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The studies described below are highlights of the research in CDP devoted to the prediction and predictability of climate variations and extreme events. These studies are integral to our section goals of extending and defining the spatio-temporal domain over which scientifically and societally useful forecasts can be made. CDP scientists have continued their interest in the inherent predictability of atmospheric phenomena and have utilized their expertise gained in ensemble prediction techniques to address the prediction of extreme events.

Working in conjunction with Jeff Yin of the Climate Analysis Section, Branstator has undertaken a new project devoted to characterizing the effect of intra-annual and longer time-scale fluctuations in the circulation on the likelihood and strength of extreme near-surface wind events. They have found it useful to subdivide this influence into two categories. One category concerns the simple additive effect of fluctuations in the mean winds whereby the probability distribution of wind speeds shifts without changing its shape. The second category concerns a multiplicative effect whereby the changing low-frequency state changes the character of the statistical distribution of high-frequency perturbations. They have found that both mechanisms are important but their relative importance is highly dependent on the geographical region being studied. Simplifying matters is the fact that the multiplicative effect is largely manifested through a simple change in the variance of high-frequency variations though the shape of statistical distributions can also be affected by low-frequency circulation changes. A promising outcome of their work is that when they compare relationships between low-frequency circulation changes and the statistics of extremes in nature to corresponding relationships in Climate of the Twentieth Century integrations with CCSM3, the relationships are very similar. This includes the regional dependence of the relationships. The verisimilitude of the CCSM3 integrations, together with the large samples made possible by ensemble experiments, will make it possible to derive statistically robust relationships between large-scale circulation states and wind storm extreme statistics. These statistical relationships can then be used to estimate changes in wind storm likelihood and strength in climate change experiments without the need for having large enough ensembles to explicitly derive the changes in the statistics of extremes.

In traditional prediction studies, Tribbia has been developing and analyzing the ENSO predictive skill of the NCAR CCSM. Over the previous year, he had produced a number of experimental hindcasts demonstrating the skill of CCSM3. This suite of hindcasts was used as a testbed for the further development of CAM3 and CCSM3. A remediation of the errors in the climatology of the simulated interannual variability in CCSM has ensued with the developments in the convective parameterization included in CCSM3.5. New forecast studies are currently underway with the latest version of CCSM to quantify the degree of improvement in ENSO hindcasts and to elucidate the root causes of the remaining deficiencies in the simulation of interannual variability.

One means of characterizing those dynamics of a system that affect its slow (and thus potentially predictable) evolution is to identify preferred or recurring trajectories that the system traverses through phase space. In past years Branstator has characterized these prominent trajectories in long integrations of AGCMs. Recently, working with Christian Franzke (IMAGe) and Andrew Majda (NYU), he has developed a theory for understanding which dynamical interactions can produce the trajectory signatures found in the earlier work. This theory leads to the conclusion that some of the most interesting trajectory features result from subtle departures from Gaussianity in the probability density functions of prominent flow patterns. Thus a necessary condition for forecast models to be able to reproduce these trajectories is that their climates have these same nonGaussian features. A more detailed description is included in (FMB).