Lead Researchers: Soledad Collazo, Mariana Barrucanda, Matilde Rusticuccia

Year: 2022

Published in: Climate Services Volume 26

Several socio-economic sectors are sensitive to the occurrence of extreme climate events. The ability to predict these extremes will allow precautionary measures to reduce their impacts. This work aims to disseminate a seasonal statistical forecast of daily temperature extremes in Argentina to the international scientific community. At the local level, this forecast is shared at monthly meetings organized by the Argentine National  Meteorological Service  and attended by different users. For the temperature extremes modeling, several predictors and statistical techniques were applied. We estimated the probability of each tercile category (above-normal, near-normal, and below-normal) by quantifying the percentage of models that predict each of them. The forecasts were verified by calculating different metrics. In general, we observed that the forecast system has less skill to discriminate the near-normal category in all seasons, and the other categories present a skill highly variable according to the season, region, and extreme index. The verification process revealed that predictability increases for all extreme indices with a previous  La Niña  phase. This product represents an advance towards an operational seasonal forecast of extreme temperatures in Argentina because it offers predictions based on a detailed study of predictors in the region, the incorporation of multiple statistical methodologies, and the predicted variables are not the most typical ones offered by forecasting centers. Finally, it is highlighted that the accuracy rate obtained with this product exceeds a forecast based on  climatology , i.e., despite the uncertainties, our forecasts provide additional information to users for decision making.

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