Ice cores have, in recent decades, produced a wealth of palaeoclimatic insights over widely ranging temporal and spatial scales. Nonetheless, interpretation of ice-core-based climate proxies is still problematic due to a variety of issues unrelated to the quality of the ice-core data. Instead, many of these problems are related to our poor understanding of key transfer functions that link the atmosphere to the ice. This study uses two tools from the field of artificial neural networks (ANNs) to investigate the relationship between the atmosphere and surface records of climate in West Antarctica. The first, self-organizing maps (SOMs), provides an unsupervised classification of variables from the mid-troposphere (700 hPa temperature, geopotential height and specific humidity) into groups of similar synoptic patterns. An SOM-based climatology at annual resolution (to match ice-core data) has been developed for the period 1979-93 based on the European Centre for Medium-Range Weather Forecasts (ECMWF) 15-year reanalysis (ERA-15) dataset. This analysis produced a robust mapping of years to annual-average synoptic conditions as generalized atmospheric patterns or states. Feed-forward ANNs, our second ANN-based tool, were then used to upscale from surface data to the SOM-based classifications, thereby relating the surface sampling of the atmosphere to the large-scale circulation of the mid-troposphere. Two recorders of surface climate were used in this step: automatic weather stations (AWSs) and ice cores. Six AWS sites provided 15 years of near-surface temperature and pressure data. Four ice-core sites provided 40 years of annual accumulation and major ion chemistry. Although the ANN training methodology was properly designed and followed standard principles, limited training data and noise in the ice-core data reduced the effectiveness of the upscaling predictions. Despite these shortcomings, which might be expected to preclude successful analyses, we find that the combined techniques do allow ice-core reconstruction of annual-average synoptic conditions with some skill. We thus consider the ANN-based approach to upscaling to be a useful tool, but one that would benefit from additional training data.
All Science Journal Classification (ASJC) codes
- Atmospheric Science