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Heterogeneous chemistry and aerosol-radiation interaction (ARI) are considered two of the key mechanisms affecting haze formation and their reasonable parameterizations in atmospherically chemical models play an important role in accurate haze or air quality prediction. However, until the present, their relative contributions to severe haze have rarely been evaluated. Using the double-way atmospherically chemical model GRAPES_Meso5.1/CUACE CW, the relative contributions of heterogeneous chemistry and ARI to PM 2.5 are investigated under different pollution levels in Middle-Eastern China (MEC) during December 2016. Study results show that the combined effects of heterogeneous chemistry and ARI generate 29.7% of total PM 2.5 mass in MEC of December 2016, of which ARI contributes 19.7%, much higher than heterogeneous chemistry (6.1%). However, heterogeneous chemistry is important for reasonable reproduction of the proportion of secondary inorganic aerosols (SIA) of PM 2.5 , especially during heavy haze. Both the contributions of heterogeneous chemistry and ARI to PM 2.5 increase with the haze pollution aggravation but to different degrees. The contribution of heterogeneous chemistry to modeled PM 2.5 concentration rises from 4.0% on clean days to 6.6% under moderate pollution and 7.8% under heavy haze. And that of ARI increases rapidly from 9.1% on clean days to 21.0% under moderate pollution and 29.1% during heavy pollution. It is worth noticing that there is an additional contribution to PM 2.5 caused by the combination of heterogeneous chemistry and ARI except for their respective contributions, which reaches up to 7.3% of PM 2.5 mass and 18.5% of SIA generation under heavily polluted conditions. This is because ARI further promotes heterogeneous oxidation of SO 2 and NO x by increasing the aerosol surface area concentration (SAC), causing additional SIA production via heterogeneous pathways. This work indicates the contribution of ARI to severe haze formation not only by altering meteorological conditions but also by reforming the chemical processes in the model. (a) The interaction mechanism between heterogeneous chemistry and ARI in atmospheric chemistry model GRAPES_Meso5.1/CUACE; (b) Percentage contributions of the combined effects of heterogeneous chemistry and ARI (Δ1), heterogeneous chemistry alone (Δ2), ARI alone (Δ3), and the interaction between the two (Δ4) to PM 2.5 mass concentration in MEC. [Display omitted] • Relative contributions of heterogeneous chemistry and ARI to PM 2.5 are evaluated under different pollution levels. • ARI contributes much more to extreme PM 2.5 during severe haze than heterogeneous chemistry. • Their combined effects exceed the sum of the two because ARI further promotes the heterogeneous oxidation of SO 2 and NO x. [ABSTRACT FROM AUTHOR]

Low visibility event, as a disastrous weather, has great impacts on traffic and transportation, aircraft, and people's daily life, etc. Timely and accurate forecasts of low visibility events are urgently needed and meaningful. The reasonable algorithm of atmospheric extinction in atmospheric chemistry models is the basis for quantitatively predicting low visibility. The revised IMPROVE algorithm (RIMP) of atmospheric extinction is incorporated into the chemistry-weather interacted model GRAPES_Meso5.1/CUACE CW V1 to improve the prediction of low visibility events (LVEs) in the urban agglomerations in eastern China, which is compared with the original IMPROVE algorithm (OIMP) used in this model. The study results show that the RIMP effectively reduces the overestimation of low visibility prediction by OIMP in general, leading to a decrease of root-mean-square errors (RMSEs) and an increase of Threat Score (TS) of visibility <3 km, 5 km, and 10 km overall both at regional and city scales in varying degrees due to its more detailed processing of aerosols' size, optical feature and hygroscopic growth; The improvements of visibility prediction of LVEs by RIMP depends on the combined contribution of high relative humidity (RH) and PM 2.5 instead of single high RH or PM 2.5. The relative contributions of RH and PM 2.5 concentration on different levels of low visibility are different in Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions due to their different RH and PM 2.5 , which leads to the different improvement of RIMP in the two regions. The larger improvements by RIMP occur for visibility <5 km in BTH, while in YRD, the larger improvements by RIMP occur for visibility <10 km and >5 km. Moreover, the improvements by RIMP were more evident with higher RH conditions in both regions. The uncertainty created by the extinction algorithm is one important factor of the multiple factors affecting LVEs prediction; accurate modeling of high RH near saturation is also very important for LVEs prediction. • The revised IMPROVE (RIMP) algorithm is applicated to the atmospheric chemistry model to improve low visibility prediction. • RIMP generally improves low visibility prediction than the original IMPROVE algorithm (OIMP). • The improvements in low visibility prediction by RIMP vary depending on different aerosol and humidity conditions. [ABSTRACT FROM AUTHOR]

Compared with climate models, the role of aerosol-cloud interaction (ACI) in mesoscale numerical weather prediction (NWP) models still needs to be better understood, especially in haze regions with relatively high aerosol concentration. Here, we perform two sensitivity experiments with and without ACI (ACI and NO-ACI) in the atmospheric chemistry model GRAPES_Meso5.1/CUACE to investigate the impact of ACI on mesoscale NWP during the low-cloud period in winter 2016 over varying haze regions (severe polluted Jing-Jin-Ji (JJJ), polluted Yangtze River Delta (YRD), and weak polluted Pearl River Delta (PRD)) in China. The study results show that the real-time ACI improves underestimated cloud optical thickness (COT) and cloud water liquid path (CLWP) in haze regions, with the mean bias of simulated COT (CLWP) decreased by 27% (3%), 60% (14%), and 55% (3%) in JJJ, YRD, and PRD, respectively. The increased COT and CLWP lead to a decrease of 6.8, 21, and 13 W m−2 in daytime surface downward shortwave radiation (SDSR) in JJJ, YRD, and PRD, helping to reduce the mean bias of daytime SDSR by 6%, 13%, and 9%. In addition, ACI mitigates the warm bias of temperature at 2 m and dry bias of relative humidity (RH) at 2 m to a certain extent in haze regions, particularly in YRD with the mean absolute bias improved by 13% and 6%. The simulated vertical structure of temperature and RH in the ACI experiment is more consistent with observations than in the NO-ACI experiment. Further investigations find that the ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude over varying haze regions. Higher COT and CLWP, hence more significant meteorology changes due to ACI, occur in YRD, followed by PRD and JJJ. This study demonstrates the importance and complexity of ACI in modifying mesoscale NWP over varying haze regions of China, which promotes the further understanding of ACI in operational NWP models and bridges the gap with climate models. •ACI increases COT and CLWP when low-cloud and haze coexist. • ACI further leads to decreased SDSR, lower temperature, and higher RH. • ACI-induced changes improve mesoscale NWP to a certain extent. • The ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude. [ABSTRACT FROM AUTHOR]

The aerosol-radiation interaction mechanism (ARI) is important for air quality modeling, and its absence in atmospheric chemistry models may cause considerable uncertainties. In this study, the real-time calculated aerosol optical parameters, i.e., mass extinction coefficient (K ext), single scattering albedo (ω), and asymmetry factor (g) from the simulated aerosol concentrations are introduced into the Global/Regional Assimilation and Prediction System 5.1 version coupled with the Chinese Unified Atmospheric Chemistry Environment model (GRAPES_Meso5.1/CUACE) to establish the ARI mechanism for the two-way feedback between aerosols and weather processes. Focusing on Beijing-Tianjin-Hebei (BTH), China, in January 2017, ARI impacts on meteorology and haze-fog are simulated by using this atmospheric chemistry model. The results show that during the severe pollution, ARI can significantly alter the meteorology, especially for the heavily polluted central and southern BTH: The surface solar radiation (SSR), the temperature at 2 m (T2), the vertical diffusion coefficient (VDC), and the planetary boundary layer height (PBLH) are reduced by up to 50 W m−2, 2.5°C, 4 m2 s−1, and 100 m, respectively. In addition to surface meteorology, ARI affects the vertical structure of the planetary boundary layer (PBL), by cooling and humidifying the lower air and heating and drying the higher air. These modifications stabilize the PBL, resulting in an increase of 8% in relative humidity at 2 m (RH2), a key factor influencing secondary aerosol generation and aerosol hygroscopic growth. Stabilized PBL and increased RH2 ultimately result in a 100 μg m−3 increase in PM 2.5 concentrations and a 1.5 km reduction in atmospheric visibility (VIS). By considering the ARI, the model generally improves numerical weather prediction (NWP) and haze-fog prediction. For the entire BTH region, the mean biases for T2 and RH2 are reduced by up to 2°C and 4% respectively. This more accurate NWP will lead to an overall improvement in haze-fog prediction, particularly in a heavily polluted city like Shijiazhuang (SJZ), the biases for PM 2.5 and VIS are reduced by 28% and 125% respectively. The results show the positive feedback between meteorology and haze-fog, and demonstrate the importance of ARI in NWP and haze-fog prediction. • Aerosol-radiation interaction (ARI) has a critical influence on the outbreak of severe haze-fog. • The ARI mechanism is achieved in GRAPES_Meso5.1/CUACE. • The two-way coupled model considering ARI significantly improves the prediction of both meteorology and haze-fog. [ABSTRACT FROM AUTHOR]