High spatial resolution WRF-Chem model over Asia: Physics and chemistry evaluation

Abstract The representation of air quality and meteorology over Asia remains challenging for chemical transport models as a result of the complex interactions between the East Asian monsoons and the large uncertainty (in space and time) of the high anthropogenic emissions levels over the region. High spatial resolution models allow resolving small-scale features induced by the complex topography of this region. In this study, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the spatial and seasonal variability of main physical and chemical variables over Asia for the year 2015 at 8-km horizontal resolution to enable resolving small-scale features induced by the region complex topography. The simulated atmospheric composition was evaluated against satellite retrievals (MOPITT, IASI + GOME2, MODIS and OMI) in addition to ground-based observations in China for the year 2015, while the meteorological variables were evaluated by several observational-based datasets (ERA5, CRU, MODIS, MTE). Results showed low to moderate seasonal biases for major meteorological variables, i.e. air temperature, relative humidity, precipitation, latent heat, sensible heat and snow cover fraction. Overall, WRF-Chem reproduced well the spatial and seasonal variability of lowermost tropospheric ozone content, total column carbon monoxide and aerosol optical depth, while large discrepancies were found for tropospheric nitrogen dioxide content, mainly during the warm season. In consistency with previous studies, the different biases between model-simulated and satellite-retrieved values can be mainly attributed to i) the large uncertainties in anthropogenic and natural nitrogen oxides emission estimates, as well as dust and sea salt emissions in the case of aerosol optical depth, and ii) some coarse parameterizations used to reproduce main small-scale features (e.g. meteorology, chemical processes, dry deposition to vegetation). Compared to ground-based observations, the WRF-Chem model reproduced well the mean annual cycle of surface nitrogen dioxide, ozone and fine particles concentrations in all seasons across China. Our results suggest that WRF-Chem provides reliable spatio-temporal patterns for most of the meteorological and chemical variables, adding thus confidence to its applicability in the context of air pollution risk assessment to human and ecosystems health.