Dynamical and statistical downscaling: intercomparison in three extreme temperature events in southern South America

ROCIO BALMACEDA HUARTE; LLUÍS FITA BORELL; PABLO G. ZANINELLI

Beijing

Congreso; ICRC-CORDEX 2019; 2019

Global Climate Models are the main tools used to generate weather and climate predictions atdifferent time scales. However, it is well recognized that these models are unable to provideinformation at the spatial scale required by many stakeholders. Hence, dynamical and statisticaldownscaling (RCM and ESD) approaches are necessary for adapting the global model predictions tosmaller spatial scales, providing suitable products for a range of applications. Despite of the largenumber of works that applied these techniques, in South America the comparison of strengths andweaknesses of ESD and RCM has not comprehensively been performed yet, especially in the simulationof extreme events.In this context, the aim of this work is to compare ESD and RCM in representing extreme temperatureevents in central-eastern Argentina. To this end, three hot summers in which record heat wave eventsoccurred were selected: 2002-2003, 2013-2014 and 2015-2016.For the comparison of the two downscaling methodologies, the WRF RCM was used with two differentconfigurations, in which the soil physics were altered. Jointly three ESD models based on linearregression and analogues were considered. All models were driven by ERA Interim. Also, to train theESD models and to evaluate the downscaling approaches, daily station data from Argentina was used.Two different bioclimatic indices based on the wet bulb temperature were also simulated andcompared.The results show that both approaches are able to reproduce the persistence and spatial distributionsof the extreme events as well as the seasonal characteristics of each hot summer. The spread in thesimulation of the intensities of the heat waves varies depending on the particular event, the regionand the simulation considered. They also showed a good performance in simulating the bioclimaticindices highlighting the importance of the generation of detailed climate information for impactassessment.