1. UK Sea Ice Meeting, 8-9th Sept 2005
Improving the Spatial Thickness Distribution of Modelled Arctic Sea Ice
Paul Miller, Seymour Laxon, Daniel Feltham
Centre for Polar Observation and Modelling
University College London

2. Motivation
Rothrock et al., (2003) showed Arctic sea ice thickness as predicted by various models
Significant differences, both in the mean and anomaly of ice thickness during the last 50 years
Causes of differences not well understood, but there is both parameter and forcing uncertainty
How can we reduce this uncertainty and increase our confidence in conjectures based on model output?

3. Reducing Parameter Uncertainty in Sea Ice Models
Use one of the best available sea ice models (CICE) and force it with the best available fields (ERA-40 & POLES)
Optimise and validate the model using a comprehensive range of sea ice observations:
Sea ice velocity, (SSM/I + buoy + AVHRR, Fowler, 2003, NSIDC)
Sea ice extent, (SSM/I, NSIDC)
Sea ice thickness, (ERS radar altimeter, Laxon et al., 2003)
We used this model and forcing to reduce uncertainty surrounding sea ice model parameters

4. Parameter Space We explored the model’s multi-dimensional parameter
space to find the ‘best’ fit to the observational data
Our parameter space has three dimensions
Uncertainty surrounds correct values to use
Space includes commonly-used values
168 model runs needed to optimise model
Albedo, ice
0.62
0.54
0.0003
Ice strength, P*
0.0016
2.5
100
(kPa)
Air drag coefficient, Cair

5. Arctic Basin Ice Thickness (<81.5oN)
Miller et al 2005a
{ice, Cair, P*} = {0.56, , 5 kPa}

6. Validation Using ULS Data from Submarine Cruises
We consider data from 9 submarine cruises between 1987 and 1997
Rothrock et al. (2003) used this data to test their coupled ice-ocean model
Modelled cruise means of ice draft are in good agreement with ULS observations
Rothrock et al., 2003, JGR, 108(C3), 3083
R = 0.98
RMS difference = 0.28m

7. Spatial Draft Discrepancy
Rothrock et al., 2003, JGR, 108(C3), 3083
Optimised CICE Model

8. Sea Ice Rheology 2 1 e=√.5 CICE sea ice rheology is plastic
CICE has an elliptical yield curve, with ratio of major to minor axes, e (Hibler, 1979)
Maximum shear strength determined by P*, thickness, concentration and e
Decreasing e reduces ice thickness in the western Arctic and increases it near the Pole S 1
P/2 S
e=2 C

9. Spatial Draft Discrepancy

10. Improvements Due to Increased Shear Strength
Improved Cruise Averages
Improved Zonal Averages

11. Model vs ERS Mean Winter Ice Thickness (<81.5oN)
Model-Satellite Thickness (m)

12. Truncated Yield Curve 2 1 S
e=2
e=√.5

13. Truncated Yield Curve 2 1 S
e=2
e=√.5
Truncated

14. Truncated Yield Curve 2 1 e=2 e=√.5 Truncated < 80% IceS
e=2
e=√.5
Truncated
< 80% Ice
Concentration

15. Arctic Basin Ice Thickness Since 1980
(Truncated for IC < 80%)

16. Conclusions
Initial work reduced parameter uncertainty in a stand-alone sea ice model
Observations of thickness/draft from submarine cruises were used to independently test the optimised model
By increasing the shear strength (by changing e from 2 to .5), we reproduced the observed spatial distribution of ice draft
Found that some tensile strength is necessary
These results are in press, Miller et al 2005b

17. Paul Miller, Seymour Laxon, Daniel Feltham
Centre for Polar Observation and Modelling, UCL

18. Slide 5
Melt Season Length
Ice Concentration
Ice Motion
Extend observational validation back to mid-1980’s using intermittent submarine data
Use optimised model to examine changes in radiative, thermal, and mechanical forcing to determine primary mechanisms in ice mass change from present

19. ERS-derived Mean Winter Ice Thickness
Model-Observed Thickness (m)