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Abstract
Abstract. Clouds play a key role in radiation and hence O3 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 O3 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 O3 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 O3 concentrations on some days. The average difference in summertime surface O3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O3 (MDA8 O3) over the CONUS. This represents up to ∼ 40 % of the total MDA8 O3 bias under cloudy conditions in the tested model version. Surface O3 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 O3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NOx-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.
Highlights
Ozone (O3) is a secondary pollutant that is formed by chemical reactions involving nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOCs) in the presence of ultraviolet radiation
We performed quantitative analyses of the WRF-Chem model mesoscale (12 km) simulations to determine how much errors in cloud predictions contribute to errors in surface O3 predictions during summertime over the contiguous United States (CONUS)
It is found that the WRF-Chem model is able to generate roughly 55 % of the clouds in the right locations by comparing to satellite clouds
Summary
Ryu et al.: Quantifying errors in surface ozone predictions to ∼ 80 %. It is not quantitatively understood how much the individual processes contribute to O3 biases. Clouds can be one of the key factors because they greatly modulate the ultraviolet radiation that is critical for O3 formation. They remain one of the largest sources of uncertainties in air quality modeling as Dabberdt et al (2004) pointed out a decade ago. Accurate cloud predictions in numerical weather models are still challenging, and it has not yet been quantified how much errors in cloud prediction impact surface O3 predictions
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