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

Abstract

Abstract. Clouds play a key role in radiation and hence O 3 photochemistry by modulating photolysis rates and lightdependent 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.

Sensitivity of Meteorological Skill to Selection of WRF‐Chem Physical Parameterizations and Impact on Ozone Prediction During the Lake Michigan Ozone Study (LMOS)

“…Journal of Geophysical Research: Atmospheres 3.1.4. Cloud Coverage Clouds are known to influence ozone concentrations directly by modulating photolysis (Ryu et al, 2018). Furthermore, they can influence ozone concentrations indirectly through lightning NO x (Hauglustaine et al, 2001) and by changing biogenic VOC emissions via shifts in radiation and temperature (Ryu et al, 2018).…”

“…Cloud Coverage Clouds are known to influence ozone concentrations directly by modulating photolysis (Ryu et al, 2018). Furthermore, they can influence ozone concentrations indirectly through lightning NO x (Hauglustaine et al, 2001) and by changing biogenic VOC emissions via shifts in radiation and temperature (Ryu et al, 2018). Errors in WRF-Chem cloud fields were found by Ryu et al (2018) to propagate to changes in average MDA8 ozone by 1-5 ppb over Contiguous United States (CONUS).…”

Comprehensive isoprene and terpene gas-phase chemistry improves simulated surface ozone in the southeastern US

“…Model bias in surface ozone is a good indicator that the budgets and processing of nitrogen oxides (NO x ) or volatile organic compounds (VOCs) are poorly constrained. Past studies have suggested different solutions including the need for updates to model representation of clouds (Ryu et al, 2018), emissions of NO (Travis et al, 2016;McDonald et al, 2018b) or biogenic hydrocarbons (Kaiser et al, 2018), chemistry (Squire et al, 2015), chemical solver (Sun et al, 2017), and deposition (Val Martin et al, 2014;Clifton et al, 2019).…”

Comprehensive isoprene and terpene chemistry improves simulated surface ozone in the southeastern U.S.

“…The Henry's law and isoprene updates do not change ozone much above the surface while the terpene updates do reduce ozone in the PBL. The vertical profile shapes for ozone, NO, isoprene, and monoterpenes are quite different between the model and observations suggesting that updates to the PBL height, mixing schemes, clouds (Ryu et al, 2018), vertical resolution, or ozone dry deposition schemes (Clifton et al, 2019) may be needed to further reduce ozone biases in CESM/CAM-chem.…”

“…model and observations for NO 2 photolysis because field campaigns typically avoid sampling clouds on a scale not resolved by the model (Hall et al, 2018). In Ryu et al (2018), WRF-chem similar to CAM-chem under-predicts NO photolysis above 2 km and when clouds derived from satellites are incorporated into WRF-chem the NO 2 photolysis bias is removed and MDA8 surface ozone decreases by 1 to 5 ppb. Additionally, studies using large eddy simulations have determined that shallow cumulus clouds enhance vertical transport of passive (i.e., no aqueous-phase processing) chemical compounds as compared to clear sky conditions (Vila-Guerau de Arellano et al, 2005;Li et al, 2017).…”