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Dynamic response of Arctic sea ice to the atmospheric Dipole Anomaly (DA)

Dynamic response of Arctic sea ice to the atmospheric Dipole Anomaly (DA). Jia Wang Arctic Modeling Group, International Arctic Research Center (IARC) Eiji Watanabe, Bingyi Wu, John Walsh, and Moto Ikeda Univ. of Tokyo, IMS, IARC, Hokkaido University 2006 Alaska Marine Science Symposium

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Dynamic response of Arctic sea ice to the atmospheric Dipole Anomaly (DA)

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  1. Dynamic response of Arctic sea ice to the atmospheric Dipole Anomaly (DA) Jia Wang Arctic Modeling Group, International Arctic Research Center (IARC) Eiji Watanabe, Bingyi Wu, John Walsh, and Moto Ikeda Univ. of Tokyo, IMS, IARC, Hokkaido University 2006 Alaska Marine Science Symposium January 23-25, Anchorage

  2. Outline • Introduction and Motivation (Brief) • Data and Methodology • Arctic Atmospheric Circulation Regimes: AO and DA • The DA Signature in the Ice-Ocean System • The AO Signature in the Ice-Ocean System • Conclusions and Future Efforts

  3. 1. Introduction and Motivation

  4. Arctic Atmospheric Variability: NAO/AO • van Loon and Rogers (1978): NAO (Walker and Bliss 1932), SAT seesaw • Thompson and Wallace (1998): AO; (2000 a,b): AO • Wang and Ikeda (2000): AO and ASIO • Wu and Wang (2002a,b): AO/SH vs. East Asian winter monsoon • Ikeda, Wang, Makshtas, 2003: AO and cloud feedback=> sea ice thinning

  5. Arctic Atmospheric Variability Question remaining: Is the AO the only dominant mode interacting with the Arctic ice-ocean system?

  6. One regime with max. ice export in coupled climate model Other Evidence:1) Skeie (2000): BO; 2) Holland (2003); 3) Goose et al. 2003; 4) Semenov and Bengttson (2003)plus Wang et al. (1995)—Internal report of CCGCR of McGill EOF2 of Wang et al. (1995) internal report. Mode 1: Mysak et al. 1996 Ice export regresses to SLP in CCM2, Holland (2003) BO: EOF2, Skeie (2000)

  7. Arctic Sea IceAssociation with Atmosphere • Walsh and Johnson (1979): Arctic sea ice dataset and atmospheric circulation anomalies • Wang et al. (1994): Sea ice anomalies in Hudson Bay and Labrador Sea related to NAO and SO using data 1953-88; Sea ice seesaw between the Labrador Sea out of phase with the GIN seas. • Mysak et al. (1996): Three-case studies: Extreme positive ice anomalies during simultaneous NAO and SO episodes: 1972/73, 82/83, and 91/92 in the Labrador Sea • Mysak and Venegas (1998): Arctic sea ice decadal variability and feedback loop • Wang and Ikeda (2001): Sea ice variability in the last century: decadal ASIO • Ikeda et al. (2001): Decadal ASIO as hypersensitive mode due to ice albedo feedback • Wang et al (2005): sea-ice decline trend amplifies the decadal ASIO, modifying the MV’s feedback loop including the positive feedbacks due to ice-albedo and cloudiness • Kwok and Rothrock (1999): dP vs. ice export across FS • Vinje (2001): No correlation between AO and ice export via FS • Rigor et al. (2002): Response of sea ice to AO, slight increase in sea ice export via FS

  8. Sea ice flux vs. SLP difference across Fram Strait (Kwok and Rothrock 1999); No correlation between the sea ice export in Fram Strait and AO (Vinje 2001; Hilmer and Jung 1999)

  9. Arctic Sea Ice Variability Question remaining: Are both Arctic sea ice circulation and sea ice export (sea ice thinning) only related to the AO?

  10. Motivation • What are the dominant modes of the Arctic atmosphere? AO/NAO, DA? Global or local? • Can we confirm the Arctic atmospheric modes with signatures in sea ice motion and its export? • What is the response of the Arctic Ocean and subpolar seas to the AO/NAO and DA?

  11. 2. Data and Methods • Atmosphere: NCEP/NCAR Reanalysis, 1948-2002 • SIC: Conventional (1901-Sep. 1978; Walsh and Chapman 1990), and SMMR/SSMI (NASA, Oct. 1978-2002; Parkinson 1989), 1x1 degree grid, Arctic Ocean and subpolar regions, 1901-2002 • Sea ice drift: IABP Dataset,1979-2002 • Barents Sea hydrographic dataset (Russia) and Labrador Sea hydrographic archive (BIO)

  12. Data and Methods (cont.) • EOF analysis • Climatology/Anomaly • Composite analysis and T/F-test • Correlation analysis/Regression, & Monte Carlo simulation • Case study • Modeling: IARC Coupled Ice-Ocean Model (CIOM) in the pan Arctic and North Atlantic Ocean (Wang et al. 2002, 2005)

  13. 3. Arctic Atmospheric Circulation Regimes: AO and a new atmospheric Dipole-Anomaly (DA) regime(Wu, Wang, Walsh, 2005, J.Climate, next issue)

  14. Spatial distributions of EOF analysis of winter SLP, (a) EOF1 (AO) and (b) EOF2 (DF) (Honda et al. 1999; Skeie 2000).

  15. AO Time series of the first two leading modes and the AO index (dashed line), (a) EOF1 and (b) EOF2. (C(AO, EOF1)=0.93--NH; C(BO, EOF2)=0.26)--Local EOF1 EOF2

  16. Composite Analysis and F-test • The winter DA index (EOF mode 2) 1.0 or -1.0: DA1.0: 1972, 1974, 1976, 1977, 1980, 1990, 1991, 1999 DA-1.0:1962, 1967, 1968, 1975, 1978, 1989, 1993, 1995 • The winter AO index (the EOF mode 1) 1.0 or -1.0: AO 1.0: 1967, 1976, 1973, 1989, 1990, 1992, 1993 AO -1.0:1958, 1960, 1966, 1969, 1970, 1977

  17. Anomalies for SLP composites for low (left) and high (right) phases based on the time series of EOF2. Shaded regions are F-test over the 99.9% (dark red), 99% (red), 95% (purple) and 90% (blue) significance levels, respectively.

  18. Anomalies of surface air temperature composites for the low (left) and high (right) phases of EOF2.

  19. Anomalies of Geopotential height composite at 700 hPa (gpm) for the low (left) and high (right) phases of EOF2.

  20. Correlations of surface air temperature with the time series of (a) EOF1 (SAT seesaw) and (b) EOF2.

  21. Correlations of the time series of EOF1 with (a) u and (b) v. (c ) and (d) same as (a) and (b), respectively, except for EOF2.

  22. The Student t-test for differences of mean: (a) u and (b) v between the two regimes of EOF 2.

  23. The F-test (for deviation) of u (left) and v (right).

  24. Wave activity flux at 500 hPa Plumb (1985)

  25. 4. The DA Signature in the Ice-Ocean SystemThis study: modeling

  26. Model Experimental Design • (A) Response of the Arctic Ocean to the winter DA forcing (winter: January-March). Based on the time series of the winter DA index (the EOF2), we chose these winters with the standard deviation of the winter DA index 1.0 and -1.0 as the atmospheric forcing cases: DA1.0: 1972, 1974, 1976, 1977, 1980, 1990, 1991, 1999 DA-1.0:1962, 1967, 1968, 1975, 1978, 1989, 1993, 1995 • (B) Same as the DA in (A) except for the AO atmospheric forcing (the EOF1): AO 1.0: 1967, 1976, 1973, 1989, 1990, 1992, 1993 AO -1.0:1958, 1960, 1966, 1969, 1970, 1977

  27. IARC-Coupled Ice-Ocean Model (CIOM):(Wang et al. 2002, 2005) Applications: pan Arctic, Beaufort Sea, Bering Sea; Labrador Sea) Ocean Model: POM, 3-D, primitive-eqs. free surface, sigma-coord. T/S, 2.5 turbulence closure 27.5km, vertical-16 levels Sea Ice Model: Hibler dynamics, VP Thermodynamics of WP multi-category thickness Coupling: Mellor and Kantha (1989) Forcing: NCEP/NCAR reanalysis Spinup: 26 years Simulation: Each winter from Jan.-March.

  28. Overall Modeling Activities at Arctic Modeling Group • CIOM: Pan-Arctic, Bering Sea, Beaufort-Chukchi seas (Hu, Mizobata, Wang) • CCSR/NIES/FRCGC Global Atmos-Ocean-Ice Model (Suzuki, Takahashi, Hasumi, Jin, Hu, Walsh, Wang) • ROMS (ice-ocean): Bering Sea, Arctic Ocean (S. Zhang, Hedstrom, Wang) • 1-D PhEcoM+Ice Algae: Bering Sea, Chukchi Sea (Jin, Deal, Tanaka, Wang) • 3-D PhEcoM: Bering Sea (Hu, Jin, Deal, Wang) • Bering Sea Ecosystem Satellite, Process Modeling and Field Studies (Mizobata, Iida, Saitoh, Wang)

  29. Poster: Downscaling characteristics of sea ice and ocean circulation in the Beaufort and Chukchi Seas Jia Wang, Haoguo Hu, and Kohei Mizobata

  30. Simulated SIC (a) high phase and (b) differences of the low minus the high phase for EOF 2.

  31. Simulated SITH (a) high phase and (b) differences of the low minus the high phase for EOF2. Increase in ice export in FS&BS Decrease in ice thickness in the Arctic

  32. Sea ice concentration composite from HadISST: Increase in ice export Decrease in Arctic sea ice concentration

  33. Sea ice extent in 2004 summer

  34. Simulated sea ice velocity (left) vs. IABP sea ice velocity regressed to EOF2 (lower left) and EOF1 (lower right)

  35. The F-test for sea ice velocity differences (low minus high phase) for EOF2.

  36. EOF1 (AO) Sea ice volume flux EOF2 (dipole)

  37. 5. The AO Signature in the Ice-Ocean System

  38. Simulated sea ice concentration composite for the AO forcing: sea-ice seesaw (Wang et al. 1994; Mysak et al. 1996)

  39. Simulated sea ice thickness composite Increase in Labrador and Hudson Bay Decrease in Barents, Kara, Laptev seas (indicating NAW intrusion/melting)

  40. Simulated sea ice velocity composite for the AO forcing, consistent with Rigor et al. (2002).

  41. The F-test of the simulated ocean surface currents. Wang et al. (2004), GRL

  42. Simulated seawater temperature variations at 120m: SUBSURFACE temperature SEESAW

  43. Simulated heat content in the upper 200m layer

  44. 6. Conclusions • AO is the NH leading mode with its center in the Arctic. Its thermodynamic impact (warming) in the last 3-4 decades played an important role in the Arctic. However, the wind anomalies induced by the AO are not statistically significant in the central Arctic, although the SLP change since 1989 was significant (Walsh et al. 1996). Its dynamic impact is through the North Atlantic water intrusion. • DA is the second dominant mode in the central Arctic (local). Its dynamic impact is more important than the AO, while the local thermodynamic effect is also important (Skeie 2000; Semenov and Bengtsson 2003), reflecting the feedback of the local ice anomaly to the atmosphere (i.e., interactions; Wu et al., MWR, 2004). The mechanism maintaining the DA is due to the Arctic atmospheric wave flux (energy propagation).

  45. Conclusions (cont.) • Using the CIOM, it is confirmed that the critical atmospheric regime to influence the Arctic Ocean is dipole forcing (EOF 2) rather than the AO (EOF 1), particularly, dipole forcing’s influence on sea ice export out of the Arctic Basin via Fram Strait and the northern Barents Sea (Vinje 2001; Hilmer and Jung 1999; Kwok and Rothrock 1999; Holland 2003). This leads to thinning of the Arctic Basin (Goose et al. 2003; Koberle and Gerdes 2003) • Using the CIOM, it is found that the AO is related to North Atlantic Water intrusion driven by sub-Arctic anomalous wind stress (v), causing the subsurface seesaw between the Barents Sea and the Labrador Sea. Mechanisms are oceanic advection (primary) and local cooling, similar to the SAT seesaw (van Loon and Rogers 1978) and sea-ice seesaw (Wang et al. 1994).

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