WRF-Chem is a fully coupled, online chemistry transport model that integrates atmospheric chemistry and aerosol processes within the Advanced Research WRF framework (Skamarock et al., 2019), allowing for direct interactions between meteorology and atmospheric composition. Unlike offline chemical transport models, WRF-Chem simultaneously simulates the evolution of weather and chemical constituents, enabling a more accurate representation of feedback mechanisms between aerosols, radiation, and clouds (Grell et al., 2005). This capability makes it a powerful tool for studying air quality, regional and urban-scale pollution, and the climatic impacts of anthropogenic and natural emissions. The model supports multiple chemical mechanisms and includes various aerosol schemes to simulate secondary organic aerosols, BC, dust, and sea salt (Emmons et al., 2010; Zaveri et al., 2008). The model offers a wide range of dynamical and chemical parameterizations, with multiple physics options, including microphysics, radiation, and planetary boundary layer schemes, tailored to different geographical regions and atmospheric conditions (Powers et al., 2017; Zhang, 2008). Additionally, its fully coupled online feedback mechanisms allow for detailed simulations of aerosol-cloud-radiation interactions, capturing the direct and indirect effects of aerosols on weather and climate (Saide et al., 2012; Baklanov et al., 2014; Glotfelty et al., 2019). These capabilities make WRF-Chem a crucial tool for assessing the impacts of emissions on atmospheric processes.

We adopt the same core WRF-Chem (v.3.9.1) (Skamarock et al., 2008) model setup and configuration as used in the operational AIRWISE system (Ghude et al., 2020; Kumar et al., 2020; Jena et al., 2021). The study region, depicted in Fig. S1 in the Supplement, is centered over Delhi (28.7° N, 77.5° E) and consists of two nested domains (d01 and d02). The outer domain (d01) encompasses North India, extending from 62 to 93° E and 21 to 36° N, with a horizontal resolution of 10km×10km and a grid size of 300×170 points. The inner domain (d02) focuses on Delhi and the National Capital Region (NCR), covering an area from 75 to 78° E and 26 to 30° N, with a higher resolution of 2km×2km and a grid size of 161×201 points. Each domain consists of 50 vertical levels, with 8 levels within the lowest one km from the surface to better resolve boundary-layer processes, and the model top is set at 50 hPa. To focus on urbanized regions, such as Delhi, the output from both domains (d01 and d02) has been masked to represent Delhi specifically. Simulations were conducted in the period from September 2023 to January 2024 capturing the high aerosol loading during the stubble burning season (mid-October to November) and the winter period in the study region. The physics schemes used in the model simulations include several key components designed to accurately represent various atmospheric processes. The WRF Single-Moment 6-class (WSM6) scheme is employed for microphysics, which simulates cloud processes (including cloud water, rain, snow, and ice) based on a single moment approach (Hong and Lim, 2006). Longwave and shortwave radiation is handled by the RRTMG, which provides efficient and accurate computation of radiative transfer and detailed representation of the solar radiation absorbed and scattered by the atmosphere (Mlawer et al., 1997; Iacono et al., 2008). The boundary-layer physics are represented by the Mellor-Yamada Nakanishi and Niino 2.5 level (MYNN 2.5) scheme, which is designed to model turbulent mixing and the vertical structure of the boundary layer (Nakanishi and Niino, 2004). For convective parameterization, the Grell-Freitas Ensemble scheme is used, for its ability to represent deep convection and its interaction with larger-scale weather systems (Grell and Freitas, 2014). The scheme is designed to provide a seamless transition between parameterized and explicitly resolved convection across different model resolutions by progressively reducing the contribution of parameterized convection as grid spacing decreases (Kwon and Hong, 2017). The Unified Noah Land-Surface Model is utilized to simulate land-surface interactions, including the exchange of heat, moisture, and momentum between the surface and the atmosphere (Chen and Dudhia, 2001). Finally, the Eta Similarity scheme is applied in the surface layer, providing near-surface turbulent fluxes based on atmospheric stability (Businger et al., 1971). These configurations of the physics schemes have been summarized in Table S1 in the Supplement.

In the WRF-Chem model, the PM_2.5 is composed of several aerosol species (Govardhan et al., 2024), including hydrophobic and hydrophilic BC (BC1 and BC2), hydrophobic and hydrophilic OC (OC1 and OC2), Dust with two size bins (Dust1 and Dust2, with effective radii of 0.5 and 1.4 µm, respectively), Sea-Salt with two size bins (SEAS1 and SEAS2, with effective radii of 0.3 and 1.0 µm, respectively), sulfate, and an un-speciated primary PM_2.5 category (as P25) representing other inorganic fine particulate matter. In the aerosol–radiation calculations, P25 is assigned the same optical properties and refractive indices as mineral dust and is therefore treated as a dust-like aerosol species for estimating its radiative effects. The optical properties of these aerosol species, including their scattering and absorption cross-sections, are separately computed in the model. These properties depend primarily on the complex RI and the morphology (shape and size) of the aerosols (Barnard et al., 2010; Tuccella et al., 2015). These RIs are basically used in the calculation of optical properties of the aerosols (such as SSA, AF etc.) using 3 methods: (a) Volume averaging mixing rule, (b) Maxwell-Garnett mixing rule and, (c) Shell-core method in the WRF-Chem model. For this study we have used the volume averaging mixing rule for the calculation of optical properties keeping in mind the mixing state of aerosols in the ambient environment of Delhi (Srivastava et al., 2018). The detailed explanation of these rules can be found in the literature (Fast et al., 2006; Barnard et al., 2010). In addition to aerosol composition, aerosol optical properties in WRF-Chem are affected by hygroscopic growth and aerosol water uptake. Aerosol water content is diagnosed as a function of aerosol composition and ambient relative humidity and is added to the dry aerosol volume to determine the wet particle size. Aerosol water is also included in the volume-averaged mixing of complex RIs, yielding an effective wet refractive index. Consequently, aerosol optical properties such as extinction, AOD, SSA, and AF are calculated using the wet particle radius and wet effective refractive index, allowing ARIs to respond dynamically to relative humidity. The optical properties are then used in the Mie subroutines to solve the radiative transfer equations in the model. The full Mie calculations are computationally expensive in the sense that they solve each equation separately and hence require more time for the model to simulate radiative transfer fluxes. An alternate subroutine that is computationally fast, but can be less accurate, uses the Chebyshev economization method (detailed explanation in Ghan et al., 2001) for the calculation of radiative fluxes. The downwelling shortwave radiation at the ground level of the model is then produced after interacting with various entities present in the atmosphere. Hence, inaccurate prescription of aerosol optical parameters can lead to erroneous simulations of their radiation interactions (Haywood and Boucher, 2000).

To improve the representation of ARIs over the study region, the RIs of Dust, BC, OC, and P25 were modified in the model. The values of complex RIs used in the study for the aerosol species were obtained from the available literature. These values were either region specific, calculated using the sampling techniques or laboratory tested values calculated under the controlled environment which may be valid for any region of interest. The details of the modified complex RIs are summarized in Table 1.

The original values of the RI were found to be independent of wavelength in WRF-Chem i.e., these species can scatter (real part of RI) and absorb (imaginary part of RI) the radiation with the same factor for all the 4 shortwave radiation bands present in the model. However, in the literature it was found that the RI values of these species depend on wavelength (except BC) and have different scattering or absorbing nature in each shortwave radiation band. Furthermore, OC was assumed to be non-absorbing in the model, but literature suggests an imaginary part of RI at few shortwave radiation bands (Table 1). The real part of RI values prescribed in the model for all the species mentioned above are approximately in agreement with the literature. However, the imaginary parts differ. Our modifications to the RI, thus make the model aerosol mixture more absorbing. Since aerosol optical properties are also calculated for hygroscopically grown particles under ambient relative humidity conditions, the revised RIs influence the optical response of hydrated aerosols. Consequently, aerosol water uptake and particle growth modify the scattering and absorption characteristics of the aerosol population, while the updated RIs alter the magnitude of these optical effects. This combined influence affects aerosol optical properties, and ARIs simulated by the model.

Eight experiments were conducted to assess the impact of ARI under various conditions. Among these, three experiments were designed to evaluate which radiation scheme, namely RRTMG, Goddard, or CAM, produces surface fluxes most consistent with observations. To isolate the role of aerosol–radiation feedback, experiment No-ARF was performed with the aerosol–radiation feedback turned off. Additionally, four experiments (RI-DUST, RI-BC, RI-OC and RI-ALL) were carried out to investigate the individual contributions of specific chemical species to the shortwave radiation reaching the surface. A summary of all experiments is provided in Table 2.

To examine the impacts of modifications in the optical properties of aerosols on the downwelling shortwave radiation in the WRF-Chem, we selected extreme pollution events that occurred over Delhi during the year 2023–2024. Figure 1 presents the mean observed PM_2.5 concentrations recorded over Delhi from 20 September 2023 to 31 January 2024. During this period, concentrations peaked at ∼565 µgm-3, primarily due to local emissions from firecrackers burning on the eve of the Diwali festival. The observed PM_2.5 time series also highlights two periods of persistently elevated aerosol loading, which were selected as special case studies to evaluate the impact of modified aerosol optical properties in the model: (a) a post-monsoon episode and (b) a typical winter event over Delhi.

The first case (Event-I), covers a 10 d period from 1 to 10 November 2023, chosen to represent intense aerosol loading due to enhanced anthropogenic activity and widespread crop residue burning in neighbouring states. During this event, PM_2.5 concentrations reached as high as 500 µgm-3, with some days recording around 400 µgm-3 (Fig. 1). Daytime concentrations typically ranged between 200–300 µgm-3. The second case (Event-II), spanning from 8 to 18 January 2024, was selected to investigate the influence of the modified optical properties under winter meteorological conditions characterized by low temperatures, high relative humidity, and stagnant wind patterns. This event was marked by persistent fog and low-level clouds that significantly diminished incoming solar radiation. PM_2.5 concentrations during this period peaked at ∼550 µgm-3, with values on most days ranging between ∼100 to 300 µgm-3.

For these high pollution events (Event-I and Event-II), a spin-up time of two days was applied for each simulation to allow meteorological and MOZART-derived chemical initial conditions to adjust to the model dynamics and emissions. Also, the comparison of these experiments was carried out for the outer domain (d01) with the observations to understand the regional impacts and variations in ARI.

The same set of experiments (No-ARF, RI-DUST, RI-BC, RI-OC and RI-ALL) was also conducted for the period from 20 September 2023 to 31 January 2024, with a 10 d spin-up, over domains d01 and d02. This long-term four-month simulation was designed to examine the impacts of modified aerosol optical properties on downwelling shortwave radiation (hereafter SWDOWN) over an extended period and during non-episodic pollution conditions. The results were analysed under both all-sky conditions (atmosphere with clouds, aerosols, trace gases etc.) and clear-sky conditions (atmosphere without clouds). For the clear-sky analysis, all data points influenced by cloud cover were excluded by retaining only those grid points and time steps with a cloud fraction less than 0.1 at all models' vertical levels.

The comparison of model-simulated meteorological variables (SWDOWN, T2, RH2, WS10), and mass concentrations of PM_2.5 with observational datasets was carried out using various statistical parameters to assess the model's performance. These parameters included Mean Bias (MB), Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), and Fractional Bias (FB). These statistical metrics offer a comprehensive evaluation of how well the model simulations align with the observed datasets. The detailed formulas and descriptions for each of these statistical parameters are provided in the Supplement (Table S2).

To choose the best amongst multiple shortwave radiation parameterization schemes available in WRF-Chem, we ran the model with the default optical properties of aerosols with three different schemes. We chose RRTMG, Goddard and CAM for this exercise and compared the diurnal cycles of averaged SWDOWN simulated over Delhi using these schemes with the corresponding observations. The comparisons are done for the month of December 2023 under clear-sky conditions. It should be noted that WRF-Chem does not provide a separate clear-sky SWDOWN diagnostic; therefore, clear-sky conditions were identified through cloud screening rather than by using a model-generated clear-sky radiation output. In the model simulations, cloudy or foggy periods were excluded by retaining only those hours for which the cloud fraction remained below 0.1 at all model levels over Delhi. For the observations, cloudy and fog-affected periods were identified using ceilometer backscatter measurements and associated cloud detection products and were subsequently removed. The SWDOWN evaluation was therefore restricted to periods classified as cloud-free in both the model and observational datasets (Fig. 2). It can be seen that, in comparison with RRTMG, the simulated SWDOWN in Goddard and CAM, overestimate the observations. Overall, all three radiation schemes overestimate SWDOWN during the morning hours (00:00 to 12:00 LT), whereas Goddard and CAM maintain a positive bias throughout the day. In, contrast, RRTMG shows a closer match to the observed values especially during the afternoon hours. However, the persistent morning overestimation is evident even after excluding foggy periods from the analysis, suggesting that it is not solely associated with fog or cloud contamination.

The biases between Goddard and CAM are roughly at least twice or even more of those for RRTMG (Table S3). In fact, RRTMG shows a very good agreement with the observations during the mid-day hours of 12:00–15:00 LT. The maximum differences in the model schemes and the observations occur around 09:00 LT, while the minima for RRTMG comes at 13:00 LT and that for Goddard and CAM is reached at 17:00 LT. Thus, RRTMG is found to be more accurate in capturing the SWDOWN vis-a-vis the other available radiation parameterization schemes. Therefore, we choose the RRTMG scheme to parameterize the shortwave radiative processes in the further experiments of this study. We refer to this configuration as CTRL in the next sections.

As mentioned in Sect. 2.3, we examine the impacts of modified aerosol optical properties on the SWDOWN for the two extreme pollution cases. The simulated near-surface PM_2.5 dry mass concentration for both these events are shown in Fig. S2. Since, we are analysing the impacts of the modified aerosol optical properties on SWDOWN mainly in clear-sky conditions, here, we show the comparison of modelled PM_2.5 with the observations in the clear-sky. For Event-I i.e., 1 to 10 November 2023 (Fig. S2a), the observations depict a peak in PM_2.5 mass concentrations during the afternoon hours of 5 November, and the values go beyond 500 µgm-3. The model in the CTRL configuration rises a little up to 400 µgm-3, capturing the signature of the peak, but failing to capture the exact magnitude. On the contrary, during the afternoon hours of 4 November, the observations just manage to cross 400 µgm-3, but the CTRL simulations reach beyond 450 µgm-3. For all the other days, the CTRL configuration captures observed tendency of the PM_2.5, even the values also are seen to be within ±20 % of the observations. However, on the 6 and 7 November, the model shows an overestimation in comparison with observations. This is likely related to the uncertain biomass burning emissions from fires mainly from the neighbouring states of Punjab and Haryana. To confirm this, we have done more simulation in CTRL configuration without the prescription of biomass burning emissions. The modelled PM_2.5 in that configuration (Fig. S3) can be seen to reduce, especially on 6 and 7 November, thus highlighting the role played by fire emissions in causing the large disagreements between model and observations on those days. Overall, the CTRL configuration fails to capture the exact magnitude of the observed PM_2.5, but the signals of the extreme pollution are well captured with PM_2.5 magnitudes not too low vis-a-vis observations.

For Event-II of extreme pollution i.e. from 8 to 18 January 2024 (Fig. S2b), most of the days were mainly affected by dense fog spells (Kant et al., 2025), especially up to 14 January. During this period, the model underestimated PM_2.5 concentrations relative to the observations. The underestimation is likely associated with the model's difficulty in reproducing the observed fog and near-saturated conditions because of a negative RH bias (Sengupta et al., 2022). Lower simulated RH can limit aerosol hygroscopic growth and suppress aqueous-phase processing, secondary aerosol formation, and gas-particle partitioning, thereby contributing to lower PM_2.5 concentrations during foggy periods. Nevertheless, the model fairly captures the signals of the observed PM_2.5 under clear-sky conditions, though the exact values are missed. Thus, for both the events, the model in CTRL configuration captures the observed signatures of peak signals, however the peak magnitudes are missed. Nevertheless, the model's atmosphere has enough ambient aerosols under clear-sky conditions, to interact with the incoming shortwave radiation and show impacts of the modified optical properties on it.

We now evaluate the simulated SWDOWN in the CTRL and RI-ALL configuration which is the combined modified RIs of all chemical species and compare it with the corresponding observational data at the WiFEX site. Due to unavailability of data during the Event-I of high Air Quality Index (AQI) (i.e. during November 2023), we have done the model-observation comparisons only for Event-II i.e. during 8 to 18 January 2024 (Fig. 3a). It may be noted that the comparisons are done only for the daytime SWDOWN values for cloud- and fog-free scenarios. This results in screening-out of most of the early morning and nighttime data. The simulated SWDOWN shows a satisfactory comparison with the observations by capturing the day-to-day variation in high and lows, however the magnitudes differ substantially. The RI-ALL configuration tends to reduce the SWDOWN in comparison with CTRL. The bias in the SWDOWN reduces by around 10–15 Wm-2 especially from 10–12 January (Fig. 3b). Towards latter half of the study period (14–18 January), the bias vis-a-vis observations in fact increases by as high as 25 Wm-2. In general, the RI-ALL configuration reduces the downwelling shortwave radiation at the surface by 10–25 Wm-2. The MB, RMSE, NMSE, and the FB are 4.58 (0.86) Wm-2, 22.45 (21.61) Wm-2, 0.043 (0.042), and 0.042 (0.008) for the CTRL (RI-ALL) experiment (Table S5). Thus, modifying the aerosol optical properties improves the agreement between simulated and observed SWDOWN, primarily through a reduction in mean bias, while the improvement in RMSE is relatively modest.

To isolate the impacts of the modifications in the individual aerosol species' optical properties in causing such changes in SWDOWN, we plot the hourly difference in clear-sky SWDOWN in each of the sensitivity experiments vis-a-vis the CTRL configuration in Fig. 4a (for Event-I) and 4c (for Event-II), the event-mean hourly differences in the SWDOWN are plotted in Fig. 4b and d for Event-I and the Event-II, respectively. Additionally, the total impacts of ARI on the SWDOWN have also been computed as the difference between the No-ARF and CTRL configuration. Those values have also been plotted in hourly as well as event-mean hourly basis in Fig. 4a–d. It may be seen that the hourly changes in SWDOWN due to the modified optical properties of all aerosol species could go as high as 80 Wm-2 on the polluted days of 7 and 8 November (Fig. 4a). These changes are roughly 1/3 of the total clear-sky aerosol radiation impact on the given day. On the contrary, during the winter month of January 2024, the corresponding changes on the extreme pollution days are around 30–40 Wm-2. This difference mainly arises due to the lesser availability of the solar radiation in the month of January compared to November. It can also be noticed that the SWDOWN is more sensitive to the changes in the optical properties of the carbonaceous aerosols compared to the mineral dust species. The mean changes in SWDOWN due to the modification in the optical properties of mineral dust (RI-DUST) is +1.4 and -0.73 Wm-2 during Event-I and Event-II respectively (Table S6). On the other hand, those due to the changes in the optical properties of BC (RI-BC) are -22.46 and -9.34 Wm-2 during Event-I and Event-II, respectively. The corresponding changes due to modified OC optical properties (RI-OC) are -11.35 and -5.69 Wm-2, respectively. Figure S4 further shows that the mean PM_2.5 composition remains broadly similar between CTRL and RI-DUST configurations during both events, indicating that the differences in simulated SWDOWN are not primarily associated with changes in aerosol mass concentrations. In particular, dust contributes only about 6 %–10 % of PM_2.5, whereas carbonaceous aerosols (BC + OC) account for nearly 37 %–40 % of the total aerosol mass during both events. Therefore, the stronger SWDOWN response to RI-BC and RI-OC configurations mainly reflects the larger radiative influence of carbonaceous aerosols rather than changes in aerosol abundance. It may be noted that the real parts (scattering ability) of the RIs of aerosol have not been substantially modified in this study (Table 1). The major modifications have occurred in the imaginary parts (absorption ability). Moreover, the changes in the imaginary parts of BC and OC are much higher in magnitude than that in the imaginary parts of the RI of dust. Therefore, RI-BC and RI-OC experiments produce more changes in SWDOWN compared to the RI-DUST. The mean changes in SWDOWN during clear-sky due to RI-ALL experiment are -27.52 and -13.39 Wm-2 for the Event-I and Event-II respectively. With the noon-time values going as high as 50 and 25 Wm-2 respectively, which are roughly 1/3 to 1/4 of the total impacts of direct radiative effects of aerosols.

The changes in SWDOWN due to changes in the optical properties of all aerosol species are around 20 % (in magnitude) less than the sum of the changes in SWDOWN due to the modification of individual species' optical properties, suggesting that the changes in SWDOWN are not strictly linear and additive.

We now examine the impacts of modified optical properties of aerosols on the meteorological parameters mainly through the direct radiative interaction and the fast-atmospheric response to those. We compare the impacts on the near-surface temperatures (T2), relative humidity (RH2) levels, wind speeds (WS10), and the simulated planetary boundary layer height (PBLH). We compare the simulated values with the corresponding measurements carried out at the WiFEX measurement site at the IGI Airport, New Delhi in Fig. 5. Figure 5a, c, e, and g, depict the comparisons between the meteorological parameters simulated by CTRL and RI-ALL configurations, and the measurements, for the Event-I. Figure 5b, d, f, and h, show the impacts of modified optical properties of aerosols on the simulated meteorological parameters in the model. For T2, it may be seen that both the model configurations underestimate the temperatures during the pre-noon hours and overestimate those in the afternoon through early midnight hours. The modification in the optical properties results in the reduction of surface temperatures by 0.2 °C in the morning hours, while the post-noon enhancements in temperatures also reach up to 0.3 °C. The late morning reduction in the temperatures is expected, as the more optically thick aerosol mixture in the RI-ALL configuration scatters and absorbs more incoming shortwave radiation than that in the CTRL configuration, thus resulting in surface-cooling. However, the temperature response remains small and changes sign during different times of the day. While the reduction in SWDOWN tends to cool the surface, the simultaneous decrease in PBLH (Fig. 5h) may reduce turbulent mixing and modifies the redistribution of heat within the near-surface layer, partially offsetting the radiative cooling. Consequently, the net impact on T2 remains modest, with differences generally within ±0.3 °C throughout the day. The corresponding RMSE values are 1.43 and 1.59 °C for CTRL and RI-ALL, respectively (Table S4). As a consequence of reducing the surface temperature during the daytime, the RH2 values are seen to be higher in the RI-ALL configuration vis-a-vis CTRL case. On an average the changes in RH2 are ∼2 %–4 %. Nevertheless, the simulated RH2 values are much lesser compared to the measurements. One of the reasons behind this underestimation in RH could be related to the drier soil moisture levels in the model, as shown by Parde et al. (2022). The changes in WS10 due to the modified optical properties of aerosols are however relatively higher and sustained morning through evening. On an average, the winds are found to be calmer in the RI-ALL configuration vis-a-vis CTRL. The maximum reductions of around 0.4 ms-1 are seen to be during the 12:00–16:00 LT with the mean winds in the CTRL configuration around 2.5–3.25 ms-1. Thus, the near-surface winds are seen to be slowing down by ∼12 %–16 %, throughout the day, with maximum changes during the afternoon hours. This in turn also results in better agreements with the observations in comparison with the CTRL run. For WS10, the MB, RMSE, NMSE, and the FB are 0.20 (0.01) ms-1, 0.83 (0.75) ms-1, 0.283 (0.262), and 0.115 (-0.01) for the CTRL (RI-ALL) experiment (Table S4).

As a result of the reduction in surface radiative heating due to enhanced aerosol extinction, together with weaker near-surface wind speeds, the boundary layer turbulence is also expected to be reduced in RI-ALL configuration. The PBLH which represents the height of the turbulence in the planetary boundary layer is seen to be reduced in the RI-ALL configuration vis-a-vis CTRL. Observational estimates of PBLH were not available for the study period for validation. The mean reductions in the simulated PBLH are seen to be around 100–200 m during the afternoon hours, with as high as 200–300 m during the late afternoon hours (∼17:00–18:00 IST), which are ∼15 %–20 % of PBLH in CTRL configuration. Thus, the modified optical properties suppress the boundary-layer turbulence by increasing stability, thereby weakening vertical mixing and reducing the transport of near-surface aerosols into the overlying atmosphere.

To further investigate the dynamical response of the boundary layer, Fig. 6 presents the mean diurnal time–height differences in temperature, PM_2.5, and turbulent kinetic energy (TKE) between the RI-ALL and CTRL simulations for the Event-I. The modified aerosol optical properties produce a pronounced daytime reduction in TKE indicated by contours during the boundary-layer growth period (approximately 10:00–17:00 IST), with the largest negative differences (-0.20 to -0.35 m2s-2) confined below ∼1.5 km a.g.l. These reductions coincide with warming between approximately 700 and 1500 m along with surface cooling (up to ∼500 m height) and enhanced the PM_2.5 concentrations within the lower boundary layer. Together with the reduced surface heating caused by enhanced aerosol extinction, the elevated warming strengthens atmospheric stability and suppresses buoyancy-driven turbulence. Consequently, the daytime mixed layer becomes shallower, vertical transport is weakened, and pollutants are more efficiently confined within the lower atmosphere, favouring the accumulation of PM_2.5 near the surface. However, a positive TKE (max. ∼0.02 m2s-2) during the early morning and nighttime hours above 1 km may have locally destabilised the atmosphere which resulted in deprived PM_2.5 concentrations between approximately 100 and 700 m height and the formation of a residual PM_2.5 layer between 1 to 2 km.

The changes in the gradient Richardson number (Fig. S5) further supports the mechanism of the stability of the atmosphere. During the daytime, the CTRL simulation exhibits a layer of relatively low R (R<0.25) within the growing boundary layer, indicating dynamically unstable conditions favourable for turbulent mixing due to solar heating. In contrast, the RI-ALL simulation exhibits substantially larger R over the same period, particularly below about 1 km a.g.l. due to reduction in SWDOWN, indicating a more stable boundary layer. The positive R differences are consistent with enhanced atmospheric stability and suppressed turbulent mixing (Fig. S5c). The concurrent increase in R and decrease in TKE therefore provide independent dynamical evidence that the modified aerosol optical properties inhibit daytime boundary-layer development, leading to a shallower PBLH, weaker ventilation, and reduced vertical dispersion of aerosols. These results demonstrate that the impact of the modified aerosol optical properties extends beyond radiative cooling at the surface by altering the dynamical structure of the boundary layer, thereby reinforcing the positive aerosol–boundary layer feedback responsible for enhanced PM_2.5 accumulation over Delhi.

A similar analysis of the impacts of the modified optical properties of aerosols on the meteorological parameters has been performed for the Event-II of extreme pollution. The results are shown in Fig. S6. Similar to the findings in Event-I during the late morning hours, the modification of aerosol optical properties results in colder near-surface temperatures (change of -0.2 °C in T2) for the Event-II too. The consequent enhancements (of 1 %–2 %) in RH are also noticed. Similarly, the wind speeds also seem to reduce by around 0.2 ms-1 in the modified configuration. The PBLH also shows a decline by 40–50 m in RI-ALL configuration vis-a-vis CTRL. It may be noticed that the changes in all the analysed meteorological parameters in the month of January are lesser compared to that in the month of November. As mentioned previously, this behaviour is primarily due to the lesser availability of solar radiation in the month of January, which reduces the magnitude of the impact that modified aerosols have on the radiation and thus limits the atmospheric response to the perturbations. Nevertheless, the nature and the sign of the impacts on meteorological parameters remains broadly similar to that in November.

The reduced winds speeds and boundary layer heights would result in reduced ventilation coefficient (VC) in the RI-ALL configuration vis-a-vis CTRL. The same has been verified in Fig. 5. The VC which is the product of PBLH and winds in the boundary layer, has been plotted for both the events, for CTRL as well as RI-ALL configuration (Figs. 5i and S6i). The changes in VC have also been plotted in Figs. 5j and S6j. Furthermore, the VC maximizes during the afternoon hours mainly due to higher boundary layer heights owing to the well-mixed boundary layer. Conversely, the lowest values of VC are noticed during the evening to early morning hours (Figs. 5i and S6i) due to shallow boundary layer and calmer winds. It may be noted that the VC in the “RI-ALL” is consistently lower compared to that in the CTRL configuration, especially during the sunlit hours and even after sunset. The mean reductions are up to 20 % in the month of November (Event-I), and up to 10 %–12 % in the month of January (Event-II). The maximum changes are expectedly seen during the sunlit hours, while they are less in magnitude and/or in relative terms during the night hours.

As discussed above, the enhanced aerosol extinction increases the daytime atmospheric stability, suppresses TKE, and reduces the boundary layer depth. These dynamical changes weaken the VC, which governs the near-surface concentration of aerosols (Govardhan et al., 2015). Therefore, one would expect enhanced accumulation of PM_2.5 in the RI-ALL simulation. The higher (lower) values VC signifies greater (reduced) horizontal advection and vertical mixing of aerosols in the atmosphere. As shown in Fig. 5, VC in the RI-ALL configuration is reduced throughout the sunlit hours of the day, one would expect a consequent increase in the near-surface mass concentrations of PM_2.5 in the model. To confirm that, we have examined the near-surface mass concentrations of PM_2.5 in both the configurations and for both the events in Fig. 7.

It may be seen that the PM_2.5 indeed increases in RI-ALL configuration vis-a-vis CTRL. The mean changes are seen to be the highest during the afternoon hours. On average, the maximum changes during the polluted events are ∼37–40 µgm-3 for the November-event and ∼25 µgm-3 during the January-event. For the Event-I, the mass concentrations show enhancements throughout the day barring minor reductions during some late-night to early morning hours. On the other hand, for the Event-II, the increments in PM_2.5 are seen only during the period of 10:00 to 20:00 LT. Nevertheless, during the sunlit hours the mass concentrations of PM_2.5 show expected enhancements.

To examine the effects of the modifications in the optical properties of aerosols on meteorological as well as aerosol related parameters on a longer timescale, we carried out four month-long simulations (for domain d01 and d02) of WRF-Chem for the months of October, November, December of 2023 and January 2024, in CTRL, and all other sensitivity configurations. The clear-sky impacts of modifications in optical properties of individual aerosol species and that of the all species put together on the surface reaching SWDOWN from domain d01 and d02 are shown in Figs. S7 and S8. Similar to that for the extreme pollution events, the impacts of modified optical properties of dust aerosols alone have minimal effect on SWDOWN. This is partly due to the fact that the period under consideration in this study is mainly post-monsoon and winter, which has relatively slower winds and humid conditions, thus lesser long-range transport of mineral dust from the deserts of Thar and Arabia to Delhi. This limits the concentrations of mineral dust over this region vis-a-vis the pre-monsoonal months of March to May. Moreover, as shown in Table 1, even after modification, the imaginary part of the RIs of dust is still much lesser than that for BC or OC, hence the consequent impacts of the modification in mineral dust RI as minimal.

The SWDOWN reduces by as high as ∼12 Wm-2 at the noon time, across all the months barring November, due to the changes in the RI of OC and BC individually. For November, the corresponding changes are approximately twice (∼25 Wm-2) of that for the other months. This is mainly due to the enhanced concentrations of these aerosol species in November mainly owing to the agricultural burning activities that occur predominantly in the neighbouring states of Punjab and Haryana in north-western India. The maximum impacts of the modifications in RIs of all aerosols crosses 25 (37) Wm-2 for October, December, and January (November). As shown previously also, the noontime impacts of ARI on the SWDOWN at surface go as high as 80–100 Wm-2 for October, December, and January, while that for November goes beyond 120 Wm-2. Therefore, as shown for the pollution episodes, the impacts of modifications in the RIs of aerosols are around 1/3 to 1/4 of the total impacts of aerosols on radiation in clear-sky conditions in WRF-Chem, even on monthly-mean timescales. Thus, the modifications induce substantial changes in the total ARI in WRF-Chem over this region. It is noteworthy that results from the coarser domain (d01; 10 km resolution) do not differ significantly from those of the finer domain (d02; 2 km resolution). A detailed comparison of simulated SWDOWN for sensitivity experiments of modified RIs of chemical species as mentioned in Table 2 across all months under clear-sky conditions is provided in the Supplement (Tables S7 and S8) for the domains d01 and d02.

Comparison with WiFEX observed shortwave fluxes for December 2023 and January 2024 further demonstrates improvement in model performance. Under clear-sky (non-foggy/cloudy) conditions, the mean SWDOWN bias is substantially reduced in the RI-ALL configuration relative to CTRL (Table S9). In December, the mean bias decreased from 10.53 to 3.96 Wm-2, corresponding to a reduction of approximately 62 %, while the RMSE decreased by ∼9 %. In January, it decreased from 23.63 to 15.26 Wm-2 (∼35 % reduction), accompanied by a reduction in RMSE of ∼12 %. These reductions demonstrate that improved representation of ARI through revised aerosol optical properties enhances model skill.

The associated impacts of the modified RIs of aerosols on the meteorological parameters as well as PM_2.5 concentrations on the monthly-mean time scales are tabulated in Table S10. The monthly-mean SWDOWN in RI-ALL configuration is lesser than the CTRL configuration for all the months by around 8–20 Wm-2. The numbers are expectedly smaller than the noon-time maximums shown in Fig. S7. The resultant changes in T2 are also evident especially for the months of December and January, albeit with subdued values. The mean RH2 in the model shows greater changes (5 %–10 %) in the months of December and January, while showing changes of ∼2 %–3 % in October and November. The near-surface wind speeds (WS10) show a reduction on a monthly-mean basis. Similarly, the monthly-mean PBLH depicts a reduction across all the months upon modification in the RIs. The consequent changes in the monthly-mean near-surface PM_2.5 mass concentrations are also evident, though smaller in magnitudes.

The overall impacts of the modified aerosol optical properties in this study are summarized schematically in Fig. 8. The implementation of more realistic and optically stronger aerosol species enhances the extinction of incoming solar radiation, thereby reducing the downwelling shortwave radiation reaching the surface. The resulting increase in atmospheric heating and decrease in surface heating weakens buoyancy production within the planetary boundary layer, leading to increased atmospheric stability. The enhanced stability suppresses turbulent kinetic energy and weakens vertical turbulent mixing, resulting in a shallower planetary boundary layer together with reduced near-surface wind speeds. Consequently, the ventilation of pollutants is diminished, promoting the accumulation of PM_2.5 within the lower atmosphere. The increased aerosol loading further enhances the extinction of incoming solar radiation, thereby reinforcing the suppression of boundary-layer mixing and establishing a positive aerosol–boundary layer feedback. This feedback mechanism is consistent with previous studies that have demonstrated the role of absorbing aerosols in stabilizing the lower atmosphere, suppressing boundary-layer development, and aggravating severe air pollution episodes (Ding et al., 2016; Sharma et al., 2023; Stjern et al., 2023). In the present study, this feedback is initiated through the implementation of updated and more realistic aerosol optical properties, highlighting the importance of accurately representing aerosol refractive indices in aerosol–radiation interaction calculations.