An evaluation of global chemistry-climate model output bias correction techniques for surface ozone burdens

Despite continuous improvement during recent decades, state of the art global chemistry-climate models (CCMs) are still showing biases compared to observational data, illustrating remaining difficulties and challenges in the simulation of atmospheric processes. Therefore, CCM output is frequently bias-corrected in studies seeking to explore changing air quality burdens e.g., in form of the number of exceedances of threshold values for the protection of human health [e.g. Rieder et al., 2018].This study focuses on assessing strengths and limitations of different bias correction methods for global CCM simulations with focus on maximum daily 8-hour average surface ozone data. Ozone is chosen as it is known as regional pollutant and thus shows smaller spatial heterogeneity in its burden than e.g. particulate matter. Within the comparison a set of different innovative, as well as, common bias correction techniques are applied to output of several global coupled CCMs contributing hindcast simulations to the Coupled Model Intercomparison Project Phase 6 (CMIP6). For bias correction and evaluation, data from ground-based observations pooled by the European Environment Agency is used. To this end, the station data is spatially averaged by adopting an inverse distance weighting method proposed by Schnell et al. [2014] to match the individual model grid cells. For the actual bias correction four different methods are used and compared. These include quantile mapping, delta-function, relative and mean bias correction approaches. As surface ozone pollution is commonly associated with a strong seasonal cycle, the adjustment techniques are applied to model data on both seasonal and annual basis, and skill scores for individual bias correction techniques are compared across CMIP6 models.

 

References:

Rieder, H.E., Fiore A.M., Clifton, O.E., Correa, G., Horowitz, L.W., Naik, V.: Combining model projections with site-level observations to estimate changes in distributions and seasonality of ozone in surface air over the U.S.A., Atmos. Env., 193, 302-315, https://doi.org/10.1016/j.atmosenv.2018.07.042, 2018.

Schnell, J. L., Holmes, C. D., Jangam, A., and Prather, M. J.: Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model, Atmos. Chem. Phys., 14, 7721–7739, https://doi.org/10.5194/acp-14-7721-2014, 2014.