Quantifying errors in surface ozone predictions associated with clouds over the CONUS: a WRF-Chem modeling study using satellite cloud retrievals

Clouds play a key role in radiation and hence Oinline-formula₃ photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in Oinline-formula₃ predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface Oinline-formula₃ predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface Oinline-formula₃ concentrations on some days. The average difference in summertime surface Oinline-formula₃ concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average Oinline-formula₃ (MDA8 Oinline-formula₃) over the CONUS. This represents up to inline-formula∼ 40 % of the total MDA8 Oinline-formula₃ bias under cloudy conditions in the tested model version. Surface Oinline-formula₃ concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for inline-formula∼ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface Oinline-formula₃ concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NOinline-formula_x-limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.