The influence of Madden–Julian Oscillation (MJO) on the dynamics of extreme rainfall events during austral Spring in Uruguay is investigated. The research is focused on the southern region of the country, which includes 15 weather stations. Extreme events are defined as days in which accumulated rainfall exceeds the 90 percentile of rainy days, and MJO is classified according to the Real‐time Multivariate MJO series 1 (RMM1) and 2 (RMM2) indices. Given that the extratropical teleconnections associated with MJO take at least 1 week to set up, we explore the influence of MJO up to 11 days prior to extreme events. A nonlinear time series analysis is performed (using symbolic patterns known as ordinal patterns) in order to consider the effects of the persistence of particular phases of MJO on the dynamic of extreme rainfall events. We find that MJO has the highest influence on extreme rainfall events in the region when it shows a persistence in phases 4 and 5 for more than 5 days, which intensifies the polar jet, influencing the trajectories of the transient waves that propagate in high latitudes, favouring geopotential disturbances over Uruguay. In other cases, the atmospheric pattern that leads to extreme rainfall events is characterized by a blocking episode that prevents transient activity from high latitudes to reach Uruguay, and the disturbances associated with the extreme events propagate along the subtropics.

We investigate the characteristics of extreme rainfall events in southern Uruguay during the summer season. The focus of this work is to understand the dynamics of these events. Therefore, we define the southern area with a clustering analysis of the meteorological stations. Then, we divide the events into three classes, considering the principal components of the 2m temperature. For each class, we calculate composites of atmospheric variables. We find three basic dynamics that can be associated to extreme events. The first group is characterized by atmospheric instability and a cold front that triggers the event. The second group is associated with deep convection that was initiated in central Argentina. The third group is represented by an intense surface low pressure in Uruguay that is favoured by the 200 hPa circulation. The results are in good agreement with the previous studies.

Recebido em 29 de Abril de 2015 -Aceito em 15 de Dezembro de 2015 ResumenSe desarrollan diferentes modelos pre-operativos de pronóstico dinámico-estadístico de precipitación estacional en el sur de Uruguay para primavera y verano. Para ello se utilizan regresiones lineales entre predicciones de variables dinámicas y observaciones de precipitación. Los pronósticos se inicializan en Agosto y Noviembre para los trimestres de Setiembre-Noviembre y Diciembre-Febrero, respectivamente. Las predicciones de las variables dinámicas son salidas de un ensamble del modelo de circulación general de la atmósfera ICTP MCGA forzado con condiciones de borde de temperatura de superficie del mar (TSM) pronosticadas por NCEP-CFSv2. Las observaciones de precipitación provienen de 10 estaciones meteorológicas ubicadas al sur del Río Negro. Los mejores índices predictores se encuentran mediante validación cruzada, utilizando ventanas de un año, con las variables dinámicas. Se concluye que el mejor índice predictor es el viento meridional en 200 hPa promediado en una región que incluye el Sudeste de Sudamérica y es tal que anomalías de componente norte están asociadas a lluvias por encima de lo normal en el sur del país. Se encuentra que para todo el sur del país los pronósticos tienen habilidad únicamente en primavera mientras que para la zona metropolitana de Montevideo los pronósticos muestran habilidad para ambas estaciones y principalmente para verano. Palabras-clave: Predicción estacional, precipitación. Seasonal Forecast of Accumulated Rainfall in Southern UruguayDuring Spring and Summer AbstractWe develop dynamical-statistical forecast models in order to predict seasonal rainfall in southern Uruguay during summer and spring. The statistical technique consists of linear regressions between dynamic variables and rainfall observations. The forecasts for September-October-November are initialized in August and the ones for December-January-February in November. The dynamic variables are ICTP-MGCAs outputs, forced with sea surface temperature, predicted by NCEP-CFSv2, as boundary conditions. The observational data is accumulated monthly, values are measured in 10 meteorological stations that are cross-correlated, using one year windows, with the dynamic variables in order to find the best predictor indexes. We conclude that the best predictor index is the meridional wind in the 200 hPa level averaged in an area that includes the Southeast of South America. Northern wind anomalies in this area are associated with positive rainfall anomalies in Southern Uruguay. We found that while forecasts for the south of the country are skillful only in spring , forecasts for the metropolitan area of Montevideo are skilfull in both seasons showing their best performance in summer.

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