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Melting Glaciers Help Fuel Productivity Hot-spots Around Antarctica
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Kevin. R. Arrigo and Gert L. van Dijken
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Department of Environmental Earth System Science, Stanford University, Stanford, California,
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USA
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Abstract
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Antarctic coastal polynyas are biologically rich ecosystems that support large populations of
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mammals and birds and are strong sinks of atmospheric carbon dioxide. To support local
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phytoplankton blooms, these ecosystems require a large input of iron, the source of which is
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poorly known. Satellite data suggest that melting glaciers are a primary supplier of iron to
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coastal polynyas, with basal melt rates explaining 58% of the between-polynya variance in
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annual mean chlorophyll a concentration. Iron upwelled from sediments, which is partly
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controlled by continental shelf width, is also important. Given the sensitivity of these biological
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hotspots to iron released from melting ice shelves, future changes in glacial melt rates could
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dramatically impact coastal ecosystems and the ability of continental shelf waters to sequester
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atmospheric carbon dioxide.
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1. Introduction
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Antarctic coastal polynyas are areas of open water surrounded by sea ice that are maintained
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throughout the year through the upwelling of warm water or as continental winds blow ice away
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from shore [Bromwich, 1989]. Because polynya waters are already ice-free in early spring when
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solar elevation is increasing rapidly, they often harbor extremely large phytoplankton
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concentrations compared to surrounding ice covered waters; these blooms persist even after sea
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ice has disappeared in summer [Arrigo and Van Dijken, 2003]. This enhanced biological
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productivity enables polynyas to support the highest densities of upper trophic level organisms in
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the Southern Ocean [Karnovsky et al., 2007] and increases the efficiency of the biological pump,
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making areas like the Ross Sea polynya disproportionately large sinks of anthropogenic carbon
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dioxide [Arrigo et al., 2008a].
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Phytoplankton growth and abundance in most Southern Ocean waters is limited by the
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availability of iron, which is usually in short supply due to the lack of an efficient supply
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mechanism [Boyd et al., 2012]. While many parts of the global ocean receive large iron fluxes
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through atmospheric dust deposition, the isolation of high latitude Antarctic waters from
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terrestrial iron sources limits its supply by this means [Boyd et al., 2012]. Consequently, some
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of the most productive waters of the Southern Ocean are in the Scotia Sea, where iron from
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shallow shelves is mixed into surface waters during advection of the Antarctic Circumpolar
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Current [Ardelan et al., 2010; Chever et al., 2010; Planquette et al., 2011; Frants et al., 2013].
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In other parts of the Southern Ocean, iron is also brought into surface waters through convective
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and turbulent mixing as well as upwelling [Fitzwater et al., 2000; Planquette et al., 2013;
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Measures et al., 2013], from melting of drifting icebergs [Smith et al., 2007; Raiswell et al.,
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2008; Schwartz and Schlodlok, 2009; Lin et al., 2011] and sea ice [Fitzwater et al., 2000; Grotti
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et al., 2005; Lannuzel et al., 2010], and a small amount from atmospheric deposition [Boyd et al.,
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2012].
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Like the rest of the Southern Ocean, the growth of phytoplankton in coastal polynyas
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depends on the availability of iron [Coale et al., 2005; Alderkamp et al., 2012; Gerringa et al.,
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2012]. However, despite the ecological and biogeochemical importance of coastal polynyas,
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little is known about the processes that fuel the growth of local phytoplankton populations. Of
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the dozens of Antarctic coastal polynyas that have been identified [Arrigo and Van Dijken,
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2003], only a handful has been studied in any detail. A recent study of polynyas in the
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Amundsen Sea showed that iron introduced from melting at the base of the Pine Island Glacier
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was the dominant dissolved iron pool on the continental shelf, despite relatively high
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concentrations of sediment-derived iron [Gerringa et al., 2012]. This glacially-derived iron,
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produced primarily as ice streams abrade continental crust while flowing to the coast [Shaw et al
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2011], was needed to satisfy the iron requirement of the intense phytoplankton bloom that
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formed in the Pine Island Bay polynya [Gerringa et al., 2012; Alderkamp et al., 2012; Mills et
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al., 2012].
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2. Data
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In the present study, we quantify the amount of phytoplankton biomass and rates of net
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primary production (NPP) in all coastal polynyas around the Antarctic continent between 1998
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and 2013 and relate these to local environmental conditions. We identified and mapped 46
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coastal polynyas (Fig. 1) using satellite-derived sea ice concentrations from the Special Sensor
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Microwave/Imager (SSM/I). For each polynya, we used satellite data to quantify daily open
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water area (from SSM/I), weekly sea surface temperature (SST, from the Advanced Very High
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Resolution Radiometer, AVHRR, OISST), 8-day chlorophyll a (Chl a) concentrations (from
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SeaWiFS and MODIS-Aqua), and calculated the daily rate of net primary production (NPP)
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[Arrigo et al., 2008]. At each polynya location, we used bathymetric data to calculate the width
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of the continental shelf (the shortest distance between the local coastline and the 1000 m
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isobath). Because 80% of Antarctic coastal polynyas are adjacent to ice shelves, we obtained ice
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shelf basal melt rates calculated from recent satellite-based estimates of the balance between ice
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accumulation and thinning [Rignot et al., 2013]. Basal melt rates were considered a proxy for
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the amount of iron introduced into adjacent polynyas. To determine what factors control
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phytoplankton abundance within the polynyas, we regressed annual mean Chl a concentration
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for each polynya against 1) local SST, 2) the length of the phytoplankton growing season (the
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number of days per year that open water area in the polynya exceeded 50% of its maximum
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value), 3) the continental shelf width, and 4) the total basal melt rate (Gt yr-1) of any ice shelves
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located in the vicinity of each polynya. For all data, means are presented with standard
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deviations.
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3. Results
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While coastal polynyas, by definition, have either reduced or no ice cover during winter, all
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46 polynyas expanded greatly in size in October and November due to intensified rates of sea ice
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melt, and contracted in March as sea ice began to freeze. The annual mean open water area
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varied by approximately three orders of magnitude between polynyas, ranging from 954±196
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km2 in the West Lazarev Sea polynya to 239,603±39,132 km2 in the Ross Ice Shelf polynya
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(Table 1). Although the annual mean size of all polynyas was 14,063±35,803 km2, most
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polynyas were substantially smaller than the mean, with 31 polynyas having annual mean open
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water areas <10,000 km2 and 24 with open water areas <5,000 km2.
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Surface Chl a concentrations in coastal polynyas began to increase in October and peaked
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around January. By March, low solar elevation precluded net phytoplankton growth and Chl a
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concentrations fell back to pre-bloom levels. The annual mean Chl a concentration calculated
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over the phytoplankton growing season in the 46 polynyas ranged from 0.17±0.10 mg m-3
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(Lutzoh-Holm Bay polynya) to 2.28±0.63 mg m-3 (Amundsen Sea polynya), averaging
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0.60±0.42 mg m-3 (Table 1). The mean Chl a concentration for coastal polynyas was more than
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double the concentration in non-shelf waters of the Southern Ocean [Arrigo et al., 2008b].
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The annual rate of NPP varied by a factor of 16 between coastal polynyas, ranging from 6.6 g
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C m-2 yr-1 in the Lutzoh-Holm Bay polynya to 106 g C m-2 yr-1 in the Amundsen Sea polynya
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(Table 1). The annual mean Chl a concentration was the most important factor controlling
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annual NPP, explaining 91% of the variance between polynyas. Total NPP in each polynya is
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calculated as a product of the annual NPP rate and the polynya area. Because of the large
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difference in area between polynyas, total NPP was far more variable than annual NPP, ranging
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over four orders of magnitude between polynyas (0.01-22.2 Tg C yr-1). Of the 46 polynyas, 40
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had total NPP values below 1.0 Tg C yr-1 and 20 had values below 0.1 Tg C yr-1 (Table 1).
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Although temperature and light availability can be important regulators of phytoplankton
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populations in polar waters, linear regression analysis demonstrated that neither SST nor the
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length of the phytoplankton growing season had a significant impact on annual mean Chl a
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concentrations within the polynyas (p >0.05). Because Antarctic coastal polynyas are located in
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very high latitude waters, SST varies little throughout the year and variability between polynyas
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is small, minimizing its effect. The lack of a relationship between the length of the open water
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season and either annual mean Chl a concentration or NPP is likely because coastal polynyas
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remain near their annual maximum size for an average of 126 days, more than twice as long as
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the average phytoplankton bloom (62 days, defined as the number of days that Chl a
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concentration exceeded 50% of its annual maximum, Table 1). As a result, light availability did
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not limit phytoplankton growth in the polynyas. It should be noted, however, that we were
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unable to assess the impact of mixed layer depth, which also controls the amount of light
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available to phytoplankton, on either Chl a or NPP. While existing data from a small number of
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polynyas suggests that mixed layers are relatively shallow and do not limit phytoplankton growth
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rates [Arrigo et al., 1999; Alderkamp et al., 2012], we cannot rule out the possibility that MLD
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may be responsible for some of the unexplained variance in Chl a concentration between
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polynyas.
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In contrast to the negligible impact of temperature and light, the width of the continental
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shelf explained 40% of the variance in annual mean Chl a concentration between polynyas [Fig.
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2a). This high correlation is consistent with a sediment source of iron to coastal polynyas.
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Dissolved iron concentrations increase with proximity to the Antarctic coast [Sedwick et al.,
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2008] and shelf waters are especially enriched in iron [Fitzwater et al., 2000; Gerringa et al.,
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2012]. As waters upwell onto the continental shelf [Gerringa et al., 2012] or during winter
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convection [Blain et al., 2008], wider continental shelves increase the contact between upwelling
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water and iron-rich sediments, allowing more iron to be entrained into surface waters, and
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support greater phytoplankton abundance [Bruland et al., 2005; Biller and Bruland, 2013; Biller
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et al., 2013].
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Although continental shelf sediments are an important iron source, iron from melting glaciers
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appears to be even more critical in controlling phytoplankton abundance in coastal polynyas. In
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our study, 59% of the variance in annual mean Chl a concentration was explained by the basal
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melt rate of nearby ice shelves (Fig. 2b). The y-intercept in Fig. 2b, which provides an estimate
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of the annual mean Chl a concentration to be expected under conditions of no basal melt, was
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0.39±0.37 mg m-3, indistinguishable from the mean Chl a concentration in the nine polynyas that
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are not located near an ice shelf (0.44±0.19 mg m-3). Therefore, including the nine polynyas not
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associated with an ice sheet and giving them a basal melt rate of zero had no impact in the
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amount of variance explained. A multiple linear regression that included both continental shelf
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width and basal melt rate of nearby ice shelves was able to explain 70% of the variance in mean
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annual Chl a concentration among the 46 coastal polynyas.
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4. Conclusions
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From the high correlation between basal melt rates and phytoplankton biomass in Antarctic
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polynyas, we infer that iron released from melting glaciers stimulates phytoplankton growth in
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polynyas throughout the Antarctic. We recognize, though, that there may be alternative, albeit
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less likely, explanations for this correlation. First, sea ice in the vicinity of expanding polynyas
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can also release iron in spring and summer when it melts [Grotti et al., 2005]. However, sea ice
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is not a true source of iron since it accumulates iron from other sources (mostly from the water
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column, Fitzwater et al., 2000]. Second, intrusions of warm Circumpolar Deep Water can
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resuspend sediment iron and also accelerate basal melting [Dutrieux et al., 2014]. Thus, high
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phytoplankton abundance in polynyas near ice shelves experiencing enhanced basal melting
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could be driven by increased upwelling rates and a greater flux of sediment-derived iron, rather
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than a glacial iron source. We consider this unlikely given that melting ice shelves are known
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sources of dissolved iron to local waters [Gerringa et al., 2012] and our observation that basal
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melt rates explained 50% more of the variance in Chl a between polynyas than shelf width.
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Finally, melting glaciers could reduce the density of local waters, increasing stratification and
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enhancing surface light levels, thereby increasing phytoplankton growth, irrespective of any iron
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input. However, the limited available data suggest that as meltwater upwells at the face of the
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glaciers, they promote deep mixing, rather than stratification, and that phytoplankton abundance
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in these upwelled waters is very low [Gerringa et al., 2012, Alderkamp et al., 2012].
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Annual rates of NPP in Antarctic coastal polynyas are among the highest in the Southern
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Ocean [Arrigo and Van Dijken, 2003] due in large part to the relaxation of nutrient limitation by
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iron released from melting glaciers. Our results also show that 50% of total NPP in Antarctic
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polynyas, which are important but often overlooked carbon sinks, is associated with the Ross Sea
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polynya and 50% is associated with the other 45 polynyas (Table 1). If we assume that the ratio
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of NPP to the magnitude of the anthropogenic carbon dioxide sink measured in the Ross Sea
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(0.24) [Arrigo et al., 2008] is applicable to other polynyas, all Antarctic polynyas combined
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would represent an anthropogenic carbon dioxide sink of about 0.019 Pg C yr-1. Thus, despite
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that fact that coastal Antarctic polynyas represent only 0.06% of the ocean area south of 62°S,
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accounting for them in analyses of oceanic carbon dioxide sequestration would shift these waters
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from being a 0.01 Pg C yr-1 source [Takahashi et al., 2009] to a 0.01 Pg C yr-1 sink of
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anthropogenic carbon dioxide. Given the expectation that the melt rate of Antarctic ice shelves
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is likely to increase in the future [Bell 2008], the ecological and biogeochemical importance of
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these coastal polynyas is also likely to increase.
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Acknowledgements. Data supporting Figure 2 are available in Table 1. This research was
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supported by NSF grant (ANT-1063592) to K. Arrigo. We thank A. Alderkamp, M. Mills, L.
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Gerringa, and Z. Brown for their helpful comments.
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Figure legends
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Figure 1. Location of the 46 coastal polynyas included in this study.
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Figure 2. Regression of annual mean chlorophyll a concentration against (A) continental shelf
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width and (B) basal melt rate (Gt yr-1) of nearby ice shelves for the 37 polynyas that were
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located near an ice shelf (Rignot et al., 2013).
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Table 1. Mean (±SD) statistics for 46 Antarctic polynyas for the years 1998-2013. The column heading #days>50%max OW gives the number of
days per year that the open water area in each polynya exceeded 50% of the annual maximum open water area. The column heading
#days>50%max NPP gives the number of days per year that NPP in each polynya exceeded 50% of the annual maximum NPP. The basal melt
rates and corresponding glacier names are from Rignot et al. [2013].
Name
Polynya
Sulzberger Bay
1
Hull Bay
2
Wrigley Gulf
3
Amundsen Sea
4
Pine Island Bay
5
Eltanin Bay
6
Ronne Entrance
7
Marguerite Bay
8
Larsen Ice Shelf
9
Ronne Ice Shelf
10
Brunt Ice Shelf
11
Lyddan Island
12
Seal Bay
13
Cape Norvegia
14
Jelbart Ice Shelf
15
Fimbul Ice Shelf
16
W. Lazarev Ice Shelf 17
E. Lazarev Ice Shelf 18
Breid Bay
19
Vestvika Bay
20
Lutzoh-Holm Bay
21
Amundsen Bay
22
Cape Borle
23
Stefansson Bay
24
Holme Bay
25
Bjerkø Peninsula
26
MacKenzie Bay
27
Prydz Bay
28
West Ice Shelf
29
Farr Bay
30
Mill Island
31
Cape Nutt
32
Open water
area (km2)
6473±1689
6359±828
11100±1151
31844±4679
19688±5448
11283±1483
15120±1986
4348±550
4727±2774
44680±56686
15189±4153
6003±1434
4340±865
2337±297
2195±371
3493±402
954±196
1995±292
1129±175
2360±440
1657±1881
1845±721
4258±980
2087±438
4650±1060
12110±899
1867±237
54541±5655
2148±584
26723±4260
2491±1139
1463±353
Annual NPP
(g C m-2 yr-1)
33.0±22.9
30.2±15.8
43.0±19.1
105.4±21.9
72.2±28.2
46.1±14.1
61.7±18.3
67.1±24.2
20.3±29.6
43.8±32.5
35.5±18.2
27.9±17.8
23.0±17.1
16.4±9.3
16.5±10.5
17.6±6.9
10.7±7.1
17.0±5.8
15.3±8.9
17.7±9.3
6.7±10.8
8.6±4.6
21.3±12.8
26.9±9.1
36.8±13.4
47.7±11.9
44.1±18.5
88.3±19.4
12.7±6.3
52.6±15.1
17.3±16.3
13.8±11.9
Total NPP
(Tg C yr-1)
0.24±0.19
0.20±0.12
0.49±0.26
3.38±0.90
1.55±0.96
0.53±0.22
0.95±0.38
0.30±0.13
0.15±0.28
3.41±7.04
0.59±0.38
0.19±0.13
0.11±0.09
0.04±0.02
0.04±0.03
0.06±0.03
0.01±0.01
0.03±0.01
0.02±0.01
0.04±0.03
0.03±0.07
0.02±0.01
0.10±0.08
0.06±0.03
0.18±0.08
0.58±0.17
0.08±0.04
4.84±1.29
0.03±0.02
1.45±0.52
0.06±0.07
0.02±0.02
Chl a
(mg m-3)
0.68±0.55
0.60±0.45
0.97±0.61
2.28±0.63
1.25±0.60
0.74±0.25
1.10±0.47
1.23±0.51
0.50±0.54
0.85±0.79
0.55±0.27
0.52±0.25
0.46±0.28
0.36±0.15
0.32±0.11
0.33±0.12
0.25±0.14
0.25±0.08
0.28±0.10
0.28±0.11
0.17±0.10
0.25±0.13
0.29±0.20
0.33±0.12
0.49±0.19
0.64±0.17
0.83±0.36
1.43±0.43
0.28±0.12
0.72±0.27
0.34±0.21
0.29±0.21
Shelf
# days>
# days>
Basal melt
width (km) 50%max OW 50%max NPP (Gt yr-1)
163.5
90
69
18.2
71.3
101
64
4.2
139.3
105
53
144.9
264.2
114
45
181.2
444.7
108
51
101.2
372.8
139
62
24.5
346.6
135
70
56.8
266.2
211
83
89
196.2
128
69
20.7
453.9
64
76
113.5
176.6
95
65
1
174.6
102
71
8.7
109.9
84
72
8.7
53.0
106
52
5.7
53.0
113
59
0
130.2
120
54
23.5
77.8
138
40
3.2
85.0
176
77
6.3
45.1
184
63
7.5
109.2
143
77
14.1
128.8
104
52
5.7
67.4
94
53
6.7
44.5
140
89
92.2
115
62
88.9
107
61
96.8
136
68
215.2
217
65
270.8
217
65
37
130.2
124
74
27.2
158.9
129
68
72.6
168.7
144
61
3
117.1
138
61
3.6
Glacier name
Sulzberger
Land
Getz
Dotson/Crosson/Thwaites
Pine Island
Venable/Ferrigno
Strange/Bach/Wilkins
George VI
Larsen C
Ronne
Brunt/Stancomb
Riiser-Larsen
Riiser-Larsen
Quar/Ekstrom
Jelbart
Fimbul
Vigrid
Lazarev
Borchgrevink
Baudouin
Shirase
Rayner-Thyer
Amery/Publications
West
Shackleton
Tracy/Tremenchus
Conger/Glenzer
13
321
322
323
324
325
326
327
Vincennes Bay
Cape Poinsett
Totten Glacier
Henry Bay
Paulding Bay
Porpoise Bay
Davis Bay
33
34
35
36
37
38
39
12339±1906
3986±744
1414±493
6512±1225
1568±412
1653±758
11608±981
37.4±14.4
18.9±10.1
10.4±7.7
20.7±9.5
7.6±7.7
13.2±12.9
29.8±8.6
0.48±0.23
0.08±0.05
0.02±0.01
0.14±0.09
0.01±0.02
0.03±0.04
0.35±0.12
0.46±0.19
0.36±0.30
0.36±0.24
0.31±0.18
0.25±0.15
0.32±0.30
0.40±0.16
124.3
181.2
71.9
142.6
160.2
113.8
209.3
138
138
141
134
113
126
142
69
56
36
65
48
30
72
5
Vincennes
27.4
Moscow University
6.7
Holmes
14