Assessment of CMIP6 in Representing Precipitation Patterns in Peru

Abstract Submission: Climate change is affecting the precipitation patterns around the world, especially in Andean tropical countries as Peru, causing extreme hydrological events that put at risk ecosystems and communities. The aim of this study is to assess the variation of precipitation patterns up to 2100 by using climate change scenarios from global circulation model CMIP6 models to design an effective risk management frame for future conditions. We assessed the performance of ten CMIP6 models considering annual and seasonal rainfall during the reference period from 1995 to 2014. This assessment was conducted through relative error mapping based on the inverse distance weighting method for each temporary scale. Initial results suggest that CNRM-CM6-1 and CNRM-ESM2-1 are the best performing CMIP6 models due to the low relative errors they exhibit in all temporary scales. Simulated precipitation values are overestimated in all time scales, except in winter when GFDL-ESM4, MIROC6 and CNRM-CM6-1 mostly underestimated precipitation values with median relative errors of -0.16, -0.22 and -0.03, respectively. The areas corresponding to the Atlantic and Titicaca basin are best simulated by the CMIP6 climate models, while the Pacific basin exhibited the highest relative errors. Future work will focus on assessing downscaling techniques to reduce the relative error and bias from the CMIP6 models’ simulations.

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