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Gayana (Concepción)

versión impresa ISSN 0717-652Xversión On-line ISSN 0717-6538

Gayana (Concepc.) v.68 n.2 supl.TIIProc Concepción  2004 

Gayana 68(2): 1-5, 2004, págs: 606-608



Xiaojun Yuan

Lamont-Doherty Earth Observatory of Columbia University 61 Rt. 9W, P. O. Box 1000, Palisades, NY 10964

Numerous studies suggest a strong link between the tropical El Niño-Southern Oscillation (ENSO) phenomenon and Antarctic sea ice variability (Simmonds and Jacka, 1995; White and Peterson, 1996; Harangozo, 2000; Yuan and Martinson, 2000; 2001; Rind et al., 2001; Kwok and Comiso, 2002; Martinson and Iannuzzi, 2003). The strongest tropical-polar teleconnections in temperature and sea ice fields is the Antarctic Dipole mode (ADP) with poles centered in the northeastern Ross Gyre in the Pacific sector, and the central Weddell Gyre in the Atlantic sector of the Southern Ocean. The ADP temperature anomalies present the largest ENSO signal outside of the tropical Pacific (Liu et al., 2002). However, besides the ENSO time scale, a number of other distinct high latitude climate modes exist at interannual and higher time scales in the Southern Hemisphere. Those regional climate variabilities likely influence Antarctic sea ice but their impacts are less understood. This study investigates the influence of high latitude climate variability on the Antarctic sea ice distribution.

The climate variability examined here includes the following distinct climate modes in southern mid-high latitudes: (1) The Southern Annular Mode (SAM), marked by a zonally symmetric but out-of-phase pressure anomalies between mid and high latitudes, is a dominant climate mode in the Southern Hemisphere (Thompson and Wallace, 2000), explaining 50% of the monthly SLP variance over the Antarctica (Gong and Wang, 1999). This climate mode is capable of influencing the sea ice field, particularly in the South Indian Ocean and South Atlantic Ocean (Visbeck, personal communication). (2) The Semi-annual oscillation (SAO) describes another more-or-less zonally symmetric mode in the southern extra-tropics, which is characterized by the twice-yearly enhancement in meridional gradients of temperature and pressure fields (van Loon, 1984, Simmonds and Jones, 1998, Walland and Simmonds, 1999). The atmospheric convergence line with a strong half-year cycle exerts significant influences on the seasonal asymmetric behavior of ice extent: slowly advancing equatorward in fall and fast retreating in spring (Enomoto and Ohmura, 1990). The SAO also influences the open water areas within the sea ice pack (Watkins and Simmonds, 1999). (3) The quasi-stationary wave-3 pattern in the southern mid-high latitudes, a predominant winter mode in pressure/wind fields (van Loon, 1972), actively interacts with the sea ice field beneath. Yuan et al., (1999) showed that three southerly branches of the wave-3 pattern coincide with three northward maximum extent of sea ice edge, indicating the role of the wave-3 pattern in advancing the ice edge. The wave-3 pattern is also positively coupled with the sea ice distribution, promoting eastward propagation of the ice edge maxima and providing preferred locations for cyclonegenesis in the open ocean north of the ice cover. (4) The Pacific South American (PSA) pattern dominates climate variability in the subpolar region of the South Pacific. It is formed by an ENSO-related Rossby Wave train (Mo and Higgins, 1998) and has a significant impact on the sea ice variability in the Antarctic Dipole region (Yuan, 2004). (5) Another well-known coupled high latitude mode is the Antarctic Circumpolar Wave (ACW) a propagating wave-2 pattern in the Atmosphere, ocean and sea ice (White and Peterson, 1996).

To examine the coupled relationship between sea ice and atmosphere, the singular value decomposition analysis is applied to satellite observed sea ice data and several atmospheric variables (300mb height, 500mb height, sea level pressure, surface air temperature, surface vector winds, and vector winds at 300mb height) from NCEP/NCAR reanalysis. The analysis reveals that the sea ice field is mostly coupled with the atmosphere in the forms of those above-mentioned climate patterns, particularly SAM, SAO, Wave-3 pattern and PSA pattern. The leading coupled mode between sea ice and sea level pressure, reflecting SAM and PSA patterns in sea level pressure and ADP pattern in sea ice, is accountable for 50% to 60% of total squared covariance for all seasons. The leading coupled mode between sea ice and surface air temperature is also accountable for more than 50% of total squared covariance of these fields. The leading mode of meridional wind and sea ice presents the wave-3 pattern in both fields. The impacts of the atmosphere on sea ice are much stronger in winter than in summer, while the strongest influence of sea ice on the atmosphere occurs in summer.

To focus on the impacts of these climate modes on sea ice variability, I constructed time series of these climate modes from atmospheric variables that best represent these modes. The PSA index is defined by 500mb height anomalies at three anomalous centers in east of New Zealand, the Amundsen Sea and Southwest Atlantic. The principle component of the leading EOF mode in surface meridional wind provides the time series of wave-3 pattern that is most profound in meridional wind field. The lead EOF mode of sea level pressure describes the SAM variability. Last, the index of SAO is defined by subtracting zonal mean SLP at 55°S by the zonal mean SLP at 65°S. The time series of these four climate modes are then correlated with 24 years of monthly sea ice concentration anomalies around the Antarctic from ice lagging 6 months to leading 6 months. The results suggest that the influences from the PSA pattern and wave-3 pattern on sea ice are stronger than that from the other two modes and are more profound in the western Hemisphere. The SAM and SAO have relatively weaker but more evenly distributed correlations with sea ice around the Antarctic (figure 1). The correlation at each grid point is then tested for its confidence level and the percentage of the grid points that pass 99% confidence level for each correlation map is calculated. The results from this significant test suggest that the SAM has relatively less influence on sea ice than other patterns. Moreover, sea ice usually responds to the atmospheric forcing with a two-month delay (figure 2).

Figure 1. Correlation coefficients between Antarctic sea ice concentration anomalies (lag two months) and time series of the Pacific South American pattern (PSA), Wave-3 pattern, Southern Annular Mode (SAM) and Semi-Annual Oscillation (SAO), respectively.


Figure 2: Percentage of grid points with significant correlation at 99% confidence level as a function of climate modes lagging months



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