The CORDEX Flagship Pilot Study in southeastern South America: a comparative study of statistical and dynamical downscaling models in simulating daily extreme precipitation events

The aim of this work is to present preliminary results of the statistical and dynamical simulations carried out within the framework of the Flagship Pilot Study in southeastern South America (FPS-SESA) endorsed by the Coordinated Regional Climate Downscaling Experiments (CORDEX) program. The FPS-SESA initiative seeks to promote inter-institutional collaboration and further networking with focus on extreme rainfall events. The main scientific aim is to study multi-scale processes and interactions most conducive to extreme precipitation events through both statistical and dynamical downscaling techniques, including convection-permitting simulations. To this end, a targeted experiment was designed considering the season October 2009 to March 2010, a period with a record number of extreme precipitation events within SESA. Also, three individual extreme events within that season were chosen as case studies for analyzing specific regional processes and sensitivity to resolutions. Four dynamical and four statistical downscaling models (RCM and ESD respectively) from different institutions contributed to the experiment. In this work, an analysis of the capability of the set of the FPS-SESA downscaling methods in simulating daily precipitation during the selected warm season is presented together with an integrated assessment of multiple sources of observations and available CORDEX Regional Climate Model simulations. Comparisons among all simulations reveal that there is no single model that performs best in all aspects evaluated. The ability in reproducing the different features of daily precipitation depends on the model. However, the evaluation of the sequence of precipitation events, their intensity and timing suggests that FPS-SESA simulations based on both RCM and ESD yield promising results. Most models capture the extreme events selected, although with a considerable spread in accumulated values and the location of heavy precipitation.Thanks to CORDEX for endorsing the FPS-SESA. This work was supported by the University of Buenos Aires 2018- 20020170100117BA grant; JMG, MLB, SAS, RPR funding from the Spanish Research Council (CSIC) I-COOP+ Program “reference COOPB20374”. JMG, JF and AL-G acknowledge support from the Spanish R&D Program through projects MULTI-SDM (CGL2015-66583-R) and INSIGNIA (CGL2016-79210-R), co-funded by the European Regional Development Fund (ERDF/FEDER). AL-G acknowledges support from the Spanish R&D Program through the predoctoral contract BES-2016-078158. Universidad de Cantabria simulations have been carried out on the Altamira Supercomputer at the Instituto de Física de Cantabria (IFCA-CSIC), member of the Spanish Supercomputing Network. MB acknowledges support from the Simons Associateship of the Abdus Salam International Centre for Theoretical Physics. RH acknowledges support from the project LTT17007 funded by the Ministry of Education, Youth, and Sports of the Czech Republic

The impact of climate variability on soybean yields in Argentina. Multivariate regression

Climate variability is examined and discussed in this work, emphasizing its influence over the fluctuation of soybean yield in the Pampas (central-eastern Argentina). Monthly data of rainfall, maximum and minimum temperatures, thermal range and seasonal rainfall were analysed jointly with the soybean yield in the period 1973-2000. Low-frequency variability was significant only in the minimum temperature during November in almost all the stations. This situation is favourable to the crop since during this month, seed germination, a growth stage sensitive to low temperatures, takes place. In the crop's core production region, 72% of the series of soybean yield presented a positive trend. Except in years with extreme rainfall situations, interannual variability of the soybean yield is in phase with the seasonal rainfall interannual variability. During these years, losses in the soybean crop occurred, with yield negative anomalies greater than one standard deviation. Soybean yield showed spatial coherence at the local scale, except in the crop's core zone. The association between each climate variable and yield did not show a defined regional pattern. Summer high temperature and rainfall excesses during the period of maturity and harvest have the greatest negative impact on the crop, whilst higher minimum temperatures during the growing season favour high yields. The joint effect of climate variables over yield was studied with multivariate statistical models, assuming that the effect of other factors (such as soil, technology, pests) is contained in the residuals. The regression models represent the estimates of the yield satisfactorily (high percentage of explained variance) and can be used to assess expected anomalies of mean soybean yield for a particular year. However, the predictor variables of the yield depend on the region. Copyright © 2007 Royal Meteorological Society.Fil:Penalba, O.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Bettolli, M.L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Vargas, W.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina