1. Mid-Winter Suppression of the Pacific from 50+ years of Reanalysis

2. Data Set Composition NCEP/NCAR reanalysis from 1948-2008 = 60 winters Single level Z data Take the spherical Laplacian spectrally (i.e. spherical harmonic transform multiplied by wavenumber squared-k(k+1)/a^2 Call it relative vorticity- should by multiplied by g/f (10^5) to really make it relative vorticity, sorry Deal with straight (unfiltered) variance for now

3. Seasonal Cycle of relative vorticity Variance averaged over the Pacific domain

4. This plot is STD

7. Spectra- Method Define 41 day time window, centered around the seasonal local max, min for all 60 years of data Take the power spectra of each year at each grid point smoothing by a one tenth cosine bell -> Take the mean in spectral domain of all 60 years At each meridian, find the grid points (latitudes) with the largest variance (in the 60 year mean spectra) Look at the average spectra of that point and the adjacent grid points (three total at each meridian) Expressed in units of standard deviation associated with a discrete mode. i.e. to get the standard deviation of the field, sum the across each realization

9. Winter -Fall

10. Same for Atlantic- vorticity

12. Atlantic- vorticity spectra_difference

13. There is a reduction in 300 hPa relative vorticity variance at all frequencies, especially those above a period of 10 days So filter the daily data using a double-pass, high pass butterworth filter with a cutoff period of 10 days, applied in the time domain Run track- no spatial filtering applied

14. What about Z?

15. Z std Winter

16. Z std Spring

17. Pacific Domain- Seasonal Cycle

18. Pacific Spectra (Z_300)

19. Pacific Spectra Difference

20. Z-Atlantic-seasonal cycle- 300 hPa

21. Z-Atlantic-Spectra

22. Z-Atlantic-spectra_seasonal difference

24. Power spectra in wave#/freq space 60 day windows over season defined by pacific vorticity variance extrema- 60 years NH data mirrored across the equator At each time step, T42 spectral transform is applied and spectral coefficients are kept Each basis has unit area weighted spatial variance The FFT of each time series of spectral coefficient is taken (sum of discrete powers equals variance in the time series) The powers of same wave# are added (variance sum is like adding in quadrature) Sum across rows gives area weighted mean variance of single wavenumber maps Sum across columns give area weighted mean variance of single Fourier mode

28. Tracking- number of features identified within a radius of 10 degrees of grid point- by 50 day season- positive features Number of Storms (good stats!)

29. Tracking- Average perturbation size-positive features

30. Sum of all perturbations sizes – essentially the product of the previous 2

31. Tracks of largest features with in the Pacific Dots are magnitude at each point Colorscale of 1= 18*10^-10

34. Storm Size Distributions All Pacific West Pacific

35. Initial Storm Size Distribution

36. Growth Rate PDF

37. Growth Rate Distribution

38. Best Caption Contest?