What are the dominant “modes” of extratropical atmospheric variability?

1. What do large-scale patterns of extratropical atmospheric variability imply about memory and predictability? David W. J. Thompson Department of Atmospheric Science Colorado State University

2. What are the dominant “modes” of extratropical atmospheric variability? • The Pacific-North America pattern • The Northern Hemisphere annular mode • (aka, the North Atlantic Oscillation or Arctic Oscillation) • The Southern Hemisphere annular mode • Why is it important to predict these modes? • To what extent does the midlatitude ocean impact these modes? • What alternative processes may drive low-frequency variability in these modes?

3. The Pacific-North America pattern 500-hPa height regressed on an index of the PNA e.g., Wallace and Gutzler 1981

4. The Pacific-North America pattern • Resembles a wave train emanating from the North Pacific. • Excited by barotropic instability in the vicinity of the Pacific jet. • Reminiscent of the extratropical atmospheric response to ENSO. • Time series resembles a red-noise process with an e-folding timescale of ~10 days.

5. The Northern Hemisphere annular mode (NAM) Sea-level pressure regressed on an index of the NAM e.g., Thompson and Wallace 2000

6. The Southern Hemisphere annular mode (SAM) 850-hPa height regressed on an index of the SAM e.g., Kidson, Karoly, Trenberth, etc.

7. The annular modes • Characterized by zonally symmetric fluctuations in the extratropical atmospheric circulation that extend from the surface to the stratosphere. • Driven by interactions between transient eddies and the mean flow of the extratropical atmosphere. • The NAM and the NAO are different interpretations of the same mode of variability. • The time series of the NAM (and SAM) resembles a red-noise process with an e-folding timescale of ~10 days. But the NAM (and SAM) also exhibits increased power at lower frequencies…

8. Why do we care about the annular modes? • Extensive climate impacts. • Time series exhibit low-frequency variability that appears to exceed the timescale of tropospheric variability.

9. Climate impacts of the NAM Surface temperature regressed on the NAM index Provided courtesy of Todd Mitchell, UW

10. Climate impacts of the SAM 700-hPa winds and surface temperature regressed on the SAM index (longest vector is ~4 m/s). Thompson and Solomon 2002

11. Recent climate trends and the annular modes Recent trends in SH 500-hPa Z (left; Dec-May 1979-1998) and NH SLP (right; Jan-March 1968-1997). e.g., Hurrell 1995; Thompson et al. 2000; Thompson and Solomon 2002

12. What are the dominant “modes” of extratropical atmospheric variability? • The Pacific-North America pattern • The Northern Hemisphere annular mode • (aka, the North Atlantic Oscillation or Arctic Oscillation) • The Southern Hemisphere annular mode • Why is it important to predict these modes? • To what extent does the midlatitude ocean impact these modes? • What alternative processes may drive low-frequency variability in these modes?

13. To what extent does the midlatitude ocean impact these modes? 1) To what extent can the persistence of the midlatitude oceans be explained via stochastic atmospheric processes? 2) To what extent does the midlatitude ocean impact the midlatitude atmosphere?

14. The null hypothesis for SST persistence From Frankignoul and Hasselmann (1977; FH) where SST anomaly White noise atmospheric forcing  Fixed thermal damping parameter  Density of seawater cp Heat capacity of seawater H Fixed ocean mixed layer depth FH model yields an e-folding timescale for SSTs of ~3-5 months

15. The null hypothesis of SST persistence, modified to include reemergence (from Deser et al., in press J. Climate)

16. Sample results from the extended FH model (from Deser et al., in press J. Climate) Observed SSTA Observed heat content anomaly SSTA, original FH model Heat content anomaly, modified FH model Heat content anomaly, modified FH model with λ =0 in summer The FH model, when extended to account for re-emergence, yields SST persistence on the order of years.

17. Observed vs. modeled autocorrelation Shading denotes r>0.3. Observed Theory Discrepancies occur where the mixed layer depth is shallow, near coastlines. (from Deser et al., in press J. Climate)

18. To what extent does the midlatitude ocean impact these modes? 1) To what extent can the persistence of the midlatitude oceans be explained via stochastic atmospheric processes? 2) To what extent does the midlatitude ocean impact the midlatitude atmosphere?

19. “We can now say with confidence that the extratropical ocean does indeed influence the atmosphere outside the boundary layer, but that this influence is of modest amplitude compared to internal atmospheric variability” Kushnir et al. 2002

20. The basic effects of atmosphere/ocean thermal coupling on midlatitude variability (Barsugli and Battisti 1998) • The original formulation by FH was extended by Barsugli and Battisti to include thermal feedbacks between the ocean and the atmosphere - Ta, To are the surface temperatures of the atmosphere and ocean. - a-d are dimensionless parameters that incorporate surface fluxes, radiative damping (b also includes any ocean dynamical feedback). - b corresponds to the ratio of the heat capacity of the ocean mixed layer and the troposphere (b~40). - N is atmospheric white noise forcing.

21. The basic effects of atmosphere/ocean thermal coupling on midlatitude variability (Barsugli and Battisti 1998) • Thermal coupling between the atmosphere and ocean increases the variance at low frequencies, and the persistence in both media. • Simulations run with specified SST anomalies (i.e., without damped thermal coupling) yield spuriously large surface fluxes between the ocean and atmosphere.

22. Interpretation of AMIP-style simulations Figure from Rodwell et al. 1999

23. Interpretation of AMIP-style simulations • Bretherton and Battisti (2000): • The correlation between the simulated and observed NAO reflects the ensemble averaging, which acts to filter the noise in each ensemble member. • As per the null hypothesis outlined in Barsugli and Battisti (1998), at low frequencies the correlation between the simulated and observed NAO approaches r=1.0 as the ensemble size approaches infinity. • The results do not PROVE any predictability of the NAO beyond the persistence of the SST anomalies. • Caveat: Barsugli and Battisti do NOT include advection by the gyre and/or thermohaline circulations (Czaja and Marshall 2000; Marshall et al. 2001).

24. What are the dominant “modes” of extratropical atmospheric variability? • The Pacific-North America pattern • The Northern Hemisphere annular mode • (aka, the North Atlantic Oscillation or Arctic Oscillation) • The Southern Hemisphere annular mode • Why is it important to predict these modes? • To what extent does the midlatitude ocean impact these modes? • What alternative processes may drive low-frequency variability in these modes?

25. Stratosphere/troposphere coupling and the NAM (from Baldwin and Dunkerton)

26. Concluding remarks There is no conclusive evidence that variability in the midlatitude ocean has a significant impact on the dominant modes of extratropical atmospheric variability.

27. Concluding remarks The most compelling observational evidence of predictability on timescales longer than the limits of deterministic weather prediction derives from stratosphere/troposphere coupling, not via coupling with the midlatitude ocean.

28. Concluding remarks 1) A large fraction of the persistence of midlatitude SSTs is predicted by a simple model driven by stochastic atmospheric forcing. caveat: Ocean dynamics likely plays an important role (e.g., the “inter-gyre gyre”), particularly in the vicinity of the western boundary currents. 2) There is increasing consensus that the atmospheric response to midlatitude SSTs is modest. caveat: The annular modes have a pronounced impact on the climate of their respective hemispheres. A “modest” increase in the predictability of the NAM or SAM would likely be very useful.