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Antarctic Sea Ice Variability in the CCSM2 Control Simulation

Antarctic Sea Ice Variability in the CCSM2 Control Simulation. Marika Holland National Center for Atmospheric Research Cecilia Bitz Polar Science Center, APL, Seattle Elizabeth Hunke Los Alamos National Laboratory. Introduction/Motivation.

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Antarctic Sea Ice Variability in the CCSM2 Control Simulation

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  1. Antarctic Sea Ice Variability in the CCSM2 Control Simulation Marika Holland National Center for Atmospheric Research Cecilia Bitz Polar Science Center, APL, Seattle Elizabeth Hunke Los Alamos National Laboratory

  2. Introduction/Motivation • 550 years of CCSM2 Model simulation analyzed (yrs 350-900) • To assess the realism of the CCSM2 simulation • To examine the physical processes driving simulated sea ice variability, including influence of simulated feedbacks • To determine influence of large scale modes of variability on Antarctic sea ice conditions

  3. Mean Sea Ice Conditions Ice Concentration Summer Average Winter Average

  4. Leading mode of winter variability Ice Concentration Simulated (600 yrs) Observed (1979-1998)

  5. Advection of Anomalies

  6. Atmospheric Conditions Associated with Ice Dipole Autumn Winter AMJ SAT AMJ SLP Consistent with anomalies being forced by both winds and SAT.

  7. Ocean Conditions Associated with Ice Dipole SST Considerable SST anomalies also associated w/ice. • Ocean velocity consistent with SLP. • Contribute to dynamical forcing of ice anomalies and ocn heat transport anomalies.

  8. Forcing of Pacific variability • Enhanced Pacific ice driven by processes in preceding autumn • Both thermodynamicsand • dynamics contribute • In winter thermodynamics enhance, dynamics damps • largest at 1 yr lag • Suggests feedbacks prolong anomalies Dynamic Processes AMJ JAS Solid=thermo (ice growth) Dash=dynamics (advection, ridging) AMJ JAS Thermodynamic Processes Pacific ice area tendency terms regressed on Ice EOF

  9. Forcing of Atlantic variability Dynamic Processes • Reduced ice driven by processes in preceding autumn • Both dynamics and thermodynamics contribute • In winter, thermodynamic processes continue to increase anomalies. • Less memory than Pacific • Anomalies shorter-lived AMJ JAS Thermodynamic Processes AMJ JAS Atlantic ice area tendency terms regressed on Ice EOF

  10. “Memory” of Atmospheric Anomalies Solid = max correlation Dash = -min correlation Solid=max r Dash=-min r Solid=max r Dash=-min r SLP SAT • Highest correlation near lag=0 • Enhanced correlations both lead and lag the ice dipole timeseries • Positive feedbacks • Largest correlations at lag=0 • Indications of enhanced correlations preceding ice dipole • Small correlations following ice dipole

  11. Associated Ocean SW absorption • Albedo feedback modifies SW absorption • Helps prolong life of anomalies • particularly in Pacific • in Atlantic, ocean currents transport warm SSTs away from ice formation regions, reducing their influence

  12. Relationship to large scale modes of variability • Number of observational studies have looked at the influence of ENSO on southern hemisphere sea ice conditions • results appear consistent with the ice dipole • A recent modeling study (Hall and Visbeck, 2002) has suggested a relationship between Antarctic sea ice and the Southern Annular Mode (SAM) • Wanted to determine whether these modes of variability play a role in forcing the sea ice dipole present in CCSM2

  13. Ice Area associated with ENSO • Ice anomalies small, but consistent with ADP. • NINO3 and ADP correlate at r=-0.32 • Forced by dynamics in Pacific, with thermo feedbacks amplifying in later years. • Both dynamically and thermodynamically forced in Atlantic

  14. SLP and SAT associated with SAM

  15. Ice Conditions associated with SAM • Maximum at lag=1 • Some similarities with ADP • Correlates to ADP at r=0.35 • Pacific and Indian - dynamically forced at lag=0 anomalous ice growth enhances at lag=1 • Atlantic - largely thermo driven anomalies

  16. Conclusions • As in observations, CCSM2 Antarctic ice variability exhibits a dipole pattern with enhanced Pacific ice associated with reduced Atlantic ice • These are forced by both dynamical and thermodynamical processes, consistent with atmosphere and ocean conditions • Albedo feedback prolongs anomalies in Pacific. Its influence in Atlantic is reduced due to transport of anomalous SST to regions where no ice formation occurs • Both ENSO and SAM appear to weakly influence the ice dipole pattern

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