1. LGM Eddy Diagnostics and Energetics from CCSM

2. Winter Composite of VT at 850

3. Winter Composite of VV at 850

4. Winter Composite of Z’ variance at 850

5. vT and ZZ at 850

6. Jet Core Speed at 850

7. Meridional Temperature Gradient 850

9. Barotropic Conversion at 850

10. Baroclinic Conversion at 850 Mean State PE to Eddy PE

11. EOF’s of low pass 700 hPa zonal velocity Picture is consistent (same 1st eofs at 850 and 550 and 400,250) surface is interesting 1 st two eofs cover 45% of variance)

12. PC1 composite seasonal cycle

13. EOF’s of low pass 700 hPa zonal velocity Same picture(850 and 550,400,250)

14. PC2 composite seasonal cycle

15. EOF’s of low pass 700 hPa zonal velocity Center of action at far jet exit is same at 850 and 550 550, 400 has a slightly different correlation with jet core 250 looks like linear combination of 3 and 4

16. PC3 composite seasonal cycle

17. EOF’s of low pass 700 hPa zonal velocity Similar picture at 550, 400, not below 700

18. EOF’s of low pass 700 hPa zonal velocity

20. One point regression fields –Z 700

21. One point regression fields –V 700

22. One point regression fields–V and Z700

23. One point regression fields– vt 700

25. Let the craziness begin Look at the product of reference times series of v’ and v’ everywhere else Basically, you get a compilation of snapshots of one point regressions DO THE EOF ANALYSIS OF THIS TIME SERIES If we just had a wave train moving through the storm track, all we’d get is the one point regression map If the storm track is, say, bi-modal, we get out the modes of the storm track

26. Crazy stuff-v variance and reference point at 400

27. Crazy stuff-eofs of 1 point map

32. Eof 1 and 2 are quadrature phases of same pattern

33. As expected, the 1 st EOF is the one point correlation map

34. Composite Seasonal time series of 1 point EOF 1 and 2 magnitude

35. Composite Seasonal time series of 1 point EOF 3 and 4 magnitude

36. Eof 3 and 4 are quadrature phases of some sort of disjointed storm track

37. Do these patterns of storm track variability correlate with sector averaged eddy activity? No!! Pc 1 is decently correlated with VT (.3) while pc2 has zero correlation (occluded?) If pc 1+2 and 3+4 are quadrature phases, what about taking the magnitude of the quadrature phases -mostly meaningless correlations

38. What spatial pattern of v’t’ is associated with one point eof1 and 2

39. When the sector average of v’t’ is big, what does v’t’ look like Regress Sector averaged v’t’ series onto v’t’ at each Grid point

40. When the sector average of v’t’ is big, what does u’v’ look like Regress Sector averaged v’t’ series onto u’v’ at each Grid point

41. When the sector average of v’t’ is big, what does tropic conversion look like Regress Sector averaged v’t’ series onto tropic conversion at each grid point

42. When the sector average of v’t’ is big, what does clinic conversion look like Regress Sector averaged v’t’ series onto baroclinic conversion at each grid point

43. What Am I asking? What eddy structure accounts for the change in sector averaged heat transport Best option (maybe) MCA of one point time series on sector averaged v’t’ Easier: regress sector averaged v’t’ on one point time series at each grid point

44. Compose one point series of v’400 relative to a point in jet entrance- regress result against total v’t’ Spatial correlation with one point time regression, or Eof on one point time series >.98

45. What does this mean? The dominate mode of eddy structure variability is a weakening or strengthening of the eddy structure This mode is the best we can do at predicting changes in domain averaged eddy heat transport with eddy structur- but… it isn’t very good The seasonal cycle of this mode does NOT explain the seasonal cycle of total eddy transport Something else is going on!