Antarctic sea ice variability in the ccsm2 control simulation
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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

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
Introduction/Motivation Simulation

  • 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


Mean sea ice conditions
Mean Sea Ice Conditions Simulation

Ice Concentration

Summer Average

Winter Average


Leading mode of winter variability
Leading mode of winter variability Simulation

Ice Concentration

Simulated (600 yrs)

Observed (1979-1998)



Atmospheric conditions associated with ice dipole
Atmospheric Conditions Associated with Ice Dipole Simulation

Autumn

Winter

AMJ SAT

AMJ SLP

Consistent with anomalies being forced by both winds and SAT.


Ocean conditions associated with ice dipole
Ocean Conditions Associated with Ice Dipole Simulation

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.


Forcing of pacific variability
Forcing of Pacific variability Simulation

  • 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


Forcing of atlantic variability
Forcing of Atlantic variability Simulation

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


Memory of atmospheric anomalies
“Memory” of Atmospheric Anomalies Simulation

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


Associated ocean sw absorption
Associated Ocean SW absorption Simulation

  • 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


Relationship to large scale modes of variability
Relationship to large scale modes of variability Simulation

  • 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


Ice area associated with enso
Ice Area associated with ENSO Simulation

  • 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



Ice conditions associated with sam
Ice Conditions associated with SAM Simulation

  • 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


Conclusions
Conclusions Simulation

  • 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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