Aerosol radiative impact on surface ozone during a heavy dust and biomass burning event over South Asia

Journal Pre-proof Aerosol radiative impact on surface ozone during a heavy dust and biomass burning event over South Asia T. Mukherjee, V. Vinoj, S.K. Midya, B. Adhikary PII:

S1352-2310(19)30840-4

DOI:

https://doi.org/10.1016/j.atmosenv.2019.117201

Reference:

AEA 117201

To appear in:

Atmospheric Environment

Received Date: 31 July 2019 Revised Date:

27 November 2019

Accepted Date: 2 December 2019

Please cite this article as: Mukherjee, T., Vinoj, V., Midya, S.K., Adhikary, B., Aerosol radiative impact on surface ozone during a heavy dust and biomass burning event over South Asia, Atmospheric Environment (2020), doi: https://doi.org/10.1016/j.atmosenv.2019.117201. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Author Credits: Tanmoy Mukherjee: Conceptualization, Methodology, Data Curation, Writing - Original Draft, Writing - Review & Editing, V. Vinoj: Conceptualization, Review & Editing, Subrata. K. Midya: Conceptualization, Review & Editing, Bhupesh. Adhikary: Conceptualization, Methodology, Data Curation, Writing - Original Draft, Writing - Review & Editing, Supervision

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Aerosol radiative impact on surface ozone during a heavy dust and biomass burning event over South Asia

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T. Mukherjee1, 2, 3, V. Vinoj2, S. K. Midya3, B. Adhikary1*

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1

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2

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3

International Centre for Integrated Mountain Development, Nepal

School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, India Department of Atmospheric Science, University of Calcutta, India

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Keywords

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WRF-Chem, Dust, Black Carbon, Radiative feedback, ozone

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Abstract

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Aerosols can modify both short and long term weather patterns by impacting the radiation budget

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of Earth. Numerical simulations were performed to understand the direct effect of aerosol on

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radiation during an elevated dust and black carbon (BC) concentration period over south Asia.

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The impact of the aerosol (dust and BC separately) direct effect on meteorology and air quality

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(focusing on surface ozone) was assessed using a fully coupled chemical transport model (WRF-

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Chem). The model simulates the elevated dust and BC concentration plume well qualitatively.

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Our results show that elevated BC concentration can reduce surface temperature up to 2 K.

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Incoming short wave flux at the surface and the boundary layer height reduced up to 70% due to

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the radiative impact of BC. 'This reduction in boundary layer height further increases the BC

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concentration at the source region. The radiative impact of dust on meteorological parameters are

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found to be less compared to BC at the surface level. The model simulates realistic surface ozone

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concentration using HTAP emission inventory. Results reveal that the presence of biomass

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burning can increase the surface ozone concentration by up to 40%. The radiative impact of BC

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can reduce the surface ozone concentration by more than 30% by altering the photolysis

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frequencies.

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1. Introduction

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Aerosols are able to impact the climatic variability directly by scattering or absorbing solar

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radiation (Atwater, 1970; Ensor et al., 1971) and indirectly by affecting droplet concentrations

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and therefore cloud formations (Dipu et al., 2013; Gu et al., 2012; Lohmann and Feichter, 2004;

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Menon et al., 2002a; Ning et al., 2015; Panicker et al., 2010). The presence of absorbing aerosol

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(e.g. dust, Black carbon) can modify the atmospheric forcing from negative to positive (Babu

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and Moorthy, 2002; Gogoi et al., 2017). Several studies reported that the impact of aerosol

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induced forcing can alter the hydrological cycle and change the local precipitation pattern over

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Indian region (Bollasina et al., 2008, 2011; Lau and Kim, 2006; Lau et al., 2017; Nigam and

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Bollasina, 2010; Ramanathan and Ramana, 2005; Vinoj et al., 2014).

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Black carbon (BC) and dust aerosols play a significant role to modify the Earth’s radiation

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budget and local meteorological conditions. The interaction between dust and short (long) wave

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radiation directly impact the radiation budget (Ge et al., 2010; Seinfeld et al., 2004; Zhao et al.,

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2011) and can modify the microphysical and optical properties of clouds (Cattani et al., 2006;

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Ching et al., 2016; Satheesh et al., 2006; Yang et al., 2019). Dust affects the cloud formation and

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therefore precipitation by acting as a potential cloud condensation nuclei (Miller et al., 2004;

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Teller et al., 2012; Zhao et al., 2011). It also influences the atmospheric dynamics by altering the

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radiative properties (Chaboureau et al., 2011; Stanelle et al., 2010; Tompkins et al., 2005).

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Studies revealed that dust can be transported even more than 1000 km before its removal via dry

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or wet deposition. Dust can potentially interacts with the radiation and therefore alters the

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meteorological phenomena (Ginoux et al., 2001; Mahowald et al., 2005; Prospero, 2009; Uno et

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al., 2005). Previous research indicates that dust can reduce the surface temperature up to -7ºC

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over Asia (Gao et al., 2015). Though several type of research have been conducted on dust

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radiative impact over south Asian domain (Dey et al., 2004; Kalenderski et al., 2013; Prasad and

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Singh, 2007; Zhao et al., 2011) using both in-situ observation and modelling technique (Chinnam

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et al., 2006; Dey et al., 2004; Hegde et al., 2007; Pandithurai et al., 2008; Prasad and Singh,

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2007), knowledge about the radiative impact of dust during an elevated concentration in the

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regional scale is limited over this region.

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Along with dust, several research investigations are conducted about BC aerosols which absorb

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solar radiation and can effectively modify the radiation balance (Bond et al., 2013; Jacobson,

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2001; Ramanathan and Carmichael, 2008; Surendran et al., 2013) and atmospheric

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thermodynamics (Menon et al., 2002b; Satheesh and Ramanathan, 2000). It can adversely impact

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human health (Dockery and Stone, 2007; Janseen et al., 2012) and can reduce crop yields (W.

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Chameides et al., 1999). Originates primarily due to incomplete combustion, BC is emitted from

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both anthropogenic (industries, automobiles, domestic, agricultural burning, etc.) and natural

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(forest fire) sources. With an average lifetime of ~1 week, BC is able to undergo long-range

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(regional and intercontinental) transport before its removal via dry or wet deposition (Bond et al.,

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2013; Ramanathan and Carmichael, 2008). Numerous studies have reported the change in

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radiative forcing due to enhanced BC concentration over the Indian region (Babu et al., 2002;

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Babu and Moorthy, 2002; Dey and Tripathi, 2008; Ganguly et al., 2005; Jayaraman et al., 1998;

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Pathak et al., 2016; Rajeev and Ramanathan, 2001; Ramachandran et al., 2006; Ramanathan et

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al., 2001; Satheesh et al., 2009; Tiwari and Singh, 2013; Vinoj et al., 2010). Even rural and

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island locations over South Asia are exposed to high BC concentrations (Rehman et al., 2011;

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Vinoj et al., 2010). Recent multi-model studies revealed that enhanced BC concentration can

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potentially alter the surface temperature and precipitation pattern (Liu et al., 2018a; Samset et al.,

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2016). However, despite several studies, knowledge regarding dynamical and radiative effect of

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elevated BC concentration over south Asian region is limited to date.

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Different studies attempted to quantify the aerosol radiative feedback effect throughout the world

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(Ban-Weiss et al., 2012; Ding et al., 2016; Gao et al., 2015; Ji, 2016; Previdi, 2010; Ramanathan

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and Carmichael, 2008; Wang et al., 2015; Zhao et al., 2014). Investigation of direct aerosol

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radiative forcing over Huston reported a huge overestimation of the short wave (SW) flux if the

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feedback is neglected (Fast et al., 2006). Similar results were found in Europe (Vogel et al.,

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2009). Over the Asian region, a decrease of 0.8-2.8ºC in temperature is reported over the

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Northern China Plain due to high particulate matter (PM) feedback (Gao et al., 2015).

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Along with meteorology, aerosol feedback can impact the local air quality and can alter the

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concentration of surface ozone. Tropospheric/surface ozone is acknowledged to be one of the

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most important greenhouse gas with a very complex mechanism of formation and depletion

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(Montzka et al., 2011). As a greenhouse gas it contributes 3-7% of the global warming and can

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increase the radiative forcing up to 0.47 W/m2 (Ehhalt and Prather, 2001). High concentrations

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of surface ozone are toxic which can cause damage to human health as well as crop yields

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(Ahmad et al., 2013; Ashmore, 2005; W. L. Chameides et al., 1999; Mauzerall and Wang, 2001;

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Mudway and Kelly, 2000). Considering the effect of absorbing aerosols in the planetary

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boundary layer, it is observed that UV absorbing aerosols can reduce the surface ozone

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concentration significantly (Dickerson et al., 1997). Jacobson, (1998) suggested a decrease of up

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to 8% in the ground ozone concentration due to the reduction of photolysis rate by BC.

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Researchers found a strong reduction in the photolysis rate (10%-30%) due to BC aerosols

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throughout the world (Li et al., 2005, 2011; Tie et al., 2005). Therefore, it is important to

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understand the impact of aerosol feedback on surface ozone over the south Asian domain.

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There are several observational techniques to identify the aerosol radiative impact. But to

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investigate the impact of specific aerosols on a large spatial scale, chemical transport models are

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usually utilized. The feedback effects cannot be generated in the traditional “offline” chemical

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transport model which utilizes the input of the meteorological fields from a prior meteorological

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model simulation. Thus fully coupled “online” model is required to simulate the feedback as it

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provides continuous interactions between the chemical and meteorological fields (Forkel et al.,

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2012; Grell and Baklanov, 2011). Online chemical transport models provide the opportunity to

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quantify the change due to aerosol radiative feedback. Thus in the present study a fully coupled

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online chemical transport model (WRF-Chem) was utilized to generate the aerosol radiative

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feedback. The study reports the effect of direct aerosol radiative feedback on meteorology and its

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influence on surface ozone during an elevated aerosol loading scenario. It primarily emphasizes

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to investigate the radiative effect of dust and BC aerosols during a simultaneous dust storm and

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biomass burning period.

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2. Model Setup and Methodology

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The study utilizes the 3.8.1 version of the Weather Research and Forecasting model (Skamarock

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et al., 2008) coupled with chemistry (Fast et al., 2006; Grell et al., 2005) to simulate the

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meteorology and chemistry over the selected domain. The domain ranges from 53º E to 99º E in

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the west-east direction covering 300 grid points and 7.6º N-35.6º N in the south-north directions

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(201 grid points) with a spatial resolution of 15 x 15 km2. The vertical grid is composed of 30

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vertical layers. The static geographical field is interpolated from the 10 min data generated by the

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United State Geological Survey (USGS) to the model domain using WRF preprocessing system

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(WPS). The domain covers complex terrain like the Himalayas, Desert region in the western part

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and populated areas like Indo-Gangetic Plain. National Center for Environmental Predictions

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(NCEP) Final Analysis (FNL) fields available every 6 h at a spatial resolution of 1º x 1º are

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utilized as initial and lateral boundary condition data for meteorology. Two moment cloud

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microphysics scheme (Morrison et al., 2009) is applied to the model to resolve cloud physics

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over the study domain (Sarangi et al., 2015). Rapid Radiative Transfer Model for General

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circulation models (RRTMG) (Iacono et al., 2008) is applied for the short- and long-wave

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radiative transfer in the atmosphere which allows the online interaction between aerosols and

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meteorology. Unified Noah Land Surface Model (Tewari et al., 2004) and revised MM5 scheme

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(Jiménez et al., 2012) represent the surface process in the model. The boundary layer process is

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parameterized using Mellor–Yamada Nakanishi Niino (MYNN) Level 2.5 scheme (Nakanishi

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and Niino, 2009).

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Model for Ozone and Related Chemical Tracers (MOZART-4) chemical scheme (Emmons et al.,

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2010) is used to characterize the gas phase chemistry. Goddard Chemistry Aerosol Radiation and

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Transport (GOCART) bulk aerosol scheme (Chin et al., 2002; Pfister et al., 2011) is used to

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represent the aerosol processes. The initial and lateral boundary conditions for the chemical

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species are supplied from six hourly output of MOZART-4 (Emmons et al., 2010).

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Anthropogenic emissions of different species (e.g. CO, NOx, SO2, NH3, CH4, PM 10, PM 2.5,

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BC, OC and Non-methane volatile Organic Compound (NMVOC)) are taken from the Emission

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Database for Global Atmospheric Research (EDGAR) HTAP global emission inventory

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(Janssens-Maenhout et al., 2012). Different

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supplied from NCAR Fire Inventory (FINN v1.5) data (Wiedinmyer et al., 2011). Online Plume

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rise model (Freitas et al., 2007) is applied to calculate the vertical distribution of the gases and

species originated

from biomass burning are

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particles emitted during biomass burning. Model of Emissions of Gases and Aerosols from

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Nature (MEGAN) version 2.04 (Guenther et al., 2006) is utilized to calculate the biogenic

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emission of the trace species from the terrestrial atmosphere. The aerosols are allowed to provide

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the feedback through direct effect on the radiation schemes.

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To explore the aerosol radiative feedback on meteorology and surface ozone, a set of four

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experiments were designed (Table 1). The first case (Base) contains all the aerosols and

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considers the aerosol radiative feedback in the simulation. The second case does not account for

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any aerosol radiative feedback (No-RA). The third case is the same as Base case but without BC

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aerosols (No-BC). The final case differs from the Base case by the exclusion of dust aerosols

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(No-DU).

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Table 1: List of Simulations performed Case

Description

Base

All aerosol + radiative feedback “on”

No-RA

All aerosol + radiative feedback “off”

No-BC

All aerosol except BC + radiative feedback “on”

No-DU

All aerosol except Dust + radiative feedback “on”

No-BB

All aerosol except biomass burning + radiative feedback “on”

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For the model simulation, we have selected a period where the western part of the model domain

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experienced dust storm while the eastern section experienced heavy biomass burning due to the

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forest fire. All the simulations are initiated on 12th March 2012 00 UTC and ended on 27th March

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2012 00 UTC. Numerous researches reported the dust storm period in between 19th to 23rd March

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(Aher et al., 2014). Thus the analysis period is considered between 16th and 24th March.

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The model simulated meteorological data is evaluated with the upper air radiosonde data

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collected from the University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html)

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and surface meteorological data from NCDC (https://www.ncdc.noaa.gov/cdo-web/).

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3. Results and Discussion

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3.1. Model Performance Evaluation

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Different meteorological parameters simulated by the model are compared with the observational

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data. Upper air radiosonde data (collected from the University of Wyoming) of temperature and

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relative humidity (RH) over three locations (Abu Dhabi, Jodhpur and, Kolkata) are utilized for

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model evaluation (Fig. 1). Figure 1 shows the vertical variation of temperature, RH on 19th

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March. The temperature is well captured by the model over all three locations with a low root

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mean square error (RMSE). Table 2 shows that temperature RMSE is lowest over Jodhpur and

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highest over Abu Dhabi. Simulated vertical temperature over all three locations shows a good

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correlation with the observational data.

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Table 2 Statistical details of model evaluation Cities Temperature Relative Humidity RMSE Correlation RMSE Correlation

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Abu Dhabi

6.3

0.99

12.5

-0.02

Jodhpur

2.6

0.99

25.4

0.03

Kolkata

3.1

0.99

29.0

0.32

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Fig1. Model performance evaluation on simulating the meteorological parameters

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Relative humidity shows lower RMSE over Abu Dhabi and highest over Kolkata. . The large

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variation in the temperature RMSE over Abu Dhabi can occur due to the proximity to dust

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sources and emissions that are random in nature. The high RMSE of RH over Kolkata is also

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expected due to its proximity to Bay of Bengal.

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Time series analysis of temperature, RH and wind speed (Fig. S1) is performed over three

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different locations (Jinnah International Airport, Indira Gandhi International Airport and Yangon

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International Airport). Model is able to capture the temperature variations throughout the study

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period with low RMSE over Jinnah and IGI airport but under predicts the temperature over

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Yangon. The RH and wind speed variation are also well simulated with low RMSE (Table S1).

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Further, the authors examined the qualitative performance of the model by comparing the

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simulated dust and biomass plume with the natural color image taken by MODIS aboard Aqua.

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Figure 2 shows the dust and BC concentration over the study domain on 20th March. The natural

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color image at the top panel of the figure provides the observational evidence of dust storm and

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biomass burning. The patches of dust and biomass plume is visible over the study region. The

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figure shows that dust originated from the Middle East region spreads across the Arabian Sea

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and reaches the western part of the Indian subcontinent. On the other hand, a dense smoke plume

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is visible over Myanmar which is extended over northeastern India. The bottom left panel of the

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figure depicts the dust concentration on 20th March 2012, 06 UTC while the right panel shows

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the BC concentration for the same time step. WRF-Chem simulates the spatial pattern of dust

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and BC plume fairly well as both WRF-Chem and Aqua show the presence of dense dust plume

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from the western desert area covering the Arabian Sea and the northwestern part of the Indian

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subcontinent. The south westerly winds are responsible for the redistribution of dust over this

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region (Dey et al., 2004; Prasad and Singh, 2007). On the other hand, dense smoke plume

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generated due to heavy biomass burning over Myanmar dispersed towards eastern India. We

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have chosen six different locations in the domain to understand the flow of dust and BC

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throughout the domain (black dots in Fig. 2).

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Fig 2. Qualitative evaluation of model performance on simulating the dust and BC concentration.

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The top panel shows the natural color image taken by MODIS Aqua. Bottom left panel shows

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the dust concentration on 20th April, 2012, 06 UTC while the right panel shows the Black

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Carbon.

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Figure 3, designed to observe the periodic progression of dust and BC throughout the domain,

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shows the variation of dust and BC on a logarithmic scale over six different locations. The first

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location (point A) is near the dust source region while the second (point B) is on the dust outflow

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area. The third and fourth (point C and D) points are situated at the Indo-Gangetic Plain. The

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fifth and sixth (point E and F) are on the outflow and source region of biomass burning. The

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figure is.

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Fig 3. Variation of Dust and Black Carbon over six locations throughout the domain. The point

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A and B are at the dust source and outflow region. The point C and D are over Indo-Gangetic

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Plain. Point E and F are on the outflow and source region of biomass burning.

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Figure 3 shows that the dust concentration had a spike on 19th March over point B followed by

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21st and 22nd March on C and D. The effect of high dust event reached even at the eastern part of

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the Indian subcontinent. As both C and D are located over urban areas (near Kanpur and

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Kolkata), the background BC concentration was already higher over these locations. The point E

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which is located over Bay over Bengal also experienced elevated BC level due to transport from

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the source region. There was continuous biomass burning over the Myanmar region. Thus the

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BC concentration was always higher at point F throughout the study period. The domain

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averaged BC and dust concentrations during the study period are ~3 µgm-3 and ~198 µgm-3

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which resemble well with the observational values.

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3.2. Effect of Aerosol radiative feedback on meteorology

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The current section discusses the effect of direct aerosol radiative feedback on the

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meteorological parameters (2 meter temperature, incoming short wave (SW) flux and planetary

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boundary layer height (PBLH)) (Fig. 4). The top panel of the figure describes the original

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temperature in K, incoming SW flux in Wm-2 and PBLH in meters while the bottom panel shows

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the change due to aerosol radiative feedback (Base – No-RA).

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Fig 4. Spatial distribution of averaged Temperature, SW Flux and PBLH during the study period

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(16/03/12-24/03/12) along with the difference (Base – No-RA) due to aerosol radiative feedback.

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The top panel (a, b and c) shows the average temperature, SW Flux and PBLH during the study

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period while the bottom panel (d, e and f) shows the change in temperature, SW Flux and PBLH

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due to aerosol radiative feedback.

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The surface temperature decreased by ~ 2 K due to heavy biomass burning over the eastern

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region of the domain. As already mentioned in the introduction section, several multi-model

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studies (Liu et al., 2018a; Samset et al., 2016) have reported that enhanced BC concentration

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(more than 10 times from its base concentration) can alter the surface temperature significantly.

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But these studies are based on theoretical assumptions. The current study covered a real time

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extreme biomass burning event where the BC concentration was elevated than its base

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concentration due to heavy biomass burning.. On the other hand, the incoming SW flux and

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PBLH show more than 70% decrease due to aerosol radiative feedback at the eastern part of the

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domain. The Incoming SW flux reduced up to 200 Wm-2 while PBLH decreased ~800 meters

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over Myanmar. Previous study already reported the decrease of SW flux and PBLH due to

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aerosol radiative feedback over the European region (Forkel et al., 2012) but with lesser

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magnitude. The decrease of PBLH was prominent over the eastern part which was covered by

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elevated BC concentration due to biomass burning. Researchers have reported that the PBLH can

251

be suppressed due to BC boundary layer interaction (Gao et al., 2018). Large amount of BC

252

increases the heating rate by absorbing more shortwave radiation which in turn increases the

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upper boundary layer temperature. This eventually leads to form a temperature inversion at the

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surface level which suppresses the PBL (Gao et al., 2018).

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The meteorological parameters are less effected at the western region of the domain which was

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covered by higher dust concentration. The temperature change was minimal over this region

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while the change in SW flux and PBLH ranged between 5-10% and 0-5%. Kumar et al., (2014)

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reported that shortwave perturbation during a dust storm over this region can reach up to 70 Wm-

259

2

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also potential for emission of LW radiation (due to absorption of radiation and reemission). The

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large dust loading is therefore expected to have some compensation effect on radiative cooling

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during the day through a warming due to dust emission during both day and night. This effect is

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also expected to be maximized over high albedo surfaces such as desert due to larger albedo and

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hence increasing the efficiency of dust absorption of solar radiation. Hence, we expect that

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compensation due to LW warming may be leading to reduced overall cooling over dust regions.

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Our study shows that incoming SW flux decreased up to 22 Wm-2 due to dust generated aerosol

267

radiative feedback over the western part. Several observational studies during this period also

268

reported the decrease of SW flux due to dust loading (Aher et al., 2014; Srivastava et al., 2014).

269

. . In the case of dust, in addition to its effect on SW radiation in cooling the surface, there is

3.3. Effect of Aerosol Radiative Feedback on Dust and Black Carbon

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The impact of aerosol radiative feedback on dust and BC concentration and aerosol optical depth

271

(AOD) changes has been analyzed in this section (Fig. 5). The figure shows that aerosol radiative

272

feedback can increase the BC concentration by 20-30% near the source region. As discussed in

273

the previous section, large amount of BC suppresses the BLH and creates temperature inversion

274

(Gao et al., 2018). It also decreases the surface temperature significantly (Liu et al., 2018b;

275

Samset et al., 2016). The lower temperature along with a lower boundary layer is able to trap the

276

pollutants to the source region. Here, the elevated concentration of BC over the eastern part of

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the domain decreased the PBL height and temperature which in turn trapped the BC at the source

278

region and acted as a feedback system to further enhance the BC concentration. With aerosol

279

radiative feedback, the domain averaged BC during the study period was 2.28 µgm-3 but it

280

decreased to 2.21 µgm-3 without any feedback mechanism. The model is capable of modulating

281

the aerosol loading through wet and dry scavenging. However, the period (during pre-monsoon

282

season) chosen for the study was mostly characterized by clear sky conditions. Therefore,

283

minimal effect to aerosol loading through clouds or wet scavenging by rainfall was observed.

284

285 286

Fig. 5. Spatial distribution of averaged a) Dust b) BC and c) AOD during the study period

287

(16/03/12-24/03/12) along with the difference (Base - No-RA) due to aerosol radiative feedback

288

respectively (d, e, f)

289

The elevated dust radiative feedback increased the dust concentration more than 80% in the

290

western part of the study region. The AOD is also modified more than 50% near the dust source

291

region and ~30% near the biomass burning region at the eastern part of the domain.. However,

292

the reason behind dust modification is still not clear. The modification in wind patterns at the

293

upper layer can be a possible explanation.

294

3.4. Effect of Aerosol Radiative Feedback on Air quality

295

Several researchers have simulated the surface ozone over the Indian subcontinent (Ghude et al.,

296

2014; Kumar et al., 2012; Sharma et al., 2017). However, the studies reported that further

297

research is required to quantify the impact of aerosol radiative feedback on surface ozone. The

298

current study period includes continuous biomass burning. The springtime biomass burning can

299

enhance Carbon Monoxide (CO) and Nitrogen Di Oxide (NO2) concentrations drastically near

300

the source region (Jena et al., 2015). Several other studies indicated that biomass burning

301

emissions contains ozone precursors (Andreae and Merlet, 2019; Crutzen et al., 1979). A recent

302

study revealed that biomass burning causes drastic increase in CO and NO2 concentration near

303

the biomass source region and as a result the surface ozone level increase up to 50% over

304

Burma region and can reach more than 70 ppb over this region during pre-monsoon (March to

305

May) (Jena et al., 2015). Our study shows a similar concentration of ozone during the study

306

period (Fig. 6). To understand the impact of biomass burning on ozone concentration during the

307

study period, another simulations were performed by turning off the biomass burning over the

308

study domain. Results show that biomass burning can increase the surface ozone level up to 40%

309

over the Myanmar region (Fig. 6b). Previous study informed that MOZART chemical scheme

310

along with HTAP inventory produces higher ozone concentration (5-20 ppb) during noontime as

311

HTAP-MOZART have a high bias with observation over India (~32%) (Sharma et al., 2017).

312

But our simulations are able to produce realistic ozone values with HTAP inventory.

313

314

Fig. 6. Spatial distribution of averaged a) ozone b) percentage change due to biomass burning

315

and c) percentage change due to aerosol radiative feedback

316

Further, we have explored the impact of aerosol radiative feedback on surface ozone during the

317

study period. Radiative feedback due to BC reduced the surface ozone concentration by more

318

than 30% (Fig. 6c). Several other studies reported the decrease in surface ozone due to high BC

319

concentration (Dickerson et al., 1997; Li et al., 2005, 2011; Tie et al., 2005). Li et al (2005)

320

reported that black carbon aerosols are able to reduce the photolysis frequencies of J[O3(1D)] and

321

J[NO2] in the planetary boundary layer by 10-30% during higher pollution period over Huston

322

which in turn can reduce the surface ozone by 5-20%. A recent study over China reported that

323

surface ozone reduced up to 16.4 ppb due to the BC- boundary layer interaction (Gao et al.,

324

2015). The magnitude of ozone reduction is much higher in our study. This may be explained by

325

the fact that, unlike other studies, in this case, the amount of BC is much higher due to heavy

326

biomass burning. On the other hand, the dust induced areas showed nominal or no change in the

327

surface ozone concentrations.

328 329

Fig.7. Variation of surface ozone concentration (simulated from Base, No-RA, No-BC and No-

330

DU) over six different locations throughout the domain

331

Surface ozone time series over six different points also depicted the same results. The point F

332

had undergone maximum changes while the change was least at point B (dust outflow point).

333

Maximum changes occurred in No-RA and No-BC runs. This again proves the impact of black

334

carbon radiative feedback on surface ozone. To explore whether dust and BC feedback can alter

335

ozone concentration at the vertical levels, the vertical variation of ozone is plotted over the six

336

locations (Fig. 8).

337 338

Fig. 8. Vertical variation of ozone (simulated from Base, No-RA, No-BC and No-DU) over six

339

location during the highest dust and BC times. The top panel (a, b and c) shows the vertical

340

variation of ozone during their respective highest dust concentration time while the bottom panel

341

(d, e and f) shows the ozone variation during their highest BC concentration

342

The figure shows the vertical variation of surface ozone over the locations at their highest dust

343

and BC concentration times. The vertical changes were maximum at the high BC locations (point

344

F and E). Even at the highest dust time, point A and B portrayed minor changes. This

345

summarizes that the radiative impact of BC on surface ozone is much higher than dust.

346

4. Conclusions

347

The study provides insight into the radiative impact of dust and BC on the meteorology and

348

surface ozone over south Asia. The model is well able to capture the high dust and biomass

349

plume. Surface temperature can reduce up to 2K at the BC source region as elevated BC reduces

350

the incoming SW flux significantly. The enhanced BC provides feedback to further increase BC

351

concentration by declining the PBL height. On the other hand a higher concentration of dust also

352

reduces the SW flux by ~22 Wm-2. But the temperature change is not significant at the surface

353

level. The simulated ozone concentration shows fair agreement with the previous studies.

354

Biomass burning over the eastern part of the domain increases the surface ozone concentration

355

by 40%. But the BC induced radiative feedback can reduce the surface ozone concentration more

356

than 30%. The reduction of photolysis frequencies can attribute for the reduction of surface

357

ozone. The time series and vertical analysis distribution of surface ozone produced by all four

358

simulations reveals that the BC induced reduction of surface ozone is much more effective than

359

dust. It should be noted that elevated BC and dust concentration reduce the production of surface

360

ozone whereas the biomass burning enhances the surface ozone production. Again, meteorology

361

is also altered due to the radiative impact of dust and BC. These conditions are need to be

362

recognized while planning for the air quality management.

363

Acknowledgements

364

ICIMOD gratefully acknowledges the support of its core donors: the Governments of

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Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway,

366

Pakistan, Sweden, and Switzerland.

367

The authors like to acknowledge Indian Space Research Organization (ISRO) for supporting

368

through ARFI project.

369

The authors also like to thank MODIS mission and NASA GIOVANNI portal for providing

370

valuable data.

371

Disclaimer

372

The views and interpretations in this publication are those of the authors and are not necessarily

373

attributable to ICIMOD.

374

375

References

376

Aher, G.R., Pawar, G. V., Gupta, P., Devara, P.C.S., 2014. Effect of major dust storm on optical,

377

physical, and radiative properties of aerosols over coastal and urban environments in

378

Western India. Int. J. Remote Sens. 35, 871–903.

379

https://doi.org/10.1080/01431161.2013.873153

380

Ahmad, M.N., Büker, P., Khalid, S., Van Den Berg, L., Shah, H.U., Wahid, A., Emberson, L.,

381

Power, S.A., Ashmore, M., 2013. Effects of ozone on crops in north-west Pakistan.

382

Environ. Pollut. 174, 244–9. https://doi.org/10.1016/j.envpol.2012.11.029

383 384

385

Andreae, A.O., Merlet, P., 2019. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical. At. Chem. Phys. 15 (4), 955–966. Ashmore, M.R., 2005. Assessing the future global impacts of ozone on vegetation. Plant Cell

386

Environ. 28, 949–964. https://doi.org/10.1111/j.1365-3040.2005.01341.x

387

Atwater, M.A., 1970. Planetary albedo changes due to aerosols. Science 170, 64–6.

388

https://doi.org/10.1126/science.170.3953.64

389 390

391

Babu, S.S., Moorthy, K.K., 2002. Aerosol black carbon over a tropical coastal station in India. Geophys. Res. Lett. 29, 13-1-13–4. https://doi.org/10.1029/2002GL015662 Babu, S.S., Satheesh, S.K., Moorthy, K.K., 2002. Aerosol radiative forcing due to enhanced

392

black carbon at an urban site in India. Geophys. Res. Lett. 29, 27-1-27–4.

393

https://doi.org/10.1029/2002GL015826

394

Ban-Weiss, G.A., Cao, L., Bala, G., Caldeira, K., 2012. Dependence of climate forcing and

395

response on the altitude of black carbon aerosols. Clim. Dyn. 38, 897–911.

396

https://doi.org/10.1007/s00382-011-1052-y

397

Bollasina, M., Nigam, S., Lau, K.M., 2008. Absorbing aerosols and summer monsoon evolution

398

over South Asia: An observational portrayal. J. Clim. 21, 3221–3239.

399

https://doi.org/10.1175/2007JCLI2094.1

400

Bollasina, M.A., Ming, Y., Ramaswamy, V., 2011. Anthropogenic Aerosols and the Summer

401

Monsoon. Science (80-. ). 334, 502–505. https://doi.org/10.1126/science.1204994

402

Bond, T.C., Doherty, S.J., Fahey, D.W., Forster, P.M., Berntsen, T., Deangelo, B.J., Flanner,

403

M.G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P.K., Sarofim, M.C.,

404

Schultz, M.G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N.,

405

Guttikunda, S.K., Hopke, P.K., Jacobson, M.Z., Kaiser, J.W., Klimont, Z., Lohmann, U.,

406

Schwarz, J.P., Shindell, D., Storelvmo, T., Warren, S.G., Zender, C.S., 2013. Bounding the

407

role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos.

408

118, 5380–5552. https://doi.org/10.1002/jgrd.50171

409

Cattani, E., Costa, M.J., Torricella, F., Levizzani, V., Silva, A.M., 2006. Influence of aerosol

410

particles from biomass burning on cloud microphysical properties and radiative forcing.

411

412

Atmos. Res. 82, 310–327. https://doi.org/10.1016/j.atmosres.2005.10.010 Chaboureau, J.P., Richard, E., Pinty, J.P., Flamant, C., Di Girolamo, P., Kiemle, C., Behrendt,

413

A., Chepfer, H., Chiriaco, M., Wulfmeyer, V., 2011. Long-range transport of Saharan dust

414

and its radiative impact on precipitation forecast: A case study during the Convective and

415

Orographically-induced Precipitation Study (COPS). Q. J. R. Meteorol. Soc. 137, 236–251.

416

https://doi.org/10.1002/qj.719

417

Chameides, W., Yu, H., Liu, S., Bergin, M., Xhou, X., Mearns, L., Wang, G., Kiang, C., Saylor,

418

R.D., Luo, C., Huang, Y., Steiner, A., Giorgi, F., 1999. Study of the effects of atmospheric

419

regional haze on agriculture : enhance crop yields in China through emission controls? Proc.

420

Natl. Acad. Sci. 96, 13626–13633.

421

Chameides, W.L., Xingsheng, L., Xiaoyan, T., Xiuji, Z., Chao, L., Kiang, C.S., St. John, J.,

422

Saylor, R.D., Liu, S.C., Lam, K.S., Wang, T., Giorgi, F., 1999. Is ozone pollution affecting

423

crop yields in China? Geophys. Res. Lett. 26, 867–870.

424

https://doi.org/10.1029/1999GL900068

425

Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B., Duncan, B.N., Martin, R. V, Logan,

426

J.A., Higurashi, A., Nakajima, T., 2002. Tropospheric Aerosol Optical Thickness from the

427

GOCART Model and Comparisons with Satellite and Sun Photometer Measurements. J.

428

Atmos. Sci. 59, 461–483. https://doi.org/10.1175/1520-

429

0469(2002)059<0461:TAOTFT>2.0.CO;2

430

Ching, J., Riemer, N., West, M., 2016. Black carbon mixing state impacts on cloud

431

microphysical properties: Effects of aerosol plume and environmental conditions. J.

432

Geophys. Res. 121, 5990–6013. https://doi.org/10.1002/2016JD024851

433

Chinnam, N., Dey, S., Tripathi, S.N., Sharma, M., 2006. Dust events in Kanpur, northern India:

434

Chemical evidence for source and implications to radiative forcing. Geophys. Res. Lett. 33,

435

1–4. https://doi.org/10.1029/2005GL025278

436

Crutzen, P.J., Heidt, L.E., Krasnec, J.P., Pollock, W.H., Seiler, W., 1979. Biomass burning as a

437

source of atmospheric gases CO, H2, N 2O, NO, CH3Cl and COS. Nature 282, 253–256.

438

https://doi.org/10.1038/282253a0

439

Dey, S., Tripathi, S.N., 2008. Aerosol direct radiative effects over Kanpur in the Indo-Gangetic

440

basin, northern India: Long-term (2001-2005) observations and implications to regional

441

climate. J. Geophys. Res. Atmos. 113, 1–20. https://doi.org/10.1029/2007JD009029

442

Dey, S., Tripathi, S.N., Singh, R.P., Holben, B.N., 2004. Influence of dust storms on the aerosol

443

optical properties over the Indo-Gangetic basin. J. Geophys. Res. D Atmos. 109, 1–13.

444

https://doi.org/10.1029/2004JD004924

445

Dickerson, R.R., Kondragunta, S., Stenchikov, G., Civerolo, K.L., G, D.B., Holben, B.N., 1997.

446

The Impact of Aerosols on Solar Ultraviolet Radiation and Photochemical Smog. Science

447

215, 827–830. https://doi.org/10.1126/science.1172133

448

Ding, A.J., Huang, X., Nie, W., Sun, J.N., Kerminen, V.M., Petäjä, T., Su, H., Cheng, Y.F.,

449

Yang, X.Q., Wang, M.H., Chi, X.G., Wang, J.P., Virkkula, A., Guo, W.D., Yuan, J., Wang,

450

S.Y., Zhang, R.J., Wu, Y.F., Song, Y., Zhu, T., Zilitinkevich, S., Kulmala, M., Fu, C.B.,

451

2016. Enhanced haze pollution by black carbon in megacities in China. Geophys. Res. Lett.

452

43, 2873–2879. https://doi.org/10.1002/2016GL067745

453 454

Dipu, S., Prabha, T. V., Pandithurai, G., Dudhia, J., Pfister, G., Rajesh, K., Goswami, B.N., 2013. Impact of elevated aerosol layer on the cloud macrophysical properties prior to

455

monsoon onset. Atmos. Environ. 70, 454–467.

456

https://doi.org/10.1016/J.ATMOSENV.2012.12.036

457 458

459 460

Dockery, D.W., Stone, P.H., 2007. Cardiovascular Risks from Fine Particulate Air Pollution. N. Engl. J. Med. 356, 511–513. https://doi.org/10.1056/NEJMe068274 Ehhalt, D., Prather, M., 2001. Atmospheric Chemistry and Greenhouse Gases. Clim. Chang. 2001 Sci. Basis 239–287. https://doi.org/10.2753/JES1097-203X330403

461

Emmons, L.K., Walters, S., Hess, P.G., Lamarque, J.F., Pfister, G.G., Fillmore, D., Granier, C.,

462

Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C.,

463

Baughcum, S.L., Kloster, S., 2010. Description and evaluation of the Model for Ozone and

464

Related chemical Tracers, version 4 (MOZART-4). Geosci. Model Dev. 3, 43–67.

465

https://doi.org/10.5194/gmd-3-43-2010

466

Ensor, D.S., Porch, W.M., Pilat, M.J., Charlson, R.J., Ensor, D.S., Porch, W.M., Pilat, M.J.,

467

Charlson, R.J., 1971. Influence of the Atmospheric Aerosol on Albedo. J. Appl. Meteorol.

468

10, 1303–1306. https://doi.org/10.1175/1520-0450(1971)010<1303:IOTAAO>2.0.CO;2

469

Fast, J.D., Gustafson, W.I., Easter, R.C., Zaveri, R.A., Barnard, J.C., Chapman, E.G., Grell,

470

G.A., Peckham, S.E., 2006. Evolution of ozone, particulates, and aerosol direct radiative

471

forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol

472

model. J. Geophys. Res. Atmos. 111, 1–29. https://doi.org/10.1029/2005JD006721

473

Forkel, R., Werhahn, J., Hansen, A.B., McKeen, S., Peckham, S., Grell, G., Suppan, P., 2012.

474

Effect of aerosol-radiation feedback on regional air quality - A case study with WRF/Chem.

475

Atmos. Environ. 53, 202–211. https://doi.org/10.1016/j.atmosenv.2011.10.009

476

Freitas, S.R., Longo, K.M., Chatfield, R., Latham, D., Silva Dias, M.A.F., Andreae, M.O., Prins,

477

E., Santos, J.C., Gielow, R., Carvalho, J.A., 2007. Including the sub-grid scale plume rise of

478

vegetation fires in low resolution atmospheric transport models. Atmos. Chem. Phys. 7,

479

3385–3398. https://doi.org/10.5194/acp-7-3385-2007

480

Ganguly, D., Gadhavi, H., Jayaraman, A., Rajesh, T.A., Misra, A., 2005. Single scattering albedo

481

of aerosols over the central India: Implications for the regional aerosol radiative forcing.

482

Geophys. Res. Lett. 32, 1–4. https://doi.org/10.1029/2005GL023903

483

Gao, J., Zhu, B., Xiao, H., Kang, H., Pan, C., Wang, D., Wang, H., 2018. Effects of black carbon

484

and boundary layer interaction on surface ozone in Nanjing, China. Atmos. Chem. Phys. 18,

485

7081–7094. https://doi.org/10.5194/acp-18-7081-2018

486

Gao, Y., Zhang, M., Liu, Z., Wang, L., Wang, P., Xia, X., Tao, M., Zhu, L., 2015. Modeling the

487

feedback between aerosol and meteorological variables in the atmospheric boundary layer

488

during a severe fog-haze event over the North China Plain. Atmos. Chem. Phys. 15, 4279–

489

4295. https://doi.org/10.5194/acp-15-4279-2015

490

Ge, J.M., Su, J., Ackerman, T.P., Fu, Q., Huang, J.P., Shi, J.S., 2010. Dust aerosol optical

491

properties retrieval and radiative forcing over northwestern China during the 2008 China-

492

U.S. joint field experiment. J. Geophys. Res. Atmos. 115, 1–11.

493

https://doi.org/10.1029/2009JD013263

494

Ghude, S.D., Jena, C., Chate, D.M., Beig, G., Pfister, G.G., Kumar, R., Ramanathan, V., 2014.

495

Reductions in India’s crop yield due to ozone. Geophys. Res. Lett. 41, 5685–5691.

496

https://doi.org/10.1002/2014GL060930

497

Ginoux, P., Chin, M., Tegen, I., Goddard, T., In-, G., 2001. Sources and distributions of dust

498

499

aerosols simulated with GOCART model. J. Geophys. Res. 106. Gogoi, M.M., Babu, S.S., Moorthy, K.K., Bhuyan, P.K., Pathak, B., Subba, T., Chutia, L.,

500

Kundu, S.S., Bharali, C., Borgohain, A., Guha, A., Kumar De, B., Singh, B., Chin, M.,

501

2017. Radiative effects of absorbing aerosols over northeastern India: Observations and

502

model simulations. J. Geophys. Res. 122, 1132–1157.

503

https://doi.org/10.1002/2016JD025592

504

Grell, G., Baklanov, A., 2011. Integrated modeling for forecasting weather and air quality: A call

505

for fully coupled approaches. Atmos. Environ. 45, 6845–6851.

506

https://doi.org/10.1016/j.atmosenv.2011.01.017

507

Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C., Eder, B.,

508

2005. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–

509

6975. https://doi.org/10.1016/j.atmosenv.2005.04.027

510

Gu, Y., Liou, K.N., Jiang, J.H., Su, H., Liu, X., 2012. Dust aerosol impact on North Africa

511

climate: a GCM investigation of aerosol-cloud-radiation interactions using A-Train satellite

512

data. Atmos. Chem. Phys. 12, 1667–1679. https://doi.org/10.5194/acp-12-1667-2012

513

Guenther, A., Karl, T., Harley, P., Weidinmyer, C., Palmer, P.I., Geron, C., 2006. Edinburgh

514

Research Explorer Estimates of global terrestrial isoprene emissions using MEGAN (

515

Model of Emissions of Gases and Aerosols from Nature ) and Physics Estimates of global

516

terrestrial isoprene emissions using MEGAN ( Model of Emissions of Gases an. Atmos.

517

Chem. Phys. Atmos. Chem. Phys. 3181–3210. https://doi.org/10.5194/acp-6-3181-2006

518 519

Hegde, P., Pant, P., Naja, M., Dumka, U.C., Sagar, R., 2007. South Asian dust episode in June 2006: Aerosol observations in the central Himalayas. Geophys. Res. Lett. 34, 1–5.

520

https://doi.org/10.1029/2007GL030692

521

Iacono, M.J., Delamere, J.S., Mlawer, E.J., Shephard, M.W., Clough, S.A., Collins, W.D., 2008.

522

Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative

523

transfer models. J. Geophys. Res. Atmos. 113, 2–9. https://doi.org/10.1029/2008JD009944

524

Jacobson, M.Z., 2001. Strong radiative heating due to the mixing state of black carbon in

525

atmospheric aerosols. Nature 409, 695–697. https://doi.org/10.1038/35055518

526

Jacobson, M.Z., 1998. Studying the effects of aerosols on vertical photolysis rate coefficient and

527

temperature profiles over an urban airshed Gas photochemistry Gas-to-pxticle conversion

528

Wind speed Wind direction Air pressure Nucleation Freezing / Melting. J. Geophys. Res.

529

103.

530

Janseen, N.A., Gerlofs-Nijland, M.E., Lanki, T., Salonen, R.O., Cassee, F., Hoek, G., Fisher, P.,

531

Brunkreef, B., Krzyanowski, M., 2012. Health efeccts of Black Carbon. World Heal. Organ.

532

iv–57. https://doi.org/10.1016/j.atmosenv.2007.03.042

533

Janssens-Maenhout, G., Dentener, F.J., Aardenne, J. Van, Monni, S., Pagliari, V., Orlandini, L.,

534

Klimont, Z., Kurokawa, J., Akimoto, H., Ohara, T., Wankmüller, R., Battye, B., Grano, D.,

535

Zuber, A., Keating, T., 2012. EDGAR-HTAP: a harmonized gridded air pollution emission

536

dataset based on national inventories, JRC Scientific and Technical Reports.

537

https://doi.org/10.2788/14102

538

Jayaraman, A., Lubin, D., Ramachandran, S., Ramanathan, V., Woodbridge, E., Collins, W.. .,

539

Zalpuri, K.., 1998. Direct observations of aerosol radiative forcing over the tropical Indian

540

Ocean during the January-February 1996 pre-INDOEX cruise. J. Geophys. Res. 103,

541

13827–13836.

542

Jena, C., Ghude, S.D., Pfister, G.G., Chate, D.M., Kumar, R., Beig, G., Surendran, D.E.,

543

Fadnavis, S., Lal, D.M., 2015. Influence of springtime biomass burning in South Asia on

544

regional ozone (O3): A model based case study. Atmos. Environ. 100, 37–47.

545

https://doi.org/10.1016/j.atmosenv.2014.10.027

546

Ji, Z.M., 2016. Modeling black carbon and its potential radiative effects over the Tibetan Plateau.

547

Adv. Clim. Chang. Res. 7, 139–144. https://doi.org/10.1016/j.accre.2016.10.002

548

Jiménez, P.A., Dudhia, J., González-Rouco, J.F., Navarro, J., Montávez, J.P., García-

549

Bustamante, E., 2012. A Revised Scheme for the WRF Surface Layer Formulation. Mon.

550

Weather Rev. 140, 898–918. https://doi.org/10.1175/MWR-D-11-00056.1

551

Kalenderski, S., Stenchikov, G., Zhao, C., 2013. Modeling a typical winter-time dust event over

552

the Arabian Peninsula and the Red Sea. Atmos. Chem. Phys. 13, 1999–2014.

553

https://doi.org/10.5194/acp-13-1999-2013

554

Kumar, R., Barth, M.C., Pfister, G.G., Naja, M., Brasseur, G.P., 2014. WRF-Chem simulations

555

of a typical pre-monsoon dust storm in northern India: Influences on aerosol optical

556

properties and radiation budget. Atmos. Chem. Phys. 14, 2431–2446.

557

https://doi.org/10.5194/acp-14-2431-2014

558

Kumar, R., Naja, M., Pfister, G.G., Barth, M.C., Wiedinmyer, C., Brasseur, G.P., 2012.

559

Simulations over South Asia using the Weather Research and Forecasting model with

560

Chemistry (WRF-Chem): chemistry evaluation and initial results. Geosci. Model Dev. 5,

561

619–648. https://doi.org/10.5194/gmd-5-619-2012

562 563

Lau, K.M., Kim, K.M., 2006. Observational relationships between aerosol and Asian monsoon rainfall, and circulation. Geophys. Res. Lett. 33, 1–5.

564

565

https://doi.org/10.1029/2006GL027546 Lau, W.K.M., Kim, K.M., Shi, J.J., Matsui, T., Chin, M., Tan, Q., Peters-Lidard, C., Tao, W.K.,

566

2017. Impacts of aerosol–monsoon interaction on rainfall and circulation over Northern

567

India and the Himalaya Foothills. Clim. Dyn. 49, 1945–1960.

568

https://doi.org/10.1007/s00382-016-3430-y

569 570

571

Li, G., Zhang, R., Fan, J., Tie, X., 2005. Impacts of black carbon aerosol on photolysis and ozone. J. Geophys. Res. Atmos. 110, 1–10. https://doi.org/10.1029/2005JD005898 Li, J., Wang, Z., Wang, X., Yamaji, K., Takigawa, M., Kanaya, Y., Pochanart, P., Liu, Y., Irie,

572

H., Hu, B., Tanimoto, H., Akimoto, H., 2011. Impacts of aerosols on summertime

573

tropospheric photolysis frequencies and photochemistry over Central Eastern China. Atmos.

574

Environ. 45, 1817–1829. https://doi.org/10.1016/j.atmosenv.2011.01.016

575

Liu, L., Shawki, D., Voulgarakis, A., Kasoar, M., Samset, B.H., Myhre, G., Forster, P.M.,

576

Hodnebrog, Sillmann, J., Aalbergsjø, S.G., Boucher, O., Faluvegi, G., Iversen, T.,

577

Kirkevåg, A., Lamarque, J.F., Olivié, D., Richardson, T., Shindell, D., Takemura, T., 2018a.

578

A PDRMIP Multimodel study on the impacts of regional aerosol forcings on global and

579

regional precipitation. J. Clim. 31, 4429–4447. https://doi.org/10.1175/JCLI-D-17-0439.1

580

Liu, L., Shawki, D., Voulgarakis, A., Kasoar, M., Samset, B.H., Myhre, G., Forster, P.M.,

581

Hodnebrog, Sillmann, J., Aalbergsjø, S.G., Boucher, O., Faluvegi, G., Iversen, T.,

582

Kirkevåg, A., Lamarque, J.F., Olivié, D., Richardson, T., Shindell, D., Takemura, T.,

583

2018b. A PDRMIP Multimodel study on the impacts of regional aerosol forcings on global

584

and regional precipitation. J. Clim. 31, 4429–4447. https://doi.org/10.1175/JCLI-D-17-

585

0439.1

586 587

588

Lohmann, U., Feichter, J., 2004. Global indirect aerosol effects: a review. Atmos. Chem. Phys. Discuss. 4, 7561–7614. https://doi.org/10.5194/acpd-4-7561-2004 Mahowald, N.M., Baker, A.R., Bergametti, G., Brooks, N., Duce, R.A., Jickells, T.D., Kubilay,

589

N., Prospero, J.M., Tegen, I., 2005. Atmospheric global dust cycle and iron inputs to the

590

ocean. Global Biogeochem. Cycles 19. https://doi.org/10.1029/2004GB002402

591

Mauzerall, D.L., Wang, X., 2001. P ROTECTING A GRICULTURAL C ROPS FROM THE E

592

FFECTS OF T ROPOSPHERIC O ZONE E XPOSURE : Reconciling Science and Standard

593

Setting in the United States, Europe, and Asia. Annu. Rev. Energy Environ. 26, 237–268.

594

https://doi.org/10.1146/annurev.energy.26.1.237

595

Menon, S., Genio, A.D. Del, Koch, D., Tselioudis, G., 2002a. GCM Simulations of the Aerosol

596

Indirect Effect: Sensitivity to Cloud Parameterization and Aerosol Burden. J. Atmos. Sci.

597

59, 692–713. https://doi.org/10.1175/1520-0469(2002)059<0692:GSOTAI>2.0.CO;2

598

Menon, S., Hansen, J., Nazarenko, L., Luo, Y., 2002b. Climate effects of black carbon aerosols

599

in China and India. Science (80-. ). 297, 2250–2253.

600

https://doi.org/10.1126/science.1075159

601

Miller, R.L., Tegen, I., Perlwitz, J., 2004. Surface radiative forcing by soil dust aerosols and the

602

hydrologic cycle. J. Geophys. Res. Atmos. 109, n/a-n/a.

603

https://doi.org/10.1029/2003JD004085

604

Montzka, S. a., Reimann, S., Engel, a., Kruger, K., O’Doherty, S., Sturges, W., Et Al., 2011.

605

Ozone depleting substances (ODSs) and related chemicals, Scientific Assessment of Ozone

606

Depletion: 2010.

607

Morrison, H., Thompson, G., Tatarskii, V., 2009. Impact of Cloud Microphysics on the

608

Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison

609

of One- and Two-Moment Schemes. Mon. Weather Rev. 137, 991–1007.

610

https://doi.org/10.1175/2008MWR2556.1

611 612

613

Mudway, I.S., Kelly, F.J., 2000. Ozone and the lung: a sensitive issue. Mol. Aspects Med. 21, 1– 48. Nakanishi, M., Niino, H., 2009. Development of an Improved Turbulence Closure Model for the

614

Atmospheric Boundary Layer. J. Meteorol. Soc. Japan 87, 895–912.

615

https://doi.org/10.2151/jmsj.87.895

616

Nigam, S., Bollasina, M., 2010. “Elevated heat pump” hypothesis for the aerosol-monsoon

617

hydroclimate link: “Grounded” in observations? J. Geophys. Res. Atmos. 115, 4–10.

618

https://doi.org/10.1029/2009JD013800

619

Ning, H., Li-juan, L., Bin, W., 2015. The Role of the Aerosol Indirect Effect in the Northern

620

Indian Ocean Warming Simulated by CMIP5 Models. Atmos. Ocean. Sci. Lett. 2834, 411–

621

416. https://doi.org/10.3878/j.issn.1674-2834.14.0032

622

Pandithurai, G., Dipu, S., Dani, K.K., Tiwari, S., Bisht, D.S., Devara, P.C.S., Pinker, R.T., 2008.

623

Aerosol radiative forcing during dust events over New Delhi, India. J. Geophys. Res.

624

Atmos. 113, 1–13. https://doi.org/10.1029/2008JD009804

625

Panicker, A.S., Pandithurai, G., Dipu, S., 2010. Aerosol indirect effect during successive

626

contrasting monsoon seasons over Indian subcontinent using MODIS data. Atmos. Environ.

627

44, 1937–1943. https://doi.org/10.1016/J.ATMOSENV.2010.02.015

628

Pathak, B., Subba, T., Dahutia, P., Bhuyan, P.K., Moorthy, K.K., Gogoi, M.M., Babu, S.S.,

629

Chutia, L., Ajay, P., Biswas, J., Bharali, C., Borgohain, A., Dhar, P., Guha, A., De, B.K.,

630

Banik, T., Chakraborty, M., Kundu, S.S., Sudhakar, S., Singh, S.B., 2016. Aerosol

631

characteristics in north-east India using ARFINET spectral optical depth measurements.

632

Atmos. Environ. 125, 461–473. https://doi.org/10.1016/j.atmosenv.2015.07.038

633

Pfister, G.G., Parrish, D.D., Worden, H., Emmons, L.K., Edwards, D.P., Wiedinmyer, C.,

634

Diskin, G.S., Huey, G., Oltmans, S.J., Thouret, V., Weinheimer, A., Wisthaler, A., 2011.

635

Characterizing summertime chemical boundary conditions for airmasses entering the US

636

West Coast. Atmos. Chem. Phys. 11, 1769–1790. https://doi.org/10.5194/acp-11-1769-2011

637

Prasad, A.K., Singh, R.P., 2007. Changes in aerosol parameters during major dust storm events

638

(2001-2005) over the Indo-Gangetic Plains using AERONET and MODIS data. J. Geophys.

639

Res. Atmos. 112. https://doi.org/10.1029/2006JD007778

640 641

642

Previdi, M., 2010. Radiative feedbacks on global precipitation. Environ. Res. Lett. 5. https://doi.org/10.1088/1748-9326/5/2/025211 Prospero, J.M., 2009. African Droughts and Dust Transport to the African Droughts and Dust

643

Transport to the Caribbean : 1024, 1024–1028.

644

https://doi.org/10.1016/j.scitotenv.2011.02.007

645 646

Rajeev, K., Ramanathan, V., 2001. Direct observations of clear sky aerosol radiative forcing from space during {I}ndian {O}cean {E}xperiment. J. Geophys. Res. 106, 17,217-221,235.

647

Ramachandran, S., Rengarajan, R., Jayaraman, A., Sarin, M.M., Das, S.K., 2006. Aerosol

648

radiative forcing during clear, hazy, and foggy conditions over a continental polluted

649

location in north India. J. Geophys. Res. Atmos. 111, 1–12.

650

651 652

653

https://doi.org/10.1029/2006JD007142 Ramanathan, V., Carmichael, G., 2008. Global and regional climate changes due to black carbon. Nat. Geosci. 1, 221–227. https://doi.org/10.1038/ngeo156 Ramanathan, V., Crutzen, @bullet P J, Lelieveld, J., Mitra, A.P., Althausen, D., Anderson, J.,

654

Andreae, M.O., Cantrell, W., Cass, G.R., Chung, C.E., 2001. Indian Ocean Experiment: An

655

integrated analysis of the climate forcing and effects of the great Indo-Asian haze. J.

656

Geophys. Res. 106398, 371–28.

657

Ramanathan, V., Ramana, M. V., 2005. Persistent, widespread, and strongly absorbing haze over

658

the Himalayan foothills and the Indo-Gangetic Plains. Pure Appl. Geophys. 162, 1609–

659

1626. https://doi.org/10.1007/s00024-005-2685-8

660

Rehman, I.H., Ahmed, T., Praveen, P.S., Kar, A., Ramanathan, V., 2011. Black carbon emissions

661

from biomass and fossil fuels in rural India. Atmos. Chem. Phys. 11, 7289–7299.

662

https://doi.org/10.5194/acp-11-7289-2011

663

Samset, B.H., Myhre, G., Forster, P.M., Hodnebrog, Andrews, T., Faluvegi, G., Fläschner, D.,

664

Kasoar, M., Kharin, V., Kirkevåg, A., Lamarque, J.F., Olivié, D., Richardson, T., Shindell,

665

D., Shine, K.P., Takemura, T., Voulgarakis, A., 2016. Fast and slow precipitation responses

666

to individual climate forcers: A PDRMIP multimodel study. Geophys. Res. Lett. 43, 2782–

667

2791. https://doi.org/10.1002/2016GL068064

668

Sarangi, C., Tripathi, S.N., Tripathi, S., Barth, M.C., 2015. Aerosol-cloud associations over

669

gangetic basin during a typical monsoon depression event using WRF-Chem simulation. J.

670

Geophys. Res. 120, 10,974-10,995. https://doi.org/10.1002/2015JD023634

671

Satheesh, S.K., Krishna Moorthy, K., Suresh Babu, S., Vinoj, V., Nair, V.S., Naseema Beegum,

672

S., Dutt, C.B.S., Alappattu, D.P., Kunhikrishnan, P.K., 2009. Vertical structure and

673

horizontal gradients of aerosol extinction coefficients over coastal India inferred from

674

airborne lidar measurements during the integrated campaign for aerosol, gases and radiation

675

budget (ICARB) field campaign. J. Geophys. Res. Atmos. 114, 1–11.

676

https://doi.org/10.1029/2008JD011033

677

Satheesh, S.K., Ramanathan, V., 2000. Large differences in tropical aerosol forcing at the top of

678

the atmosphere and Earth’s surface. Nature 405, 60–63. https://doi.org/10.1038/35011039

679

Satheesh, S.K., Vinoj, V., Moorthy, K.K., 2006. Vertical distribution of aerosols over an urban

680

continental site in India inferred using a micro pulse lidar. Geophys. Res. Lett. 33, 2–6.

681

https://doi.org/10.1029/2006GL027729

682

Seinfeld, J.H., Carmichael, G.R., Arimoto, R., Conant, W.C., Brechtel, F.J., Bates, T.S., Cahill,

683

T.A., Clarke, A.D., Doherty, S.J., Flatau, P.J., Huebert, B.J., Kim, J., Markowicz, K.M.,

684

Quinn, P.K., Russell, L.M., Russell, P.B., Shimizu, A., Shinozuka, Y., Song, C.H., Tang,

685

Y., Uno, I., Vogelmann, A.M., Weber, R.J., Woo, J.H., Zhang, X.Y., 2004. ACE-ASIA:

686

Regional climatic and atmospheric chemical effects of Asian dust and pollution. Bull. Am.

687

Meteorol. Soc. 85, 367–380. https://doi.org/10.1175/BAMS-85-3-367

688

Sharma, A., Ojha, N., Pozzer, A., Mar, K.A., Beig, G., Lelieveld, J., Gunthe, S.S., 2017. WRF-

689

Chem simulated surface ozone over south Asia during the pre-monsoon: effects of emission

690

inventories and chemical mechanisms. Atmos. Chem. Phys. 17, 14393–14413.

691

https://doi.org/10.5194/acp-17-14393-2017

692

Skamarock, W.C., Klemp, J.B., Dudhiya, J., Gill, D.O., Barker, D.M., Duda, M.G., Y, H.X.,

693

Wang, W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3.

694

NCAR Tech. Note. https://doi.org/10.1080/07377366.2001.10400427

695

Srivastava, A.K., Soni, V.K., Singh, S., Kanawade, V.P., Singh, N., Tiwari, S., Attri, S.D., 2014.

696

An early South Asian dust storm during March 2012 and its impacts on Indian Himalayan

697

foothills: A case study. Sci. Total Environ. 493, 526–534.

698

https://doi.org/10.1016/j.scitotenv.2014.06.024

699

Stanelle, T., Vogel, B., Vogel, H., Bäumer, D., Kottmeier, C., 2010. Feedback between dust

700

particles and atmospheric processes over West Africa during dust episodes in March 2006

701

and June 2007. Atmos. Chem. Phys. 10, 10771–10788. https://doi.org/10.5194/acp-10-

702

10771-2010

703

Surendran, D.E., Beig, G., Ghude, S.D., Panicker, A.S., Manoj, M.G., Chate, D.M., Ali, K.,

704

2013. Radiative forcing of black carbon over Delhi. Int. J. Photoenergy 2013.

705

https://doi.org/10.1155/2013/313652

706

Teller, A., Xue, L., Levin, Z., 2012. The effects of mineral dust particles, aerosol regeneration

707

and ice nucleation parameterizations on clouds and precipitation. Atmos. Chem. Phys. 12,

708

9303–9320. https://doi.org/10.5194/acp-12-9303-2012

709

Tewari, M., Chen, F., Wang, W., Dudhia, J., Lemone, M.A., Mitchell, K., Ek, M., Gayno, G.,

710

Wegiel, J., Cuenca, R.H., 2004. IMPLEMENTATION AND VERIFICATION OF THE

711

UNIFIED NOAH LAND SURFACE MODEL IN THE WRF MODEL, in: 20th Conference

712

on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction,.

713

pp. 11–15. https://doi.org/10.1007/s11269-013-0452-7

714

Tie, X., Madronich, S., Walters, S., Edwards, D.P., Ginoux, P., Mahowald, N., Zhang, R.Y.,

715

Lou, C., Brasseur, G., 2005. Assessment of the global impact of aerosols on tropospheric

716

oxidants. J. Geophys. Res. D Atmos. 110, 1–32. https://doi.org/10.1029/2004JD005359

717

Tiwari, S., Singh, A.K., 2013. Variability of Aerosol parameters derived from ground and

718

satellite measurements over Varanasi located in the Indo-Gangetic Basin. Aerosol Air Qual.

719

Res. https://doi.org/10.4209/aaqr.2012.06.0162

720

Tompkins, A.M., Cardinali, C., Morcrette, J.J., Rodwell, M., 2005. Influence of aerosol

721

climatology on forecasts of the African Easterly Jet. Geophys. Res. Lett. 32, 1–4.

722

https://doi.org/10.1029/2004GL022189

723

Uno, I., Wang, Z., Chiba, M., Chun, Y.S., Gong, S.L., Hara, Y., Jung, E., Lee, S.., Liu, M.,

724

Mikami, M., Music, S., Nickovic, S., Satake, S., Shao, Y., Song, Z., Sugimoto, N., Tanaka,

725

T., Westphal, D.., 2005. Dust model intercomparison (DMIP) study over Asia: Overview. J.

726

Geophys. Res. Atmos. 111.

727

Vinoj, V., Rasch, P.J., Wang, H., Yoon, J., Ma, P., Landu, K., Singh, B., 2014. Short-term

728

modulation of Indian summer monsoon rainfall by West Asian dust. Nat. Geosci. 7.

729

https://doi.org/10.1038/NGEO2107

730

Vinoj, V., Satheesh, S.K., Moorthy, K.K., 2010. Optical, radiative, and source characteristics of

731

aerosols at Minicoy, a remote island in the southern Arabian Sea. J. Geophys. Res. Atmos.

732

115, 1–19. https://doi.org/10.1029/2009JD011810

733

Vogel, B., Vogel, H., B̈ aumerr, D., Bangert, M., Lundgren, K., Rinke, R., Stanelle, T., 2009. The

734

comprehensive model system COSMO-ART Radiative impact of aerosol on the state of the

735

atmosphere on the regional scale. Atmos. Chem. Phys. 9, 8661–8680.

736

https://doi.org/10.5194/acp-9-8661-2009

737

Wang, Z., Liu, D., Wang, Y., Wang, Z., Shi, G., 2015. Diurnal aerosol variations do affect daily

738

averaged radiative forcing under heavy aerosol loading observed in Hefei, China. Atmos.

739

Meas. Tech. 8, 2901–2907. https://doi.org/10.5194/amt-8-2901-2015

740

Wiedinmyer, C., Akagi, S.K., Yokelson, R.J., Emmons, L.K., Al-Saadi, J.A., Orlando, J.J., Soja,

741

A.J., 2011. The Fire INventory from NCAR (FINN): A high resolution global model to

742

estimate the emissions from open burning. Geosci. Model Dev. 4, 625–641.

743

https://doi.org/10.5194/gmd-4-625-2011

744

Yang, Y., Zhao, C., Dong, X., Fan, G., Zhou, Y., Wang, Y., Zhao, L., Lv, F., Yan, F., 2019.

745

Toward understanding the process-level impacts of aerosols on microphysical properties of

746

shallow cumulus cloud using aircraft observations. Atmos. Res. 221, 27–33.

747

https://doi.org/10.1016/j.atmosres.2019.01.027

748

Zhao, C., Hu, Z., Qian, Y., Ruby Leung, L., Huang, J., Huang, M., Jin, J., Flanner, M.G., Zhang,

749

R., Wang, H., Yan, H., Lu, Z., Streets, D.G., 2014. Simulating black carbon and dust and

750

their radiative forcing in seasonal snow: A case study over North China with field campaign

751

measurements. Atmos. Chem. Phys. 14, 11475–11491. https://doi.org/10.5194/acp-14-

752

11475-2014

753

Zhao, C., Liu, X., Leung, L.R., Hagos, S., 2011. Radiative impact of mineral dust on monsoon

754

precipitation variability over West Africa. Atmos. Chem. Phys. 11, 1879–1893.

755

https://doi.org/10.5194/acp-11-1879-2011

756

757

Aerosol radiative impact on surface ozone during a heavy dust and biomass burning event over South Asia T. Mukherjee1, 3, V. Vinoj2, S. K. Midya3, B. Adhikary1* 1

International Centre for Integrated Mountain Development, Nepal

2

School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, India

3

Department of Atmospheric Science, University of Calcutta, India

Highlights •

Aerosol feedback on meteorology and surface ozone is reported over South Asia.

•

Elevated BC concentration can reduce surface temperature up to 2 K.

•

Incoming short wave flux at the surface reduced up to 70% due to elevated BC.

•

The radiative impact of dust is less compared to BC at the surface.

•

BC can reduce the surface ozone concentration by ~30% at the source region.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: