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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/acp-2017-914
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
03 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).
Quantifying errors in surface ozone predictions associated with clouds over CONUS: A WRF-Chem modeling study using satellite cloud retrievals
Young-Hee Ryu1, Alma Hodzic1,2, Jerome Barre1,a, Gael Descombes1, and Patrick Minnis3 1National Center for Atmospheric Research, Boulder, CO, USA
2Laboratoire d'Aérologie, Observatoire Midi-Pyrénées, CNRS, Toulouse, France
3NASA Langley Research Center, Hampton, VA, USA
anow at: European Centre for Medium-Range Weather Forecasts
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 that in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) at 12 km horizontal resolution with the Morrison microphysics and Grell 3D cumulus parameterization to quantify uncertainties in summertime surface O3 predictions associated with the cloudiness over contiguous United States (CONUS). To evaluate the model's own clouds and to restrain the growth of model errors, the model is driven by reanalysis 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 up to 60 ppb 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 6 ppb for the 8-h average O3 over CONUS. This represents up to ~ 40 % of the total 8-h average O3 bias under cloudy conditions in the tested model version, and the results are robust with respect to the choice of the microphysics scheme. 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 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.

Citation: Ryu, Y.-H., Hodzic, A., Barre, J., Descombes, G., and Minnis, P.: Quantifying errors in surface ozone predictions associated with clouds over CONUS: A WRF-Chem modeling study using satellite cloud retrievals, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-914, in review, 2017.
Young-Hee Ryu et al.
Young-Hee Ryu et al.
Young-Hee Ryu et al.

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Short summary
We investigate whether errors in cloud predictions can significantly impact the ability of air quality models to predict surface ozone over the U.S. during summer 2013. The comparison with satellite data shows that the model predicts ~ 55 % of clouds in the right locations and underpredicts cloud thickness. The error in summertime daytime ozone is estimated to 1–6 ppb, and represents ~ 40 % of the ozone bias. The accurate predictions of clouds particularly benefits ozone predictions in urban areas.
We investigate whether errors in cloud predictions can significantly impact the ability of air...
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