Abstract

Abstract. An online coupled regional climate–chemistry–aerosol model (RIEMS-Chem) was developed and utilized to investigate the mechanisms of haze formation and evolution and aerosol radiative feedback during winter haze episodes in February–March 2014 over the Beijing-Tianjin-Hebei (BTH) region in China. Model comparison against a variety of observations demonstrated a good ability of RIEMS-Chem in reproducing meteorological variables, planetary boundary layer (PBL) heights, PM2.5, and its chemical components, as well as aerosol optical properties. The model performances were remarkably improved for both meteorology and chemistry by taking aerosol radiative feedback into account. The domain-average aerosol radiative effects (AREs) were estimated to be −57 W m−2 at the surface, 25 W m−2 in the atmosphere, and −32 W m−2 at the top of atmosphere (TOA) during a severe haze episode (20–26 February), with the maximum hourly surface ARE reaching −384 W m−2 in southern Hebei province. The average feedback-induced changes in 2 m air temperature (T2), 10 m wind speed (WS10), 2 m relative humidity (RH2), and PBL height over the BTH region during the haze episode were −1.8 ∘C, −0.5 m s−1, 10.0 %, and −184 m, respectively. The BTH average changes in PM2.5 concentration due to the feedback were estimated to be 20.0 µg m−3 (29 %) and 45.1 µg m−3 (39 %) for the entire period and the severe haze episode, respectively, which demonstrated a significant impact of aerosol radiative feedback on haze formation. The relative changes in secondary aerosols were larger than those in primary aerosols due to enhanced chemical reactions by aerosol feedback. The feedback-induced absolute change in PM2.5 concentrations was largest in the haze persistence stage, followed by those in the growth stage and dissipating stage. Process analyses on haze events in Beijing revealed that local emission, chemical reaction, and regional transport mainly contributed to haze formation in the growth stage, whereas vertical processes (diffusion, advection, and dry deposition) were major processes for PM2.5 removals. Chemical processes and local emissions dominated the increase in PM2.5 concentrations during the severe haze episode, whereas horizontal advection contributed to the PM2.5 increase with a similar magnitude to local emissions and chemical processes during a moderate haze episode on 1–4 March. The contributions from physical and chemical processes to the feedback-induced changes in PM2.5 and its major components were explored and quantified through process analyses. For the severe haze episode, the increase in the change rate of PM2.5 (9.5 µg m−3 h−1) induced by the feedback in the growth stage was attributed to the larger contribution from chemical processes (7.3 µg m−3 h−1) than that from physical processes (2.2 µg m−3 h−1), whereas, during the moderate haze episode, the increase in the PM2.5 change rate (2.4 µg m−3 h−1) in the growth stage was contributed more significantly by physical processes (1.4 µg m−3 h−1) than by chemical processes (1.0 µg m−3 h−1). In general, the aerosol–radiation feedback increased the accumulation rate of aerosols in the growth stage through weakening vertical diffusion, promoting chemical reactions, and/or enhancing horizontal advection. It enhanced the removal rate through increasing vertical diffusion and vertical advection in the dissipation stage, and had little effect on the change rate of PM2.5 in the persistence stage.

Highlights

  • Aerosols affect radiation transfer by scattering or absorbing solar and infrared radiation, by acting as cloud condensation nuclei (CCN) to modify cloud properties, and by heating the atmosphere to alter cloud formation, termed as the aerosol direct radiative effect, indirect effect, and semi-direct effect, respectively (Twomey, 1974; Albrecht, 1989; Ramanathan et al, 2001)

  • Recent observational analyses of aerosol mixing state in Beijing (Ma et al, 2012; Wu et al, 2016) indicated that more than 80 % of aerosols were internally mixed with black carbon (BC) during haze days, whereas about 70 % of aerosols were externally mixed with BC in clean days; an internal mixing assumption was adopted for model simulation, because this study focuses on haze events

  • For PM2.5 (Fig. 11a), the net integrated process rates (IPRs) due to aerosol feedback in the growth stage was 2.40 μg m−3 h−1, with 1.40 μg m−3 h−1 from physical processes (HADV + vertical advection (VADV) + horizontal diffusion (HDIF) + vertical diffusion (VDIF) + dry deposition (DDEP)) and 1.0 μg m−3 h−1 from chemical processes (GAS + thermodynamic chemistry (Thermo) + heterogeneous chemistry (HET)), which indicated that the feedbackinduced increase in PM2.5 concentration per hour was produced through larger contributions from physical processes than chemical processes in this episode

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Summary

Introduction

Aerosols affect radiation transfer by scattering or absorbing solar and infrared radiation, by acting as cloud condensation nuclei (CCN) to modify cloud properties, and by heating the atmosphere to alter cloud formation, termed as the aerosol direct radiative effect, indirect effect, and semi-direct effect, respectively (Twomey, 1974; Albrecht, 1989; Ramanathan et al, 2001). Given the increasing concerns about severe particulate matter (PM) pollution during haze days, some modeling studies have been conducted to investigate the effect of aerosol radiative feedback on meteorology and near-surface PM2.5 concentration, with a focus on winter haze events in North China Gao et al (2020) reported that the aerosol– radiation feedback-induced daytime changes in PM2.5 concentrations were less than 6 % during haze days in the BTH region in January 2010 from six applications of different online coupled meteorology–chemistry models under the international framework of MICS-Asia Phase III. An online coupled regional climate– chemistry–aerosol model (RIEMS-Chem) was developed and applied to explore the formation and evolution of haze events during February–March 2014, in which a week-long haze episode with the daily maximum PM2.5 concentration up to 400 μg m−3 (hourly mean up to 483 μg m−3) was observed. The results from this study are expected to provide new insights into the mechanism of aerosol–radiation– meteorology feedback, which is currently the source of one of the largest uncertainties in haze formation and evolution

Model description
Process analysis
Emission inventories
Model configuration and numerical experiments
Observational data
Meteorological variables
Aerosol optical parameters
Aerosol radiative effects and feedbacks
The feedback effects on meteorological variables and aerosols
Process analysis of haze evolution and aerosol radiative feedback
Haze evolution during 20–26 February
Haze evolution during 1–4 March
Contributions of physical and chemical processes to the aerosol feedback
Findings
Conclusions
Full Text
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