Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • BCO-DMO Processing Description
  • Problem Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Water sampling:
Hydrographic profiles and discrete water samples were collected from each station using a conventional shipboard conductivity temperature-depth (CTD; Sea-Bird 911+) sensor and a 24 × 10 liter (L) Niskin bottle rosette sampler (General Oceanics). Potential temperature (θ) and salinity (S) were recorded continuously as a function of depth and at the moment of Niskin bottle closure. A few samples (particulate phosphate) reported here were collected similarly using a trace-metal-clean CTD-rosette system (see Sherrell et al., 2015) that was deployed at the same location and depths just before or after the conventional CTD.

Water samples were collected and processed according to standard protocols (Knap et al., 1996; Dickson et al., 2007) for dissolved inorganic carbon (DIC), alkalinity (ALK), nutrients, chlorophyll a (Chl a), particulate and dissolved organic carbon (POC, DOC), particulate and total dissolved nitrogen (PN, TDN), and particulate phosphorus (PP; Planquette and Sherrell, 2012; Planquette et al. 2013). Samples from the same depths and stations were also collected for microbial biomass and activity (see below; Williams et al., 2016). This dataset focuses on samples collected from the upper 100 meters (m) of the water column. Seafloor depths in the area ranged from 300 to 1300 m (Nitsche et al., 2007), although the 13 stations included here were all deeper than 400 m.

Inorganic nutrients, inorganic carbon, and organic matter analysis:
Dissolved inorganic nutrient samples were pre-filtered through 0.45-micrometer (µm) polycarbonate syringe filters, kept refrigerated, and analyzed onboard the ship within 1 day of sampling. Nitrate (NO₃-), nitrite (NO₂-), ammonium (NH₄+), phosphate (HPO₄²-), and silicic acid (Si(OH)₄) were measured using a five-channel Lachat Instruments QuikChem FIA+ 8000s series autoanalyzer in conjunction with a Lachat Instruments XYZ AutoSampler (ASX-500 Series), two Lachat Instruments RP-100 Series peristaltic Reagent Pumps, and Omnion Software, version 3.0.220.02. The nitrate + nitrite analysis uses the basic method of Armstrong et al. (1967), with minor improvements for greater precision and easier operation. Nitrate was first reduced to nitrite using a cadmium reduction column and imidazole buffer as described by Patton (1982). Sulfanilamide and N-(1-Napthyl) ethylenediamine dihydrochloride react with nitrite to form a colored diazo compound. Nitrite analysis was performed on a separate channel, omitting the cadmium reductant. Ammonium was determined using the indophenol blue method modified from ALPKEM RFA methodology (EPA, 1984). Total dissolved inorganic nitrogen (DIN) was calculated by summing NO₃- + NO₂⁻ + NH₄+. The phosphate method was a modification of the molybdenum blue procedure of Bernhardt and Wilhelms (1967), in which phosphate was determined as reduced phosphomolybdic acid employing hydrazine as the reductant. The silicic acid method was based on Armstrong et al. (1967), as adapted by Atlas et al. (1971). Addition of an acidic molybdate reagent forms silicomolybdic acid, which was then reduced by stannous chloride. Detection limits (NO₃- + NO₂- = 0.075 micromoles per liter (µmol/L); NO₂- = 0.009 µmol/L; NH₄+ = 0.040 µmol/L; HPO₄²- = 0.022 µmol/L; and Si(OH)₄ = 1.90 µmol/L) and precision (NO₃- + NO₂- = ± 0.0076 µmol/L; NO₂- = ± 0.0009 µmol/L; NH₄+ = ± 0.0041 µmol/L; HPO₄²- = ± 0.0023 µmol/L; and Si(OH)₄ = ± 0.193 µmol/L) were determined using multiple runs of standards prepared in low nutrient seawater. Samples with negative values following calibration using standard curves were converted to zeros.

Samples for DIC were preserved with mercuric chloride and sealed (Dickson et al., 2007), and then stored cool and dark until analysis using the SOMMA at UGA ( Johnson et al., 1993; Cooley and Yager, 2006). Accuracy was confirmed with Certified Reference Material from University of California, San Diego (CRM; Dickson et al., 2003) and precision was determined to be better than ± 1 micromoles per kilogram (µmol/kg) using duplicate samples from surface and 200 m depths. Alkalinity measurements were made on the same samples (following DIC analysis) using a programmable open-cell potentiometric titration system (Dickson et al., 2003; Cooley and Yager, 2006). Accuracy was established by acid-calibration using multiple daily runs of CRM. Precision was determined to be ± 5 µmol/kg using replicate samples run on multiple days.

Samples for particulate organic carbon (POC) and nitrogen (PN) were collected by cleanly filtering 100–600 milliliters (mL) of seawater onto a 25-millimeter (mm) diameter, combusted GF/F filter (nominal pore size of 0.7 µm) which was then folded sample side in and frozen at -80 degrees Celsius (°C). Samples were processed at Rutgers University and analyzed using a Carlo-Erba CHN analyzer (Hedges and Stern, 1984). Precision, based on replicate filtered volumes from the same Niskin bottle, was ± 5% for most samples, but was occasionally higher. Samples for particulate phosphate (PP) were collected separately from the same depths and locations as part of a trace metal suite and analyzed separately using a Thermo-Finnigan Element I HR-ICP-MS (Sherrell et al., 2015; Planquette and Sherrell, 2012; Planquette et al., 2013). Precision was ± 5%.

Samples for DOC and TDN were collected cleanly from the filtrate of the POC/PN samples and stored frozen until processed at the Georgia Institute of Technology by Shimadzu TOC-5000 analyzer with an associated TNM-1 Total Nitrogen Measuring Unit. Precision was ± 4%. Residual dissolved organic nitrogen (DON) was calculated by subtracting DIN from TDN. This approach involves taking the difference between two relatively large numbers, thus precision of relatively small DON concentrations is strongly affected by the precision of the DIN and TDN analyses.

Organismal abundance and biomass analysis:
Water column Chl a concentration (used as a proxy for algal biomass) was measured onboard ship using acetone extraction and a spectrofluorometer (Alderkamp et al., 2015). Shipboard values were calibrated against a second set of samples collected similarly, flash-frozen in liquid N₂, stored at -80°C, and analyzed at Mote Marine Lab using HPLC (Wright et al., 1991; see Alderkamp et al., 2015). CHEMTAX (Mackey et al., 1996; Wright et al., 1996; 2010) was applied to determine the relative abundance of phytoplankton classes based on pigment analysis (see Alderkamp et al., 2015).

Bacterial abundance samples were collected in triplicate, preserved using 1% paraformaldehyde, and deep frozen (-80 °C) until they were counted at The University of Georgia with flow cytometry and SYBR Green I nucleic acid staining (Marie et al., 1997). Abundance was calibrated with polystyrene beads, and values were crosschecked using DAPI and epifluorescence microscopy (Porter and Feig, 1980). Abundance was converted to bacterial carbon (BAC) using a conversion factor (25 femtograms C per cell (fg C cell-1); Simon and Azam, 1989). Precision was ± 3%.

Microzooplankton abundance and biovolume were determined at select depths and stations using microscopy (Goswami, 2004). Samples were gently siphoned through silicon tubes into 300 mL amber colored glass bottles, fixed in acidic Lugol’s solution (2% final concentration), and kept cool and dark until analysis. Biovolume calculations followed the HELCOM (2014) manual on appropriate geometrical shapes when making length-width measurements for each individual species. Biovolumes were corrected for shrinkage due to preservation (vol × 1.33; Stoecker et al., 1994). Heterotrophic/mixotrophic microplankton cell volumes were converted to cell carbon (Menden-Deuer and Lessard, 2000) for loricate and aloricate ciliates (CIL), and dinoflagellates (DINO).

Heterotrophic nanoflagellates (HNAN) were counted by flow cytometry (Christaki et al., 2011). Each sample was stained with SYBR Green, at final concentration of 1:10000 and a minimum staining time of 10 minutes in the dark. The flow rate was ∼250 microliters per minute (µL/min). Both green and red fluorescence were used to discriminate between autotrophs and heterotrophs. Data acquisition was 5–10 minutes depending on concentration of the sample (or depth). Samples with > 1200 events per second were diluted to allow a correct measurement. The detected abundance was checked against counts of DAPI filters. HNAN biomass was converted from abundance data assuming 3.5 cubic micrometer (µm³) per cell biovolume (Vaqué et al., 2002) and 220 femtograms C per cubic micrometer (fg C/µm³) (Børsheim and Bratbak, 1987).

Mesozooplankton biovolume and abundance were determined as described by Wilson et al. (2015) and converted to biomass (micromoles C per liter (µmol C/L)) for both daytime and nighttime tows using conversion factors from the literature (Gallienne et al., 2001; Forest et al., 2012; Trudnowska et al., 2014). The maximum value of day or night tows is reported here.

Biological rate measurements:
Phytoplankton net primary production (NPP) rates were determined for 6 light depths in the upper 100 m using standard ¹⁴C-bicarbonate incubations (Steeman-Nielsen, 1952; Knap et al., 1996) in on-deck incubators with light-filtering screens to match in situ light levels. Bacterial production (BP) rates were determined using ³H-leucine incorporation as described by Williams et al. (2016). Microbial community respiration (MCR) rates were determined for near-surface and subsurface depths by changes in CO₂ concentrations over 48 hours in dark incubations, as described in Williams et al. (2016).

- Loaded original file "Yager_et_al_2016_ASPIRE_Carbon_Budget_Data.csv" into the BCO-DMO system.
- Treated "NaN" as a missing data value (missing data are empty/blank in the final CSV file).
- Renamed columns to comply with BCO-DMO naming conventions.
- Converted "Date" column from MM/DD/YY format to ISO 8601 date format (YYYY-MM-DD).
- Saved the final file as "1001499_v1_aspire_upper_water_column_inventories.csv".

The Amundsen Sea Polynya is areally the most productive Antarctic polynya, exhibits higher chlorophyll levels during peak bloom and greater interannual variability than the better-studied Ross Sea Polynya ecosystem. Polynyas may be the key to understanding the future of polar regions as their extent is expected to increase with anthropogenic warming. The project will examine 1) sources of iron to the Amundsen Sea Polynya as a function of climate forcing, 2) phytoplankton community structure in relation to iron supply and mixed-layer depths, 3) the efficiency of the biological pump of carbon to depth and 4) the net flux of carbon as a function of climate and micronutrient forcing. The research also will compare results for the Amundsen Sea to existing data synthesis and modeling efforts for the Palmer LTER and Ross Sea. The project will 1) build close scientific collaborations between US and Swedish researchers; 2) investigate climate change implications with broad societal relevance; 3) train new researchers; 4) encourage participation in research science by underrepresented groups, and 5) involve broad dissemination of results via scientific literature and public outreach, including close interactions with NSF-supported PolarTrec and COSEE K-12 teachers.

This project brings together experienced US and Swedish investigators (trace metal and carbon chemists, phytoplankton physiologists, microbial and zooplankton ecologists, and physical oceanographers) to investigate climate controls on carbon dioxide uptake by one of the most productive ecosystems in the Antarctic.

The Amundsen Sea Polynya is the most productive Antarctic polynya per square meter, and exhibits higher chlorophyll levels during peak bloom and greater interannual variability than the better-studied Ross Sea polynya ecosystem to the west.

Polynyas, or recurring areas of seasonally open water surrounded by ice, are foci for energy and material transfer between the atmosphere, polar surface ocean and deep sea. Most help take up large amounts of carbon dioxide from the atmosphere.

These polar ecosystems are characterized by high biological productivity and intense biogeochemical cycling - a bit like an oasis. Polynyas may be the key to understanding the future of polar regions since their extent is expected to increase with anthropogenic warming. On the other hand, if seasonal sea ice disappears completely, the unique nature of polynyas may also be lost.

Regional reductions or growth in sea-ice over the past decade have been extensive and are coupled to climate-sensitive global cycles such as ENSO and the Southern Annular Mode. Without many historical measurements, this regional and interannual variability is our best present-day indication for what controls or “forces” these critical polar ecosystems and their sensitivity to future change.

Variability in the productivity of Antarctic polynyas is high for reasons the science community do not currently understand. The supply of trace metals such as iron is thought to determine phytoplankton community structure and production in the Southern Ocean, particularly in conjunction with mixed-layer depth controls on light limitation. A key question is whether interannual variability is driven by these two climate-sensitive factors, and whether we can expect climate-sensitive shifts in ecosystem function and carbon flux in the future. Understanding critical feedbacks between climate and the marine biosphere becomes increasingly urgent as we project rates of change into the future.

Special ASPIRE journal feature in ELEMENTA