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 )
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
Winter Average
Summer Average

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

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 thermodynamics and
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
Largest correlations at lag=0
Indications of enhanced correlations preceding ice dipole
Small correlations following ice dipole
Highest correlation near lag=0
Enhanced correlations both lead and lag the ice dipole timeseries
Positive feedbacks

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