1. Alexandra Jahn 1, Bruno Tremblay 1,3, Marika Holland 2, Robert Newton 3, Lawrence Mysak 1 1 McGill University, Montreal, Canada 2 NCAR, Boulder, USA 3 Lamont-Doherty Earth Observatory, New York, USA Effect of the large-scale atmospheric circulation on the variability of the Arctic Ocean freshwater export AOMIP meeting at WHOI, January 14 th 2009

2. Objective What controls changes in the FW storage and distribution in the Arctic Ocean? –Especially: What controls the variability of the liquid FW export from the Arctic Ocean?

3. Outline Motivation Part 1: Variability of the Arctic FW exchange Part 2: Comparison with CCSM3 Part 3: FW tracers in CCSM3 Conclusions Part 4: Future topics for AOMIP

4. Motivation River Runoff Bering Strait liquid P-E Barents Sea liquid Fram Strait liquid CAA liquid Barents Sea solid Fram Strait solid CAA solid Observational Arctic Ocean FW budget FW flux (km 3 /year) Aagaard and Carmack, 1989 FW flux (km 3 /year) Serreze et al., 2006

5. ARCTIC 91 ACSYS 96 Location of Stations for 91 and 96 cruise Schlosser et al., 2002 Change in surface freshwater distribution 1991 1996

6. Newton et al., 2008 Shift of horizontal distribution of runoff, due to shift in shelf- basin exchange from the Lomonosov Ridge to the Medeleyev-Alpha Ridge System Shift in shelf-basin exchange

7. Part 1: Variability of the Arctic FW exchange Method: Used the UVic ESCM to simulate the last 60 years (1948—2007) Analyzed the variability of the FW export in this simulation Analyzed simulations with different CAA configurations and CAA closed Main conclusion: Liquid FW export is driven mainly by the large scale atmospheric forcing over the Arctic (characterized by AO/NAO index) CAA FW export responds faster to AO changes than the FW export through Fram Strait

8. Resolution of 1.8° longitude by 0.9° latitude Main model components:  Ocean:  MOM 2.2; 32 unequally spaced vertical levels  Sea ice:  Zero-layer thermodynamic scheme (Bitz et al., 2001)  elastic-viscous-plastic dynamics (Hunke and Dukowicz,1997)  Atmosphere:  EMBM with prescribed daily NCEP winds and CO 2 (Weaver et al., 2001)  Climatological Arctic river runoff is prescribed (Lammers et al., 2001) Model: UVic ESCM, version 2.8

9. Variability of Arctic FW fluxes FW sinks FW sources Liquid Sea ice - - - Bering Strait River RunoffP-E CAA Fram Strait Barents Sea

10. Fram StraitCAA Cause of liquid FW export variability Liquid FW export Velocity anomaly Salinity anomaly Liquid FW export variability is mainly due to changes in the volume flux, but salinity anomalies enhance this variability by about 20%.

11. Forcing of CAA liquid FW export SSH difference Beaufort Sea – Baffin Bay CAA liquid FW export r=0.72 SSH anomaly Beaufort Sea SSH anomaly Large CAA FW export Small CAA FW export SSH difference between Beaufort Sea and Baffin Bay can explain large part (52%) of CAA liquid FW export variability Local Wind

12. Role of large scale atmospheric forcing The CAA liquid FW export is controlled by the low- frequency variability of the atmospheric forcing over the Arctic and associated SSH anomalies in the Beaufort Sea Maximum correlations (for 2y running means): AO : FW r=0.70 (1 year lag) SSH Diff : AO r=0.66 (1 year lag) Liquid CAA FW export AO index SSH difference Beaufort Sea – Baffin Bay

13. Role of large scale atmospheric forcing Composite of high CAA liquid FW export events Composite of low CAA liquid FW export events Typical AO positive SLP pattern one year before high liquid CAA FW export Typical AO negative SLP pattern one year before low liquid CAA FW export

14. Forcing of Fram Strait liquid FW export Cross correlation: SSH Diff: r=0.86 Local Wind: r=0.49 Changes in the SSH upstream of Fram Strait can explain 74% of the variance of the Fram Strait liquid FW export, the local wind forcing 24%. Liquid Fram Strait FW export SSH Diff Local Wind

15. Maximum cross correlation (for 2y running means): AO:FW r=0.50 (5 year lag) AO:SSH r=0.47 (5 year lag) Liquid Fram Strait FW export AO index Role of large scale atmospheric forcing SSH Difference

16. Differences between FW export maxima Eurasian Basin Canadian Basin Total Arctic Magnitude of lag of Fram Strait liquid FW export behind AO index depends on region of FW storage change FW column

17. Role of large scale atmospheric forcing Composite of high Fram Strait liquid FW export events Composite of low Fram Strait liquid FW export events Typical AO positive SLP pattern 5 years before high Fram Strait FW export Typical AO negative SLP pattern 5 years before low Fram Strait FW export

18. Different CAA locations As long as CAA is open and the mean liquid CAA FW export is the same, the CAA location has only a small effect on the Arctic FW storage variability a b c Closed CAA = black FW storage anomaly [km 3] Closed CAA

19. Magnitude of CAA export For increased liquid FW export through the CAA:  the amplitude of the simulated FW storage maximum in 1967 increases  the amplitude of the FW storage maximum in 1989 decreases FW storage anomaly [km 3] Highest CAA liquid FW export (~2500 km 3 /yr) Lowest CAA liquid FW export (~700 km 3 /yr) CAA closed

20. Part 2: Comparison with CCSM3 CCSM3.0 is a fully coupled global climate model –Community atmosphere model (CAM). T85  ~ 1.4° resolution –Parallel Ocean Program (CCSM3.0 POP) with 1° longitudinal resolution and up to 0.3° latitudinal resolution at the equator; 40 levels in the vertical (10 m to 250 m thick –Community Land Model (CLM)  dynamic soil- vegetation-atmosphere-transfer model –Community Sea ice Model (CSIM). EVP dynamics with a subgrid-scale ice thickness distribution.

21. Comparison UVic ESCM and CCSM3 CCSM3 UVic ESCM Much larger liquid FW exports in CCSM than in UVic ESCM Larger liquid FW export through CAA than Fram Strait in UVic ESCM, opposite in CCSM Larger variability of Fram Strait FW export in CCSM3 than UVic ESCM

22. Comparison UVic ESCM and CCSM3 CCSM results agree with UVic ESCM results on the FW export mechanism in main points, but lower cross- correlations between AO and liquid FW export in the CCSM than in the UVic ESCM –CAA: CCSM: Maximum cross-correlation with AO of r=0.48 at zero year lag for two year mean UVic ESCM: Maximum cross-correlation with AO of r=0.70 at one year lag for two year mean –Fram Strait: CCSM: Maximum cross-correlation of r=0.28 with AO index at nine year lag for two year mean UVic ESCM: Maximum cross-correlation of r=0.50 at five to six year lag for two year mean

23. CCSM3 UVic ESCM High CAA exportLow CAA export FW storage composites for CAA

24. FW storage composites for Fram Strait CCSM3 High Fram Strait exportLow Fram Strait export UVic ESCM

25. Part 3: FW tracers in CCSM3 Include passive tracers for Arctic FW from rivers and Bering Strait: –Sources: river runoff into different shelf seas Bering Strait FW import –Sinks: evaporation sea-ice formation Greenland and CAA Beaufort Sea East Siberian Sea Laptev Sea Kara Sea Barents Sea Bering Strait

26. FW tracers: Future work Use dye tracers to track FW from different sources in the Arctic Ocean to investigate: –Changes in the contribution of FW from different sources to the liquid FW export through Fram Strait and the CAA during high and low export events? –How do FW pathways change in response to atmospheric forcing? –Comparison of model results to tracer data –Look at changes in FW pathways during the 21 st century when Arctic sea-ice decreases

27. Conclusions I Liquid FW export is driven mainly by the large scale atmospheric forcing over the Arctic Atmospheric circulation influences FW export mainly through SSH changes in the Arctic Ocean CAA responds faster to AO changes than Fram Strait Fram Strait responds with a lag to AO changes; the magnitude of the lag depends on the region of FW storage change

28. Conclusions II CAA location has only a small influence on the variability of the Arctic FW storage Magnitude of FW storage maxima in 1960/70 and 1980/90 changes when magnitude of liquid FW export through CAA changes UVic ESCM and CCSM agree on the main FW export mechanisms, but CCSM3 shows lower correlation between AO and liquid FW export than UVic ESCM  need to compare with other models

29. Part 4: Future topics for AOMIP 1. Interannual and decadal variability of the FW storage in the Arctic Ocean –Compare simulated FW storage and export variability in different AOMIP models and investigate the reasons for these differences between models (e.g., wind or surface FW forcing, mixing parameterization, model domain)

30. FW storage in hindcasts and data ~6500 km 3 ~2500 km 3 Häkkinen and Proshutinsky, 2004 Köberle and Gerdes, 2007 ~7000 km 3 ~10,000 km 3 Polyakov et al. 2008 ~6000 km 3 Jahn et al, 2009 ~6500 km 3 ~1500 km 3

31. Future topics for AOMIP 2. Seasonal variability of the FW storage variability in the Beaufort Gyre –Compare model simulated seasonal cycle of FW storage with data obtained from Beaufort Gyre experiment to determine how good models capture seasonal cycle –Compare interannual variability in Beaufort Gyre FW storage in hindcasts for 2002-2008 with observations from Beaufort Gyre experiment

32. Seasonal Beaufort Sea FW storage variability A B C Proshutinsky et al (2009)

33. Thank you! Contact information: alexandra.jahn@mail.mcgill.ca www.esmg.mcgill.ca/Alexandra.htm