Using Self-Organizing Maps to Investigate Extreme Climate Events: An Application to Wintertime Precipitation in the Balkans

Tereza Cavazos Center for Integrated Regional Assessments, The Pennsylvania State University, University Park, Pennsylvania, and Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa

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Abstract

This paper examines some of the physical mechanisms and remote linkages associated with extreme wintertime precipitation in the Balkans. The analysis is assessed on daily timescales to determine the role of the circulation and atmospheric moisture on extreme events, and also at intraseasonal and interannual timescales to find possible linkages with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) patterns. A nonlinear classification known as the self-organizing map (SOM) is employed to obtain the climate modes and anomalies that dominated during the 1980–93 period. An artificial neural network (ANN) is also used to derive daily precipitation at gridpoint scale and at local scale in Bucharest, Romania. Of the predictors used, 500–1000-hPa thickness, 700-hPa geopotential heights, and 700-hPa moisture are the most important controls of daily precipitation. These results are substantiated with the climate states from the SOM classification, which show strong meridional flow over central and eastern Europe coupled to increased winter disturbances in the central Mediterranean and a tongue of moisture at the 700-hPa level from the eastern Mediterranean and the Black Sea during anomalously wet events in the Bulgarian region. Dry events are almost an inverse of these conditions. Extreme events are further modulated by changes in the circulation associated with the AO. In contrast, the NAO does not play a role on wintertime precipitation over the region. The ANN captures well synoptic events and dry spells, but tends to overestimate (underestimate) small (large) events. This suggests a problem for area-averaged precipitation, which is already biased by its spatial resolution. However, comparison between precipitation at Bucharest station and at its nearest grid point shows that the performance of the ANN is slightly better at gridpoint scale.

Corresponding author address: Tereza Cavazos, Dept. of Geography and Regional Development, The University of Arizona, Tucson, AZ 85721.

Email: cavazos@geog.climate.arizona.edu

Abstract

This paper examines some of the physical mechanisms and remote linkages associated with extreme wintertime precipitation in the Balkans. The analysis is assessed on daily timescales to determine the role of the circulation and atmospheric moisture on extreme events, and also at intraseasonal and interannual timescales to find possible linkages with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) patterns. A nonlinear classification known as the self-organizing map (SOM) is employed to obtain the climate modes and anomalies that dominated during the 1980–93 period. An artificial neural network (ANN) is also used to derive daily precipitation at gridpoint scale and at local scale in Bucharest, Romania. Of the predictors used, 500–1000-hPa thickness, 700-hPa geopotential heights, and 700-hPa moisture are the most important controls of daily precipitation. These results are substantiated with the climate states from the SOM classification, which show strong meridional flow over central and eastern Europe coupled to increased winter disturbances in the central Mediterranean and a tongue of moisture at the 700-hPa level from the eastern Mediterranean and the Black Sea during anomalously wet events in the Bulgarian region. Dry events are almost an inverse of these conditions. Extreme events are further modulated by changes in the circulation associated with the AO. In contrast, the NAO does not play a role on wintertime precipitation over the region. The ANN captures well synoptic events and dry spells, but tends to overestimate (underestimate) small (large) events. This suggests a problem for area-averaged precipitation, which is already biased by its spatial resolution. However, comparison between precipitation at Bucharest station and at its nearest grid point shows that the performance of the ANN is slightly better at gridpoint scale.

Corresponding author address: Tereza Cavazos, Dept. of Geography and Regional Development, The University of Arizona, Tucson, AZ 85721.

Email: cavazos@geog.climate.arizona.edu

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