RCAO, a coupled atmosphere-ice-ocean-land model of the Arctic

1. RCAO, a coupled atmosphere-ice-ocean-land model of the Arctic R. Döscher*, K. Wyser*, M. Meier**, G. Broström*, P. Samuelsson*, P. Graham*, M. Qian*** *SMHI/Rossby Centre/Norrköping/Sweden **SMHI/Ocean research/Norrköping/Sweden ***UQAM/Montreal/Canada

2. Sea ice extent Spinup phase

3. Summer sea ice extent in four ensemble runs Spinup phase

4. Summer sea ice extent anomaly in four ensemble runs --- coupled runs --- ERA40 or sat observation

5. Sea ice extent summer winter Ensemble mean ERA

6. Interannual variability in sea ice extent summer winter Ensemble mean ERA

7. Trends in sea ice extent summer winter Ensemble mean ERA

8. Trends in sea ice thickness summer winter Ensemble mean compare with Rothrock et al. 2003

9. Mean T2m anomaly --- red RCAO ensemble --- black ERA-40

10. Surface pressurein standalone atmosphereandcoupled model

11. Predictability • Predictability is defined as the extent to which variability of Arctic variables can potentially be controlled by external forcing. The potential predictability is low when the internal (=internally generated) variability high. Predictability studies are useful to assess the feasibility of prediction systems. • Internal variability = internally generated variability = • mean variability among the ensemble members • not predictable by boundary conditions common to all ensemble members • External variance = externally driven variability = • variance of the ensemble-averaged anomalies • SNR = external variance/internal variability = • indicator for predictability

12. Predictability of summer ice thickness internal variability external variability total variability external/internal

13. Predictability of winter ice thickness internal variability external variability total variability external/internal Red areas in the signal-to-noise ratio indicate strong control by external driving forces rather than internal non-linear processes.

14. Seasonal cycle in total variability for t2m ERA-40 Model ensemble weak signal during summer, due to almost zero ice temperature

15. Predictability t2m winter internal variability external variability total variability external/internal Dominating internal variability in Fram Straits

16. Climatology of freshwater Freshwater height Sea surface salinity Beaufort gyre Freshwater originates mostly form Siberian rivers and is transported into the Beaufort Sea. (Proshutinsky et al., 2002)‏ Data from: Polar hydrographic climatology (Steele et al.). 1x1 degree data set. Total Arctic freshwater volume: ocean: 74345 km3 Sea ice: 10450 km3

17. Freshwater height

18. Freshwater export Fram Strait 1990s event GSA

19. Freshwater export Framstraits Figure from Lemke et al. : Simulated and observed sea ice transport through Fram Strait 1990-1996 (Hilmer, 1999, private communication; Vinje et al., 1998)‏

20. Freshwater export Greenland-Island • Solid part of freshwater export reduced in Danmark/Greenlan Straits • Freshwater transport is similar in all ensemble members

21. Freshwater fluxescombined results from observations and modelling • ??? Dickson et al. 2007

22. Foreseen needed development of RCA for Arctic applications Now: only snow Future: glacier included Now: forest and open land (grass) Future: include wet land processes Now: physical lake (FLake) Future: biochemicalprocesses Now: diagnostic soil frost Future: prognostic soil ice andits impact on soil hydrology Now: Baltic Sea river routing Future: flexible river routingapplicable anywhere Now: soil depth is 3m Future: deeper soil and morelayers to include perma frostprocesses

23. RCA3 coupled to the dynamicvegetation model GUESS(Ben Smith et al., Lund University) • Air temperature • Soil temperatures • Soil water • SW net • CO2 Future: utilize the potentialin GUESS to include carbonand nitrogen soil processes • LAI • Fraction forest • Fraction deciduous • LAI • Fraction of vegetation

24. Runoff Routing in RCA2 ... focus on the Baltic Basin In: mm/km2 Out: m3/s

25. Runoff Routing in RCA2 ... focus on the Baltic Basin In: mm/km2 Out: m3/s a more universal approach with added detailis desired

26. Runoff Routing in RCA3 ... example: 0.2 deg

27. Runoff Routing in RCA3 ... Torne-Kalix Basin example: 0.2 deg River outflow

28. Runoff Routing in RCA3 ... choose a basin! example: 0.2 deg

29. New datasets coming in Dec, 2007Hydro1k/Hydrosheds

30. Results so far • The regional coupled model gives realistic sea ice extent trends during the 1980s and 1990s. • Externally forced variability in the Arctic is generally stronger or of similar amplitude than internally generated variability. • Internally generated variability (due to non-linear ocean-ice-atmosphere interaction within the Arctic) is of similar importance as external forcing for future decadal prediction efforts • Freshwater export shows some basically realistic features. • the regional coupled model potentially provides the tool for an integrated freshwater analysis

31. Future plans • Further model improvements • Land surface scheme • River routing • Ice classes • Further predictability studies under different climate • Recent climate simulations forced with with improved reanalysis products • Ocean standalone simulations of the complete 19th century within the AOMIP project. • Within the EU-IPY-project DAMOCLES, Regional Arctic climate scenarios based on Bergen Climate model and other GCMs will be carried out.