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Downloaded 09/26/21 07:11 AM UTC 1OCTOBER 2000 VENEGAS and MYSAK 3413 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 408N 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 458N 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] 408N) and SIC (in the North Atlantic sector) using the q 2000 American Meteorological Society Unauthenticated | Downloaded 09/26/21 07:11 AM UTC 1OCTOBER 2000 VENEGAS AND MYSAK 3413 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 5 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.
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