3412 JOURNAL OF CLIMATE VOLUME 13

Is There a Dominant Timescale of Natural Climate Variability in the Arctic?

SILVIA A. VENEGAS Danish Center for Earth System Science, Niels Bohr Institute for Astronomy, Physics and Geophysics, University of Copenhagen, Copenhagen, Denmark

LAWRENCE A. MYSAK Centre for Climate and Global Change Research, and Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

(Manuscript received 12 July 1999, in ®nal form 18 November 1999)

ABSTRACT A frequency-domain singular value decomposition performed jointly on century-long (1903±94) records of North Atlantic sector sea ice concentration and sea level pressure poleward of 40ЊN reveals that ¯uctuations on the interdecadal and quasi-decadal timescales account for a large fraction of the natural climate variability in the Arctic. Four dominant signals, with periods of about 6±7, 9±10, 16±20, and 30±50 yr, are isolated and analyzed. These signals account for about 60%±70% of the variance in their respective frequency bands. All of them appear in the monthly (year-round) data. However, the 9±10-yr oscillation especially stands out as a winter phenomenon. Ice variability in the Greenland, Barents, and Labrador Seas is then linked to coherent atmospheric variations and certain oceanic processes. The Greenland Sea ice variability is largely due to ¯uctuations in ice export through Fram Strait and to the local wind forcing during winter. It is proposed that variability in the Fram Strait ice export depends on three different mechanisms, which are associated with different timescales: 1) wind-driven motion of anomalous volumes of ice from the East Siberian Sea out of the Arctic (6±7-yr timescale); 2) enhanced ice motion forced by winter wind anomalies when they align parallel to the Transpolar Drift Stream (9±10-yr timescale); 3) wind-driven motion of old, thick, and very low salinity ice from offshore northern Canada into the out¯ow region (16±20-yr timescale). Also, a marked decreasing trend in ice extent since around 1970 (30± 50-yr timescale) is linked to a recently reported warming in the Arctic. The Barents Sea ice variability is associated with the nature of the penetration of Atlantic waters into the Arctic Basin, which is affected by two distinct mechanisms: 1) changes in the intensity of the northward-¯owing Norwegian Current, which is linked to variability in the North Atlantic oscillation (NAO) pattern (9±10-yr timescale); and 2) changes in the upper-ocean temperature of the Norwegian Current waters, which is likely related to the advection of temperature anomalies by the ocean gyres (16±20-yr timescale). Ice variability in the Labrador Sea, on the other hand, appears to be mainly determined by thermodynamical effects produced by the local wind forcing, which is closely related to the NAO pattern (9±10-yr timescale), and by oceanic advection of ice anomalies into this sea from the Greenland±Irminger Sea by the East Greenland Current (6±7-yr timescale).

1. Introduction ever, by working in the time domain, such analyses yield Recent analyses of Arctic and subarctic sea ice, at- modes of variability that do not necessarily have distinct mospheric, oceanic and hydrologic data, which use em- timescales. For example, the ®rst and second singular pirical orthogonal function (EOF) and related methods, value decomposition modes of the sea ice concentration have revealed the existence of natural ¯uctuations on (SIC) ¯uctuations poleward of 45ЊN both exhibit de- interannual, decadal, and interdecadal timescales (e.g., cadal-scale variability (see Figs. 5 and 13 in Yi et al. Mysak et al. 1990; Deser and Blackmon 1993; Slonosky 1999). In view of the role that some of these ¯uctuations et al. 1997; Mysak and Venegas 1998; Thompson and [e.g., the decadal-scale Arctic oscillation, (AO)] may Wallace 1998; Deser et al. 2000; Yi et al. 1999). How- play in greenhouse warming (Coti et al. 1999; Fyfe et al. 1999; Shindell et al. 1999), it is of considerable interest to provide new ways to describe and help better understand the natural climate variability in the Arctic. Corresponding author address: Silvia Venegas, Danish Center for The main purpose of this paper is to present, for the Earth System Science, Niels Bohr Institute for Astronomy, Physics and Geophysics, University of Copenhagen, Juliane Maries Vej 30 ®rst time, an analysis of century-long records of North- DK-2100, Copenhagen, Denmark. ern Hemisphere sea level pressure (SLP; poleward of E-mail: [email protected] 40ЊN) and SIC (in the North Atlantic sector) using the

᭧ 2000 American Meteorological Society

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Multi-Taper Method±Singular Value Decomposition 2. Data (MTM±SVD) approach. This is a multivariate frequen- cy-domain decomposition technique developed recently The datasets analyzed in this study consist of monthly by Mann and Park (1994, 1996, 1999) that seeks to SIC and SLP data, which were kindly provided by the isolate statistically signi®cant narrowband oscillations Hadley Centre for Climate Research in the United King- that are correlated among a large number of independent dom. The SIC data were extracted from the Global Ice time series (grid points). Since the oscillations can be and Sea Surface Temperature dataset (GISST; Parker et modulated in amplitude and frequency, an interesting al. 1995), and their integer values are reported as mid- aspect of this analysis is the study of the evolutive spec- month coverages, ranging from 0 (no sea ice in the grid tra that characterize the SLP and SIC variability. It is area) to 10 (grid area completely ice covered). Thus, well known that the 100-yr AO index shows notable for example, SIC value 5 corresponds to 0.5 of the area decadal variability only since the 1960s (see Fig. 5 in covered. The record analyzed spans 99 yr extending Thompson and Wallace 1998). The same property also from January 1900 to December 1998. The SLP data applies to the more spatially con®ned North Atlantic were extracted from the Gridded Monthly Sea Level oscillation (NAO) index (Hurrell and van Loon 1997). Pressure dataset (Allan 1993). The record analyzed Thus the question naturally arises as to whether a similar spans 92 yr extending from January 1903 to December nonstationary behavior applies to SIC and coupled SIC± 1994. In both datasets, the monthly data were converted SLP variability on the decadal as well as other time- into anomalies by subtracting the 30-yr climatology scales. 1961±90. Another goal of this study is to provide a coherent The quality of the sea ice data is heterogeneous. The characterization of observed decadal and interdecadal GISST coverage is complete from 1970 onward (the variability at northern high latitudes that can assist cli- satellite era) and reasonably complete from 1950, but mate modelers in the interpretation of decadal-scale var- the data are sparse during the ®rst half of the century iability that appears in multicentury control runs of and especially during the winter months. A number of ocean and coupled atmosphere±ocean general circula- grid points are ®lled with climatology (anomaly ϭ 0) tion models under current radiative forcing (for a re- in the pre-1950 period and hence they do not introduce view, see Mysak 1999). For example, Delworth et al. any information in our analysis. Most of the variability (1993) showed that in a 500-yr run of the Geophysical information included in the analysis come from the more Fluid Dynamics Laboratory coupled atmosphere±ocean complete post-1950 portion, and therefore, the results model, there exist approximately 50-yr oscillations in presented here certainly have a bias toward the second the strength of the thermohaline circulation and sea sur- half of the record. To test the impact of the changing face temperature in the North Atlantic. It was subse- data quality on our results, we performed two separated quently found (Delworth et al. 1997) that these oscil- analyses, using pre-1950 and post-1950 records, re- lations are highly coherent with multidecadal ¯uctua- spectively. The two analyses yielded very similar re- tions in sea surface salinity that originate in the Arctic sults, except that the pre-1950 data exhibited much and propagate into the North Atlantic via the East smaller variances. The time series containing only zero Greenland Current. In another GCM variability study, anomalies (the climatological grid points) are removed Zorita and Frankignoul (1997) showed that the output from the analysis before calculating the LFV spectrum of the control run of the Hamburg coupled ocean±at- in all cases, to ensure that the ``non-useful'' time series mosphere climate model contains two quasi-oscillatory are not affecting the results presented. Another source modes in the North Atlantic, with dominant periods of of heterogeneity in the sea ice data, which affects par- 10 and 20 yr. However, the question of possible links ticularly the pre-1950 period, is the reduced data cov- to the Arctic has not yet been addressed in this model. erage in winter compared to summer. During the pre- This paper is organized as follows. In section 2 the 1950 period, the averaged winter and summer variances data analyzed are described, and in section 3 the MTM± are respectively 0.04 and 0.20, whereas during the post- SVD method is outlined. In section 4 the dominant time- 1950 period, the same quantities are 0.28 and 0.33. The scales in the Arctic SIC and SLP variability are pre- reduced winter variance in the pre-1950 period re¯ects sented via ``local fractional variance'' (LFV) spectra. In the reduced number of time series available in winter the subsequent four sections (5±8), the spatial patterns with respect to summer. The implication of this is that associated with the four dominant decadal and longer the results corresponding to the ®rst half of the century period signals identi®ed in section 4 are analyzed: the are ``weighted'' toward the summer variability. The quasi-decadal 6±7-yr (QD6) and 9±10-yr (QD1O) aforementioned biases due to the irregular data cover- modes, and the interdecadal 16±20-yr (ID18) and 30± age, however, are partially smoothed by the fact that 50-yr (ID40) modes. In section 9 the temporal recon- each time series is normalized before analysis by the structions of the SIC anomalies are presented for the gridpoint standard deviation. four main centers of action in the northern North At- Seasonal averages were also computed from the lantic and marginal seas. A discussion and concluding monthly data in order to explore the seasonal depen- remarks are given in section 10. dence of the climate signals under investigation and

Unauthenticated | Downloaded 09/26/21 07:11 AM UTC 3414 JOURNAL OF CLIMATE VOLUME 13 their robustness. The de®nition of the seasons, however, 1987; Percival and Walden 1993). A detailed description is slightly different for the two variables. The cold sea- of the MTM±SVD technique can be found in the recent son for SIC anomaly data is de®ned as the averaged review of Mann and Park (1999). Only a brief summary winter and spring SIC anomalies, that is, 6 months from of the method is given below. December to May (DJFMAM), and the warm season is The time series of SIC and SLP anomalies at each de®ned as the averaged summer and fall anomalies, that grid point are ®rst transformed from the time to the is, 6 months from June to November (JJASON). We use spectral domain by using the MTM approach for spectral m this extended de®nition of seasons because the SIC data estimation. For each time seriesxn , we calculate K in- m shows very little variability during the normal winter dependent spectral estimatesY k ( f), by multiplying the months (DJF), due to the fact that a large number of time series by a family of K orthogonal sequences of grid points over the areas of interest are completely real numbers called Slepian data tapers (or data win- k covered by ice. An analysis of the data partitioned into dows)an : the four ``traditional'' seasons reveals that winter (DJF) N and spring (MAM) show variability on similar time- Ymkmi( f ) ϭ axe2␲ fn⌬t, knn͸ scales, and the same is valid also for summer (JJA) and nϭ1 fall (SON). Hence, we select the 6-month seasons given where m ϭ 1,...,M are the grid points, n ϭ 1,..., above, which we will loosely call winter and summer. N are the time steps, k ϭ 1,..., K are the spectral [For an EOF analysis of conventional 3-month winter estimates (one for each data taper), and ⌬t is the sam- and summer SIC data, see Deser et al. (2000).] Winters pling interval (monthly or seasonal). Only spectral ¯uc- are named by the year in which January occurs, and the tuations at frequencies greater than the Rayleigh fre- ®rst winter (1900) is computed as the 5-month average quency f R ϭ 1/N⌬t are resolved. The Slepian tapers January±May. average the energy over a window of width 2pf cen- For the SLP data, the winter season is de®ned as the R tered at a given frequency f, and only the ®rst K ϭ 2p 4-month average December±March, and the summer is Ϫ 1 are resistant to spectral leakage. The choice of the de®ned as the 4-month average June±September. Win- parameters p and hence K represents a compromise be- ters are named by the year in which January occurs, tween the variance and spectral resolution of the Fourier and the ®rst winter (1903) is computed as the 3-month transforms. As in Mann and Park (1996), we choose p average January±March. This de®nition of the winter ϭ 2 and hence K ϭ 3. This provides a frequency res- season is selected to be consistent with the de®nition of olution of 2pf ϭ 2 ϫ 2/(1200 ϫ 0.083) ϭ 0.04 cycles the winter NAO index, which we will refer to later in R yrϪ1 for a 100-yr (1200 months) data series, which per- this work. A parallel analysis using the 6-month seasons mits us to resolve decadal from interdecadal signals in as de®ned for the SIC data (winter±spring and summer± the LFV spectrum (see below). fall) was also conducted and revealed that the SLP re- Thus, at each frequency f, we obtain K spectral es- sults are not sensitive to the choice of season length. timates for each of the M gridpoint series that we or- The SIC anomalies are distributed over a 5 lat 5 Њ ϫ Њ ganize into a matrix A( f) of size M ϫ K, as follows: long grid and cover only the North Atlantic side of the 111 Arctic Ocean and marginal seas, from 90ЊWto90ЊE wY11( f ) wY 12( f ) wY 13( f ) and from 50Њ to 85ЊN. The restricted domain chosen for wY222( f ) wY ( f ) wY ( f ) the SIC data corresponds to the region where century- A( f ) ϭ 21 22 23 , long time series are believed to be of a reasonably good ___ MMM quality. The SLP anomalies are distributed over a 5Њ lat wYM 1 ( f ) wYM 2 ( f ) wYM 3 ( f ) ϫ 10Њ long grid and cover the entire Arctic Ocean and where the w are weights on the time series, which are marginal seas, over all longitudes and from 40ЊN to the m North Pole. uniformly set to 1 in the independent SIC and SLP analyses. In the joint SIC±SLP analysis, the matrix A( f) includes the grid points of the two ®elds SIC and SLP 3. Methodology one after the other (m ϭ 1,...,L for SIC and m ϭ L 1,...,M for SLP). In this case, the weights w are The method used for the analysis is the MTM±SVD, ϩ m a multivariate frequency-domain decomposition tech- set to be inversely proportional to the number of grid nique developed by Mann and Park (1994, 1996). The points for each ®eld in order to ensure that both datasets MTM±SVD technique seeks to identify statistically sig- are given equal overall weight in the analysis. ni®cant narrowband oscillations (which may be mod- We then perform a complex singular value decom- ulated in amplitude) that are correlated among a large position on A( f) in the form number of different time series (grid points). It exploits 3 A( f ) ϭ ␭ ( f )u ( f )t v ( f ), the Multi-Taper Method (MTM) of spectral analysis that ͸ kk k uses multiple orthogonal data tapers to provide a spec- kϭ1 tral estimate with an optimal trade-off between spectral where the K orthonormal-left singular vectors uk( f) rep- resolution and variance (Thomson 1982; Park et al. resent EOFs in the spatial domain, and the K ortho-

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normal-right singular vectors vk( f) represent EOFs in 4. The dominant timescales in Arctic SIC and SLP the spectral domain, from which we can recover the time-domain signal at each frequency f. The K singular The frequency-domain decomposition is performed ®rst on the SIC and the SLP anomaly ®elds separately, values ␭k( f) are proportional to the variance accounted for by the kth mode within a narrow frequency band and then on the ``joint'' SIC and SLP anomalies. The around frequency f (local fractional variance or LFV). purpose of the two independent analyses is to identify It is worth pointing out that a SVD decomposition is the timescales of oscillation preferred by each ®eld. The performed at each frequency f. goal of the joint analysis, on the other hand, is to detect In the following, we will concentrate only on the ®rst the dominant timescales of variability on which the two mode of the decomposition, which accounts for the larg- ®elds exhibit signi®cant coherence. Typically, signi®- est fraction of the variance (typically 60%±80% in our cant signals (frequencies) in the LFV spectra of the ®rst analyses) at each frequency f. The LFV accounted for mode account for about 60%±70% of the variance in by the ®rst mode is plotted as a function of frequency, the respective frequencies. Figure 1 shows the ®rst mode LFV spectrum of the which yields a spectrum-like plot that we will call the SIC anomalies based on the 99-yr period 1900±98, for ``LFV spectrum.'' Peaks in the LFV spectrum are in- all months, winter (DJFMAM) and summer (JJASON). dicative of potentially signi®cant narrowband spatio- The spectrum of the monthly anomalies (Fig. 1a) ex- temporal signals in the dataset. Signi®cance levels in hibits signi®cant peaks (at 95% signi®cance level) in the LFV spectrum are determined through a bootstrap- several frequency bands: the interdecadal band (ID), ping procedure (Efron 1990; Mann and Park 1996). Here with periods of 30±50 yr and of 16±20 yr (centered at the ®elds are permuted in time to keep their spatial 18 yr), the quasi-decadal band (QD), with periods in the structure intact. One thousand permutations are gener- range of 6±12 yr, in which we can identify two dominant ated and a new LFV spectrum is computed each time. narrow peaks at 10 yr and 6 yr, and the interannual band Empirical signi®cance levels are obtained by taking the (IA), with a period around 3 yr. The variance accounted 50, 90, 95, and 99 percentiles of this ensemble. To ac- for by these signals, in their respective frequency in- count for the month to month autocorrelation existent tervals, ranges from 67% to 72%. The peaks in the in the monthly data, the procedure is performed sepa- quasi-biennial band (QB), with periods of 2±2.5 yr, are rately on the annual sequences corresponding to each only signi®cant at the 90% level. The 10-yr QD signal of the 12 months of the year, and the resulting distri- is clearly dominant during winter, accounting for 78% butions are then averaged together. of the variance in the quasi-decadal band (Fig. 1b), while We also compute an ``evolutive'' version of the LFV the ID signal is most dominant during summer, ac- spectrum by using a moving time window to detect counting for about 65% of the variance in the inter- possible amplitude and frequency variations with time, decadal band (Fig. 1c). that is, nonstationarity in the signals. A 60-yr moving The evolutive version of the LFV spectrum for the window is used in this work. This window width allows monthly SIC anomalies, as described in section 2, is for a frequency resolution of 0.067 cycles yrϪ1 (2p/N, presented in Fig. 2 to test the stability of the signals with p ϭ 2, N ϭ 60) which means that the quasi-decadal during the 99 yr and the possible time dependence of and interdecadal signals can be resolved from each other their amplitude and frequency characteristics. The two and only periods shorter than 30 yr (N/2) can be con- components of the QD signal are clearly separated and ®dently separated from a secular trend. show some signi®cance until 1935; then they seem to The complex-valued M-vector u1( f) (the spatial merge into one weak signal in the middle of the century EOF), corresponding to the ®rst mode of the decom- and become much stronger during the last 40 yr of the position (k ϭ 1), can be used to reconstruct the spatial record (from around 1960). The quality of the sea ice pattern of a signal at a given ®xed frequency. The re- data improves considerably after 1950 (see discussion constructed spatial patterns associated with four selected in section 2), which may in part account for the abrupt frequencies are presented in sections 5±8 as sequences increase in signi®cance of the QD signal during the last of maps describing the evolution of the signal during a 40 yr. However, as we will see below (Fig. 4), the SLP ``typical'' cycle (see, e.g. Fig. 7, patterns 1±6). The data also exhibits a strong QD signal since about 1960, method used is able to detect standing and propagating although the data quality is considered to be better than signals equally well. On the other hand, the complex- that of SIC. It is interesting to note that a clear change valued K-vector v1( f) (the spectral EOF) can be inverted in behavior around 1960 is also observed in the time to obtain the slowly varying envelope of the signal near series of the Koch index (number of weeks per year frequency f, which is used to reconstruct the time-do- when ice affected the coasts of Iceland; see Mysak et main signal at grid points of interest. For an example al. 1990) and in the Iceberg Severity Index (annual num- of the temporal reconstruction, see bottom of Fig. 7. ber of icebergs crossing 48ЊN; see Belkin et al. 1998). For further details about the spatial and temporal re- Therefore, the hypothesis of a real regime change in constructions performed here, the reader is referred to climate around 1950±60 should not be discarded. Mann and Park (1999). The ID signal around 16±20 yr in Fig. 2 is signif-

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FIG. 2. The evolutive version of the LFV spectrum for the monthly SIC anomalies, using a 60-yr moving window. Black denotes the 99% signi®cance level, gray the 95% signi®cance level, and white is below the 95% signi®cance level.

et al. (1999) in North Atlantic SLP and sea surface temperature variations. Figure 3 shows the LFV spectrum of the SLP anom- alies based on the 92-yr period 1903±94, for all months, winter (DJFM) and summer (JJAS). The monthly anom- alies (Fig. 3a) show signi®cant peaks (at 95% level) in several frequency bands: an interdecadal band with pe- riods around 30±50 yr; a quasi-decadal band with a period of about 10 yr; three interannual bands with pe- riods around 5 yr, 2.7 yr, and 2.1 yr; and a slightly less signi®cant (just above the 90% signi®cance level) in- terdecadal band around 20±22 yr. The two major inter- annual signals (5 and 2.7 yr) account for 80% of the variance in the interannual band, whereas the 30±50-yr signals accounts for 74% of the variance in the inter- decadal band. The signi®cance of the quasi-decadal sig- nal increases considerably in the winter season (99% level, Fig. 3b), but the signal is insigni®cant in summer FIG. 1. The LFV spectrum of the SIC anomalies based on the 99- (Fig. 3c). The 5-yr signal is also signi®cant during win- yr period 1900±98, for (a) all months, (b) winter (DJFMAM), and ter only, while the 2.7-yr signal, though present in both (c) summer (JJASON). Signi®cant peaks for the monthly data are seasons, has a stronger signature during summer. The evident in the interdecadal band (periods of 30±40 yr and 16±20 yr), the quasi-decadal band (periods of 6±12 yr), the interannual band interdecadal signals do not have a preferred season. (periods around 3 yr), and the quasi-biennial band (periods of 2±2.5 The evolutive LFV spectrum for the monthly SLP yr). The quasi-decadal signal is dominant during winter while the anomalies is shown in Fig. 4. Dominant features in this interdecadal signal is dominant during summer. spectrum are the two interannual bands, at periods of around 5 and 2.7 yr. The quasi-decadal signal is sig- ni®cant only during the last 40 yr (beginning around icant during the entire record, but shifts slowly to higher 1960). This regime change in the SLP around 1960 has frequencies toward the end of the century, when it prac- also been documented by Kushnir (1994), Hurrell and tically merges with the QD signal. The signi®cance of van Loon (1997), Tourre et al. (1999), and Dickson et the IA and the QB signals (periods of 2±3 yr) decreases al. (1996), among others. The signi®cance of the quasi- toward the end of the century, coinciding with the in- decadal signal is fairly low in the monthly analysis when crease in signi®cance of the QD signal. A similar in- compared with that of the dominant interannual signals, triguing out of phase behavior between the QD signal but it increases substantially in the evolutive LFV spec- and the 2±3-yr period signal has been observed by Tourre trum of the winter-only analysis (not shown).

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FIG. 4. The evolutive version of the LFV spectrum for the monthly SLP anomalies, using a 60-yr moving window. Conventions as in Fig. 2.

ennial band), presumably due to the high-frequency character of the SLP ®eld in the frequency domain. The monthly spectrum exhibits signi®cant peaks at frequency bands reminiscent of those in Fig. 1a: two interdecadal bands, one with periods of around 30±50 yr, which we will call ID40, and a second with periods of around 16±20 yr, which we call ID18; two quasi- decadal bands, one with periods of around 9±10 yr, which we call QD10, and the second with periods of around 6±7 yr, which we call QD6; the IA band, with periods of around 3 yr; and the QB band, with periods of around 2.1 yr. The variance accounted for by these signals, in their respective frequency bands, ranges from 63% to 67%. The QB signal in Fig. 5a has a higher signi®cance than that in Fig. 1a due to the in¯uence of the SLP ®eld in the joint analysis. It is interesting to note that the QB signal is more signi®cant in the SLP and in the joint data than in the SIC data; presumably this is because the SIC is a longer-timescale response FIG. 3. The LFV spectrum of the SLP anomalies based on the 92- variable and hence cannot respond to the rapid biennial yr period 1903±94, for (a) all months, (b) winter (DJFM), and (c) oscillations in the SLP data. summer (JJAS). Signi®cant peaks for the monthly data are evident There are no signi®cant traces of the interannual SLP in the quasi-decadal band (period around 10 yr) and three interannual bands (around 5, 2.7, and 2.1 yr). The quasi-decadal signal is con- signals at around 5- and 2.7-yr periods in the joint anal- siderably more signi®cant in the winter season. ysis spectra, re¯ecting the very little coherence between SIC and SLP at those frequencies (in spite of their high signi®cance in the SLP ®eld, Fig. 3a). Instead, these Figure 5 shows the LFV spectrum of the joint SIC periods are likely related to the atmospheric telecon- and SLP anomalies based on the 92-yr period 1903±94, nection pattern of the ENSO signal, which exhibits an for all months, winter and summer (recall the different important node in the North Paci®c SLP (Mann and de®nitions of seasons used for each variable). Each of Park 1996). Thus, it is not surprising that they are largely the three joint spectra has a structure similar to the cor- absent in the joint analysis, since the SIC data is from responding SIC spectrum in the interannual and longer the northern North Atlantic and hence does not feel the timescale range, where the SIC data may have more ENSO atmospheric signal. Again, as in Fig. 1, the QD10 energy than the SLP data. On the other hand, the joint signal is largest for the winter season, accounting for monthly spectrum shows more resemblance with the over 70% of the variance in the quasi-decadal band (Fig. monthly SLP spectrum at high frequencies (quasi-bi- 5b), whereas the ID18 oscillation shows strongest var-

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FIG. 6. The evolutive version of the LFV spectrum for the monthly joint SIC and SLP anomalies, using a 60-yr moving window. Con- ventions as in Fig. 2.

iables and regions of the globe, not necessarily including the Arctic (Folland et al. 1984; Ghil and Vautard 1991; Deser and Blackmon 1993; Mann and Park 1994; Lau and Weng 1995; Mann and Park 1996; Chang et al. 1997; Sutton and Allen 1997; Tourre et al. 1999). Finally, the evolutive LFV spectrum for the joint SIC and SLP monthly anomalies is presented in Fig. 6. As expected, its structure resembles the SIC evolutive spec- trum (Fig. 2) at the decadal and longer periods. The ID40 signal cannot be con®dently resolved by the win- dow width used (see section 3). There is a clear shift of the ID18 signal toward higher frequencies from the beginning to the end of the century. The QD10 signal is signi®cant after around 1940, but becomes stronger after 1960, when the QD6 signal also becomes signif- icant. The IA and QB signals seem to have greater prom- inence in the middle years of the century. In the next four sections (5±8), we shall concentrate FIG. 5. The LFV spectrum of the joint SIC and SLP anomalies on four of the dominant frequencies identi®ed in the based on the 92-yr period 1903±94, for (a) all months, (b) winter, joint analysis, namely, the decadal modes QD6 and and (c) summer. Signi®cant peaks for the monthly data are evident QD10, and the interdecadal modes ID18 and ID40. The in the interdecadal band (periods of 30±50 yr and 16±20 yr), the quasi-decadal band (periods of 9±10 yr and 6±7 yr), the interannual higher-frequency IA and QB modes will not be inves- band (period around 3 yr), and the quasi-biennial band (period around tigated in this study. Following the methodology de- 2.1 yr). The 9±10-yr quasi-decadal signal is dominant in winter while scribed in section 3, we reconstruct the spatial and tem- the two interdecadal signals are dominant in summer. poral patterns of the SIC and SLP anomaly ®elds as- sociated with each of the four modes, and search for iability during summer, accounting for 60% of the var- physical mechanisms and atmosphere±ocean links that iance in the interdecadal band (Fig. 5c). The QD6 and may explain the described patterns in each case. The IA signals, on the other hand, show some signi®cance reconstructions are performed using monthly data, ex- in both seasons. The winter dominance of the QD10 cept for the QD10 signal, for which the winter-only signal has already been observed by Deser and Black- patterns are reconstructed since it is so prominent during mon (1993), Mann and Park (1996), and Slonosky et this season. In section 9 the temporal reconstructions of al. (1997). The dominant timescales of oscillation iden- the SIC anomalies are given for four main centers of ti®ed by our analysis are in good agreement with those action (the Greenland, Barents, Labrador, and Irminger previously described by other authors, for different var- Seas).

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5. The 6±7-yr quasi-decadal signal (QD6) Arctic through Fram Strait (cf. pattern 2 with Figs. 8f and 10f of Arfeuille et al.). Figures 9a and 9b show the Figures 7 and 8 show the spatial reconstructions of atmospheric circulation pattern for each situation, ob- the monthly SIC and SLP anomalies, respectively, for tained as the sum of the SLP climatology and the anom- the quasi-decadal mode QD6 (approximately 6±7-yr pe- alous SLP patterns 2 and 5, respectively. Figure 9b dis- riod). The largest SIC anomalies are found in the Green- plays a well-developed Arctic high located close to the land and Irminger Seas, and slightly weaker signals are Asian coast, which resembles that seen in Figs. 8a±10a seen in the Barents Sea, Labrador Sea, and Baf®n Bay of Arfeuille et al. Figure 9a, on the other hand, shows (see Fig. 7). The Barents Sea positive anomaly forming a strong Icelandic low extending farther than normal to in pattern 1 is followed by positive anomalies in the the east and a weak Arctic high retreating to the southern Greenland and Irminger Seas around one to two years part of the Beaufort Sea, similar to Figs. 8d±10d of later (patterns 2±3) and then in the Labrador Sea after Arfeuille et al. about another year (pattern 4). Thus, a weak dipole-like The model simulations of Arfeuille et al. (2000) iden- pattern is formed between the Labrador and the Barents tify four periods of large ice export (transport) through Seas (see patterns 1±2 and 4±5). To place the QD6 cycle Fram Strait: 1967±68, 1981±83, 1989, and 1994±95 (see in a temporal context, the time reconstruction of the their Fig. 2). Assuming that large ice transports result monthly SIC anomalies in the Greenland Sea associated in large sea ice extents in the Greenland Sea shortly with this signal is shown at the bottom of Fig. 7. Each afterward, these years approximately correspond to positive peak anomaly in this time series corresponds (with a lead of one year) the years of large ice extent to pattern 3 of the spatial reconstruction. The main fea- in the Greenland Sea depicted by the time series at the ture seen in the SLP patterns (Fig. 8), which is likely bottom of Fig. 7, during the overlapping period. The to affect the Arctic Ocean±Greenland Sea SIC anoma- large ice anomaly in 1975±76 shown in our ®gure was lies, is a dipole structure between southern Greenland not selected in their analysis as a major event, although and the Laptev Sea; this is most clearly observed in it appears as a small export in 1975 in their Fig. 4. We patterns 2 and 5. propose that in 1975±76, the effect of the QD6 signal The 6-yr oscillation described by these patterns can on the Fram Strait ice export is largely offset by the be compared with the model results recently obtained opposite effect produced by the ID18 signal on the same by Arfeuille et al. (1999), who investigated numerically export (see Fig. 16a, Greenland Sea: the QD6 and ID18 the interannual variability of the ice volume in the Arctic signals cancel each other in 1975±76). and the subsequent anomalous ice export to the Green- In the light of the above results, we suggest that the land Sea through Fram Strait during the period 1958± anomalous anticyclonic atmospheric circulation over the 98. Arfeuille et al. used 41 yr of the National Centers Laptev Sea (pattern 5 of Fig. 8) tends to pack the sea for Environmental Prediction winds to drive the sea ice ice in the East Siberian±Chukchi±Beaufort Seas. Three model developed by Tremblay and Mysak (1997). On years later, it reverses into an anomalous cyclonic cir- the basis of the model results, they proposed that large culation (pattern 2 of Fig. 8) and drives the sea ice ice transport anomalies exiting the Arctic through Fram anomaly toward Fram Strait. Based on ice motion ob- Strait are preceded by large ice volume anomalies in servations, Colony and Thorndike (1985) have shown the East Siberian Sea. The anomalous accumulation of that sea ice takes approximately 3 yr to move from the ice in the East Siberian Sea is generated by anomalous East Siberian Sea to Fram Strait, which is consistent winds blowing towards the Asian coast that result from with the timescale of the oscillation proposed here. Once a positive SLP anomaly centered in the Laptev Sea. the ice anomaly reaches Fram Strait, it is advected by When this SLP anomaly changes polarity after a few the mean East Greenland Current toward the Greenland years, the ice volume anomaly is advected by the anom- and Irminger Seas, where it arrives approximately 1 yr alous oceanic cyclonic circulation and the Transpolar later (pattern 3 of Fig. 7). Hence, the export out of the Drift Stream (TDS), and then out of the Arctic through Arctic of ice anomalies originated in the East Siberian± Fram Strait. Chukchi±Beaufort Seas and driven toward Fram Strait The SLP patterns in Fig. 8 clearly depict the two by the anomalous atmospheric con®guration (which oc- polarities of the atmospheric circulation anomalies de- curs every 6±7 yr) is proposed as a mechanism ac- scribed by Arfeuille et al. (2000): a cyclonic (patterns counting for the subsequent anomalous ice cover in the 1±3) and anticyclonic (patterns 4±6) circulation anom- Greenland and Irminger Seas. aly over the Laptev Sea, accompanied by a SLP anomaly The Barents Sea is affected by this mechanism as of opposite polarity centered over Greenland. The well since ice anomalies are also exported out of the anomalous anticyclonic con®guration tends to pack the Arctic through a path to the east of Svalbard (see pattern sea ice along the coast of the East Siberian, Chukchi, 1 of Fig. 8). The ice cover anomalies in the Labrador and Beaufort Seas (cf. pattern 5 with Figs. 8c±10c of Sea are indirectly related to the ice export anomalies Arfeuille et al.). On the other hand, the anomalous cy- through Fram Strait since the SIC anomalies are partly clonic con®guration tends to advect the sea ice accu- advected from the Greenland±Irminger Sea to the Lab- mulated in the East Siberian±Beaufort Seas out of the rador Sea by the mean ¯ow of the subpolar gyre (East

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FIG. 7. Spatial reconstruction of the monthly SIC anomalies for the quasi-decadal mode QD6 (6±7-yr period). The SIC patterns are presented at six consecutive times uniformly spanning a complete 6±7-yr cycle, that is, the time lag between two consecutive snapshots is approximately 12±14 months. Red tones and solid lines denote positive anomalies, blue tones and dashed lines denote negative anomalies. Zero contour line is omitted. Contour interval is 0.3. (bottom) The time reconstruction of the monthly SIC anomalies associated with the QD6 signal in the Greenland Sea (70ЊN±10ЊW). Each positive peak anomaly in this time series corresponds to pattern 3 of the spatial reconstruction. The anomaly values shown are converted into tenths of area covered by ice by dividing by 10.

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FIG. 8. Spatial reconstruction of the monthly SLP anomalies for the quasi-decadal mode QD6 (6±7-yr period). The SLP patterns are presented at six consecutive times uniformly spanning a complete 6±7-yr cycle, that is, the time lag between two consecutive snapshots is approximately 12±14 months. Red tones and solid lines denote positive anomalies, blue tones and dashed lines denote negative anomalies. Contour interval is 0.5 mb.

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FIG. 9. The atmospheric circulation patterns obtained as the sum of the SLP climatology and the Fig. 8 anomalous SLP patterns (a) 2, and (b) 5. Contour interval is 3 mb.

Greenland Current, see patterns 3±4 and 6±1 of Fig. 7). very similar (not shown). We note that, during the sec- In addition to this, (and probably more importantly), the ond half of the century, when the QD10 signal is most negative SLP anomaly over Greenland in patterns 4 and evident, peak positive anomalies in the Greenland Sea 5 of Fig. 8 produces anomalous northwesterly winds (pattern 2 in Fig. 10) occur around 1958, 1968, 1978, over the Labrador Sea. These anomalous winds cool the and 1988. The last three of these years correspond water by heat loss to the atmosphere (and hence favor roughly to the times of the Greenland Sea Ice and Sa- the ice formation), and advect sea ice from Baf®n Bay linity Anomaly (ISA) events described in Yi et al. (see patterns 4 and 5 of Fig. 7). The opposite mecha- (1999). The large ice anomalies seen in the Labrador nisms take place for the SLP anomaly patterns 1 and 2 Sea in the early 1970s, 1980s, and 1990s that arise be- of Fig. 8. cause of the Greenland±Labrador Seas dipole mentioned above, coincide with the three Labrador Sea ``Great Sa- 6. The 9±10-yr quasi-decadal signal (QD10) linity Anomalies'' described by Belkin et al. (1998). The SLP patterns 1±3 (Fig. 11) exhibit a large positive Figures 10 and 11 show the spatial reconstructions center of action over the central Arctic and Greenland, of the winter SIC and SLP anomalies, respectively, for surrounded by two negative centers, one over central the quasi-decadal mode QD10 (9±10-yr period). The Europe and the other over the Bering Sea. The second spatial reconstructions using all-months data (not half of the cycle (patterns 4±6) consists of the opposite- shown) show the same patterns but with smaller am- plitudes, particularly in the SLP ®eld. signed con®guration. The change of polarity of this pat- The SIC pattern 1 (Fig. 10) exhibits a positive ice tern occurs very rapidly (within a year) between snap- anomaly forming in the Greenland Sea that rapidly shots 3±4 and 6±1. This distribution of SLP anomalies grows and spreads into the Barents Sea (pattern 2). Then is reminiscent of the AO pattern de®ned by Thompson the whole anomaly diminishes its amplitude (pattern 3) and Wallace (1998), and, in the Atlantic sector, of the and disappears at the same time as a positive anomaly NAO pattern (Rogers 1984; Hurrell 1995). It is well forms over the Labrador Sea (pattern 4). This new known that the temporal representations of the AO and anomaly grows (pattern 5) and then vanishes (pattern NAO patterns oscillate over a range of frequencies, 6), and a new cycle begins. A negative SIC anomaly in which includes the quasi-decadal timescale (Hurrell and the Greenland Sea can be traced in the same way, start- van Loon 1997; Thompson and Wallace 1998; Raja- ing with pattern 4. A dipole pattern between the Ba- gopalan et al. 1998). Therefore, the SLP signal described rents±Greenland Seas and the Labrador Sea is clearly here captures the decadal-scale ¯uctuations of the stand- seen in the opposite-signed snapshots 2 and 5, a feature ing oscillation between the two phases of the AO (and ®rst noted by Walsh and Johnson (1979). The time re- NAO) pattern. A positive AO pattern (snapshot 5, Fig. construction of the monthly SIC anomalies in the Green- 11) is associated with low concentrations of ice in the land Sea associated with this signal is presented at the Barents and Greenland Seas and high concentrations of bottom of Fig. 10. The winter-only temporal signal is ice in the Labrador Sea (snapshot 5, Fig. 10). This as-

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FIG. 10. Spatial reconstruction of the winter SIC anomalies for the quasi-decadal mode QD10 (9±10-yr period). The SIC patterns are presented at six consecutive times uniformly spanning a complete 9±10-yr cycle, so the time lag between two consecutive snapshots is approximately 18±20 months. (bottom) The time reconstruction of the monthly SIC anomalies associated with the QD10 signal in the Greenland Sea. Each positive peak anomaly in this time series corresponds to pattern 2 of the spatial reconstruction. Conventions as in Fig. 7.

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FIG. 11. Spatial reconstruction of the winter SLP anomalies for the quasi-decadal mode QD10 (9±10-yr period). The SLP patterns are presented at six consecutive times uniformly spanning a complete 9±10-yr cycle, that is, the time lag between two consecutive snapshots is approximately 18±20 months. Conventions as in Fig. 8.

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FIG. 12. The atmospheric circulation patterns obtained as the sum of the winter SLP climatology and the Fig. 11 anomalous SLP patterns (a) 2, and (b) 5. Contour interval is 3 mb. sociation is also evident in the ®rst SVD mode of SIC land. This implies that the isobars are parallel to the and SLP obtained by Yi et al. (1999). ¯ow of the TDS that traverses the central Arctic from Figures 12a and 12b show the con®gurations of iso- the Laptev Sea to Fram Strait. The wind forcing acting bars obtained as the sum of the winter SLP climatology in the same direction as the TDS results in ice export and the patterns of anomalies 2 and 5 from Fig. 11, out of the Arctic at a high rate. At the same time, the respectively. We can compare these two opposite phases anomalous northerly winds blowing parallel to the east- of the QD10 SLP oscillation with the two atmospheric ern coast of Greenland help drive the ice anomalies circulation regimes described by Gudkovich (1961), southward toward Iceland and the Irminger Sea. Thus Proshutinsky and Johnson (1997), and Johnson et al. the combined effects of advection and wind forcing re- (1999). Years of anomalous anticyclonic atmospheric sult in large ice concentrations in the Greenland Sea. circulation in the Arctic (1±3 of Fig. 11) are character- On the other hand, during the years of an atmospheric ized by a positive SLP anomaly over the central Arctic, cyclonic regime (Fig. 12b), the wind anomalies are no a negative NAO index, and a well-developed Arctic high longer parallel to the east Greenland coast nor to the (Fig. 12a). On the other hand, years of cyclonic cir- mean position of the TDS, which has now shifted toward culation are characterized by a negative SLP anomaly North America (Gudkovich 1961). The ice export over the central Arctic (4±6 of Fig. 11), a positive NAO through Fram Strait is thus reduced compared to the index and a weak Arctic high con®ned to the Beaufort± anticyclonic case, giving rise to a negative SIC anomaly Chukchi Seas only (Fig. 12b). In the next three sections, in the Greenland Sea. The Fram Strait ice export on this we explore how these two atmospheric states may affect timescale is therefore directly related to the magnitude the sea ice ¯uctuations in the Greenland, Barents, and of the SLP gradient to the east of Greenland, the two Labrador Seas. extremes of which are seen in snapshots 2 and 5 of Fig. 11 and in Figs. 12a and 12b. These results have been recently corroborated by Kwok and Rothrock (1999), a. The Greenland Sea who found a signi®cant correlation between the winter As in the case of the QD6 signal, the ice export ¯ux of sea ice through Fram Strait, the SLP gradient through Fram Strait is strongly in¯uenced by the dif- across Fram Strait, and the NAO index. In conclusion, ferent atmospheric regimes in the central Arctic. How- both mechanisms (Fram Strait ice export by an enhanced ever, while on the timescale of the QD6 signal the mag- TDS and local wind forcing east of Greenland) are thus nitude of the ice export is determined by the volume of suggested to play a decisive role in the ¯uctuations of ice advected by the ocean currents from the East Si- the Greenland Sea ice extent on the 10-yr timescale. berian Sea, on the timescale of the QD10 signal the role of the wind forcing appears to be fundamental in de- b. The Barents Sea termining the Fram Strait export. During the years of an anticyclonic Arctic regime (Fig. 12a), an important In the Barents Sea, on the other hand, a very different ridge of high pressure extends toward northern Green- mechanism is proposed to explain the ice ¯uctuations,

Unauthenticated | Downloaded 09/26/21 07:11 AM UTC 3426 JOURNAL OF CLIMATE VOLUME 13 namely, the changes in the penetration of the relatively d. Summary warm Atlantic water carried northward by the Norwe- gian current (the northern branch of the North Atlantic In summary, all the mechanisms mentioned above current). The exchange of northward ¯owing warm and that are responsible for the decadal (QD10) ice ¯uctu- saline Atlantic water with cold and fresh Arctic water ations in the Greenland, Barents, and Labrador Seas that occurs in the Norwegian and Barents Seas is strong- (Fram Strait ice export and local wind forcing in the ly enhanced during the years of positive NAO pattern Greenland Sea, Atlantic water invasion into the Barents Sea, and local wind forcing in the Labrador Sea) are (snapshots 4±6 in Fig. 11). In this state, the Icelandic very closely linked to the atmospheric circulation over low is very deep and tilted in the southwest±northeast the Arctic. The enhanced amplitude of the atmospheric direction (Fig. 12b). In such a situation, the Barents Sea patterns during winter explains the marked winter sig- ice cover is reduced due to excessive melting by a stron- nature of the QD10 oscillation. The strong ice±atmo- ger than normal transport of heat by the Norwegian sphere interaction on this timescale is also implied by current into the Arctic region. When the NAO is neg- the high signi®cance of the 10-yr signal in the LFV ative, that is, the Icelandic low is weak and extends spectrum of SLP alone (Fig. 3b). Note that the other zonally (Fig. 12a), the Atlantic waters do not penetrate timescales studied in this work are much less signi®cant so far north, and ice is easily formed and remains in in the SLP data, and appear more important in the joint the Barents Sea (Ikeda 1990; Dickson et al. 1996; Steele analysis only because of their signi®cance in the SIC and Boyd 1998). These two circulation states have re- data. The signi®cance of the QD10 mode in the inde- cently been successfully modeled by Dickson (1999). pendent spectra of both variables (SIC and SLP) sug- Two different experiments were performed, one using gests the presence of feedbacks between the ice and the the atmospheric conditions of 1979 (negative NAO in- atmosphere (Mysak and Venegas 1998; Deser et al. dex) and the other, of 1990±94 (positive NAO index). 2000). We propose that the QD10 cycles shown here The results indicate a much deeper penetration of the may be a manifestation of a coupled atmosphere±ice± Atlantic waters into the Arctic in the case of the positive ocean mode, in which the three climate components NAO. sequentially force each other by means of positive and In addition to the changes in the strength of the in¯ow negative feedbacks, in a manner similar to what is shown of Atlantic waters into the Barents Sea and the Arctic in Fig. 4 of Mysak and Venegas (1998). in general, we propose that changes in the temperature of this in¯owing Atlantic water may also contribute to the sea ice ¯uctuations in the Barents Sea (Grotefendt 7. The 16±20-yr interdecadal signal (ID18) et al. 1998; Zhang et al. 1999). Positive temperature Figures 13 and 14 show the spatial reconstructions anomalies advected by the subpolar gyre may periodi- of the monthly SIC and SLP anomalies, respectively, cally reach the Barents Sea and produce enhanced ice for the interdecadal mode ID18 (approximately 16±20- melting on a decadal timescale, which is characteristic yr period). As we did for the QD6 mode, we again of the subpolar gyre. include both seasons in this reconstruction. Figure 13 shows centers of action over nearly the same regions as for the QD6 and QD10 signals. How- c. The Labrador Sea ever, in contrast to these two latter signals, the anomalies over the Barents, Greenland, and Labrador Seas are of- The Labrador Sea is mainly in¯uenced by the local ten in phase (e.g., see snapshots 2±3 and 4±5). In ad- wind anomalies that enhance (patterns 4±6 of Fig. 11) dition, we note that there is some propagation of the or weaken (patterns 1±3 of Fig. 11) the climatological signal from east to west. The time reconstruction of the northwesterlies over the region. During the years of Arc- monthly SIC anomalies in the Greenland Sea associated tic cyclonic circulation (Fig. 12b), stronger than normal with this signal is shown at the bottom of the ®gure. winds due to the well-developed Icelandic low con- The SLP patterns in Fig. 14 are characterized by a SLP stantly cool the atmosphere over the Labrador Sea. This anomaly in the central Arctic to the north of Greenland pattern is part of the NAO-related temperature seesaw surrounded by two opposite-signed anomalies south of between North America and Europe (van Loon and Rog- Greenland and over northern Russia (snapshots 6±1 and ers 1978; Hurrell 1995). The Labrador Sea is thus cooled 3±4). by large air±sea heat ¯uxes, which favors the ice for- From pattern 3 in Fig. 14 we note that there are strong mation (Ikeda 1990). The opposite mechanism occurs anomalous westerly winds north of the Canadian ar- during the years of anticyclonic circulation over the Arc- chipelago and Greenland. These winds would drive ice tic (Fig. 12a). In addition to the wind forcing, ice anom- from the waters offshore northern Canada and northern alies are also advected from the Greenland and Irminger Greenland into the out¯ow region near Fram Strait. Seas by the East Greenland Current, taking 3±4 yr to These waters contain the thickest and oldest (and there- reach the Labrador Sea. This is suggested in patterns fore least saline) sea ice of the Arctic region (Bourke 3±4 and 6±1 of Fig. 10 (see also Mysak et al. 1990). and Garret 1987; Walsh and Chapman 1990). This sug-

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FIG. 13. Spatial reconstruction of the monthly SIC anomalies for the interdecadal mode ID18 (16±20-yr period). The SIC patterns are presented at six consecutive times uniformly spanning a complete 16±20-yr cycle, that is, the time lag between two consecutive snapshots is approximately 32±40 months. (bottom) The time reconstruction of the monthly SIC anomalies associated with the ID18 signal in the Greenland Sea. Each positive peak anomaly in this time series corresponds to pattern 3 of the spatial reconstruction. Conventions as in Fig. 7.

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FIG. 14. Spatial reconstruction of the monthly SLP anomalies for the interdecadal mode ID18 (16±20-yr period). The SLP patterns are presented at six consecutive times uniformly spanning a complete 16±20-yr cycle, that is, the time lag between two consecutive snapshots is approximately 32±40 months. Conventions as in Fig. 8.

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FIG. 15. Spatial reconstruction of the monthly (a) SIC and (b) SLP anomalies for the interdecadal mode ID40 (30±50-yr period) at the point of maximum amplitude of the oscillation. (c) The time reconstruction of monthly SIC anomalies associated with the ID40 signal in the Greenland Sea. The spatial pattern in (a) corresponds to the two positive peaks in this time series (1910 and 1970). Conventions as in Figs. 7 and 8. gests that there is an enhanced ¯ux of relatively fresh con®guration of patterns 4±5 (Fig. 14) in the North At- water (and sea ice) into the Fram Strait region. This lantic region implies that the Icelandic low is strong and low-salinity water is then exported out of the Arctic into tilted in the southwest±northeast direction, similarly to the Greenland Sea where it strengthens the halocline what occurs in the QD10 mode (see patterns 4±6 of Fig. and favors the formation of sea ice. This process has 11, and Fig. 12b). Hence the Norwegian Current pen- been discussed extensively by Walsh and Chapman etrates far north into the Barents Sea reducing the sea (1990) in their search for the causes of the large ice ice extent there through melting. The opposite process anomaly of 1968 in the Greenland Sea. The SLP cor- can be inferred from patterns 1±2 of Fig. 14. relation map in their Fig. 6 closely resembles pattern 3 of Fig. 14. 8. The 30±50-yr interdecadal signal (ID40) This freshwater advection mechanism is proposed to have the largest impact during summer, when a larger Figures 15a and 15b show the spatial reconstructions amount of freshwater is put into the upper layer due to of the monthly SIC and SLP anomalies, respectively, the melting of the old, thick ice advected from offshore for the interdecadal mode ID40 (approximately 30±50- northern Canada. This is clearly re¯ected by the dom- yr period) at the point of maximum amplitude of the inance of the summer season on the ID18 timescale, as oscillation. The temporal reconstruction of the monthly seen in the LFV spectra of Figs. 1c and 5c. SIC anomalies in the Greenland Sea associated with this The Barents Sea ice variability on the ID18 timescale signal is shown in Fig. 15c. The SIC pattern is domi- can also be linked to the strength of the penetration of nated by a large positive SIC anomaly over the Green- the Atlantic waters into the Arctic. The SLP anomalous land Sea, accompanied by two weaker anomalies: a pos-

Unauthenticated | Downloaded 09/26/21 07:11 AM UTC 3430 JOURNAL OF CLIMATE VOLUME 13 itive one in the western Barents Sea (south of Svalbard) 9. Temporal reconstructions and a negative one in the Labrador Sea. The main feature in the SLP pattern is a large monopole SLP anomaly We show in Fig. 16 the temporal (monthly) recon- centered over Greenland. structions of the SIC anomalies in terms of the four Due to the low-frequency character of this oscillation signals described above in the main centers of action, and the shortness of available Arctic time series, many namely, the Greenland Sea, the Barents Sea, the Lab- rador Sea, and the Irminger Sea. The Greenland Sea studies have referred to this oscillation as a trend over reconstruction (Fig. 16a), corresponding to the time se- the past few decades, particularly when describing tem- ries shown at the bottom of Figs. 7, 10, 13, and 15 is perature data. A warming trend of the oceanic temper- dominated by the two interdecadal signals ID40 and atures in the central Arctic during the last 2±3 decades ID18, although the QD10 signal becomes comparable has been reported by several authors (Quadfasel et al. in amplitude after 1960. It is very interesting to note 1991; Carmack et al. 1995; Johannessen et al. 1995; that the four modes are in phase around 1968±70, the Grotefendt et al. 1998). While some investigators sug- time of the well-known Great Ice and Salinity Anomaly gest this is evidence of global warming, it is also pos- (GISA) event (called the GSA in Dickson et al. 1988). sible that this increase in ocean temperature may be The constructive ``interference'' of the four signals ex- associated with this low-frequency mode, which is re- plains the extremely large amplitude of the ice anomaly ¯ected by the decreasing sea ice extent after 1970 in observed that year. By way of contrast, during the small- the Greenland Sea (Figs. 15a and 15c). er-amplitude ISA event centered around 1979, the QD10 A number of variables in the Arctic and subarctic are signal is being opposed by the Q6D and ID18 signals, found to contain this low-frequency behavior with a resulting in a much weaker ice anomaly, as shown in peak near 1970, similar to what is seen in the Greenland Fig. 16b. Finally, around 1989 the QD10 and QD6 sig- Sea time series (Fig. 15c). Examples of these are the nals are in phase but are opposed by the ID40, which air temperature time series in the Greenland Sea, the results in a moderate ISA event (Fig. 16b). Barents Sea, and eastern Canada (Ikeda 1990), the up- The Barents Sea variability (Fig. 16c) is dominated per-ocean temperature and salinity in the Barents Sea, by the QD10 signal, particularly during the ®rst 20 and the Labrador Sea and the Faroe±Shetland Channel the last 40 yr of the century; the three other modes (Belkin et al. 1998), the NAO Index (Hurrell and van account for similar fractions of the anomaly variance. Loon 1997), and the winter mean pressure in western As in the Greenland Sea, the four signals add construc- Greenland and the potential temperature in the Labrador tively during the extraordinary GISA event of 1968±70 Sea (Dickson et al. 1996). The temporal component of (Fig. 16d). Due to the net dominance of the QD10 signal, the EOF analysis of Arctic sea ice extent performed by the three ISA events of around 1969, 1979, and 1989 Deser et al. (2000) also shows a decreasing trend after are more clearly distinguished here than in the Green- 1965±70, and the air temperature pattern associated with land Sea. it exhibits an important center of action in the Greenland In the Labrador Sea (Fig. 16e) all modes show similar Sea and another of opposite polarity in the Labrador amplitudes, particularly during the last half of the re- cord. Although the decadal timescale of the ISA events Sea, in agreement with the ice pattern of Fig. 15a. as determined by the QD10 oscillations is somewhat Hence, we suggest that the ID40 signal is related to disrupted by the other signals, large sea ice extent anom- long-term temperature changes in the Arctic, which are alies are seen near 1971, 1983, and 1990±93 (Fig. 16f), in turn associated with the SLP distribution. Deser et approximately 3±4 yr after the ice anomalies in the al. also show that the decreasing trend is much larger Greenland and Barents Seas. Because these quasi-de- in summer than in winter. This agrees with the LFV cadal events are so clearly seen in the Labrador Sea, spectra of Figs. 1 and 5 that exhibit a notable summer Belkin et al. (1998) argue that this is the most important signal on the ID40 timescale. region for ISAs. The mechanism for this low-frequency ID40 signal Last, the ice ¯uctuations in the Irminger Sea (Fig. is unclear. However, if its origin is in the Arctic, then 16g) are clearly dominated by the QD6 mode during it may be a manifestation of the coupled atmosphere± the past three decades. Again, however, the large GISA ocean multidecadal mode simulated by Delworth et al. event of 1968±70 results from the in-phase behavior of (1993). Delworth et al. (1997) have recently linked this the four signals, and three small but distinct ISA-like mode to salinity anomalies in the Arctic that slowly events are observed afterward with a 6-yr period (Fig. propagate out into the North Atlantic and around the 16h). subpolar gyre, in a manner similar to the Great Salinity A common feature of all the reconstructions in the Anomaly (GSA) event of the 1970s (Dickson et al. four centers of action shown in Fig. 16 is that the low- 1988). Since salinity and sea ice anomalies are closely frequency character observed in the ®rst half of the related (Mysak et al. 1990), it is conceivable that the century, when the interdecadal signals dominate, chang- 1970 peak in Fig. 15c is also further evidence of the es into a higher-frequency behavior after about 1960, GSA. when the decadal-scale signals dominate. This seems to

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FIG. 16. (left) The temporal reconstructions of the SIC anomalies associated with each of the four signals described in the text in the (a) Greenland Sea, 70ЊN±10ЊW; (c) Barents Sea, 75Њ±35ЊE; (e) Labrador Sea, 60ЊN±60ЊW; and (g) Irminger Sea, 65ЊN±30ЊW. Thin dashed line ϭ QD6 signal, thin solid line ϭ QD10 signal, thick solid line ϭ ID18 signal, and thick dashed line ϭ ID40 signal. (right) The sum of the four reconstructed time series in (a), (c), (e), and (g) (thick lines) compared with the actual SIC anomalies in the (b) Greenland Sea, (d) Barents Sea, (f) Labrador Sea, and (h) Irminger Sea (thin lines). The anomaly values shown are converted into tenths of area covered by ice by dividing by 10.

support the recently proposed hypothesis of a possible signals (thick lines in Figs. 16b,d,f and 16h) accounts change in climatic regime near 1960 (Dickson et al. for 51%, 42%, 31%, and 52% of the variance of the ice 1996), although this change may also be part of a longer extent anomaly in the Greenland, Barents, Labrador, and (century) scale oscillation. On the other hand, this Irminger Seas, respectively (thin lines). Considering change could as well be a spurious feature caused by only the last 45 yr of the century, which contain the the heterogeneity of the sea ice data, as discussed in best-quality data (see section 2), these percentages in- section 2. crease to 67%, 57%, 34%, and 66% respectively. This The sum of the four quasi-decadal and interdecadal clearly indicates that the quasi-decadal and interdecadal

Unauthenticated | Downloaded 09/26/21 07:11 AM UTC 3432 JOURNAL OF CLIMATE VOLUME 13 timescales dominate the Arctic sea ice ¯uctuations in Fram Strait, forced by the winter wind anomalies when the North Atlantic sector. In particular, the variability they align parallel to the TDS (a 9±10-yr period signal; in the Greenland±Irminger Sea is the best represented see also Mysak and Venegas 1998); and 3) the wind- by the four signals isolated here. The unexplained por- driven motion of old, thick, and very low-salinity ice tion of the variance may be accounted for by the higher- from offshore northern Canada and Greenland into the frequency interannual and quasi-biennial signals, which out¯ow region (a 16±20-yr period signal; see also Walsh have been shown to be signi®cant (section 4) but have and Chapman 1990). The ®rst mechanism relates to not been analyzed in this study. anomalous volumes of ice forced out of the Arctic by the anomalous winds, the second involves normal amounts of ice forced out of the Arctic at a high rate 10. Discussion and conclusions by anomalous winds parallel to the TDS during winter The frequency-domain decomposition performed and the third concerns the melting of low-salinity ice above provides us with a new view of the rather com- during summer and subsequent extra input of freshwater plicated Arctic and subarctic climate variability. How- into the out¯ow region. In addition, the winter wind ever, we note that this simplistic decomposition of the anomalies blowing parallel to the east Greenland coast observed variability into different timescales does not play an important role in advecting the ice exported necessarily imply that each of the physical mechanisms through Fram Strait toward Iceland and the Irminger described above act only on a narrow frequency band. Sea. Finally, the marked decreasing trend in ice extent On the contrary, the effects of some of them may be in the Greenland Sea since around 1970 may be linked felt over a broad range of frequencies. For example the to a recently reported warming in the Arctic. It is also variability of the Atlantic water in¯ow into the Barents conceivable that this low-frequency signal in the Green- Sea seems to occur on timescales between 10 and 20 land Sea has its origins in the Arctic (Delworth et al. yr (as inferred from the analysis of the QD10 and ID18 1997). However, there are no long records of ice cover signals), the ¯uctuations in the Fram Strait ice export in the Arctic itself that can be used to verify this hy- may be explained by both the QD6 and the QD10 sig- pothesis. nals, and the local wind forcing in the Labrador Sea The Barents Sea ice ¯uctuations may also be asso- seems to take place, with variable intensity, on the four ciated with the Fram Strait ice export. However, we described timescales. Nevertheless, the analysis per- propose that the most signi®cant factor accounting for formed is very useful in helping to identify different the ice variability in this sea is the nature of the pen- physical mechanisms responsible for the ice ¯uctuations etration of Atlantic waters into the Arctic Basin. Two in the North Atlantic sector of the Arctic, and to obtain distinct mechanisms are suggested to induce variability a simple but comprehensive picture of the Arctic climate in this process: 1) changes in the intensity of the north- variability during this century. Also, the frequency-do- ward-¯owing Norwegian Current that have been linked main decomposition provides us with an answer to the to variability in the NAO pattern (Dickson et al. 1996), question as to which timescales dominate the natural and 2) changes in the upper-ocean temperature of the climate variability in the Arctic. Atlantic waters carried by the Norwegian current that In answer to this question, we have in fact isolated are likely related to the advection of sea surface tem- four dominant year-round signals that account for a large perature anomalies by the subpolar and/or subtropical fraction of the variance (over 60%) of the joint Arctic gyres (Grotefendt et al. 1998). The ®rst mechanism op- and subarctic SIC and SLP ¯uctuations on the decadal erates on the quasi-decadal timescale (the QD10 signal) and interdecadal timescales, namely, those with periods and is closely linked to coherent atmospheric variations of 6±7 yr, 9±10 yr, 16±20 yr, and 30±50 yr. However, on this timescale (the NAO), which likely involve air± the 9±10-yr oscillation especially stands out during win- ice±ocean feedbacks. The second mechanism has a lon- ter: it accounts for over 70% of the variance at that ger period of oscillation (the ID18 signal), presumably period. Two signi®cant higher-frequency signals (inter- determined by the advection time of the ocean gyres. annual and quasi-biennial) have also been detected, but A recent model analysis by Zorita and Frankignoul they have not been analyzed in detail in this study. (1997) identi®es two quasi-oscillatory modes of vari- Ice variability in the Greenland Sea, particularly dur- ability in the North Atlantic sector with dominant pe- ing the second half of the century, is largely accounted riods of about 10 and 20 yr. In agreement with our for by the ¯uctuations in ice export through Fram Strait results, they suggest that the 10-yr oscillation re¯ects a and by the local wind anomalies during winter. The coupled atmosphere±ocean mode whereas the 20-yr one variability of the Fram Strait ice export is proposed to involves the ocean alone with no apparent atmospheric depend mainly on three different mechanisms that are feedbacks. associated with different timescales: 1) the wind-driven Ice cover variability in the Labrador Sea is mainly motion of anomalous volumes of ice from the East Si- determined by thermodynamical effects produced by the berian Sea out of the Arctic through Fram Strait (a 6±7- local wind anomalies on all of the four timescales an- yr period signal; see also Arfeuille et al. 2000); 2) the alyzed. This wind forcing is closely related to the at- enhanced transport of ice from the central Arctic toward mospheric circulation ¯uctuations of the NAO pattern.

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In addition, it is suggested that ice anomalies are ad- Arfeuille, G., L. A. Mysak, and L. B. Tremblay, 2000: Simulation of vected from the Greenland±Irminger Sea into the Lab- the interannual variability of the wind driven Arctic sea ice cover during 1958±1998. Climate Dyn., 16, 107±121. rador Sea by the East Greenland Current in the subpolar Belkin, I. M., S. Levitus, J. I. Antonov, and S.-A. Malmberg, 1998: gyre. This results in a time lag of 3±4 yr between the ``Great Salinity Anomalies'' in the North Atlantic. Progress in ice ¯uctuations in these two locations. Oceanography, Vol. 41, Pergamon, 1±68. A fundamental question remains as to what causes Bourke, R. H., and R. P. Garret, 1987: Sea ice thickness distribution the different oscillations described above. Are the ice in the Arctic Ocean. Cold Reg. Sci. Technol., 13, 259±280. Carmack, E. C., R. W. Macdonald, R. G. Perkin, and F. A. Mc- ¯uctuations all atmospherically driven? 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