This talk is concerned with estimating the predictable variations of extratropical daily weather statistics ("stormtracks") associated with global sea surface temperature (SST) changes on interannual to interdecadal scales, and its magnitude relative to the unpredictable noise. The SST-forced stormtrack signal in each northern winter in 1950-99 is estimated as the mean stormtrack anomaly in an ensemble of atmospheric general circulation model (AGCM) integrations for that winter with prescribed observed SSTs. Since the stormtrack signals cannot be derived directly from the archived monthly AGCM output, they are diagnosed from the SST-forced winter-mean 200 mb height signals using an empirical linear stormtrack model (STM). For two particular winters, the El Nino of JFM 1987 and the La Nina of JFM 1989, the stormtrack signals and noise are estimated directly, and more accurately, from additional large ensembles of AGCM integrations. The linear STM is remarkably successful at capturing the AGCM's stormtrack signal in these two winters, and is thus also suitable for estimating the signal in other winters. We find that a predictable SST-forced stormtrack signal exists in many winters, but its strength and pattern can change substantially from winter to winter. The pattern correlation of the SST-forced and observed stormtrack anomalies is high enough in the Pacific-North American sector to be of practical use. In the Euro-Atlantic region, we find much lower correlations, which we argue arise from substantial AGCM error in representing the regional response to tropical SST forcing, rather than intrinsically low predictability.

Studies of climate variability and change are increasingly focused on understanding and predicting regional changes of daily weather statistics. Assessing the evidence for such variations over the last hundred years requires a daily tropospheric circulation dataset. The only available data for the early 20th century are error-riddled hand- drawn analyses of the mean sea level pressure field over the northern hemisphere. Modern data assimilation systems have the potential to improve upon these maps, but prior to 1948, there are few digitized upper-air sounding observations available for such a reanalysis. We investigate the possibility that the quantity of newly recovered surface pressure observations is sufficient to generate a useful reanalysis of at least the lower tropospheric circulation back to 1900. Surprisingly, we find that with an ensemble data assimilation system, one should be able to produce high-quality reanalyses of even the upper troposphere using only surface pressure observations.

In the presentation, the speaker will address several issues regarding the atmosphere-ocean-land processes associated with the variability of the Asian-Australian monsoon, with a focus on the seasonal-to-interannual time scales. He will discuss the problems and opportunities in studies of the monsoon in areas covering (a) general data and modeling problems, (b) interaction between monsoon and El Nino-Southern Oscillation, (c) influence of land surface process on monsoon, (d) importance of extra-tropical atmospheric process, (e) impact of local sea surface temperatures, and (f) the role of the Indian Ocean in Asian-Australian monsoon climate. He will also provide evidence of the active role of Asian monsoon in the variability of global climate.

The relative abilities of three, dimensioned error statistics---the root-mean-square error (RMSE), the mean absolute error (MAE) and the mean bias error (MBE)---to describe average error or difference between climate fields are examined. Three examples are used to illustrate our points. The first example is drawn from a recent comparison of gridded, global precipitation data sets that appeared in the Journal of Climate (Fekete et al. 2004). Our second example is constructed (by us) from climatologically averaged weather-station air temperatures, which we alternately interpolate spatially (grid) using two interpolation procedures. Within a third example, we use MAE to evaluate the relationships between grid resolution and spatial resolution error for monthly precipitation within the Amazon Basin. The RMSE and its square, the mean-square error (MSE), are of particular interest, because they are the most widely reported average-error measures, and they are fundamentally flawed. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error, and that (unlike RMSE) they are unambiguous.