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 12km 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 60ppb 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 5ppb for maximum daily 8h 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.
|Number of pages||17|
|Journal||Atmospheric Chemistry and Physics|
|Publication status||Published - 2018 May 30|
Bibliographical noteFunding Information:
Acknowledgements. We acknowledge Samuel Hall and Kirk Ullmann for providing actinic flux data that are used for supplementary analysis and George Grell and Geoff Tyndall for helpful discussions. This study is supported by NASA-ROSES grant NNX15AE38G. Patrick Minnis was supported by the NASA Modeling, Analysis, and Prediction Program. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We would like to acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation.
© Author(s) 2018.
All Science Journal Classification (ASJC) codes
- Atmospheric Science