Climate impact on interannual variability of Weddell SeaBottom Water

Darren C. McKee,1,2 Xiaojun Yuan,1 Arnold L. Gordon,1,2 Bruce A. Huber,1

and Zhaoqian Dong3

Received 24 June 2010; revised 18 February 2011; accepted 3 March 2011; published 26 May 2011.

[1] Bottom water formed in the Weddell Sea plays a significant role in ventilating theglobal abyssal ocean, forming a central component of the global overturning circulation.To place Weddell Sea Bottom Water in the context of larger scale climate fluctuations,we analyze the temporal variability of an 8‐year (April 1999 through January 2007)time series of bottom water temperature relative to the El Niño/Southern Oscillation(ENSO), Southern Annular Mode (SAM), and Antarctic Dipole (ADP). In addition to apronounced seasonal cycle, the temperature record reveals clear interannual variabilitywith anomalously cold pulses in 1999 and 2002 and no cold event in 2000. Correlations ofthe time series with ENSO, SAM, and ADP indices peak with the indices leading by14–20 months. Secondary weaker correlations with the SAM index exist at 1–6 monthlead time. A multivariate EOF analysis of surface variables shows that the leading moderepresents characteristic traits of out‐of‐phase SAM and ENSO impact patterns and iswell separated from other modes in terms of variance explained. The leading principalcomponent correlates with the bottom water temperature at similar time scales as did theclimate indices, implying impact from large‐scale climate. Two physical mechanismscould link the climate forcing to the bottom water variability. First, anomalous winds mayalter production of dense shelf water by modulating open‐water area over the shelf.Second, surface winds may alter the volume of dense water exported from the shelfby governing the Weddell Gyre’s cyclonic vigor.

Citation: McKee, D. C., X. Yuan, A. L. Gordon, B. A. Huber, and Z. Dong (2011), Climate impact on interannual variability ofWeddell Sea Bottom Water, J. Geophys. Res., 116, C05020, doi:10.1029/2010JC006484.

1. Introduction

[2] Bottom waters formed in the marginal seas of Antarc-tica play a significant role in ventilating the global abyssalocean. The Weddell Gyre facilitates input of source waters tothe Weddell Sea, encourages complex modifications throughair‐sea‐ice and ice shelf‐water interactions, and serves as aconduit for export via complicated bottom topography. Itsglobal relevance has made the Weddell Gyre the subject ofmuch attention in the past, including studies of trendsand interannual variability in either the gyre itself or thesurrounding ocean‐ice‐atmosphere system [e.g., Fahrbachet al., 2004; Martinson and Iannuzzi, 2003; Meredith et al.,2008; Kerr et al., 2009; Lefebvre et al., 2004].[3] The Weddell gyre’s cyclonic circulation is partly

driven by climatological wind systems over the gyre

[Schroder and Fahrbach, 1999]. Warm saline waters escapethe Antarctic Circumpolar Current near 20°E, where theyare carried in the cyclonic gyre to the continental shelf in thesouthwest, losing heat along the way. There, entrainmentwith dense shelf water results in the formation of bottomwater masses, steered within the cyclonic circulation bytopography to their destinations. The densest water mass,Weddell Sea Bottom Water (WSBW), is defined as bottomwater with potential temperature (�°C) less than −0.7°C[Orsi et al., 1993].[4] Different varieties of WSBW exist with different

source properties or formation mechanisms [see Gordon,1998; Gordon et al., 2001; Huhn et al., 2008]. Acceptedformation processes can be summarized in two mechanisms.The common requirement is upwelling of saline warm deepwater (WDW), cooled sufficiently in winter with increaseddensity from brine rejection. The first process calls forWDW interacting with high‐salinity shelf water (HSSW)and winter water (WW) near the shelf break, mixing withfurther entrainment upon descent [Foster and Carmack,1976]. Foster and Carmack’s [1976] mixing scheme sug-gests WSBW is about 25% shelf water and 62.5% WDW,with the remainder being WW. Isotope data agree ratherwell with these ratios (about 25% and 70%, respectively)

1Lamont‐Doherty Earth Observatory, Columbia University, Palisades,New York, USA.

2Department of Earth and Environmental Sciences, ColumbiaUniversity, New York, New York, USA.

3Polar Research Institute of China, Shanghai, China.

Copyright 2011 by the American Geophysical Union.0148‐0227/11/2010JC006484

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, C05020, doi:10.1029/2010JC006484, 2011

C05020 1 of 17

[Weppernig et al., 1996]. The second process involvesHSSW under the southern ice shelves reaching a super-cooled (and relatively fresher) state to form ice shelf water(ISW), ultimately capable of descending the slope andmixing with surrounding WDW [Foldvik et al., 1985]. Ofthese two processes, western shelf water contributes about2–3 times more to the water column below 0°C than doesISW [Weppernig et al., 1996]. In total, the transport ofsource waters is about 0.97–2.5 Sv, while the transport ofWSBW is about 2–5 Sv [Carmack and Foster, 1975; Fosterand Carmack, 1976; Fahrbach et al., 1995; Gordon et al.,2001; Grumbine, 1991].[5] Various studies have observed a plume‐like nature

in water mass properties [Drinkwater et al., 1995; Barberand Crane, 1995; von Gyldenfeldt et al., 2002; Foster andMiddleton, 1980], highlighting the roles of cabbeling andthermobaric effects in allowing dense shelf waters todescend the slope [Gordon et al., 1993] as well as the role ofseasonality in surface conditions such as sea ice formationand brine rejection [Drinkwater et al., 1995]. Location ofthe shelf slope front and degree of baroclinicity in theWeddell Gyre [Meredith et al., 2008; Jullion et al., 2010]place further constraints on the export of dense water.[6] The importance of brine rejection and cyclonic vigor

highlight the potential roles of sea ice production over thesouthwestern continental shelf and cyclonicity of thewind fieldover the Weddell Sea, respectively, in describing interannualvariability ofWSBW. The surface forcing from the atmospheredirectly affects these two processes. The atmospheric vari-ability at the surface, in turn, is largely controlled by large‐scale regional and extrapolar climate variabilities. Particularly,El Niño‐Southern Oscillation (ENSO) and the SouthernAnnular Mode (SAM) have well‐documented responses in theocean‐ice‐atmosphere system of theWeddell Sea region [Yuanand Martinson, 2000, 2001; Yuan, 2004; Martinson andIannuzzi, 2003; Lefebvre and Goosse, 2005; Lefebvre et al.,2004; Holland et al., 2005]. Such responses could link large‐scale climate to the observed variability in WSBW.[7] For ENSO, while a detailed analysis is shown by Yuan

[2004], the important result for this study is the presence ofa low‐pressure anomaly over the Bellingshausen Sea regionduring cold events with a corresponding high‐pressureanomaly during warm events. This yields greater meridionalheat flux on the Atlantic side of the peninsula and lesseron the Pacific side in cold events, causing opposite phases ofanomalies in ice edge extent, meridional winds, and surfacetemperature in these two basins, termed as the AntarcticDipole [Yuan and Martinson, 2000; Yuan, 2004]. ENSO hasan equally significant impact on the vigor of the WeddellGyre. With warm ENSO events, the gyre center contractsand shifts southward under enhanced cyclonic forcing[Martinson and Iannuzzi, 2003].[8] The Antarctic Dipole (ADP), triggered by ENSO events

[Rind et al., 2001; Yuan, 2004] and representing the greatesttemperature response to ENSO outside the equatorial Pacific[Liu et al., 2002], is the predominant interannual signal in theSouthern Ocean sea ice fields [Yuan and Martinson, 2001].Its surface signature is an out‐of‐phase pattern in the sea iceedge and surface air temperature between the Atlantic andPacific sectors. Because of the positive feedback within theocean‐ice‐atmosphere system in polar seas, the ADPanomalies are amplified and matured after the tropical forcing

demises, making it a unique high‐latitude mode [Yuan, 2004]that sets surface conditions in the Weddell Sea.[9] The SAM consists of an out‐of‐phase oscillation in

pressure anomalies between middle and high latitudes and isa dominant climate mode in the Southern Hemispherepressure field [Gong and Wang, 1999; Thompson andWallace, 2000]. A positive SAM strengthens westerlies toisolate the Antarctic continent and results in cooling over thecontinent [Thompson and Solomon, 2002] except in thepeninsula region where a pressure anomaly causes warmingthrough advection of warmer air in the form of southwardwinds to the east of the peninsula and northward winds tothe west: it is not a perfectly annular mode [Lefebvre et al.,2004]. The nonannular impact of the SAM is very similarto the local pressure anomaly of ENSO events and thesurface anomalies mimic those of the ENSO triggereddipole anomalies. The enhanced westerlies of a positiveSAM phase also enact a spin‐up of the Weddell Gyre due tothe “annular” impact of the SAM.[10] The interference of the SAM and ENSO, particularly

in the southeastern Pacific and over the Bellingshausen Searegion allows for complex modulations of impact[Simmonds and King, 2004; Fogt et al., 2011]. Stammerjohnet al. [2008] have pointed out that when cold ENSO eventsare coincident with a positive SAM (and vice versa), theytend to reinforce each other’s impacts on sea ice. More spe-cifically, the associated pressure anomaly in the southeastPacific is reinforced and shifted farther southeast, offeringgreater breadth of impact on the Weddell and AntarcticPeninsula regions. Figure 1 shows that this opposite‐phasingof the two climate modes is present during much of the late1990s and early 2000s when we have hydrographic obser-vations, further motivating our study.[11] Even though there is evidence that large‐scale climate

variability has impact on surface temperature and sea icedistributions in the Weddell Sea, whether these climateinfluences could be extended to the deep ocean remainsunknown because of data scarcity. Perennial sea ice has madehydrographic observation difficult and as a result, all timeseries of hydrographic parameters to date have been too shortto assess interannual variability relevant to external climate.Now, with an 8‐year time series of bottom water potentialtemperature observed by deep‐sea moorings in the northwestWeddell Sea [Gordon et al., 2010], we have for the first timea record long enough to observe interannual variability. Thegoal of this study is to statistically relate the interannualvariability observed in this record to large‐scale polar andextrapolar climate variability (ENSO, SAM, ADP) and thento explain these relations through physical mechanisms ofsurface forcing, one related to dense water production andone related to dense water export.

2. Data

2.1. Mooring Data

[12] We use hydrographic data collected from a series ofmoorings south of the South Orkney Islands in the north-west Weddell Sea [Gordon et al., 2010] (Figure 2). Col-lectively, they document the properties of bottom waterat the northern limb of the Weddell Sea. The mooringswere initially deployed in April 1999 and are redeployedevery 2–3 years. Moored instruments document temperature,

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

2 of 17

salinity, and velocities from near‐bottom to 501 m abovebottom at approximately 100 m intervals, with samplingrates varying from 7.5 to 30 min. The primary data used inthis study are potential temperature records spanning fromApril 1999 through January 2007.[13] Of particular interest is the lowest instrument at

mooring M3, which at 4560 m depth represents the deepestand densest water of the Weddell Sea (Figure 3). There is

a significant gap in the bottom‐level potential temperaturetime series from 29 July 2004 through 7 March 2005, whilethe second‐deepest instrument does not have this gap.Since the signal is vertically coherent, the record from thesecond‐lowest instrument was substituted over this gap bysubtracting the mean offset between the two instruments(−0.027°C). Potential temperature records from the bottominstrument at mooringM2 (depth 3096m) overall demonstrate

Figure 2. Locations of moorings M2 and M3 as well as the XCTD lines used in this study. Flow pathsfrom potential source locations to the moorings are indicated, including the possible path around PowellBasin for the less dense water reaching M2. The permanent pycnocline base depths as calculated from theXCTD data are shown in Figure 9.

Figure 1. SAM (solid) and NINO3.4 (dashed) indices from 1997 through 2007. Out‐of‐phase occur-rences of ENSO and SAM are in gray shading.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

3 of 17

an identical interannual signal with a muted seasonal signal(Figure 3), so all analyses use data from both moorings. Inaddition to the potential temperature records we include somediscussion of bottom salinity (Figure 3) and bottom speedat M3. There are more significant gaps in these records(explained by Gordon et al. [2010]), so we do not study sta-tistics of these time series.[14] The data are highly seasonal with cold pulses in

austral winter. A detailed description of the seasonal vari-ability is presented by Gordon et al. [2010]. While thestructure of the temperatures is clearly vertically coherent,the increased vertical derivative of potential temperatureduring cold pulses indicates benthic intensification, suggest-ing export of dense water from the shelf occurs via gravityplumes. Velocities of approximately 12 cm/s at M3 suggest atransit time from likely shelf water source to mooring M3 of∼6 months. Of interest in this study is substantial interannualvariability at M2 and M3 with anomalously cold pulses in1999 and 2002 and an absence of a cold pulse in 2000, clearlyevident in Figure 3. Data in year 2000 are warmer year‐round,and the pulse in 2002 occurs earlier than usual.[15] On the basis of �‐S profiles, the bulk of the water

observed at M3 is believed to be sourced from shelf water inthe southwestern Weddell, particularly the Ronne Ice Shelfarea with some sourced farther north (Figure 2, middle andupper dark blue arrows, respectively) [Gordon et al., 2010].Though upon mixing Filchner Ice Shelf outflow derivedbottom waters tend to become warmer and saltier andtherefore fill the inner Weddell Sea as opposed to steeringits outer rim [Gordon, 1998], we admit the possibility oftheir influence at M3 and therefore include the bottom dark

blue arrow in Figure 2. M2 is believed to be sourced byshelf waters farther north [Gordon et al., 2010], and thispath is marked by the light blue arrow. The paths themselvesare drawn on the basis of intense frontal mixing of shelfwater and modified WDW at the slope and steering by theCoriolis force upon descent, thereby following topography.[16] The fact that the potential temperature at M2 lacks

seasonal variability and has a mean value near the WSBW/WSDW boundary of � = −0.7°C invites questions thatparcels of water at M2 may not be derived from the samesurface forcing that parcels at M3 respond to and that M2may primarily be recording the variability of the ambientWSDW. We argue that the interannual variability observedat both moorings is forced by the same surface condition.The lack of a seasonal signal at M2 can be explained asfollows. Parcels at M2 are likely derived from farther north[Gordon et al., 2010] where the continental shelf is narrowerand the slightly less dense shelf water over the westernmargin and northwest corner is less prone to seasonal forces.That is, the outflow is more constant in time since the shelfwater is forced to run off a continuously narrowing shelf.Further, the sea ice over this region is much less variableseasonally with a climatological concentration near 100%year‐round, limiting local atmospheric communication.[17] A second interpretation is that while the water at

M3 receives the direct impact of the seasonal injection ofgravity currents, the shallower water of M2 sees this signalattenuated as the gravity current effects are mixed upwardover a longer advective path. There is an offset of severalmonths in timing of pulses between the two time series (maxcross‐correlation at 3 months offset), potentially due to

Figure 3. (top) Monthly potential temperature (�°C) time series from bottommost instruments at moor-ings M2 and M3. The bottommost instruments are ∼15 m off their respective mooring depths. The graysegment on the M3 temperature line marks the period where the record from the second‐lowest instrumentwas substituted by subtracting the mean offset between the two instruments (−0.027°C). Also included aremean temperature values (dashed lines) at each mooring to help illustrate variability. (bottom) Monthlysalinity time series from bottom instrument at mooring M3.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

4 of 17

water traveling to M2 taking a longer path around the PowellBasin. Though an additional 3 months may not seem longenough to attenuate any seasonality since it is small incomparison to the ∼6 month transit time toM3, which recordsa strong seasonal signal, there is reason to suspect strongvertical mixing occurs within the Powell Basin as suggestedby data from the DOVETAIL program [Muench et al., 2002].The steep topography of the South Scotia Ridge generatesa strong mixing intensity [Heywood et al., 2002]. Morespecifically, dissipation of internal tide energy across theridge from the conversion of barotropic to baroclinic tides cangenerate locally strong vertical mixing within the PowellBasin, even with weak stratification [Muench et al., 2002;Padman et al., 2006]. While lateral mixing with overlyingWSDW along this longer advective path may contribute to areduced seasonal signal, we argue that the properties of waterat M2 are representative of WSBW by appealing to profilesof dissolved oxygen and potential temperature along thelocation of the moorings from the DOVETAIL program in1997 [Gordon et al., 2001] which show a clear WSBW coreat both mooring sites.

2.2. Climate Indices

[18] A number of climate indices are used in this study.The NINO3.4 index is defined as the average sea surfacetemperature anomaly in the region bounded by 5°N to 5°S,from 170°W to 120°W, and is retrieved from the IRI/LDEOClimate Data Library. The SAM index is retrieved from theBritish Antarctic Survey, where they use the methodologyoutlined by Marshall [2003]. The definition is the same asthe zonal mean definition proposed by Gong and Wang[1999], which is the difference in zonally averaged SLPanomalies between 65°S and 40°S, though to avoid usingreanalysis data, 12 stations are used to calculate a proxyzonal mean sea level pressure at 65°S and 40°S. The ADPindex is defined as the difference between DP1 and DP2(DP1‐DP2), where DP1 is the averaged sea ice concen-tration at the Pacific center of the ADP (130°–150°W and60°–70°S) and DP2 is the averaged ice concentration at theAtlantic center (20°W–40°W and 55°–65°S).

2.3. Surface Variables

[19] The atmospheric data (sea level pressure, zonal andmeridional winds, surface air temperature) are monthly meansfrom NCEP‐NCAR CDAS 1 data on a 2.5° latitude × 2.5°longitude grid. It is well‐documented that, due to the limitednumber of observations in the southern high latitudes, NCEP‐NCAR reanalysis data are not ideal [e.g., Marshall andHarangozo, 2000], especially before the satellite era; how-ever, all data used are from after 1978. Sea ice concentrations,which are derived from space‐born passive microwave mea-surements, are obtained from the National Snow and Ice DataCenter. These satellite measurements have typical spatialresolution of 25 km and near‐daily temporal resolution. Herewe use monthly sea ice concentration derived by the bootstrapalgorithm [Comiso et al., 1997] and averaged them into a0.25° latitude × 1° longitude grid. The ADP index describedabove was generated from these data.

2.4. XCTD Data

[20] To examine the spin up or down of the Weddell Gyre,sections of expendable conductivity, temperature, depth

(XCTD) lines across approximately 60°S are used in thisstudy. These data were taken from Chinese vessel M/VXueLong under U.S./Chinese ship‐of‐opportunity samplingprograms [Yuan et al., 2004] in the summers 1998, 2000,2002, and 2005 (Figure 2). All XCTD profiles went through acareful quality control procedure guided by Bailey et al.[1993]. The manufacturer’s specified accuracy of XCTD isabout +/− 0.02°C for temperature and +/− 0.07 ppt for salinity,both of which are adequate for the purpose of identifyingthe base of the permanent pycnocline. These repeat sectionswere taken in the same season with relatively consistent loca-tions, making the data set very valuable to assess interannualvariability in the upper ocean’s response to climate forcing.

3. Results

3.1. Relation to Large‐Scale Climate Variability

[21] Our first approach to assess the relationship betweenthe potential temperature variability at moorings M2 and M3and climate forcing was through linear correlations betweenthe time series of temperature anomalies at each mooringand the three climate indices. All linear correlations con-sidered 24 months of lead/lag relationships, allowing forassessment of a broad range of surface forcings, tele-connections, and transit times. The method of lagged cor-relation is described in section A1. We filtered all timeseries with a Butterworth filter with width of 6 months priorto correlation calculation to reduce subannual variability.We evaluated correlation significance with a bootstrappingmethod described in section A2.[22] Correlations peak with indices leading on the order of

14–20 months. Maximum correlations with their significancevalues are presented in Table 1 with their corresponding lagtime, with both time series filtered by 6 months. Table 1 alsoreports a significance value for the lag time (see section A2).Correlations as a function of lag time are shown in Figure 4.While we have very good confidence in the lag time ofmaximum ∣r∣, the distributions of high ∣r∣ values acrossadjacent lag times are broad. This is due in part to the memoryof the time series and also to any complex sequence of eventslinking large‐scale anomalies to local forcings and then to thedeep ocean (and the time‐variant nature of these events).[23] The difference in response to NINO3.4 between M2

and M3 may be in part due to the reduced seasonal signal atM2. The correlations of temperature anomalies at M2 andM3 with NINO3.4 index and with ADP index over differentleads reveal one peak that gradually dissipates over adjacentleads. This, combined with the fact that ∣rADP∣ ≥ ∣rNINO3.4∣for M3 (though they are approximately equal for M2),suggests that ENSO’s impact on bottom water temperaturevariability is realized through the development of ADP,which happens during 3 to 6 months after the peaks ofENSO events [Yuan, 2004]. On the other hand, the corre-lations of temperature anomalies at M2 and M3 with SAMindex reveal a second peak of opposite sign (though lesssignificant with all p > 0.1) at approximately 1–6 monthslead time. This could imply bottom water production orexport responds to the SAM at various time scales.

3.2. Coherent Regional Surface Forcing

[24] The statistically significant correlations with the cli-mate indices and the complex nature of modulation between

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

5 of 17

ENSO and SAM in the Antarctic Peninsula region suggestthe need to capture coherent surface forcing patterns thatare combined results from different influencing sources. Todo so, we computed a multivariate empirical orthogonalfunction (MEOF) analysis using surface forcing variables ofsea level pressure (SLP), surface air temperature (SAT),zonal and meridional winds, and sea ice concentration (SIC).For all fields, anomalies were calculated about a climatologyof January 1997 to December 2006 and were detrended,standardized, and smoothed by a Butterworth filter with filterwidth of 6 months.[25] The spatial pattern of the first MEOF mode is pre-

sented in Figure 5. The first mode is clearly indicative ofsurface anomalies (SLP and zonal winds) associated withthe SAM [Thompson and Wallace, 2000; Lefebvre et al.,2004; Simmonds and King, 2004] and classical ADP pat-terns in SAT, meridional winds, and sea ice [Yuan, 2004].The SLP field demonstrates the high‐pressure anomalies at40°S and low anomalies at 65°S associated with enhancedzonal winds, a signature of positive phase SAM. The zonalasymmetry of SAM is seen, with a clear dent in the SLPpattern near 90°W, 60°S, which is coincident with thecoherent ENSO/Pacific South America (PSA) pressureanomaly over the Bellingshausen Sea. This pressure anomalyresults in coherent ADP anomalies in meridional wind, SAT,and sea ice concentration fields [Yuan, 2004]. The anomalousmeridional winds associated with the anomalous pressurecenter advect warm oceanic air south in the Pacific sector andcold continental air north in the Atlantic sector, resulting intemperature anomalies and thermodynamically driven outerice extent anomalies. This anomalous wind pattern also affectssea ice concentration dynamically, resulting in a north/southcontrast with inner ice pack variability related to dynamicalpacking (potentially thickening) near the coast in the WeddellSea and vice versa in the Pacific sector.[26] Mode two (not shown) seems to be a transition mode,

dominated by Western Hemisphere wave‐3 patterns in SAT,zonal wind, meridional wind, and sea ice concentration. Thewave‐3 is a well‐established climate pattern in the SouthernHemisphere, supposedly caused by the land/ocean distri-bution [Raphael, 2004]. It acts to advance the ice edge atsynoptic time scales and provides preferred locations forcyclogenesis in the open ocean north of the ice cover [Yuanet al., 1999]. It demonstrates a significant relation to localice cover [Raphael, 2007; Yuan and Li, 2008].[27] The variance explained by mode one (22%) is com-

parable to findings for the first mode of SLP anomalies

attributed to SAM in other studies [e.g., Simmonds andKing, 2004]. Figure 6 shows the leading principal compo-nent and its correlations with the temperature anomaliesat M2 and M3. The phasing of the time series is similarto that of the SAM index and it correlates significantly withWSBW potential temperature anomalies on the same leadscales as did the SAM index, though with higher correlationcoefficient values (on the order of 0.5–0.6) when the corre-lations are significant. As with correlations involving theSAM index, opposite sign, marginally significant, and weakercorrelations are present with the principal component leadingby a shorter 1–6 month lead.[28] Computing MEOF analyses with other combinations

of variables always yields essentially identical spatialmodes, though with slightly varying variance explained andpeak correlation leads for each mode. The variability invariance explained is likely due to inherent lead‐lag times inthe ice‐atmosphere system, causing a bleeding betweenfields. For example, the highest variance explained wasobtained when considering only dynamical variables (32%by mode 1 SLP and winds), omitting the time necessary forsea ice concentration (and to a lesser extent, SAT) torespond to advected air.

Table 1. Maximum Correlation Coefficients Between PotentialTemperature Anomalies and Climate Indicesa

� Anomaly at M3 � Anomaly at M2

r Lead r Lead

NINO3.4 −0.41, 89% 21, 99% −0.73, 97% 21, 99%SAM +0.38, 98% 14, 99% +0.54, 99% 20, 99%ADP +0.48, 99% 15, 99% +0.71, 99% 17, 98%

aHere r is the linear correlation coefficient between given index andpotential temperature time series. Significance levels are reported next tor value and lead value. The methods of lagged correlation andsignificance testing are described in sections A1 and A2, respectively.Lead time corresponds to number of months the index leads the tempera-ture time series.

Figure 4. Correlations between temperature time series(M2 dashed, M3 solid) and climate indices, plotted as afunction of lag time (number of months the climate indiceslag the temperature time series). Correlations significantabove 90% are marked as circles and correlations significantabove 95% are marked as crosses.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

6 of 17

3.3. Local Winds and Sea Ice Fields

[29] The spatial patterns of the leading MEOF mode andits strong correlations with WSBW temperature anomaliesinvite physical interpretations at the local scale. First, weexamine relationships with meridional winds and sea iceconcentrations over the continental shelf. With a residencetime of ∼5 years [Mensch et al., 1996], the shelf water thatescapes each austral summer is a blend of dense water typesformed from previous winters. The residence time is largely

due to a rather sluggish circulation in the vertical planewhere shelf salinity increases to the west, leading to ageostrophic vertical circulation [Gill, 1973]. Nevertheless,contributions from the immediately preceding winter can bestrong in the case of anomalous surface conditions via eithergreatly increasing or failing to replenish supply. Bearing thisin mind, in combination with the estimated transit time of∼6 months from likely shelf water source to mooring M3,anomalous ice conditions 4–5 seasons before corresponding

Figure 5. Spatial patterns of the first mode of MEOF analysis. First mode resembles +SAM andPSA pattern.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

7 of 17

observations at the moorings should be relevant. As such,relationships between potential temperature and both sea iceconcentration anomalies and meridional wind anomalieswere assessed through linear correlations. This is not to saythat the properties (�‐S) of the shelf water formed in a givenyear will necessarily be reflected in the immediately fol-lowing export but rather that a greater formation of denseshelf water in a given year will yield a greater supply ofsource water on the shelf, and therefore a stronger contri-bution from the “cold end‐member” along the mixing linefor WSBW, resulting in a colder product.[30] Figure 7 shows correlation coefficients between sea

ice concentration anomalies and potential temperatureanomalies at the moorings. Of primary interest is the north/south contrast in correlations, separated by the shelf slope.The patterns suggest that increased (decreased) summer seaice concentration over the continental shelf the summerbefore export corresponds to a warmer (colder) pulse in thedeep ocean later or less (more) dense shelf water exported.Increased sea ice concentration over the shelf and reducedsea ice extent is consistent with strong southward windanomalies in the western Weddell, mechanically preventingadvection of sea ice and thermodynamically hindering theformation of new sea ice the following winter, consistentwith the anomalous winds of both positive SAM and LaNiña [Yuan, 2004; Lefebvre and Goosse, 2005]. Somecharacteristic correlation values are presented in Table 2.

3.4. Wind Stress Curl and Upper Ocean Structure

[31] The WSBW temperature could also be affected bythe volume of dense water exported from the shelf each

year. As mentioned before, the degree of baroclinicity in theWeddell Gyre is a factor influencing the escape of densewater and this in turn is related to the wind stress above thegyre [Jullion et al., 2010]. To evaluate cyclonic forcing, wecalculated the wind stress using the algorithm of Smith[1988], modified to allow for zonal and meridional com-ponents, and then computed the curl of this vector field. Wethen calculated curl anomalies with the climatology fromJanuary 1997 to December 2006 and spatially averagedthem to obtain a single time series (Figure 8), as done byGordon et al. [2010]. The averaging domain (marked inFigure 8) includes the area bounded by the continental slopein the central Weddell Sea west of the Prime Meridian.While correlations of this time series with the temperatureanomalies at M2 and M3 are low (best r of 0.25 at theconfidence level of 73%), they are qualitatively reasonable.Approximately 6 months before the coldest anomaly of2002, the wind stress curl anomaly reaches its most negative(cyclonic) value in the entire period of 1996–2008. Further,the wind stress curl anomaly is negative for a prolongedperiod before 1999’s cold pulse and is largely positivethroughout 1999 and 2002, preceding the lack of a coldpulse in 2000 and the warm period of late 2002 to early2003, respectively. The largest increases in cyclonic forcingseem to coincide with sudden shifts in the phase of the SAMfrom negative to positive (Figures 1 and 8).[32] To demonstrate the spin up or down of the gyre, we

examine the variability of the permanent pynocline base(PPB) depth as calculated from the XCTD data (stationsshown in Figure 2). The PPB depth is identified from den-sity profiles. Each density profile was visually inspected forthe selection of key points because, as these are summer‐time observations, we needed to distinguish between theseasonal and permanent pycnocline. Then, we calculatedthe PPB depth as the depth of the maximum value ofjr2� j, where r is density [Martinson and Iannuzzi, 1998].Figure 9 shows the PPB depth of these repeat sections incomparison with historical data. The samplings west ofapproximately 15–20°W correspond to the gyre rim, whereasdata east of this divide are more indicative of the gyre’scenter. As such, the PPB depth relative to the mean in thecentral gyre can serve as a proxy for degree of baroclinicitywithin the gyre.[33] The PPB depths are in good agreement with the wind

stress curl observations. Between 0° and 20°W where thedata fit within the center of the Weddell Gyre, the mean PPBdepth in January 1998 when there was enhanced annualmean cyclonic forcing is shallower than in December 1999when there was diminished annual mean cyclonic forcing.In the same region, the mean PPB depth for January 2002when there was enhanced annual mean cyclonic forcing isshallower than the historical mean, and the mean PPB depthfor January 2000 when there was diminished annual meancyclonic forcing is deeper than the historical mean. West of20°W where the data more accurately correspond to thegyre’s outer rim, the mean PPB depth in January 2002 isdeeper than the historical mean, a clear signature of gyrespin up associated with strong cyclonicity in winds.[34] A few words should be said about the Weddell Gyre

to express the shortcomings of our method. The transport ofthe gyre is primarily barotropic [Klatt et al., 2005]. West ofthe Prime Meridian, the gyre is flanked by the Weddell

Figure 6. (top) The principal component of the leadingmode from MEOF analysis shown in Figure 5. (bottom)Correlation of the principal component with potential tem-perature anomalies (M2 dashed, M3 solid). Horizontal axisindicates number of months the principal component lagsthe temperature time series. Correlations significant above90% are marked as circles and correlations significant above95% are marked as crosses.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

8 of 17

Front to the north and the Antarctic Coastal Current to thesouth. Only the coastal current demonstrates clear seasonalvariability, which is also primarily barotropic. While thebaroclinic component of this current is small, it can beimportant in that this component of the gyre forms the freshv‐shaped front (and its outer limb contains the WDW)involved in the shelf‐slope mixing processes [Gill, 1973]. Itis found that the Sverdrup transport (mean wind stress curl)explains 30% of this baroclinic variability [Nuñez‐Riboniand Fahrbach, 2009]. The idea that a mean wind curl canrelate to the spin up or down of the Weddell Gyre implies agyre that is quasi‐fixed in space with one mode of vari-ability, though we attempt to be as flexible as possible withthis constraint by carefully defining our averaging domain.For example, because the gyre demonstrates a “double cell”structure centered about the Prime Meridian [Beckmannet al., 1999, and references therein], we defined our east-ern bound to be west of this divide. Further, a meridionaldisplacement of the west wind (and corresponding currents)has been observed, with west winds low in the north andeast winds high in the south through the early 1990s andmid 2000s with the opposite pattern in the time between

(E. Fahrbach et al., Warming of deep and abyssal watermasses along the Greenwich meridian on decadal timescales: The Weddell gyre as a heat buffer, submitted to DeepSea Research, Part II, 2011). So, our northern and southernbounds cover the full extent of the central latitude bands.

4. Discussion

[35] As shown above, we propose that there are twomechanisms that could produce temperature fluctuations inthe deep ocean. First is the process affecting the volume ofdense shelf water available, operating on 14–20 monthscales. Second is the process affecting the export of denseshelf water, operating on 1–6 month scales. While firstmentioned by Gordon et al. [2010], here we discuss theseprocesses in more detail and explain their relation to large‐scale climate modes.[36] The two mechanisms we consider are those that we

believe should have the most impact on the variability ofWSBW properties. In theory, the observed variability at themoorings could be related to a shift in the current’s core;variable shelf water source, properties, or export; variableamount of modified WDW in mixture or properties ofinjected WDW; variable air‐sea heat fluxes during low icecover; or variable circulation patterns of water on the shelf.[37] We do not believe that the variability recorded at the

moorings is due to a shift in the cold core of the currentgiven the moorings’ locations at steep topographic escarp-ments, which serve to guide the bottom current and createa fixed WSBW stream [Gordon et al., 2010]. While wecannot test for a shift in source without in situ measure-ments on the shelf, the fact that M2 and M3 demonstrate thesame interannual signal while being sourced from differentregions suggests that source location is probably not of pri-mary importance. We cannot completely refute the possibil-ity of variable WDW properties or amount of modifiedWDW injected being significant as the thermohaline prop-erties of WDW have in some observations been directlyrelated to those in WSBW [Fahrbach et al., 1995]. Insteadwe can only emphasize our measurements are strictly ofbottom‐most WSBW directly affected by gravity plumes lesssusceptible to vertical mixing. We assume the shelf circula-

Table 2. Correlation Coefficients Between Surface Anomaliesand Potential Temperature Anomaliesa

� Anomaly at M3 � Anomaly at M2

r Lead r Lead

Mean meridionalwind anomaly

−0.64, 99% 13, 99% −0.71, 99% 16, 99%

Sea ice concentrationanomaly (56°W,73.25°S)

+0.52, 99% 14, 99% +0.53, 99% 17, 98%

Sea ice concentrationanomaly (33°W,76.25°S)

+0.40, 94% 16, 99% +0.52, 99% 18, 98%

aHere r is the linear correlation coefficient between given surfaceanomaly time series and potential temperature time series. The methodsof lagged correlation and significance testing are described in sectionsA1 and A2, respectively. Lead time corresponds to number of monthsthe surface anomaly time series leads the temperature time series. Themean meridional wind anomaly is averaged over the region 65°W–45°W,77.5°S–67.5°S.

Figure 7. Correlation coefficients between sea ice concentration anomaly as well as wind anomaly andtemperature anomalies at (left) M2 and (right) M3. Sea ice and winds lead M2 by 17 months and M3 by14 months. Sea ice correlations are color‐coded while wind correlations are presented as vectors whose xcomponent is rzonal and y‐component is rmeridional. Vector of length 1 is shown in top left corners.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

9 of 17

tion is approximately constant given its slow nature. Last,atmospheric heating should not have a significant effect onshelf water properties as the effect is limited to the upper 30 m,a cap which is overcome rapidly by late fall or early wintercooling [Martinson, 1990; Martinson and Iannuzzi, 2003].[38] The range of salinity we observe at the moorings is

within the range of the net sea ice effect on bottom watersalinities which is about 0.15–0.20 difference betweensource/product waters [Toggweiler and Samuels, 1995].Given this and the presence of gravity current injection, wesuspect that the two processes affecting the production andexport of dense water should be the most important indetermining WSBW thermohaline properties.

4.1. Mechanism I: Dense Shelf Water Production

[39] The dense water production is directly related to thebrine release from sea ice formation. The anomalous sea iceconcentration patterns in the southwestern Weddell Seaappear strongly related to mean meridional wind anomalies

over the western Weddell Sea (Figure 10) as suggested byGordon et al. [2010]. Sea ice concentration anomalies over thecontinental shelf can be negative only in the austral summerbecause by winter the nearshore sea ice pack is mostly 100%covered. Our results show that winter cold pulses in WSBWare related to sea ice and wind anomalies in summer with alead time of 14–20 months. In summer, an enhanced north-ward meridional wind advects more ice and surface fresh-water, increasing leads and coastal polynyas. Then, comewinter, the larger open area allows more sea ice formation,producing more dense shelf water. The very strong El Niño of1997–1998 demonstrates this impact quite well, resulting inthe opening of the Ronne Polynya with a greatly reduced seaice cover throughout the summer and into the fall. The verystrong anomalous northward winds associated with this ENSOevent were responsible for opening the Ronne Polynya andcreated an anomalously large glut of HSSW [Ackley et al.,2001; Nicholls and Østerhus, 2004]. This would, conse-quently, relate to 1999’s cold anomaly in WSBW.

Figure 9. Permanent pycnocline base depth as calculated from XCTD data. Dashed line is historicalmean with gray shading indicating one standard deviation. Values for each year are color‐coded anddo not necessarily span all longitudes. For a corresponding map of locations used, see Figure 2.

Figure 8. Time series of mean wind stress curl anomaly averaged over the Weddell Sea, with averagingdomain indicated in the map to the left. Important to note are the strongly negative values in late 2001 aswell as the subtly positive values in 1999.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

10 of 17

[40] Likewise, a weakened northward meridional windwill advect less ice and increase sea ice concentration overthe continental shelf, which reduces freezing and thus denseshelf water formation in winter. A modeling study byTimmermann et al. [2002] made the link between meridio-nal wind, sea ice concentration, and HSSW. They showedthat after a period of sustained southward winds (that is, theanomaly is large enough so that the mean winds reversedirection), the volume of HSSW decreased, ultimately tozero. The mean meridional wind over the western WeddellSea was indeed due southward for the entirety of 1999(Figure 11), completely reversing the direction from theclimatological mean, which should result in a reduction indense shelf water. This would explain the absence of a coldpulse of WSBW in 2000. While other warm potential tem-perature anomalies at M2 and M3 correspond to increasedsea ice concentration over the continental shelf and negativemeridional wind anomalies, no other event has the meanmeridional wind in our averaging region of the westernWeddell southward for the period of a year.[41] Salinity observations support the validity of this

mechanism. A �‐S diagram shows that salinity during thecold pulses reveals a fan‐like appearance with 2001 and2002 being notably saltier than other events [Gordon et al.,2010]. This trait was originally attributed to source vari-ability [Gordon et al., 2010] though we offer an alternateinterpretation. A time series of salinity reveals that salinityduring cold pulses increases from a low in 1999 to a high in2002 before being notably lower again in 2003 (Figure 3).The increase from 1999 to 2002 appears near linear. Thetrend line for this portion of the time series has a slopeabout two orders of magnitude higher than that of observedWDW salinification over the 1990s [Fahrbach et al., 2004].Instead, we refer to the tremendous amount of HSSW pro-duced in early 1998. The prolonged ice‐free region near theRonne Ice Shelf and the very strong and directionally con-stant winds may have fostered a more vigorous shelf cir-culation. As such, while more HSSW was formed with thewinter freeze, the primary pulse would likely be rather freshas water parcels would spend less time underneath thepolynya collecting salt [Grumbine, 1991]. This would yielda cold but fresh pulse in 1999. As the brines incorporate intothe shelf water, their influence on subsequent exports wouldincrease, yielding increasingly salty water through 2002.The reversion to low salinity in 2003 suggests that the effectof this anomaly originating in early 1998 was finished bythe late 2001 export, fitting rather well with Gill’s estimateof 3.5 years required for complete exchange of shelf water,estimated using a salinity budget [Gill, 1973].[42] The prevailing circulation of sea ice within the gyre

includes a clockwise circulation with old ice arriving fromthe east and packing in the southwest before circulatingnorth. For more shelf water production, it is important thatthe summer ice response over the southwestern shelf bedriven by cold northward winds and not local melt. This isprobably not a problem as temperatures are subfreezingyear‐round and upwelling of WDW has little influence onpolynya formation [Ackley et al., 2001]. Similarly, for lessdense water production, that effect is enhanced if warmsouthward wind anomalies increase summer ice concentra-tion over the shelf. To maximize the efficacy of the mecha-nism, the enhanced (weakened) summer wind may continue

into winter to maintain (hinder) a polynya at the ice shelfbeyond the initial freeze (or lack thereof).

4.2. Mechanism II: Shelf Water Export

[43] An enhancement of cyclonic wind‐forcing corre-sponds to a more vigorous gyre circulation through bar-oclinic adjustment, which favors export of dense water andcan be revealed by a doming of isopycnals in the centralgyre and deeper isopynals at the gyre’s rim [Meredith et al.,2008; Jullion et al., 2010]. As stated above, the correlationsbetween mean wind stress curl anomaly and potential tem-perature anomalies at the moorings are weak. However, thelead time of 1–6 months and the clear qualitative fit betweenthe largest anomalies in each time series are suggestive[Gordon et al., 2010]. For one, the lag time is physicallyrealistic and sits well with the proposed amount of timerequired for the gyre to respond to wind‐forcing [Jullionet al., 2010]. Second, the lead time matches the lead timeof the weaker secondary correlations of the SAM index (andfirst principal component of MEOF analysis) with potentialtemperature anomalies at the moorings.[44] The link between wind stress curl and PPB depth

suggests that the mechanism of spinning the Weddell Gyreup or down in response to cyclonic forcing is indeed real-istic. Moreover, the fact that the mean wind stress curlanomaly is consistent with the phase of the SAM other thanin years of strong and unfavorable ENSO related anomalies(for example, La Niña event in calendar year 1999) suggeststhat the SAM influences the ocean‐ice‐atmosphere systemat different time scales. A baroclinic adjustment of theWeddell Gyre to wind forcing is attributed to ENSO byMartinson and Iannuzzi [2003] and to the SAM by Jullionet al. [2010]. Our lagged correlations (Figure 4) favor theidea that the SAM has more of an impact on a shorter termrelevant for spin‐up.[45] One might expect that an increased export of dense

water toward the moorings would result in a higher speed inthe bottom layer accompanied by a stronger vertical shear(d∣u∣/dz). Figure 12 shows that bottom temperature decreaseswith the increase in both speed and shear in the bottom layer,as expected. Even with large scatter, the approximate linearrelationship supports a fast flow associated with increasedexport. We are limited to a crude estimate of the shear(computed from two instruments separated vertically by 501m). This may not be sufficient for accurate vertical resolution,especially for vigorous plumes (which may be thicker), asdemonstrated by the notable deviation from the trend atthe coldest temperature.

4.3. Specific Roles of SAM and ENSO in DrivingSurface Forcings

[46] We begin this section with the presentation andinterpretation of a linear statistical model designed to linkthe indices to the relevant surface variables and then linkthese variables to the observed bottom water variability. Themodel is an ensemble of multiple linear regressions. On thebasis of proper lag times (those of maximum correlations),the three indices (NINO3.4, SAM, and ADP) were fit to shelf‐averaged meridional wind; sea ice concentration at 56°W,73.25°S; and gyre‐averaged wind stress curl in three separ-ate multiple linear regressions. Then, the indices were runthrough each of these models to create three time series of

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

11 of 17

Figure 10

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

12 of 17

“predicted” mean meridional wind, sea ice concentration,and mean wind stress curl. Finally, these predicted timeseries were fit to the M3 temperature time series in a finalmultiple linear regression at lag times of maximum correlationwhich are consistent with those described in mechanisms I andII (Figure 13). Given the multicolinearity within the indicesand the short nature of the time series, it is unrealistic topredict the bottom temperature or assess the significance ofthese regressions.[47] The result seems to capture the main interannual

variability of the M3 temperature time series when climateforcing is large (1999–2004). We believe that this impliesa degree of predictability from the climate indices. Morespecifically, it is our belief that the correlations with theindices are indicative of causality between ENSO and SAMrelated anomalies and WSBW properties.[48] In section 4.1 we have shown that the physics implied

by the spatial pattern of correlations in Figure 7 are con-sistent with Mechanism I. What remains to be shown iswhether these patterns are related to large‐scale climatemodes. The wind correlations demonstrate a strong merid-ional component and are centered about a pressure anomalyin the eastern Pacific. The ice response suggests a thermo-dynamic component (reduced extent due to southwardadvection of warm air) and a dynamic component (increasedshelf concentration due to reduced northward advection ofice). These patterns are indicative of the ADP [Yuan andMartinson, 2001; Yuan, 2004].[49] We believe the primary role of the ENSO is in the

generation of these ADP anomalies which drive MechanismI. The “hierarchy” of correlation coefficients with bottomtemperature and an assessment of the their respective leadtimes supports this. First, it is true that ∣rmerid wind∣ ≥ ∣rADP∣ ≥∣rNINO34∣ at M3 (though these values are approximatelyequal at M2) (Tables 1 and 2). Second, the lead time forNINO3.4 with bottom potential temperatures is approxi-

mately 3–5 months longer than that for ADP with bottompotential temperatures while the lead times for both sea iceand meridional wind with bottom potential temperatures areapproximately equal to those of ADP with bottom waterpotential temperatures. Yuan [2004] shows that ADP anoma-lies begin to manifest about a season after climate forcingmatures in the Tropical Pacific, consistent with this value.[50] Our results suggest that a positive (negative) phase

SAM corresponds to a warmer (colder) temperature anom-aly 14–20 months later. The main contribution of SAM atthis time lead comes from its nonannular component, and itis well‐documented that this nonannular component caninduce a very similar dipole pattern of anomalies [Lefebvreet al., 2004; Holland et al., 2005]. However, our record isdominated by periods where SAM is out of phase withstrong ENSO events and, for potential temperature at bothM2 and M3, ∣rNINO3.4∣ ≥ ∣rSAM∣. Instead, the primary roleof the SAM on this time scale might be in modulating thehigh‐latitude response to ENSO.[51] Fogt et al. [2011] provide a reasonable mechanism

for the modulation of ENSO by SAM, the crux being thatduring favorable phase relationships (Figure 1), the ADPteleconnection mechanism is strengthened. Once estab-lished, the ADP is maintained in part by cyclone activityassociated with the polar front jet [Yuan, 2004]. We find thatthe first EOF of zonal wind anomalies at 300 mb between70°S and 25°S during our period of study reveals anenhanced polar front jet with a reduced subtropical jet (notshown) and its principal component correlates significantlywith the SAM (r = 0.79). Then, the SAM is capable ofmodulating the mechanisms both enacting and maintain-ing the ADP. While the first mode of our MEOF analysis(Figure 5) clearly represents the SAM and its principalcomponent correlates very well with the SAM index (r =0.81), the ENSO‐driven PSA imprint is overwhelming witha spatial pattern more like a La Niña/+SAM composite

Figure 11. Time series of spatially averaged meridional wind values (solid line) and 12 month runningmean (dashed line). The averaging domain is indicated in the map to the left. This shows the reversal ofthe climatological winds in 1999.

Figure 10. (a) Six month running mean of meridional wind anomalies (dashed line) at 52.5°W, 75°S and sea ice concen-tration anomalies averaged over the shelf in the western Weddell Sea (solid line). Blue shading marks calendar years whenmore shelf water is formed while red shading years when less is formed, both based on the principles of mechanism I.(b) Temperature anomalies at M2 and M3, where blue indicates calendar years of anomalously cold pulses and red indicatescalendar years of anomalously warm pulses (mean temperature anomaly for that year, negative or positive, respectively).(c) Schematic of the relationship between winds, summer sea ice, and dense shelf water formation in winter.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

13 of 17

[Fogt et al., 2011]. This confirms that our period of study isdominated by favorable phase occurrences of ENSO andSAM. The hierarchies of correlation values may suggestthat such occurrences are necessary to sustain the anomaliesrequired for anomalous dense water production.[52] As for Mechanism II, our results suggest that a posi-

tive (negative) phase SAM corresponds to a colder (warmer)temperature anomaly 1–6 months later. The enhanced west-erlies and the prevailing low pressure over the Weddell Gyre

under a positive SAM act to promote a stronger mean windstress curl. The correlations (for both the SAM at this timescale and the mean wind stress curl with potential tempera-ture) might be low because the period of strongest anomaliesobserved at the moorings is one where the phases of ENSOand SAM are opposite as illustrated in Figure 1. While such acoincidence of the modes tends to reinforce the meridionalwind and sea ice anomalies, the variability of gyre vigor isobfuscated by decreased (increased) cyclonic forcing from

Figure 13. Actual bottom temperature anomalies at M3 (solid) and predicted temperature anomaliesat M3 (dashed) from linear model described in section 4.3. The r value is 0.66, while the adjusted r valueis 0.65.

Figure 12. (top) Bottom speed at M3 and (bottom) estimated shear at M3 as functions of bottom poten-tial temperature. Black lines indicate trend lines while gray curves indicate a 95% confidence interval.R values are −0.44 and −0.38 for speed and shear, respectively, and both are significant at a 99% level.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

14 of 17

cold (warm) ENSO anomalies with increased (decreased)cyclonic forcing from positive (negative) phase SAM anoma-lies competing for influence. As a result, the signal is much lessdistinct. There is no correlation with ENSO at this time scaleso Mechanism II is probably primarily induced by the SAM.[53] We conclude this discussion with a timeline of events

to summarize the forcing process. In the austral summer(∼18–21 months before pulse) a warm ENSO event maydevelop. A teleconnection to high latitudes develops ADPanomalies in sea ice and winds that may be enhanced by anegative phase SAM. Associated cold winds through thesummer and fall increase offshore advection of sea ice to pro-vide a greater open area at the ice shelf edge (∼14–18 monthsbefore pulse). By austral winter these ADP anomalies maypersist, allowing the greater area of open water to freeze,increasing brine rejection and increasing the shelf’s supplyof dense water (∼12 months before pulse). Major export ofdense water is constrained to occur in austral spring/summer[Gordon et al., 2010] (∼4–8 months before pulse), duringwhich a positive phase SAM may enhance the cyclonic circu-lation, dome the pycnocline and upper isopycnals towardthe shelf edge, and facilitate this export. Finally, the coldanomaly is advected to the moorings. The opposite holds for awarm anomaly.

5. Conclusions

[54] In an 8‐year time series of potential temperaturedocumenting the outflow of the Weddell Gyre south of theSouth Orkney Islands, we found substantial interannualvariability with anomalously cold pulses in austral winters of1999 and 2002 with a bypassed cold pulse in austral winter2000. The temperature anomalies suggest an influence fromENSO at 14–20 months lead time and influences from theSAM at 14–20 months lead time as well as 1–6 months leadtime. A MEOF analysis of surface forcings again suggeststhat these are the dominant modes of climate variability inthe spatial and temporal scales of interest and that these areappropriate time scales for forcing.[55] The enhanced northward meridional wind associated

with warm ENSO events and the nonannular component ofthe negative phase SAM is associated with increasedadvection of sea ice for more (and/or broader) coastalpolynyas and increased open sea area over the continentalshelf for more freezing and brine rejection, all leading to agreater volume of dense shelf water available. The weak-ened (or reversed) meridional wind associated with coldENSO events and the nonannular component of positivephase SAM is associated with the opposite conditions. Suchforcings have impact on WSBW properties 14–20 monthslater. The enhanced cyclonic forcing associated with theannular component of positive phase SAM yields domedisopycnals due to a more vigorous Weddell Gyre whoseconsequences permit increased outflow. Such a forcing hasimpact on WSBW properties 1–6 months later.[56] While the physical mechanisms enacting the tem-

perature anomalies are clear, the links between large‐scaleclimate modes and these anomalies are less so. Neverthe-less, a linear model suggests that at least the sign of WSBWtemperature anomalies can be predicted from values of theclimate indices. An assessment of surface anomalies, cor-relation values, and their lag times suggests that the ENSO

more likely directly drives the ADP related anomalies whilethe SAM modulates this impact. The SAM may additionallyimpact the mean wind stress curl over the gyre. It is verylikely that the observed anomalies in WSBW temperatureare responses to different contributions from each of theabove forcings, as well as to internal climate variability,intermittent activities of local icebergs and spatially varyingsources, and the interaction of all of the above with a resi-dence time of several years. It also appears that the regionalresponse to extrapolar forcing is strong only when theforcing signal is large. Without large ENSO and SAMevents after 2004, the WSBW signal is reduced to its sea-sonal variability (Figures 1 and 3). This suggests that for theforcing mechanisms proposed to be fully realized, the cli-mate anomalies need to be large in magnitude and possiblyalso in favorable phases.

Appendix AA1. Lagged Correlations

[57] Given the short nature of the temperature time seriesinvolved, maximizing the number of points used in com-puting the cross‐correlation function (CCF) is important toenhance both significance and physical interpretation. Thecross‐correlation function for two time series of equal lengthn involves summing over n‐∣k∣ pairs of data at each lag k,where k is an integer between [1‐n, n‐1]. Necessarily,this precludes the possibility of capturing the influence ofthe sustained large‐amplitude climate variability (Figure 1)occurring prior to our earliest temperature observations. Thisvariability is central to our hypothesis.[58] Because we are correlating our temperature time

series with forcing time series that are subsets of longerseries, it is possible to compute the CCF using two timeseries of unequal length. The net effect of this procedure isto create a “sliding window” in which every lagged corre-lation involves nT points (the number of points in the tem-perature time series). In computing the CCF in this case, wecannot recalculate the mean and standard deviation of theforcing time series in each window because this violates theassumption of stationarity. On the other hand, if the forcingtime series is much longer than the temperature time series,the mean and standard deviation of the longer forcing timeseries may not be representative of the surface and large‐scale climate forcing over the period of temperature obser-vation and may lead to artificially high or low r values.Therefore we do not use such a method.[59] As a compromise, for all lagged correlations in this

study we use the standard lagged correlation of two‐timeseries of equal length while extending both temperature timeseries back 24 months to April 1997 by substituting therespective climatology over this period. We stress that thesame method without “padding” and the methods using timeseries of unequal length are able to reproduce the featuresof this method’s CCF that serve as the basis for our phys-ical interpretations. All correlations are biased, or divided by(n‐1), so that the CCF decays toward zero at large lags.A2. Bootstrap Method for Correlationand Lag Significance

[60] For all correlations, we generate 1000 random tem-perature time series to evaluate a random response to the

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

15 of 17

surface and large‐scale climate forcing. The random timeseries are produced so as to maintain the spectral color of theoriginal temperature time series. First, the discrete Fouriertransform of the temperature time series T is computed andthe amplitude of this complex series Aorig is kept. Then, thephase of this complex series for the positive frequencies isgenerated from a pseudorandom number generator scaledto the range [−p, +p] while the phase for the negative fre-quencies is derived from this distribution. Calling the ran-dom phases �rand, we create a new complex series Aorig*exp(i�rand). Because of our definition of �rand, this complexseries preserves the Hermitian symmetry demanded of real‐valued time series. We take the inverse Fourier transformof this complex series to get Trand. The process is repeated1000 times for the time series at M2 and M3.[61] These random temperature time series are correlated

with the surface or climate time series of interest at the fullrange of lags. For each lag, the sorted distribution of 1000 rvalues is used to create significance levels via a percentileapproach. For example, the 25th and 975th r values deter-mine the bounds for the 95% level.[62] To evaluate the significance of the lag time, we

evaluate these random lagged correlations in tandem withthe equivalent lagged correlations of the real temperaturetime series. We first note at what relevant lag time the realCCF provides max(∣r∣). Then, among the 1000 randomcorrelations, we note how many times each lag time pro-vides max(∣r∣) of the corresponding CCF. This frequencydivided by 1000 and subtracted from 1 provides the “sig-nificance of the lag time” or literally how confident weare that our observed lag time did not provide max(∣r∣)by chance. The usefulness of this statistic is that for highsignificance values, we are confident that the observed lagtime is not due to some intrinsic quality of the temperaturetime series.

[63] Acknowledgments. We would like to thank the two anonymousreviewers whose insightful comments greatly improved this manuscript.We would also like to thank Doug Martinson for his help in developingthe bootstrapping methods used. This research was funded in full or inpart under the Cooperative Institute for Climate Applications Research(CICAR) award number NA08OAR4320754 from the National Oceanicand Atmospheric Administration, U.S. Department of Commerce. Thestatements, findings, conclusions, and recommendations are those ofthe author(s) and do not necessarily reflect the views of the NationalOceanic and Atmospheric Administration or the Department of Commerce.Additionally, McKee was supported in part by the LDEO Climate Center,Yuan was supported by NSF grants ANT 07–39509 and OPP 02–30284,and Dong was supported by the Program on Sustaining Science andTechnology (grant 2006BAB18B02) from the Ministry of Science andTechnology, China. Lamont‐Doherty Earth Observatory contribution 7455.

ReferencesAckley, S. F., C. A. Geiger, J. C. King, E. C. Hunke, and J. Comiso (2001),The Ronne polynya of 1997/98: Observations of air‐ice‐ocean interac-tion, Ann. Glaciol., 33, 425–429, doi:10.3189/172756401781818725.

Bailey, R., A. Gronell, H. Phillips, G. Meyers, and E. Tanner (1993),CSIRO Cookbook for Quality Control of Expendable Bathythermograph(XBT) Data, Rep. 220, CSIRO Mar. Lab., Hobart, Tas., Australia.

Barber, M., and D. Crane (1995), Current flow in the north‐west WeddellSea, Antarct. Sci., 7, 39–50, doi:10.1017/S0954102095000083.

Beckmann, A., H. H. Hellmer, and R. Timmermann (1999), A numericalmodel of the Weddell Sea: Large‐scale circulation and water mass distri-bution, J. Geophys. Res., 104(C10), 23,375–23,391, doi:10.1029/1999JC900194.

Carmack, E. C., and T. D. Foster (1975), On the flow of water out of theWeddell Sea, Deep Sea Res. Oceanogr. Abstr., 22, 711–724, doi:10.1016/0011-7471(75)90077-7.

Comiso, J. C., D. J. Cavalieri, C. L. Parkinson, and P. Gloersen (1997),Passive microwave algorithms for sea ice concentration: A comparisonof two techniques, Remote Sens. Environ., 60(3), 357–384, doi:10.1016/S0034-4257(96)00220-9.

Drinkwater, M. R., D. Long, and D. Early (1995), Comparison of variationsin sea‐ice formation in the Weddell Sea with seasonal bottom‐wateroutflow data, in 1995 International Geoscience and Remote SensingSymposium, IGARSS ’95: Quantitative Remote Sensing for Science andApplications, vol. 1, edited by T. I. Stein, pp. 402–404, IEEE Comput.Soc. Press, Silver Spring, Md.

Fahrbach, E., G. Rohardt, N. Scheele, M. Schroeder, V. Strass, andA. Wisotzki (1995), Formation and discharge of deep and bottom waterin the northwestern Weddell Sea, J. Mar. Res., 53(4), 515–538,doi:10.1357/0022240953213089.

Fahrbach, E., M. Hoppema, G. Rohardt, M. Schröder, and A. Wisotzki(2004), Decadal‐scale variations of water mass properties in the deepWeddell Sea, Ocean Dyn., 54, 77–91, doi:10.1007/s10236-003-0082-3.

Fogt, R. L., D. H. Bromwich, and K. M. Hines (2011), Understanding theSAM influence on the South Pacific ENSO teleconnection, Clim. Dyn.,36, 1555–1576, doi:10.1007/s00382-010-0905-0.

Foldvik, A., T. Gammelsrød, and T. Tørresen (1985), Circulation and watermasses on the southern Weddell Sea shelf, in Oceanology of the Antarc-tic Continental Shelf, Antarct. Res. Ser., vol. 43, edited by S. S. Jacobs,pp. 5–20, AGU, Washington, D. C.

Foster, T. D., and E. C. Carmack (1976), Frontal zone mixing and AntarcticBottom Water formation in the southern Weddell Sea, Deep Sea Res.Oceanogr. Abstr., 23, 301–317, doi:10.1016/0011-7471(76)90872-X.

Foster, T. D., and J. H. Middleton (1980), Bottom water formation in thewestern Weddell Sea, Deep Sea Res., Part A, 27, 367–381, doi:10.1016/0198-0149(80)90032-1.

Gill, A. E. (1973), Circulation and bottom water production in the WeddellSea, Deep Sea Res. Oceanogr. Abstr., 20, 111–140, doi:10.1016/0011-7471(73)90048-X.

Gong, D., and S. Wang (1999), Definition of Antarctic Oscillation Index,Geophys. Res. Lett., 26(4), 459–462, doi:10.1029/1999GL900003.

Gordon, A. L. (1998), Western Weddell Sea thermohaline stratification, inOcean, Ice and Atmosphere: Interactions at the Antarctic ContinentalMargin, Antarct. Res. Ser., vol. 75, edited by S. S. Jacobs and R. Weiss,pp. 215–240, AGU, Washington, D. C.

Gordon, A. L., B. A. Huber, H. H. Hellmer, and A. Ffield (1993), Deep andbottom water of the Weddell Sea’s western rim, Science, 262, 95–97,doi:10.1126/science.262.5130.95.

Gordon, A. L., M. Visbeck, and B. Huber (2001), Export of Weddell SeaDeep and Bottom Water, J. Geophys. Res., 106(C5), 9005–9017,doi:10.1029/2000JC000281.

Gordon, A. L., B. Huber, D. McKee, and M. Visbeck (2010), A seasonalcycle in the export of bottom water from the Weddell Sea, Nat. Geosci.,3(8), 551–556, doi:10.1038/ngeo916.

Grumbine, R.W. (1991), Amodel of the formation of high‐salinity shelf wateron polar continental shelves, J. Geophys. Res., 96(C12), 22,049–22,062,doi:10.1029/91JC00531.

Heywood, K. J., A. C. Naveira Garabato, and D. P. Stevens (2002), Highmixing rates in the abyssal Southern Ocean, Nature, 415, 1011–1014,doi:10.1038/4151011a.

Holland, M. M., C. M. Bitz, and E. C. Hunke (2005), Mechanisms forcingan Antarctic dipole in simulated sea ice and surface ocean conditions,J. Clim., 18, 2052–2066, doi:10.1175/JCLI3396.1.

Huhn, O., H. H. Hellmer, M. Rhein, C. B. Rodehacke, W. Roether, M. P.Schodlok, and M. Schröder (2008), Evidence of deep‐ and bottom‐water formation in the western Weddell Sea, Deep Sea Res., Part II,55, 1098–1116, doi:10.1016/j.dsr2.2007.12.015.

Jullion, L., S. C. Jones, A. C. Naveira Garabato, and M. P. Meredith(2010), Wind‐controlled export of Antarctic Bottom Water from theWeddell Sea,Geophys. Res. Lett., 37, L09609, doi:10.1029/2010GL042822.

Kerr, R., M. M. Mata, and C. A. E. Garcia (2009), On the temporal variabil-ity of the Weddell Sea Deep Water masses, Antarct. Sci., 21, 383–400,doi:10.1017/S0954102009001990.

Klatt, O., E. Fahrbach, M. Hoppema, and G. Rohardt (2005), The transportof the Weddell Gyre across the Prime Meridian, Deep Sea Res., Part II,52, 513–528, doi:10.1016/j.dsr2.2004.12.015.

Lefebvre, W., and H. Goosse (2005), Influence of the Southern AnnularMode on the sea ice‐ocean system: The role of the thermal and mechan-ical forcing, Ocean Sci., 1, 145–157, doi:10.5194/os-1-145-2005.

Lefebvre, W., H. Goosse, R. Timmermann, and T. Fichefet (2004), Influenceof the Southern Annular Mode on the sea‐ice‐ocean system, J. Geophys.Res., 109, C09005, doi:10.1029/2004JC002403.

Liu, J., X. Yuan, D. Rind, and D. G. Martinson (2002), Mechanism studyof the ENSO and southern high latitude climate teleconections, Geophys.Res. Lett., 29(14), 1679, doi:10.1029/2002GL015143.

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

16 of 17

Marshall, G. J. (2003), Trends in the Southern Annular Mode from obser-vations and reanalyses, J. Clim., 16, 4134–4143, doi:10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2.

Marshall, G. J., and S. A. Harangozo (2000), An appraisal of NCEP/NCARreanalysis MSLP data viability for climate studies in the South Pacific,Geophys. Res. Lett., 27(19), 3057–3060, doi:10.1029/2000GL011363.

Martinson, D. G. (1990), Evolution of the Southern Ocean winter mixedlayer and sea ice: Open ocean deepwater formation and ventilation,J. Geophys. Res., 95(C7), 11,641–11,654, doi:10.1029/JC095iC07p11641.

Martinson, D. G., and R. A. Iannuzzi (1998), Antarctic Ocean‐ice interac-tion: Implications from ocean bulk property distributions in the WeddellGyre, in Antarctic Sea Ice: Physical Processes, Interactions and Vari-ability, Antarct. Res. Ser., vol. 74, edited by M. Jeffries, pp. 243–271,AGU, Washington, D. C.

Martinson, D. G., and R. A. Iannuzzi (2003), Spatial/temporal patterns inWeddell gyre characteristics and their relationship to global climate,J. Geophys. Res., 108(C4), 8083, doi:10.1029/2000JC000538.

Mensch, M., R. Bayer, J. L. Bullister, P. Schlosser, and R. Weiss (1996),The distribution of tritium and CFCs in the Weddell Sea during themid 1980s, Prog. Oceanogr., 38, 377–388, doi:10.1016/S0079-6611(97)00007-4.

Meredith, M. P., A. C. Naveira Garabato, A. L. Gordon, and G. C. Johnson(2008), Evolution of the deep and bottom waters of the Scotia Sea,Southern Ocean, 1995–2005, J. Clim., 21(13), 3327–3343, doi:10.1175/2007JCLI2238.1.

Muench, R. D., L. Padman, S. L. Howard, and E. Fahrbach (2002), Upperocean diapycnal mixing in the northwestern Weddell Sea, Deep Sea Res.,Part II, 49, 4843–4861, doi:10.1016/S0967-0645(02)00162-5.

Nicholls, K. W., and S. Østerhus (2004), Interannual variability and venti-lation timescales in the ocean cavity beneath Filchner‐Ronne Ice Shelf,Antarctica, J. Geophys. Res., 109, C04014, doi:10.1029/2003JC002149.

Nuñez‐Riboni, I., and E. Fahrbach (2009), Seasonal variability of the Ant-arctic Coastal Current and its driving mechanisms in the Weddell Sea,Deep Sea Res., Part I, 56, 1927–1941, doi:10.1016/j.dsr.2009.06.005.

Orsi, A. H., W. D. Nowlin Jr., and T. Whitworth III (1993), On the circu-lation and stratification of the Weddell Gyre, Deep Sea Res., Part I, 40,169–203, doi:10.1016/0967-0637(93)90060-G.

Padman, L., S. Howard, and R. Muench (2006), Internal tide generationalong the South Scotia Ridge, Deep Sea Res., Part II, 53, 157–171,doi:10.1016/j.dsr2.2005.07.011.

Raphael, M. N. (2004), A zonal wave 3 index for the Southern Hemisphere,Geophys. Res. Lett., 31, L23212, doi:10.1029/2004GL020365.

Raphael, M. N. (2007), The influence of atmospheric zonal wave three onAntarctic sea ice variability, J. Geophys. Res., 112, D12112, doi:10.1029/2006JD007852.

Rind, D., M. Chandler, J. Lerner, D. G. Martinson, and X. Yuan (2001),Climate response to basin‐specific changes in latitudinal temperature gra-dients and implications for sea ice variability, J. Geophys. Res., 106(D17),20,161–20,173, doi:10.1029/2000JD900643.

Schroder, M., and E. Fahrbach (1999), On the structure and the transportof the eastern Weddell Gyre, Deep Sea Res., Part II, 46, 501–527,doi:10.1016/S0967-0645(98)00112-X.

Simmonds, I., and J. C. King (2004), Global and hemispheric climate var-iations affecting the Southern Ocean, Antarct. Sci., 16, 401–413,doi:10.1017/S0954102004002226.

Smith, S. D. (1988), Coefficients for sea surface wind stress, heat flux, andwind profiles as a function of wind speed and temperature, J. Geophys.Res., 93(C12), 15,467–15,472, doi:10.1029/JC093iC12p15467.

Stammerjohn, S. E., D. G. Martinson, R. C. Smith, X. Yuan, and D. Rind(2008), Trends in Antarctic annual sea ice retreat and advance and theirrelation to El Niño‐Southern Oscillation and Southern Annular Modevariability, J. Geophys. Res., 113, C03S90, doi:10.1029/2007JC004269.

Thompson, D. W. J., and S. Solomon (2002), Interpretation of recentSouthern Hemisphere climate change, Science, 296, 895–899, doi:10.1126/science.1069270.

Thompson, D. W. J., and J. M. Wallace (2000), Annular modes in theextratropical circulation, part I, Month‐to‐month variability, J. Clim.,13, 1000–1016, doi:10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.

Timmermann, R., H. H. Hellmer, and A. Beckmann (2002), Simulations ofice‐ocean dynamics in theWeddell Sea: 2. Interannual variability 1985–1993,J. Geophys. Res., 107(C3), 3025, doi:10.1029/2000JC000742.

Toggweiler, J. R., and B. Samuels (1995), Effect of sea ice on the salinityof Antarctic Bottom Waters, J. Phys. Oceanogr., 25, 1980–1997,doi:10.1175/1520-0485(1995)025<1980:EOSIOT>2.0.CO;2.

von Gyldenfeldt, A.‐B., E. Fahrbach, M. A. Garcia, and M. Schroder(2002), Flow variability at the tip of the Antarctic Peninsula, Deep SeaRes., Part II, 49, 4743–4766, doi:10.1016/S0967-0645(02)00157-1.

Weppernig, R., P. Schlosser, S. Khatiwala, and R. G. Fairbanks (1996),Isotope data from Ice Station Weddell: Implications for deep water forma-tion in the Weddell Sea, J. Geophys. Res., 101(C11), 25,723–25,739,doi:10.1029/96JC01895.

Yuan, X. (2004), ENSO‐related impacts on Antarctic sea ice: A synthesisof phenomenon and mechanisms, Antarct. Sci., 16(4), 415–425,doi:10.1017/S0954102004002238.

Yuan, X., and C. Li (2008), Climate modes in southern high latitudes andtheir impacts on Antarctic sea ice, J. Geophys. Res., 113, C06S91,doi:10.1029/2006JC004067.

Yuan, X., and D. G. Martinson (2000), Antarctic sea‐ice extent variabilityand its global connectivity, J. Clim., 13, 1697–1717, doi:10.1175/1520-0442(2000)013<1697:ASIEVA>2.0.CO;2.

Yuan, X., and D. G. Martinson (2001), The Antarctic dipole and its predict-ability, Geophys. Res. Lett., 28(18), 3609–3612, doi:10.1029/2001GL012969.

Yuan, X., D. G. Martinson, and W. T. Liu (1999), Effect of air‐sea‐iceinteraction on Southern Ocean subpolar storm distribution, J. Geophys.Res., 104(D2), 1991–2007, doi:10.1029/98JD02719.

Yuan, X., D. G. Martinson, and Z. Dong (2004), Upper ocean thermohalinestructure and its temporal variability in the southeast Indian Ocean, DeepSea Res., Part I, 51, 333–347, doi:10.1016/j.dsr.2003.10.005.

Z. Dong, Polar Research Institute of China, 451 Jinqiao Rd., Shanghai,200129, China.A. L. Gordon, B. A. Huber, D. C. McKee, and X. Yuan, Lamont‐Doherty

Earth Observatory, Columbia University, 61 Rte. 9W, Palisades, NY10964, USA. (dcm2117@columbia.edu)

MCKEE ET AL.: CLIMATE IMPACT ON BOTTOM WATER C05020C05020

17 of 17