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Supplementary Information for
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Remote influence of Atlantic multidecadal variability on Siberian
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warm season precipitation
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Cheng Sun, Jianping Li and Sen Zhao
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*To whom correspondence should be addressed. E-mail: ljp@bnu.edu.cn
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This file includes:
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Supplementary Figs. 1–10
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AMV leads
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Supplementary Figure 1 Lead–lag correlation between the SWP and AMV indices for different
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smoothing time scales. The blue, red, and green lines are for unsmoothed, 5-yr, and 9-yr running
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mean time series, respectively. The definitions of the SWP and AMV indices are given in the text.
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The dashed lines are the 95% confidence levels based on the effective numbers of degrees of
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freedom. This figure was plotted using NCL.
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(a)
GPCC
Kaplan
HadISST3
StdDev
CRU
StdDev
(b)
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Supplementary Figure 2 SWP and AMV indices from different SST and precipitation data sets. (a)
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Normalized time series of the SWP indices from the CRU and GPCC precipitation data sets (thin
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lines) and the 11-yr running averages (thick lines). (b) Normalized time series of the AMV
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indices from the Kaplan and HadISST3 SST data sets for the period 1901–2013 (thin lines) and
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the 11-yr running averages (thick lines). In (a) and (b) the long-term linear trends in SST and
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precipitation data have been removed to highlight the fluctuations. This figure was plotted using
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NCL.
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Supplementary Figure 3 Regression of the warm season land precipitation anomalies over
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northern Asia (mm month−1) with respect to the normalized AMV index at decadal time scales.
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Dots indicate regressions significant at the 95% confidence level. This figure was plotted using
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NCL.
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Supplementary Figure 4 Regression of the warm season NH 900-hPa geopotential height (m)
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with respect to the normalized AMV index at decadal time scales. Dots indicate regressions
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significant at the 95% confidence level. This figure was plotted using NCL.
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Supplementary Figure 5 The AMV-type SST anomalies (K) used to force the sensitivity
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experiments in the SPEEDY model. This figure was plotted using NCL.
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Supplementary Figure 6 Warm season precipitation (shading; mm month−1) and vertically
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integrated moisture flux (vectors; kg m−1 s−1) simulated by the SPEEDY model over the eastern
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NH in response to North Atlantic SST warming. Dots and arrows indicate the regions where the
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results from the sensitivity simulations are significantly different from the control at the 95%
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confidence level (Student’s t-test). This figure was plotted using NCL.
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(a)
(b)
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Supplementary Figure 7 SPEEDY model simulated fields from a longer 30-yr sensitivity
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experiment forced by the AMV-type warm SST anomalies. (a) 300-hPa geopotential height
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(shading; m), (b) precipitation (shading; mm month−1), and vertically integrated moisture flux
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(vectors; kg m−1 s−1) anomalies over the NH during the warm season. Dots in (a) and (b), and
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arrows in (b) indicate the regions where the results from the sensitivity simulations are
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significantly different from the control at the 95% confidence level (Student’s t-test). This figure
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was plotted using NCL.
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(a)
(b)
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Supplementary Figure 8 Simulated fields from the SPEEDY model of the warm season in
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response to North Atlantic SST cooling. (a) 300-hPa geopotential height (shading; m), (b)
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precipitation (shading; mm month−1), and vertically integrated moisture flux (vectors; kg m−1 s−1)
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anomalies over the NH. Dots in (a) and (b), and arrows in (b) indicate the regions where the
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results from the sensitivity simulations are significantly different from the control at the 95%
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confidence level (Student’s t-test). This figure was plotted using NCL.
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Supplementary Figure 9 Stationary Rossby wave trajectories (blue curves) with zonal wave
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number (a) k = 3 and (b) k = 4. The Rossby waves start from the mid-latitude North Atlantic
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according to the RWS analyses shown in Fig. 4c, and are forced by the background state of the
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warm season climatology. The climatological mean 300-hPa zonal wind for the warm season
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(color shading; m s–1) serves as the background field for the Rossby wave trains. The trajectories
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are computed by Rossby wave ray tracing analysis, following ref. 46. This figure was plotted
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using NCL.
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Supplementary Figure 10 Climatological Rossby stationary wavenumber ( (   U yy ) / U , where
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U and  are mean zonal wind and meridional gradient of planetary vorticity, respectively, and
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U yy is the approximated meridional gradient of relative vorticity). The stationary Rossby
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wavenumber calculation is based on the long-term climatological zonal wind at 300 hPa for the
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warm season. This figure was plotted using NCL.
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