This thesis describes the undertaking of multiple studies designed to evaluate and reduce global modelled surface O3 biases in CTMs/ESMs. Specific focus is placed on the evaluation of rural surface O3 seasonal variability in a global CTM (GEOS-Chem). A major observational data collation is undertaken, processing 1,033,463,750 measurements of O3 and some of its major precursors, from 16,996 sites, through a number of rigorous data quality checks, to ensure data is of a high enough quality for global model evaluation.
Through a model–measurement comparison, applying spectral analysis, substantial seasonal biases of surface O3 in GEOS-Chem are found, with a general overestimation of the seasonal amplitudes in North America and Europe (by up to 16 ppbv), together with delayed phase maxima by 1–5 months. The main cause of these biases is found to be homogenous overestimates of summertime O3 in all observed areas, by a minimum of 10 ppbv.
An extensive global sensitivity study is undertaken to evaluate the sensitivity of modelled surface O3 biases to alterations of anthropogenic emissions, biogenic emissions, and the O3 dry deposition flux. Constraining model biases jointly by O3, NO and CO observations yields regional optimal monthly scaling factors. Driving GEOS-Chem with these derived factors results in the modelled summertime overestimates of surface O3 being removed almost entirely, across all regions. The removal of this bias is dominantly controlled through increases to the summertime O3 dry deposition flux (by factors of 2–4), with modifications to this term providing the only viable pathway for substantial reduction of modelled summertime biases, in all evaluated regions. Surface O3 is found to be NOx sensitive in all regions, with scalings of VOC emissions generally not imparting significant change on O3. General modelled winter underestimates of surface O3 are able to be removed through reductions of NOx emissions.
