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Challenges in the bias correction of multi-variate parameters under climate change conditions

Conference: 2nd Workshop on Bias Correction in Climate Studies
Year: 2018
Contribution type: Oral
Abstract URL: Book of abstracts
PDF file: 2018_Casanueva_BCworkshop_Santander_final.pdf
Poster: 2018_Casanueva_BCworkshop_Santander_short.pdf
Authors:
, Kotlarski, S., , Schwierz, C., Liniger, M.A.

Along with the higher demand of bias-corrected data for climate impact studies, the number of available data sets has largely increased in the recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on Global Climate Models (GCMs). Complementary, the CORDEX initiative has incorporated experiments based on regionally-downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distill the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses.

In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCMs and EURO-CORDEX Regional Climate Models (RCMs), at approximately 150km, 50km and 12km horizontal resolution, respectively) in a multi-variate framework. The analysis is carried out for the Wet Bulb Globe Temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dewpoint temperature, which in this work are individually bias-corrected prior to the index calculation. The aim of this work is twofold: First, the potential added value of bias-corrected RCMs over their bias-corrected GCM counterparts is assessed in present and future climate conditions. For this purpose, we evaluate the resulting WBGT and the models’ ability to represent the inter-variable relationships. Secondly, the two bias correction methods are compared in order to screen their strengths and weaknesses in present and future climate conditions.