$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg01.eps} \end{figure}$ Figure 1: Fragment of the computer generated phases $\phi _1(t_i)$ (diamonds) and $\phi _2(t_i)$ (points). Thick line is a half year average of the principal maxima (M₁(t)), thin line is a half year average of the secondary maxima (M₂(t)). The mean curves can cross each other.
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In the text

$\begin{figure} \par\includegraphics[height=3.71cm,width=8.5cm,clip]{1357fg02.eps} \end{figure}$ Figure 2: Distribution of the differences between phases $\phi _1(t_i), \phi _2(t_i)$ and the half year mean M¹(t) for one particular run (first from the series of 100).
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In the text

$\begin{figure} \par\includegraphics[height=3.71cm,width=8.5cm,clip]{1357fg04.eps} \end{figure}$ Figure 4: Two rows of random phases with phase adjustments applied. The upward trend is due to the accumulation of the added full cycles.
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In the text

$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg05.eps} \end{figure}$ Figure 5: Distribution of the differences between phases and the mean M₁(t) after adjustment. Crossing parameter $\delta \phi = 1/18$ . The bimodal structure of the distribution demonstrates that century-scale persistence of the active longitudes appear even in randomly distributed phases.
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In the text

$\begin{figure}\par\includegraphics[height=3.35cm,width=8.5cm,clip]{1357fg06.eps} \end{figure}$ Figure 6: Mean distribution of the differences for 100 runs. Crossing parameter $\delta \phi = 1/18$ . The bimodality in Fig. 5 is not an exceptional fluctuation.
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In the text

$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg07.eps} \end{figure}$ Figure 7: Fragment of random phases for constrained data. The series of maxima were generated by checking against minimum allowed distance between main and secondary maximum. Crossing parameter $\delta \phi = 1/18$ and distance parameter $\Delta \phi = 0.30$ . Adjustments to follow phase drift were also applied.
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In the text

$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg10.eps} \end{figure}$ Figure 10: Two rows of random phases and corresponding mean curves for locally correlated data. Crossing parameter $\delta \phi = 1/18$ , distance parameter $\Delta \phi = 0.3$ . In this kind of simulation even "flip-flop'' events can be seen.
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In the text

$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg01.eps} \end{figure}$	Figure 1: Fragment of the computer generated phases $\phi _1(t_i)$ (diamonds) and $\phi _2(t_i)$ (points). Thick line is a half year average of the principal maxima (M₁(t)), thin line is a half year average of the secondary maxima (M₂(t)). The mean curves can cross each other.
Open with DEXTER

$\begin{figure} \par\includegraphics[height=3.71cm,width=8.5cm,clip]{1357fg02.eps} \end{figure}$	Figure 2: Distribution of the differences between phases $\phi _1(t_i), \phi _2(t_i)$ and the half year mean M¹(t) for one particular run (first from the series of 100).
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$\begin{figure} \par\includegraphics[height=3.35cm,width=8.5cm,clip]{1357fg03.eps} \end{figure}$	Figure 3: Mean distribution of the 100 separate runs. The distribution is flat in the centre.
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$\begin{figure} \par\includegraphics[height=3.71cm,width=8.5cm,clip]{1357fg04.eps} \end{figure}$	Figure 4: Two rows of random phases with phase adjustments applied. The upward trend is due to the accumulation of the added full cycles.
Open with DEXTER

$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg05.eps} \end{figure}$	Figure 5: Distribution of the differences between phases and the mean M₁(t) after adjustment. Crossing parameter $\delta \phi = 1/18$ . The bimodal structure of the distribution demonstrates that century-scale persistence of the active longitudes appear even in randomly distributed phases.
Open with DEXTER

$\begin{figure}\par\includegraphics[height=3.35cm,width=8.5cm,clip]{1357fg06.eps} \end{figure}$	Figure 6: Mean distribution of the differences for 100 runs. Crossing parameter $\delta \phi = 1/18$ . The bimodality in Fig. 5 is not an exceptional fluctuation.
Open with DEXTER

$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg07.eps} \end{figure}$	Figure 7: Fragment of random phases for constrained data. The series of maxima were generated by checking against minimum allowed distance between main and secondary maximum. Crossing parameter $\delta \phi = 1/18$ and distance parameter $\Delta \phi = 0.30$ . Adjustments to follow phase drift were also applied.
Open with DEXTER

$\begin{figure} \par\includegraphics[height=3.4cm,width=8.5cm,clip]{1357fg08.eps} \end{figure}$	Figure 8: Phase differences from mean M₁(t) for constrained data.
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$\begin{figure} \par\includegraphics[height=3.35cm,width=8.5cm,clip]{1357fg09.eps} \end{figure}$	Figure 9: Mean distribution of the phases for distributions like in Fig. 8.
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$\begin{figure} \par\includegraphics[height=3.45cm,width=8.5cm,clip]{1357fg10.eps} \end{figure}$	Figure 10: Two rows of random phases and corresponding mean curves for locally correlated data. Crossing parameter $\delta \phi = 1/18$ , distance parameter $\Delta \phi = 0.3$ . In this kind of simulation even "flip-flop'' events can be seen.
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$\begin{figure} \par\includegraphics[height=3.4cm,width=8.5cm,clip]{1357fg11.eps} \end{figure}$	Figure 11: Phase differences from mean M₁(t) for locally correlated data.
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$\begin{figure} \par\includegraphics[height=3.35cm,width=8.5cm,clip]{1357fg12.eps} \end{figure}$	Figure 12: Mean distribution of the phase differences for distributions like in Fig. 11.
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