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 O 3 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 O 3 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 O 3 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 O 3 concentrations on some days. The average difference in summertime surface O 3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O 3 (MDA8 O 3 ) over the CONUS. This represents up to  ∼  40 % of the total MDA8 O 3 bias under cloudy conditions in the tested model version. Surface O 3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for  ∼  80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O 3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NO 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.