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Manuscript number: 2014GL062078
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Auxiliary Material for
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On the bipolar origin of Heinrich events
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Xiao Yang, J A. Rial, and Elizabeth P. Reischmann
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(Wave Propagation Laboratory, Department of Geological Sciences, University of North
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Carolina at Chapel Hill, Chapel Hill, North Carolina, USA)
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Geophysical Research Letters
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Introduction
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This auxiliary material has provided a brief introduction to the development of the polar
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synchronization, the method to match difference age models, and the concept of analytic
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signal. The detailed data processing techniques and parameters used to obtain the results
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in this paper have been recorded in the “Calculation of the Energy and inter-polar
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gradient” section of the supporting information (see in text01.pdf). Along with the above
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auxiliary text, six auxiliary figures (Figure S1-S6 as in fs01.eps – fs06.eps) have been
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included in separate files. These supporting figures have been described and cited in
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either the body of the manuscript or in the auxiliary text to reinforce the claims and to
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demonstrate the robustness of the methods.
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1. text01.docx Supporting background information on the polar synchronization, age
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model matching, analytic signal, and temperature difference/energy calculation.
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2.1 fs01.eps (Figure S1) Phase difference between NGRIP (Greenland) and Antarctica’s
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EDML isotope records calculated using the AICC2012 age model [Veres et al., 2013]
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(blue dots). The evolution of the phase difference with the 2π jumps removed is shown in
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the lower part of the plot (red dots). The persistence of the π/2 phase shift is a condition
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for the signals to be considered synchronized. Histograms show that the distribution of
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phase differences strongly peaks at π/2 (mod 2π). Similar phase difference relation have
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been observed between methane age-matched NGRIP and DOME C, and between GRIP
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and BYRD (see Figure 3 in [Oh et al., 2014]). It should also be noticed that the phase
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difference exists for all values from 0 to 2π in the histogram and synchronization in
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nature is rarely if ever perfect. The jumps in the phase difference (blue dots) are usually
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known as phase slippage.
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2.2 fs02.eps (Figure S2) Comparison of time errors after age-match to the time errors
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without age-match. For the timing comparison, 17 peaks that have amplitudes greater
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than one standard deviation (marked with red circles in the bottom figure) were selected
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from methane matched GRIP records. Age matching results reduce time errors (standard
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deviations) from ±2400 years to less than ±400 years at the corresponding peak
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amplitudes, except 2 peak points that possibly due to the low sampling rate of GISP2 at
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these locations.
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2.3 fs03.eps (Figure S3) Polar climate difference calculation base on age-matched
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records (a) and unmatched original records (b). The peaks in the matched records align
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much better with occurrence of H events and IRDs when compared with the same
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calculation based on unmatched records, demonstrating the importance of a unifying age
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model when comparing polar climate history. Lower panel (b) demonstrates that the
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delicate pi/2 polar synchronization relationship, thus the predictive power of the polar
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temperature gradient calculation, demolishes if the original age model of the two records
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were used. Two records used here are NGRIP and DOME C.
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2.4 fs04.eps (Figure S4) Temperature and power calculation for records based on
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AICC2012 age model [Veres et al., 2013] as well as age-matched records. The southern
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records from Antarctica have been averaged to reduce local climate variations. From top
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to bottom for both AICC2012 and age-matched calculation, the y-axis labels are:
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“Normalized δ18O-derived temperature”, “Normalized δ18O-derived temperature”,
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“Rectified normalized temperature gradient”, and “Arrival of energy (arbitrary units)”.
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2.5 fs05.eps (Figure S5) Variations in the baseline of the southern records and their
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impact on the power estimation. A sequence of shift values (0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2,
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1.4) have been added to the averaged southern records; each of the resulting shifted
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record then was used to calculate power estimation. One can observe that, with a slight
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boost in the baseline value ( ³ 0.2 ), the resulting power estimation became much more
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clean, at the same time, has a much stronger correlation with the timings of H events.
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2.6 fs06.eps (Figure S6) S-N polar temperature difference based on bandpass filtered
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records (a) and EMD reconstructed records (b). In (a), the age-matched records have been
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bandpass filtered with corner frequency 0.0001 year -1 to 0.0009 year -1 . In (b) the same
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pair have been decomposed using EMD and reconstructed by summing up selected IMFs
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(here we selected IMF 2-6). The resulting energy density shares almost all of the peaks
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with that from using bandpass filter. The advantage of using EMD is that it has no
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assumption to the nature of the data thus has been widely used to decompose nonlinear
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signal [Huang et al., 1998], but it has bias to compare two different reconstructed records
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by summing the same range of IMFs, since the total number of IMFs being generated
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using EMD is determined by the number of extremes in the record itself, thus IMFs
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summation with the same range from different data may represents slightly different
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frequency bands.
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2.7 fs07.eps (Figure S7) Comparison between S-N polar temperature gradient and two
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proxy records (organic carbon and Fe/Ca ratio) from GeoB3912-1 [Jennerjahn et al.,
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2004] sediment core in equatorial Atlantic. After removing the high frequency variations
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in (a) and (b), the correlation coefficient can reach as great as 0.8. Despite the possible
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error introduced by different age models, such similarity suggests transmission of polar
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climate signal across the equator.
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References:
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