Challenges in Modeling Global Sea Ice in a Changing Environment
Marika M HollandNational Center for Atmospheric
Research
•Systems of equations that describe fluid motion, radiative transfer, etc. •Include ocean, atmosphere, land, sea ice components•Conservative exchange of heat, water, momentum across components •Unresolved processes are parameterized
Coupled Climate Models
Sea Ice Models Used in Climate Simulations
• Two primary components– Dynamics
• Solves force balance to determine sea ice motion– Thermodynamics
• Solves for vertical ice temperature profile • Vertical/lateral melt and growth rates
• Some (about 30% of IPCC-AR4) models also include– Ice Thickness Distribution
• Subgridscale parameterization• Accounts for high spatial heterogeneity in ice
Simulated Ice Thickness
Climatology1980-1999
Thickness varies
considerably across models
Differences in mean and distribution
Largest inter-model
scatter is in the Barents Sea region
Ensemble Mean Standard Deviation
3.02.01.0 m0.0
Ice Thickness• Equilibrium Reached when
– Ice growth is balanced by ice melt + ice divergence– Illustrative to consider how different models achieve this
balance and how mass budgets change over time
Ice volume change
Thermodynamic source
Divergence
Assessing Sea Ice Mass Budgets
Climate model archive of monthly averaged ice thickness and velocityAssess Arctic ice volume, transport through Arctic straits, and solve for ice growth/melt as residual
FramStrait
CAA
BarentsSea
BeringStrait
NorthAmerica
Eurasia
Holland et al., 2010
20th century mass budgets
Across the 14 models:Annual Ice melt varies from 0.6m-1.8mAnnual growth has a similar range (0.9m-1.9m)Annual ice divergence varies from 0.03m-0.6m
Mean of 14 Models
20th century mass budgets
Intermodel scatter in ice melt strongly related to net SW fluxSuggests a dominant role for albedo variations across models, which may be caused by:
Albedo parameterizationsSimulated surface state (e.g. snowfall)
Correlation of ice melt and SHF
Regression of ice melt and SHF
Net SW
Net SW
Net Flux
Mean of 14 Models
Arctic Ice Thickness Change
•By 2100, in response to rising GHGs, considerable ice volume loss of about 1.5m on annual average •Large intermodel scatter in ice loss is strongly related to initial ice thickness•Models with initially thicker ice have larger ice volume loss
Ensemble Mean
Ense
mbl
e Ra
nge
Average Arctic ice thickness change (SRES A1B Scenario)
Ice Mass Budget Change
Over 21st century, increased net ice melt occursPartially balanced by reduced divergence (less transport from Arctic to lower latitudes).
Multi-model ensemble meanMass Budget Change Relative to
1950-1970 mean
For different models:Nature of ice mass budget changes varies considerablyDifferent in•Magnitude of net change•Magnitude and sign of terms that produce change
Model scatter in evolving ice mass budgets
•All models exhibit reduced ice transport, related to thinning ice•Net melt increase strongly related to initial thickness (thicker models have more melt)•Relative role of changes in melt and growth are related to evolving September ice extent•Increases in ice melt give way to decreases in ice growth as Arctic loses the summer ice cover
Melt Change at 2050
Growth Change at 2050
Ice mass budgets affected by climate feedbacks
• Fundamental sea ice thermodynamics gives rise to a number of important feedbacks
Surface albedo changes modify SW absorption in ice and ocean heat fluxIce loss lowers albedo – positive feedback
Ice mass budgets affected by climate feedbacks
• Fundamental sea ice thermodynamics gives rise to a number of important feedbacks
Heat conduction related to vertical temperature gradient
Causes ice growth to vary as 1/h Has a stabilizing effect on ice thickness
since thin ice grows more rapidly
Model scatter in evolving ice mass budgets
Melt Change
Growth Change
MeltGrowth
Divergence
•Influence of ice thickness on ice growth rates causes ice growth to increase (for some models) even with large Arctic warming•However, when summer ice cover becomes sufficiently low, the albedo feedback overwhelms this and results in ice growth reductions
CCSM3 Model
Albedo FeedbackThe surface albedo feedback can be isolated as:
where
Changes potentially due to:• Changing area of open water• Changing albedo of sea ice
Importance of surface albedo changes is assessed from:
Albedo FeedbackAnalysis
Assess the change in albedo per
change in surface temperature (Da/DT)
using transient climate integrations
DT
Da
Surface Albedo Feedback Analysis
For Arctic Ocean domain, sensitivity of surface albedo to air temperature change exhibits a three-fold variation across models
By year 2100, 80% of intermodel scatter related to scatter in summer open water area change
At year 2050, changes in sea ice albedo play a larger role
Evidence that model parameterizations influence feedback strength
Enhanced albedo feedback in ITD run
Larger albedo change per temperature change for thinner initial ice With ITD have larger a change for ice with same initial thicknessSuggests surface albedo feedback enhanced in ITD run
ITD (5 cat)1 cat.
1cat tuned
Holland et al., 2006
Larger increase in net ice melt in models with larger Da/DTThis is consistent with analysis of surface heat flux changes.Models with larger net ice melt increases exhibit:•Larger increases in net SW •Larger increases in downwelling longwave (winter)•Larger compensating increases in turbulent and longwave heat loss (cold season)
For some changes, difficult to attribute cause-and-effect
Scatter in net ice melt relative to surface heat flux
changes
(Holland et al., 2010)
Translating ice volume change to
ice extent lossFor thick ice: small extent loss per meter of ice thickness loss
For 1-2m ice: • large ice extent loss per ice volume change• variable across models
How do changes in ice volume translate into ice extent loss?
For 1-2m thickness, scatter in ice extent loss per thickness change is related to the distribution of ice thickness within the ArcticModels with a broader distribution have smaller ice extent loss per ice thickness change. Stabilizing effect of thick ice regions?
Challenges in Modeling Sea Ice in a Changing Environment
• Sea ice is a complex material and numerous processes are excluded/idealized in models
• However these models are based on physical principals and validated against observations
• Climate models differ widely in their simulation of sea ice – both climatology and change
• Simulated feedbacks vary considerably and can be parameterization dependent
• However, even models with nearly identical sea ice components can have large differences as simulated sea ice is highly dependent on atmosphere and ocean conditions
• To model correct sea ice requires adequate simulations of atmosphere and oceans
Challenges in Modeling Sea Ice in a Changing Environment
• So, is it all hopeless?• Recent studies providing insight on what is
needed if we are to accurately simulate sea ice change:– present day ice conditions, including extent and
the spatial distribution of ice thickness; – the evolving surface energy budget
• To achieve this involves numerous and interacting factors across the coupled system
• Models are continuously improving and have provided considerable insight into the functioning of sea ice and its role in the climate system
Simulated September Arctic Extent
(Updated from Stroeve et al., 2007)
Range in model 2007 extent from natural variability ~ 4.8 to 7 million km2
Arctic OceanSeptember Ice ExtentCCSM3 – Ensemble Members
Observations
Questions?
What stabilizes the ice cover?Run with increasing GHG
MeltGrowth
Divergence
Run with GHG stabilized after 2020
Melt
DivergenceGrowth
Model parameterizations modify ice growth rate feedback
For ice of the same mean thickness,• The ITD has fewer locations with increased ice growth. • This suggests a reduced negative feedback on ice thickness
5 category1 category1cat tuned
Sea Ice Model - Dynamics• Ice treated as a continuum with an effective large-scale
rheology describing the relationship between stress and flow
• Force balance between wind stress, water stress, internal ice stress, coriolis and stress associated with sea surface slope
• Ice freely diverges (no tensile strength)• Ice resists convergence and shear• Multiple ice categories advected with same velocity
field
€
m ∂u∂t
= −mfk × u + τ a + τ o − mg∇H +∇ • σ
Coriolis Airstress
Oceanstress
Sea Slope
InternalIce Stress
Ice Thickness Distribution
Evolution depends on: Ice growth, lateral melt, ice divergence, and mechanical redistribution (riding/rafting)
(Thorndike et al., 1975)
€
∂g∂t
= − ∂∂h
( fg) + L(g) −∇ • (r v g) + Ψ(h,g,
r v )
Vertical heat transfer
(from Light, Maykut, Grenfell, 2003)(Maykut and Untersteiner, 1971; Bitz and Lipscomb, 1999; others)
• Assume brine pockets are in thermal equilibrium with ice
• Heat capacity and conductivity are functions of T/S of ice
• Assume constant salinity profile• Assume non-varying density• Assume pockets/channels are brine filled•
€
ρc ∂T∂t
= ∂∂z
k ∂T∂z
+ QSW
€
QSW = − ddz
ISW e−κzwhere
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ISW = i0(1−α )FSW