Climate variability on seasonal to interannual scales is dominated by the tropical ENSO phenomenon and its global impacts. CDC scientists have been at the forefront in providing evidence that much of the predictable evolution of ENSO and its remote atmospheric and oceanic impacts are governed by low-dimensional, linear dynamics. This clarification has proved extremely valuable for basic understanding and for building simple, useful, forecast models. However, it has also raised important new questions. Foremost among these is perhaps that raised by Figure 2.4. How much predictability, especially extratropical predictability, exists on this time scale beyond that associated with simple linear ENSO signals? What additional useful predictive information can be extracted by running large GCM ensembles? CDC scientists have addressed these questions by exploring the nonlinearity and sensitivity of the global response to the details of anomalous tropical Pacific SST fields and by focusing on the distributional aspects of the response, especially the changes of variance, rather than just shifts of the mean. We have also contributed to improved understanding of the predictability of SSTs in other ocean basins, through both "atmospheric bridge" and "re-emergence' mechanisms, and the impact of such SSTs on atmospheric predictability. There is encouraging evidence that one will be able to provide substantially improved forecast guidance on this time scale, especially on the tails of the distributions, through better understanding and prediction of these formally second-order but still important effects. The overarching theme of new research in this area must be a community-wide shift from deterministic to probabilistic seasonal predictions. Ultimately, one can extract only so much information from deterministic predictions of a chaotic system. The utility of probabilistic predictions, on the other hand, is unbounded in an important practical sense, in that it is determined to a large degree by the needs of particular users. Future CDC research on this time scale will increasingly reflect this shift of focus to probabilistic predictions, with the climate-society interface and the needs of different categories of user groups in mind.
Contributed by: M. Alexander, J. Barsugli, G. Compo, M. Hoerling, S. Peng, C. Penland, and P. D. Sardeshmukh.