The aerosol–radiation–meteorology feedback loop is the process by which aerosols interact with solar radiation to influence boundary layer meteorology. Through this feedback, aerosols cause cooling of the surface, resulting in reduced buoyant turbulence, enhanced atmospheric stratification and suppressed boundary layer growth. These changes in meteorology result in the accumulation of aerosols in a shallow boundary layer, which can enhance the extent of aerosol–radiation interactions. The feedback effect is thought to be important during periods of high aerosol concentrations, for example, during urban haze. However, direct quantification and isolation of the factors and processes affecting the feedback loop have thus far been limited to observations and low-resolution modelling studies. The coupled large-eddy simulation (LES)–aerosol model, the University of California, Los Angeles large-eddy simulation – Sectional Aerosol Scheme for Large Scale Applications (UCLALES-SALSA), allows for direct interpretation on the sensitivity of boundary layer dynamics to aerosol perturbations. In this work, UCLALES-SALSA has for the first time been explicitly set up to model the urban environment, including addition of an anthropogenic heat flux and treatment of heat storage terms, to examine the sensitivity of meteorology to the newly coupled aerosol–radiation scheme. We find that (a) sensitivity of boundary layer dynamics in the model to initial meteorological conditions is extremely high, (b) simulations with high aerosol loading (220
Severe air pollution events are a major health issue for megacities worldwide, particularly in nations with large populations and high levels of industrialisation such as India and China. Beijing, situated in the North China Plain, is well known for its air quality issues, where concentrations of PM
In addition to the unfavourable meteorological conditions, heavy emissions and regional transport of pollutants into Beijing cause high concentrations of urban aerosol particles to accumulate. These particles can either scatter or absorb solar radiation, depending on their composition. Observations predominantly show that aerosol particles cause net cooling at the surface and warming in the upper atmosphere. This consequently alters the thermal profile of the atmosphere, reducing turbulence due to buoyancy. Reduced turbulent mixing suppresses boundary layer development during the day, minimises the vertical mixing of pollutants and increases surface aerosol concentrations. Furthermore, reduction in planetary boundary layer (PBL) height due to the feedback effect also increases water vapour concentrations which can result in enhanced aqueous heterogeneous reactions, thus increasing the rate of secondary aerosol formation. If the aerosol particles are hygroscopic, increased water vapour concentrations will also cause particle growth, resulting in stronger aerosol–radiation interactions. This positive feedback loop between aerosols, radiation and meteorology can lead to sustained periods of stagnation and has been found to enhance pollution events (Fig.
Schematic of the positive feedback loop between aerosols, radiation and meteorology thought to enhance pollution episodes in Beijing.
Aerosol composition and size are the main factors impacting an aerosol particle's single scattering albedo, thus impacting the extent by which it will interact with radiation. Most aerosol particles predominately scatter radiation and thus have an overall cooling effect, stabilising the boundary layer and allowing for further accumulation of aerosol particles. However, black carbon (BC), an absorbing aerosol which can contribute up to 10 % of PM in Beijing
Research examining the feedback effect on Beijing haze episodes has thus far relied upon observations or regional modelling studies.
Observational studies also show a link between aerosol concentrations and boundary layer meteorology.
Despite an increase in research in this area, quantification of aerosol perturbations on boundary layer meteorology is still uncertain. In WRF-Chem, results are strongly dependent on the boundary layer scheme or parameterisation employed throughout the simulations, while observations of this effect, although useful, only show links between the phenomena without being able to quantify the processes or separate factors. High-resolution sensitivity studies which allow for direct analysis of boundary layer meteorology are therefore needed to be able to assimilate the major contributions to haze events.
Large-eddy simulations (LESs) can explicitly resolve large, high-energy eddies while parameterising smaller eddies for computational efficiency. This allows for direct investigation of boundary layer meteorology, turbulent fluxes and statistics, while easily controlled conditions allow for insight into the sensitivity of aerosol interactions on PBL dynamics
The model used in this work is UCLALES-SALSA. A comprehensive description of the model and its previous uses can be found in the paper by
UCLALES is a large-eddy simulation which has mainly been used in idealised cloud and fog studies. It is based on the Smagorinsky–Lilly subgrid model and solves the Ogura–Phillips anelastic equations with an Asselin filter. Boundary conditions are doubly periodic in the horizontal and fixed in the vertical. Momentum variables are advected with leapfrog time stepping and scalar variables through forward time stepping. In the standard model, a two-moment warm rain microphysical scheme is used, the vertical is spanned by a stretchable grid, and a sponge layer is applied at the domain top to prevent gravity waves being released into the boundary
SALSA was developed by
Schematic of the size bin layout for SALSA including the internal and external mixing size bins and the cloud and rain droplet bins
UCLALES-SALSA couples the UCLALES with SALSA and is primarily described in the paper by
The solution for radiative transfer in UCLALES is based on the four-stream method integrating over 6 shortwave bands and 12 longwave bands according to
In the past few decades, rapid urbanisation has transformed the landscape in Beijing, creating a microclimate which can be represented by its own distinct physics. Part of this is the urban heat island (UHI), which refers to the phenomenon where a city is significantly warmer than its surrounding areas. This is a result of increased SW radiation absorption, decreased longwave (LW) radiation loss, decreased turbulent transport, increased heat storage and anthropogenic heat sources. Furthermore, urban environments often consist of mainly impervious surfaces, and therefore the urban heat island is also often characterised by low latent heat and comparatively higher sensible heat fluxes
Studies by
In order to set up UCLALES-SALSA for an urban environment, alterations to the heat storage term and a simplistic additional anthropogenic heat flux were included in the surface scheme and sensitivity studies were performed for a non-polluted day in Beijing (Fig.
Potential temperature
Of all surface parameters altered, the largest sensitivity the model showed was to volumetric heat capacity (
This sensitivity work provides the setup for UCLALES-SALSA in an urban environment and this is utilised for the remainder of results presented below which all include a diurnal
All measurements used in this study were taken at IAP, Chinese Academy of Sciences, as part of the APHH Beijing campaign. Measurements taken include but are not limited to NR-PM
The domain size for all model simulations spanned 5.4 km in the horizontal with a resolution of 30 m, and the model top was set to 1.8 km in the vertical with a resolution of 10 m. The model uses an adaptive time step with a maximum time step of 1 s. A haze period which took place within the APHH winter campaign period from 24 to 26 November 2016 was used to examine the sensitivity of boundary layer meteorology to varying aerosol concentrations. Meteorological data taken from ECMWF-ERA5 reanalysis and tower meteorological data were used to initialise vertical profiles at 08:00 LST on 24, 25 and 26 November. Simulations were run from 08:00 LST for 14 h (22:00 LST) including 1 h spin-up time. Simulations for all days were considered to be cloudless. Case studies for each day were simulated and compared to each other and are described as follows: case 1 – no aerosols, case 2 – high and low aerosol loading, case 3 – aerosol vertical profiles. For case 2, aerosol vertical profiles were constant in the column, whereas case 3 examined the impact of including a varying aerosol vertical profile. Aerosol size distribution parameters and volume fraction of aerosol components were the same for all simulations, detailed in Tables 1 and 2. The values for aerosol size distribution data used were measured by a scanning mobility particle sizer (SMPS) and aerosol composition measurements were taken with an aerosol mass spectrometer (AMS). All in situ measurements were taken at IAP and values used were averaged between 07:30 and 08:30 LST on 24 November 2016. In all cases, BC can be considered to be the primary absorbing aerosol, with sulfate (
Real (
Size distribution parameters initialised for simulations examining measured aerosol feedback on meteorology.
The results highlighted in this section aim to test the sensitivity of the newly coupled aerosol–radiation scheme in UCLALES-SALSA to aerosol loading, using meteorological conditions, urban characteristics and simplified aerosol conditions, associated with Beijing haze episodes. Case 1 shows boundary layer development for 24, 25 and 26 November with no aerosols, case 2 examines the effect of high and low aerosol loading for each of the days, and case 3 focuses on the impact of varying aerosol vertical profiles.
Simulations in case 1 examine the development of boundary layer dynamics for 24, 25 and 26 November without aerosol–radiation interactions. All 3 d are initialised with different meteorological vertical profiles, taken from ECMWF profiles. On 25 November, there is a strong temperature inversion throughout the whole profile, while on 26 November there is strong vertical wind shear, higher surface humidity and strong stability in the lowest 300 m (Fig.
SHF is similar in magnitude for all 3 d, while TKE and simulated PBL height are significantly lower for the 25 November simulation. A well-mixed, turbulent boundary layer forms quickly on 24 November; however, on 25 November, a shallow, weakly turbulent boundary layer remains throughout the day, and on 26 November a turbulent boundary layer is much slower to develop (Fig.
Case 1 shows that simulated boundary layer dynamics are impacted by initial meteorological conditions. In case 2, the sensitivity of boundary layer dynamics to aerosol loading is examined, where aerosol mixing ratios were constant throughout the profile as shown in Fig.
In all cases, inclusion of aerosols causes cooling in the lower planetary boundary layer and warming above it. This is due to the aerosols absorbing and scattering incoming SW radiation (Fig.
Potential temperature profiles at 17:00 LST for 24, 25 and 26 November, with no aerosols (red), low aerosol loading (blue) and high aerosol loading (turquoise).
Potential temperature (
For the case of 25 November, the PBL is already low due to synoptic conditions and aerosols from the previous day causing strong temperature inversions in the morning. Therefore, even though the aerosols cause cooling in the PBL to the same extent on 26 and 25 November, a strong temperature inversion already exists on 25 November and so the PBL is low even without the inclusion of aerosols.
To assess the sensitivity of the model to a varied aerosol vertical profile, case 3 uses the same setup as case 2 but varies the aerosol mass mixing ratio with altitude, as shown in Fig.
Aerosol mass mixing ratio vertical profiles for low and high aerosol loading simulations on 26 November, for constant aerosol profile (red) and vertically varying aerosol profiles (blue) at initial time step (solid) and after 9 h simulation (dashed). For the case of the constant vertical profiles (red lines), the aerosol mass mixing ratio remains constant through time and so the dashed red lines are hidden behind the solid red ones.
Figure
Number mixing ratio (solid) and potential temperature (dashed) vertical profiles at 17:00 LST for constant vertical aerosol profiles on 24 November (blue) and 26 November (turquoise) and varied aerosol vertical profiles on 24 November (red) and 26 November (green). The mixing ratios for constant aerosol vertical profiles (blue and turquoise) remain constant in time for both simulations; the solid blue line is equivalent to the solid turquoise line.
Variance in vertical velocity (
Figure
The results highlighted above show the use of a novel coupled LES–aerosol radiation model to investigate haze in the urban environment of Beijing. Simulated sensitivity to urban surface parameters is high and these will be different for other urban locations. It is therefore necessary to evaluate and tune these parameters to observations in specific environments in order to use an LES model to fully explore boundary layer dynamic sensitivities. Aerosol–radiation interactions were tested for the first time in the model framework and showed that sensitivity of boundary layer meteorology and turbulence to aerosol loading was strong while also being dependent on initial meteorological conditions.
Case 1 identifies the importance of meteorological conditions on boundary layer dynamics throughout the day. Many observations in Beijing found that meteorological conditions are a main driver for both the onset and longevity of haze. Large-scale synoptic conditions such as southerly winds and low pressure often pre-empt pollution episodes which tend to occur every 4–7 d during wintertime in Beijing
The initial meteorological profiles for the simulations on 24 November are taken prior to the onset of the haze and are associated with clean conditions. This is likely the reason for the quick turbulent boundary layer development along with high TKE and SHF throughout the day. Observations show that aerosol concentrations begin to build up around midday on 24 November and remain constant until the afternoon of 25 November when concentrations build up rapidly, peaking overnight on 25 November and remaining high until the afternoon of 26 November. Therefore, the initial conditions used in the simulation of 25 November will have been slightly affected by aerosol–radiation interactions of the previous evening. Aerosol–radiation interactions reduce the amount of solar radiation reaching the surface which causes cooling; simultaneously, black carbon aerosols will absorb radiation at the top of the PBL. Although absorption by BC occurs throughout the column, several studies have shown that due to the higher incidence of solar radiation and lower density of air, BC causes warming at PBL top to a greater extent than at the surface
Aerosol–radiation interactions cause a reduction in SHF, surface SW radiation and TKE, resulting in a reduction in the daily maximum PBL height for all 3 d examined. In these simulations, the aerosols interact with radiation to cause heating and cooling in different layers which perturbs the temperature profile of the PBL and decreases the sensible heat flux term. The aerosols also take up water to a small extent which decreases latent heat. These effects lead to decreased turbulence in the PBL. High aerosol concentrations enhance this effect due to an increased number of aerosols interacting with radiation. This leads to a reduction in maximum SHF of 44, 33 and 45 W m
Volume fraction of aerosols included in SALSA for all simulations in case 2 and case 3.
Change in maximum PBL height (taken as the height between 12:00 and 16:00 LST with a maximum gradient in potential temperature) and SHF for all 3 d with high and low aerosol loading.
High aerosol concentrations are known to stabilise the boundary layer through the reduction of vertical transport of momentum to the surface
In these simulations, aerosol profiles are constant through the column and high aerosol concentrations aloft. Figure
Case 3 examined the impact of meteorological feedback on aerosol vertical mixing for high and low aerosol loading simulations by including aerosol vertical profiles on 24 and 26 November. Simulations with a varied vertical aerosol profile had the same aerosol concentrations at the surface as the high aerosol simulations in case 2 but reduced concentrations at higher altitudes (Fig.
The large degree of cooling on 26 November compared to 24 November is due to the effects of initial meteorology feeding back on aerosol–radiation interactions. Figure
UCLALES-SALSA was set up to model an urban environment for the first time, in order to investigate the impact of aerosol–radiation interactions on urban haze. During setup, sensitivity to urban surface parameters was shown to be high and accounted for the slower release of heat throughout the day as observed in urban Beijing. Inclusion of a diurnal anthropogenic heat flux in simulations resulted in a warmer environment typical of an urban heat island. Given the sensitivity to such parameters, accurate measurements of these properties can be considered paramount in order to improve modelling of the urban environment. Turbulent motion throughout the day in each simulation is further impacted by initial meteorological profiles. Conditions associated with clean periods in Beijing allow for the development of a highly turbulent boundary layer, while strong morning temperature inversions prevent the growth of a turbulent boundary layer throughout the day. Aerosol–radiation interactions in all cases decreases SHF, TKE and PBL height, in addition to causing cooling at the surface and reducing surface wind speeds. All simulations also show a large sensitivity to aerosol loading, with more than a one-third reduction in SHF due to high aerosol loading in all simulations. Through comparing simulations with and without aerosol vertical profiles (case 3), we observe that on 26 November the simulated development of a turbulent boundary layer in the afternoon is impacted by high aerosol loading aloft (case 2). This is due to aerosols at high altitudes reducing mechanical shear as well as the reduction in buoyancy. However, overall, the effect of including a vertical aerosol profile is minimal compared to the effect of overall aerosol loading, which suggests a higher effect of surface aerosols.
The sensitivity work outlined above aims to isolate the aerosol and dynamical effects on pollution episodes through using a specific period with varying meteorological conditions and simplified aerosol conditions. LES models are limited in their ability to represent changing synoptic conditions without additionally forcing or nudging simulated profiles with mesoscale model results or through observations. However, these simulations do show the sensitivity to and importance of meteorological conditions in the development of boundary layer turbulence in Beijing in addition to assessing the importance of aerosol loading on the aerosol–meteorology feedback loop and the impact on PBL turbulent statistics. The aerosol feedback loop is thought to have the largest impact on haze episodes during the cumulative and dissipation stages of the pollution episode. Future work will focus particularly on these stages and the impact of aerosol–radiation–meteorology interactions. As aerosol optical properties play an important role in the feedback, future work will also take advantage of the SALSA framework to vary aerosol optical properties in a case study of Beijing haze.
The input data for the simulations performed in this study can now be found at
The idea for the study was conceived by JS, GM and HC. All model simulations were performed by JS with the assistance of JT. JS wrote the paper with input from JT and TK. All co-authors discussed the results and commented on the manuscript.
The authors declare that they have no conflict of interest.
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Model simulations were carried out on the ARCHER UK National Supercomputing Service (
This research has been supported by the National Centre for Atmospheric Science (NCAS), AIRPRO (grant no. NE/N00695X/1) and the Academy of Finland (projects 283031 and 309127).
This paper was edited by Pingqing Fu and reviewed by three anonymous referees.