Improving the Spatial Thickness Distribution of Modelled Arctic Sea Ice

Paul Miller, Seymour Laxon, Daniel FelthamCentre for Polar Observation and Modelling

University College London

UK Sea Ice Meeting, 8-9th Sept 2005

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?

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, 1994-2001 (SSM/I

+ buoy + AVHRR, Fowler, 2003, NSIDC)

– Sea ice extent, 1994-2001 (SSM/I, NSIDC)

– Sea ice thickness, 1993-2001 (ERS radar altimeter, Laxon et al., 2003)

We used this model and forcing to reduce uncertainty surrounding sea ice model parameters

Parameter Space

Ice strength, P*

Albedo, ice

Air drag coefficient, Cair

• Our parameter space has three dimensions

• Uncertainty surrounds correct values to use

• Space includes commonly-used values

• 168 model runs needed to optimise model

0.62

0.54

2.5 (kPa)100

0.0003

0.0016

We explored the model’s multi-dimensional parameter space to find the ‘best’ fit to the observational data

Arctic Basin Ice Thickness(<81.5oN)

{ice, Cair, P*} = {0.56, 0.0006, 5 kPa}

Miller et al 2005a

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

R = 0.98RMS difference = 0.28m

Rothrock et al., 2003, JGR, 108(C3), 3083

Spatial Draft Discrepancy

Rothrock et al., 2003, JGR, 108(C3), 3083 Optimised CICE Model

Sea Ice Rheology

• 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

2

1

e=2

e=√.5

C

S

S

P/2

Spatial Draft Discrepancy

e=2 e=√.5

Improvements Due to Increased Shear Strength

Improved Zonal Averages

Improved Cruise Averagese = 2 e = √.5

Model vs ERS Mean Winter Ice Thickness (<81.5oN)

Model-Satellite Thickness (m)

e = 2 e = √.5

Truncated Yield Curve2

1

e=2

e=√.5

S

Truncated Yield Curve2

1

e=2

e=√.5

S

Truncated

Truncated Yield Curve2

1

e=2

e=√.5

S

Truncated < 80% IceConcentration

Arctic Basin Ice Thickness Since 1980

e = 2e = √.5

e = √.5(Truncated for IC < 80%)

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

Paul Miller, Seymour Laxon, Daniel FelthamCentre for Polar Observation and Modelling, UCL

Slide 5

Melt Season LengthIce ConcentrationIce 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 1948 - present

ERS-derived Mean Winter Ice Thickness

Model-Observed Thickness (m)

e = 2 e = √.5