Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Parameters
• Deployments
• Project Information
• Funding

1-D vertical mixing/biogeochemical Regional Ocean Modeling System (ROMS) output of October 2010 - March 2011 of the Amundsen Sea Polynya, modeled at twelve bloom stations. Data are 3-hourly averages, and saved in NetCDF files. In the NetCDF files, data are distributed over a 6x6 grid with 30 depths (ranging from the surface down to 210 m, with higher resolution near the surface). ocean_avg.nc files are the standard model output, while files named ocean_avg_sensitivity_lowWW.nc are from runs using a lower winter water initial dissolved iron concentration.

This dataset includes the following model parameters:

averaged time since initialization, background vertical mixing coefficient for momentum, background vertical mixing coefficient for tracers, bathymetry at RHO-points, bottom roughness, coagulation rate, Coriolis parameter at RHO-points, curvilinear coordinate metric in ETA, curvilinear coordinate metric in XI, detrital sinking rate, detritus remineralization rate, disaggregation rate, domain length in the ETA-direction, domain length in the XI-direction, empirical Fe:C coefficient, empirical Fe:C power, Fe:C ratio, free-surface nudging/relaxation inverse time scale, grid type logical switch, inverse half-saturation for phytoplankton NO3 uptake, iron remineralization rate, iron scavenging rate on large POM, iron scavenging rate on small POM, iron uptake timescale, Ivlev constant for zooplankton grazing, large detritus sinking rate, latitude of PSI-points, latitude of RHO-points, latitude of U-points, latitude of V-points, light attenuation due to phytoplankton, light attenuation due to sea water, linear drag coefficient, longitude of PSI-points, longitude of RHO-points, longitude of U-points, longitude of V-points, mass point sources and sink activation switch, mean density used in Boussinesq approximation, momentum point sources and sink activation switch, nitrate uptake rate, number of iterations to achieve convergence, number of long time-steps, number of short time-steps, number of time-steps between history records, number of time-steps between restart records, number of time-steps between the creation of average files, number of time-steps between the creation of history files, number of time-steps between time-averaged records, photosynthetically available radiation fraction, phytoplankton initial slope of the P-I curve, phytoplankton mortality rate to the detritus pool, phytoplankton mortality rate to the Nitrogen pool, phytoplankton sinking rate, Power-law shape barotropic filter parameter, quadratic drag coefficient, rate of large detritus nitrogen re-mineralization, S-coordinate at RHO-points, S-coordinate at W-points, S-coordinate bottom control parameter, S-coordinate parameter,  critical depth, S-coordinate stretching curves at RHO-points, S-coordinate stretching curves at W-points, S-coordinate surface/bottom layer width, S-coordinate surface control parameter, sea surface height climatology processing switch, size of long time-steps, size of short time-steps, slipperiness parameter, starting time-step for accumulation of time-averaged fields, surface roughness, time-averaged available dissolved iron concentration, time-averaged detritus concentration, time-averaged free-surface, time-averaged iron on large particulate organic matter, time-averaged iron on small particulate organic matter, time-averaged large fraction nitrogen detritus concentration, time-averaged net latent heat flux, time-averaged net longwave radiation flux, time-averaged net sensible heat flux, time-averaged nitrate concentration, time-averaged phytoplankton,  associated iron concentration, time-averaged phytoplankton concentration, time-averaged potential temperature, time-averaged salinity, time-averaged solar shortwave radiation flux, time-averaged surface net heat flux, time-averaged surface net salt flux,  (E-P)*SALT, time-averaged surface u-wind component, time-averaged surface v-wind component, time-averaged temperature vertical diffusion coefficient, time-averaged u-volume flux, time-averaged vertical momentum component, time-averaged v-volume flux, time-averaged zooplankton concentration, time stamp assigned to model initilization, tracer climatology nudging activation switch, tracer climatology processing switch, tracer point sources and sink activation switch, Tracers nudging/relaxation inverse time scale, vertical terrain-following stretching function, vertical terrain-following transformation equation, zooplankton excretion efficiency to detritus pool, zooplankton excretion efficiency to Nitrogen pool, zooplankton grazing rate, zooplankton half-saturation constant for ingestion, zooplankton mortality rate to the detritus pool, zootoplankton mortality rate to the Nitrogen pool, 2D momentum climatology nudging activation switch, 2D momentum climatology processing switch, 2D momentum nudging/relaxation inverse time scale, 3D momentum climatology nudging activation switch, 3D momentum climatology processing switch, 3D momentum nudging/relaxation inverse time scale

We use the 1-D ROMS/biogeochemical model at twelve ASPIRE bloom stations  from 1 October 2010 through 31 March 2011 (6 months). These stations are the same as those considered by Yager et al. (2016), except for Station 68, a sea ice-covered station on the shelf break that we did not include since it was far from the polynya. The 1-D model uses a 9-minute time step and represents the upper 210 m of the water column with 30 vertical levels. The model vertical resolution is highest in the surface layer (2 – 4 m resolution in the top 25 m), and lowest in the bottom layer (maximum of 15.5 m resolution at 210 m). The model physics evolve over time according to vertical mixing and surface forcing. 

The biogeochemical model used in this study is a modified version of the iron-, light- and nitrate-limited NPZD model developed by Fiechter et al. (2009) for ROMS in the Gulf of Alaska. Phytoplankton growth is determined by a maximum specific rate and a multiplicative function of iron, nitrate, and light availability relative to saturating values. The model considers the cycles of both nitrogen and iron, allowing for explicit iron and nitrate limitation on the rate of nutrient uptake using Michaelis-Menten kinetics.

BCO-DMO Processing Notes:
 - served the zip package 

The Amundsen Sea, in the remote Pacific sector of the Southern Ocean, is one of the least well studied Antarctic continental shelf regions. It shares characteristics in common with other Antarctic ice shelf regions, but exhibits unique aspects also. The Amundsen Sea Polynya (ASP), an open region at the base of several of the terminal glaciers draining the West Antarctic Ice sheet exhibits: 1) large intrusions of heat delivered from the warming modified circumpolar deep water (mCDW) rising up onto the continental shelf, 2) the fastest melting ice sheets in Antarctica, 3) the most productive coastal polynya (161 g C m-2) together with a significant atmospheric CO2 sink, and 4) some of the most rapidly declining regions of seasonal off-shore sea ice on Earth.

Following on from an earlier oceanographic field program, the Amundsen Sea Polynya International Research Expedition (ASPIRE; 2011), this study seeks to better synthesize and model the relative contributions of both physical ocean-ice linkages and biological production and carbon export terms and to compare these with other circumpolar Antarctic regions. A central feature will be the use of a regionally coupled physical-biogeochemical model to follow the dynamics of the large phytoplankton blooms that occur annually in the Amundsen Sea Polyna. This study will provides a means to locate the Amundsen Sea properties along the continuum of Antarctic ice shelf systems, and to understand how these system might change in response to climate change.

Pedagogical techniques will be used to provide educational outreach for three distinct target populations: secondary students, pre-service science teachers, and in-service science teachers. Partnerships will be developed with science teacher educators to implement the STEM career-development lessons in undergraduate and graduate level science teacher education courses.