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Modeling Research on Seasonal to Interannual Variability

The discovery in the 1980s of a significant extratropical impact of the tropical Pacific ENSO phenomenon, and the demonstration that several elements of the phenomenon could be predicted two or more seasons in advance using relatively simple models, were important developments in climate research. They rekindled interest in gaining a better understanding of ENSO dynamics, in the hope of making even better predictions. They also raised hopes that at least some aspects of interannual variability might be similarly predictable in other parts of the globe, not only from ENSO's remote impact but also through possibly predictable slow variations in other ocean basins. International research programs such as TOGA and CLIVAR were launched in pursuit of these hopes. Largely from their impetus, the observing system in the tropical Pacific was vastly improved, global gridded observational datasets spanning several decades were generated through various 'reanalysis' efforts, and extensive numerical simulations and predictions of interannual climate variations were undertaken with both uncoupled and coupled global climate models. Our understanding of the variability and predictability of the climate system on this time scale has matured considerably as a result. CDC has contributed significantly to the present store of knowledge in this area, and is at the forefront in addressing many of the remaining questions.

CDC scientists have provided important evidence that the predictable evolution of ENSO and of the associated remote teleconnections are governed largely by linear, low-dimensional dynamics. This may be one reason why simple empirical linear models remain competitive with sophisticated GCMs at making seasonal predictions. Indeed the question of how much extratropical predictability exists on this scale beyond that associated with simple linear ENSO signals has become a recurring theme in CDC research. In the last four years, we have identified the situations in which the dynamics of ENSO are significantly nonlinear, clarified the extent to which the remote impacts can be nonlinear, and explored the extent to which those impacts might be sensitive to the details of the tropical SST anomaly patterns, i.e. be "higher-dimensional". We have also explored how the nonlinear and higher-dimensional dynamics might affect the tails of the probability distributions of atmospheric variables on synoptic, intraseasonal, and seasonal scales, and thus the risk of extreme anomalies on those scales. We have investigated the predictability of SSTs in other oceans basins, both through "atmospheric bridge" ENSO teleconnections and through the year-long persistence and subsequent re-emergence of SST anomalies from below the seasonally-varying surface mixed layer. We have also continued to assess the impact of such SSTs on the atmospheric circulation.

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