On all timescales, from weekly to monthly to intraseasonal to interannual to decadal, the climate system is dominated by large-scale spatial patterns or “modes” of atmospheric and oceanic variability that control regional climate. These patterns are most often defined in terms of Empirical Orthogonal Function (EOF) analysis (equivalent to principal component (PC) analysis). The resulting climate patterns or climate modes organize coherent variations in climate over large regions, and have proven useful in creating indices that document the strength of the relevant pattern, e.g., in the atmosphere, and can be used to correlate the pattern with related phenomena in other components of the climate system, such as patterns of sea surface temperature and sea ice concentration. Limitations of current EOF analysis include the absence of associated measures of uncertainty or variability, and the implicit assumption of stationarity underlying the use of the first EOF calculated from long time-series. The focus of this presentation is on the wintertime pattern of the North Atlantic Oscillation (NAO), motivated in part by the desire to relate the NAO to fluctuations in sea ice concentration. I will show some results from observations then go on to describe ongoing work with colleagues in the Statistics Department at UC Irvine. A low-dimensional parametric representation of the NAO has been developed. It can be applied over shorter time scales than conventional EOF analysis and allows easy estimates of uncertainty in parameter estimates and therefore in the NAO. This model has been extended to accommodate time varying parameters. The parameters have physical interpretations such as locations of the centers of action of the NAO. (Joint work with Hal Stern, Xu Tian, Yi-Hui Wang and Yaming Yu)