1. On Remote Weather Associated with the Subtropical Highs of Pacific Sea Level Pressure Richard Grotjahn Atmospheric Science Program, Dept. of LAWR, Univ. of California Davis, CA 95616, USA

2. Subtropical High climatology North Pacific (JJA) South Pacific (DJF)

3. Remote Forcing Hypotheses for SP high 3 remote sources Some connections will be visible through the divergent circulations. P or OLR are proxy for rising motion. Simplest tests: is SLP linked to P in target regions? P intensity? P shifts? P timing? (1) Hadley and Walker circulations, (2) Rossby wave forcing from East,  v = f dw/dz (3) traveling frontal cyclones and anticyclones

4. Precipitation Climatology JJA DJF

5. Meridional x-section – 100 WZonal x-section – 20 S Divergent Circulations  Vertical motion largest over Amazonia, but  Stronger motions from SW, W, and N feed surface divergence H H

6. Physical Interpretation of Gill’s Model Form vorticity eqn Invisicid form:

7. Rossby Wave Mechanism deduced from Gill’s Tropical Circulation Model

8. Analysis Procedures (Monthly Data)  Preliminary study to identify coincident behavior.  Monthly NCEP/NCAR Reanalysis data (1979-97).  Seasonal groupings, local “summer” emphasized.  Total and monthly anomaly (MA) fields. (MA defined as deviations from the average constructed from all occurrences of that month).  Monthly data cannot distinguish cause from effect.  Tools (significance test) shown here: composites (bootstrap resampling) 1-point rank correlations (t- and D-statistics).

9.  T1 – target group chose based on a criterion. Each member 2-D field of F1.  T2 – similar to T1. Target group for field F2 using same times as for T1.  R1 n – “n th ” random group drawn from field F1. Times randomly chosen from the entire record with replacement but no duplication. Sample size matches target sample. Many random groups. (e.g. 1000).  R2 n - similar to R1 n except randomly choices from F2. Times used differ from those for R1 n For each grid point: compare the mean of the target group vs the means of the random samples at that grid point. Composite of the target group for F2 is Composite of n th random group for F1 is Composite of n th random group for F2 is Compare against all the Bootstrap Resampling (part 1) t = 1 t = 2 t = 3 t = 4 t = NT t = 5 T2 T1 R2 n t = 6 R1 n F1 F2 F2(nx,ny,nt) (P, OLR, DWS, VP, SLP,…) F1(nt) (SLP, MJO, SOI,…)

10. Bootstrap Resampling (part 2) Significance:  Determine separately for each location point  Distribution from random composites at each pt.  Level determined by number at a tail times 2  Distribution can be ‘normal-like’, bimodal, etc  Significant if target composite lies at either tail (2-tailed test) Example: At point (i,j) of an observed distribution. The star indicates a significant target composite T2 Figure II.2: example of null distribution. This null distribution was generated while assessing the significance of the 850 hPa mean temperature. This histogram refers to the grid point closest to Sacramento, and gathers 1000 random samples. The target value has been added and is shown by a star. 99% of the values stand between the two dashed lines. (i.e. 5 random to right tail, 5 to the left)

11. Composites: ONDJF Monthly Anomaly Data: E and NE: lower SLP (purple) more P (N of South America) for strong high and vice versa. N and NW: More P and Northward shift of ICZ W: More P (green) & westward shift of SPCZ NW & N MJO? ENSO? S and SW: Dipole (P) storm track shift to S for strong SP high. Tracks may be broader for weak SP high.  6 strongest – 6 weakest  Green: significant above (1%)  Purple: significant below (1%)

12. Composites: JJA Monthly Anomaly Data:  6 strongest – 6 weakest  Green: significant above (1%)  Purple: significant below (1%) SLP:  Highest when high is NW  SP high coordinated  Weak & strong composites not “opposite” Precip:  Shift of ICZ southward  Shift of midlat NW-ward  Stronger over Indonesia

13. 1-Point Rank Correlations Day 1 Day 2 Day 3 Day NT F2(i,j,NT) F2(i,j,3) F2(i,j,2) F2(i,j,1) F1(1) F1(2) F1(3) F1(NT) F2(nx,ny,nt) (P, OLR, DWS, VP, SLP,…) F1(nt)=F1(M,N,nt) (SLP, MJO, SOI, …) R(i,j) (M,N) y x R(i,j) y x NHST (Null Hypothesis Significance Test): “Given that F2 at (i,j) is not correlated with F1 at (M,N), what is the probability that the indicated correlation could occur by chance?” ≤ 1% chance is shaded

14. 1-pt correlations of Monthly Anomaly Data: Intersection of 2 signif. tests used shaded & tend to match 0.3 correlation. correlation points respond to events on the same side. NE to E Pacific ICZ (shift) and more Amazonian P NW side to ICZ & SPCZ shift E, NE, N, and NW of the SP high center correlate with less P in the Kiribati area like composites results W, SW & S: Total and MA data both show: dipolar correlation pattern implies poleward shift of storm track P for higher SLP P shown, OLR similar Blue: significant above (1%) Orange: significant below (1%)

15. 1-pt correlations of MA Data: NP High  Signif. R at remote spots on the same side of the high as the correl. point.  P near Central America not compelling. For key points on the East side of the high, less P for stronger SLP.  Results consistent w/ composites* P shown, OLR similar Blue: significantly (2.5%) more P for higher SLP at * Brown: significantly (2.5%) less P for higher SLP at * H is total data mean location

16. Work with Daily Mean Data: SP high only Data Source: NOAA/CDC (Boulder CO, USA) NCEP/NCAR reanalysis data SLP, U, V U d, V d, Velocity Potential (VP) from NCL commands. Data record: 90-day DJF periods shown (122 day NDJF similar) Drawn from 01/1990 through 08/2002 Goal: Prior work showed remote links now wish to establish cause and effect by using lags and leads.

17. Lags and Leads (expressed as F1 Relative to F2) Example: 1 day lag t = 1 t = 2 t = 3 t = 4F2(i,j,4) F2(i,j,3) F2(i,j,2) F2(i,j,1) F1(1) F1(2) F1(3) F1(4) F2(nx,ny,nt) (P, OLR, DWS, VP, SLP,…) F1(nt) (SLP, MJO, SOI, …) R(i,j) (M,N) y x R(i,j) (1 day lead is similar; but F1 leads F2)

18. SLP lagged autocorrelations lag (L) and lead (R) SLP @ pt-8 correlations (CW: 8, 4, 0, -4, -12d)

19. SLP lagged autocorrelations lag (L) and lead (R) SLP @ pt-11 correlations (CW: 4, 2, 0, -2, -4d)

20. SLP 1-pt Autocorrelation animation

21. Velocity Potential (“VP”) at 200 hPa lag (L) and lead (R) SLP @ pt-8 correlations (CW: 8, 6, 4, 2, 0, -2, -4,-6 d)

22. SLP-Vel.Pot. 1-pt correlation animation

23. V d – Meridional Divergent Wind at 200 hPa & SLP @ pt-11 correlations (CW: 4, 2, 0, -2, -4d)

24. Cross-correlation points for SLP & VP

25. VP cross-correlations for SLP on NE side

26. DWS cross-correlations for SLP max

27. 1-pt Correlations of Climate Indices SLP is 2-D field, climate index is the “point value” Red: significant (1%) positive correlation Blue: significant (1%) negative correlation Correlations between SOI and Nino 3+4 and monthly SLP: Nino 3+4 tends to be positive when the SOI is negative Both indices correlate with SLP on equatorial side of SP high Both indices have some like to opposite change in midlat storm track. MJO results like VP shown: mainly correlation only on N & NE side of high

28. Conclusions Equatorial and NE side of SP high highly correlated with pressure in equatorial & E Pacific. Stronger SLP on N side of SP high is followed by lower SLP over SE Asia. Equatorial side of NP high correlated with ICZ. Relation to precip over Central America inconsistent with Rossby wave model. Stronger SP highs are those SW of the mean position & reinforced by divergent winds from midlatitude cyclones. Stronger NP highs are those NW of mean position & reinforced by midlat cyclones and Indonesian precip. Expansion of Amazonian velocity potential (VP) min. leads to stronger SP high when reinforced by weaker E. Indonesian VP min. Both lead to westward move of VP max over Pacific. This last item leads a westward migration of higher than normal SLP on equatorial side of SP high. For many points cross spectrum (not shown) has strong frequency ~40d. Presumably consistent MJO correlations found (not shown).