Climate change projections and extremes for Costa Rica using tailored predictors from CORDEX model output through statistical downscaling with artificial neural networks

Abstract

Despite intense research on climate change (CC), regional studies for Central America, which is considered a CC hot spot, remain scarce. The information provided by general circulation models (GCMs) is too coarse to accurately reproduce local-scale climatic features, which are needed for impact assessment. Thus, downscaling techniques are employed to address this scale mismatch. Costa Rica is the present case study, for which suitable predictors were tailored for downscaling related to regional climatic characteristics, such as the Inter-Tropical Convergence Zone, El Niño Southern Oscillation, the Caribbean Low-Level Jet, and the Mid-Summer Drought. Statistical downscaling models were calibrated for precipitation, maximum and minimum temperature, using the perfect prognosis methodology by means of station data, ERA-INTERIM reanalysis and artificial neural networks, yielding satisfactory results. As found in several studies, the temperature models replicated more accurately the statistics of the observed datasets. However, here, through the implemented approach and the tailored predictors, the precipitation models conveyed an improvement compared to standard methods. Projected daily climate was obtained employing CORDEX data under the RCP8.5 scenario for the central region of the country. Overall, the changes in climate estimated by the end of the 21st century agree with coarser-scale projections. Finally, projected climate extremes indices were calculated and rendered further details on the intensity of future CC by the end of the century.