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. Clouds play a key role in radiation and hence O 3 photochemistry bymodulating photolysis rates and light-dependent emissions of biogenicvolatile organic compounds (BVOCs). It is not well known, however, how mucherror in O 3 predictions can be directly attributed to error in cloudpredictions. This study applies the Weather Research and Forecasting withChemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrisonmicrophysics and Grell 3-D cumulus parameterization to quantify uncertaintiesin summertime surface O 3 predictions associated with cloudiness overthe contiguous United States (CONUS). All model simulations are driven byreanalysis of atmospheric data and reinitialized every 2 days. In sensitivitysimulations, cloud fields used for photochemistry are corrected based onsatellite cloud retrievals. The results show that WRF-Chem predicts about55 % of clouds in the right locations and generally underpredicts cloudoptical depths. These errors in cloud predictions can lead to up to 60 ppbofoverestimation in hourly surface O 3 concentrations on some days. Theaverage difference in summertime surface O 3 concentrations derived fromthe modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximumdaily 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 inthe tested model version. Surface O 3 concentrations are sensitive tocloud 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 cloudcorrections is about 2 times larger in VOC-limited than NO x -limitedregimes. Our results suggest that the benefits of accurate predictions ofcloudiness would be significant in VOC-limited regions, which are typical ofurban areas.

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