Figure 1. The correlation of Pacific SST anomalies (based on 3-month running mean) and January(1) Niño4 index at different lags from 1958–
2010. The Niño4 index has lags of (a) 15 months; (b) 12 months; (c) 9 months; (d) 6 months; (e) 3 months; and (f) 0 months (no lag). Only p<0.05
is shown. The contours lines correspond to R=0.6 (bold black), 0.5 (thin black) and 0.4 (grey). The grey box in (b) shows the specific PAMS
region.
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Figure 2. Climatological mean SLP (contour, units: +1000 mbar) and standard deviation of monthly SLP anomalies (color) from (a) the
observation (1958–2010) and (b) CESM simulation. (c) and (d) are the same except for SST (units: °C). Linear trends are removed.
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Figure 3. Scatter diagram of averaged zonal wind speed (or stress) vs. Niño4 SST anomalies (160°E–150°W), normalized by their respective
standard deviations from (a) the observation and (b) CESM simulation.
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Figure 4. The CEOF1 of (a, c) SLP anomalies (mbars) and (b, d) SST anomalies (°C) (3-month running mean) in the North Pacific (top:
observation; bottom: CESM simulation).
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Figure 5. Same as Fig. 4 except for the CEOF2.
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Figure 6. (a) Lag-correlation between Niño3 (and EMI index) and PC1/PC2 of the CEOF (3-month running mean) based on observation. Positive
axis means Niño3 (and EMI index) lags. (b) is the same as (a) except for CESM simulation. Grey dashed line shows the 95% confidence level.
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Figure 7. Wavelet power spectrum (left) and global power spectrum (right) of PC1 (colors) and PC2 (black contour lines) from the observation and
CESM simulation (9-month smoothing is applied to emphasize the interannual to decadal variability). The local wavelet power spectrum provides
a measure of the variance distribution of the time series according to time and periodicity; high variability is represented by red, whereas blue
indicates weak variability in the wavelet power spectrum. For the global power spectrum, the dashed lines indicate 95% significant level and the
periods of 3, 5 and 12 years, respectively.
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Figure 8. (a) Observed lead-lag correlation between the 3-month averaged zonal wind anomalies (U10ps) in PAMS (110~140°E, 5~25°N) and PC1.
(b) Same as (a) except for the meridional wind anomalies (-V10ps) and PC2. Areas with a correlation that is significant at the 95% confidence level
are shaded. (c) and (d) are the same as (a) and (b), except for CESM simulation. Contours are the lead-lag correlation between NSI and PC2 (see
text for definition).
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Figure 9. Time series of NDJ-averaged -V10ps, JAS-averaged U10ps in PAMS (superimposed by their associated JFM-averaged PC2 and DJFaveraged PC1 with maximum correlations in Figs. 8a and 8b, respectively). All time series are aligned with their corresponding months. Black
solid (dashed) lines label the El Niño (La Niña) years in December.
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Figure 10. Correlation of the NDJ-averaged meridional wind anomalies (-V10ps) in PAMS (grey box in the
top-left panel) with several lags (NDJ, DJF, JFM, FMA, MAM) of SLP anomalies (left), SST anomalies
(middle) and latent heat flux anomalies (right) in the observation (contours in the left and middle panels
are the CEOF2 of SLP and SST anomalies in Fig. 5). Grey dashed boxes in the top-left panel show the
domain to define the EAWM index.
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Figure 11. Same as Fig. 10 except for the vertical section along 25°N of geo-potential height (left), vertical velocity Ω (middle) and air
temperature (right).
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Figure 12. Correlation of the JAS-averaged zonal wind anomalies (U10ps) in PAMS (grey box in the topleft panel) with several leads/lags (MAM, MJJ, JAS, SON, NDJ) of SLP anomalies (left), SST anomalies
(middle) and latent heat flux anomalies (right) in the observation (contours in the left and middle panels
are the CEOF1 of SLP and SST anomalies in Fig. 4).
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Figure 13. Same as Fig. 12 except for the vertical section along 5°N of geo-potential height (left), vertical velocity Ω (middle) and air temperature
(right).
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Figure 14. Correlation of the JAS-averaged zonal wind anomalies (U10ps) in PAMS (grey box in the topleft panel) with several leads/lags (MAM, MJJ, JAS, SON, NDJ) of SLP anomalies (left) and SST
anomalies (right) in the CESM simulation from year 50 to 150 (contours in the left and middle panels are
the CEOF1 of SLP and SST anomalies in Fig. 4).
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Figure 15. Correlation of the DJF-averaged meridional wind anomalies (-V10ps) in PAMS (grey box in the
top-left panel) with several lags (DJF, JFM, FMA, MAM, AMJ) of SLP anomalies (left) and SST
anomalies (right) in the CESM simulation from year 50 to 150 (contours in the left and middle panels are
the CEOF2 of SLP and SST anomalies in Fig. 5).
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Figure 16. SLP (color shaded) and wind (vectors) anomalies averaged in NDJ, DJF, JFM, FMA and
MAM in 1981~1982 (left), 1993~1994 (middle) and 1997~1998 (right).
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Figure 17. Schematic diagram of the dynamical processes linking the two dominant modes through the
PAMS origin. The SLP (contours) and SST (color) anomalies associated with CEOF2 patterns are
overlaid.
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