Precipitation over Southern China in April, largely associated with mesoscale convective systems (MCSs), has declined significantly in recent decades. It is unclear how this decline in precipitation may be related to the concurrent increase of anthropogenic aerosols over this region. Here, using observation analyses and model simulations, we showed that increased levels of anthropogenic aerosols can significantly reduce MCS occurrences by 21% to 32% over Southern China in April, leading to less rainfall. Half of this MCS occurrence reduction was due to the direct radiative scattering of aerosols and the indirect enhancement of non‐MCS liquid cloud reflectance by aerosols, which stabilized the regional atmosphere. The other half of the MCS occurrence reduction was due to the microphysical and dynamical responses of the MCS to aerosols. Our results demonstrated the complex effects of aerosols on MCSs via impacts on both the convective systems and on the regional atmosphere.

Abstract. We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. In WRF-GC, meteorological and chemical calculations are performed on the exact same 3-D grid system; grid-scale advection of meteorological variables and chemical species uses the same transport scheme and time steps to ensure mass conservation. Prescribed size distributions are applied to the aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. WRF-GC is computationally efficient and scalable to massively parallel architectures. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016. Our sensitivity simulations show that including ARI and ACI improves the model's performance in simulating regional meteorology and air quality. WRF-GC generally reproduces the magnitudes and spatial variability of observed aerosol and cloud properties and surface meteorological variables over East Asia during January 2015 and July 2016, although WRF-GC consistently shows a low bias against observed aerosol optical depths over China. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations of PM2.5 in January 2015 (normalized mean bias of −9.3 %, spatial correlation r of 0.77) and afternoon ozone in July 2016 (normalized mean bias of 25.6 %, spatial correlation r of 0.56) over eastern China. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org (last access: 20 June 2021).

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Based on sounding data from 1975 to 2005 and TM/ETM+ remote sensing images in 1989, 2001 and 2007, the climate changes in Harbin City, Northeast China in recent 30 years were analyzed and forecasted. Results show that in the lower troposphere the meridional wind speed and mean annual wind speed decrease, and in the lower stratosphere the temperature decreases while the meridional wind speed increases significantly. In the study area, the climate is becoming warmer and wetter in the middle lower troposphere. The expansion of urban area has great effects on the surface air temperature and the wind speed, leading to the increase of the surface air temperature, the decrease of the surface wind speed, and the increase of the area of urban high temperature zone. The quantitative equations have been established among the surface air temperature, the carbon dioxide (CO 2 ) concentration and the specific humidity (the water vapor content). It is predicted that the future increasing rate of the surface air temperature is 0.85℃/10yr if emission concentration of CO 2 remains unchanged; if emission concentration of CO 2 decreases to 75%, 50% and 25%, respectively, the surface air temperature will increase 0.65 /10 ℃ yr, 0.46 /10 ℃ yr and 0.27 /10 ℃ yr, respectively. The rise of the surface air temperature in the study area is higher than that of the global mean temperature forecasted by IPCC.