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18 Pages·2015·4.38 MB·English

TheCryosphereDiscuss.,9,6187–6222,2015 D
www.the-cryosphere-discuss.net/9/6187/2015/ isc
doi:10.5194/tcd-9-6187-2015 us
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©Author(s)2015.CCAttribution3.0License. io
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Thisdiscussionpaperis/hasbeenunderreviewforthejournalTheCryosphere(TC). p
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PleaserefertothecorrespondingfinalpaperinTCifavailable. r
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Antarctic slush-ice algal accumulation not D
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quantified through conventional satellite s
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imagery: Beware the ice of March P
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J.L.Lieser1,M.A.J.Curran1,2,A.R.Bowie1,3,A.T.Davidson2,S.J.Doust2, |
A.D.Fraser4,1,B.K.Galton-Fenzi1,2,R.A.Massom1,2,K.M.Meiners1,2,
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J.Melbourne-Thomas1,2,P.A.Reid5,P.G.Strutton3,6,T.R.Vance1, is
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M.Vancoppenolle7,K.J.Westwood1,2,andS.W.Wright1,2 us
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1AntarcticClimate&EcosystemsCooperativeResearchCentre,UniversityofTasmania, n
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PrivateBag80,7001Hobart,Australia a
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2AustralianAntarcticDivision,ChannelHighway,7050Kingston,Tasmania,Australia e
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3InstituteforMarineandAntarcticStudies,UniversityofTasmania,PrivateBag129,
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7001Hobart,Tasmania,Australia
4InstituteofLowTemperatureScience,HokkaidoUniversity,Sapporo, D
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Hokkaido,060-0819,Japan c
5BureauofMeteorology(CAWCR),G.P.O.Box727,7001Hobart,Tasmania,Australia us
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6AustralianResearchCouncilCentreofExcellenceforClimateSystemScience, io
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UniversityofTasmania,7001Hobart,Tasmania,Australia P
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6187 |
7Laboratoired’OcéanographieetduClimat(ExpérimentationsetApprochesNumériques), D
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L’OCÉAN–UMR7159CNRS/IRD/UPMC/MNHN,IPSLBote100,4PlaceJussieu, c
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75252ParisCEDEX05,France ss
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Received:17September2015–Accepted:27October2015–Published:11November2015 P
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Correspondenceto:J.L.Lieser([email protected]) pe
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PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
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6188 |
Abstract D
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Our current knowledge of broad-scale patterns of primary production in the Southern s
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Ocean is derived from satellite ocean-colour estimates of chlorophyll a (Chl a) in the ion
open ocean, typically in spring-summer. Here, we provide evidence that large-scale P
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5 intra-ice phytoplankton surface aggregation occur off the coast of Antarctica during pe
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austral autumn, and that these “blooms” are largely undetected in satellite ocean-
colour time series (which mask the ice-covered ocean). We present an analysis of |
(i) true-colour (visible) satellite imagery in combination with (ii) conventional ocean- D
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colour data, and (iii) direct sampling from a research vessel, to identify and charac- c
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10 terise a large-scale intra-ice algal occurrence off the coast of East Antarctica in early ssio
autumn(March)2012.Wealsopresentevidenceoftheseautumn“blooms”inotherre- n
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gions(forexample,PrincessAstridCoastin2012)andotheryears(forexample,Terra a
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NovaBayin2015)implyingregularandwidespreadoccurrenceofthesephenomena. e
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Theoccurrenceofsuchundetectedalgalaccumulationsimpliesthatthemagnitudeof
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primaryproductionintheSouthernOceaniscurrentlyunderestimated.
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1 Introduction s
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InAntarctica,phytoplanktonbloomsaretypicallyobservedduringspringandsummer P
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inthevicinityoftheseaiceedgeasitrecedes(Arrigoetal.,2008a),andcanalsooc- p
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curwithintheseaiceitself(Massometal.,2006).Broad-scaleassessmentofprimary
productionisderivedfromsatelliteestimatesofChlabasedonocean-colourobserva- |
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tions in the open ocean. However, ice-covered regions are masked in the processing D
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(Massometal.,2006;Bélangeretal.,2007). c
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In autumn, phytoplankton production declines in the Antarctic sea-ice zone due to ss
limitation of growth by seasonal changes in light and temperature. Declining solar el- ion
evations reduce incoming photosynthetically active radiation (PAR); reduced stratifi- P
25 a
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cation allows deeper mixing of phytoplankton to depths with little light; and declining e
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6189 |
temperaturesallowseaicetoreform,furtherattenuatinglightenteringtheocean(e.g. D
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MitchellandHolm-Hansen,1991;KangandFryxell,1993;SmithJr.etal.,2000;Arrigo c
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etal.,2008b;ArrigoandLizotte,2010). ss
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Using cloud-free data from NASA’s TERRA satellite MODIS (MODerate resolution n
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5 Imaging Spectrometer) true-colour bands 1, 2 and 4 (645, 859 and 555nm), we de- a
tectedalarge-scaleregion( 30000km2)ofice-associatedphytoplanktonsurfaceac- pe
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cumulationthatdevelopedbetween21Februaryand19March2012(intheearlyau-
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tumn)offCapeDarnley,EastAntarctica,centredon67.5◦S,70◦E(Fig.1a,apolynya
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region during winter). The phytoplankton accumulation was not detected in conven- is
10 tionalmapsofChlafromtheMODISocean-colourbands(443,488and551nm).This cus
intense early autumn phytoplankton occurrence, which we suggest was caused by s
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auniquecombinationofphysicalandbiologicalprocesses,challengestheassumption n
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thatprimaryproductionisnegligibleinthisregionduringearlyautumn(SmithJr.etal., a
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2000). While we were able to perform some opportunistic sampling, this was insuffi- er
cient to conclusively establish whether this was a conventional phytoplankton bloom.
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However, such was the extraordinary concentration of phytoplankton within the sea-
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icematrix,asobservedfromspaceandonthesurface,thatweusethetermintra-ice is
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“bloom”. u
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AutumnbloomsinconsolidatedseaicehavebeenobservedaroundAntarcticapre- io
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20 viously (Fritsen et al., 1994), including in the interior parts of ice floes in the pack ice P
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zone(Meinersetal.,2012).However,thesebloomswererelativelysmallinscaleand p
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of low intensity. Increased autumn phytoplankton biomass has been observed in as- r
sociationwithcoastalpolynyasaroundEastAntarctica(Arrigoetal.,2015;Arrigoand |
vanDijken,2003)andhasbeeninferredfromicecorerecords(Curranetal.,2002)or D
hypothesisedfrommodelsofice-algalproduction(seeforexampleAppendixA).Late is
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summer blooms have also been reported from Antarctic waters (e.g. Comiso et al., s
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1990; Smith Jr. and Nelson, 1990), but only where the recent disappearance of sea io
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icehasbelatedlyexposedcoastalwaterstohighlightlevels.Inthisstudy,weprovide P
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first evidence that enhanced concentration of ice-associated phytoplankton occurs in p
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autumn, and that this is not accounted for in current satellite-based Antarctic primary D
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productionestimates. c
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2 Materialandmethods P
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2.1 Chronologyof2012ice-slushphytoplanktonevent r
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DailyMODIStrue-colourimages(pseudoanimationinAppendixB)showthatinitiation
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oftheobservedphytoplanktonwereassociatedwithbreak-upoflandfastseaice(fast is
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ice)totheeastofCapeDarnleybetween10Februaryand10March2012.Theinitial u
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stagesweredetectedclosetothisdisintegratingfastice.Asthephytoplanktonoccur- sio
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rence expanded in scale, it discoloured the sea ice (Fig. 2), which likely consisted of
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10 amixtureofpulverisedexistingiceandnewly-formedice(Massometal.,2006).This ap
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icewastransportedbythewinds,firsttothesouth(particularlyinwesternPrydzBay) r
andthentothenorth(relevantatmosphericmodeloutputcanbefoundinAppendixC) |
withthechangeinwinddirectionoccurringaround27February.Wealsonoteevidence
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ofwind-drivenstripsandpatchesofseaiceatthesouthernedgeofthe“bloom”,visible is
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15 assoutheast-northwestalignedstreakswestofCapeDarnleyaround12March.This uss
processispresumablydrivenbykatabaticwinds. io
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2.2 Watersampling ap
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On5March2012,sevenopportunisticseawatersamplesweretakeninthevicinityof
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the “bloom” by the research and supply vessel Aurora Australis from the uncontami-
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20 nated seawater line intake system at 4.8m depth (see Fig. 1 for the ship track and is
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samplinglocation).Photos(Fig.2)suggestaveryhighbiomassofice-associatedalgae u
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attheseasurface.Samplingsiteswerelocatedbothwithinandjustoutsidethebloom sio
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region, as identified from MODIS true-colour imagery. Samples were preserved with
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1%vol:volLugol’siodineandstoredinglassbottlesinthedarkat4◦C.Protistswere ap
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identified using an inverted microscope at 400 and 640 magnification, and counted r
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×
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in 20 randomly chosen fields of view using a Whipple grid. Cell abundances were D
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converted to biovolumes using the formulae of Hillebrand et al. (1999) and Cornet- c
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Barthaux et al. (2007). Cell carbon values were then calculated from biovolumes us- ss
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ingtheconversionsofMenden-DeuerandLessard(2000)andCornet-Barthauxetal. n
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5 (2007). a
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2.3 Evidenceofice-associatedphytoplanktonoccurrenceinautumnforother
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The early autumn, ice-associated phytoplankton phenomenon documented here is isc
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not an isolated event in this region. Using MODIS true-colour data, we identified s
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10 six other years since February 2000 when ice-associated early autumn blooms ion
occurred in the same region: 2000, 2002, 2005, 2006, 2007, 2008 and 2012 P
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(http://lance-modis.eosdis.nasa.gov/cgi-bin/imagery/realtime.cgi). These blooms are e
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generally most pronounced around 1 March 5days, suggesting a regular mecha-
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nism,whichweexplorebelow.
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15 3 Resultsanddiscussion ss
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3.1 Biologicalcharacteristicsoftheseawatersamples P
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Seawater samples taken on 5 March 2012 indicated differences in the abundance, r
composition and size of the phytoplankton at sites inside and outside the bloom (Ta-
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ble D1). Concentrations of Chla inside the bloom (2.11mgm−3 average, 3.3mgm−3 D
20 maximum)werethreetofivetimeshigherthanthoseoutsidethebloom(0.66mgm−3). isc
Total protist abundance reached 2.6 106cellsdm−3 in the midst of the bloom com- us
paredto1.2to1.7 106cellsdm−3 o×utside.Ice-associatedphytoplanktonabundance sio
× n
atthesurfacecouldhavebeenlargerthanat4.8mdepth,butunfortunatelycouldnot P
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bemeasuredatthetime. p
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Overall, our results show that the phytoplankton biomass was higher in the bloom D
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due to higher densities and the large cell size of the dominant taxa. The phytoplank- c
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toncommunitywasdominatedbymembersofthediatomgenusFragilariopsis,which ss
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comprised56to75%ofallautotrophssampled.TheabsoluteabundanceofFragilar- n
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5 iopsis spp. differed little between sampling sites, but cells sampled within the bloom a
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were larger (most 20 to 50µm long, with some up to 80µm long) compared to cells e
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outside(commonly<10µmlong).ThehaptophytePhaeocystisantarcticawasthesec-
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ond most abundant taxon, averaging 18% of all autotrophs by number and reaching
10 3n.o1t×va1r0y5scyeslltsedmma−ti3caaltlybowthithiciec-ecocvoevreerdbauntdthiceeir-frsemeallolcsaiztieon(s∼.2Cetollc5oµnmcenintradtiiaomnsetdeird) Discus
meant they contributed little to overall protistan biomass. Phytoplankton taxa that in- s
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habitseaice,namelyEntomoneissp.andPolarellaglacialis,werealsopresentatlow n
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abundanceswithinthebloombutwereabsentoutside(TableD1). a
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3.2 Underestimatedimportanceofice-associatedphytoplanktoninautumn
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The ice-associated phytoplankton in the region off Cape Darnley was not detected in D
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conventionalsatelliteocean-colourimages.Figure1ashowsMODISlevel2Chladata c
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intheCapeDarnleyregion,EastAntarctica,acquiredon2March201210:30UTC.The s
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highest values are at the fringe of the area that was masked by sea ice and/or cloud n
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cover, and scattered within that region. The MODIS true-colour composite has been a
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overlaid in regions where sea ice and/or cloud cover prohibited Chla retrievals, and e
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thediscolouredseaiceandiceslushisobviousinFig.1a.Aship-borneimage(taken
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5March2012)oftheseaice/slushmatrixisgiveninFig.2.Inthisregion,thecombi-
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nation of different products from the same MODIS instrument (Fig. 1a) demonstrates is
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that satellite-derived estimates of Chla are lower than actual values. Also included in u
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25 Fig.1aistheship’strackwhilesamplingthebloomthreedayslater.Thistrackshows sio
Chla calculated from underway fluorometry samples with the highest values of up to n
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3mgm−3 inside the bloom area, matching the highest values of the remote sensing ap
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Chla estimates along the edges of the detection. Figure 1b demonstrates the close r
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agreement between remote sensing data and samples in an along-track view where D
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the satellite data are not masked by sea ice and/or clouds, and the high values of c
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Chlaofupto6mgm−3insidethemaskedareaofthebloompatch.Becausetheyhave ss
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notbeendetectedinthepast,large-scaleautumnbloomswithintheseaicezonehave n
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5 notbeenfactoredintocalculationsofregional-scaleprimaryproductionfortheSouth- a
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ern Ocean (e.g. Arrigo et al., 2008b). These results are evidence that ice-associated e
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phytoplanktonproductionisunderestimatedinotherseasonstoo(e.g.Massometal.,
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2006;MassomandStammerjohn,2010).
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3.3 Proximaldriversofbloomformationinautumn c
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10 Wehypothesisethatthecombinationoffiveenvironmentalvariablescreatethecondi- ion
tionsthatleadtothisearlyautumnbloomformation;seaice,winds,light,nutrientavail- P
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abilityandlackofgrazingpressure(seeconceptualrepresentationshowninFig.3a). pe
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In early March, the interaction of these variables allows for an environmental window
inwhichtheseice-associatedbloomscanform(Fig.3b),asfollows. |
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15 3.3.1 Seaiceconditions iscu
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BasedontheMODIStimeseriesandinformationinFig.2,thisearly-autumn“bloom” io
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appears to be associated with the decay of fast ice and new sea-ice formation. Sea- P
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icegrowthintheCapeDarnleyregiontypicallybeginsinlateFebruarytoearlyMarch p
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(Massom et al. (2013) and Fig. 3b), driven by the air-sea temperature gradient and
winds. Frazil ice formation can act to concentrate phytoplankton in the newly-forming |
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sea ice matrix, with frazil crystals “scavenging” algal cells from the water column as D
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they rise (Garrison et al., 1983). One-dimensional biophysical model simulations (de- c
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tailed in Appendix A) suggest that frazil scavenging (Garrison et al., 1989) is not es- ss
sential to Chla accumulation in the ice, but would accelerate it. MODIS ocean-colour ion
imageryindicatesthattherewaschlorophylinthesameregionpriortoinitiationofthe P
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ice-associated bloom (see Fig. E1), possibly as a result of decay of fast ice. Fast ice e
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decay occurs through break-up and pulverisation by wind and wave activity. That de- D
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cay releases both concentrated algal cells and nutrients to the surrounding ice slush c
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(Massom et al., 2006). We suggest that concentrated phytoplankton cells, within this ss
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interstitial sea ice matrix between brash ice and floes, comprise the discolouration of n
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5 seaiceobservedinMODIStrue-colourimagery(Fig.1a)andfromtheship(Fig.2). a
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3.3.2 Windconditions
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Strong winds in the Cape Darnley region in March 2012 are indicated in the high- D
resolution polar realisation ACCESS-P model output (Appendix C). Such conditions isc
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(Fig. 3b) favour both the break-up and pulverisation of fast ice, and the formation of s
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newandfrazilice.Windactionmayalsoplayakeyroledeliveringnutrientsbyaeolian io
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transport (Winton et al., 2014) from the continent, as well as increasing the spatial P
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extentbydispersionofthebloomthroughoutitslifetime. pe
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3.3.3 Light |
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AtthehighlatitudeatwhichthebloomwasobservedinMarch,theavailabilityofincom- is
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15 ingPARissufficienttoinitiatebloomconditionsifphytoplanktonareconfinedtoashal- us
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lowice-slushmatrixonthesurface.Inthisway,theyareexposedtohigherlightlevels io
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thanisthecaseinice-freeareaswheredeepermixingcanresultinlightlimitation,or P
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withinconsolidatedseaicewithasnowcoverwheresnowsignificantlyattenuatesPAR p
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(Massometal.,2006).Atsuchalowlightangle,seaicealsoactstotrapandscatter r
thelightwhichthephytoplanktoncanuse(BuckleyandTrodahl,1987). |
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3.3.4 Nutrientavailability is
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PrimaryproductionintheSouthernOceanisstronglylimitedbytheavailabilityofnutri- sio
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ents(Blainetal.,2007).Followingspringblooms,theexhaustionofnutrients,particu-
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larlyiron,togetherwithincreasedgrazingpressureanddecreasedstratificationleads ap
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toadeclineinproductionintheopenocean(deBaaretal.,1995). r
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IntheregionoftheCapeDarnleybloom,thepresenceoflargediatomsisconsistent D
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with iron enrichment (Coale et al., 2004). Sea ice and fast ice has been shown to c
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contain high concentrations of iron relative to adjacent areas of open water (van der ss
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Merweetal.,2011),soitssubsequentpulverisationbywaveslikelyreleasedironinthe n
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5 bloomregion,toinitiateormaintainalgalgrowth. a
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Other potential sources of iron to support this early autumn bloom include: iceberg e
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melt, wind-driven snow deposition, perhaps containing iron-rich dust, from the conti-
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nent and wind-driven upwelling (see Fig. F1). Of these, iceberg sources of nutrients
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aredifficulttoquantifyfortheCapeDarnleybloom,butfasticebreak-upwasobserved is
10 and upwelling can be inferred from wind strength and direction. Fast ice break-up in cus
western Prydz Bay was observed in MODIS true-colour imagery ten days before the s
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detectionoftheice-associatedbloom(AppendixB). n
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3.3.5 Grazing e
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Within the bloom region, the habitat provided by sea ice provides a protective matrix
against zooplankton grazing (Krembs et al., 2000). Microbial grazers, such as het- D
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erotrophic flagellates, dinoflagellates and ciliates, were more abundant in the bloom, c
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buttheirgrazingwasinsufficienttolimitphytoplanktongrowth.Intheabsenceofsam- s
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ples to indicate metazoan grazer densities, it is not possible to quantify total grazing n
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pressurefortheice-associatedbloom. a
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3.4 Climateindices
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The discovery of this bloom and the satellite-based evidence of other such blooms in D
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different years and regions, and implications for primary production estimates, raises c
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the question of inter-annual variability. Our initial investigation suggests that the pos- ss
itive phase of El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode ion
25 (SAM)mayplayarole(seediscussioninAppendixG).Understandingthesedriversof Pa
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inter-annual variability, and their association with blooms of this type, is important for D
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estimatingthescaleofthisadditionalsourceofprimaryproduction. c
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4 Conclusions P
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Thisintra-icephytoplankton“bloom”appearstobetheconsequenceofacombination r
5 ofseaiceconditionsresultingfrombreakupoffasticeandinitialgrowthstagesofnew |
sea ice. We suggest that the combination of sufficient light, nutrients released from
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disintegrating ice, subsequent wind redistribution and a reduction in grazing pressure is
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allows for this phytoplankton bloom. Blooms of this type are a potentially important s
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sourceofprimaryproductionintheSouthernOcean,whichisnotcurrentlyquantified io
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10 withocean-colourremotesensing,andthereforehaveimplicationsforSouthernOcean Pa
foodwebsandbiogeochemicalcycling(Murphyetal.,2012). p
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AppendixA: One-dimensionalbio-physicalmodelresults
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ThemodelcouplesasimpleN-Pmodulewiththehalo-thermodynamicmodel(Vancop- u
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penolleetal.,2010).Thismodelcapturesicethermodynamics,halo-dynamics,aradi- sio
ation scheme, and a simple nutrient-phytoplankton ice algae model, characterised by n
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one species of ice algae (diatoms) and two limiting nutrients (nitrates and silicates). a
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Provided that iron is plentiful, the algal biomass observed in MODIS true-colour im- r
agery and in-situ (from the research vessel Aurora Australis) originates either from
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(i) in-ice growth of algae, or (ii) scavenging of algal cells from the water column by
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20 risingfrazilicecrystals.Thesealternativehypothesesweretestedusingsimplecalcu- isc
lationsandmodelsimulations. us
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A1 Simplecalculations D
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A1.1 In-icegrowthoficealgae s
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We first compute the amount of Chla that is achievable from the in-situ growth of ice P
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algaebyconsumptionofthethelocallyavailablenutrients.Assumingthat30%ofthe p
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5 oceanicconcentrationofsilicatesofCSii =40mmolm−3 isstoredintheseaiceduring r
formation(therestbeingrejectedbybrinegravitydrainage)andfullyexhaustedbyice |
algae,thenwegetthatchlorophyllaconcentrationinseaiceis: D
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CCi hla=rCChlarSCiCSii×30%≈17mgChlam−3 (A1) ussio
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where rCChla=0.15mgmmol−1 is a standard chlorophyll:carbon ratio in sea ice di- Pa
atomsandrC=9.09istheC:Simolarratio(Sarthouetal.,2005). pe
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A1.2 Scavengingoffrazilcrystals |
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TheCapeDarnleypost-polynyaishighlyproductiveinlatesummer(seeArrigoandvan isc
Dijken,2003),withsurfaceChlaconcentrationsreachingupto5mgChlam−3.Assum- uss
ingthatafractionα=10%ofseawaterChlawithaconcentrationCCohla=1mgm−3 is ion
15 scavenged by raising frazil crystals over an oceanic depth of ho=50m, the resulting Pap
averageChlaconcentrationin30cmthickseaiceshouldbe: e
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αCChlah |
CCi hla= ohi o ≈17mgChlam−3. (A2) Dis
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Thesetwoscenariosgiveequivalent(high)Chlaconcentrationsanddonotdistinguish ss
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betweenourtwohypothesesforthebloomsource. n
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A2 Modelsimulations D
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Themodelisdesignedtorepresentun-deformedlevel(columnar)ice,weapplyithere s
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totheseaicemixturethatcharacterisedtheCapeDarnleyearlyautumnbloom.Thisis io
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basedontheassumptionthatnutrientpathwaysinlevelice(e.g.brinechannels)that P
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5 arecapturedintheone-dimensionalmodel,behavesimilarlytothefrazilmixturefound pe
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betweenpancakes(althoughthismixturelikelyoccupiesmorevolumethanbrinechan-
nels,becauseoftheconstantswellforcingthattendstopreventfulliceconsolidation). |
AsChlaisaparticulatetracer,weassume100%ofincorporation(CChla=CChla).The D
i o is
modelthereforearguablyprovidesarough,minimal,representationoftheCapeDarn- cu
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leybloomconditions. s
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Forcingandinitialconditions Pa
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Theseaicemodelisforcedatboundarieswiththefollowingnumbers:
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– seasurfacesalinity=34gkg−1;
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– nitrate concentration in seawater = 30mmolm−3 (typical value from that region, iscu
15 seeSarmientoandGruber,2006); ssio
– silicateconcentrationinseawater=40mmolm−3 (typicalvaluefromthatregion, nP
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seeSarmientoandGruber,2006); p
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– atmosphericheatfluxesfromempiricalformulaforsensible,latent,shortwaveand
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long wave fluxes, fed by daily NCEP temperatures, winds and specific humidity
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20 (multipliedby0.83tocorrectaknownsystematicbias),togetherwithaclimatology is
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ofcloudfraction; u
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– aprescribedverysmallsnowfallrate. ion
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Modelrunsstartatday-of-year50(19February)whenthesurfaceenergybudgetbe- ap
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comes negatives, which (approximately) corresponds with the onset of ice formation. r
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Runs last 30 days, until day-of-year 80 (20 March), and are initialised with standard D
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thinicecharacteristics: c
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– icethickness=5cm; io
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– snowdepth=2cm; a
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5 – icesalinity=22.93gkg−1(fromtheformulaofKovacs(1996)givingbulkicesalin- r
ityforyoungiceasafunctionofitsthickness); |
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– nitrateconcentration=20.23mmolNm−3 (sameice-oceanratioasforsalt); is
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– silicateconcentration=26.97mmolSim−3 (sameice-oceanratioasforsalt). ssio
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TheconcentrationofcarbonstoredinicealgaeCDA isalsoprescribedattheoceanic P
i a
10 boundary(CDoA).Asthisconcentrationintheoceanisusedasaninitialconcentration per
informingnewice,accretedattheicebase,wemadeseveralsensitivityexperiments.
|
– Exp.1:CDoA=0.5mmolCm−3;µ=1.3×10−5s−1; D
is
– Exp.2:CDoA=0.5mmolCm−3;µ=1.95×10−5s−1; cus
s
– Exp.3:CDoA=5mmolCm−3;µ=1.3×10−5s−1; ionP
15 – Exp.4:CDoA=50mmolCm−3;µ=1.3×10−5s−1, aper
whereµisthemaximumspecificphotosynthesisrate.
|
Experiment1correspondsto<0.1mgChla m−3(nobiologicalactivityintheocean).
D
In experiment 2, we tested the role of the maximum specific growth rate µ, which is
c
is rather uncertain. It would be bigger if the diatoms are adapted to high-light levels u
s
20 (ocean diatoms) and lower if they are adapted to low-light levels (ice diatoms). In ex- sio
n
periment 3, the seawater that sea ice is forming from is typical of bloom conditions
P
( 1mgChla m−3), but there is no harvesting of organic matter by frazil crystals. In ap
≈ e
experiment 4, the seawater is typical of bloom conditions, and we assume that initial r
6200 |
concentration in forming ice is enhanced by frazil scavenging (initial concentration of D
Chlainseaice≈10mgChla m−3). iscus
s
A3 Results io
n
P
a
The model grows 50cm of ice thermodynamically within the 30day simulation period p
e
(Fig. A1). The cold air temperatures imply low temperatures and high brine salinities r
5
in the upper ice, which is improper to ice algae growth, as can be seen from the lim- |
itation factors in the lower panels of the Fig. A1 (0 = improper to ice algal growth, D
1 = ice algal growth is possible). The four sensitivity experiments give quite different isc
u
blooms, particularly in terms of Chla concentration and timing of maximum Chla val- s
s
10 ues(Fig.A2).IntegratingChlavertically(Fig.A3a)anddividingbyicethicknesstoget ion
meanconcentrationsinseaice(Fig.A3b)indicatesthefollowingdifferencesbetween P
a
experiments: pe
r
– Experiment1giveslowconcentrationsthatareprobablyunrealistic;
|
– Experiment4(frazilscavengingexperiment)givesthefastesticealgalgrowthrate D
is
andthemostabundanticealgae; c
15 u
s
s
– Experiments2and3areinbetweentheseextremes.Experiment2indicatesthat io
n
light-acclimateddiatomswithhigherµmightoutcompeteicealgaeinthebloom. P
a
Theconcentrationsinseaicearehighandthetimingofmaximumconcentrations p
e
(around day-of-year 60) is consistent with observations. Experiment 3 indicates r
thatevenwithoutfrazilscavenging,accumulationoforganicmatterfromtheupper |
20
oceancreatesasufficientbasisforicealgalgrowth. D
is
c
A4 Conclusion us
s
io
n
Modelsimulationssuggestthatfrazilscavengingwouldleadtorapidandintenseiceal-
P
galdevelopment(Experiment4).Sensitivityexperimentsalsosuggestthatsubstantial ap
e
activity is possible in the ice (i) with no frazil scavenging but accumulation of surface r
25
6201 |
material in forming ice (Experiment 3); (ii) with no activity in the water, but with light- D
is
acclimateddiatomsoutcompetingicediatomsintheslush(Experiment2).Onthebasis c
u
oftheseresults,itisnotpossibletodistinguishbetweenalternativehypothesesforthe ss
io
originofalgalcellsintheice-associatedbloom. n
P
a
p
e
AppendixB: AnimationofMODIStrue-colourimagery r
5
|
A series of daily MODIS true-colour images is shown in this animation, available
D
fromftp://ftp.acecrc.org.au/pub/jllieser/MODIS_Animation.pps(9.3MB).Thesequence is
c
u
startswiththeinitiationoftheobservedoccuranceon10February2012undfinishes s
s
on10March2012. io
n
P
a
p
e
10 AppendixC: LinktoACCESS-Pwinds r
|
AtmosphericmodeloutputfromACCESS-Pmodel(Shresthaetal.,2013),withrelation
D
totheobservedbloomphenomenoncanbefoundat:ftp://ftp.bom.gov.au/anon/home/ is
c
cawcr/perm/preid/research/algal_bloom/access_p_algal_bloom.html. u
s
s
io
n
AppendixD: Watersamplebiologicalcharacteristics Pa
p
e
r
Sampling sites were located within and outside the region identified from MODIS
15
true-colour imagery as experiencing a bloom. Samples of 1L were preserved with |
1% vol:vol Lugol iodine and stored in glass bottles in the dark at 4◦C. Protists were D
is
identified and counted using phase and Nomarski interference optics using an Olym- c
u
pusIX71andIX81invertedmicroscopeat400to640 magnification.Brightfieldop- ss
× io
20 ticswasalsousedtodiscriminatetaxathatcontainedchloroplasts.Protistantaxawere n
countedin20randomlychosenfieldsofview,exceptforhighlyabundanttaxathatwere Pa
p
countedinasubsetofthefieldofviewdefinedbyanocularquadrant(Whipplegrid). e
r
6202 |
Cell biovolumes and carbon conversion statistics were used to calculate the cell D
is
biomassofprotistantaxa/groups.Theaveragecellvolumeofeachprotistantaxonwas c
u
calculated using formulae presented by Hillebrand et al. (1999) and Cornet-Barthaux ss
io
et al. (2007). Cell carbon was then calculated using the volume-specific carbon con- n
P
5 versionsofMenden-DeuerandLessard(2000)andCornet-Barthauxetal.(2007). a
p
Species composition and biomass differed significantly between sampling sites in- e
r
side and outside the bloom area (PERMANOVA, P =0.028 and P =0.026, respec-
|
tively)(Andersonetal.,2008).BasedonSIMPERanalysis(Primer-ELtd)largediatoms
D
10 (apnrded>o5m0in%anbtalyseFdraognilabriioompsaisss)e(Cxplalarkineeadn∼d4G0o%rleoy,f2th0e06d)iff.eTrheunsc,ethbeasoebdseornveadbupnrodtaisntacen iscus
communitywithinthebloomareadifferedmarkedlyfromthecommunitiesinsurround- s
io
ingice-freeareasintermsofcomposition,abundanceandcellsizedistribution. n
P
a
p
e
r
AppendixE: OceancolourimageofCapeDarnleyregionfor11February2012
|
WithFig.E1weprovideaMODISocean-colourscenebeforetheiceassociatedbloom D
15 (11February2012)toshowtheexistingprimaryproduction. iscu
s
s
io
n
AppendixF: TruecolourimageofCapeDarnleyregionfor23February2012 P
a
p
e
FigureF1showsaMODIStrue-colourimage,illustratingpotentialiceseedingmecha- r
nisms,including(1)wind-drivenformationofstripsandpatches,(2)disintegratingfast
|
ice,and(3)wind-blownsnowoffthefaceoftheAmeryIceShelf.
D
is
c
u
s
AppendixG: InteractionsbetweenENSOandSAMintheSouthernHemisphere s
20 io
n
Weidentifiedanassociationbetweentheyearsinwhichice-associatedearlyautumn Pa
p
bloomsweredetectedinMODIStrue-colourimageryforCapeDarnley,andmodesof e
r
6203 |
climatevariabilitythatinfluencetheSouthernOcean,specificallytheElNiño-Southern D
is
Oscillation(ENSO)andtheSouthernAnnularMode(SAM).TheSouthernOscillation c
u
Index(SOI)isanindexdescribingtheatmosphericoscillationbetweenElNiño(warm) ss
io
and La Niña (cool) temperature states in the tropical eastern Pacific Ocean. Vance n
P
5 et al. (2013) showed that there is a circumpolar ENSO signal in the westerly winds a
p
around Antarctica. This strengthening was further enhanced by interaction of ENSO e
r
with the SAM. By austral summer in El Niño (La Niña) years, tropospheric anoma-
|
lies develop into a circumpolar ridge-like (trough-like) anomaly across Antarctica that
D
weakens (strengthens) circumpolar westerlies (Fig. G1) and interacts with the SAM is
10 (L’HeureuxandThompson,2006). cus
In the Cape Darnley region, these ice-associated early autumn blooms occurred s
io
in years when La Niña and positive SAM conditions generated high winds (Fig. G1), n
P
whichisoneoftheimportantdriversidentifiedforthisbloom(seeFig.3boftheoriginal a
p
paper).Theshortsatellitedataset(2000–2012)limitsourabilitytoconfirmthisENSO- er
SAM-bloomrelationshipinastatisticallysignificantway,butitoccursmoreoftenthan
15 |
by chance (Fogt et al., 2011) and is likely linked to greenhouse gas forcing (IPCC,
D
2007). Therefore, these previously unquantified blooms may increase in frequency, is
c
furthercontributingtoprimaryproductionintheSouthernOcean. u
s
s
io
AcircumpolarpressuresignalofENSO n
P
a
p
The interaction between ENSO and SAM during DJF is primarily the result of coinci- e
20 r
dentLaNiña-SAMpositive,ElNiño-SAMnegativeorENSO-weakSAMeventsoccur-
|
ringmoreoftenthanbychance(Fogtetal.,2011).Otheriterations(i.e.LaNiña-SAM
D
negative) resulted in anomalous transient eddies in the mid-latitudes that opposed is
c
ratherthanreinforcedtheSAM-ENSOinteraction.DuringLaNiña-SAMpositiveevents u
s
25 (Fig.G1,upperpanel),ananomalouscircumpolartroughovertheAntarcticcontinent sio
andcoastalregionsispairedwithadecidedlyun-SAM-likezonalwavenumber3(ZW3) n
P
patternatmid-latitudes.ZW3isassociatedwithtransitionsfromstronglymeridionalto a
p
e
stronglyzonalflow(Raphael,2007).ZW3isthoughttobeassociatedwithENSO,and r
6204 |
anAustraliansectorwave 3index(AZ3–vanOmmen andMorgan,2010)isstrongly D
is
correlatedwithSOIduringsummer(TableG1). c
u
The NCEP/NCAR reanalysis (Kistler et al., 2001) was used to investigate GPH ss
io
anomalies. All reanalysis datasets have high latitude data problems related to data n
P
5 scarcityandtrendeffectsintheSouthernHemisphere,particularlypriorto1979(Hines a
p
et al., 2000; Kistler et al., 2001). Data coverage improves greatly after 1979, thus we e
r
usedcompositesfrom1979–2009.
|
TheSupplementrelatedtothisarticleisavailableonlineat D
doi:10.5194/tcd-9-6187-2015-supplement. isc
u
s
s
10 Acknowledgements. We acknowledge the use of MODIS data products or imagery from io
n
the Land, Atmosphere Near real-time Capability for EOS (LANCE) system operated by the
P
NASA/GSFC/Earth Science Data and Information System (ESDIS) with funding provided by a
p
NASA/HQ(https://earthdata.nasa.gov).ModeldatacanbeobtainedonrequestfromM.Van- er
coppenolle([email protected]).Underwayandsampledataareavail-
|
15 ablefromK.Westwood([email protected]).
We thank Barbara Frankel, Julie McInnes, Fanny Chever, Maurice Levasseur and Martine D
is
Lizotte for their work on sample processing and analysis, Nobuo Kokubun (NIPR) for in-situ c
u
photography,andSoKawaguchi,RobKingandTomTrullfortheirhelpfulcontributionstoini- s
s
tial discussions regarding the autumn “bloom”. We are grateful to science support staff and io
n
20 crewaboardRSVAuroraAustralisformakingitpossibleforsamplingtooccur.Finallywethank P
a
SteveNicol,IanAllison,TomTrull,IanMurrayandRowanTrebilcoforconstructivecomments p
e
thatgreatlyimprovedanearlyversionofthemanuscript.ThisworkwassupportedbytheAus- r
tralian Government’s Cooperative Research Centre program through the Antarctic Climate &
|
EcosystemCRC(ACECRC).JLLwassupportedunderAustralianResearchCouncil’sSpecial
25 ResearchInitiativeforAntarcticGatewayPartnership(ProjectIDSR140300001). Dis
c
u
s
s
io
n
P
a
p
e
r
6205 |
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