| description abstract | There are several well-documented studies showing the shifts in seasonal mean rainfall and temperature conditional on El Niño?Southern Oscillation (ENSO) phase. Here the shifts in the seasonal histograms of daily rainfall over South America conditional on ENSO phase are examined. The authors are motivated to analyze daily rainfall statistics over seasons by the demands for information on the shorter temporal scales voiced by users of climate data. In the first stage of the analysis the Kolmogorov?Smirnov (K-S) test is used, comparing El Niño to La Niña histograms of daily station data, to identify regions where there are significant shifts in the histograms. The K-S statistic analyses of daily station data are then compared to the same analyses performed on existing publicly available gridded station datasets. The degree to which the station and gridded data agree in showing geographical regions of significance provides evidence that the gridded fields might provide guidance on the nature of the ENSO signal where station data are not available. Further, the analysis of the gridded datasets can be used to motivate and guide efforts to obtain more complete daily data where the gridded datasets suggest an ENSO signal. As an example a detailed comparison of one station in southern Brazil and its nearest neighbors in the gridded data are presented, suggesting that, despite biases, the gridded fields are generally consistent with the station data where both are available. For many regions of the world neither daily station data nor daily gridded datasets are available for analysis. Thus despite documented and well-known regional biases in the precipitation fields available in the NCEP?NCAR reanalysis the extent to which shifts in the daily statistics of the NCEP?NCAR reanalysis precipitation are consistent with station and gridded station analyses is also examined. The preliminary work described here suggests that while the reanalysis does not ideally replicate the gridded station results the reanalysis may be useful as a tool for indicating candidate regions for further analysis with station or gridded data. | |
