A deeper understanding of regional meteorology in the Antarctic is required for improved interpretations of the ever-growing body of ice-core-based paleoclimate records from this region. Benefits would also accrue in other areas such as operational forecasting at McMurdo and global-change research. Artificial neural network (ANN) techniques offer new approaches to improving our record of surface observations and our understanding of the regional atmospheric circulation, two keys to this important problem.

Automatic weather stations (AWS) currently provide the only year-round, continuous direct measurements of near-surface weather on the interior West Antarctic ice sheet. Unfortunately, many records are relatively short (less than 10 years) and/or incomplete due to the harsh climate. Multilayer feed-forward (FF) ANNs can address these problems through their ability to downscale GCM-scale meteorological data sets (e.g., ECMWF reanalysis products) to the AWS observations. FFANNs have successfully predicted AWS surface data (temperature, pressure) at multiple sites in West Antarctica using large-scale features of the atmosphere (e.g., 500 mb geopotential height) from a region around each AWS. Our results for temperature prediction are approximately equal to those from a satellite-based methodology but with no exposure to problems from surface melt events or sensor changes. Similarly, the significant biases seen in ECMWF surface temperatures are absent from our predictions.

Previously infeasible climatological studies become possible with complete (1979-93) records from multiple AWS sites. For example, annual temperature anomalies show values significantly above average in 1980 at all sites (Siple Station, Byrd, Lettau, Elaine, Marilyn, Ferrell). Anomalously cold values follow two years later on the Ross ice shelf and three years later for the sites on the West Antarctic ice sheet (Byrd and Siple Station). Principal component analysis also suggests significant differences both between the ice-shelf and ice-sheet sites and between the two ice-sheet sites.

Self-organizing maps (SOMs), a second ANN technique, have proved useful for analysis of synoptic-scale circulation in temperate latitudes. The use of SOMs allows development of synoptic climatologies with an arbitrary number of smoothly transitioning climate states, in contrast to traditional synoptic classification techniques. Results from SOM analyses are applicable both to the ice core interpretation problem and to studies of global climate change. SOM-derived maps of synoptic variables such as temperature and geopotential height can be compared to ice core data to examine the relationship between the proxy and the atmosphere. Climate change can be studied by looking at how state transitions evolve over time. We anticipate new insights and improved ice-core interpretations from application of SOMs to the West Antarctic atmosphere.