Propagating Atmospheric Patterns Associated with Sea Ice Motion through the Fram Strait

Jessica Liptak Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

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Courtenay Strong Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

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Abstract

A novel analysis method involving phase-shifted complex Hilbert empirical orthogonal functions (HEOFs) was used to examine how variations in predominant propagating patterns of Arctic surface wind influence daily Fram Strait sea ice export F during extended winter (October–April), a primary control on Arctic sea ice volume. Northwesterly winds favorable to F were provided by poleward-moving anticyclones upstream over the Canadian Arctic associated with the leading HEOF of wind and also by eastward-moving cyclones downstream over the Barents Sea associated with the second HEOF of wind. A suite of spatial and statistical analyses indicated that the aggregate of the two propagating patterns largely explains a sea level pressure pattern analyzed in several prior studies as a standing wave oriented east–west across the strait.

Corresponding author address: Courtenay Strong, University of Utah, 135 S, 1460 E, Rm. 819 (WBB), Salt Lake City, UT 84112-0110. E-mail: court.strong@utah.edu

Abstract

A novel analysis method involving phase-shifted complex Hilbert empirical orthogonal functions (HEOFs) was used to examine how variations in predominant propagating patterns of Arctic surface wind influence daily Fram Strait sea ice export F during extended winter (October–April), a primary control on Arctic sea ice volume. Northwesterly winds favorable to F were provided by poleward-moving anticyclones upstream over the Canadian Arctic associated with the leading HEOF of wind and also by eastward-moving cyclones downstream over the Barents Sea associated with the second HEOF of wind. A suite of spatial and statistical analyses indicated that the aggregate of the two propagating patterns largely explains a sea level pressure pattern analyzed in several prior studies as a standing wave oriented east–west across the strait.

Corresponding author address: Courtenay Strong, University of Utah, 135 S, 1460 E, Rm. 819 (WBB), Salt Lake City, UT 84112-0110. E-mail: court.strong@utah.edu

1. Introduction

The Fram Strait is the primary conduit of sea ice export in the Arctic. Ice motion associated with Arctic ice export depends largely on atmospheric forcing, which is often measured in terms of storm tracks and standing-wave teleconnections such as the North Atlantic Oscillation (NAO). The NAO is typically defined as the first empirical orthogonal function (EOF) of sea level pressure (SLP) over the North Atlantic (Hurrell et al. 2003). The positive phase of the NAO, characterized by an anomalously strong Icelandic low, is associated with enhanced ice export through the Fram Strait during the winter (Kwok and Rothrock 1999; Kwok et al. 2004; Kwok 2009). Hilmer and Jung (2000) found that the correlation between the NAO and sea ice motion was positive and stronger in decades following the late 1970s, when the Icelandic low shifted eastward relative to its mean position in 1958–77, resulting in enhanced northerly winds over the Fram Strait. Similar inconsistencies in the temporal correlation between winter Fram Strait sea ice flux and the NAO were noted by Vinje (2001).

Other modes of atmospheric variability influence Fram Strait sea ice flux. The “Barents Oscillation” (Skeie 2000; Tremblay 2001) appeared as the second (Skeie 2000) or third EOF (Tremblay 2001) of winter (December–March) SLP north of 25°–30°N, depending on the analysis period used, and depicted northerly geostrophic flow through the Fram Strait in its positive polarity. Tsukernik et al. (2010) showed that daily Fram Strait sea ice flux during the winter and summer was correlated with an index of the cross-strait pressure gradient based on a dipole in the SLP anomaly field that resembled the Barents Oscillation. Wu et al. (2006) and Wang et al. (2009) termed the pattern in the second EOF of SLP north of 70°N the dipole anomaly. The positive phase of the dipole anomaly depicted enhanced cyclonic circulation over the Barents Sea and was associated with a southward Fram Strait sea ice flux.

The gradient associated with SLP anomalies determines surface wind stress, which acts with the Coriolis force, internal ice stress, and ocean stress to determine the motion of low-concentration ice present in the Fram Strait. A discussion of the importance and dynamical interpretation of Ekman drift of ice is given in Ogi et al. (2008). Provided that sea ice is sufficiently far from land, on average the ice moves approximately 30° to the right of the surface wind as observed by Nansen (1902) and Zubov (1943), implying that sea ice motion is roughly parallel to surface isobars. Model results from Koenigk et al. (2006) showed that annual ice export through the Fram Strait was primarily driven by the cross-strait SLP gradient. Vihma et al. (2012) found that a greater portion of the variance in annual mean Arctic ice drift speed was explained by the difference in SLP between 270° and 90°E at 84°N than by the dipole anomaly in Wu et al. (2006). Furthermore, annual and fall (September–November) mean ice drift speeds in the Fram Strait were determined by the SLP difference at 82°N.

On time scales of one to two weeks, the passage of cyclones strongly influences Fram Strait sea ice export. Brümmer et al. (2003) observed an increase in the average ice drift speed from approximately 0.2 to 0.6 m s−1 during the passage of a cyclone. In addition, the location of a cyclone relative to the Fram Strait played a major role in determining the direction of ice motion and amount of convergence. Rogers et al. (2005) found that decreased Fram Strait sea ice export was associated with increased winter cyclone frequency in the Fram Strait and decreased cyclone frequency over the Norwegian and Barents Seas.

Here, a novel analysis method involving phase-shifted complex Hilbert empirical orthogonal functions was used to examine how variations in predominant propagating patterns of Arctic surface wind during extended winter (October–April) influence daily Fram Strait sea ice export. By using circulation patterns that are both propagating and of leading statistical importance, this analysis method incorporates strengths of storm track and EOF analyses.

2. Data and methods

Following Tsukernik et al. (2010), sea ice motion vectors obtained from the National Snow and Ice Data Center (Fowler 2003) were used to compute an index F of daily sea ice motion through the Fram Strait during winter (defined as 15 October–14 April) over the period 1979–2006. The index F was the meridional component of sea ice motion averaged over the region 79°–81°N, 20°W–15°E (Fig. 1, red box), with a positive F indicating southward sea ice motion (export).

Fig. 1.
Fig. 1.

Arrows indicate the vector correlation between F and the surface wind field. Arrow length indicates the magnitude of the correlation, and arrow direction indicates the size and sign of the two correlation components. The red box indicates the region used to define F. Shading corresponds to the sea ice concentration.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00599.1

A complex Hilbert empirical orthogonal function (HEOF) analysis (e.g., Hannachi et al. 2007) was performed on the combined field of the zonal and meridional components of daily surface wind from 60° to 90°N obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996). The phase of each HEOF was shifted by the angle φ that maximized the temporal correlation between F and the real part of the principal component (i.e., time series) associated with each HEOF. As shown in Strong and Liptak (2012), the desired phase shift φ is the argument of the complex correlation between F and the HEOF time series, and application of the phase shift facilitates presentation of the HEOF pattern by consolidating the statistically relevant information entirely in the real domain; thus, the HEOF is set in the phase most relevant to the geographically fixed variations of interest (F in this case).

Here, we present the first two phase-shifted HEOFs of , which are and . We refer to their eigenvectors as and “patterns” and their principal components as and “time series” where needed for clarity. Further details on the rationale, derivation, and utility of the phase shift are given in Strong and Liptak (2012).

To interpret the results of the HEOF analysis in the context of prior work on SLP standing waves, traditional EOF analysis was performed on daily winter SLP data from the NCEP–NCAR reanalysis over the North Atlantic region, which is defined as 45°–90°N, 90°W–90°E. The first EOF Ep1 represented the NAO (Hurrell et al. 2003) and accounted for 29% of the SLP variance. Attention here focused on the second EOF Ep2, which accounted for 13% of the variance and was also well separated according to the criterion of North et al. (1982).

To estimate the power spectrum of a principal component, power spectra were calculated for each winter using Hanning windows, and then the spectra were averaged (Welch 1967). Red noise spectra and associated 95% confidence limits were calculated according to the method outlined by Gilman et al. (1963). Reported correlations were tested for statistical significance at the 95% confidence level by bootstrapping the distribution of the Pearson correlation coefficient r by resampling with replacement 1000 times (e.g., Efron 1979).

3. Results

The vector correlation between the surface wind velocity and F (Fig. 1) shows that Fram Strait sea ice flux is associated with a cyclonic circulation over the Barents Sea and an anticyclonic circulation located off of northwestern Greenland. Assuming that the interaction of ocean stress, internal ice stress, and the Coriolis force produces sea ice motion that is oriented approximately 30° to the right of the surface flow (Nansen 1902; Zubov 1943), the northwesterly winds over the Fram Strait are conducive to sea ice export (defined by large positive values of F).

Together, and account for 25% of the variance in the surface wind field and are well separated according to the criteria of North et al. (1982). The real part of the pattern (Fig. 2a) depicts northwesterly flow through the Fram Strait between an upstream anticyclonic circulation (filled blue circle) and downstream cyclonic circulation (filled red circle), and the correlation between the time series and F is 0.27. The median temporal rate of change of the angle of the time series is positive, meaning that the transients tend to propagate toward increasing phase along a transpolar trajectory from North America toward Asia (open symbols in Fig. 2a). The imaginary part of the pattern (Fig. 2b) is the real part shifted by π/2, and the associated northeasterly flow over the strait (arrows, Fig. 2b) is conducive to westward sea ice motion, yielding zero correlation with F.

Fig. 2.
Fig. 2.

(a) The real and (b) the imaginary part of the first phase-shifted HEOF of surface wind . (c) The real and (d) the imaginary part of the second phase-shifted HEOF of surface wind . Red symbols mark the centers of cyclonic and blue symbols mark the centers of anticyclonic circulations. Correspondingly colored open symbols show these circulation features advancing forward by π/4 (open circle), π/2 (open square), and 3π/4 (open triangle). Curves connect locations where circulation centers follow a continuous path. Percent values in the top-right corners of (a),(c) indicate the amount of variance in the surface wind field explained by each HEOF.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00599.1

The real part of the pattern (Fig. 2c) also depicts an upstream (filled blue circle) and downstream (filled red circle) cyclone, and the correlation between the time series and F is 0.30. The median temporal rate of change of the time series is positive, meaning that captures eastward-moving transients over the subpolar Atlantic seas and the Canadian Arctic (open symbols in Fig. 2c). As with , the imaginary part of (Fig. 2d) advances the circulation features by π/2 downstream to positions where they have zero correlation with F.

Mapped inspection and statistical analysis of and suggest that these two patterns combine to form the standing-wave east–west SLP dipole pattern linked to F in several prior studies. The east–west dipole appears as the second EOF of SLP over the North Atlantic domain (Ep2; section 2), and is nearly identical (Fig. 3) to the one presented in Fig. 2 of Tsukernik et al. (2010). The Ep2 pattern is also similar to the dipole anomaly (Wu et al. 2006; Wang et al. 2009) and the Barents Oscillation (Skeie 2000; Tremblay 2001), although the latter two use different analysis domains and time periods. The correlation between F and Ep2 is 0.39 and reflects southward ice motion driven by the northerly flow indicated by the north–south orientation of the isobars over the Fram Strait. Notably, the anticyclonic circulation center in is collocated with the Ep2 positive center of action, and the cyclonic circulation center in is collocated with the Ep2 negative center of action. The Ep2 time series is correlated with the real parts of and at r = 0.25 and r = 0.41, respectively. In contrast, Ep1 (the NAO) is correlated with at r = 0.02 (not significant) and at r = 0.04 (significant).

Fig. 3.
Fig. 3.

The second EOF of SLP over the domain 45°–90°N, 90°W–90°E. The contour interval is 0.005.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00599.1

Providing further evidence of the strong linkage from and to Ep2, the temporal power spectra of these patterns are remarkably similar (Fig. 4). Each spectrum differs significantly from a null red noise spectrum for periods between 4 and 15 days (dashed vertical lines in Fig. 4). Peaks occur at periods of 13 days for (Fig. 4a), 14 days for (Fig. 4b), and 13 days for Ep2 (Fig. 4c).

Fig. 4.
Fig. 4.

Power spectra (bold black curves) for (a) , (b) , and (c) the second empirical orthogonal function of sea level pressure Ep2. Thin solid curves indicate the null red noise spectrum, dashed curves show the 95% confidence limit for the null red noise spectrum, and dotted vertical curves indicate periods of 4 and 15 days.

Citation: Journal of Climate 26, 9; 10.1175/JCLI-D-12-00599.1

4. Summary and conclusions

Complex HEOF analysis was used to identify the two leading patterns of variability in the surface wind field poleward of 60°N during winter (i.e., and ). The HEOFs were then phase shifted to optimize the real correlation between the associated principal component time series and an index of Fram Strait sea ice export F, revealing that each pattern supports northwesterly flow over the Fram Strait. Here, depicts synoptic-scale cyclones and anticyclones that followed a transpolar track from the Canadian Arctic to Siberia, and the correlation with F is 0.27. The pattern is consistent with the tendency for Arctic cyclones to travel eastward or poleward from the Canadian Archipelago, Greenland Sea, Barents Sea, and Kara Sea over the eastern Arctic and, to a lesser extent, the Chukchi and East Siberian Seas in the western Arctic in the winter (Serreze et al. 1993; Sickmöller et al. 2000; Brümmer et al. 2000; Zhang et al. 2004; Sorteberg and Kvingedal 2006). Thus, captures the variability associated with propagating synoptic-scale cyclones that cross into the high latitudes, not a transpolar storm track per se. Here, resolves eastward-moving circulations over the subpolar Atlantic and Canadian Arctic consistent with patterns observed in several storm track analyses (e.g., Sickmöller et al. 2000; Brümmer et al. 2000; Sorteberg and Kvingedal 2006), and its correlation with F is 0.30.

Centers of action in the SLP standing-wave dipole associated with F in prior work (i.e., the second EOF of North Atlantic SLP Ep2) align remarkably well with the real parts of the and spatial patterns after phase shifting. Specifically, the positive center of action of Ep2 is collocated with the anticyclonic circulation center, and the negative center of action of Ep2 was collocated with the cyclonic circulation center. In addition, the real parts of and together account for nearly the same amount of variance in F as Ep2. Considering how Ep2 correlates with (r = 0.25) and (r = 0.41) and that the associated power spectra show significant variability for 4–15-day periods, this study supports the conclusion that the east–west SLP dipole linked to F variability in prior work represents the aggregation of poleward-propagating and eastward-propagating synoptic-scale cyclones and anticyclones.

Together, and account for a modest 16% of F, and wind-based indices could have easily been constructed to account for far more (e.g., one of the correlation vectors in Fig. 1 alone has a length of 0.57). The purpose here was not to optimally account for F but rather to determine the two most important propagating patterns of daily Arctic surface wind variability and to investigate how they influence F and illuminate aspects of prior related work based on analyses of storm tracks and standing-wave SLP patterns.

Acknowledgments

This research was supported by the National Science Foundation Arctic Sciences Division Grant 1022485.

REFERENCES

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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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  • Welch, P. D., 1967: The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust., 15, 7073.

    • Search Google Scholar
    • Export Citation
  • Wu, B., J. Wang, and J. E. Walsh, 2006: Dipole anomaly in the winter Arctic atmosphere and its association with sea ice motion. J. Climate, 19, 210225.

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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Save
  • Brümmer, B., S. Thiemann, and A. Kirchgäßner, 2000: A cyclone statistics for the Arctic based on European Centre re-analysis data. Meteor. Atmos. Phys., 75, 233250.

    • Search Google Scholar
    • Export Citation
  • Brümmer, B., G. Müller, and H. Hoeber, 2003: A Fram Strait cyclone: Properties and impact on ice drift as measured by aircraft and buoys. J. Geophys. Res., 108, 4217, doi:10.1029/2002JD002638.

    • Search Google Scholar
    • Export Citation
  • Efron, B., 1979: Bootstrap methods: Another look at the jackknife. Ann. Stat., 7, 126.

  • Fowler, C., cited 2003: Polar Pathfinder daily 25 km EASE-grid sea ice motion vectors. NSIDC. [Available online at http://nsidc.org/data/docs/daac/nsidc0116_icemotion.gd.html.]

  • Gilman, D. L., F. J. Fuglister, and J. M. Mitchell, 1963: On the power spectrum of “red noise.” J. Atmos. Sci., 20, 182184.

  • Hannachi, A., I. T. Jolliffe, and D. B. Stephenson, 2007: Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Climatol., 27, 11191152, doi:10.1002/joc.1499.

    • Search Google Scholar
    • Export Citation
  • Hilmer, M., and T. Jung, 2000: Evidence for a recent change in the link between the North Atlantic Oscillation and Arctic sea ice export. Geophys. Res. Lett., 27, 989992.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: The North Atlantic Oscillation: Climate Significance and Environmental Impact.Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 279 pp.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471.

  • Koenigk, T., U. Mikolajewicz, H. Haak, and J. Jungclaus, 2006: Variability of Fram Strait sea ice export: Causes, impacts and feedbacks in a coupled climate model. Climate Dyn., 26, 1734.

    • Search Google Scholar
    • Export Citation
  • Kwok, R., 2009: Outflow of Arctic Ocean sea ice into the Greenland and Barents Seas: 1979–2007. J. Climate, 22, 24382457.

  • Kwok, R., and D. A. Rothrock, 1999: Variability of Fram Strait ice flux and North Atlantic Oscillation. J. Geophys. Res., 104 (C3), 51775189.

    • Search Google Scholar
    • Export Citation
  • Kwok, R., G. F. Cunningham, and S. S. Pang, 2004: Fram Strait sea ice outflow. J. Geophys. Res., 109, C01009, doi:10.1029/2003JC001785.

  • Nansen, F., 1902: The Oceanography of the North Polar Basin. Longmans, Green, and Company, 427 pp.

  • North, G. R., T. L. Bell, R. F. Cahlan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699706.

    • Search Google Scholar
    • Export Citation
  • Ogi, M., I. G. Rigor, M. G. McPhee, and J. M. Wallace, 2008: Summer retreat of Arctic sea ice: Role of summer winds. Geophys. Res. Lett., 35, L24701, doi:10.1029/2008GL035672.

    • Search Google Scholar
    • Export Citation
  • Rogers, J. C., L. Yang, and L. Li, 2005: The role of Fram Strait winter cyclones on sea ice flux and on Spitsbergen air temperatures. Geophys. Res. Lett., 32, L06709, doi:10.1029/2004GL022262.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., J. E. Box, R. G. Barry, and J. E. Walsh, 1993: Characteristics of Arctic synoptic activity, 1952–1989. Meteor. Atmos. Phys., 51, 147164.

    • Search Google Scholar
    • Export Citation
  • Sickmöller, M., R. Blender, and K. Fraedrich, 2000: Observed winter cyclone tracks in the Northen Hemisphere in re-analysed ECMWF data. Quart. J. Roy. Meteor. Soc., 126, 591620.

    • Search Google Scholar
    • Export Citation
  • Skeie, P., 2000: Meridional flow variability over the Nordic Seas in the Arctic Oscillation framework. Geophys. Res. Lett., 27, 25692572.

    • Search Google Scholar
    • Export Citation
  • Sorteberg, A., and B. Kvingedal, 2006: Atmospheric forcing of the Barents Sea winter ice extent. J. Climate, 19, 47724784.

  • Strong, C., and J. Liptak, 2012: Propagating atmospheric patterns associated with winter Midwest precipitation. J. Hydrometeor., 13, 13711382.

    • Search Google Scholar
    • Export Citation
  • Tremblay, L. B., 2001: Can we consider the Arctic Oscillation independently from the Barents Oscillation? Geophys. Res. Lett., 28, 42274230.

    • Search Google Scholar
    • Export Citation
  • Tsukernik, M., C. Deser, M. Alexander, and R. Tomas, 2010: Atmospheric forcing of Fram Strait sea ice export: A closer look. Climate Dyn., 35, 13491360.

    • Search Google Scholar
    • Export Citation
  • Vihma, T., P. Tisler, and P. Uotila, 2012: Atmospheric forcing on the drift of Arctic sea ice in 1989–2009. Geophys. Res. Lett., 39, L02501, doi:10.1029/2011GL050118.

    • Search Google Scholar
    • Export Citation
  • Vinje, T., 2001: Fram Strait ice fluxes and atmospheric circulation: 1950–2000. J. Climate, 14, 35083517.

  • Wang, J., J. Zhang, E. Watanabe, M. Ikeda, K. Mizobata, J. E. Walsh, X. Bai, and B. Wu, 2009: Is the dipole anomaly a major driver to record lows in Arctic summer sea ice extent? Geophys. Res. Lett., 36, L05706, doi:10.1029/2008GL036706.

    • Search Google Scholar
    • Export Citation
  • Welch, P. D., 1967: The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust., 15, 7073.

    • Search Google Scholar
    • Export Citation
  • Wu, B., J. Wang, and J. E. Walsh, 2006: Dipole anomaly in the winter Arctic atmosphere and its association with sea ice motion. J. Climate, 19, 210225.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda, 2004: Climatology and interannual variability of Arctic cyclone activity: 1948–2002. J. Climate, 17, 23002317.

    • Search Google Scholar
    • Export Citation
  • Zubov, N. N., 1943: Arctic Ice. U.S. Navy Electronics Laboratory, 506 pp.

  • Fig. 1.

    Arrows indicate the vector correlation between F and the surface wind field. Arrow length indicates the magnitude of the correlation, and arrow direction indicates the size and sign of the two correlation components. The red box indicates the region used to define F. Shading corresponds to the sea ice concentration.

  • Fig. 2.

    (a) The real and (b) the imaginary part of the first phase-shifted HEOF of surface wind . (c) The real and (d) the imaginary part of the second phase-shifted HEOF of surface wind . Red symbols mark the centers of cyclonic and blue symbols mark the centers of anticyclonic circulations. Correspondingly colored open symbols show these circulation features advancing forward by π/4 (open circle), π/2 (open square), and 3π/4 (open triangle). Curves connect locations where circulation centers follow a continuous path. Percent values in the top-right corners of (a),(c) indicate the amount of variance in the surface wind field explained by each HEOF.

  • Fig. 3.

    The second EOF of SLP over the domain 45°–90°N, 90°W–90°E. The contour interval is 0.005.

  • Fig. 4.

    Power spectra (bold black curves) for (a) , (b) , and (c) the second empirical orthogonal function of sea level pressure Ep2. Thin solid curves indicate the null red noise spectrum, dashed curves show the 95% confidence limit for the null red noise spectrum, and dotted vertical curves indicate periods of 4 and 15 days.

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