Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans

This paper examines some of the physical mechanisms and remote linkages ass ociated with extreme wintertime precipitation in the Balkans. The analysis is assessed on daily timescales to determine the role of the circulation an d atmospheric moisture on extreme events, and also at intraseasonal and int erannual timescales to find possible Linkages with the North Atlantic Oscil lation (NAO) and the Arctic Oscillation (AO) patterns. A nonlinear classifi cation known as the self-organizing map (SOM) is employed to obtain the cli mate modes and anomalies that dominated during the 1980-93 period. An artif icial neural network (ANN) is also used to derive daily precipitation at gr idpoint scale and at local scale in Bucharest, Romania. Of the predictors u sed, 500-1000-hPa thickness, 700-hPa geopotential heights, and 700-hPa mois ture are the most important controls of daily precipitation. These results are substantiated with the climate states from the SOM classification, whic h show strong meridional how over central and eastern Europe coupled to inc reased winter disturbances in the central Mediterranean and a tongue of moi sture at the 700-hPa lever from the eastern Mediterranean and the Black Sea during anomalously wet events in the Bulgarian region. Dry events are almo st an inverse of these conditions. Extreme events are further modulated by changes in the circulation associated with the AO. In contrast, the NAO doe s not play a role on wintertime precipitation over the region. The ANN capt ures well synoptic events and dry spells, but tends to overestimate (undere stimate) small (large) events. This suggests a problem for area-averaged pr ecipitation, which is already biased by its spatial resolution. However, co mparison between precipitation at Bucharest station and at its nearest grid point shows that the performance of the ANN is slightly better at gridpoin t scale.