Amplification of South Asian haze by water vapour-aerosol interactions

Air pollution and wintertime fog over South Asia is a major concern due to its significant implications on air quality, visibility and health. Using a coupled regional climate model with on-line chemistry, we assess the contribution of the hygroscopic growth of aerosols (wet-dry) to the total aerosol optical depth and demonstrate that the increased surface cooling due to the hygroscopic effects of aerosols further increases the humidity in the boundary layer and thus enhances the 10 confinement of pollutants through aerosol-boundary layer interactions. This positive feedback mechanism plays an important role in the prevalence of wintertime fog and poor air quality conditions over South Asia, where water vapor contributes more than half of the aerosol optical depth. The aerosol-boundary layer interactions lead to moistening of the boundary layer and drying of the free-troposphere, which amplifies the long-term trend in relative humidity over the Indo-Gangetic Plain during winter. Hence, the aerosol-water vapor interaction plays a decisive role in the formation and maintenance of the wintertime 15 thick fog conditions over South Asia, which needs to be considered for planning mitigation strategies.

RegCM4 has an online chemistry module, which is extensively used for understanding aerosol-climate interactions (Nair et al., 2012;Shalaby et al., 2012;Solmon et al., 2015). The aerosol and trace gas emission fluxes are adopted from the IIASA dataset, and chemical boundary conditions are from the global model MOZART (model of ozone and related 100 chemical tracers). Gas-phase chemistry is based on the CBMZ (carbon-bond mechanism version Z) scheme and the ISORROPIA II is used for inorganic aerosols (Shalaby et al., 2012). The aerosol scheme includes sulphate, nitrate, ammonia, sea salt (2 size bins), mineral dust (4 size bins), black carbon (hydrophilic and hydrophobic) and organic carbon (hydrophilic and hydrophobic). The sources, sinks, atmospheric processes and transport of the aerosol species are detailed in Solmon et al., (2006). The mass concentration of each aerosol species is converted into optical properties using a mass extinction cross-105 section, which depends on the ambient relative humidity and species-specific hygroscopic growth functions (Solmon et al., 2006). We use the growth function described by Kiehl et al., (2000) for sulphate, nitrate and ammonia aerosols. In this scheme, the growth function increases exponentially with humidity. For example, at 80% relative humidity, the particle mass extinction cross-section increases by a factor of 3 compared to its dry value. The model emits carbonaceous aerosols as hydrophobic and the optical properties of this nascent carbonaceous aerosols are invariant with relative humidity. These 110 particles changes from hydrophobic to hydrophilic due to ageing at a fixed time scale of 1.15 days (~27.6 hours, Solmon et al., (2006)). The hydrophilic part of the carbonaceous aerosols (black carbon and organic carbon) has a weak affinity to water compared to sulphate aerosols (Kasten, 1969). At 80% relative humidity, the growth factor is 1.37 and 1.49 for https://doi.org/10.5194/acp-2020-503 Preprint. Discussion started: 6 July 2020 c Author(s) 2020. CC BY 4.0 License. considered as control runs for wet and dry aerosol cases. Since the effect of aerosol-radiation interactions on land and meteorological parameters are switched off (no climate feedback), the difference between these two experiments provides the contribution of hygroscopic growth to the aerosol properties (AODRH) Experiments 3 and 4 are similar to Exp 1 and 2 but with climate feedbacks (Table 1). The difference between the Exp3 and 4 130 provides the net effect of the hygroscopic growth of aerosols on the regional climate, which includes (i) changes due to the radiative forcing of the aerosol hygroscopic growth and (ii) its climate feedback. For example, the change in temperature (T) due to the climate feedback of AODRH is estimated as The effect of total aerosol system on meteorological variables (for eg: Temperature) is estimated as 135 The change in AOD solely due to climate feedback of total aerosol system (dry + hygroscopic growth) is estimated as Hence the AOD(Ambientfeedback) is the sum of dry AOD, its hygroscopic growth and climate feedback = + ∆ + ∆ (5) 140 validation of chemical constituents simulated by the model are rather limited over South Asia (Nair et al., 2012;Solmon et al., 2015;Usha et al., 2020). A qualitative inter-comparison of the chemical composition of aerosols simulated by RegCM with in-situ measurements at distinct locations over the Indian region is shown in Figure 2. The measurements of column aerosol optical depth are taken from AERONET (Aerosol Robotic Network) radiometer observations at Jaipur, Kanpur, Gandhi College and Delhi. The black carbon measurements were carried out using Aethalometers installed under the ARFI 150 (Aerosol Radiative Forcing over India) project. Since the measurements of aerosol composition are sparse over the Indian region, we have taken values reported from earlier studies for the winter season, and therefore the organic carbon and sulphate measurements are for different years than those of the model simulations (Ali et al., 2019;Aswini et al., 2019;George et al., 2008;Ram et al., 2010Ram et al., , 2012Rengarajan et al., 2011;Safai et al., 2008;Satsangi et al., 2012). Despite these shortcomings, compared to earlier studies, the present model configuration simulates black carbon mass loading and AOD 155 which are closer to the observed values (Nair et al., 2012). Though RegCM has a simple scheme for organic aerosols, the organic carbon mass concentration broadly matches the observed seasonal mean values. In fact, most climate and chemical transport models fail to capture the high aerosol loading over this region, a problem which has been mostly attributed to the parameterization of stable boundary layer conditions (Nair et al., 2012), unaccounted emissions (eg: Nair et al., (2012), references are therein) and biases in simulating relative humidity and precipitation (Chatani and Sharma, 2018;Feng et al., 160 2016). In the present study, AOD and the near-surface mass concentrations of black carbon, organic carbon, sulphate and dust simulated by the model are in the range of values reported by several studies over the region. One of the main challenges in simulating the AOD over the region is the accurate simulation of relative humidity, which affects hygroscopic growth (Chatani and Sharma, 2018). The mean relative humidity estimated from several re-analysis datasets and measurements in different locations of the IGP varies from 60 to 80% (Chatani and Sharma, 2018;Gautam et al., 2007;165 Ghude et al., 2017;Goswami and Sarkar, 2015). A good agreement is found between measured and simulated (with aerosol feedback) relative humidity over the IGP ( 3 Results and discussions 180

Hygroscopic growth of aerosols
The particulate mass loadings (PM2.5) measured over most cities in the IGP during winter are well above the air quality standards, as shown in Figure 1. The total column AOD at ambient humidity simulated by RegCM4 with climate feedback shows very high values over the IGP, mostly consisting of boundary layer aerosols. The comparison of measured and simulated AOD for dry and ambient humidity conditions (Fig. 3) over the distinct environments of IGP indicates an 185 almost two-fold increase in AOD due to the hygroscopic growth of aerosols and its climate feedback. The mean observed https://doi.org/10.5194/acp-2020-503 Preprint. Discussion started: 6 July 2020 c Author(s) 2020. CC BY 4.0 License.

Effects on regional meteorology
The effects of the increase in AOD solely due to the hygroscopic growth of particles (∆AODRH) on surface temperature and relative humidity (Eqn. 2), illustrated in Figure 5 (top panel), showing a surface cooling of about 0.5°C and a 3% increase in relative humidity over the IGP during winter. This is mostly due to the direct solar dimming at the surface 245 and to a cloud cover change induced by the ∆AODRH. The total effect of aerosols on temperature and relative humidity (Eqn. Figures 5c & 5d. Nearly 40 to 50% of the aerosol-induced surface cooling is contributed by the water vapor through the hygroscopic growth of particles. These effects are more prominent over the IGP and central India, whereas high AODRH and surface dimming was seen over eastern IGP (Fig. 4). Although AOD and AODRH are high, the change in relative humidity due to hygroscopic growth of aerosols is negligible over Bangladesh and Eastern IGP. Hence the strength 250 of land-atmosphere interactions through the exchange of heat and moisture fluxes plays a major role in deciding the aerosol induced dimming effects on meteorology (Bharali et al., 2019;Li et al., 2017). At the surface, an observed decrease in evapotranspiration (and associated rainfall) due to solar dimming further confirms a slowing down of the hydrological cycle induced by the aerosol hygroscopic growth (Liepert et al., 2004;Ramanathan, 2001). In other words, water vapour itself reduces the evaporation through dimming associated with the aerosol hygroscopic growth. 255 https://doi.org/10.5194/acp-2020-503 Preprint. Discussion started: 6 July 2020 c Author(s) 2020. CC BY 4.0 License.

3) is shown in
The mean vertical profiles of change in temperature and relative humidity over the IGP due to total aerosol and hygroscopic effects are shown in Figure 6. Significant cooling and associated moistening are noticeable in the boundary layer followed by a weak drying in the lower free troposphere (1 to 3 km) and a negligible influence above (>3 km). Based on extensive measurements from Delhi during wintertime fog campaign, Ghude et al., (2017) have reported more than 70% humidity within the boundary layer and dry condition (<20%) above 3 km. A similar pattern is observed for specific 260 humidity with the highest increase of 0.6 g kg -1 at the surface. This figure further confirms that the hygroscopic growth is the single largest contributor to the total aerosol effects over the IGP during winter.
The surface cooling, along with the weak entrainment of dry airmass from the free troposphere, leads to an increase in relative and specific humidity at the surface (Fig. 5a). Based on the extensive measurements of aerosol and humidity profiles, Feng et al., (2016) reported up to 30% underestimation of relative humidity within the boundary layer and 20% 265 overestimation in the free troposphere by WRF-Chem model over a north Indian station (Nainital) during the winter season.
These authors attributed this dry bias in the boundary layer humidity as one of the major reasons for the underestimation of AOD by climate models over the region. Most re-analysis datasets (NCEP and ECMWF) have a negative bias (underestimate) in simulating relative humidity over this region, especially during winter (Chatani and Sharma, 2018;Feng et al., 2016), which significantly affects the simulation of aerosol optical depth, aerosol radiative forcing and fog prediction. 270 The present study shows that the negative bias (underestimation) of relative humidity could be reduced, especially at a high relative humidity (RH > 70%), by including the climate feedback of ambient aerosols (Exp 4). In contrast to boundary layer moistening, drying in the lower free troposphere (1 to 3 km) by 1 to 2% was attributed to the warming of the top of the boundary layer, which decreases the humidity and suppresses the mixing of airmass between the free troposphere and boundary layer. Compared to dry condition, the warming of the top of the boundary layer occurs at a lower altitude for 275 ambient aerosol forcing.
We analysed re-analysis data and in-situ observations for the long-term trend in wintertime relative humidity within the boundary layer and free-troposphere over the IGP during the last four decades (Fig. 7). In contrast to the global scenario, relative humidity is increasing over the Indian region at the rate of 1% per decade, together with a concurrent increase in aerosol loadings (Babu et al., 2013) and the number of foggy days (Ghude et al., 2017;Syed et al., 2012). Based on the 280 above discussions (Fig. 6), it can be argued that the aerosol forcing contributes significantly to the observed increasing trend of wintertime moistening of the boundary layer and enhanced occurrence of fog events over the IGP. The aerosol radiative forcing increases the stability of the atmosphere and decreases the transport of moisture from the boundary layer to the free troposphere, which favours the drying trend in the lower free troposphere as shown in Figure 7b. Earlier studies also attributed the humidification of the boundary layer and drying of the free troposphere during winter to the aerosol forcing (Li 285 et al., 2017;Tie et al., 2017). Our analysis shows that aerosol feedback processes significantly increase near-surface relative humidity due to weak turbulent diffusivity in the stratified boundary layer (Bharali et al., 2019;Li et al., 2017). The increase in relative humidity, when surface temperature increases, is due to the increase in water vapour content in the atmosphere as seen in the specific humidity trend reported by Mukhopadhyay et al., (2017). In contrast to the negative trend in relative https://doi.org/10.5194/acp-2020-503 Preprint. Discussion started: 6 July 2020 c Author(s) 2020. CC BY 4.0 License. humidity at lower free troposphere during winter (Fig. 7b), Mukhopadhyay et al., (2017) show a positive trend for annual 290 mean humidity, which is largely dominated by the positive trend during summer. Global analyses have shown that most continental regions exhibit a long term warming trend and decrease in relative humidity (Dai, 2006), which has strong implications for the land-ocean warming contrast (Hodnebrog et al., 2019). The long-term trend in the moistening of the boundary layer and drying of the free troposphere over the Indian region during winter is further amplified by the aerosol radiative forcing, primarily through the hygroscopic growth. However, the masking effects of surface warming by aerosols 295 and the contribution of aerosols to the increasing trend in humidity is yet to be quantified.   observations (b) relative humidity in the free troposphere (700 hPa) taken from re-analysis data.

Implications on air quality
Regional mean AOD increase of ~0.23 is contributed by the hygroscopic growth of aerosols as shown in Figure 3.
This hygroscopically grown aerosol system further increases the AOD through climate feedbacks (AODfeedback), whose https://doi.org/10.5194/acp-2020-503 Preprint. Discussion started: 6 July 2020 c Author(s) 2020. CC BY 4.0 License. contribution can be estimated using Eqn. 4 and is shown in Figure 8a. Overall, hygroscopic growth (AODRH ~ 0.23) and 310 associated climate feedback (AODfeedback ~ 0.17) contribute significantly to the total AOD (0.72) over the IGP. This positive feedback is stronger over the central IGP than the eastern IGP, though eastern IGP has higher AOD and relative humidity. The moisture content in the atmosphere increases the AOD directly by its hygroscopic effect (Fig. 3) while high AOD increases the surface relative humidity through its radiative and climate feedbacks (Fig. 8a). Even though the spatial patterns of change in AOD and relative humidity due to aerosol feedbacks are slightly different, the anomalies in AOD and 315 relative humidity show a significant correlation over most of the Indian sub-continent, especially over the IGP (Fig. 8b). The frequency of occurrence of AOD and PM2.5 for dry and ambient humidity conditions with climate feedback over the IGP is shown in Figure 9. The inclusion of climate feedback and hygroscopic aerosol growth (∆AODRH) produces an increase in AOD along the IGP (Fig. 5). The narrow frequency distribution for dry AOD changes to a broad pattern for AOD at ambient humidity, which implies an increase in the number of days with hazy skies and significant dimming at surface. There is a substantial increase in the number of days having high AOD for ambient humidity condition. The 325 frequency distribution of the ratio of AOD for ambient and dry humidity with and without climate feedback is shown in Figure 9b. The narrow distribution for no-feedback simulation with a mean ratio of 1.72 changes to a broad distribution having a mean increase of dry AOD by 2.3 times at ambient humidity with climate feedbacks. Due to the positive feedback as mentioned above, AOD at ambient humidity increases more than 3 times of its dry AOD, depending on the strength of the land-atmosphere and aerosol-boundary layer interaction. The magnitude of the climate feedback due to the total aerosol 330 https://doi.org/10.5194/acp-2020-503 Preprint. Discussion started: 6 July 2020 c Author(s) 2020. CC BY 4.0 License.