Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

M. Ruiz-Ramos, A. Rodríguez, A. Dosio, C. M. Goodess, C. Harpham, M. I. Mínguez, E. Sánchez

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Abstract

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management.
Original languageEnglish
Pages (from-to)283-297
Number of pages15
JournalClimatic Change
Volume134
Issue number1
Early online date1 Oct 2015
DOIs
Publication statusPublished - Jan 2016

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