Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry-climate model surface ozone fields

Abstract. State of the art chemistry-climate models (CCMs) still show biases compared to ground level ozone observations, illustrating remaining difficulties and challenges in the simulation of atmospheric processes governing ozone production and loss. Therefore, CCM output is frequently bias-corrected in studies seeking to explore changing air quality burdens and associated impacts. Here we assess four statistical bias correction techniques of varying complexity, and their application to surface ozone fields of four CCMs, and evaluate their performance against gridded observations in the EU and US. For the evaluation of the raw CCM outputs and the performance of the individual adjustment techniques we focus on two time periods (2005–2009 & 2010–2014), where the first period is used for development and training and the second to evaluate the performance of techniques when applied to model projections. Our results show, that while all methods applied are capable of significantly reducing the model bias, better results are obtained for more complex approaches such as quantile-mapping and delta-functions. We also highlight the sensitivity of the correction techniques to individual CCM skill at reproducing the observed distributional change in surface ozone. Ensemble simulations available for one CCM indicate the ozone bias arises from sensitivities in chemical mechanisms or emissions rather than driving meteorology.