overestimates by an average of 20.89±6.75%, al-
tionofourdatasetshowssignificantaccumulationvariability thoughitoverestimatesbyasmuchas44.7±7.8%inseveral
notcapturedinmodeloutputs. locations(Fig.8e–f).
The model output and IceBridge accumulation time do- Basin D in the southeast is poorly covered by our data,
mains do not match identically, but these minor differences butwefindthatMARsignificantlyoverestimatesaccumula-
do not significantly affect our results. The largest time do- tion by an average of 23.31±5.36%. Koenig et al. (2016)
maindiscrepancyiswiththePolarMM5comparison,where similarlyfoundthatMARoverestimatesaccumulationinthe
model output is averaged from 1958 to 2008 and IceBridge SE region for the years 2009–2011 in comparison to Ice-
accumulationisaveragedfrom1957to2014.Thetoppanel Bridgesnowradaraccumulationrates.Averagedacrossbasin
of Fig. 7 shows Polar MM5 output averaged from 1958 to E, there are no statistically significant differences between
www.the-cryosphere.net/11/773/2017/ TheCryosphere,11,773–788,2017
780 G.Lewisetal.:RegionalGreenlandaccumulationvariability
Figure7.Comparisonof1957–2004averagedIceBridgeaccumulation(solidline)anduncertainty(shadedregion)toaveragedPolarMM5
(cid:48) (cid:48)(cid:48)
(1958–2008;triangles)alonga977kmflightinnorthernGreenland.LocationofflightshownasB–B–B onFig.2.Comparisonof1957–
2014 averaged IceBridge accumulation to averaged Polar MM5 (1957–2008), MAR (1948–2015), RACMO2 (1958–2015), and Bales09
accumulationalongthesameflight.Thedifferencebetween1957–2004and1957–2014IceBridgeaccumulationacrossthisflightisinsignif-
icant.
Table2.Percentandmagnitudedifferencesbetweenaverage1957–2014IceBridgeaccumulationandaveragemodelaccumulationineachof
thesixGrISdrainagebasins.Positivenumbersindicatethatthemodeloverestimatesaccumulationinthatbasin.Plusminusfiguresrepresent
1standarddeviation.Statisticallysignificantdifferencesareindicatedinbold.
A(n=135) B(n=815) C(n=234) D(n=102) E(n=1064) F(n=831)
PolarMM5(%) −2.73±3.73 −7.60±8.00 −9.45±3.80 4.33±5.85 −7.96±4.73 −11.32±5.28
RACMO2(%) 6.22±5.25 1.67±6.65 4.73±5.36 −0.20±2.44 1.97±5.98 −5.12±5.15
MAR(%) 3.66±4.22 −18.68±9.29 20.89±6.75 23.31±5.36 3.39±5.88 2.83±6.96
Box13(%) −6.85±4.47 −17.29±6.30 −0.14±5.28 −0.35±2.33 3.99±5.97 5.36±8.66
Bales09(%) −4.28±4.64 3.35±9.26 5.62±5.65 16.91±9.03 4.76±5.00 −9.13±5.24
PolarMM5(mw.e.a−1) −0.005±0.006 −0.010±0.011 −0.027±0.012 0.015±0.022 −0.031±0.020 −0.036±0.020
RACMO2(mw.e.a−1) 0.009±0.009 0.001±0.009 0.015±0.017 −0.002±0.010 0.007±0.022 −0.014±0.015
MAR(mw.e.a−1) 0.006±0.007 −0.024±0.012 0.075±0.026 0.085±0.015 0.012±0.023 0.011±0.023
Box13(mw.e.a−1) −0.012±0.007 −0.021±0.008 −0.001±0.017 −0.001±0.009 0.014±0.022 0.016±0.029
Bales09(mw.e.a−1) −0.008±0.008 0.003±0.012 0.022±0.017 0.058±0.028 0.016±0.019 −0.028±0.017
IceBridge accumulation and any of the RCMs used in this in basins B and C. We note that RACMO2.3 does not sig-
study. Likewise, Vernon et al. (2013) finds little difference nificantly differ from IceBridge accumulation in any of the
in basin E between the climate models used in that study. basins. Areas where RCM and IceBridge accumulation dif-
On the other hand, Polar MM5 underestimates accumula- fer the most are concurrent with areas without many in situ
tion in basin F, with a statistically significant underestima- measurements (e.g., in the southeast), and where ice cores
tion of 11.32±5.28%. Figure 8 shows that the differences were collected several decades ago (e.g., NASA-U, Camp
areparticularlylargenearCampCentury(seeFig.2forrefer- Century). Additional field measurements would be benefi-
ence), where Polar MM5 underestimates by 16.15±3.75% cialtovalidatebothourIceBridgeaccumulationandRCMs
andMARoverestimatesby22.98±6.79%. inthesedata-poorregions.
Insummary,theRCMsdoanexcellentjobofcalculating Averaged across all 25 flights, the Bales09 accumulation
accumulation averaged over basins A and E, but there are modelkriggedfromicecoreandsnowpitmeasurementsdif-
largedifferencesbetweenmodelandIceBridgeaccumulation fers from averaged 1957–2014 IceBridge accumulation by
TheCryosphere,11,773–788,2017 www.the-cryosphere.net/11/773/2017/
G.Lewisetal.:RegionalGreenlandaccumulationvariability 781
Figure8.
0.033±0.023mw.e.a−1 (Fig. 8i–j). There are no statisti- cent difference in Table 2 by the annual regional SMB in
callysignificantdifferencesbetweenBales09andIceBridge each basin over 1961–1990 (Table 3 from Vernon et al.,
accumulationinanyofthesixdrainagebasins(Table2),al- 2013). For basins B, E, and F, MAR differs by a com-
thoughdifferencesarealsolargestinareaswithsparseinsitu binedtotalof−19.63to10.17Gta−1,RACMO2differsbe-
measurements. tween −13.97 to 10.77Gta−1, and Polar MM5 underesti-
Basins B, E, and F have sufficient data coverage to ex- matesby6.84to30.78Gta−1.GivenamodeledGrISSMB
trapolate over these basins’ spatial domain to estimate the of363±89Gta−1 (Vernonetal.,2013),theuncertaintiesin
model uncertainty of their SMB estimates. We obtain to- thesethreebasinsrepresentatotalSMBdifferenceof−5.41
tal model uncertainty (in Gta−1) by multiplying the per- to2.80%(MAR),−3.84to2.96%(RACMO2),oranunder-
www.the-cryosphere.net/11/773/2017/ TheCryosphere,11,773–788,2017
782 G.Lewisetal.:RegionalGreenlandaccumulationvariability
Figure8.Magnitude(left)andpercent(right)differencesbetweenaveraged1957–2014IceBridgeaccumulationand(a–b)PolarMM5,(c–
d)RACMO2,(e–f)MAR,(g–h)Box13,and(i–j)Bales09averagedaccumulation.AlsoshownaresixdrainagebasinsoftheGrISdiscussed
inthetext(cf.Vernonetal.,2013).
estimation of 1.88 to 8.48% (Polar MM5). Today, it would NGRIP (underestimation of 0.002±0.002mw.e.a−1) than
take360Gtoficemasslosstoraiseglobalsealevelby1mm. theydoalongthefirsthalfofthetransect(overestimationof
Thus,thecombinedMARSMBunderestimationfrombasins 0.007±0.004mw.e.a−1).Theaverage1817–1921measure-
B, E, and F could represent up to 0.054mma−1 of less sea ments(thisstudy)are0.01mw.e.a−1 higherthanthe1811–
levelrisethanpreviouslycalculatedfromtheGrIS. 1911 Karlsson16 values, and the 1712–1811 measurements
(thisstudy)are0.0081mw.e.a−1higherthanthe1711–1811
Karlsson16values.Thus,ourresultsarenearlyidenticalwith
3.4 ComparisonwithKarlssonetal.(2016)
Karlsson16 over the time domain of this study, despite the
two studies using different methods to calculate accumu-
AstudybyKarlssonetal.(2016;hereafterKarlsson16)uses
lation, analyzing different IceBridge flights from different
averydifferentmethodtocalculateaccumulationfromIce-
years,andtracingIRHsfromdifferenticecores.
BridgeaccumulationradardatanearNEEMandNGRIP.We
compare data from their study, representing flight lines in
2011and2012,toarepeatflightduringthe2014IceBridge 3.5 IceBridgeaccumulationtemporaltrends
seasonanalyzedusingourmethod.InFig.9,the1921–2014
accumulation rates (this study) are plotted against 1911– We can analyze spatiotemporal trends in snow accumu-
2011 Karlsson16 accumulation rates and the RCMs used lation using our IceBridge accumulation record spanning
for comparison in this study. On average along the 350km 17700km of flight paths over the past 300 years. We per-
flight line, the accumulation rates calculated in this study formanempiricalorthogonalfunction(EOF)analysisonthe
are0.002±0.005mw.e.a−1higherthaninKarlsson16,well data set to evaluate temporal changes in accumulation and
within calculated error, and in better agreement than either assess potential atmospheric forcing mechanisms (Fig. 10).
data set with the RCMs. Our accumulation values agree WelimitourEOFanalysisto1889–2014tocapturethemax-
better with Karlsson16 from 150km along the transect to imumspatialvariabilitysincelayersolderthan1889aredif-
TheCryosphere,11,773–788,2017 www.the-cryosphere.net/11/773/2017/