www.ncof.gov.uk

Results of the assimilation of sea ice concentration and velocity into a sea-ice-ocean model.John Stark, Mike Bell, Matt Martin, Adrian Hines, Alistair Sellar, Jeff Ridley.

www.ncof.gov.uk

Motivation & Outline of Talk.

Motivation• Improve operational forecasts of sea-ice and ocean

• Improve seasonal and decadal forecasts

• Improve NWP forecasts by using better sea-ice data

• Reduce uncertainties in sea-ice models by detailed intercomparison of models & data.

Overview of talk• Overview of the assimilation system.

– Ice concentration assimilation.– Ice velocity assimilation.

• Results from year-long reanalysis integrations– Period from October 1999 December 2000.

• Summary

www.ncof.gov.uk

The Sea-Ice-Ocean Model

1° Global FOAM.• Ocean component is identical to the operational FOAM model.• 6-hourly NWP fluxes.• Ice thickness distribution (ITD), 5 categories (Lipscomb 2001).• Elastic viscous plastic rheology (Hunke & Dukowicz 1997)

1° FOAM, Operational since 1997

• Derived from the CICE model (Hunke & Lipscomb, LANL)

• Configuration of sea ice model is identical to our latest climate model, HadGEM1.

• Regular Lat-Long grid (Polar island participates in ice flow)

www.ncof.gov.uk

Sea Ice Concentration Assimilation Scheme

• Obs from SSM/I (passive microwave).• ASI algorithm converts brightness temperature to ice concentration estimates.• Optimal interpolation used :

– Can process many observations efficiently.– Can cope with poor estimates of the error.– Does not require accurate knowledge of error correlations (but these can be used).– Does not require an adjoint or inverse model or ensemble runs.

ASI algorithm & Q.C.

O.I. Analysis Forecast

NWP surface temperature

SST & Ice

Ice concentration

Swath data

An

aly

sis

Incr

em

en

ts

www.ncof.gov.uk

Observation Pre-Processing

• Very large number of observations : more than 300,000 per day.– Sub-sampled to include only observations with all 7 channels

(85GHz is sampled at twice the frequency in both directions). – Helps to reduce error correlation, since beam footprint is larger than

spatial sampling.

• Used a maximum ice extent mask to reduce spurious observations in sub-tropics and reduce the total number of observations.– Land masks extended to 100km

Ice extent mask for March

www.ncof.gov.uk

Ice Conc. Observation Pre-Processing

• Observations that fail the weather filter are treated as 0% ice cover.

Swath Obs, Tb

NASA-Team

Weather Filter Fail Conc=0%

NTA > 30% ? Discard

Conc(ASI)

Ice Extent Filter

Error Estimate

NWP Surface T. Fail

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Effect of 30% NTA threshold.

Threshold = 5% Threshold = 30%

• Trade off between spurious ice observations & resolving the marginal ice zone:

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Application of Analysis Increments

• Preferentially altering the thinnest ice.– Smallest change in heat content– Represents thermodynamic changes to the sea

ice.

• Scaling all categories– Smallest RMS change in category ice area.– Represents convergence / divergence in the flow.– Can result in large changes to thick ice.

• The analysis scheme provides an estimate of total sea ice concentration– This must be mapped onto the ice thickness distribution

(ITD) in the model.

Ice Thickness

Fra

ctio

nal C

over

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Application of Analysis Increments

Scaling all categories

Total Ice Volume Comparison : North

0.00E+00

5.00E+12

1.00E+13

1.50E+13

2.00E+13

2.50E+13

3.00E+13

28/08/1999

17/10/1999

06/12/1999

25/01/2000

15/03/2000

04/05/2000

23/06/2000

12/08/2000

01/10/2000

20/11/2000Time

Ice

Volu

me,

m^3

ControlConc. AssimVel. AssimConc.&Vel Assim

Total Ice Volume Comparison : North

0.00E+00

5.00E+12

1.00E+13

1.50E+13

2.00E+13

2.50E+13

3.00E+13

28/08/1999

17/10/1999

06/12/1999

25/01/2000

15/03/2000

04/05/2000

23/06/2000

12/08/2000

01/10/2000

20/11/2000Time

Ice

Volu

me,

m^3

ControlIceConcIceVelConcVel

Red = Free runCyan = Assim

Red = Free runCyan = Assim

Ice Thickness

Fra

ctio

nal C

over

Ice Thickness

Fra

ctio

nal C

over

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Ice Velocity Assimilation Scheme

Obs Q.C.

Analysis

Analysis increments Balancing stress

Computed using free drift

i

Forecast

Model ice velocity bias

Ice-motion data from CERSAT-IFREMER and NSIDC

Ice motion forecast

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Ice Vel. Observation Pre-Processing

CERSAT Obs (NetCDF)

DiscardQuality Flag==1 ? Fail

Fowler Obs (ASCII)

Coordinate Conversion & Vector Rotation

DiscardV > 1m/s ? FailModel N-day

Mean Velocities

Interpolate to Obs

Combine with Ob Errors, output to Obs file.

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Velocity analysis scheme

• Assimilation– Assimilation scheme based on OI, similar to ice

concentration.– Assimilation scheme able to control divergence introduced

by the assimilation.– Relative amount of divergence can be controlled.– Uses a ‘balancing stress’ based on free drift dynamics to

apply the increments.– Uses 2 background error length scales.

• Quality Control– Observations near coastlines have reduced impact to

prevent velocities into land.

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Ice Velocity : Application of Increments

• EVP scheme rapidly adjusts to maintain equilibrium with external forcing on ice.– Direct application of the analysis increments to the velocity fails.– Previous work (Meier, Zhang) has diagnosed an additional velocity which is

added to the model and plays no direct part in the dynamics.– We used an alternative approach : balancing stress.

Arctic Sea Ice Spin up : RMS Speed

0

2

4

6

8

10

12

14

16

18

00:00 06:00 12:00 18:00 00:00Time (Hours Since 00:00)

RM

S V

elo

city

(cm

/s)

Control

Zero-Velocity at 00:00

Velocity Vector Difference

Ice velocity after a perturbation compared to a control run.

www.ncof.gov.uk

Ice Velocity : Application of Increments

Ice velocity assimilation using a ‘balancing stress’• An additional term was added to the model sea-ice

momentum equation to apply the velocity increments.

• The additional stress term is computed by assuming the sea-ice is in free drift.

• The first term on the right is the additional stress required to balance the ice-ocean stress, the second term comes from the Coriolis term.

• This is a non-linear term since it depends on the sea ice velocity and not just the increment.

a w iˆm a a k mf at

u

τ τ u τ

i w i iˆ1k w w kc k mfa τ u u u u

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Outline of Talk.

• Overview of the assimilation system.– Ice concentration assimilation.– Ice velocity assimilation.

• Results from year-long reanalysis integrations– Period from October 1999 December 2000.

• Summary

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Snap Shots : March 1, 2000 After 150 days of assim.

Control Conc. Assim.

Vel. Assim Conc. & Vel. Assim

ASI Gridded.Ice Concentration

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Snap Shots : Sept 1, 2000 After 335 days of assim.

Control Conc. Assim.

Conc. & Vel. Assim

ASI Gridded.Ice Concentration

Vel. Assim

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Ice concentration statistics.

• Significant improvement with ice conc. assimilation.

• Small difference with ice vel. assimilation.

• Melt season a particular problem.

R.M.S Ice Concentration Compared to HadISST : North

00.05

0.10.15

0.20.25

0.30.35

0.40.45

0.5

28/08/1999

17/10/1999

06/12/1999

25/01/2000

15/03/2000

04/05/2000

23/06/2000

12/08/2000

01/10/2000

20/11/2000Time

RM

S C

onc.

Err

or

Control

IceConc

IceVel

ConcVel

R.M.S Ice Concentration Compared to HadISST : South

0

0.05

0.1

0.15

0.2

0.25

0.3

28/08/1999

17/10/1999

06/12/1999

25/01/2000

15/03/2000

04/05/2000

23/06/2000

12/08/2000

01/10/2000

20/11/2000Time

RM

S C

onc.

Err

or

Control

IceConc

IceVel

ConcVel

R.M.S Ice Concentration Compared to HadISST : North

00.05

0.10.15

0.20.25

0.30.35

0.40.45

0.5

28/08/1999

17/10/1999

06/12/1999

25/01/2000

15/03/2000

04/05/2000

23/06/2000

12/08/2000

01/10/2000

20/11/2000Time

RM

S C

onc.

Err

or

ControlA

IceConcA

ConcVelA

1° Global

1° Global1/3° Arctic

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Velocity Comparison with Buoy Motion

• Motion computed offline from 1-day mean model fields.

• Assimilation improves representation of buoy motion during winter. Less improvement during summer.

Modelled Arctic Buoy Drift : 10 day drift

Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00Nov-990

0.01

0.02

0.03

0.04

0.05

0.06

Mea

n V

elo

city

Err

or

(m/s

)

ControlG IceVelGlabels ticks

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Impact of velocity assim. on thickness.

• Velocity assimilation led to changes in thickness of order 30cm.

• Thicker ice around Greenland and Fram Strait.

• Thinner in Beaufort Gyre.

Annual avg. thickness change (m)

(assim. – control)

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Correlation between velocity increments and wind stress.

Zonal Wind / Assim, October 1999 Meridional Wind / Assim, October 1999

Zonal Wind / Assim, March 2000 Meridional Wind / Assim, March 2000 Strong negative correlation in many areas indicates wind stress too strong.

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Summary

• The ice concentration and velocity assimilation has been shown to give quantitative improvements in modelled sea ice.

• Very little coupling between ice concentration and ice velocity in this model.– Ice dynamics has little impact on ice concentration in this model.

• Velocity analysis increments applied indirectly, via a balancing stress.– Ice velocity cannot be directly assimilated using an EVP model.

• Use of bias correction techniques allows forecast improvement & reduces RMS errors.

• Diagnosis of deficiencies in the model and forcing are possible using the assimilation.