Clim Dyn
DOI 10.1007/s00382-009-0647-z

Atmospheric forcing of Fram Strait sea ice export: a closer look
Maria Tsukernik Æ Clara Deser Æ Michael Alexander Æ
Robert Tomas

Received: 20 March 2009 / Accepted: 6 August 2009Ó Springer-Verlag 2009

Abstract Fram Strait is the primary region of sea ice
export from the Arctic and therefore plays an importantrole in regulating the amount of sea ice and freshwater
within the Arctic. We investigate the variability of Fram
Strait sea ice motion and the role of atmospheric circulation forcing using daily data during the period 1979–2006.
The most prominent atmospheric driver of anomalous sea
ice motion across Fram Strait is an east–west dipole patternof Sea Level Pressure (SLP) anomalies with centers ofaction located over the Barents Sea and Greenland. Thispattern, also observed in synoptic studies, is associatedwith anomalous meridional winds across Fram Strait and isthus physically consistent with forcing changes in sea ice
motion. The association between the SLP dipole patternand Fram Strait ice motion is maximized at 0-lag, persists
year-round, and is strongest on time scales of 10–60 days.
The SLP dipole pattern is the second empirical orthogonal
function (EOF) of daily SLP anomalies in both winter andsummer. When the analysis is repeated with monthly data,
only the Barents center of the SLP dipole remains significantly correlated with Fram Strait sea ice motion. However, after removing the leading EOF of monthly SLP
variability (e.g., the North Atlantic Oscillation), the fulleast–west dipole pattern is recovered. No significant SLP
forcing of Fram Strait ice motion is found in summer using

M. Tsukernik (&)  C. Deser  R. Tomas
Climate and Global Dynamics Division, National Center
for Atmospheric Research (NCAR), P.O. Box 3000, Boulder,
CO 80307, USAe-mail: masha@ucar.edu
M. Alexander
NOAA Earth System Research Laboratory, Boulder, USA

monthly data, even when the leading EOF is removed. Our
results highlight the importance of high frequency atmospheric variability in forcing Fram Strait sea ice motion.
Keywords Fram Strait  Sea ice motion  Sea ice export North Atlantic Oscillation  East–west dipole High frequency atmospheric variability

1 Introduction
Fram Strait, located between Greenland and Svalbard, isthe primary gateway for the export of sea ice out of the
Arctic (e.g., Kwok et al. 2004). Fram Strait sea ice export ishighly variable from day to day and from year to year
(Vinje 2001; Brummer et al. 2001, 2003; Kwok 2009).
Such high variability affects other components of the
Arctic climate system: for example, anomalous Fram Strait
export has been linked to the ‘‘Great Salinity Anomaly’’ in
the North Atlantic (Dickson et al. 1988) and to the recent
decline of summer sea ice extent (Rigor and Wallace2004).
The relationship between the large-scale patterns ofatmospheric variability especially the North Atlantic
Oscillation (NAO; Hurrell 1995) and the related Arctic
Oscillation (AO; Thompson and Wallace 1998) with sea
ice export through Fram Strait has been investigated innumerous studies, for example: Kwok and Rothrock 1999;
Hilmer and Jung 2000; Jung and Hilmer 2001; Vinje 2001;
Rigor et al. 2002; Kwok et al. 2004. During the last two
decades of the twentieth century (e.g., 1978–1997) the
correlation between the NAO and sea ice export throughFram Strait was highly positive (e.g., Hilmer and Jung
2000, Kwok et al. 2004); however, the correlation duringother time periods (e.g., 1958–1977) was near zero or even123M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

slightly negative (Hilmer and Jung 2000; Vinje 2001; Jungand Hilmer 2001).
Given the ambiguity in the relationship between the
NAO/AO and Fram Strait sea ice export, Wu et al. (2006)
and Wu and Johnson (2007) investigated whether otherpatterns of atmospheric variability are related to the ice
export in winter. Wu et al. (2006; see also Koenigk et al.
2006) identified an east–west dipole pattern with centers ofaction over the Kara/Laptev Seas and the CanadianArchipelago to be an important forcing for sea ice export
through Fram Strait, while Wu and Johnson (2007) argued
that another pattern with a center of action over the Barents
Sea plays even a bigger role. Maslanik et al. (2007) indicated that the strength and position of the centers of actionof atmospheric circulation variability associated with sea
ice motion within the Arctic basin are affected by cyclone
frequency and strength, and that both factors vary considerably from year to year.
To examine the link between sea ice export and atmospheric circulation patterns in more detail, Brummer et al.
(2003) analyzed how a single cyclone passing throughFram Strait influences sea ice motion. They found that ice
velocity increased by a factor of three during the passage ofthe cyclone, and that the ratio of ice drift to wind speed also
increased. Brummer et al. (2001) analyzed 16 years of
cyclone statistics from ERA-40 and corresponding sea ice
drift observations. They found that sea ice motion is quitesensitive to the particular cyclone trajectory and concluded
that, on average, cyclones increase sea ice export throughFram Strait. Rogers et al. (2005) investigated the role ofwinter cyclones in Fram Strait sea ice export and found acorrespondence between increased cyclogenesis along thenortheast coast of Greenland and low sea ice export. Highsea ice export years, on the other hand, corresponded to thepersistent cyclones in the Norwegian and Barents Seas.
Using a case study approach, Tsukernik (2007) illustrated
how a particular cyclone trajectory influences sea ice
motion: a cyclone passing through Fram Strait can completely reverse the direction of sea ice export, while a
cyclone passing east of Fram Strait dramatically increases
the sea ice export. The relationship between Fram Strait sea
ice flux and the SLP gradient across the Strait has been
noted in several studies, including Vinje (2001), Widell
et al. (2003), and Kwok (2009). In particular, Widell et al.
(2003) found that the SLP gradient explained approximately 60% of the variability in Fram Strait sea ice motion
in both daily and monthly averaged data.
Although the topic of atmospheric influence on Fram
Strait sea ice export has received a lot of attention, there is
still a gap between the monthly averaged studies that relate
the sea ice export to large-scale atmospheric patterns andthe synoptic-scale studies that investigate the role of highfrequency atmospheric disturbances in sea ice export. To

123bridge this gap, we use daily data to investigate the relationship between the atmospheric circulation and sea ice
export over a range of time scales. Due to the scarcity ofsea ice thickness measurements, we focus on the areal fluxof sea ice through Fram Strait based on satellite estimatesof sea ice motion. We investigate the spatial structure andtemporal evolution of the SLP patterns associated withvariations in sea ice motion through Fram Strait, including
its seasonal and frequency dependence. This paper isorganized as follows. In Sect. 2 we describe the datasetsand methods used in this study. In Sect. 3 we present main
results, and in Sect. 4 we summarize our results and discussthem along with findings from previous research.

2 Data and methods
We use 6-hourly NCEP/NCAR Reanalysis (Kalnay et al.
1996) SLP and daily sea ice motion vectors from the 25 km
Polar Pathfinder product available from the National Snowand Ice Data Center (Fowler 2003) during 1979–2006.
Since we are interested in Fram Strait sea ice export, wederive an index of the meridional component of sea ice
motion averaged across the Strait (20°W–15°E, 79°–81°N;
region in red outlined in Fig. 1). A negative sea ice index
indicates northward ice motion, while a positive sea ice
index indicates southward ice motion across Fram Strait.
We smooth the 6-hourly NCEP SLP data using a runningfive-point centered average to produce daily averages thatmatch the resolution of the sea ice index. To define daily
anomalies we remove the first two harmonics of the seasonal cycle from both the sea ice index and the gridded
SLP time series.
We use linear correlation and regression analysis todefine anomalous SLP conditions associated with changes
in sea ice export. The statistical significance of the correlation and regression values is assessed using a 2-sided
student t test, taking into account the autocorrelation ofboth series (Press et al. 1986). In order to investigate therelationship between the atmospheric circulation and sea
ice export on different timescales we perform cross-spectrum analysis (Bloomfield 1976) and estimate the 99%
significance level following Julian (1975) which takes
auto-correlation into account. Based on the cross-spectrum
results, we define a band pass filter with half-power points
at 10 and 60 days (Duchon 1979).
To investigate the seasonal dependence of the sea ice–
atmosphere relationship, we divide record into two seasons: winter (15 October–14 April) and summer (15 April–
14 October). We subsequently apply all of the techniques
described above to the two seasons separately. As the
cross-spectrum can only be calculated for a continuous
time period, we calculate the spectrum for each winter andM. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 1 Simultaneous correlation map between daily SLP anomalies
and daily sea ice motion through Fram Strait during 1979–2006.
White contours indicate the 99% significance levels. Fram Strait isoutlined by the open red box. Black square boxes show the areas used
in SLP gradient index calculation

summer during 1979–2006 separately and then average
individual power spectra together.

3 Results
3.1 Daily data
Figure 1 depicts the correlation coefficient map between
SLP north of 40°N and the Fram Strait sea ice motion index
based on 10,227 days of data during 1979–2006. Due to thelarge sample size, correlation coefficients exceeding*0.05 in absolute value are statistically significant at the
99% level (outlined by white contour in Fig. 1). There are
two main centers of action associated with the anomalous
sea ice motion: one over Barents Sea and another one overnorthern Greenland and Canadian Archipelago. As the signof the correlation coefficients suggest, southward Fram
Strait sea ice motion is maximized with a Barents Sea Low
and a Greenland High. Such an east–west dipole pattern is
associated with geostrophic northerly winds in Fram Strait
and therefore is physically consistent with increased sea icetransport. As previous studies have indicated, sea ice in the
Arctic Ocean moves nearly parallel to the geostrophic wind(Thorndike and Colony 1982; Kimura and Wakatsuchi2000).

An analogous SLP pattern has been described in theliterature related to the cold-air outbreaks in Scandinavia(Kolstad et al. 2008). A similar east–west dipole pattern
emerges as the second empirical orthogonal function(EOF) of the daily SLP anomalies over the Atlantic sector(908W–908E, 458–908N) during 1979–2006 and explains
14% of the variance; while the leading EOF resembles theNorth Atlantic Oscillation (NAO; Hurrell 1995) andaccounts for 32% of the variance (Fig. 2). Nearly identical
EOF patterns and percent variances explained are obtained
for the earlier period 1958–1977 (not shown), confirming
the robustness of the results. Previous studies have also
obtained a dipole pattern from EOF analysis based onmonthly SLP anomalies (e.g., ‘‘Barents Oscillation’’described by Skeie 2000, Tremblay 2001; winter dipole
pattern described by Wu et al. 2006).
We employ the two centers of action revealed by thecorrelation coefficient map (Fig. 1) to construct a SLP
gradient index (SLPGI). For simplicity we define the centers as rectangular boxes, both outlined in Fig. 1. The
Barents center of action stretches from 72.5° to 77.5°N andfrom 17.5° to 50.0°E, and the Greenland center of action
occupies the area from 75.0° to 80.0°N and from 60.0° to
42.5°W. We define the SLPGI as the difference between
the two. Our results are not sensitive to the exact definitionof the Barents and Greenland centers of action—bigger andsmaller boxes defining the SLPGI provide similar results(not shown). It is interesting to note that the two centers ofaction are not significantly correlated with one another(correlation coefficient is 0.1 based on daily SLP anomalies
during 1979–2006). However, when the variability associated with the leading EOF is removed from the data, thetwo centers of action become significantly anti-correlated(correlation coefficient is -0.4, significant at the 99% level).
We interpret this as competing influences on the two centers of action, with EOF1 contributing to an in-phaserelationship and EOF2 to an out-of-phase connection (e.g.,
dipole).
The standardized time series of the SLPGI and Fram
Strait sea ice motion index for one particular winter season(1985–1986) are presented in Fig. 3. The winter of 1985–
1986 is chosen for illustration only as it is fairly representative of the entire record. The SLPGI and ice motion
index exhibit similar behavior, with an overall correlation
coefficient of 0.54, significant at the 99% level. Both timeseries experience substantial high frequency (sub-monthly)
variability and therefore monthly averages cannot sufficiently describe these variables. Peaks and troughs of the
SLPGI and ice motion time series often occur simultaneously, with no systematic lead or lag between the two.
There are, however, short periods of non-simultaneouschange (for example the second half of November 1985),
which is expected from noisy high resolution indices.

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M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 2 First and second EOF(s) of Atlantic sector (45–90°N, 90°W–90°E) daily SLP anomalies during 1979–2006. The patterns north of 40°N
are obtained by regressing the daily SLP anomalies at all grid point upon the PC time series

Fig. 3 Time series of the standardized daily sea ice motion index
through Fram Strait (dashed) and the SLPGI (solid) during the 1985–
1986 winter season. The correlation coefficient between the two
indices is 0.54

Figure 4 depicts the anomalous SLP pattern associatedwith enhanced southward sea ice motion through Fram
Strait, obtained by regressing the daily SLP anomaly timeseries at each grid point upon the daily ice motion anomaly
time series. The top panel shows results based on all
10,227 days in the 1979–2006 record; the middle andlower panels depict results based on the winter (15 October–14 April) and summer (15 April–14 October) seasonsof the year. The SLP regression coefficients are in the unitsof hPa per cm s-1 and are thus representative of a 1 cm s-1
increase in the southward ice motion through Fram Strait.
Overall, the seasonal variations are quite small: both theGreenland and Barents centers of action persist year-round,although they are *15% stronger (and the Barents center

123

is also more extensive) in winter compared to summer.
Thus, the same ice motion anomaly is associated with astronger geostrophic northerly wind anomaly in winter thansummer. Considering that the ice volume flux in winter isgreater and more variable than that in summer (e.g., Kwok2009), these differences are not surprising.
Due to the persistence of the east–west dipole patternyear-round, we define the SLPGI for winters and summers
based on the same two centers of action (see Fig. 1). The
lead/lag correlation and regression coefficients between
Fram Strait sea ice motion index and the SLPGI for all daysof the year, and for winter and summer separately are presented in Fig. 5. The maxima of both the correlation andregression curves occur at zero lag, and decline sharply withlag, reaching their e-folding values (0.37) at ±3 days (20%of their maximum values at ±5 days). Such a sharp decline
suggests a lack of inertia in the wind–sea ice relationship,consistent with previous results (Gudkovich 1961; Campbell
1965; Thorndike and Colony 1982). Winter values exhibit
slightly greater inertia than summer as evidenced by thesmall correlation values at lags of ±8 to 15 days. Consistent
with the results shown in Figs. 1 and 4, the simultaneous
correlation (regression) coefficients range from 0.58
(1.33 hPa per cm s-1) in winter to 0.49 (1.12 hPa per
cm s-1) in summer. Correlation (regression) coefficientsexceeding 0.05 (0.12 hPa cm-1 s) are significant at the 99%
level. The SLP gradient regression values correspond togeostrophic wind regression coefficients of 0.56 cm s-1 perm s-1 in winter and 0.44 cm s-1 per m s-1 in summer. The

M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 5 Lead/lag correlation (top) and regression (bottom) coefficients
between the daily SLPGI and daily Fram Strait sea ice motion during
1979–2006. Black line represents all days of the year, blue line
represents winter (15 October 15 through 14 April) and red line
represents summer (15 April–14 October). Dashed gray lines show
the 99% significance levels. Green line in bottom panel shows thelead/lag regression between an NAO-like index (see text for details)
and sea ice motion through Fram Strait in winterFig. 4 Simultaneous regression of daily SLP anomalies upon daily
anomalies of sea ice motion through Fram Strait based on the period
1979–2006. The top, middle and bottom panels are based on all daysof the year, winters (15 October through 14 April) and summers (15April–14 October), respectively. White contours indicate the 99%
significance levels

simultaneous correlation coefficient between the SLPGI and
ice motion index (0.56) is somewhat lower than that (0.76)
obtained by Widell et al. (2003). Several factors may contribute to the lower correlation, including the removal ofseasonal cycle in our calculation, but not in Widell et al.
(2003); different time series (1979–2006 vs. 1996–2000)

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M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export
Fig. 6 Lead/lag autocorrelation
coefficients between the daily
SLPGI (left) and daily Fram
Strait sea ice motion (right) for
1979–2006. Black line
represents all days of the year,
blue line represents winter (15October through 14 April) andred line represents summer (15April–14 October). Dashed graylines show the 99% significancelevelsand different ice motion and SLP gradient datasets. These
factors also contribute to lower geostrophic wind regression
as compared to Kimura and Wakatsuchi (2000) wind
reduction estimates (their Fig. 3).
It is interesting to note that the autocorrelation curves
for the SLPGI and the sea ice motion index (Fig. 6) bothexperience a sharp decline with an e-folding time of±3 days. The ice motion index, however, shows a stronger
low-frequency component (e.g., memory) than the SLPGI,
as evidenced by non-zero autocorrelation values at lags
greater than ±5 days. Seasonal variations in both SLPGIand sea ice motion index are small (shown by blue and redlines in Fig. 6).
The daily data used in this study allow us to investigate
the spectral character of the relationship between the
SLPGI and Fram Strait sea ice motion index in detail.
Figure 7 depicts the coherence between the two variables,
which is equivalent to the correlation coefficient as afunction of frequency. Coherence values are shown for
periods between 2 days and 5 years; values exceeding 0.22
are significant at the 99% level (dashed grey line).
Coherence values peak in the 10–60 day band, with values
between 0.6 and 0.75. Coherence values are lower than 0.5
at periods shorter than 5 days and longer than 200 days.
Lower coherence values for periods Z200 days suggest
factors in addition to the localized wind forcing areimportant in connection with sea ice motion through Fram
Strait on interannual timescales.
The seasonality of the coherence values, calculated byaveraging the power spectra for each year separately, isshown in Fig. 8. Note, that this method yields higher
coherence values for periods shorter than *5 days thanthose in Fig. 6 due to the averaging procedure. It also can
only resolve periods shorter than *180 days. Both wintersand summers exhibit maximum coherence values in the
10–60 day band, with higher coherence values in winter123

Fig. 7 Coherence between the SLPGI and Fram Strait ice motion
index based on the daily anomalies during 1979–2006. Dashed grayline indicates the 99% significance value. Highest coherence values([0.6) are observed in the 10–60 day band, outlined by dotted blacklines(0.70–0.75) than those in summer (0.60–0.70). The wintercoherence curve is very similar to that based on all days ofthe year, except for the higher values at periods longer than
60 days.
Given that the strongest association between the SLPGIand the Fram Strait sea ice motion index occurs in the
10–60 day range, we have recomputed the SLP regression
coefficients upon the ice index using 10–60 day band passfiltered daily data. Figure 9 depicts the time evolution of

M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 8 Coherence values between the SLPGI and Fram Strait ice
motion index, calculated by averaging the power spectra for each year
separately. Black line represents all days of the year, blue line
represents winter (15 October through 14 April) and red line
represents summer (15 April–14 October). Note that this method
yields higher coherence values for shorter periods ([5 days) than thatin Fig. 6. Due to discontinuity from year to year both winter andsummer records extend to 180 days only, while year-round values
extend to 365 days

the regression coefficients for the winter season from a lagof -8 days (SLP leading) to a lag of ?8 days (SLP lagging). Similar patterns are obtained using year-round data(not shown). At -8 day lag (top left) there is a weakly
defined dipole pattern of reversed sign, compared to that at
0 lag (Fig. 4 and middle panel of Fig. 9). The sign reversal
is partially due to the response curve of the 10–60 day
filter, while the weak amplitude of the regression values
suggests a lack of inertia in the system as mentioned
before. As time progresses (-6 and -4 day lags) a low
SLP anomaly moves into the Barents Sea and by -2 daylag (middle left panel) the Barents and Greenland centers
are well-defined with regression coefficients values
increasing dramatically. The regression coefficients reach
their maximum values at 0 lag, consistent with the results
based on unfiltered data. The regression pattern dissipates
almost as quickly as it develops. The bottom row of panels
depicts the Barents low center gradually moving southeastward at ?2, ?4 and ?6 day lags. By ?8 day lag(bottom right), the regression pattern once again is weak
and of reversed polarity.
Figure 10 shows the lead/lag correlation and regression
coefficients between the SLPGI and Fram Strait ice

motion index based on the 10–60 day band pass filtereddata. As expected, the simultaneous correlation regression
coefficients increase after filtering (compare Figs. 5, 9),withcorrelation(regression)values
of
0.69
-1
-1
(*2 hPa cm s) in winter and 0.64 (1.9 hPa cm s) insummer. Both regression and coefficient values experience sharp declines beyond weekly lags. Negative values
observed at periods of 1–3 weeks are likely to be an
artifact of the filtering.
Because the Greenland center of action encompasses theregion near Iceland (see Fig. 9, middle row, -2 days to?2 days lags) and because the regression values near the
Azores (40°N, 30°W) are of opposite polarity, a statistical
relationship exists between the NAO-index and the sea ice
motion through Fram Strait. To examine this association in
more detail, we develop an NAO-like index based on the
difference between the Icelandic (55°-65°N and
40°-10°W) and Azores centers of actions (35°–45°N and
40°–20°W). Note that our sign convention is opposite to
the traditional definition of the NAO (Hurrell 1995). The
lag regression between the NAO-like index and the sea ice
motion index based on unfiltered and 10–60 day filtereddata for winter only are depicted by green curves in Figs. 5
and 10, respectively. As evident from these figures, theNAO-like relationship with sea ice motion is much weaker
than that of the SLPGI, although significant at the 99%
level.
3.2 Monthly data
We repeat the correlation/regression analysis using
monthly averages for direct comparison to previous studies. Figure 11 (top panel) shows the simultaneous regression of monthly SLP anomalies on the monthly sea ice
motion index based on all months of the year during 1979–
2006. The striking feature of the monthly regression map
compared to the daily regression map (Fig. 4) is the disappearance of the Greenland center of action and therefore
the dipole structure of the pattern. The Barents center ofaction is still present and statistically significant at the 99%
level. Although the dipole atmospheric pattern is notpresent, the Barents low pressure center still produces a
SLP gradient across Fram Strait, providing the necessary
forcing for the underlying sea ice.
The seasonal structure of the monthly regression map isalso noticeably different from that of the daily regression
map. With monthly data, the Barents center of action is
active in winter only, while no significant relationshipbetween SLP and sea ice motion exists in summer. The
latter result is consistent with previous studies that foundthat summer sea ice export is not correlated with themonthly averaged atmospheric wind forcing (e.g., Kwok
et al. 2004; Wu et al. 2006; Wu and Johnson 2007).

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M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 9 Regression of daily SLP anomalies upon daily anomalies ofsea ice motion through Fram Strait based on 10–60 day band passfiltered data for the winter season (15 October through 14 April).
Panels represent time evolution of the regression coefficients: from

SLP leading sea ice motion by 8 days (-8 day lag) to SLP lagging ice
motion by 8 days (?8 day lag). White contours represent the 99%
significance levels

The monthly SLP regression map shows some projection onto the NAO centers of action in winter, although theregression values are not statistically significant (Fig. 10,
middle panel) and of opposite sign to those observed indaily regression maps (Fig. 9). To clarify the role of the

NAO in forcing sea ice motion on monthly and longer
timescales, we removed the leading EOF from the monthly
SLP dataset and recomputed the simultaneous regressions
on the monthly sea ice index. The leading EOF of themonthly SLP anomalies (Fig. 12) resembles the NAO in123

M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 10 Lead/lag correlation (top) and regression (bottom) coefficients between the daily SLPGI and daily Fram Strait sea ice motion
based on 10–60 day band-pass filtered data for 1979–2006. Black line
represents all days of the year, blue line represents winter (15 Octoberthrough 14 April) and red line represents summer (15 April–14
October). Dashed gray lines show the 99% significance levels. Greenline in bottom panel shows the lead/lag regression between an NAOlike index (see text for details) and sea ice motion through Fram Strait
in winterboth winter and summer, and is also similar to the leading
EOF of daily SLP anomalies (Fig. 2a). With the removal of
the leading EOF, the monthly regression map based on
year-round data (Fig. 13, top panel) is very similar to thedaily regression map (Fig. 4), with both the Barents andFig. 11 Simultaneous regression of monthly SLP anomalies uponmonthly anomalies of sea ice motion through Fram Strait based on the
period of 1979–2006. The top, middle and bottom panels are based on
all months of the year, winters (October through March) and summers(April through September), respectively. White contours indicate the
99% significance levels

123M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Fig. 12 The leading EOF of Atlantic sector (45–90°N, 90°W–90°E)
monthly SLP anomalies during 1979–2006. The top, middle andbottom panels are based on all months of the year, winters (Octoberthrough March) and summers (April through September), respectively. The patterns north of 40°N are obtained by regressing the daily
SLP anomalies at all grid point upon the PC time series

123

Fig. 13 As in Fig. 10, but the leading EOF of monthly SLP
anomalies is removed from the data before the SLP regressions arecomputed. The top, middle and bottom panels are based on all monthsof the year, winters (October through March) and summers (April
through September), respectively. The leading EOFs for year-round,
winter and summer seasons are computed separately. White contours
indicate the 99% significance levels

M. Tsukernik et al.: Atmospheric forcing of Fram Strait sea ice export

Greenland centers of action present. The amplitude of the
SLP dipole is slightly weaker than that based on daily data,
but it is still significant at the 99% level.
Similar results are found for winter (Fig. 13, middle
panel), with both centers of the east–west SLP dipole significant at the 99% level. However, the Barents center ofaction in winter is noticeably weaker when the leading
EOF is removed than when it is included (compare
Figs. 11, 13, middle panels). This can be partially attributed to the fact that the Barents region is included in thepolar center of action of the leading EOF in winter(Fig. 12, middle panel) and thus contributes to the SLP
gradient across Fram Strait. In summer (Fig. 13, bottom
panel) there is no significant relationship between monthly
EOF-residual SLP and Fram Strait sea ice motion. The
leading EOF in summer is shifted northward compared tothat in winter (Fig. 12, bottom panel). The lack of relationship between monthly SLP anomalies and Fram Strait
sea ice motion in summer (Figs. 11, 13, bottom panels)suggests that high-frequency (e.g., sub-monthly) atmospheric variability plays a dominant role in forcing sea ice
motion in summer (Fig. 4, bottom panel).

4 SummaryWith the help of daily data for SLP and sea ice motion, we
found that an east–west dipole pattern with Barents andGreenland centers of action is the most prominent atmospheric driver of sea ice through Fram Strait. The dipole
pattern persists year-round, being slightly stronger inwinter than in summer. The strongest relationship between
the SLP dipole pattern and Fram Strait sea ice motion issimultaneous, with an e-folding time of *5 days. Spectral
analysis shows maximum coherence values in the 10–
60 day band. Such a time scale suggests that both high- andlow-frequency atmospheric patterns are essential in driving
sea ice out of the Arctic.
Atmospheric circulation variability on the 10–60 day
time scale has been described in previous studies inassociation with blocking events in high latitudes (e.g.,Michelangeli and Vautard 1998) and westward propagating
planetary-scale perturbations (Doblas-Reyes et al. 2001)
also known as Branstator–Kushnir oscillations (Branstator
1987; Kushnir 1987). The possible link between these
atmospheric phenomena and the Barents–Greenland SLP
dipole pattern identified in this study merits further investigation. However, we find no evidence of westward
propagation of the SLP dipole (not shown). While the east–west dipole projects onto the zonal wave 1 structure of
atmospheric variability identified in Cavalieri (2002) athigh latitudes (70°–80°N), it is primarily an Atlantic sector
pattern farther south (45°–70°N),

Repeating our analysis using monthly data revealed amodified spatial pattern of SLP anomalies associated withFram Strait sea ice motion. While the Barents center ofaction remains prominent, the Greenland center of action
and therefore the dipole structure of the pattern disappeared. However, removing the leading EOF from themonthly averaged SLP data (e.g., the NAO) resulted in the
return of the east-west dipole pattern. Based on theseresults, we argue that in monthly data the NAO—theleading intrinsic pattern of atmosphere variability—partially masks the relationship between the SLP dipole pattern and the Fram Strait sea ice motion response. That is,
the NAO is not the most dynamically relevant pattern for
explaining the variations in sea ice motion through Fram
Strait. Rather, the east–west SLP dipole pattern is theimportant driver of the anomalous sea ice motion both indaily and monthly averaged data. These results help
explain why previous studies based on monthly data (e.g.,
Hilmer and Jung 2000; Vinje 2001; Kwok et al. 2004)
found no consistent relationship between the NAO andFram Strait sea ice motion.
This study investigated the role of atmospheric forcing
in driving Fram Strait sea ice motion. It will be interesting
to extend this study to examine the relationship between
atmospheric forcing and Fram Strait sea ice volume flux byincorporating observational estimates of sea ice thickness.
Ice volume changes in the Arctic sea ice are crucial for
determining the future behavior of sea ice extent andimportant for linking the thermodynamic and dynamic
components of sea ice change (Holland et al. 2008). As
Rigor and Wallace (2004) have argued, the loss of sea ice
extent in recent years was preconditioned by the loss ofolder and thicker sea ice through Fram Strait in the 1990s.
We plan to investigate the processes that triggered the sea
ice loss of the 1990s in greater detail,Acknowledgments We thank the reviewers for their insightfulcomments and suggestions that substantially improved the manuscript. We also thank Christophe Cassou for helpful suggestions, andAdam Philips and Dennis Shea for technical assistance in preparationof the figures. This work was supported by a grant from the National
Science Foundation Arctic System Science Program. The National
Center of Atmospheric Research is sponsored by the National Science
Foundation.

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