Wintertime Climate Dynamics and Snow Feedback

Mitigating hazards through advanced warnings and improving the performance of climate-sensitive economic sectors through seasonal prediction are thus of interest to industry and government agencies. The most important advance in understanding climate variability and its application to seasonal prediction has been the linkage of the dominant tropical atmosphere and ocean signal (El Niño/Southern Oscillation or ENSO) with surface temperatures and precipitation patterns across the globe. However, predictive skill for temperature forecasts outside of the tropics, including the U.S., has been mixed (Barnston et al. 1999; Spencer and Slingo 2003). For example, temperature anomalies during the winter of 2002/03 were poorly predicted by U.S. forecast centers, despite the occurrence of a moderate El-Niño. Clearly, much room for improvement remains in our understanding of climate variability, in particular in the extratropics and in winter-time, where the dominance of ENSO is more tenuous. Better understanding of the dominant mode of Northern Hemisphere (NH) winter climate variability, referred to as the North Atlantic Oscillation (NAO) or the Arctic Oscillation (AO), which could lead to improved predictability is often recognized as the next most important anticipated advance in seasonal climate forecasting (Cohen 2003), especially for the eastern U.S. and Europe, regions where forecasts based on ENSO have little or no skill.

The surface-temperature and surface-circulation signatures of the NAO/AO are strongest in the North Atlantic sector (Hurrell 1995, 1996; Thompson and Wallace 2001; Ambaum et al. 2001; Cohen and Saito 2002). The NAO/AO has been linked with sea surface temperature (SST) variability, snow cover variability, sea ice variability, stratospheric forcing, and aerosols (Rodwell et al. 1999; Watanabe and Nitta 1999; Mysak and Venegas 1998; Baldwin and Dunkerton 1999; Perlwitz and Graf 1995). Three of these mentioned linkages have been proposed as possible forcing mechanisms and/or leading indicators for the dominant pattern and therefore potentially possess inherent forecasting skill (Mehta et al. 2000; Cohen and Entekhabi 1999; Baldwin and Dunkerton 2001). Nonetheless, recent articles on the subject have emphasized the lack of understanding of the underlying dynamics driving NAO variability and consequently its poor predictability (Hurrell et al. 2001).

We propose complementing the patterns of variability derived from the strictly statistical description of the NAO/AO with a more dynamically oriented approach. The desired payoff of enhancing our understanding of the genesis, growth and decay of dominant patterns of seasonal variability is not limited to abstract considerations but can be applied to improving climate forecasts.