1. Introduction
Arctic sea ice plays an important role in the climate system. Arctic sea ice variation is an important indicator of changes in the climate system, such as global warming and polar amplification. Observational evidence and climate modeling suggests that sea ice can itself be an agent of climate change (Walsh 1983; Kahl 1990; Mysak et al. 1990; Deser et al. 2000; Rigor et al. 2002; Wu et al. 2002, 2004). The presence of sea ice strongly influences air–sea interactions and high-latitude weather and climate. Sea ice reflects a large part of the incoming solar radiation and restricts exchanges of heat, moisture, and momentum between the ocean and atmosphere. Through latent heat release during the frozen season and the heat absorbed during the melting of sea ice, sea ice strongly affects the atmospheric energy budget. Wu et al. (2004) show that sea ice variations can influence stratification and stability of the boundary layer and further produce dynamic influences on the atmosphere, which in turn feeds back on sea ice and the ocean.
Processes related to sea ice variations, including the growth and melting (producing freshwater) of sea ice, influence seawater salinity flux and sea ice export out of the Arctic basin into the Nordic seas and the Barents Sea. Sea ice export through Fram Strait and its interaction with the North Atlantic give sea ice a potential role in the North Atlantic climate variations and deep-water formation (Aagaard and Carmack 1989), and the variability of thermohaline circulation (THC). Evidence shows that the Great Salinity Anomaly (GSA) occurred in the northern North Atlantic during 1968–82 and was connected with an anomalously large export of sea ice from the Arctic (Dickson et al. 1988; Häkkinen 1993). Recent evidence (Dong and Sutton 2002) indicates that atmospheric feedbacks could spread globally the influence of a change in the THC much more quickly and efficiently than ocean processes alone.
With respect to atmospheric forcing regimes of the Arctic sea ice, many previous studies focused mainly on the North Atlantic Oscillation (NAO) (Wang et al. 1994; Kwok and Rothrock 1999; Dickson et al. 2000; Wu et al. 2000; Zhang et al. 2000; Kwok 2000; Jung and Hilmer 2001; and many others) and the Arctic Oscillation (AO) (Wang and Ikeda 2000; Vinje 2001a; Rigor et al. 2002; Zhang et al. 2003; Holland 2003). The response of sea ice to the NAO (AO) shows out of phase variations of sea ice concentration between the Labrador and Greenland–Barents Seas (Walsh and Johnson 1979; Wang et al. 1994; Slonosky et al. 1997; Deser et al. 2000; Wu et al. 2000) and a cyclonic (anticyclonic) circulation anomaly in sea ice motion in the Arctic Ocean (Kwok 2000; Rigor et al. 2002). However, it has become apparent that the relationship between the NAO (AO) and sea ice export out of the Arctic basin through Fram Strait is complicated, depending on the sea ice flux dataset (Kwok and Rothrock 1999; Vinje 2001a) and the specific period (Hilmer and Jung 2000).
Proshutinsky and Johnson (1997) revealed an anticyclonic and a cyclonic circulation regime over the Arctic basin, with each regime persisting about 5 to 7 years. During the former, anticyclonic winds are prevalent in the Arctic basin so that sea ice and the water flow toward Fram Strait is reduced (Proshutinsky and Johnson 1997; Tremblay and Mysak 1998; Proshutinsky et al. 2002). Consequently, sea ice flux from the Arctic basin into the Greenland Sea is weaker than normal.
Although previous studies have revealed the response of sea ice motion to the NAO (AO) (Kwok 2000; Rigor et al. 2002), the NAO (AO) cannot explain all of the phenomena present in sea ice motion and export. A few studies have suggested that it is not the NAO (AO) that is forcing sea ice export anomalies through Fram Strait (Jung and Hilmer 2001; Holland 2003). Thus, the question is raised whether there is other forcing beyond the NAO (AO); that is, there are likely other processes in the atmospheric circulation state that also influence sea ice motion and export out of the Arctic basin. The objective of this study is to identify a meridional anomaly of atmospheric variability over the Arctic region and its effects on sea ice motion and sea ice export through Fram Strait. The meridional anomaly shows a dipole structure that strongly influences sea ice motion and sea ice export.
2. Data and methods
The primary datasets used in this study are monthly mean sea level pressure (SLP), surface wind, surface air temperature, and geopotential heights at 500 hPa obtained from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis datasets for the period from 1960 to 2002 and monthly sea ice motion (SIM) obtained from International Arctic Buoy Program (IABP) for the period 1979–98. For detailed information about the SIM dataset, see Rigor et al. (2002). The monthly mean sea ice concentration dataset (on a 1° latitude × 1° longitude grid for the period 1871–2002) was obtained from the British Atmospheric Data Centre (BADC; http://badc.nerc.ac.uk/data/list_all_datasets.html). In this study, a sea ice concentration dataset after 1960 was used to calculate the climatology and anomalies.
We applied empirical orthogonal function (EOF) analysis to monthly mean SLP north of 70°N to reveal dominant modes of variations in SLP. In this study, we focus our attention on the winter season (October to March). Following the procedure of Vinje (2001a), we used monthly mean SLP grid points (80°N, 10°W and 72.5°N, 20°E) data to approximately calculate the wind-induced ice volume flux through Fram Strait. In this study, more attention is paid to the seasonal mean averaged for the six winter months (October–March). Simultaneously, as a comparison, this paper also preliminarily explores intraseasonal variations.
3. Results
a. Dipole anomaly at an intraseasonal time scale
Based on those autocorrelations, the number of degrees of freedom was found to be Neffective = 243. According to the literature of North et al. (1982), the EOF2 is well separated from the other modes. Wang et al. (1995) had derived the similar dipole anomaly using SLP data of 45°N for the period of 1953–93, accounting for 12% of the total variance. Nevertheless, no further analysis was conducted.
The leading mode represents the Arctic Oscillation, with −0.95 correlation between the principle component of the leading mode and Thompson and Wallace’s (1998) monthly mean AO index (Fig. 1a). Figure 1b shows two centers with opposite signs over the eastern and the western Arctic: one center is over northern Eurasia and the Arctic marginal seas, and the other is over northern Canada and Greenland, a dipole structure (hereafter the dipole anomaly). The zero isoline cuts through the central Arctic basin and Barents Sea. Such distributions of SLP anomalies are favorable for both sea ice export out of the Arctic basin and cold air outbreaks over the Nordic seas and the Barents Sea.
Owing to a strong annular structure, the AO (NAO) obstructs cold air outbreaks over the Barents Sea and northern Eurasia, and there is a weak connection between the AO (NAO) and sea ice efflux through Fram Strait (Häkkinen and Geiger 2000; Vinje 2001a; Jung and Hilmer 2001). Considering sea ice and freshwater export out of the Arctic basin and its potential effect on the thermohaline circulation in the North Atlantic and further on the global climate system, the dipole mode of atmospheric variations could play an important role in the effect of sea ice export on climate, although it only accounts for 13% of the total variance.
b. Differences from the “Barents Oscillation”
It should be pointed out that, although the dipole anomaly is similar to the “Barents Oscillation” (Skeie 2000) in the Arctic (strong meridionality) (Fig. 2a), it differs from the Barents Oscillation: 1) The Barents Oscillation is not well separated from the third and fourth EOFs, as Skeie (2000) suggested. Consequently, in the literature of Tremblay (2001), the Barents Oscillation was characterized by the EOF3 of SLP fields rather than EOF2. In addition, because the Barents Oscillation did not appear in the first four EOFs of SLP fields during the period from 1977 to 1999, as Tremblay (2001) suggested, it is not a stable mode. However, the dipole anomaly is a stable mode (see the following section). 2) The Barents Oscillation is not a dipole structure because there are four centers in SLP anomalies from the Arctic basin extending southward to the Nordic seas, as shown in Fig. 1b of Skeie (2000). Skeie (2000) only mentioned that the surface air temperature anomalies associated with the Barents Oscillation display a dipole pattern between the Nordic seas and central Siberia. He never suggested the existence of a dipole in SLP anomalies. 3) The spatial distribution of the dipole anomaly differs from the Barents Oscillation over the Siberian marginal seas (Fig. 2b), and the correlation between the dipole anomaly and the Barents Oscillation is only marginally significant (0.26 at the 0.1 significance level).
To confirm stability of the dipole anomaly in this study, an EOF analysis is again carried out for the winters from 1977/78 to 2001/02 (October–March). The spatial distribution of EOF2 is shown in Fig. 3, and the mode accounts for 12.78% of the total variance. Apparently, Fig. 3 closely resembles Fig. 1b. This demonstrates that the dipole anomaly in this study is a stable mode, differing from the Barents Oscillation. Additionally, it is should be pointed out that Hilmer and Jung (2000) and Tremblay (2001) also respectively revealed the dipole structure of SLP anomalies, which centered over the Greenland Sea. They attributed the dipole structure in SLP anomalies to an eastward shift of the northern center of action associated with the NAO (AO), which occurred circa the late 1970s. However, Fig. 3 implies that the dipole anomaly cannot be attributed to an eastward shift of the northern center of action associated with the NAO (AO). Furthermore, Fig. 4d of Hilmer and Jung (2000) clearly shows that no strong meridionality can be found over the Arctic basin.
c. Regression and composite analyses of the dipole anomaly at interannual time scale
In this section, regression and composite analyses are performed to illustrate atmospheric circulation anomalies related to the dipole anomaly at an interannual time scale. On an interannual time scale, the dipole anomaly accounts for 10.4% of total variance. Meanwhile, for comparison and as a complement, we preliminarily discuss intraseasonal variation of the dipole anomaly. The dipole anomaly displays a strong interannual variability with no apparent interdecadal trend (Fig. 4), which differs from the AO. Figures 5a and 5b show SLP and 500-hPa geopotential height anomalies obtained from a linear regression analysis (regression on the monthly mean principal component PC2), respectively. Figure 5a shows a –4 hPa isoline covering the northern Asian coast and between the Kara Sea and the Laptev Sea, with a concurrent 2-hPa isoline covering the area from North America via Greenland southeastward to the Nordic seas. At 500 hPa, negative geopotential height anomalies occupy the northern Kara Sea and the northeastern Barents Sea, and anomalies exceeding 20 gpm extends from the Canadian Archipelago southeastward to the Nordic seas (Fig. 5b). Consequently, the dipole anomaly shows a quasi-barotropic structure. It is apparent that the dipole anomaly is a “local scale” phenomenon rather than a Northern Hemisphere phenomenon. Interannual variations of the variables averaged for the winter months shows a large similarity to their intraseasonal variations, particularly in the locations of the centers of action (Figs. 5c and 5d). Certainly, there are some apparent differences between them; that is, interannual variations are obviously weaker than intraseasonal variations. Additionally, positive anomalies in SLP and 500-hPa geopotential heights become robust over the Aleutian area when compared to intraseasonal variations, and the center of negative SLP anomalies seems to shift toward the Laptev Sea.
Because a new center in SLP anomalies exists over the Aleutian area (Fig. 5c), it is natural to ask whether or not the dipole anomaly still exists at an interannual time scale? To answer this question, we first removed influences of the winter AO on SLP by means of a linear regression method. Then, a regionally averaged (70°–80°N, 90°–120°E) residual SLP is used to represent approximately the intensity index of the center of action associated with the dipole anomaly, which is situated in the area between the Kara Sea and Laptev Sea. Figure 6 shows correlations of the intensity index with the residual SLP at each grid point. As can be seen, the center of significant negative correlations emerges over the Nordic seas and Greenland, consistent with the center of positive SLP anomalies shown in Figs. 5a and 5c. Consequently, the two centers of action associated with the dipole anomaly are negatively correlated, and positive SLP anomalies over the Aleutian and Alaska area (Fig. 5c) could not influence the existence of the dipole anomaly.
Based on the time series of the winter PC2 shown in Fig. 4, we chose those cases with winter-averaged standard deviation ≥ 1.0 (1961/62, 1967/68, 1968/69, 1980/81, 1988/89, 1992/93, 1994/95, and 1995/96) or ≤ −1.0 (1969/70, 1976/77, 1982/83, 1984/85, 1989/90, 1993/94, 1998/99, 1999/2000, and 2000/01) to construct a composite analysis. Compared to the positive phase of the dipole anomaly, the Siberian high becomes robust and the area occupied by the 1020-hPa isoline extends to the Arctic basin, with a weakened Icelandic low (not shown).
During the high-index state of the dipole anomaly, negative SLP anomalies occupy nearly the whole Arctic, with a center close to the Laptev Sea and concurrent positive SLP anomalies in the Canadian Archipelago via Greenland extending southeastward to the North Atlantic (Fig. 7a). Thus, SLP anomalies show a strong meridional structure over the Arctic basin and Greenland Sea. Compared to the high-index state, the low-index state produces near-opposite SLP anomalies over the Arctic Ocean and the Siberian marginal seas with the center of positive SLP anomalies between the Kara Sea and Laptev Sea (Fig. 7b). Although there are negative SLP anomalies over northern Canada and Greenland, its center is situated in the northeastern North Atlantic. Obviously, the low-index state corresponds to a strengthened Icelandic low (Fig. 7b). Significant differences in mean SLP fields between the positive and negative phase occurs over northern Eurasia and the eastern Arctic, with the center of negative SLP anomalies (below −7 hPa) situated in the Laptev Sea (Fig. 7c). However, no significant SLP differences can be found over northern Canada and Greenland.
Removing the influence of the AO on SLP by using a linear regression method, the dipole anomaly becomes robust, as shown in Fig. 8. It is apparent that Figs. 8a and 8b closely resemble Figs. 7a and 7b, respectively. As can be seen, over the Arctic basin and the Siberian marginal seas anomalies in SLP become weaker; however, over northern Canada, Greenland, and the Nordic seas anomalies become robust, when compared to Fig. 7. Differences between Fig. 7c and Fig. 8c are significant SLP differences appearing over the Canadian Archipelago extending via Greenland southeastward to the Nordic seas. The area where correlations of the winter PC2 with the residual SLP after removing the influence from the AO on SLP are significant (Fig. 9) is consistent with the shaded area shown in Fig. 8c.
At 500 hPa, the center of the polar vortex appears over the Arctic basin close to the northern Kara Sea and Laptev Sea during the positive phase of the dipole anomaly (not shown). In contrast, when the negative phase prevails, the center of the polar vortex moves to the Canadian Archipelago and northwestern Greenland (not shown). The 500-hPa geopotential height anomalies after removing the influence of the winter AO on SLP show a large similarity to Fig. 8 (not shown).
Surface wind over much of the Arctic basin flows toward Fram Strait and the Barents Sea during the positive phase, which is favorable for sea ice export out of the Arctic basin into the Greenland and Barents Seas (Fig. 10a). Compared to the positive phase in the Arctic basin, the surface wind displays a strong anticyclonic circulation pattern during the negative phase (Fig. 10b). As a response of the ocean to the anticyclonic circulation, the oceanic circulation including sea ice motion (shown later) also tends to strengthen an anticyclonic motion in the Arctic basin, which is unfavorable for sea ice export out of the Arctic basin into the Greenland and Barents Seas because the strengthened Beaufort gyre accumulates sea ice in the Arctic basin (Proshutinsky et al. 2002). In Fig. 10c, there is a strong cyclonic circulation anomaly covering the Arctic Ocean and much of the Arctic marginal seas, with the center of the cyclonic circulation anomaly close to the Laptev Sea. It is clear that the center of the cyclonic circulation anomaly is consistent with the center of negative SLP anomalies (Fig. 7c). It is apparent that the positive phase of the dipole anomaly would drive more sea ice export out of the central Arctic basin than its negative phase.
Statistical significance tests for mean surface wind differences between the positive phase (σ ≥ 1) and negative phase (σ ≤ −1) demonstrate that over the Arctic basin, particularly over most of the Barents Sea and the vicinity of Fram Strait, surface wind differences are significant (not shown). In addition, over northern Eurasia and the Arctic marginal seas, surface wind differences also exceed statistical significance levels. Consequently, the dipole anomaly strongly influences surface wind and further influences SIM (Thorndike and Colony 1982). Correlation coefficients of the surface wind with the winter AO show that no significant correlation (in this study, we only discuss the significant levels over the 0.01 and 0.001) can be found over the Barents Sea, the Greenland Sea, and the vicinity of Fram Strait (not shown), implying that the winter AO is not a major factor to directly drive sea ice into the Greenland and Barents Seas. In contrast, the dipole anomaly shows a close relationship with the surface wind over most of the Barents Sea, the vicinity of Fram Strait, and the Arctic basin, particularly with a meridional wind (not shown).
d. Response of SIM to the dipole anomaly at an interannual time scale
Certainly, there should be different responses of SIM to different atmospheric forcing. In this section, we discuss the response of the winter mean SIM to the dipole anomaly. Composite maps of the winter mean SIM fields during the high-index (σ = 1) and low-index (σ = −1) states of the dipole anomaly, constructed by simply adding and subtracting the regression result to the winter SIM climatology (1979–98), are shown in Fig. 11. Figure 11b shows the regression map of the winter mean SIM to the dipole anomaly, and a coherent cyclonic circulation anomaly covering nearly the whole Arctic Ocean and the Siberian marginal seas with its center in the Kara and Laptev Seas. One exception is in the Canadian and Greenland marginal seas where an anomalous anticyclonic sea ice motion is visible, reflecting the effect of positive SLP anomalies over the Canadian Arctic and Greenland on sea ice motion. This implies that changes in sea ice motion in the Arctic basin and sea ice export through Fram Strait not only are associated with SLP anomalies over the Kara Sea and the Laptev Sea but also depend on SLP anomalies over northern Canada and Greenland. Comparing to Fig. 5a with Fig. 5c, it is found that the cyclonic circulation anomaly shown in Fig. 11b can be attributed to a northward shift of the center of the dipole anomaly over the Siberian marginal seas.
Compared to the climatology (Fig. 11a), during the positive phase of the dipole anomaly there are coherent large-scale changes in the intensity and character of sea ice transport in the Arctic basin. The significant changes include a weakening of the Beaufort gyre; an increase in sea ice export out of the Arctic basin through Fram Strait and the northern Barents Sea; and enhanced sea ice import from the Laptev Sea, the East Siberian Sea, and the Beaufort Sea into the Arctic basin. The Transpolar Drift Stream tends to parallel to the Greenwich meridian (Fig. 11c). Holland’s (2003) numerical simulation results also support that ice export through Fram Strait is closely related to reduced SLP along the Eurasian coast and increased SLP in northern Canada and Greenland (see her Fig. 14), which is similar to Fig. 5c. With a coupled model Jung and Hilmer (2001) investigated the linkage between the NAO and the Arctic sea ice export via Fram Strait and they suggested that in this coupled GCM it is not the NAO that is forcing sea ice export anomalies through Fram Strait. While the dipole anomaly is in its negative phase (Fig. 11d), compared to Fig. 11a, the strengthened Beaufort gyre is apparent, and sea ice export from the Laptev Sea into the Arctic Ocean is weaker. Since the negative phase of the dipole anomaly corresponds to an anticyclonic circulation in the Arctic basin (Fig. 10b), exports of sea ice and freshwater from the Arctic basin into the Nordic seas and the Barents Sea are weaker than usual because the Beaufort gyre accumulates sea ice and freshwater in the Arctic basin (Proshutinsky and Johnson 1997; Tremblay and Mysak 1998; Proshutinsky et al. 2002). Consequently, the negative phase of the dipole anomaly obstructs sea ice export out of the Arctic basin into the Nordic seas and the Barents Sea (Fig. 11b).
Additionally, we constructed case composite maps of winter SIM according to the time series of the winter PC2 (σ ≥ 1: 1988/89, 1992/93, 1994/95, and 1995/96 and σ ≤ −1: 1982/83, 1984/85, 1989/90, and 1993/94 during the period 1979–98). Composite maps of SIM for the positive and negative phase of the dipole anomaly closely resemble Figs. 11c and 11d, respectively (not shown).
The circulation patterns in the dipole anomaly extremes are unlike the two regimes of wind-forced ice circulation discussed by Proshutinsky and Johnson (1997). The correlation coefficient between the time series of the dipole anomaly and the sea level gradients in the central Arctic basin is poor (0.16); positive sea level gradients correspond to an anticyclonic regime and negative gradients correspond to a cyclonic regime of circulation (Proshutinsky and Johnson 1997, see their Fig. 18).
e. Sea ice export and the dipole anomaly
Most of the sea ice formed in the Arctic Ocean is transferred into the Greenland Sea and the Barents Sea. Estimated winter maximum sea ice volume flux through Fram Strait is 300–400 km3 per month (Vinje 2001a). Many previous studies have explored the relationship between sea ice export from Fram Strait and atmospheric forcing (NAO or AO). Using the satellite observations for 1979–96, Kwok and Rothrock (1999) indicated that there is a high correlation (0.86) between positive phases of the NAO and sea ice area transport through Fram Strait. However, Vinje (2001a) revealed a weak connection between the AO and sea ice volume flux from Fram Strait (−0.15). Hilmer and Jung (2000) showed that a high correlation (0.7) between ice export through Fram Strait and the NAO exists since the mid-1970s but not earlier because the spatial structure of the NAO has been unusual since the mid-1970s. In the present study, the correlation between the winter AO and winter sea ice volume flux through Fram Strait is only 0.04 (Fig. 12), which is very low and consistent with the previous studies (Vinje 2001a; Jung and Hilmer 2001). According to simulation results from a coupled ice–ocean model, Häkkinen and Geiger (2000) concluded that the AO does not have much influence on sea ice export through Fram Strait. Jung and Hilmer (2001) also show that the NAO cannot directly force sea ice export anomalies through Fram Strait. In contrast, the dipole anomaly shows a higher correlation coefficient with the winter sea ice export (0.33 at the 0.05 significance level) (Fig. 12).
Additionally, Fig. 12 shows that during the winters from 1960/1961 to 1968/1969, a positive phase prevailed (mean value: 0.7); during the following winters, from 1969/1970 to 1979/1980, the negative phase was dominant (mean value: −0.4). Thus, the positive phase epoch corresponded to more winter sea ice in the Nordic seas and the Barents Sea, which is consistent with the result of Deser et al. (2000, see their Fig. 3). A composite of sea ice concentration further verified that the positive phase of the dipole anomaly directly causes increases in sea ice concentration in the Nordic seas and the Barents Sea with concurrent decreases of sea ice concentration in much of the Arctic basin (Fig. 13). Decreases of sea ice concentration in the Laptev Sea are most apparent compared to that in the other Siberian marginal seas, consistent with the preceding composite map of the winter SIM. Simulation results produced by Arfeuille et al. (2000) also show that there was more sea ice export out of the Arctic basin during 1967/68, which corresponds to the positive phase of the dipole anomaly (Fig. 4). While the response of SIM to atmospheric forcing (Arfeuille et al. 2000, see their Fig. 8) is in agreement with that shown in Fig. 11c, it seems that there is more sea ice export into the Barents Sea than into the Greenland Sea.
f. Influence of sea ice on surface air temperature
Vinje (2001b) suggested that variations in the ocean temperature and its positive or negative correlation with wind direction seems to be of crucial importance for the variation in sea ice and that air temperature plays a minor role. On the other hand, changes in SIM and sea ice extent produce significant influences on surface air temperature. For example, the divergence of sea ice motion produces more open water and thin-ice area where heat flux from the ocean directly heats the atmosphere in the boundary layer. Wu et al. (2004) show that changes in winter sea ice extent in the Greenland and Barents Seas produce significant influences on the local air temperature and stratification. Based on observations, Rigor et al. (2002) come to similar conclusions regarding the impact of sea ice transport on the heating of the atmosphere. Influenced by sea ice export out of the Arctic basin, surface air temperatures show large differences over the Barents and the Greenland Seas during the positive and negative phases of the dipole anomaly, as shown in Fig. 14. It should be pointed out that, as suggested by Deser et al. (2000), air temperature changes in the Barents Sea, part of the Greenland Sea, the vicinity of Fram Strait, and some areas of the Arctic basin shown in Fig. 14a may be exaggerated because of the crude binary representation of the marginal ice zonal in the NCEP–NCAR reanalysis. In fact, the marginal sea ice zone is a mixture of ice types and open water. Figure 14c shows that the most significant differences in mean air temperature between the positive and negative phase of the dipole anomaly occur over the northern Barents Sea, parts of the Greenland Sea, the vicinity of Fram Strait, and some areas of the Arctic basin north of Spitsbergen. The largest differences are approximately 8°C. Additionally, influenced by divergence (convergence) of SIM, surface air temperature anomalies in the Laptev Sea are obviously different for different phases of the dipole anomaly. As shown in Figs. 13 and 14, variations in sea ice concentration and surface air temperature produced by the dipole anomaly show a seesaw structure in the Laptev Sea and the Barents Sea.
4. Discussion
Figure 15a shows that the contribution from the winter AO forcing exceeds 40% of the total variance, with the maximum center appearing in the eastern Arctic, consistent with that shown in the regression map of SIM on the AO index (Fig. 16a). The contribution from the dipole forcing also exceeds 40% of the total variance in the Arctic basin, showing an annular structure (Fig. 15b). The maximum value is in the central Arctic basin (Fig. 15b), consistent with that shown in Fig. 16b. Apparently, the contribution from the dipole forcing is greater than that driven by the winter AO, particularly in the central Arctic basin, the northern Barents Sea, and the vicinity of Fram Strait. The contribution from the two modes to the total variance (Fig. 15c) qualitatively resembles that shown in Fig. 15b. Consequently, the strong annular structure shown in Fig. 15c has masked the effects of the AO on the winter SIM. It is clear that the AO strengthens the north–south gradient of the total variance and causes alongshore gradients along the Canadian Archipelago. Compared to Fig. 15b, the maximum center moves toward the eastern Arctic (Fig. 15c). Figure 15 further demonstrates that the dipole anomaly plays a more important role than the winter AO in driving sea ice export out of the Arctic basin through the northern Barents Sea and Fram Strait.
Jung and Hilmer (2001) and Holland (2003), respectively, showed the linkage between SLP variations and sea ice export through Fram Strait based on simulation results, as mentioned previously. They showed the monopole and dipole structure in SLP anomalies, respectively. Although the dipole anomaly identified in this study also relates to sea ice export via Fram Strait, it differs from the two previous studies owing to the following facts. First, the monopole and dipole structure in SLP anomalies are obtained from simulation results, and thus one cannot affirm that they really exist in observations. Second, the two studies cannot answer how sea ice motion in the Artic basin responds to the monopole and dipole structure, and how to describe their variations. Third, the center of SLP anomalies over northern Canada and Greenland, missed in Jung and Hilmer (2001), would influence sea ice motion in the Canadian and Greenland marginal seas and further sea ice export through Fram Strait. In fact, sea ice export through Fram Strait is more dependent on SLP gradient. Thus, Vinje (2001a) used SLP gradient data to approximately calculate the wind-induced ice volume flux through Fram Strait. In this study, an anomalous anticyclonic sea ice motion is visible in the western Arctic basin (Fig. 16b), reflecting the effect of positive SLP anomalies over northern Canada and Greenland on sea ice motion (Fig. 5a). The monopole structure of Jung and Hilmer (2001) may be one of atmospheric modes of variability in the coupled model; however, it does not exist in the first two modes of atmospheric variability in observations. The dipole anomaly plays a more important role in influencing sea ice motion than the AO. However, Jung and Hilmer (2001) did not show any role of the monopole structure except its connection with sea ice export through Fram Strait.
What mechanism is responsible for the existence of the dipole anomaly in the Arctic atmosphere? Composite and regression analysis of SLP and geopotential heights at 500 hPa demonstrate that anomalies related to the dipole anomaly show a quasi-barotropic structure in the atmosphere. Consequently, the Siberian high could not give a reasonable explanation for this anomaly due to its strong baroclinicity (Wu and Wang 2002). The atmospheric dynamic process should be a major factor in maintaining the existence of the dipole anomaly.
5. Conclusions
This study identified the dipole anomaly in the Arctic atmosphere and its relationship with the winter SIM in the Arctic basin, based on the NCEP–NCAR reanalysis dataset, the IABP dataset, and a sea ice concentration dataset. After the winter AO, the dipole anomaly corresponds to the second dominant mode in the Arctic atmosphere, accounting for 13% of the variance. One of its two anomalous centers is stably located between the Kara Sea and the Laptev Sea; the other is situated over the Canadian Archipelago extending through Greenland southeastward to the Nordic seas. The dipole anomaly differs from the one described in Hilmer and Jung (2000) and Tremblay (2001), and they attributed the dipole to an eastward shift of the center of action of the North Atlantic Oscillation. The finding shows that the dipole anomaly also differs from the “Barents Oscillation” revealed by Skeie (2000).
The results show that the dipole anomaly plays an important role in driving sea ice export out of the Arctic basin into the Greenland Sea and the Barents Sea. When the dipole anomaly remains in its positive phase, it contributes to coherent large-scale changes in the intensity and character of sea ice transport in the Artic basin. The significant changes include a weakening of the Beaufort gyre; an increase in sea ice export out of the Arctic basin through Fram Strait and the northern Barents Sea; and enhanced sea ice exports from the Laptev Sea and the East Siberian Sea into the Arctic basin (Transpolar Drift Stream tends to parallel to the Greenwich meridian). Simultaneously, there is a coherent cyclonic SIM circulation anomaly covering nearly the whole Arctic basin and the Siberian marginal seas, with its center in the Laptev Sea. One exception is in the Canadian and Greenland marginal seas where an anomalous anticyclonic sea ice motion is visible, reflecting the effect of positive SLP anomalies over the Canadian Arctic and Greenland on sea ice motion. Correspondingly, there are negative SLP anomalies appearing over the northern Eurasian coast and the Arctic Ocean with its center in the Laptev Sea, concurrent with the center of positive SLP anomalies over the south of Greenland. The negative phase of the dipole anomaly directly causes a decrease in sea ice export out of the Arctic basin because the strengthened Beaufort gyre accumulates sea ice and freshwater in the Arctic basin.
Influenced by the dipole anomaly, variations in sea ice concentrations and surface air temperatures exhibit a seesaw structure in the Laptev Sea and the Barents Sea. The winter (October–March) sea ice volume flux through Fram Strait shows a higher correlation (0.33, 42 samples) with the dipole anomaly than the winter AO forcing (0.04). The finding also demonstrates that influences of the dipole anomaly on winter SIM are greater than that of the winter AO. We suggest that the dipole anomaly is closely related to the atmosphere–ice–ocean interactions that influence the Barents Sea sector.
Acknowledgments
We thank the anonymous reviewers for helpful comments and constructive suggestions. The first author is grateful to Prof. Mark A. Johnson and Roger L. Colony for constructive discussions. Jia Wang thanks the Coastal Marine Institute, University of Alaska Fairbanks for financial support (MMS/CMI). This study was also supported by the National Nature Foundation of China (Grants 40475030 and 40225012). Wang and Walsh appreciate Frontier Research System for Global Change and CAMP of IARC/NSF for financial support. J. Wang appreciated Prof. M. Wallace for his constructive comments on this study.
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Spatial distributions of the first two leading EOF modes of winter monthly mean SLP (October–March): (a) EOF1 and (b) EOF2, accounting for 61% and 13% of total variance, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Spatial distributions of the first two leading EOF modes of winter monthly mean SLP (October–March): (a) EOF1 and (b) EOF2, accounting for 61% and 13% of total variance, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Spatial distributions of the first two leading EOF modes of winter monthly mean SLP (October–March): (a) EOF1 and (b) EOF2, accounting for 61% and 13% of total variance, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Reproduced (a) “Barents Oscillation” and (b) its north side from 70°N (Skeie 2000).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Reproduced (a) “Barents Oscillation” and (b) its north side from 70°N (Skeie 2000).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Reproduced (a) “Barents Oscillation” and (b) its north side from 70°N (Skeie 2000).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Spatial distributions of EOF2 of winter monthly mean SLP for the period 1977/78–2001/02 (October–March). The mode accounts for 12.78% of the total variance.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Spatial distributions of EOF2 of winter monthly mean SLP for the period 1977/78–2001/02 (October–March). The mode accounts for 12.78% of the total variance.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Spatial distributions of EOF2 of winter monthly mean SLP for the period 1977/78–2001/02 (October–March). The mode accounts for 12.78% of the total variance.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Interannual variations of PC2. All the data have been normalized.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Interannual variations of PC2. All the data have been normalized.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Interannual variations of PC2. All the data have been normalized.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Regression maps of (a) SLP (hPa) and (b) 500-hPa geopotential height north of 20°N (gpm), regression on the monthly PC2. Contour interval: 1 hPa in (a); 10 gmp in (b); (c), (d) as in (a) and (b), respectively, but for regression on the winter mean PC2. Contour interval: 0.5 hPa in (c) and 5 gmp in (d).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Regression maps of (a) SLP (hPa) and (b) 500-hPa geopotential height north of 20°N (gpm), regression on the monthly PC2. Contour interval: 1 hPa in (a); 10 gmp in (b); (c), (d) as in (a) and (b), respectively, but for regression on the winter mean PC2. Contour interval: 0.5 hPa in (c) and 5 gmp in (d).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Regression maps of (a) SLP (hPa) and (b) 500-hPa geopotential height north of 20°N (gpm), regression on the monthly PC2. Contour interval: 1 hPa in (a); 10 gmp in (b); (c), (d) as in (a) and (b), respectively, but for regression on the winter mean PC2. Contour interval: 0.5 hPa in (c) and 5 gmp in (d).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Correlations of the region-averaged (70°–80°N, 90°–120°E) residual SLP with residual SLP at each grid point after removing the influence of the winter AO on SLP. The blue and yellow areas represent correlations exceeding the 0.05 and 0.01 significance levels, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Correlations of the region-averaged (70°–80°N, 90°–120°E) residual SLP with residual SLP at each grid point after removing the influence of the winter AO on SLP. The blue and yellow areas represent correlations exceeding the 0.05 and 0.01 significance levels, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Correlations of the region-averaged (70°–80°N, 90°–120°E) residual SLP with residual SLP at each grid point after removing the influence of the winter AO on SLP. The blue and yellow areas represent correlations exceeding the 0.05 and 0.01 significance levels, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composites of SLP anomalies: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b). The blue and yellow areas in (c) represent differences exceeding the 0.05 and 0.01 significance levels, respectively. Contour interval: 1 hPa.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composites of SLP anomalies: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b). The blue and yellow areas in (c) represent differences exceeding the 0.05 and 0.01 significance levels, respectively. Contour interval: 1 hPa.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composites of SLP anomalies: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b). The blue and yellow areas in (c) represent differences exceeding the 0.05 and 0.01 significance levels, respectively. Contour interval: 1 hPa.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
As in Fig. 7 but for composites of the residual SLP anomalies after removing the influence of the winter AO on SLP.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
As in Fig. 7 but for composites of the residual SLP anomalies after removing the influence of the winter AO on SLP.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
As in Fig. 7 but for composites of the residual SLP anomalies after removing the influence of the winter AO on SLP.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Correlations of the winter mean PC2 with the residual SLP after removing the influence of the AO on SLP. The blue and yellow areas represent correlations exceeding the 0.05 and 0.01 significance levels, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Correlations of the winter mean PC2 with the residual SLP after removing the influence of the AO on SLP. The blue and yellow areas represent correlations exceeding the 0.05 and 0.01 significance levels, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Correlations of the winter mean PC2 with the residual SLP after removing the influence of the AO on SLP. The blue and yellow areas represent correlations exceeding the 0.05 and 0.01 significance levels, respectively.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composites of surface wind: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b). Unit: m s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composites of surface wind: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b). Unit: m s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composites of surface wind: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b). Unit: m s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
(a) Winter (October–March) mean sea ice motion averaged for the period 1979–98; (b) regression of winter mean sea ice motion on the winter mean PC2; (c) composite of winter sea ice motion for the positive phase of the dipole anomaly (winter sea ice motion climatology adding the regression analysis shown in Fig. 11b); (d) as in (c) except for the negative phase composite (winter sea ice motion climatology subtracting the regression analysis shown in Fig. 11b). Unit: cm s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
(a) Winter (October–March) mean sea ice motion averaged for the period 1979–98; (b) regression of winter mean sea ice motion on the winter mean PC2; (c) composite of winter sea ice motion for the positive phase of the dipole anomaly (winter sea ice motion climatology adding the regression analysis shown in Fig. 11b); (d) as in (c) except for the negative phase composite (winter sea ice motion climatology subtracting the regression analysis shown in Fig. 11b). Unit: cm s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
(a) Winter (October–March) mean sea ice motion averaged for the period 1979–98; (b) regression of winter mean sea ice motion on the winter mean PC2; (c) composite of winter sea ice motion for the positive phase of the dipole anomaly (winter sea ice motion climatology adding the regression analysis shown in Fig. 11b); (d) as in (c) except for the negative phase composite (winter sea ice motion climatology subtracting the regression analysis shown in Fig. 11b). Unit: cm s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Normalized winter sea ice volume flux through Fram Strait (solid line) and the winter PC1 (dashed line) and PC2 (dashed line with dots). The year refers to the winter (October–March) season, taken as the year for October.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Normalized winter sea ice volume flux through Fram Strait (solid line) and the winter PC1 (dashed line) and PC2 (dashed line with dots). The year refers to the winter (October–March) season, taken as the year for October.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Normalized winter sea ice volume flux through Fram Strait (solid line) and the winter PC1 (dashed line) and PC2 (dashed line with dots). The year refers to the winter (October–March) season, taken as the year for October.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composite of sea ice concentration: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composite of sea ice concentration: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Composite of sea ice concentration: (a) the positive phase, (b) the negative phase, and (c) differences between (a) and (b).
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
As in Fig. 7 but for composites of surface air temperature anomalies. Units: °C.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
As in Fig. 7 but for composites of surface air temperature anomalies. Units: °C.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
As in Fig. 7 but for composites of surface air temperature anomalies. Units: °C.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Ratios of the accumulated variance of SIM velocity driven by (a) the monthly mean AO, (b) the monthly mean dipole anomaly, and (c) combination of (a) plus (b), the accumulated total variance of SIM velocity.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Ratios of the accumulated variance of SIM velocity driven by (a) the monthly mean AO, (b) the monthly mean dipole anomaly, and (c) combination of (a) plus (b), the accumulated total variance of SIM velocity.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Ratios of the accumulated variance of SIM velocity driven by (a) the monthly mean AO, (b) the monthly mean dipole anomaly, and (c) combination of (a) plus (b), the accumulated total variance of SIM velocity.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Regression maps of monthly mean sea ice motion on (a) the monthly AO and (b) the monthly PC2. Unit: cm s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Regression maps of monthly mean sea ice motion on (a) the monthly AO and (b) the monthly PC2. Unit: cm s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
Regression maps of monthly mean sea ice motion on (a) the monthly AO and (b) the monthly PC2. Unit: cm s−1.
Citation: Journal of Climate 19, 2; 10.1175/JCLI3619.1
