| Contributors | Affiliation | Role |
|---|---|---|
| Yager, Patricia Lynn | University of Georgia (UGA) | Principal Investigator |
| Sherrell, Robert M. | Rutgers University | Co-Principal Investigator |
| Stammerjohn, Sharon E. | University of Colorado at Boulder | Co-Principal Investigator |
| Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Water samples were collected aboard R/V Nathaniel B. Palmer during NBP 22-02 (January to March 2022) in the Amundsen Sea, Antarctica. Water sampling was conducted with a profiling rosette of 24 12-liter (L) Niskin bottles equipped with an SBE 911CTD (conductivity-temperature-depth recorder; Sea-Bird Scientific, Bellevue, WA, USA) with additional chlorophyll a (Chl-a) fluorescence (ECO FLRTD-1482; WETLabs, Philomath, OR, USA) and dual dissolved oxygen sensors (SBE43 Sea-Bird Scientific, Bellevue, WA, USA). At each of 21 stations (January 15 - February 25, 2022), seawater samples were collected by Niskin bottle from 3 to 6 depths (2 to 1271 meters (m)), including Antarctic Surface Water (AASW) within the surface mixed layer, intermediate Winter Water (WW), and near-bottom Circumpolar Deep Water (CDW; Randall-Goodwin et al., 2015).
Nutrient samples for nitrate, nitrite, ammonia, and phosphate were collected at the same stations and depths from the profiling rosette/CTD used to collect DNA samples, filtered (0.45-micrometer (µm)), frozen (-80 degrees Celsius), and analyzed at the Oceanographic Data Facility (ODF) Chemistry Laboratory at Scripps Institution of Oceanography, University of California San Diego (Becker et al., 2019). Water column Chl a samples were filtered onto 45-millimeter (mm) GFF filters, dried, and flash frozen (-80 degrees Celsius), and transported back to Georgia where the concentration was measured using acetone extraction and a spectrofluorometer (Knap et al., 1994). Meltwater fraction was determined using oxygen isotope ratios of seawater samples collected and measured (British Antarctic Survey; Meredith et al., 2008) in combination with temperature, salinity, and dissolved oxygen (Randall-Goodwin et al., 2015). Dissolved iron (dFe) samples were also collected at similar stations and depths (using a Trace-Metal Clean CTD that was deployed immediately after the conventional CTD) and analyzed by the Sherrell lab at Rutgers University using standard protocols (Sherrell et al., 2015; Chinni et al., 2026).
- Imported original file "ARTEMIS_DNA_METADATA_FINAL.csv" into the BCO-DMO processing system.
- Flagged "na" as a missing data value (note: missing data are empty/blank in the final CSV file).
- Renamed columns to comply with BCO-DMO naming conventions.
- Converted Date column from string format %m/%d/%y to date type with output format %Y-%m-%d.
- Renamed column "In_situ_Fluorescense_mg_m3" to "In_situ_Fluorescence_mg_m3" and "Amonium_umol_L" to "Ammonium_umol_L" to correct spelling.
- Saved the final data file as "1004881_v1_artemis_dna_metadata.csv".
| File |
|---|
1004881_v1_artemis_dna_metadata.csv (Comma Separated Values (.csv), 69.00 KB) MD5:59cd3ad1e7e93cb7b5254ed72a19a403 Primary data file for dataset ID 1004881, version 1 |
| Parameter | Description | Units |
| Sample_ID | Name of DNA sample | unitless |
| ARTEMIS_ID | Unique Niskin bottle identifier | unitless |
| Station | Station number | unitless |
| Event | Event number | unitless |
| CTD | Conventional CTD Cast number | unitless |
| NISKIN | Niskin Bottle number (1-24) | unitless |
| Date | Date sampled (GMT) | unitless |
| VolumeFiltered_L | Volume of water filtered for DNA Sample | liters |
| FilterDiameter_mm | Diameter of filter used | millimeters |
| FilterType | Type of filter used | unitless |
| FilterPoreSize_um | Pore size of filter | micrometers |
| Community | Designation of sample as 'free-living', 'particle-associated', or both, according to filter and pre-filter pore size. | unitless |
| DepthThreshold | Designation of water column region samples | unitless |
| Watermass | Designation of water mass based on Temperature and Salinity | unitless |
| Location | Designation of sample based on general location in the region | unitless |
| Latitude | Latitude | degrees |
| Longitude | Longitude | degrees |
| Pressure_db | Water pressure of sample | decibars |
| Depth_m | Water depth of sample | meters |
| InsituTemp_C | In situ temperature | degrees C |
| PracticalSalinity | Practical Salinity | (psu) or unitless |
| PotentialTemp_C | Potential Temperature | degrees C |
| AbsoluteSalinity_g_kg | Absolute Salinity | gram per kilogram seawater |
| Potential_Density_Anomaly_kg_m3 | Potential Density Anomaly | kilograms per cubic meter seawater |
| Dissolved_Oxygen_ml_L | Dissolved Oxygen | milliliters per liter seawater |
| Beam_Transmissivity_pcnt | Beam Transmissivity | percent (%) |
| In_situ_Fluorescence_mg_m3 | In situ fluorescence | milligrams per cubic meter seawater |
| Nitrate_umol_L | Nitrate concentration | micromole per liter seawater |
| Phosphate_umol_L | Phosphate concentration | micromole per liter seawater |
| Nitrite_umol_L | Nitrite concentration | micromole per liter seawater |
| Ammonium_umol_L | Ammonium concentration | micromole per liter seawater |
| Lab_Chlorophyll_a_ug_L | Chlorophyll a concentration measured in the lab | microgram per liter seawater |
| Dissolved_Iron_nmol_kg | Dissolved iron concentration | nanomole per kilogram seawater |
| delO18_ppt | Oxygen 18 to Oxygen 16 ratio relative to a standard | parts per thousand |
| Fraction_CDW_pcnt | Calculated fraction of circumpolar deep water | percent |
| Fraction_Sea_Ice_Melt_pcnt | Calculated fraction of sea ice melt | percent |
| Fraction_Meteoric_pcnt | Calculated fraction of meteoric water | percent |
| meltwater_fraction_TS_pcnt | meltwater fraction calculated using Temperature and Salinity | percent |
| Meltwater_Fraction_O2S_pcnt | meltwater fraction calculated using dissolved oxygen and Salinity | percent |
| Meltwater_Fraction_O2T_pcnt | meltwater fraction calculated using dissolved oxygen and temperature | percent |
| Dataset-specific Instrument Name | Seal Analytical continuous-flow AutoAnalyzer 3 (AA3) |
| Generic Instrument Name | Bran+Luebbe / SEAL Analytical AutoAnalyzer 3 (AA3) continuous-flow analyzer |
| Dataset-specific Description | Nutrient analyses are performed at Scripps on a Seal Analytical continuous-flow AutoAnalyzer 3 (AA3). |
| Generic Instrument Description | The AutoAnalyzer 3 (AA3) is a segmented continuous-flow analyzer (continuous flow analyzer, CFA) used for automated colorimetric analysis of dissolved nutrients and other analytes in environmental, seawater, freshwater, wastewater, soil, and agricultural samples. The AA3 was originally manufactured by Bran+Luebbe and, following acquisition of the product line in 2006, has continued to be manufactured and supported by SEAL Analytical. The AA3 is the third-generation instrument in the Technicon AutoAnalyzer family and is widely used for determination of nitrate, nitrite, ammonium, phosphate, silicate, and other dissolved nutrients. See the description from the manufacturer. |
| Dataset-specific Instrument Name | Niskin bottles |
| Generic Instrument Name | Niskin bottle |
| Dataset-specific Description | Water sampling was conducted with a profiling rosette of 24 12-L Niskin bottles. |
| Generic Instrument Description | A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. |
| Dataset-specific Instrument Name | SBE 43 oxygen (O2) sensors |
| Generic Instrument Name | Sea-Bird SBE 43 Dissolved Oxygen Sensor |
| Dataset-specific Description | The sensor suite for each comprised dual SBE temperature (T), conductivity (C), and SBE 43 oxygen (O2) sensors, a WET Labs ECO-AFL/FL fluorometer, and a C-Star transmissometer. Post-cruise corrections were applied to the dual T/C/O2 sensors based on Seabird pre- and post-cruise calibrations and discrete-depth O2 titrations performed during NBP2202. |
| Generic Instrument Description | The Sea-Bird SBE 43 dissolved oxygen sensor is a redesign of the Clark polarographic membrane type of dissolved oxygen sensors. More information from the manufacturer: https://www.seabird.com/products/sbe-43-dissolved-oxygen-sensor |
| Dataset-specific Instrument Name | Seabird 911+ |
| Generic Instrument Name | Sea-Bird SBE 9plus CTD |
| Dataset-specific Description | Hydrographic variables were measured with the use of a CTD/rosette system. The conventional (CNV) package included a Seabird 911+ system on a 24-bottle SBE32 rosette. |
| Generic Instrument Description | High precision and accuracy CTD comprising an SBE 9plus underwater unit (SBE 3plus temperature, SBE 4C conductivity, and Paroscientific Digiquartz pressure sensors, and an SBE 5T submersible pump). Can be used for either real-time data acquisition or for autonomous operations at a sampling speed of up to 24 Hz. The instrument package also includes a TC duct, to reduce salinity spiking caused by ship heave for improved resolution of water column features, and to ensure that temperature and conductivity measurements are made on the same parcel of water. Supplied with both an aluminium and titanium main housing, allowing for use up to 6800 and 10,500 metre depths respectively. Also capable of measuring from eight auxiliary sensors. |
| Dataset-specific Instrument Name | dual SBE temperature (T) sensors |
| Generic Instrument Name | Sea-Bird SBE-3 Temperature Sensor |
| Dataset-specific Description | Hydrographic variables were measured with the use of a CTD/rosette system. The conventional (CNV) package included a Seabird 911+ system on a 24-bottle SBE32 rosette. The sensor suite for each comprised dual SBE temperature (T), conductivity (C), and SBE 43 oxygen (O2) sensors, a WET Labs ECO-AFL/FL fluorometer, and a C-Star transmissometer. Post-cruise corrections were applied to the dual T/C/O2 sensors based on Seabird pre- and post-cruise calibrations and discrete-depth O2 titrations performed during NBP2202. |
| Generic Instrument Description | The SBE-3 is a slow response, frequency output temperature sensor manufactured by Sea-Bird Electronics, Inc. (Bellevue, Washington, USA). It has an initial accuracy of +/- 0.001 degrees Celsius with a stability of +/- 0.002 degrees Celsius per year and measures seawater temperature in the range of -5.0 to +35 degrees Celsius. More information from Sea-Bird Electronics: https://www.seabird.com/products/sbe-3-oceanographic-temperature-sensor |
| Dataset-specific Instrument Name | dual SBE conductivity (C) sensors |
| Generic Instrument Name | Sea-Bird SBE-4 Conductivity Sensor |
| Dataset-specific Description | Hydrographic variables were measured with the use of a CTD/rosette system. The conventional (CNV) package included a Seabird 911+ system on a 24-bottle SBE32 rosette. The sensor suite for each comprised dual SBE temperature (T), conductivity (C), and SBE 43 oxygen (O2) sensors, a WET Labs ECO-AFL/FL fluorometer, and a C-Star transmissometer. Post-cruise corrections were applied to the dual T/C/O2 sensors based on Seabird pre- and post-cruise calibrations and discrete-depth O2 titrations performed during NBP2202. |
| Generic Instrument Description | The Sea-Bird SBE-4 conductivity sensor is a modular, self-contained instrument that measures conductivity from 0 to 7 Siemens/meter. The sensors (Version 2; S/N 2000 and higher) have electrically isolated power circuits and optically coupled outputs to eliminate any possibility of noise and corrosion caused by ground loops. The sensing element is a cylindrical, flow-through, borosilicate glass cell with three internal platinum electrodes. Because the outer electrodes are connected together, electric fields are confined inside the cell, making the measured resistance (and instrument calibration) independent of calibration bath size or proximity to protective cages or other objects. |
| Dataset-specific Instrument Name | SBE32 rosette |
| Generic Instrument Name | Seabird SBE 32 Carousel Water Sampler |
| Dataset-specific Description | Hydrographic variables were measured with the use of a CTD/rosette system. The conventional (CNV) package included a Seabird 911+ system on a 24-bottle SBE32 rosette. |
| Generic Instrument Description | The SBE 32 is a Carousel Water Sampler. With an accessory Deck Unit, the Carousel provides water sampling and real-time CTD data acquisition with any Sea-Bird profiling CTD (requires electro-mechanical cable and slip-ring equipped winch). With an accessory underwater unit, the Carousel can operate autonomously with a Sea-Bird Scientific profiling CTD and can be programmed to close bottles at selected depths, allowing deployment using non-electrical wire or line. The Carousel is available in two models: • Full-size SBE 32 for a 12 or 24-position system (36-position custom). • Compact SBE 32C for a 12-position sampler with bottles up to 8 liters, for use with limited vertical clearance. |
| Dataset-specific Instrument Name | WET Labs ECO-AFL/FL fluorometer |
| Generic Instrument Name | Wet Labs ECO-AFL/FL Fluorometer |
| Dataset-specific Description | The sensor suite for each comprised dual SBE temperature (T), conductivity (C), and SBE 43 oxygen (O2) sensors, a WET Labs ECO-AFL/FL fluorometer, and a C-Star transmissometer. Post-cruise corrections were applied to the dual T/C/O2 sensors based on Seabird pre- and post-cruise calibrations and discrete-depth O2 titrations performed during NBP2202. |
| Generic Instrument Description | The Environmental Characterization Optics (ECO) series of single channel fluorometers delivers both high resolution and wide ranges across the entire line of parameters using 14 bit digital processing. The ECO series excels in biological monitoring and dye trace studies. The potted optics block results in long term stability of the instrument and the optional anti-biofouling technology delivers truly long term field measurements.
more information from Wet Labs |
| Dataset-specific Instrument Name | C-Star transmissometer |
| Generic Instrument Name | WET Labs {Sea-Bird WETLabs} C-Star transmissometer |
| Dataset-specific Description | The sensor suite for each comprised dual SBE temperature (T), conductivity (C), and SBE 43 oxygen (O2) sensors, a WET Labs ECO-AFL/FL fluorometer, and a C-Star transmissometer. Post-cruise corrections were applied to the dual T/C/O2 sensors based on Seabird pre- and post-cruise calibrations and discrete-depth O2 titrations performed during NBP2202. |
| Generic Instrument Description | The C-Star transmissometer has a novel monolithic housing with a highly integrated opto-electronic design to provide a low cost, compact solution for underwater measurements of beam transmittance. The C-Star is capable of free space measurements or flow-through sampling when used with a pump and optical flow tubes. The sensor can be used in profiling, moored, or underway applications. Available with a 6000 m depth rating.
More information on Sea-Bird website: https://www.seabird.com/c-star-transmissometer/product?id=60762467717 |
| Website | |
| Platform | RVIB Nathaniel B. Palmer |
| Report | |
| Start Date | 2022-01-06 |
| End Date | 2022-03-08 |
| Description | See more information at R2R: https://www.rvdata.us/search/cruise/NBP2202 |
NSF Award Abstract:
Part I: Non-technical summary:
The Amundsen Sea is adjacent to the West Antarctic Ice Sheet (WAIS) and hosts the most productive coastal ecosystem in all of Antarctica, with vibrant green waters visible from space and an atmospheric carbon dioxide uptake rate ten times higher than the Southern Ocean average. The region is also an area highly impacted by climate change and glacier ice loss. Upwelling of warm deep water is causing melt under the ice sheet, which is contributing to sea level rise and added nutrient inputs to the region.
This is a project that is jointly funded by the National Science Foundation’s Directorate of Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award, each Agency funds the proportion of the budget and the investigators associated with its own country.
In this collaboration, the US team will undertake biogeochemical sampling alongside a UK-funded physical oceanographic program to evaluate the contribution of micronutrients such as iron from glacial meltwater to ecosystem productivity and carbon cycling. Measurements will be incorporated into computer simulations to examine ecosystem responses to further glacial melting. Results will help predict future impacts on the region and determine whether the climate sensitivity of the Amundsen Sea ecosystem represents the front line of processes generalizable to the greater Antarctic. This study is aligned with the large International Thwaites Glacier Collaboration (ITGC) and will make data available to the full scientific community. The program will provide training for undergraduate, graduate, post-doctoral, and early-career scientists in both science and communication. The team will also develop out-of-school science experiences for middle and high schoolers related to climate change and Antarctica.
Part II: Technical summary:
The Amundsen Sea hosts the most productive polynya in all of Antarctica, with atmospheric carbon dioxide uptake rates ten times higher than the Southern Ocean average. The region is vulnerable to climate change, experiencing rapid losses in sea ice, a changing icescape and some of the fastest melting glaciers flowing from the West Antarctic Ice Sheet, a process being studied by the International Thwaites Glacier Collaboration. The biogeochemical composition of the outflow from the glaciers surrounding the Amundsen Sea is largely unstudied. In collaboration with a UK-funded physical oceanographic program, ARTEMIS is using shipboard sampling for trace metals, carbonate system, nutrients, organic matter, and microorganisms, with biogeochemical sensors on autonomous vehicles to gather data needed to understand the impact of the melting ice sheet on both the coastal ecosystem and the regional carbon cycle. These measurements, along with access to the advanced physical oceanographic measurements will allow this team to 1) bridge the gap between biogeochemistry and physics by adding estimates of fluxes and transport of limiting micronutrients; 2) provide biogeochemical context to broaden understanding of the global significance of ocean-ice shelf interactions; 3) determine processes and scales of variability in micronutrient supply that drive the ten-fold increase in carbon dioxide uptake, and 4) identify small-scale processes key to iron and carbon cycling using optimized field sampling. Observations will be integrated into an ocean model to enhance predictive capabilities of regional ocean function.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Project metadata and links to related datasets in project ARTEMIS are available from USDAP-DC site: https://www.usap-dc.org/view/project/p0010249
NERC Award Abstract:
The Amundsen Sea hosts the most productive polynya in coastal Antarctica, with its vibrant green waters visible from space, and an atmospheric CO2 uptake flux density 10x higher than average for the Southern Ocean. The region is vulnerable to climate change, with rapid losses in sea ice, episodic shifts in the coastal icescape, and the fastest melting glaciers in the adjacent West Antarctic Ice Sheet (WAIS). In an ecosystem experiencing such dramatic change, it is critical to resolve the climate-sensitive drivers and feedbacks of the meltwater-associated iron (Fe) delivery, which underpins productivity in this otherwise high-nutrient, low chlorophyll region. Our previous field research (ASPIRE) identified a clear link between the melting WAIS and the delivery of micronutrient Fe to the polynya ecosystem, and its role in rapid CO2 drawdown. Our recent numerical modeling effort (INSPIRE) suggests several pathways for Fe delivery, ways to optimize fieldwork, and guidance for improving mechanistic understanding of Fe supply and cycling. An ongoing physical oceanographic field program (TARSAN, part of the International Thwaites Glacier Collaboration, ITGC) offers an ideal physical framework for our next research effort. We propose here to collaborate with TARSAN-supported UK scientists, providing significant value added to both teams. TARSAN explored the eastern Amundsen Sea by ship in Feb-Mar 2019 and expects to operate in the Thwaites region again in Feb-Mar 2021. They will use a full suite of physical oceanographic techniques, including 2 under-ice-shelf AUVs, gliders, surface vehicles, a microstructure profiler, shipboard CTD, seal tags, noble gases, and underway sensors to characterize the ice-ocean interactions responsible for rapid glacial melting. During 2019, TARSAN and THOR (also ITGC) collected detailed bathymetric, sedimentary, and ice-shelf cavity information (available Sept 2019) that will immediately improve and update the INSPIRE model to present-day boundary conditions. Our combined NSFGEO-NERC project (ARTEMIS) will facilitate collaboration between ASPIRE/INSPIRE team members and TARSAN/ITGC, add biogeochemical measurements to the funded 2021 expedition, and build on existing glider infrastructure and seal tag expertise (adding biogeochemical sensors to autonomous vehicles) at modest additional logistical cost. Numerical runs with ARTEMIS's updated model will inform TARZAN's 2021 field effort. Observations made will improve our understanding and our model, allowing a more sophisticated assessment of the role of Fe in present and future scenarios. Our team (ARTEMIS) would add shipboard biogeochemical observations (trace metals, carbonate system, nutrients, organic matter, microorganisms) and autonomous vehicle biogeochemical observations (nitrate, Chl a, optical backscatter) to gather knowledge critical to understanding the impact of WAIS melting on both the polynya ecosystem and the regional carbon (C) cycle. ARTEMIS combines the expertise of a US component comprising a carbonate system and microbial ecologist (Yager), a trace metal biogeochemist (Sherrell), a trace metal isotope geochemist (Fitzsimmons), an organic geochemist (Medeiros), an ice-ocean-atmosphere interactions expert (Stammerjohn), and a numerical ocean modeler (St-Laurent), with a UK component comprising 3 physical oceanographers: TARSAN lead PI (Heywood), a biogeochemically savvy autonomous vehicle expert (Queste), and an oceanographer whose vehicles are marine mammals (Boehme). This international team will work together at sea and with shore-based analyses to address a set of interconnected questions arising from the findings of ASPIRE/ INSPIRE.