WRF-GC: online two-way coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling

We developed the two-way version of the WRF-GC model, which is an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem chemical transport model, for regional air quality and atmospheric chemistry modeling. WRF-GC allows the two parent models to be updated independently, such that WRF-GC can stay state-of-the-science. The meteorological fields and chemical variables are transferred between the two models in the coupler to simulate the feedback of gases and aerosols to meteorological processes via interactions with radiation and cloud microphysics. We used the WRF-GC model to simulate surface PM_2.5 concentrations over China during January 22 to 27, 2015 and compared the results to the outcomes from classic GEOS-Chem nested-grid simulations as well as the surface observations. For PM_2.5 simulations, both models were able to reproduce the spatiotemporal variations, but the WRF-GC (r = 0.68, bias = 29%) performing better than GEOS-Chem (r = 0.72, bias = 55%) especially over Eastern China. For ozone simulations, we found that including aerosol-chemistry-cloud-radiation interactions reduced the mean bias of simulated surface ozone concentrations from 34% to 29% compared to observed afternoon ozone concentrations. WRF-GC is computationally efficient, with the physical and chemical variables managed in distributed memory. At similar resolutions, WRF-GC simulations were three times faster than the classic GEOS-Chem nested-grid simulations, due to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. We envision WRF-GC to become a powerful tool for advancing science, serving the public, and informing policy-making.