Modeled Ross Sea Changes
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SUPPLEMENTAL MATERIAL
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1. Model setup and simulations
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The Ross Sea model [Dinniman et al., 2007, 2011] now includes a dynamic sea ice model
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[Budgell, 2005] that prognostically calculates sea-ice concentration and thickness [Stern et al.,
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2013] and accurately depicts circulation and shelf water formation [Dinniman et al., 2007, 2011].
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Open-ocean momentum, as well as heat and fresh water (imposed as a salt flux) fluxes, are
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calculated based on the COARE 3.0 bulk flux algorithm [Fairall et al., 2003] with no relaxation
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of surface temperature or salinity. The Ross Sea model also simulates the mechanical and
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thermodynamic interactions between the floating Ross Ice Shelf and the water underneath
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[Holland and Jenkins, 1999; Dinniman et al., 2011]. Inputs along the open boundaries of the
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regional model [Dinniman et al., 2007] have been updated to use sea-ice concentrations derived
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from Special Sensor Microwave/Imager (SSM/I). Ocean tides are not included. However, this
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model has now been run with tides [Padman et al., in prep; Mack et al., in prep] and the addition
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of tides does not have a significant impact on the aspects of the circulation discussed here.
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High resolution (30-km grid spacing) winds and atmospheric temperatures were obtained
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from the Antarctic Mesoscale Prediction System [AMPS; Powers et al., 2003; Bromwich et al.,
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2005]. Atmospheric forecasts for September 2003 to September 2005 were used to force the
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Ross Sea circulation model. The forcing fields resulted in accurate simulation of the maximum
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summer extent of the Ross Sea polynya (Fig. S1). Similar results were obtained with the ERA-
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Interim winds (0.75° horizontal spacing) for this and other time periods (see results in Stern et al.
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[2013]). Simulations that used the CMIP3 MPI-ECHAM5 winds (1.875° horizontal spacing)
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produced less accurate representations of the summer polynya extent, suggesting that resolution
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in the wind forcing is a critical aspect of simulating sea ice distribution in the Ross Sea.
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Modeled Ross Sea Changes
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Daily winds and atmospheric temperature fields for 9/1996-9/2000 were obtained from the
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MPI ECHAM5 climate model [Jungclaus et al., 2006] 20th-century experiment. In an evaluation
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of the IPCC AR4 climate model simulations, Connolley and Bracegirdle [2007] concluded that
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MPI ECHAM5 was one of the two best performers in the Antarctic (and best globally), and as a
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result we used this atmospheric model. MPI ECHAM5 was also one of the CMIP3 models that
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included 21st century stratospheric ozone recovery [Miller et al., 2006]. Simulations using a
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global sea ice-ocean model forced with future emissions scenarios from ECHAM5 or the Hadley
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Centre Climate Model (the other top performer in the Antarctic) showed little difference between
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the two in sea ice extent, although there were significant differences in other aspects of the ocean
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simulation [Timmermann and Hellmer, 2013]. Comparison of these atmospheric temperature
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fields with the ECMWF-Interim (ERA-Int) Reanalysis temperature data showed that the
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ECHAM5 temperatures were colder (summer climatology differences of up to 8 °C), especially
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near the coast and Ross Ice Shelf front. Simulations using the 20th-century temperatures did not
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produce significant summer expansion of the Ross Sea Polynya (not shown). Therefore, a
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monthly climatology constructed from the ERA-Int air temperatures was used as input for the
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20th-century simulations.
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Daily winds for 9/2046-9/2050 and 9/2096-9/2100 were taken from the CMIP3 A1B
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emissions scenario of the ECHAM5 model. Monthly climatologies of the difference between the
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ECHAM5 A1B and the 20th-century air temperatures were computed for both future scenarios,
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and added to the ERA-Int climatology to create forcing fields for the ocean model. The adjusted
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air temperatures were 1.66 ± 2.58°C warmer for 2046-2050 (Year 2050) and 2.50 ± 2.32°C
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warmer for 2096-2100 (Year 2100) relative to the 20th-century air temperatures.
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Modeled Ross Sea Changes
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The observed freshening of the Ross Sea [salinity changes of -0.03 decade-1 near Ross Island,
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-0.04 decade-1 in MCDW near the Ross Ice Shelf, and -0.08 decade-1 above 200 m along the
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Ross Ice Shelf and near the slope front have been observed; Jacobs and Giulivi, 2010] was
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simulated using a boundary condition that uniformly reduced salinity at the boundaries by 0.12.
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While this value of freshening may not be realistic on the model’s northern or western
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boundaries, as is it believed that most of the freshening is driven from upstream in the
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Amundsen-Bellingshausen Seas [Jacobs and Giulivi,2010], the largest transport into the model
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domain from outside is the shelf break current at the eastern boundary, and thus the only part of
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the boundary freshening that has a significant effect on the simulation is the setup on the eastern
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boundary. Simulations that used the 2050 or 2100 winds and adjusted air temperature, but
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without freshening were done to isolate the effects of wind and air temperature on oceanographic
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properties (Table S1).
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2. Simulated sea-ice area and influence on productivity
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Observed and simulated sea-ice concentrations over the Ross Sea continental shelf are near
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100% in winter, but decrease substantially with the seasonal development of the Ross Sea
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polynya (Fig. 2). Under the 2050 scenario, winter sea-ice concentrations are similar to present
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conditions, but the minimum summer sea-ice cover decreases to a mean ice area that is 44% of
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present conditions. In 2100 the summer ice cover is only 22% of that at present.
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The change in the mass flux between the ocean and the sea ice over the continental shelf
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(Fig. S2) suggests a significant increase in melting (negative mass flux) for the 2050 and 2100
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scenarios, indicating that much of the difference in the summer ice-free extent is due to in situ
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melting rather than enhanced advection of sea ice out of the Ross Sea. Changes in the boundary
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salinity (e.g., 2050SBC vs. 2050) made little difference in the local melting/freezing patterns.
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Modeled Ross Sea Changes
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The timing of the greatest in situ melting is earlier in 2050 than present (Fig. S2) and becomes
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even earlier in 2100. This represents a shift in the timing of the polynya formation, with the
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mean day of retreat below 15% concentration (calculated in the same manner as in Stammerjohn
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et al. [2008]) over the continental shelf in 2050 occurring 5 days earlier, but the mean day of
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advance occurring at the same time . Some locations have a shorter ice-free period due to a
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slight shift in the location of the summer polynya (Fig. S3a), suggesting the importance of spatial
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variability in the changes. By 2100, the duration of the ice-free season is even longer (Fig. S3b)
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with the mean day of retreat now occurring 11 days earlier and the advance occurring 16 days
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later.
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The simulated changes in the ice-free period were used to estimate summer mean primary
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productivity, which was added to annual mean primary productivity estimates to determine
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potential effects on the biological production of the Ross Sea (Table 1). Diatoms frequently
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dominate summer production in the Ross Sea [Smith et al., 2011], and this contribution is also
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estimated (Table 1). The Ross Sea productivity estimates [Smith et al., 2014] are based on
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nitrogen and silicon nutrient deficits from a limited number of samples taken in different years.
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Total productivity is comparable to satellite-derived estimates [Arrigo et al., 2008].
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3. Location of differences in surface layer CDW dye
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A substantial volume of MCDW is advected onto the continental shelf along the troughs
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(Fig.3), where it is mixed into the upper water column (Fig. S4a). In the 2050 and 2100
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simulations, the amount of MCDW that enters the upper 50 m of the water column (averaged
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over the entire continental shelf) decreased by 24 and 8%, respectively (Fig. S4b,c). More
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CDW/MCDW dye gets mixed into the surface layers in the western Ross Sea, which presently is
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Modeled Ross Sea Changes
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the site of the greatest vertical mixing and thus the location of the greatest changes in the surface
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CDW inputs (Fig. S4).
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Modeled Ross Sea Changes
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References
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Arrigo, K.R., G.L. van Dijken, and S. Bushinsky (2008), Primary production in the Southern
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Ocean,1997-2006. J. Geophys. Res.,113, C08004, doi:10.1029/2007JC004551.
Bromwich, D.H., A.J. Monaghan, K.W. Manning, and J.G. Powers (2005), Real-time forecasting
for the Antarctic: An evaluation of the Antarctic Mesoscale Prediction System (AMPS).
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Budgell, W.P. (2005), Numerical simulation of ice-ocean variability in the Barents Sea region
towards dynamical downscaling. Ocean Dyn., 55, 370-387.
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Dinniman, M.S., J.M. Klinck, and W.O. Smith, Jr. (2007), The influence of sea ice cover and
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icebergs on circulation and water mass formation in a numerical circulation model of the
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Ross Sea, Antarctica. J. Geophys. Res., 112, C11013, doi:10.1029/2006JC004036.
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Dinniman, M.S., J.M. Klinck, and W.O. Smith, Jr. (2011), A model study of Circumpolar Deep
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Water on the West Antarctic Peninsula and Ross Sea continental shelves. Deep-Sea Res. II,
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58, 1508-1523.
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Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson (2003), Bulk
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parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. J.
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Climate, 16, 571-591.
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Holland, D.M., and A. Jenkins (1999), Modeling thermodynamic ice-ocean interactions at the
base of an ice shelf. J. Phys. Oceanogr., 29, 1787-1800.
Jacobs, S.S., and C.F. Giulivi (2010), Large multidecadal salinity trends near the PacificAntarctic continental margin. J. Climate, 23, 4508-4524, doi:10.1175/2020JCLI3284.1.
Jungclaus, J.H. et al. (2006), Ocean circulation and tropical variability in the coupled model
ECHAM5/MPI-OM. J. Climate, 19, 3952-3972.
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Modeled Ross Sea Changes
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Miller, R.L., G.A. Schmidt, and D.T. Shindell (2006), Forced annular variations in the 20th
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century Intergovernmental Panel on Climate Change Fourth Assessment Report models. J.
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Geophys. Res., 111, D18101, doi:10.1029/2005JD006323.
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Powers, J.G., A.J. Monaghan, A.M. Cayette, D.H. Bromwich, Y.-H. Kuo, and K.W. Manning
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(2003), Real-time mesoscale modeling over Antarctica: The Antarctic Mesoscale Prediction
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System (AMPS). Bull. Amer. Meteor. Soc., 84, 1533-1545.
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Smith, W.O. Jr, D.G. Ainley, K.R. Arrigo, and M.S. Dinniman, M.S. (2014), The oceanography
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and ecology of the Ross Sea. Ann. Rev. Mar. Sci., 6, 469-487, doi:10.1146/annurev-marine-
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Smith, W.O. Jr., V. Asper, S. Tozzi, X. Liu, X. and S.E. Stammerjohn (2011), Continuous
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fluorescence measurements in the Ross Sea, Antarctica: scales of variability. Prog.
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Stammerjohn, S.E., D.G. Martinson, R.C. Smith, X. Yuan, and D. Rind (2008), Trends in
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Stern, A.A., M.S. Dinniman, V. Zagorodnov, S.W. Tyler, and D.M. Holland (2013), Intrusion of
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warm surface water beneath the McMurdo Ice Shelf, Antarctica. J. Geophys. Res., 118, 1-
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13, doi:10.1002/2013JC008842.
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Modeled Ross Sea Changes
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Supplemental Figure Legends
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S1. Simulated sea ice cover over the Ross Sea continental shelf obtained using 30-km resolution
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AMPS winds and air temperatures for September 2003 to September 2005. The observed
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(SSM/I) sea-ice cover for the same two-year period is shown for comparison.
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S2. Averages of total mass flux (m3 water s-1), including frazil ice production, between the ocean
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and sea ice on the Ross Sea continental shelf over the four years of each of the five
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simulations.
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S3. Simulated change in the number of days when sea ice concentrations are below 15%
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obtained from the difference in a) the 2050 and the 20th century simulations and b) the 2100
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and the 20th century simulations. Positive values correspond to a longer sea ice-free period.
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S4. Concentration (in arbitrary units; a.u.) of CDW dye in the model surface layer at the end of
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four-year simulations that used conditions for a) present, b) 2050, and c) 2100.
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