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Arctic Sea Ice Variability in the Context of Recent Atmospheric Circulation Trends
CLARA DESER
National Center for Atmospheric Research,* Boulder, Colorado

JOHN E. WALSH
Department of Atmospheric Sciences, University of Illinois, Urbana–Champaign, Urbana, Illinois

MICHAEL S. TIMLINCIRES–Climate Diagnostics Center, University of Colorado, Boulder, Colorado(Manuscript received 2 August 1998, in final form 9 March 1999)
ABSTRACT
Forty years (1958–97) of reanalysis products and corresponding sea ice concentration data are used to document
Arctic sea ice variability and its association with surface air temperature (SAT) and sea level pressure (SLP)
throughout the Northern Hemisphere extratropics. The dominant mode of winter (January–March) sea ice variability exhibits out-of-phase fluctuations between the western and eastern North Atlantic, together with a weaker
dipole in the North Pacific. The time series of this mode has a high winter-to-winter autocorrelation (0.69) andis dominated by decadal-scale variations and a longer-term trend of diminishing ice cover east of Greenlandand increasing ice cover west of Greenland.
Associated with the dominant pattern of winter sea ice variability are large-scale changes in SAT and SLP
that closely resemble the North Atlantic oscillation. The associated SAT and surface sensible and latent heat
flux anomalies are largest over the portions of the marginal sea ice zone in which the trends of ice coverage
have been greatest, although the well-documented warming of the northern continental regions is also apparent.
The temporal and spatial relationships between the SLP and ice anomaly fields are consistent with the notionthat atmospheric circulation anomalies force the sea ice variations. However, there appears to be a local responseof the atmospheric circulation to the changing sea ice cover east of Greenland. Specifically, cyclone frequencies
have increased and mean SLPs have decreased over the retracted ice margin in the Greenland Sea, and thesechanges differ from those associated directly with the North Atlantic oscillation.
The dominant mode of sea ice variability in summer (July–September) is more spatially uniform than that inwinter. Summer ice extent for the Arctic as a whole has exhibited a nearly monotonic decline (24% decade21 )during the past 40 yr. Summer sea ice variations appear to be initiated by atmospheric circulation anomalies
over the high Arctic in late spring. Positive ice–albedo feedback may account for the relatively long delay (2–3months) between the time of atmospheric forcing and the maximum ice response, and it may have served toamplify the summer ice retreat.

1. Introduction
Sea ice is a sensitive component of the climate system, influenced by conditions in both the atmosphereand ocean. Variations in sea ice may in turn modulate
climate by altering the surface albedo; the exchange ofheat, moisture, and momentum between the atmosphereand ocean; and the upper ocean stratification in areas* The National Center for Atmospheric Research is sponsored by
the National Science Foundation.
Corresponding author address: Dr. Clara Deser, NCAR/CGD, P.O.
Box 3000, Boulder, CO 80307-3000.
E-mail: cdeser@ncar.edu

q 2000 American Meteorological Societyof deep water formation. The surface albedo effect isconsidered to be one of the dominant factors in thepoleward amplification of global warming due to increased ‘‘greenhouse gas’’ concentrations simulated inmany climate models (e.g., IPCC 1990; Manabe et al.
1992; Randall et al. 1998).
Observational studies of sea ice extent indicate thatvariability on timescales of weeks and longer tends tobe organized into large-scale geographical patterns thatare closely associated with the dominant structures ofatmospheric circulation variability, particularily during
winter (Walsh and Johnson 1979; Overland and Pease
1982; Fang and Wallace 1994; Slonosky et al. 1997;
Prinsenberg et al. 1997). The consensus of the observational studies is that ‘‘interannual variability in sea
ice conditions is caused by the variability in the large-

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scale atmospheric circulation which locally manifests
itself as surface air temperature and wind anomalies’’(Prinsenberg et al. 1997). Feedbacks or other influencesof winter ice anomalies upon the atmosphere have been
more difficult to detect due to the dominance of atmospheric forcing of sea ice in the observed associations, as shown by temporal asymmetries in ice–atmosphere cross-correlation functions (e.g., Lemke et al.
1980).
Over the past 3–4 decades, the dominant patterns of
wintertime atmospheric circulation variability in the
Northern Hemisphere, particularily the Pacific–North
American and the North Atlantic oscillation (NAO) or
Arctic oscillation (AO) teleconnection patterns, have exhibited trends that are unprecedented in the observational record. (For the purposes of this study, the NAO
may be regarded as a subset of the spatially broader
AO; see Thompson and Wallace 1998). Sea level pressures (SLPs) have decreased over the central Arctic andover the climatological Icelandic and Aleutian low centers, and increased farther south (Hurrell 1996; Thompson and Wallace 1998). The most pronounced SLP reductions over the central Arctic have occurred since1988 (Walsh et al. 1996). Associated with these circulation changes, winter surface air temperatures over land
(Eurasia in particular) have increased to record values
while those over the oceans have cooled slightly (Hurrell 1996; Thompson et al. 2000). The latter authors
hypothesize that anthropogenic forcing may be a contributing factor to the recent unprecedented strengthening of the polar vortex (reflected by the AO) andassociated warming over the Northern Hemisphere continents.
Trends in Arctic sea ice extent have been evaluatedmost recently by Chapman and Walsh (1993), Maslanik
et al. (1996), and Cavalieri et al. (1997). Sea ice cover
over the Arctic as a whole has decreased by 2.9% per
decade during 1979–96 (Cavalieri et al. 1997), with thelargest reductions (6% decade21 ) in summer (Maslanik
et al. 1996) and negligible change in winter. A similarseasonal dependence was reported by Chapman andWalsh (1993) for Arctic sea ice trends during 1961–90.
Although winter ice extent for the Arctic as a whole
has remained relatively stable, large trends of opposing
sign are apparent in different regions, both for the more
recent 1979–96 period (Parkinson et al. 1999, their Plate
2) and for several preceding decades (Chapman andWalsh 1993, their Fig. 9). Regionally, the magnitudesof the sea ice trends are as large in winter as in summer.
The purpose of this study is to examine the interannual variations in winter Arctic sea ice extent within
the context of the recent hemispheric changes in atmospheric circulation and surface air temperature (SAT)
during the last 40 yr (1958–97). Specifically, we address
the following three questions.
R How are the temporal and spatial patterns of ice var-

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iability related to those of SAT and atmospheric circulation?
R Do the ice changes represent a response to the recent
trends in atmospheric circulation and SAT?
R Is there any discernible influence of the ice covervariations upon the atmosphere?
We also examine Arctic sea ice variability during
summer and its relation to spring atmospheric circulation changes. While earlier work has addressed thetrends of Arctic sea ice coverage (e.g., Lemke et al.
1980; Chapman and Walsh 1993; Parkinson et al. 1999),
the present analysis extends these earlier studies byquantitatively relating the sea ice variations to the largescale atmospheric circulation. Moreover, the examination of cyclone variations in the context of a possible
feedback from sea ice has not previously been undertaken with data of the past several decades. Finally, theevaluation of the linkage between summer and wintersea ice, particularly with regard to forcing by the winter–spring circulation, may be regarded as an original feature of this study.
2. Data and methods
a. Sea ice concentration
We use an updated version of the monthly mean sea
ice concentration dataset (on a 18 lat 3 18 long grid forthe period 1953–97) described in Chapman and Walsh(1993). This dataset combines conventional estimateswith microwave measurements from polar-orbiting satellites since 1972. The data sources and processing techniques are summarized in Chapman and Walsh (1993);
the update is from satellite passive microwave data provided by the National Snow and Ice Data Center(NSIDC 1998). The sea ice concentration record exhibits a discontinuity in October 1978 associated with achange in the satellite sensor and passive microwave
retrieval algorithm that depicts lower concentrations
within the pack ice during during the melt season(April–September; see appendix). For this reason, weshall present results based upon the full period of record
for the winter (January–March) season only; for thesummer (July–September) season, we carry out a separate analysis for the periods before and after 1978.
Since the correlations between the winter time series of
ice concentration and ice extent exceed 0.9 in the Beaufort, Greenland, and Labrador Seas (not shown), theconclusions obtained here are insensitive to the choiceof concentration rather than extent as the primary sea
ice variable for the winter season. Similar results arefound for summer.
b. NCEP reanalyses of surface air temperature andsurface sensible and latent heat flux
Four times daily values of surface air temperature,
sea level pressure, and sensible and latent heat fluxes

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at the air–sea interface for the period 1958–97 wereobtained from the National Centers for Environmental
Prediction–National Center for Atmospheric Research(the NCEP–NCAR) reanalysis project, a state-of-the-art
atmospheric data assimilation system (Kalnay et al.
1996). Surface air temperature is determined prognostically over land and sea ice, and coupled to the sea
surface temperature from Reynolds and Smith (1995)
over open water. Sea ice coverage, determined fromessentially the same observational archives used here
(see Kalnay et al. 1996), is specified to be either 0% or
100% in the NCEP–NCAR reanalyses depending onwhether the observed values are less than or greater than
55%. The temperature and flux data were provided ona T62 Gaussian grid, and the SLP data on a 2.58 lat 3
2.58 long grid. Monthly means were computed from the
6-hourly data.
c. Cyclone frequency
Cyclone counts were obtained from Dr. M. Serreze
at the University of Colorado. Following the cyclone
detection and tracking algorithms developed in Serreze(1995) and Serreze et al. (1997), storm positions were
determined from the 6-hourly NCEP–NCAR reanalysis
SLP fields projected onto a 250 km 3 250 km equalarea grid. We summed the number of cyclones for each
winter (January–March) season during 1958–97 andthen smoothed the cyclone totals with a three-point binomial filter in the zonal and meridional directions. We
then interpolated the smoothed cyclone counts onto a
18 lat 3 18 long grid.
d. Methods
For each year during the period of record common
to all the datasets (1958–97), we formed winter (January–March) averages. Anomalies were then computedby subtracting the 40-yr mean from each individual winter. We applied empirical orthogonal function (EOF)
analysis to the winter sea ice anomalies to extract thedominant structure of variability. The EOF is based
upon the area-weighted covariance matrix. The associated principal component (PC time series) of the EOF
was constructed by projecting the anomaly fields foreach winter upon the EOF spatial pattern. Thus, the PC
is a measure of the degree of similarity between the
EOF pattern and the ice anomaly pattern in individual
winters. The PC is normalized to unit variance, and the
EOF pattern has amplitudes proportional to the percent
ice concentration anomaly per unit anomaly of the PC.
Linear regression analysis and compositing were used
to determine the anomalies in SLP, cyclone frequency,
surface air temperature, and surface energy flux associated with the leading ice PC. Similar measures andtechniques were used to diagnose the summer (July–
September) sea ice variations.

FIG. 1. Map of the Arctic Seas.

3. Resultsa. Sea ice variability
The names and locations of the Arctic Seas mentioned
in this study are indicated in Fig. 1. The geographicaldistribution of the standard deviation of winter (January–March) sea ice concentration anomalies during theperiod 1958–97 is shown in Fig. 2. The climatologicalmarginal ice zone (MIZ), as delineated by the 3% and
97% mean ice concentration isopleths, is also indicatedon the figure. The variability is confined to the MIZ,
with the largest standard deviations near its center. The
variability is greater in the Atlantic sector (values exceeding 25%–30% in the Greenland and Barents Seasand the Davis Strait) than in the Pacific (values exceeding 15%–20% in the Bering Sea and the Sea of
Okhotsk). Note that only the northern portion of the Seaof Okhotsk is contained in the Chapman and Walsh(1993) dataset. Similar results are shown in Fang andWallace (1994) based on a shorter period of record.
The region of maximum variability in the Greenland
Sea in Fig. 2 coincides with the sea ice feature known
as the ‘‘Odden’’ (Shuchman et al. 1998). The Odden isa tongue of sea ice that episodically advances rapidly
northeastward into the Greenland Sea from the edge ofthe main ice pack near 108W between 728 and 748N. At
least one such advance (and retreat) occurs during most
winters; the maximum area of the Odden ranges fromclose to zero in some winters to nearly 3 3 10 5 km 2 inother years (Shuchman et al. 1998). The episodic nature

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FIG. 2. Standard deviation of January–March ice concentration
anomalies during 1958–97 (shading and thin contours). Light shading
denotes values between 5% and 15% and dark shading values greaterthan 15%. The thin solid contour is the 15% isopleth and the thickdashed contour is the 25% isopleth. The thick solid contours denotethe 3% and 97% isopleths of the climatological mean winter ice
concentration field.

of the Odden is undoubtedly a primary reason why theeastern Greenland Sea contains a variance maximum in
Fig. 2.
The leading EOF of winter sea ice concentration
anomalies over the Arctic, which accounts for 35% of
the variability (compared to 13% for the second model),exhibits largest amplitude in the Atlantic sector, with
out-of-phase variations between the Labrador andGreenland–Barents Seas (Fig. 3). A similar but weaker
phase opposition is also evident within the Pacific sector
between the Bering Sea and the Sea of Okhotsk. The
maximum values are 20%–25% (per unit anomaly of
the PC) in the Greenland and Barents Seas, and 10%–
15% in the Labrador Sea. The out-of-phase behavior of
winter sea ice within the Atlantic and Pacific basins is
consistent with results from previous studies (e.g.,
Walsh and Johnson 1979; Rogers and van Loon 1979;
Fang and Wallace 1994; Slonosky et al. 1997).
The PC of the leading EOF exhibits low-frequency
behavior, with decadal-scale variations superimposed
upon a general downward trend (Fig. 3). (Note that negative values of the PC correspond to reduced ice concentrations east of Greenland and increased ice coverwest of Greenland.) The high lag-one autocorrelation of
the PC (0.69) reflects the predominance of decadal andlonger timescale variations. The largest positive anomalies occurred in the late 1960s in association with theGreat Salinity Anomaly (Dickson et al. 1988), a periodof enhanced wind-driven ice transport from the Arctic
through Fram Strait. The maximum negative anomalies

FIG. 3. (a) Leading EOF of winter ice concentration anomalies
during 1958–97. The contour interval is 5%, and the zero contour
has been omitted. Light (dark) shading denotes values greater than
2% (less than 22%). The EOF accounts for 35% of the variance of
winter sea ice over the Arctic. (b) Principal component time seriesof the leading ice EOF.

occurred during the early 1990s. Thus the temporal trend
over the past several decades has been toward less ice
in the eastern North Atlantic and more ice in the western
North Atlantic. A similar PC was presented in Slonosky
et al. (1997) based on the period 1954–90, although it
accounted for a lower fraction of the total variance(23%) than that found here. The east–west contrast in
the trends over the North Atlantic is also clearly apparent in Parkinson et al.’s (1999) analysis of sea ice
data for the post-1978 period.
As in the case of the total variance (Fig. 2), the EOF
loadings of Fig. 3 contain a maximum in the Greenland
Sea where the Odden feature occurs. The temporal variations of EOF 1 indeed correspond well with the annual

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time series of Odden mean sea ice area for 1979–94 ascompiled from satellite passive microwave data by
Shuchman et al. (1998). The correlation between the
Odden time series and PC1 (Fig. 3) during this 16-yr
period is 0.64, which is surprisingly large consideringthat EOF1 describes the variations of the sea ice margin
throughout the Northern Hemisphere. The implicationis that the atmospheric factors controlling the variabilityof the Odden (see Shuchman et al. 1998) covary witha larger-scale atmospheric circulation pattern that affects
sea ice hemispherically. The associated hemisphericscale atmospheric variability is addressed in the following sections.
b. Relation between sea ice and sea level pressure
The SLP anomaly field associated with the leading
EOF of sea ice was determined from a linear regressionof the winter SLP anomalies at each grid point upon
the ice PC time series. The regression coefficients arein units of mb per standard deviation of the ice PC. To
assign more convenient and physically meaningful units
to the regression field, we multiplied the regression coefficients by 22.3, the value of the 40-yr linear trend
in the ice PC. Thus, the resulting regression field isrepresentative of the SLP anomaly associated with the
40-yr trend in the ice PC. A composite of SLP anomalies
based upon extreme years in the ice PC record yields apattern that is nearly identical to that obtained from theregression procedure but with slightly larger amplitude,
as we shall show in section 3e.
The SLP anomaly pattern that accompanies the leading EOF of Arctic sea ice (Fig. 4) exhibits below-normal
values over the Arctic, with maximum negative anomalies [28 to 29 mb (40 yr)21 ] directly east of Greenland,
and above normal values [4–6 mb (40 yr)21 ] along 408N
over the eastern Atlantic extending into Europe. Weaker
SLP anomalies [62 mb (40 yr)21 ] are found over the
North Pacific and Kamchatka. This pattern (excluding
the North Pacific sector) is strongly reminiscent of thepositive polarity of the NAO (Hurrell 1995) and AO(Thompson and Wallace 1998), although the details of
the pattern east of Greenland are distinctive (see section
3e). The Aleutian lobe in Fig. 4 is opposite in sign tothe corresponding feature in the AO of Thompson andWallace.
The SLP pattern shown in Fig. 4 implies anomalous
southerly geostrophic winds across the Greenland andBarents Seas and anomalous north-northwesterly flowin the Labrador Sea when the ice PC is below normal.
Such wind variations are qualitatively consistent with
the notion that the sea ice anomalies are directly forcedby the atmosphere, either thermodynamically throughadvected surface air temperature anomalies or dynamically through wind-driven ice drift anomalies. For example, anomalous advection of warm air from the south
over the Greenland and Barents Seas will promote icemelt and as well a dynamic retreat of the ice edge.

FIG. 4. Winter SLP (contours) and ice concentration (shading)anomalies associated with the leading ice EOF. The patterns wereconstructed by linearly regressing the SLP and ice anomalies upon
the ice PC and multiplying the regression coefficients by 22.3, thevalue of the 40-yr linear trend in the ice PC. The SLP anomaly field
is in units of mb (40 yr)21 , contoured every two units. Light (dark)
shading denotes ice concentration anomalies greater than 8% (40 yr) 21[less than 28% (40 yr)21 ].

Analogous arguments can be made for the Labrador Sea
and for the weaker SLP–ice anomalies in the Pacificsector. Indeed, a comprehensive sea ice model forced
with observed geostrophic surface winds during 1946–
93 obtains a qualitatively similar pattern of sea ice
anomalies (S. Hakkinen 1998, personal communication). Similar relationships between wintertime SLP and
sea ice anomaly fields have been documented by Rogers
and van Loon (1979), Fang and Wallace (1994), Mysak
et al. (1996), and Slonosky et al. (1997), among others.
Further support for the notion that the atmosphericcirculation controls the sea ice anomaly pattern depicted
in Fig. 4 comes from lead–lag relationships between theice PC and SLP. Figure 4 contains an implicit 2-weeklead between the SLP and ice fields since the SLP data
are centered at the midpoint of the month while the sea
ice data are reported at the end of the month. A simple
index of the magnitude of the SLP pattern shown in
Fig. 4 is the difference between the maximum value
near 408N and the minimum value over the Greenland
Sea (15 mb). For comparison, the December–February(February–April) SLP index value is 15.5 mb (8.6 mb).
Thus, the implied wind forcing of the sea ice field issubstantially stronger when SLP leads the ice by 2–6
weeks than when it lags the ice by 2 weeks. This result
is consistent with the findings of Fang and Wallace(1994). The temporal correlation coefficient between thetime series of the SLP pattern shown in Fig. 4 (obtainedby projecting the SLP regression pattern poleward of

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FIG. 5. Winter NAO index and the ice PC. Units are standard deviations. Note that the scale for the NAO index has been inverted.

358N onto the SLP anomaly field each winter) and theice PC is 0.63 (0.61 for February–April and 0.73 for
December–February).
As mentioned above, the pressure pattern shown in
Fig. 4 is reminiscent of the NAO. Figure 5 shows the
NAO record (defined as the December–March normalized SLP difference between Lisbon, Portugal, andStykkisholmur, Iceland) superimposed upon the ice PC
index (note that the NAO scale has been inverted). The
two time series are broadly similar (their linear correlation coefficient is 0.63), but individual winters can beradically different. For example, the (inverted) NAO
index was low in both 1989 and 1990, yet the ice PC
was near normal in 1989 and low in 1990. Figure 6
shows that the January–March SLP anomalies in 1989
were nearly uniform from Iceland to the central Arctic,
while in 1990 they were amplified near Iceland (verysimilar patterns obtain for December–February, notshown). Thus, the implied geostrophic meridional wind
pattern across the Greenland and Barents Seas was very
different in the two winters, with strong southerly wind
anomalies favoring ice retreat as observed in 1990, andnegligible meridional wind anomalies consistent with
the near-normal ice conditions in 1989. Other years inwhich the NAO was extreme but the ice PC was nearnormal (e.g., 1961, 1973, and 1985) exhibit a similarlack of geostrophic meridional wind component east ofGreenland (not shown). Thus, a simple index such as
the NAO, while a useful indicator of the large-scaleatmospheric circulation, may be inadequate for inferring
the details of atmospheric forcing relevant for the sea
ice margin.
c. Relation between sea ice and surface airtemperature
The linear regression pattern of SAT upon the ice PC
record is shown in Fig. 7. Since SAT variations are
considerably smaller over the oceans than over land, we
have included the 60.58C (40 yr)21 contour over ocean

FIG. 6. Winter SLP anomalies (mb) for (a) 1989 and (b) 1990. The
contour interval is 3 mb and negative contours are dashed.

areas only. As with SLP, the large-scale pattern of SAT
anomalies associated with the leading EOF of sea ice
is similar to that accompanying the NAO (Hurrell andvan Loon 1997) and AO (Thompson et al. 2000): positive anomalies over northern Eurasia and northwestern
Canada, and negative anomalies over northeastern Canada and Greenland, with values as large as 28–38C (40yr)21 over the land surface. The largest SAT changes[approximately 108C (40 yr)21 ] occur over the sea ice
anomalies, with warming coincident with the reduced
ice cover in the Greenland and Barents Seas and coolingover the increased ice amounts in the Davis Strait. Am-

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FIG. 8. As in Fig. 4 but for upward surface latent-plus-sensible
heat flux over the Atlantic sector. The contour interval is 50 W m22(40 yr)21 starting at 625 W m22 (40 yr)21 . Light (dark) shading
denotes heat flux anomalies greater than 75 W m22 (40 yr)21 [lessthan 275 W m22 (40 yr)21 ].

FIG. 7. As in Fig. 4 but for surface air temperature. The contour
interval is 28C (40 yr)21 and the zero contour is omitted. Light (dark)
shading denotes temperature anomalies greater than 28C (40 yr)21[less than 228C (40 yr)21 ]. The 60.58C contours are included overocean areas only.

plified SAT anomalies also occur over the sea ice perturbations in the Pacific sector. [Note that the stationbased SAT dataset used in the studies of Hurrell andvan Loon (1997) and Thompson et al. (1999) does notcontain any information over sea ice.]
The broad-scale pattern of temperature anomalies inFig. 7 is consistent with advection by the large-scaleatmospheric circulation (see also Thompson et al. 2000),
while the more localized SAT anomalies over sea ice
may be attributed to the sea ice anomalies themselves.
Although large changes in SAT over ice cover anomalies
are to be expected due to the strong contrast between
sea surface and ice surface temperatures, the changes
in Fig. 7 may be exaggerated because of the crude binary
representation of the marginal ice zone in the NCEP–
NCAR reanalyses (sea ice concentrations are specified
to be either 0% or 100%). The marginal ice zone is, inreality, a mixture of ice types and surfaces; total ice
concentrations range from essentially zero to unity. Results from field programs such as Surface Heat Budgetof the Arctic Ocean (SHEBA) (Moritz et al. 1993)
should provide a more realistic assessment of the SATchanges associated with changes in ice concentration.
d. Relation between sea ice and surface latent andsensible heat flux
Changes in sea ice concentration can affect the atmospheric circulation through changes in surface albedo
and surface fluxes of heat, moisture, and momentum.
In winter, the albedo effect is supressed due to the low

insolation. The linear regression pattern of surface turbulent (sensible 1 latent) heat flux anomalies upon theice PC record is shown in Fig. 8 for the Atlantic sector.
Positive values indicate above-normal heat transfer fromthe ocean to the atmosphere when the ice PC index is
below normal. The largest heat flux changes occur near
the ice edge, where they are nearly an order of magnitude larger than those over the open ocean (250 W
m22 as opposed to 25 W m22 ). In particular, positive
upward heat flux anomalies are associated with reduced
ice cover in the Greenland/Barents Seas, and negative
upward heat flux anomalies with enhanced ice concentration in the Labrador Sea. The maximum flux anomalies exceed 250 W m22 (40 yr)21 in the Greenland Sea.
For comparison, we note that the mean upward heat flux
over the Gulf Stream in winter is 350–400 W m22 .
The large heat flux anomalies over the MIZ are accompanied by anomalies of opposite sign equatorwardof the ice edge. We interpret this dipole pattern to be
the result of the atmospheric circulation anomalies responsible for the ice cover changes. For example, in thecase of strong northerly wind anomalies over the Greenland and Barents Seas, the ice edge advances, reducing
the ocean-to-atmosphere heat flux locally but enhancing
it on the equatorward side of the ice boundary due tothe flow of abnormally cold air over the relatively warm
ocean surface. A similar dipole pattern has been foundin general circulation model experiments with perturbed
sea ice concentrations (Simmonds and Budd 1991).
The surface flux anomalies in Fig. 8 are reanalysis
derived and hence are dependent on the NCEP–NCAR
model parameterizations. As with the SAT fields, thesurface heat flux anomalies associated with changes in
ice cover may be overestimated in the NCEP–NCAR
reanalyses, primarily because of the binary representation of ice coverage. However, the amplification of
the heat flux changes over ice relative to open ocean isundoubtedly correct in a qualitative sense because evena partial ice cover reduces substantially the surface–624

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atmosphere exchanges of heat and moisture by virtueof the strong contrast between ice surface and sea surface temperatures.
e. Relation between sea ice and cyclone counts
The large heat flux perturbations associated withchanges in sea ice cover suggest that the atmosphericcirculation may be sensitive to the observed sea ice
variations in the Atlantic sector, particularily in the active stormtrack region east of Greenland. It is worth
noting that the heat flux anomalies due to ice coverchanges (200–250 W m22 ) are approximately an orderof magnitude larger than those induced by midlatitude
SST variations. In particular, a 0.58C SST anomaly (typical of the extratropics in winter; recall Fig. 7) would
produce an upward turbulent heat flux anomaly of lessthan 20 W m22 according to the standard bulk formulas(cf. Hsiung 1986).
To investigate whether the changes in surface turbulent heat flux induced by variations in sea ice coverage have any impact upon cyclone frequencies, we
composited storm counts for the winters in which theice PC index exceeds one standard deviation (1963,
1966, 1967, 1968, 1969) and for the winters in which
the ice PC index is less than minus one standard deviation (1974, 1983, 1984, 1990, 1991, 1992, 1993,
1995). Recall that the storm positions are identified according to the location of low pressure centers at sea
level (section 2c). We also composited the ice concentration values according to the same criteria. The highand low composites and their difference are shown in
Fig. 9 for the subpolar North Atlantic.
In the high ice composite (Fig. 9a), the ice edge eastof Greenland extends from northern Iceland to Svalbard
to the southern tip of Novaya Zemlya. The stormtrackreflects the position of the sea ice boundary, with thenorthern portion of the four storm count isopleth approximately coincident with the 60% ice concentration
isopleth. In the low ice composite (Fig. 9b), the ice edge
east of Greenland is retracted poleward and the stormtrack expanded northwestward into the Greenland Sea
relative to the high ice composite (note again the correspondence between the four storm count isopleth andthe 60% ice concentration isopleth, both of which are
farther to the northwest in Fig. 9b than in Fig. 9a). The
low-minus-high composite difference field (Fig. 9c) exhibits an increase in cyclone numbers between Greenland and Iceland in the region of the climatological mean
Icelandic low and directly east of Greenland where thesea ice concentrations have decreased.
We hypothesize that the increase in storm counts directly over the reduced sea ice coverage in the Greenland Sea is a result of the ice-induced variations in thesurface energy flux into the atmosphere from the lower
boundary. We note that the proximity of the stormtrack
to the ice edge east of Greenland may enhance the sensitivity of cyclone activity in this region to changes in

FIG. 9. Composites of winter cyclone counts (contours) and sea
ice concentrations exceeding 60% (shading) for winters with (a) highand (b) low values of the ice PC. (c) Low-minus-high composite
differences. The cyclone counts represent the number of storms per
250 km 3 250 km area per winter season. The contour interval forthe storm count difference field is 1, with the zero contour omitted.
Light (dark) shading denotes ice concentration differences greaterthan 20% (less than 220%). Cyclone count differences greater than
one are statistically significant at the 95% confidence level according
to a Student’s t-test.

sea ice extent. Indeed, the low-level baroclinicity associated with the mean ice–ocean boundary has beenshown to influence the formation of polar lows in theGreenland Sea, both in observations (Carleton 1985a,b;
Rasmussen 1985) and in model simulations (Sardie andWarner 1985). Thus, changes in Greenland Sea ice coverinduced by large-scale atmospheric circulation anomalies may feed back upon the atmosphere by altering
cyclone activity locally over the MIZ. We also note thepossibility that the increase in cyclone counts over theGreenland Sea in winters of reduced ice cover may result from the interaction of the anomalous large-scaleatmospheric flow with the orography over Greenland.

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FIG. 10. Regional index of cyclone counts over the Greenland Sea
and the ice PC. Units are standard deviations. Note that the scale forthe cyclone index has been inverted.

The time series of the number of cyclones per winterseason in the area directly east of Greenland and northof Iceland (delineated by the 11 contour in the bottom
panel of Fig. 9) is shown together with the ice PC index
in Fig. 10. Note that the cyclone scale has been inverted.
The correspondence between the two records is quite
good on the decadal timescale: their linear correlation625

coefficient is 0.65 (0.85 if a 3-yr running mean is applied). However, the (inverted) cyclone index does notremain as low as the ice PC during the 1990s.
The NAO plays a major role in the variability of North
Atlantic cyclone activity (Rogers 1990; Serreze et al.
1997). Are the cyclone frequency changes that occur
near the ice edge over the Greenland Sea unique tocomposites keyed to the ice PC, or do they also occur
in composites based upon the NAO index? Figure 11
shows the composite differences of SLP and cyclone
frequency based upon the ice PC index (left) and the
NAO index (right). The high NAO composite is based
upon the winters for which the NAO index exceeds one
standard deviation (1983, 1989, 1990, 1992, 1993, 1994,
1995). Similarily, the low NAO composite is based upon
the winters for which the NAO index is less than minus
one standard deviation (1962, 1963, 1964, 1965, 1969,
1977, 1979, 1996). Note that the high NAO and low
ice PC composites contain two years in common andthe low NAO and high ice PC composites contain five
years in common. The statistical signficance of the lowminus-high composite differences was assessed according to a local Student’s t-test. As a rough guide, cyclone
differences exceeding unity and SLP differences lessthan 28 mb are significant at the 95% level for thedomain shown in Fig. 11.

FIG. 11. (Left) Low-minus-high composite differences of winter (a) SLP (mb) and (c) cyclone counts based upon the ice PC record. (Right)
High-minus-low composite differences of winter (b) SLP (mb) and (d) cyclone counts based upon the NAO index. The shading in all four
panels denotes the corresponding composite differences of sea ice concentration (light shading for values greater than 35% and dark shadingfor values less than 235%).

626

JOURNAL OF CLIMATEThe composite differences based upon the ice PC
index (Fig. 11, left) exhibit a local minimum in SLP
and a local maximum in cyclone frequency directly over
the reduced sea ice concentrations in the Greenland Sea–
Fram Strait corridor. In constrast, the belt of lowest
pressure and highest cyclone counts in the NAO-based
composite differences (Fig. 11, right) are not confinedto the region of reduced ice cover (which is much lesspronounced than in the ice PC composite), but occur
south of the ice edge where the mean stormtrack and
Icelandic low are located. It may be noted that the cyclone frequency differences over the Norwegian andBarents Seas are of opposite polarity in the NAO andice-based composites.
Many earlier studies, ranging from Wiese (1924) to
Mysak and Power (1992), have hypothesized that reduced sea ice coverage should contribute to changes incyclone activity. The results in Figs. 9–11 suggest that
cyclone frequency (and mean SLP) may respond to sea
ice and that the response is not simply a reflection of
the large-scale circulation variability associated with the
NAO. We note that the local decrease in SLP over the
reduced ice amounts in the Greenland Sea in the ice PC
composite difference (Fig. 11a) is qualitatively consistent with the results of idealized atmospheric generalcirculation experiments in which a prescribed sea ice
margin is altered artificially (Herman and Johnson 1978;
Simmonds and Budd 1991; Rind et al. 1995).
f. Summer sea ice variability
As discussed in the introduction, summer Arctic sea
ice extent has been diminishing at a rate of 4%–6%
decade21 , both for the most recent period (1979–95;
Cavalieri et al. 1997; Parkinson et al. 1999; Maslanik
et al. 1996) and for previous decades (1961–91; Chapman and Walsh 1993). How are the sea ice trends insummer related to those in winter? Do atmospheric circulation anomalies play a role in forcing the summer
ice variations?
Although an analysis of the full record (1957–96) ofsummer sea ice concentrations is desirable, the pronounced downward discontinuity from 1978 to 1979
(due to the introduction of satellite passive microwave
sensing techniques that depict lower summer ice concentrations within the pack ice) precludes such an analysis with the present dataset (see Fig. A1 in the appendix
for the summer Arctic ice concentration record). However, the record of summer ice extent as opposed toconcentration appears to be less sensitive to the changes
in remote sensing techniques, presumably because theoccurence of open water with the pack ice occurs preferentially at relatively high ice concentrations (higher
than 15%, which is the conventional definition used forice extent). Thus, we rely on ice extent as our primaryvariable for studying the long-term summer variability,
although we also show results based upon ice concentration for the two subperiods 1957–78 and 1979–96.

VOLUME 13

FIG. 12. Time series of Arctic sea ice extent averaged over thepolar cap in winter (dashed curve) and summer (solid curve). Notethe different scales for winter and summer. Units are 10 6 km 2 .

The merging of the passive microwave ice concentration
measurements with earlier data sources during the melt
season remains an important topic for future research.
Figure 12 shows the winter (1958–97) and summer(1957–96) sea ice extent records for the Arctic as awhole, where ice extent is defined according to the position of the 15% ice concentration boundary. The linear
trend in summer Arctic ice extent during 1957–96 is
20.30 3 10 6 km 2 decade21 or 24.0% decade21 relative
to the mean, comparable to the results shown in Chapman and Walsh (1993) based upon the period 1961–90(23.6% decade21 ). In winter, the linear trend is small:
20.08 3 10 6 km 2 or 20.6% decade21 , also in agreementwith Chapman and Walsh. On a regional basis, however,
the trends in winter are just as large as those in summer(Fig. 13). In winter, the positive trends in the Labrador
Sea offset the negative trends elsewhere to produce nearzero change in the zonal average.
The summer Arctic ice extent record is nearly identical to the leading principal component of the summer
ice extent field (not shown), which accounts for 31%of the variance: the correlation coefficient between the
two time series is 0.98. Similarly, the spatial pattern of
the leading EOF of summer ice extent (not shown) resembles closely the pattern of linear trends in Fig. 13.
It is instructive to compare the dominant pattern of
sea ice variability in winter with that in summer. Bothseasons contain long-term trends during the past 40 yr(Figs. 3 and 12). However, in summer the trend is relatively monotonic, while in winter it is superimposed

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627

FIG. 13. Linear trends in sea ice extent as a function of longitude
based on the period 1958–97 for winter (dashed curve) and 1957–
96 for summer (solid curve). Units are 10 3 km 2 decade21 .

upon decadal-scale fluctuations. The temporal correlation coefficient between the leading winter and summer
ice PC records is 0.74 for winter leading summer by 6months (0.63 for summer leading winter by 6 months),
reflecting that the downward trend is common to bothtime series. The spatial patterns of sea ice variability
also differ between the seasons. The winter pattern (Fig.
3) consists of regional anomalies of opposing sign thatlargely cancel in the zonal average, while the summer
pattern (Fig. 13) is more zonally uniform. The spatial
and temporal differences between winter and summer
variability in Arctic sea ice are reminiscent of those in
Northern Hemisphere air temperature documented byWallace et al. (1993, 1996). These authors argue thatthe thermal variations in summer are radiatively forced
while those in winter contain both radiative and ‘‘circulation-induced’’ components.
The mean and standard deviation of the summer(July–September) sea ice concentration field is shownin Fig. 14. Compared to winter, the summer ice pack is
contracted poleward with much of the MIZ located northof 758N. As in winter, the variability is confined to the
MIZ, with maximum values (15%–20%) in the Siberian
sector.
The spatial distributions of the linear trends in summer ice concentration during 1957–78 and 1979–96 areshown in Fig. 15. The average of the trends in the two
subperiods is also shown; these provide an estimate of
the total summer ice concentration trends during 1957–
96. Decreases in sea ice concentration [5%–30% decade21 ] are evident during both periods, but the spatial
patterns are slightly different. In the earlier period, negative trends extend from Greenland eastward to the dateline, with the largest values in the Barents, Kara, andLaptev Seas (208–1358E). In the later period, the negative trend is focused largely in the east Siberian Sea.
The trends for the later period are consistent with thoseshown in Parkinson et al. (1999).
The time series of summer ice extent for the Barents–

FIG. 14. Standard deviation of July–September ice concentration
anomalies during 1957–91 (shading and thin contours). Light shading
denotes values between 5% and 15% and dark shading values greaterthan 15%. The thin solid contour is the 15% isopleth and the thickdashed contour is the 25% isopleth. The thick solid contours denotethe 3% and 97% isopleths of the climatological mean summer ice
concentration field. For this figure only, the summer sea ice concentrations are based upon a dataset that does not include the discontinuity in 1979 associated with the introduction of satellite passive
microwave data (W. Chapman 1998, personal communication). Standard deviation maps for the early (1957–78) and later (1979–96)
portions of the record (the latter containing passive microwave data)
look qualitatively similar (not shown).

Kara–Laptev Sea region (208–1358E) and the east Siberian Sea (1358E–1808) are shown in Fig. 16. Summer
ice extent in the Barents–Kara–Laptev Sea sector is highduring the 1960s, with relatively stable conditions since(apart from the large negative anomaly in 1995). In
contrast, summer ice extent in the east Siberian Sea isrelatively stable during the 1960s–1980s followed by aperiod of low ice cover during 1989–95 [note that thevalues during 1957–62 in the Siberian sector likely suffer from a shortage of data; Chapman and Walsh(1993)]. These results are consistent with the ice concentration trend maps shown in Fig. 15.
g. Relation of summer sea ice trends to theatmospheric circulation
Has the atmospheric circulation played a role in reducing the summertime Arctic sea ice extent, or are thesummer ice declines a reflection of the general warming
trend over the Northern Hemisphere during the past 30–
40 yr? To investigate this question, we have regressed
the late spring (April–June) SLP anomaly field (otherseasons are examined below) upon an index of summer
sea ice extent. In view of the differences in the spatial

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JOURNAL OF CLIMATEVOLUME 13

FIG. 15. Linear trends in summer sea ice concentration during (a) 1957–78, (b) 1979–96, and (c) their average. Light (dark) shading
denotes values less than 25% decade21 (greater than 5% decade21 ). The contour interval is 5% decade21 , and the zero contour has been
omitted.

patterns of ice retreat during 1958–78 and 1979–96 (recall Fig. 15), we have performed a separate regression
analysis for each subperiod. For the early years, we haveused the record of summer ice extent averaged from 208
to 1348E as our ice index, while for the later period wehave used ice extent averaged from 1358E to 1808. Bothindices reflect the region of largest sea ice declines during the period for which they are defined (recall Fig.
15).

The spring SLP regression map for 1958–78 (Fig.
17a) exhibits positive values centered over the high Arctic (poleward of approximately 708N) and over the
North Atlantic west of the United Kingdom (note that
the SLP regression coefficients have been multiplied by
21 to correspond to a negative unit departure of thenormalized sea ice index). Thus, when summer ice extent is below normal across the Barents, Kara, and Laptev Seas, spring SLP is above normal over the high

1 FEBRUARY 2000

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629

FIG. 16. Time series of summer ice extent in (a) the Barents–Kara–
Laptev Sea region (208–358E) and (b) the east Siberian Sea (1358E–
1808). Units are 10 6 km 2 .

Arctic and North Atlantic, and below normal in surrounding regions. This SLP regression pattern is indicative of an anomalous southerly wind component over
the region of below-normal summer sea ice concentrations (see Fig. 17). The spring SLP regression map for
1979–96 (Fig. 17, bottom panel) exhibits negative values over the high Arctic and positive values over muchof the surrounding area. Thus, when summer ice extent
is below normal in the east Siberian Sea, spring SLP is
below normal in the high Arctic and above normal to
the south. The SLP regression pattern is indicative ofan anomalous southerly wind component over the regionof reduced summer sea ice concentrations in the east
Siberian Sea, and an anomalous northerly wind component over the area of increased sea ice in the Barents
and Kara Seas (see Fig. 17). It should be noted that the
SLP regression maps for the two subperiods differ considerably, particularly over the high Arctic where thevalues are of opposite sign. Nevertheless, both SLP patterns are physically consistent with the notion thatspring atmospheric circulation anomalies were instrumental in forcing the observed summer sea ice changes.
To give an indication of how strongly coupled the
SLP patterns depicted in Fig. 17 are to the sea ice indicesupon which they were based, we show the time seriesof the sea ice index with its associated SLP pattern

FIG. 17. (a) Regression map of spring (April–June) SLP (thickcontours) and summer (July–September) sea ice concentration (shading and thin contours) anomalies based upon the record of summer
ice extent in the region (208–1358E) during the period 1958–78. Boththe SLP and ice regression fields have been multiplied by 21. Units
are mb for SLP (ice concentration). Light (dark) shading denotes ice
concentration values less than 25% (greater than 5%) per unit standard deviation of the ice extent index. The contour interval is 5%
per decade, and the zero contour has been omitted. (b) As in (a), but
based on summer ice extent in the region (1358E–1808) during theperiod 1979–96.

index, defined by projecting the SLP regression patternpoleward of 708N upon the SLP anomaly field in eachspring (Fig. 18). The temporal correspondence between
the regional summer sea ice indices and their associated630

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FIG. 18. (a) Time series of normalized summer ice extent in the
Barents–Kara–Laptev Sea region (208–1358E; solid curve) and theassociated spring SLP anomaly pattern index (dashed curve; see text
for details). (b) Time series of normalized summer ice extent in theeast Siberian Sea (1358E–1808; solid curve) and the associated spring
SLP anomaly pattern index (dashed curve).

spring SLP pattern indices is remarkable: the correlation
coefficient between each sea ice–SLP pair is 0.73.
We have also performed regression analyses between
the summer ice extent indices and the SLP anomaly
fields during early summer (May–July) and high summer (June–August). For the period 1958–78, the correlation coefficient between the summer sea ice index
and the time series of the projection of the SLP regression pattern poleward of 708N is weaker in May–
July (0.65) and June–August (0.55) than in April–June(0.73). For the period 1979–96, the correlation coefficient is slightly stronger in May–July (0.76) than in
April–June (0.73), and weaker in July–August (0.58).
On the basis of these results, we conclude that atmospheric circulation anomalies in late spring–early summer (April–June or July) exert a stronger influence uponsummer sea ice cover than those in high summer (June–
August).
The spatial relation and close temporal correspondence between the late spring SLP and summer ice extent anomaly patterns shown in Figs. 16 and 17 indicatethat spring atmospheric circulation anomalies likely initiated the observed summer sea ice cover changes during
the past 40 yr. Similar conclusions were reached bySerreze et al. (1995) regarding the low ice cover anom-

VOLUME 13

aly during summer 1990, and by Maslanik et al. (1996)
for the period of generally low summer ice cover during
1990–95. We note that once ice melt is under way,
positive ice–albedo feedback can quickly accelerate ice
retreat during the summer months. This mechanism may
account for the relatively long delay (longer than thatfound in winter) between the time of anomalous atmospheric forcing (April–June or July) and the periodof maximum ice anomalies (July–September). Positive
ice–albedo feedback may also account for the amplification of negative sea ice trends relative to positive ones
during 1979–96 (Fig. 17, lower panel), especially considering that the spring SLP anomaly pattern would likely have produced a more balanced pattern of ice anomalies (i.e., larger positive trends in the Greenland, Barents, and Kara Seas due to anomalous northerly winds).
Preexisting negative ice trends in winter may also haveplayed a role in reducing the magnitude of the positivesummer ice anomalies across the Greenland, Barents,
and Kara Seas.
The spring SLP regression maps based upon summer
ice extent (Fig. 17) are very similar to those based upon
the winter NAO index (not shown), suggesting thatspring atmospheric circulation anomalies over the Arctic
are tied to winter NAO fluctuations within each subperiod. Note, however, that there is no consistent relation between Arctic SLP in late spring and the winter
NAO index, since a high NAO winter tends to be followed by high (low) Arctic SLP in late spring during
1958–78 (1979–96). The correlation coefficient between the winter NAO index and the regional summer
sea ice extent indices is 20.57 during 1958–78 and20.65 during 1979–96.
4. Summary and discussion
a. Winter
We have documented the leading pattern of variabilityof winter (January–March) sea ice concentration anomalies in the Arctic during 1958–97 and its association
with surface air temperature and sea level pressure
throughout the Northern Hemisphere extratropics. Consistent with previous studies, the dominant structure ofsea ice variability (which accounts for 35% of the variance) exhibits out-of-phase fluctuations between the
Labrador and Greenland/Barents Seas. A smaller amplitude seesaw occurs in the Pacific sector between the
Bering Sea and the Sea of Okhotsk. The time series ofthe leading EOF exhibits a high winter-to-winter autocorrelation (0.69) and is dominated by decadal-scalevariations and a long-term trend of diminishing ice cover east of Greenland and increasing ice cover west ofGreenland.
Associated with the dominant pattern of ice variability are large-scale SLP changes reminiscent of thepositive polarity of the NAO and AO circulation patterns. The spatial and temporal relationships between

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631

DESER ET AL.

the SLP and ice anomaly fields are consistent with thenotion that the atmospheric circulation anomalies force
the sea ice variations.
The ice pattern is also associated with hemispheric
SAT anomalies: warming over the northern continents
and cooling over the subpolar oceans. However, the largest SAT anomalies [108C (40 yr)21 ] are coincident withchanges in ice cover, with positive SAT anomalies overreduced ice amounts in the Greenland Sea and negative
SAT anomalies over enhanced ice cover in the Labrador
Sea. The amplification of the SAT perturbations over
ice relative to land or ocean results from the strong
contrast between ice surface and sea surface temperatures and thus is a direct consequence of the sea ice
variations prescribed in the NCEP–NCAR reanalysis.
Consistent with the SAT changes, surface sensible
and latent heat flux anomalies are strongly tied to thepattern of sea ice variability, with the largest upward
heat flux anomalies [200 W m22 (40 yr)21 ] directly over
the reduced ice cover in the Greenland Sea. The enhanced ocean-to-atmosphere heat and moisture fluxes
may have contributed to the observed increase in thenumber of cyclones over the Greenland Sea. Whether
such localized changes in transient eddy activity have
any impact upon the atmospheric general circulation
through changes in heat and vorticity flux convergenceremains to be investigated.
Because the quantitative validity of the reanalysisderived surface fluxes is open to question, an immediatepriority is the use of independent information (e.g., fromsatellites and field programs) for the assessment andpossible improvement of sea ice and surface flux parameterizations in models used to perform atmosphericreanalyses. However, we note that the cyclone statistics
used here were determined largely by the directly assimilated SLP data in the atmospheric reanalysis. The
assimilated pressure data did not ‘‘know’’ that the model’s prescribed sea ice was crude and simplistic. In thisrespect, the cyclone statistics were obtained independently of the sea ice data to which they were comparedin this study, and the SLP–sea ice association cannot
be considered an artifact of a model that treats sea ice
in a questionable manner.
The recent and historically unprecedented trends inthe wintertime NAO and AO circulation patterns overthe past three decades have been imprinted upon the
distribution of Arctic sea ice. The cause of the circulation trends and the sensitivity of the atmospheric flow
to the resulting changes in Arctic sea ice are in need offurther investigation, especially since feedbacks involving sea ice are likely to shape decade-to-century-scale
climate changes in high latitudes.
b. Summer
The dominant mode of sea ice variability in summer
is more spatially uniform than that in winter. Summer
ice extent for the Arctic as a whole has exhibited a nearlymonotonic decline (24% decade21 ) during the past 40
yr. The spatial patterns of ice retreat during the early(1957–78) and late (1979–96) portions of the record
differ slightly, with the maximum ice declines locatedin the Barents, Kara, and Laptev Seas during the early
period and in the east Siberian Sea during the later period. Both patterns of summer ice retreat appear to be
initiated by atmospheric circulation anomalies over thehigh Arctic in late spring: Positive ice–albedo feedback
may account for the relatively long delay (2–3 months)
between the time of atmospheric forcing and the maximum ice response.
An outstanding issue not resolved by our study is
why the winter NAO index (or equivalently, a winter
SLP pattern index based upon the leading winter sea
ice PC) exhibits correlations with spring SLP that areof opposite sign over the high Arctic during the early(1958–78) and late (1979–96) portions of the record.
Those opposing spring Arctic SLP anomaly patterns
both appear to initiate a net reduction in Arctic summer
sea ice cover, but in different areas within the Eurasiancoastal waters. We speculate that positive ice–albedo
feedback, by amplifying negative sea ice anomalies relative to positive ones, can alter an initially balanced
pattern of sea ice changes forced by the atmosphericcirculation into one with negative areal mean. Preexisting negative ice anomalies in winter may also have
served to reduce the magnitude of positive summer ice
anomalies forced by spring atmospheric circulation
changes. In this case, winter circulation changes may
also be implicated in the summer ice declines.
Acknowledgments. We thank Drs. Mark Serreze andJames Maslanik for helpful discussions. We also thankan anonymous reviewer and Professor J. M. Wallace forencouraging us to pursue the summer sea ice variability.
Drs. M. Alexander, U. Bhatt, J. Hurrell, S. Peng, D.
Thompson, and K. Trenberth provided helpful comments on an earlier version of the manuscript. Dr. Jim
Hurrell kindly provided the NAO record. The graphics
were produced with the GrADS software package developed by Brian Doty. This study was supported inpart by grants from NSF Physical Oceanography ‘‘Atlantic Circulation and Climate Experiment’’ to C. Deserand M. Alexander and from NASA Grant POLAR970015 to J. Walsh.
APPENDIX
Summer Sea Ice Record
Figure A1 shows the time series of Arctic summer(July–September) sea ice concentrations (%) averaged
north of 608N. The discontinuity between 1978 and 1979is due to the introduction of satellite passive microwave
sensing techniques that depict lower summer ice concentrations within the pack ice.

632

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FIG. A1. Time series of Arctic summer sea ice concentrations (%)
averaged north of 608N. The discontinuity between 1978 and 1979is due to the introduction of satellite passive microwave sensing techniques that depict lower summer ice concentrations within the pack
ice.

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