A decadally delayed response of the tropical Pacific to Atlantic multidecadal
variability
Davide Zanchettin, Oliver Bothe, Hans F. Graf, Nour-Eddine Omrani, Angelo Rubino,
Johann H. Jungclaus
SUPPLEMENTARY ONLINE MATERIAL
Figure S1 – Maps of linear regression coefficients (K/K) of annual-average North
Atlantic sea-surface temperatures (SSTs) on annual-average AMO indices for the three
MPI-ESM-P simulations with AMO nudging (reported in the title of each panels).
The three simulations consistently feature a basin-wide AMO signature on North Atlantic
SSTs, with a relatively weaker imprint in the Gulf Stream region, in the Labrador and
Nordic Seas and more generally in areas affected by sea ice. Regressions coefficients
generally increase with the AMO amplitude.
ONI (K)
a) amo0.5k
2
0
-2
50
100
150
50
100
150
200
250
300
350
400
200
250
300
350
400
Period (years)
3
5
7
10
20
30
50
70
100
ONI (K)
Time (year)
b) amo1k
3
2
1
0
-1
50
100
150
200
250
300
350
400
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100
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300
350
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Period (years)
3
5
7
10
20
30
50
70
100
ONI (K)
Time (year)
c) amo2k
4
2
0
-2
50
100
150
50
100
150
Period (years)
3
5
7
10
20
30
50
70
100
Time (year)
Figure S2 (previous page) – Time series and wavelet power spectrum of the winter (DJF)
ONI index for the three MPI-ESM-P simulations with AMO nudging. Only significant
regions of the wavelet spectrum that are statistically significant are shown: shading (black
line) is above 90% (95%) confidence against a theoretical AR1 background spectrum.
Dashed vertical lines indicate the beginning of the six 70-year AMO cycles represented
in each simulation. The thick line marks the cone of influence, where edge effects occur.
Wavelets were computed using the software provided by A. Grinsted.
amo0.5k
4
0.9
Period
8
16
0.8
32
0.5
64
128
50
100
150
200
250
300
350
400
amo1k
4
0.9
Period
8
16
0.8
32
0.5
64
128
50
100
150
200
250
300
350
400
amo2k
4
0.9
Period
8
16
0.8
32
0.5
64
128
50
100
150
200
250
300
350
400
Figure S3 (this and previous page) – Wavelet coherence spectra between annual-average
AMO and winter (DJF) ONI indices for the three MPI-ESM-P simulations with AMO
nudging, whose name is reported in the title of each panel. Period is in years, the x-axis is
time, in years. The thick line individuates regions of the wavelet coherence spectrum that
are statistically significant at 95% confidence. Arrows indicate the phases. The thin line
marks the cone of influence, where edge effects occur. The wavelet coherences were
calculated using the software provided by A. Grinsted.
Note the broadband robust significant coherence at multidecadal timescales and the
robust AMO-ONI phasing at the 70-year period of imposed AMO oscillations. The
average phase angles at period 70.05 years are -0.772 π in amo0.5k and amo1k, and 0.758 π in amo2k, which correspond to AMO leading ONI of about 27 years and 26.5
years, respectively (regions affected by borders excluded in the calculation).
Figure S4 – Differences in the Walker circulation in the tropical Pacific from the cold to
the warm AMO phase. The difference between velocity potential at 200 hPa and 850 hPa
is used to estimate the strength of upward atmospheric motion. Mapped are winter (DJF)
values of such difference averaged during the warm AMO phase (years 7-27 of the AMO
cycle) minus corresponding values averaged during the cold AMO phase (years 44-64 of
the AMO cycle). Contours are the climatology for the amo0.5k simulation (black are
positive values, grey are negative values, thick black is zero; contours are plotted at 4e6
m2/s intervals). Velocity potential anomalies are proportional to convergence, so negative
anomalies in the maps correspond to anomalous upward motion (e.g., due to stronger
divergence in the upper troposphere and stronger convergence in the lower troposphere),
and vice versa.
Hence, Figure S4 shows a westward shift of both upward and downward branches of the
Walker circulation during the warm AMO phase compared to the cold AMO phase. The
strengthening of the western downward branch of the Walker cell over the eastern Pacific
Ocean, together with the enhanced southern flanks of subtropical highs in both hemispheres mark the eastern edge for the strengthened trade winds simulated between 120°E
and 150°W (Figure 2b). The response depends linearly to the strength of AMO forcing.
Figure S5 – As Figure 3 in the main manuscript, but for the NINO3 region.
Figure S6 – As Figure 3 in the main manuscript, but for the NINO1.2 region.
Figure S7 - Average evolution of winter (DJF) seawater vertical diffusivity (m2/s) in the
NINO4 (a), NINO3.4 (b) and NINO1.2 (c) regions through the AMO cycle. Anomalies
are calculated from each level’s climatology. Shadings are statistically significant
anomalies for the amo2k simulation; big (small) black dots indicate where anomalies are
significant in all (two of) the AMO simulations. Data were smoothed with an 11-year
running mean low-pass filter. In all panels, the grey vertical lines mark the approximate
maxima of the warm and of the cold phase of the AMO. Significance is based on 5-95
percentile range.
Figure S8 – Same as Figure S7 but for horizontal mass transport (kg/s).
Figure S9 - Same as Figure S7 but for vertical (upward) mass transport (kg/s).
Figure S10 – Left panels: evolution of AMO index (a) and latent heat flux (b), large-scale
(c) and convective (d) precipitation and evaporation (e) spatially averaged between 150180°E and 10°S-10°N during AMO oscillations in the nudged simulations (thin lines:
individual cycles, thick lines: average cycle). Fluxes are positive downward, so positive
values correspond to gain by the ocean. Right panels are the cross-correlations with the
AMO index of variables plotted in the corresponding left panels (AMO leads by the years
reported in the x-axis); statistically significant values are marked with a circle. Data are
annual-mean values smoothed with a 5-year running mean low-pass filter.
Figure S11 - Migration of simulated maximum annual-average sea-surface temperature
(SST) anomalies in the equatorial Pacific Ocean through the AMO oscillation. Contour
plots are for the 90th percentile of SST anomalies calculated for ensemble-cycle averages
of SST anomaly for subsequent decades through the AMO cycles (paced at 5-year
intervals). Numbers in the color bar individuate the beginning of the decades.
Figure S12 – Evolution of average potential temperature (°C) and salinity (psu) in
selected areas of the world ocean simulated in the three MPI-ESM-P simulations with
pattern nudging (color code as for Figure 1 in the main manuscript, i.e., blue: amo0.5k;
green: amo1k; red: amo2k), for two reference depths (top panels: upper 200 m; bottom
panels: upper 800 m). The AMO index is also reported for comparison.
Figure S13 – Left panels: evolution of AMO index (a), winter (DJF) North-Pacific-Index
(NPI) describing atmospheric variability in the Aleutian Low region (b), winter Arctic
sea-ice cover (c) and winter 10 m zonal wind averaged over the western Pacific warm
pool (d) during AMO oscillations in the nudged simulations (thin lines: individual cycles,
thick lines: average cycle). Right panels are the cross-correlations with the AMO index of
variables plotted in the corresponding left panels (AMO leads by the years reported in the
x-axis); statistically significant values are marked with a circle. Data are smoothed with a
5-year running mean low-pass filter. The NPI index is computed as the sea level pressure
spatially averaged over 30–65° N and 160–220° E. The western Pacific warm pool region
is defined as between 150-180°E and 10°S-10°N.