1. Evaluation of AOMIP Modeled Arctic Sea Ice Thickness (Using Observational ULS data) Mark Johnson 1, Andrey Proshutinsky 2, Yevgeny Aksenov 4, Igor Ashik 3, Beverly de Cuevas 4, Nikolay Diansky 5, Christian Haas 6, Sirpa Hakkinen 7, Ron Kwok 8, Ron Lindsay 9,Wieslaw Maslowski 10, An T. Nguyen 8, Jinlun Zhang 9 1 Institute of Marine Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA 2 Wood Hole Oceanographic Institution, Woods Hole, MA, US 3 Arcticand Antarctic Research Institute, St. Petersburg, Russia 4 National Oceanography Centre, Southampton, Southampton, UK 5 Institute of Numerical Mathematics Russian Academy of Sciences, Moscow, Russia 6 University of Alberta, Edmonton, Canada 7 Goddard Space Flight Center, Greenbelt, MD, USA 8 Jet Propulsion Laboratory, Pasadena, CA, USA 9 Polar Science Center University of Washington, Seattle, WA, USA 10 Naval Postgraduate School, Monterey, CA, USA

2. Ice Thickness from models and ULS –Linear regression –Histogram –Differences (models-observations) –Correlations –Taylor Diagram (modified) –Model issues Ice Concentration – seasonality –methodology and validations

3. AWI IOS ULS Locations BGEP NPEO

4. ( NPEO observed 3.87m removed) Comparison using monthly means from models and ULS obs

6. Model is too thin Model is too thick

7. Model – Observations Thickness

10. n=1 Correlations

11. 30 cm-30 cm models>obsModels<obs

12. 30 cm-30 cm These model-obs have differences > |30 cm|

13. We will look at the model vs observation correlations and the model minus observation values using a modified Taylor Diagram as follows

14. model – obs = 75 m model – obs = -30 m model – obs = 2m model – obs = - 2m model = obs model – obs = -75 m model – obs = 30 m Taylor Diagram (modified) rotation scaled to 2m model-obs thickness model thickness > obs model thickness < obs

15. model – obs = 75 m model – obs = -30 m model – obs = 2m model – obs = - 2m correlation=0.6 correlation=1 model = obs model – obs = -75 m model – obs = 30 m Taylor Diagram (modified)

16. model – obs = 75 m model – obs = -30 m model = obs model – obs = -75 m model – obs = 30 m Taylor Diagram (modified) model – obs = 2m model – obs = - 2m “good”

17. model – obs = 75 m model – obs = -30 m model = obs model – obs = -75 m model – obs = 30 m Taylor Diagram (modified) model – obs = 2m model – obs = - 2m “not so good”

18. Performance by model

19. n=1 “good” AWI3 “not so good” AWI1 AWI4 AWI11

20. “good” IOS4 IOS1, 2, 3, 4, 5, 6, 7, and 8 are underestimated BGEP A, B, C and D are overestimated

21. “good” IOS8 AWI2 AWI3 “not so good” AWI2 AWI7 BGEP A,B,C

22. “good” IOS8 AWI1 BGEPB BGEPC

23. “good” IOS2 IOS3 IOS8 AWI3 AWI5 AWI6

24. “good” IOS1 IOS2 IOS3 IOS8 AWI2 AWI3 AWI4 BGEPA BGEPB BGEPC

25. Performance by instrument

26. “good” UW - 4 ECCO2 – 3 NPS – 1 GSFC - 1 INMOM - 1

27. n=1 “good” UW - 3 ECCO2 – 3 NPS – 2 INMOM - 2 “not so good” INMOM - 2 ORCA - 2 n=1

28. “good” UW – 3 NPS – 2 “not so good” INMOM - 3

29. UWECCO2NPSGSFCINMOMORCA IOS 1 IOS 2 IOS 3 IOS 4 IOS 5 IOS 6 IOS 7 IOS 8 AWI 1 AWI 2 AWI 3 AWI 4 AWI 5 AWI 6 AWI 7 AWI 8 AWI 9 AWI 10 AWI 11 BGEP A BGEP B BGEP C BGEP D good:1075131 poor:53 n=1 correlation = -0.76

30. Where do the “good” data show up on the model vs observations?

31. ( NPEO observed 3.87m removed)

32. Conclusions from ULS data Generally the models overestimate the ice thickness compared to ULS observations Models don’t have enough sea ice in the thin or FY ice range up to 1m thick Models have too much sea ice greater than 2m thick Models do better in the Beaufort than Fram Strait Questions –Why do the models have too much MY ice and not enough “new” ice? –Do the models not melt or advect away the thicker MY ice? –Do tides “open up” the sea ice allowing for more of the “thin” ice less than 1m thick? –What models have tides in them?

33. Seasonality of Sea-Ice Concentration Subsistence hunters along coastal Alaska have observed for years the start and end dates of “freeze-up” and “break-up” Algorithm to compute these event times using SSM/I has been developed with good agreement (Hajo Eicken) Seasonality can be computed using satellite record. Compare with model results?

37. Conclusions UW does well integrating thickness Most models agree with IOS data but not with AWI data TPD and Fram Strait export may play a key role Seasonality of sea-ice concentration appears to be an attractive validation tool.

38. CSFCECCO2INMOMNOCSNPSUW Domain b Resolution c Ice t regional 0.35 ⁰ - 045 ⁰ 720 s regional 15-22km 600 s regional 0.25 ⁰ 3600 s global 3-6 km 7200 s regional 9 km 3600s regional 6-75 km 1152 s Vertical coordinateσzzz Vertical levels26506430 Minimum depth25m5m6.065m Bering StraitRestoredNot restored Fully represented in global domain open Equation of stateMellor Jackett and McDougal, 1995 Jackett & McDougall (1995) UNESCO Vertical mixingMY2.5KPP, no double diffusion TKE (Gaspar et al.( 1990), Blanke & Delecluse (1993)) KPP Tracer advection Lin et al 1994 Piecewise parabolic 7 th order monotonicity- preserving (Direct space time with flux limiter) [Daru and Tenaud, 2004] TVD (Lévy et al. 2001) Central diff. Momentum advectioncenteredvector invariantEEN (Barnier et al. 2006) Central diff.

39. GSFCECCO2INMOMNOCSNPSUW Ice Physical parameterizations Salinity5Function of surface S64 Thickness categories d 2: ice and no ice 8 (7 for ice and 1 for open water) 112 Advection Centered mom. Upwind A+D Centered 2 nd order Prather, 2 nd order, 2 nd moment conserving Central diff. Dynamics e Generalized viscous Viscous plasticVP Teardrop plastic rheology, LSR solver Albedos

40. CSFCECCO2INMOMNOCSNPSUW Albedos Melting snow Cold snow - 0.85 0.78 – melting snow 0.8085 0.5-0.65 (clear sky, snow thickness dependent) 0.70 Cold ice0.740.7 0.1-0.72 (clear sky, ice thickness dependent) 0.75 Melting ice0.70.7060 01.-0.5 (clear sky, ice thickness dependent) 0.64 Ocean0.10.15560.060.1 Surface Momentum Exchange Coefficients Atmos.-ice g 1.4E-31.14 x 10^-31.63 x 10 -3 Surface BL Ice-OceanBL model5.4 x 10^-35.0 x 10 -3 Cw=0.0055

41. StartEndLatitude (degrees, minutes) Longitude (degrees, minutes) MooringInstrumentWater Depth Instrument Depth Data directory Aug-91Nov-9275 00'N12 40'WAWI411APL261002m48muls26-91-92 Aug-92Dec-9274 52'N11 43'WAWI412-2APL312362m50muls31-92 Aug-93Jul-9474 53'N07 38'WAWI414-2APL323425m70muls32-93-94 Jul-94Oct-9574 58'N12 59'WAWI410-2APL49413m73muls49-94-95 Aug-97Sep-9879N02WV10-1APL322600m58muls32-97-98 Sep-98Sep-9979N02 03'WV10-2APL472609m54muls47-98-99 Sep-99Aug-0079N02 03'WV10-3APL252582m53muls25-99-00 Oct-99Sep-0074 25'N10 15'WAWI419-1APL323229m63muls32-99-00 Aug-00Oct-0179 2'N02 03'WF10-4APL482554m67muls48-00-01 Sep-00Sep-0174 24'N10 12'WAWI419-2APL313160m65muls31-00-01 Sep-01Sep-0274 24'N10 12'WAWI419-3APL473160m82muls47-01-02

42. Preliminary conclusions The major one is that it seems that the UW model is good and a bit better than others. ECCO2 models results (this is MIT model which An T Nguen runs at JPL are also good and better than the others except UW model. I will continue working with conclusions.