Predictability of summer daily extreme precipitation in Central-Eastern Argentina

PENALBA, OLGA C; POGGI, M MERCEDES; BETTOLLI MARIA LAURA

Santiago de Chile

Conferencia; 11th meeting of the International Conference on Southern Hemisphere Meteorology and Oceanography; 2015

American Meteorological Society

There is a growing need for climate information from various productive sectors in order to plan their activities and implement adaptation measures to reduce the impacts of climate variability, especially of extreme events. In Argentina, having estimators or predictors of daily precipitation extremes in seasonal scale would provide more and better products and specific climatic applications required for various sectors, through climate services. In turn, from these predictors and their relationship with the extreme precipitation, statistical and dynamic models of seasonal rain forecast could be develop in order to progress in medium-term climate forecasting, scale of variability little explored to present.The latitudinal extent of Argentina, its topography and the different productive sectors force to make climate forecast studies at different time scales, both seasonal and intraseasonal, and by region. Therefore, for this work, the center-east of the country has been chosen as the study area.In this context, the main objective was to explore climate indices as potential predictors of summer daily extreme precipitation in this area with the purpose of statistically model the relationship found in seasonal scale. To this end, the intensity of seasonal and monthly mean daily extreme precipitation was used, estimated as the rate between the seasonal (December, January and February) and monthly cumulative precipitation, respectively, above the daily 75th percentile and the number of days that recorded this precipitation, from observed daily precipitation data of 30 weather stations included in the database CLARIS-LPB, in the period 1960-2012. Different climate indices (those related to the ENSO phenomenon, Antarctic Oscillation, the Indian Ocean Dipole, the sea surface temperature in regions of the Atlantic Ocean, etc.) as well as regional indices (blocking index, index of the onset of convection in the tropics, hydric balance in previous months, etc.) were considered as potential predictors. The relationship between predictors and daily extreme precipitation was studied using the Pearson correlation coefficient with a phase shift up to 6 months prior, considering a significance level of 5%. Subsequently, the statistical modeling of the relationship between predictors and predictand was made based on singular value decomposition analysis.The first results in seasonal scale indicated significant associations between the variable and diverse global indices in different lagged months, but with little spatial homogeneity. In some stations, indices that estimate the variable with up to 6 months in advance were found, being the ENSO phenomenon the one that provides the highest signal. Regional indices, however, showed weak correlations with summer daily extreme precipitation with even lower homogeneity, both spatially and temporally.In monthly terms, it was found that December is the month of the warm season with higher predictability based on the indices associated with the ENSO, with signal of the phenomenon getting lost in January and February. In these months, the relationship with different regions of the Atlantic Ocean becomes more important along with the local conditions of soil moisture in previous months.The different signals found in the three summer months forced to make statistical modeling per month instead of considering summer in seasonal term. The results of the models show that the combination of the selected predictors in each case has a good performance in forecasting extreme precipitation events in the study region.