Data Evaluation and Presentation

As Antarctica exhibits strong spatial contrasts it is important to evaluate how well the sampled locations represent regional to continent scale gradients. Most parameters, such as elevation, distance from the sea, annual accumulation etc., change simultaneously along many transects and are therefore difficult to assess individually. The comparison between the Antarctic topography as inferred by the RAMP 5km elevation model (Liu and others, 2001) (Fig.1a) and the reconstructed surface using only elevation information from the sampled sites provides a means to evaluate how well Antarctic geographic features are represented by the sampled locations. The reconstructed surfaces in Fig.1b-d are calculated using the interpolation method of linear kringing between sampling sites. In Figure 1b the Antarctic surface is reconstructed using only sites that provide data from the chosen 1992 to 1997 time period (45 data points). While the data are clustered and separated by large geographical gaps, they represent contemporary glaciochemical concentration, generally excluding time-driven factors, such as climate variability. The reconstructed topography lacks many of the significant Antarctic features, e.g. neither ice shelf nor the Antarctic Peninsula are yet represented. A number of sites provide 5-year averages for slightly different time periods or have an associated dating error of more than ±1 year. Incorporating these sites enlarges the database significantly. In Fig. 1c the reconstructed topography using all multi-year data is shown (194 data points). While main geographical features, such as the East and West Antarctic Ice Sheets, the Ross and Ronne/Filchner Ice Shelves are represented, other significant details, such as the Transantarctic Mountains, the Antarctic Peninsula, and the Lambert Glacier system are poorly or not represented. The reconstructed topography in Fig.1d incorporates all available data (520 data points), including non-annual samples. This is the most comprehensive data set currently available. As the data do not all represent the same time period or might represent only seasons, their interpretation in an Antarctic-wide comparison requires careful attention. Although the reconstructed map incorporating all available data is more detailed than Fig.1c, it still lacks important elements across large regions of the Antarctic continent. Overall this comparison highlights the need for many more traverses to provide better coverage, especially of well dated, multi-year, contemporary time series.

Figure 1

Figure 1: Reconstructed topography of Antarctica, derived from a) RAMP 5km elevation model (Liu and others, 2001), b) sample locations providing data for the 1992-1997 time period, c) sample locations providing multi-year averages, d) all glaciological sample locations.

Ion Concentration versus Elevation

As discussed above, many site physical characteristics influencing glaciochemistry change simultaneously, either geographically or temporally. These include annual accumulation, elevation, and distance from the sea. Accurate, high resolution annual accumulation data are difficult to obtain, as they require high resolution dating and density measurements. Furthermore, there are no well documented, straightforward linear associations between chemistry and accumulation rate. In order to determine distance from the sea it is necessary to understand the pathway of the precipitating air mass for both wet and dry deposition. Local atmospheric circulation patterns can be highly variable and might change true distance to the sea from 10 kilometres to 1000 kilometres depending on the pathway of the air mass (e.g. Bertler and others, 2004a; Xiao and others, 2004; Kaspari and others, this volume). Furthermore, large seasonal changes in sea-ice cover further complicate the measurement of true distance to the sea. One parameter that is relatively easy to obtain and does not change significantly over short time periods is elevation.

However, as annual accumulation and distance from the sea exhibit a correlation with elevation in Antarctica, any observed patterns are likely to be caused by a varying combination of all three. Correlation between ion concentration and elevation is shown in Fig. 2. Ion concentration variability across Antarctica exhibits an amplitude of up to four orders of magnitude. Therefore, ion concentrations are plotted on logarithmic scales, with the exception of the Cl/Na ratio.

Table 1: Correlation between elevation and ion concentration of multi-year samples
Number of samples
correlation formula
Na 150 y = −503•In(x) + 4078 -0.73 13.18 >0.999
Cl 172 y = −421•In(x) + 4040 -0.51 7.21 >0.999
Cl/Na 144 y =+421•In(x) + 4040 +0.56 8.08 >0.999
NO3 166 not significant >0.999
SO4 160 not significant >0.999
MS 89 y = -793•In(x) + 3994 -0.42 4.37 >0.999
Ca 116 y = -429•In(x) + 3030 -0.70 10.47 >0.999
Mg 122 y = -474•In(x) + 2966 -0.73 11.87 >0.999
K 76 y = -294•In(x) + 2388 -0.52 5.23 >0.999

The correlations between elevation and Na or Cl (Fig. 2a & b) show a statistically significantly inverse relationship (logarithmic) of decreasing ion concentration with increasing altitude of r = -0.73 and r = -0.51, respectively (Table 1). Furthermore, the scatter in both data sets is larger at lower elevation than at higher locations. When correlating the Cl/Na ratio with elevation (r = 0.56, Table 1), sites below 2000 m predominantly show values close to the marine ratio of ~1.8 (Warneck, 1991), while the scatter in the data increases significantly above 2000 m, reaching values of up to 20 (Fig. 2c). This confirms that sites below 2000 m are predominantly influenced by sea-salt and also suggests no significant post-depositional aerosol loss or enrichment. The larger scatter with increasing elevation is indicative of a number of potential processes leading to relative enrichment or depletion of either species (Gayley and Ram, 1985; Mulvaney and Peel, 1988; Mulvaney and Wolff, 1994; De Angelis and Legrand, 1995; Yang and others, 1996a; Legrand and Mayewski, 1997; Kreutz and others, 1998; Stenberg and others, 1998; Wagenbach and others, 1998a; Wolff and others, 1998a; Wolff and others, 1998b; Kreutz and Mayewski, 1999; Udisti and others, 1999; Kreutz and others, 2000; Aristarain and Delmas, 2002; Proposito and others, 2002; Becagli and others, in press; Udisti and others, in press; Benassai and others, Annals, 41).

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