Radiative impact of a heavy dust storm over India and surrounding oceanic regions

Accepted Manuscript Radiative impact of a heavy dust storm over India and surrounding oceanic regions Sumita Kedia, Rajesh Kumar, Sahidul Islam, Yogesh Sathe, Akshara Kaginalkar PII:

S1352-2310(18)30302-9

DOI:

10.1016/j.atmosenv.2018.05.005

Reference:

AEA 15996

To appear in:

Atmospheric Environment

Received Date: 7 December 2017 Revised Date:

26 April 2018

Accepted Date: 4 May 2018

Please cite this article as: Kedia, S., Kumar, R., Islam, S., Sathe, Y., Kaginalkar, A., Radiative impact of a heavy dust storm over India and surrounding oceanic regions, Atmospheric Environment (2018), doi: 10.1016/j.atmosenv.2018.05.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

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Radiative impact of a heavy dust storm over India and surrounding oceanic regions

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Sumita Kedia1*, Rajesh Kumar2, Sahidul Islam1, Yogesh Sathe1, Akshara Kaginalkar1 1

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Centre for Development of Advanced Computing, Pune, India – 411 008 2

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National Center for Atmospheric Research, Boulder, CO *

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Corresponding Address: [email protected]

6 Abstract

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Efficient management of frequently occurring destructive dust storms requires an in-depth

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understanding of the extent of impacts of such events. Due to limited availability of observational

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data, it is difficult to understand/estimate the impact of dust aerosols on the Earth’s radiation budget

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in detail. This study, applies a regional model, Weather Research and Forecasting model with

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chemistry (WRF-Chem), to investigate the impact of an intense dust storm that originated over the

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Arabian peninsula during 01-02 April 2015 and transported towards the Indian subcontinent by the

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westerly winds. Two identical numerical experiments are designed, each for 15 days, one with and

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another without dust aerosols, to estimate the impact of the dust storm over the Indian subcontinent

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and adjoining regions. WRF-Chem model reproduced the spatial, temporal as well as the vertical

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distribution of dust plume reasonably well.

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Model results show significant changes in aerosol optical, physical and radiative properties

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due to the dominance of coarse mode aerosols in the atmosphere during the dust storm. Analysis of

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vertical profiles of particulate matter (PM10) concentration reveals the presence of dust aerosols

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extending from the surface to altitudes as high as 3-4 km during the dust storm period. The dust

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storm induced a cooling effect at the surface via reduction in shortwave (SW) radiative flux. A

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substantial decrease in temperature is also seen at 850 hPa due to dust, indicating a significant

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impact of dust layer on the atmospheric temperature profile. Atmospheric heating due to dust

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aerosols in the SW region is found to be compensated up to a large extent by longwave (LW)

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cooling effect of dust. The net dust induced radiative perturbation at the top of the atmosphere

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(TOA) over different regions is negative and varied from -2.49 to -0.34 Wm-2, while it is in the

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range of -0.62 to + 0.32 Wm-2 at the surface.

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1 Introduction

Mineral dust is one of the most important atmospheric aerosols because of its ability to

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interact with both the shortwave (SW) and longwave (LW) radiation, and because it has the largest

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mass abundance as compared to other aerosols (Seinfeld and Pandis 1997; Miller and Tagen 1998;

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Prospero 1999; Mallet et al. 2009; IPCC 2013). Dust aerosols exert a significant influence on the

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Earth's climate system directly by scattering and absorbing the solar and terrestrial radiation (Miller

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and Tegen 1998; Ramanathan et al. 2001; Lau et al. 2009), semi-directly through changing the

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evaporation rate of cloud droplets due to changes in atmospheric temperature structure in the air

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column (Rosenfeld et al. 2001), and indirectly by affecting cloud optical and microphysical

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properties (Su et al. 2008). Dust aerosols alter atmospheric heating rates, and therefore the

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atmospheric stability, which affects the surface energy balance and the hydrological cycle

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(Choobari et al. 2014; and references therein). Many recent studies have shown through

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simulations that dust aerosols not only delays the onset but also weakens the East Asian summer

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monsoon (Sun et al. 2012; Guo and Yin 2015). In contrast, the Elevated Heat Pump hypothesis

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states that absorptive aerosols such as dust and black carbon can strengthen the Indian Summer

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Monsoon (ISM) by heating the mid-upper troposphere and enhancing the updraft motion which

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accelerates the transport of moist air from the nearby oceanic regions to northern India (Lau et al.

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2006). In addition, some recent studies have reported that dust can strengthen the ISM system by

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heating the troposphere over the Iranian Plateau and the Arabian Sea (Jin et al. 2014; Vinoj et al.

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2014; Solmon et al. 2015). Mineral dust also affects ambient air quality, visibility, human health,

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crops and plant growth, and the marine ecosystem (Farmer 1993; Prospero 1999; Keil et al. 2016;

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IPCC 2013), and exerts a significant impact on human activities like aviation, agriculture, real-

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estate construction, telecommunication, and water resource management. Despite being a core

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element of radiation and climate forcing, many processes related to atmospheric dust and the

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associated climate impacts are poorly understood and the knowledge of dust-radiation-climate

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impacts are still limited (IPCC 2013).

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Mineral dust emission processes are generally determined by many factors such as

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atmospheric instability, soil moisture and texture, vegetation, temperature, and precipitation (Titos

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et al. 2017). Dust storms often occur in arid and semi-arid regions situated mostly in subtropical

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latitudes when strong winds blow loose sand from a dry surface. These events inject a large amount

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of mineral dust into the atmosphere which can be transported horizontally for about thousands of

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kilometers and vertically up to 6-10 km by large scale wind system (Tagen and Fung 1994;

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Prospero 1999; Gobbi et al. 2004) with diverging transport pathways as a function of the season.

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Depending on the size, atmospheric conditions and altitude in the atmosphere, dust particles can

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have varying residence time before they are removed from the atmosphere by gravitational settling

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or by rain washout (Choobari et al. 2014). Larger dust aerosols (> 2 µm diameter) are primarily

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removed by dry deposition near the source regions (Tegen and Fung 1994), while smaller particles

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that experience long range transport are mostly scavenged through wet deposition (Miller and Tegen

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1998) because of the inefficiency of the latter to dry deposition (Seinfeld and Pandis 1997).

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The Arabian Peninsula, located in southwest Asia, is well known as one of the world's

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largest dust sources. While this region acts as a source of dust aerosols for the Arabian Sea,

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north/northwest India, and the north Indian Ocean throughout the year, the intensity and frequency

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of dust storms is the highest during spring and summer (Léon and Legrand 2003; Washington et al.

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2003; Prakash et al. 2015; Sijikumar et al. 2016). The dominance of dust aerosols over the central

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and northern parts of the Arabian Sea during pre-monsoon season was also observed during the

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Integrated Campaign for Gases, Aerosols and Radiation Budget (ICARB) conducted during 2006

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(Kedia and Ramachandran 2009). Kim et al. (2011) analysed the seasonal variation of optical

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properties of mineral dust and found that dust over the Arabian Peninsula is more absorbing than the

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Saharan dust in the shortwave range. Studies have shown that internal mixing of natural dust

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aerosols during transport with anthropogenic aerosols (e.g., sulphates, nitrates, and black carbon)

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results in a drastic modification of their optical properties (Dey et al. 2004; Gautam et al. 2011; Pan

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et al. 2015; Kedia et al. 2014). Gautam et al. (2011) have shown that dust transported in the Indo-

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Gangetic Plain (IGP) becomes more absorbing due to mixing with the carbonaceous aerosols. Pan

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et al. (2015) showed a gradual alteration of dust properties due to mixing processes as they were

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transported. The radiative impact of dust is dependent on many factors such as aerosol size

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distribution, mixing state, shape, composition, the altitude of dust layer as well as the underlying

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surface properties (Seinfeld and Pandis 1997).

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Many recent studies have highlighted the potential impact of dust aerosols over the Indian

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region on local to regional scale (Dey et al. 2004; Prasad et al. 2007; Hegde et al. 2007; Gautam et

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al. 2011; Singh and Beegum 2013; Kumar et al. 2014; Prakash et al. 2015; Ramachandran et al.

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2015; Singh et al. 2016; Sijikumar et al. 2016). Although these studies have assessed the radiative

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impact of dust events, a detailed high-resolution analysis of dust impacts over south/south-west

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Asia and adjoining oceanic regions is still missing. Moreover, each of such events is unique because

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of the complexity in intensity, duration, and extent of spatial transport of the dust particles (Titos et

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al. 2017). In addition, it is well known that the spatiotemporal distribution of dust aerosols is crucial

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to understanding their impact on climate (Kaufman et al. 1997).

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The present study investigates the transport pathways and the impact of an extreme dust

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event which originated over the Arabian Peninsula and affected the entire Arabian Sea and Indian

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region during April 2015. During this period, the columnar aerosol concentrations attained

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considerably high values and caused extremely low ranges of visibility and substantially

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deteriorated air quality. In the present work, WRF-Chem model is used to simulate the evolution of

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this dust storm and transport patterns of dust plume to understand the impact of this severe dust

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storm on atmosphere over India and surrounding oceanic regions of the Arabian Sea and the Bay of

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Bengal. The model simulated dust properties are evaluated against various observational datasets

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from satellite and ground based in situ measurements.

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2.

Model and Observation Details:

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2.1 Model Setup Details

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WRF-Chem (version 3.6.1) is configured to simulate the prevalent weather conditions over

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the west and south Asia encompassing the region of 36oE to 90oE and 8oN to 30oN at a horizontal

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grid spacing of 20 km. There are 46 vertical layers in the model between the surface and 10 hPa

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with variable resolution (fine resolution near the surface and coarser resolution above the boundary

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layer). The initial and lateral boundary conditions for the model simulations are obtained from the

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6-hourly ERA-Interim reanalysis data (Dee et al. 2011) which is produced by the European Centre

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for Medium range Weather Forecasting (ECMWF). The physical parameterizations used in the

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simulations are listed in Table 1. The static geographical fields such as land-use, terrain height, soil

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properties, vegetation fraction, etc. are interpolated from United States Geological Survey (USGS)

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data to the model domain by using the WRF pre-processing system.

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The anthropogenic emissions of carbon monoxide (CO), nitrogen oxides (NOx), black

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carbon (BC), organic carbon (OC), sulfur dioxide (SO2), ammonia (NH3), and non-methane volatile

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organic compounds (NMVOCs) are obtained from the Emission Database for Global Atmospheric

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Research developed for the assessment of the Hemispheric Transport of Air Pollutants (EDGAR-

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HTAP) (Janssens-Maenhout et al. 2015). The EDGAR-HTAP database provides global monthly

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varying emission of the aforementioned pollutants at a spatial resolution of 0.1o × 0.1o based on the

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combination of nationally reported emissions and region-specific inventories. Biogenic emissions of

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trace species are calculated online using the Model of Emissions of Gases and Aerosols from Nature

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(MEGAN; Guenther et al. 2006). Fire inventory from the National Center for Atmospheric

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Research (NCAR) version 1 (FINN v1; Wiedinmyer et al. 2011) is used to represent daily emissions

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of trace gases and aerosols from biomass burning. Dust and sea-salt emissions are also calculated

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online within the model.

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The model simulations are carried out for a period of 15 days (25 March - 10 April 2015)

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and the model output is saved every 3 hours. Two simulations with similar configurations, one with

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and another without dust aerosols are conducted to assess the impact of additional atmospheric dust

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injected by the dust storm on aerosol optical properties and radiation budget over the entire pathway

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of dust plume.

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2.2 Observation data: Satellite and Reanalysis datasets Six major observational data are used to evaluate the model simulation: (a) ERA-Interim

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reanalysis data available daily at 0.75o x 0.75o resolution at every 6 hours has been used to evaluate

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the model simulated meteorological condition (winds and temperature) at varying pressure levels.

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The ERA-Interim atmospheric reanalysis is built upon a consistent assimilation of radiosonde

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observations as well as remote sensing data (Dee et al. 2011). It has been demonstrated by many

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recent studies that ERA-Interim provides a reasonable approximation of the current large-scale

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atmospheric situation and adequately captures the variability of meteorological condition (Simmons

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et al. 2010; Mooney et al. 2011); (b) daily mean columnar aerosol optical depths (AODs) version 6,

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derived using dark target and deep blue combined algorithms, archived at 1o x 1o spatial resolution

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from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the National

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Aeronautics and Space Administration (NASA) Terra (crosses equator at 1030 LST) and Aqua

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(crosses equator at 1330 LST) satellites (Remer et al. 2005) are averaged, and used for validating

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model simulated AOD. MODIS derived AODs have been used extensively to characterize aerosols

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and their radiative impacts over continental and oceanic regions (IPCC 2013; Levy et al. 2013). The

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error in MODIS collection 6 AODs is ± (0.05+15%) over the land and (+(0.04+10%), -(0.02+10%))

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over the oceans (Levy et al. 2013); (c) the Cloud Aerosol Lidar with Orthogonal Polarization

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(CALIOP) aboard the CALIPSO satellite retrieved daytime/night-time Level 2 version 4.10 data of

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vertical profile of aerosol extinction at 530 nm has been used to validate the model simulation

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(Winker et al. 2009). Vertical feature mask image from CALIPSO gives information on the vertical

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distribution of aerosols and clouds and classifies each data point both vertically and horizontally

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into seven different layer types: clear air, cloud, aerosol, stratospheric layer, surface, sub surface,

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and totally attenuated. This information of aerosol types/size from vertical feature mask image has

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also been used for model validation; (d) daily Level 3, version 006 data of outgoing longwave

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radiation (OLR) available at 1o x 1o resolution from the Atmospheric Infrared Sounder (AIRS)

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aboard Aqua (Aumann et al. 2003) are used to evaluate the model simulated outgoing longwave

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radiation (OLR); (e) the Ozone Monitoring Instrument (OMI) Level 3, version 003 ultraviolet

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aerosol index (AI) available at a spatial resolution of 1o x 1o are used (Torres et al. 2007) to identify

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the source region, as well as pathways of dust aerosols; (f) ground based measurement of daily

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averaged particulate matter of aerodynamic diameter smaller than 10 µm (PM10) data from the

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Central Pollution Control Board (CPCB) air quality monitoring network under National Ambient

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Air Quality Monitoring Programme (NAMP) has been utilized in this work. These ground based

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monitoring stations measured PM10 mass concentration (in µg/m3) using the Tapered Element

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Oscillating Microbalance (TEOM) method (CPCB 2011).

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3 The Arabian Peninsula Dust Outbreak during 01-06 April 2015

This section briefly describes the heavy dust storm event studied here. The Arabian

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Peninsula located in the sub-tropical belt in southwest Asia is a difficult environment to characterize

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as this region is considered as the largest confluences of dust and anthropogenic emissions in the

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world (Kim et al. 2011; Sabbah et al. 2012). This region experiences frequent dust storms

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throughout the year due to lack of rainfall and warmer temperature. The work aims to study the

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radiative impact of an intense dust storm that originated over the Arabian Peninsula during 1-2 April

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2015. These dust particles were transported towards the Indian subcontinent across the Arabian Sea

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by westerly winds, where it adversely affected human lives and activities through a reduction in

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visibility, air quality and increased health risk. Consequently, the event drew large media attention

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(https://timesofindia.indiatimes.com/city/mumbai/Dust-storm-in-Gulf-leaves-city-under-haze

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blanket/articleshow/46818421.cms). WRF-Chem model simulation was performed to estimate and

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investigate the impact of this dust storm over the entire dust affected area of west Asia, India and

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the Arabian Sea. The study period has been divided into two parts: pre-dust (26-31 March; hereafter

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PD) and dust period (02-07 April; hereafter DD), and the difference in various optical and radiative

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parameters are analysed to estimate the influence of the dust storm.

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4.1 Model Evaluation

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4.1.1 Meteorological condition

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The meteorological conditions play a vital role in the emission of dust aerosols, their

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transport, altitudinal variation, as well as in modification in aerosol optical and physical properties,

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and their climatic impact. Wind and temperature are two of the most crucial meteorological

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parameters for dust emission and transport. Therefore, model simulated horizontal wind and

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temperature at different pressure levels (surface, 850 hPa, and 700 hPa) are averaged for the DD

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period, and compared against the output from the European Centre for Medium-Range Weather

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Forecast (ECMWF) ERA-Interim reanalysis datasets (Figure 1) to assure that high-resolution WRF-

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Chem fields do not deviate significantly from the driving global meteorological forcing. In addition,

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change in temperature and winds after dust storm are calculated as the difference in the average

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meteorological condition for the PD and DD period and shown at three pressure levels (Figure 1c).

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At the outset, broad features of meteorological conditions from the model simulations

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generally compare well with the reanalysis fields at all the three pressure levels. Air temperature is

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greater than 24 oC at the surface while it is below 15 oC at 700 hPa over the entire Indian

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subcontinent and the surrounding oceanic region for the DD period. The temperature is mostly in

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the range of 15-24 oC at 850 hPa with a relatively higher value over land as compared to the ocean.

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Temperature is higher over the southern Arabian Sea near the Indian west coast region at the surface

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in the model as well as reanalysis data.

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After the dust storm, air temperature decreased at the surface and 850 hPa by about 1-3 oC

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while temperature increased at 700 hPa by as much as 3-4 oC over the Arabian Sea due to its

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proximity to source region. The temperature has decreased at all the three pressure levels by about

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1-2 oC over north India, while it has decreased by as high as 3-4 oC over the west coast, west and

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northwest India at the surface and 850 hPa (Figure 1c). In contrast, 700 hPa temperatures have

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increased over the west and southwest India due to additional dust during the DD period as

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compared to the PD period. The model simulated wind pattern is also consistent with the ERA-Interim reanalysis data

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for the DD period. Winds are stronger, westerly/north-westerly over the north Arabian Sea at all the

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three pressure levels during the DD period both in the model simulation and reanalysis data. Winds

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are relatively calm over the north Indian region at the surface while these are stronger and westerly

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at higher pressure levels during the DD period facilitating the transport of dust particles over the

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entire IGP region. Majority of the dust particles transported to the IGP are trapped in the IGP by

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towering Himalayas, however, some dust particles make it to the Tibetan Plateau (Shao and Dong

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2006). Change in wind pattern due to dust storm has also been shown as a difference in winds

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during the PD and DD periods in Figure 1c. Winds changed to south-westerly at the surface and

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westerly at higher pressure levels during the dust storm when compared to the PD scenario. A

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similar change in the wind pattern has also been reported previously during a severe dust storm that

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originated over the western part of the Middle East during March 2012 (Singh et al. 2016).

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Choobari et al. (2012) have stated that direct radiative forcing by mineral dust can change the

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vertical temperature profile, and thus can affect the atmospheric stability which in turn influences

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the wind speed. They also reported an increase in wind speed at higher altitudes because of a more

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stable atmosphere built due to the dust induced cooling at the surface and warming aloft.

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4.1.2 Aerosol properties

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Daily mean AOD at 550 nm averaged for the MODIS Terra and Aqua are used for model

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validation during the dust storm period (DD) as shown in Figure 2. The spatial map of average

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AOD over the study domain reveals high aerosol loading over the Arabian Peninsula, the northern

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Arabian Sea and the north-western part of India in both the model simulation as well as MODIS

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retrievals. Both the model and satellite show higher AOD values near the eastern IGP which can be

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attributed to locally emitted pollutants as well as to the transport of highly polluted air masses from

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the upwind IGP sources. The model overestimates the MODIS AOD over India but captures many

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gradients of the AOD reasonably well, e.g., decrease in AOD values as we move from Rajasthan

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and Haryana toward Uttar Pradesh but increasing AOD values in Bihar, West Bengal and

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Bangladesh. Similarly, the model simulates the increase in AOD values as we move from

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Maharashtra to Andhra Pradesh. These results suggest that the model is able to capture dust storm

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induced changes in AOD distribution despite some differences between the model and observed

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values. Several factors can contribute to the difference between model and satellite observations

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including uncertainties in emission inventories, inadequate knowledge of some of the aerosol

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processes included in the model (e.g., lifecycle of secondary aerosols), and the uncertainties in

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satellite retrievals derived from inverting the radiance measurements. However, a detailed

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investigation of the causes of model overestimation is not possible here due to lack of relevant

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measurements especially those of aerosol chemical composition, vertical distribution, and surface

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reflectance, etc.

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Figure 2c shows OMI derived aerosol index (AI), which is a useful parameter to identify

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the presence of absorbing and scattering aerosols. A positive value of AI represents the presence of

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UV absorbing aerosols such as dust and black carbon in the atmosphere, while negative and non-

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zero values indicate scattering aerosols abundance (Herman et al. 1997). Significantly high positive

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values of AI are observed over the dust loaded area which concurs with the model simulated high

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AOD region over the Arabian Sea as well as India. A large positive AI values over northwest India

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as well as some parts of the IGP coinciding with higher AOD values confirms the presence of

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absorbing dust aerosols over these regions (Figure 2).

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4.1.3 Aerosol vertical profile

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The overpass of CALIPSO satellite over the study region on 29th March (PD scenario;

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Figure 3a) and 04th April (DD scenario; Figure 3b) provided an opportunity to validate the vertical

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distribution of aerosols over the west Indian region. The vertical distribution of aerosol extinction

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profile from CALIPSO satellite at 532 nm for two days representing PD and DD scenarios, is

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presented in Figures 3c-d. The WRF-Chem simulated vertical extinction coefficient at 550 nm is

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extracted corresponding to time and track of CALIPSO overpass and compared with CALIPSO

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retrieved aerosol extinction profile in Figures 3c-d. WRF-Chem is found to be able to capture

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magnitude and vertical distribution of extinction coefficient retrieved by CALIPSO for both the

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days quite well. A significant increase in the aerosol extinction in the altitude between 1-4 km after

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the dust storm is clearly visible in WRF-Chem and CALIPSO data. Aerosol extinction at 2 km is

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about 0.1 on 29th March which increased at least by a factor of 2 due to dust aerosols on 4th April in

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both the CALIPSO and WRF-Chem. Aerosol extinction profile is nearly similar for both the days

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after 4km altitude.

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Figure 4a depicts CALIPSO derived vertical feature mask on 4th April. A thick dust layer

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extending between 1-4 km is observed in the northwest Indian region. This coincides with the high

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aerosol extinction values as observed in CALIPSO as well as model simulation at the same altitude

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level. To further validate the size distribution and type of aerosols in the vertical level simulated by

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model, vertical profile of Angström exponent (α) and PM ratio are computed and shown for 4th

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April 2015 in Figures 4b-c. Angström exponent is derived from the Angström power law (AOD =

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βλ-α, where λ is the wavelength, α is Angström exponent, and β is turbidity coefficient) using AODs

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at 550 and 1060 nm from WRF-Chem. The value of α depends on the ratio of concentration of

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smaller to larger aerosols in the aerosol size distribution. Typical values of α≥2 indicate size

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distribution dominated by fine mode aerosols that are usually associated with the urban pollution

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and biomass burning, and values of α <1 indicate size distribution dominated by coarse mode

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aerosols such as dust and sea salt (Seinfeld and Pandis 1997; Kedia and Ramachandran 2009). PM

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ratio is defined as the ratio of PM2.5 and PM10 (PM2.5/PM10) concentration and is an important

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parameter which provides crucial information regarding the aerosol type (Ram et al. 2012). This

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ratio can be used as an indicator of fine and coarse mode aerosol abundance over any region. A

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higher value of PM ratio can be attributed to the dominance of anthropogenic aerosols which are

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generally of smaller size while smaller PM ratios indicate the dominance of naturally emitted coarse

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mode aerosols (Ram et al. 2012). Low values of α (< 0.2) are observed for the entire column till

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~4km after which it increases (Figure 4b). This suggests that the coarse mode aerosols are dominant

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over the selected region from surface till about 4 km. Figure 4c shows the vertical profile of PM

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ratio which is less than 0.4 till 4 km after which this has increased. These results confirm that the

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atmosphere till about 4 km was loaded with dust aerosols both from model and CALIPSO.

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Surface level PM10 concentration from ground based observations was available over two

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locations in Mumbai (Bandra and Airoli) from CPCB network during the study period. In order to

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access the accuracy of the WRF-Chem simulated surface concentration of pollutants, model

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simulated PM10 concentrations are compared with CPCB monitored data over Bandra and Airoli.

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Figure 5 shows a comparison of time series of the daily mean surface level PM10 over Bandra and

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Airoli from CPCB and WRF-Chem simulations during 04-07 April. Over both the locations, PM10

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value has significantly increased during 5-6 April, at least by a factor of two, as compared to

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previous days from CPCB observation as well as WRF-Chem simulation. This analysis confirms

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that model simulation, even if it is performed at a coarser resolution, is able to capture the trends

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and magnitudes of observed surface level pollutant concentrations fairly well.

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4.1.5 Outgoing longwave radiation

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Mineral dust absorbs and re-emits the terrestrial longwave radiation due to their large size

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and chemical composition. The outgoing longwave radiation (OLR) determines the large-scale

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atmospheric circulation and subsequently the synoptic weather condition. Figure 6 compares the

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change in average OLR (∆OLR; calculated as the difference in OLR for PD and DD period) over

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the study domain as simulated by WRF-Chem with that observed by the AIRS satellite to assess the

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model performance. Both the simulated and AIRS observed ∆OLR values are mostly in the range of

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-60 to +45 Wm-2 over the study domain. A significant increase in the OLR is observed over the

317

entire Arabian Sea and some parts of central India from model simulation and AIRS satellite with a

318

∆OLR value as high as -60 Wm-2 (Figure 6). ∆OLR is also negative over Gujarat in west India

319

indicating an increase in OLR due to dust aerosols. In contrast, OLR has decreased and/or has not

320

changed significantly over the northern India and the IGP both in model simulation and the AIRS

321

measurements indicated by a positive or near zero ∆OLR value. A positive (negative) ∆OLR

322

represents an overall increase (decrease) of LW radiation at the TOA due to dust which depends on

323

the balance between the (a) absorption of LW radiation by dust thereby causing a decrease in OLR,

324

and (b) emission of LW by dust layer because it absorbs SW flux causing a heating of the dust layer,

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and consequently some part of LW radiation gets re-emitted in all the directions.

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In general, the model has well captured the air temperature, wind circulation, outgoing

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longwave flux, AOD, PM10 concentrations, and the vertical profile of aerosol extinction and size

328

distribution reasonably well. The simulated results are further used to investigate the impact of the

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dust storm on radiative and optical properties of aerosols in detail over its entire pathway, including

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the Arabian Sea and the Indian subcontinent. To examine the spatiotemporal impact of the dust

331

storm, the entire study area has been divided into 4 sub-regions (Figure 6a), and the average aerosol

332

properties as well as the impact of dust aerosols on the radiation budget are analysed and discussed.

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Table 2 shows the median values of various parameters over each of these regions. It can be seen

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that the OLR has increased over all the four regions in the range of 5-11 % after the dust storm.

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4.2 Spatial and vertical distribution of Dust

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Figure 7a and b show the change in the total columnar concentration of PM10 and PM2.5 over

338

the study domain due to additional dust produced by the dust storm. A significant increase in PM10

339

and PM2.5 is observed when averaged for the DD period over the north of the Arabian Sea and the

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northwest/west part of India. This is in agreement with the higher AODs and AI values observed in

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Figure 2. In a similar way, more than five times increase in the concentration of columnar aerosol

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mass concentration due to dust storms over the Asian region has been reported in earlier studies

343

(Singh et al. 2016; Prakash et al. 2015). It is observed that PM10 and PM2.5 have increased over the

344

northern Arabian Sea and northwest India followed by lesser but considerable increase over the IGP

345

(Figure 4). Table 2 summarizes the region-wise change in PM mass concentration due to dust storm

346

(see Table 2 caption for region definitions). The PM10 concentrations have increased over all the

347

four regions due to emissions and transport of additional dust aerosols in the atmosphere during the

348

storm. The magnitude of increase in PM10 is the highest over R1 (because of its proximity to the

349

source region) while PM2.5 increased the most in R2. Increase in PM10 has gradually reduced from

350

R1 to R4 because of dry deposition of dust aerosols as they progressed eastward. However, the

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percentage increase in the PM10 and PM2.5 concentration after dust storm is the highest over R2 and

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the lowest over R4 which is centred over the Bay of Bengal. The highest percentage change in PM

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concentration over R2 is likely because dust aerosols are trapped in the IGP after reaching there.

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From the IGP, the north-westerly winds transport the dust aerosol to the Bay of Bengal (Figure 1).

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The PM ratio is observed to decrease significantly over the entire dust affected areas of the

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Arabian Sea, India as well as over the Bay of Bengal during the DD period indicating that the

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transported dust has a higher concentration of PM10 aerosols (Figure 4c; Table 2). PM ratio has

358

decreased the most near the source region and over northwest India while the lowest reduction is

359

observed over R4. This lowest change in PM ratio over the Bay of Bengal is attributed to the fact

360

that this region was least affected by the dust storm and change in PM2.5 was found to be negligible

361

over here (Table 2). This analysis highlights the sudden increase in columnar PM concentrations

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due to dust storm which in turn can significantly impact the radiative balance as discussed later

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(sections 4.3 and 4.6) in detail.

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Vertical distribution of dust particles in the atmosphere is another crucial factor affecting the

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Earth’s radiation budget (Sicard et al. 2014). The model simulated vertical profile of PM10 averaged

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for the PD and DD period has been shown over four different regions in Figure 8. Vertical profile of

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PM10 confirms that an elevated layer of dust aerosols is present over all the regions. It is observed

368

that the dust aerosols are mainly confined between surface till about 3 km (700 hPa) over R1 but are

369

uplifted to higher altitudes as they travelled further away from the source (e.g., R2 and R3). The

370

increase in PM10 is not very significant over R4 which is expected as it is the farthest region to the

371

source. It is also remarkable that PM10 has increased significantly up to 400 hPa over R2 and R3

372

regions which is expected due to topographic uplifting. This uplifting of dust aerosols can have

373

important implications for monsoonal rainfall as suggested in the EHP hypothesis (Lau et al. 2006).

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4.3 Dust storm impact on aerosol optical properties

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To investigate the change in optical properties of aerosols due to additional dust particles

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after the storm, we have analysed two major aerosol parameters, viz. single scattering albedo (SSA)

378

and asymmetry parameter (ASY) which determines the aerosol radiative forcing and their impact.

379

SSA is defined as the fraction of the light scattered over the total extinction by the aerosols and

380

provides important information about the scattering and absorption properties of atmospheric

381

aerosols. Asymmetry parameter is the cosine weighted average of the scattering angles for the

382

scattered radiation by aerosols. The ASY varies between -1 (total backscattering) and 1 (total

383

forward scattering) and is dependent on the size, shape and composition of aerosols.

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Figure 9 depicts the change in SSA and ASY during the DD period (calculated as the

385

difference between the PD and the DD periods) due to dust within the atmospheric column from

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surface to 850 hPa. SSA over the study domain is mostly in the range of 0.92 to 0.98 at 550 nm

387

when averaged for the DD period. SSA has decreased over the northern Arabian Sea and northwest

388

India by > 0.01 indicating more absorption due to additional dust in the atmosphere. Region wise

389

change in the average SSA for DD period are not found to be very significant as seen in Table 2. A

390

decrease in SSA by about 0.005 is observed over the entire IGP due to dust aerosols. A decrease in

391

SSA could be due to the mixing of dust aerosols with other anthropogenic aerosols of local origin.

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Note that the change in SSA is not very significant because these values represent average SSA in

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the atmospheric column from surface to 850 hPa (Figure 6a). A reduction in ASY is observed during

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the dusty days over most of the dust affected areas including the northern Arabian Sea, north and

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northwest India (Figure 6b). The decrease in ASY values is relatively larger over the land region

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(India) as compared to the Arabian Sea. Region wise change in ASY is also negative over all the

397

regions (Table 2) due to increase in the coarse mode dust aerosols in the atmosphere after the dust

398

storm.

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399 4.4 Dust impact on the ground reaching shortwave flux

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Dust aerosols can reduce the surface reaching downward radiation flux by scattering and

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absorbing the SW radiation and this cause a cooling at the surface. This effect can be characterized

403

by calculating the difference in the downward solar flux reaching the surface (Fluxsfc) for dusty and

404

dust free atmospheric condition. For this calculation, the difference in Fluxsfc is calculated for two

405

simulations (with and without dust aerosols) and used. A negative value of change in Fluxsfc

406

represents a cooling effect at the surface and a positive value indicates warming effect due to dust.

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Region-wise Fluxsfc values are given in Table 2 for the PD and the DD period. Averaged value

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Fluxsfc is found to gradually decrease from R1 (~2%) to R4 (~1%) confirming that dust storm led to

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cooling of the surface in the SW over all the regions during the DD period.

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4.5 Short-wave and long-wave radiative perturbation by dust

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Radiative perturbation can be calculated as the difference in the net (downward minus

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upward) flux with and without aerosols. Radiative impact is sensitive to aerosol shape, mixing state,

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size distribution, composition, altitude of the aerosol layer, and the underlying surface properties.

415

Present study focuses on the estimation of the impact of dust aerosols on the Earth’s radiative

416

balance over India and the surrounding oceanic regions of the Arabian Sea and the Bay of Bengal.

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For this, we have calculated the change in net flux at the top of the atmosphere (TOA), and at the

418

surface (SFC) in both short wavelengths (0.3-4.0 µm) and long wavelength (4.0-100 µm) range

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using two identical simulations, one with dust aerosols and other without any dust aerosols.

420

Radiative perturbation due to additional dust in the atmosphere is calculated by taking the

421

difference of with dust and without dust simulations by WRF-Chem. This difference between the

422

two simulations are used to estimate shortwave (SW), longwave (LW), and NET (SW + LW)

423

radiative perturbation at the TOA and SFC as,

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(DRFTOA/SFC)sw/lw = ( Flux (net)With dust TOA/SFC - Flux (net)Without dust TOA/SFC )sw/lw

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The difference of the dust radiative perturbation estimated at TOA and SFC is termed as the

428

atmospheric radiative perturbation (ATM) and is written as,

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(DRFATM)sw/lw = (DRFTOA - DRFSFC )sw/lw

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Figure 10 depicts the spatial map of the dust induced radiative perturbation (SW, LW, and

433

NET) at the TOA, SFC, and in the atmosphere averaged for the DD period. At the outset, cooling of

434

the TOA due to dust is linearly proportional to the AODs (Figure 2) with a high value over the

435

northern Arabian Sea and west/northwest India.

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At the TOA, dust induced SW radiative perturbation is negative (-8 to -2 Wm-2) with a large

437

negative value over the northern Arabian Sea, north/northwest India and the IGP (Figures 7). LW

438

radiative perturbation at the TOA is mostly positive over this area with a higher magnitude over

439

north India and the IGP (in the range of 0-6 Wm-2) as compared to that over the Arabian Sea (in the

440

range of 0-4 Wm-2). NET dust radiative perturbation at the TOA due to dust interaction with both

441

SW and LW is negative over the Arabian Sea while it ranged from -2 to 2 Wm-2 over the Indian

442

region. A positive (negative) value of NET radiative perturbation at the TOA signals that the SW

443

absorption by dust is more (less) as compared to the SW scattering and the LW emission by dust. It

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is clear from Table 2 that TOA radiative perturbation has decreased significantly over all the four

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regions due to dust particles during the DD period. The magnitude of this decrease is directly

446

proportional to the proximity of the region from the dust source. SW radiative perturbation at the SFC is negative indicating a cooling effect due to scattering

448

and absorption of solar radiation by dust aerosols, while in the LW range it is mostly positive due to

449

trapping of the infrared radiation. Dust induced NET (SW + LW) radiative perturbation at the

450

surface is found to be mostly in the range of -4 to +4 Wm-2 over the dust affected areas. Region-

451

wise averaged dust radiative perturbations at the SFC has decreased over all the four regions (Table

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2). This suggests that even if the dust induced radiative perturbation at the SFC is negative which

453

caused cooling of the surface; dust interaction with the LW radiation has compensated this cooling

454

to a great extent over this region depending on the dust concentration, altitude and also the

455

underlying surface type. A negative (positive) value of NET surface radiative perturbation can be

456

directly related to a decrease (increase) in the sensible heat flux thereby altering the surface

457

temperature.

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Radiative perturbation is strongly positive in SW while it is negative in LW in the

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atmosphere due to dust. Average SW radiative perturbation in the ATM is positive and is as high as

460

+10 Wm-2 over the dust affected areas indicating substantial warming of the atmosphere; however, it

461

is compensated by the LW cooling which is of similar magnitude but opposite in sign (Figure 10).

462

The NET radiative perturbation in the ATM is mostly in the range of -6 to + 6 Wm-2 over the dust

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affected areas.

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This work revealed that the dust aerosol induced SW (LW) radiative perturbations produces

465

cooling (warming) effects at both the TOA and the earth's surface. LW warming effect is generally

466

less significant during night time because of a lower skin temperature and a decrease in the

467

planetary boundary layer height (PBLH). The sign and magnitude of the dust radiative perturbation

468

depend on dust optical properties for the SW range whereas it depends on the vertical distribution of

469

dust for the LW. Note that the radiative perturbations are found to be smaller because the values

470

represent the mean for the entire day (both day and night). Even these small changes in the radiative

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471

balance can be locally significant and might play important role in affecting the atmospheric

472

stability and circulation.

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4.6 Dust impact on the planetary boundary layer height It is well known that dust aerosols can alter the balance between incoming solar radiation at

476

the surface and outgoing terrestrial radiation, leading to changes in the surface as well as

477

atmospheric temperature which consequently influences the PBLH, surface-atmosphere exchange

478

processes and also the atmospheric dynamics. The response of the PBLH to dust aerosols is strongly

479

dependent on the balance between the cooling of surface layer due to a reduction in the SW

480

radiation reaching the surface and additional heating caused in the LW due to additional absorption

481

by dust layer. Table 2 presents region wise average change in PBLH over the study domain due to

482

additional dust aerosols in the atmosphere after the dust storm which is calculated as the difference

483

of the average PBLH for PD and DD period. The PBLH has increased over all the regions except

484

over R4 (the farthest region) where it has slightly decreased during the DD period as compared to

485

the PD period. The highest increase in the PBLH during the DD period is observed over the north of

486

the AS (R1) followed by northeast region (R3) while the PBLH is not very significantly affected in

487

R2. This is because of the fact that the LW warming effect is dominant over the SW cooling as

488

indicated by an overall positive value of NET radiative perturbation at the SFC (Figure 10). An

489

increased PBLH favours vertical dispersion of dust aerosols which is also evident from Figure 8.

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Change in the air temperature (∆T) at three different pressure levels (surface, 850 and 500

491

hPa) due to additional dust over the study domain during the DD period is shown in Figure 11. This

492

figure presents the difference in temperature between the two simulations (with dust and without

493

dust) averaged for the DD period. Change in temperature is considerable over all the three altitudes

494

with the maximum change observed at 850 hPa; the sign of temperature change varied from

495

negative to positive. Note that both day and night temperatures are taken into account while

496

calculating the ∆T value. During daytime, dust layer causes a cooling below dust layer by scattering

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497

and absorbing the incoming SW radiation and thus reduces the OLR. Dust layer attenuates the OLR

498

by absorbing LW radiation which is emitted by Earth’s surface but it also emits a part of this

499

throughout the day and night which causes warming of the atmosphere above and below the dust

500

layer. It is observed that the 2m temperature has increased over most of the dust affected areas

502

over northwest Indian region while a slight decrease in temperature is observed over northern part

503

of the Arabian Sea. A heterogeneous alteration of the surface temperature over the study domain can

504

be imputed to the dynamic response of the surface to the SW cooling and the LW warming imposed

505

by the dust layer. Air temperature has decreased significantly over the entire dust affected areas

506

including the Arabian Sea and northwest India at 850 hPa with a high value of >1 oC (Figure 11b).

507

Temperature has also decreased over the entire Indian region but the change is smaller compared to

508

that over the Arabian Sea because of its proximity to the source and also due to the difference in

509

surface albedo of the land and the ocean surface. Change in temperature is positive over both the

510

Arabian Sea as well as Indian region at 500 hPa because of scattering of SW radiation and emission

511

of additional LW radiation due to heating of the dust layer.

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4.7 Atmospheric heating and cooling rates due to dust

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The rate of change of atmospheric temperature within a layer due to the interaction of SW

515

and LW radiation by mineral dust can be expressed in terms of atmospheric heating/cooling rate

516

given by

517 518

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∂T g ∆F = ∂t Cp ∆ P

519

Where g is the gravitational acceleration, Cp is the specific heat capacity of the air at

520

constant pressure, ∆F is the atmospheric forcing and ∆P is the pressure difference between two

521

layers. In the present case, ∆P is taken as 300 hPa which represents the pressure difference between

522

surface and 700 hPa layer. This has been chosen as a major fraction of dust aerosols are

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523

concentrated below the 3 km (~700 hPa) height in the atmosphere (Figure 8). The dust

524

heating/cooling rate is calculated as the difference in heating rates between simulations with and

525

without dust aerosols. It is well known that, apart from the aerosol optical properties and chemical composition, the

527

type of underlying surface also plays an important role in determining aerosol radiative impact.

528

Dust induced a heating/cooling of the atmosphere due to its interaction with SW/LW radiation is

529

found to be in the range of -0.12 to 0.12 K/day in the lower atmosphere (up to 300 hPa) as shown in

530

Figure 12. It is also observed that the western and central part of the IGP are more affected as

531

compared to eastern IGP as they are closer to the source region of the dust storm. It is clear that

532

there exist significant differences in the effect of dust aerosols, which are emitted from frequent

533

dust storm over the Arabian Peninsula, on western and eastern parts of the Himalaya.

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5. Summary

This study examines the impact of a massive dust storm that originated in the Arabian

537

Peninsula region during 01-02 April 2015. Two identical numerical simulations are carried out (one

538

with dust and another without dust) to quantify the radiative impacts of dust aerosols emitted over

539

the Arabian Peninsula and reaching over west/northwest part of India using a high-resolution

540

regional chemistry-climate model WRF-Chem. Changes in aerosol characteristics, which includes

541

their optical and radiative properties, have been investigated over the Indian subcontinent and the

542

adjoining Arabian Sea.

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The WRF-Chem model captures the large-scale synoptic conditions as compared to the

544

reanalysis data. The simulated results also agreed reasonable well with various available satellites

545

and ground based measurements. Region-wise averaged PM10 from surface to 3 km altitude is

546

found to increase significantly over the northern Arabian Sea, and north/northwest Indian region.

547

Air temperature is found to decrease over the entire dust storm affected area including the Arabian

548

Sea and northwest India at 850 hPa with a value of more than 1 oC. Aerosol optical parameters such

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as the SSA is found to decrease showing absorbing nature of dust aerosols. Region-wise averaged

550

value of shortwave flux reaching the surface has decreased due to scattering and absorption by dust

551

which is in the range of 0.5 - 3%. Average NET radiative perturbation at the TOA due to dust is in

552

the range of -2 to 2 Wm-2; while it is mostly in the range of -4 to 4 Wm-2 at the surface with large

553

regional variability. The results clearly indicated that the radiative impact of dust is significant over

554

land and ocean both at the surface as well as at the TOA. The atmospheric heating rates are also

555

modulated due to dust aerosols which may play a significant role in affecting the atmospheric

556

stability as well as on monsoon circulation.

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This study shows that the dust aerosols from the Arabian Peninsula can be transported to

558

north India, where they can combine with large anthropogenic emissions and can potentially affect

559

the onset and advancement of the Indian summer monsoon, and might affect the Himalayan glaciers.

560

Future efforts should focus on understanding the impact of such events on monsoonal rainfall to

561

reduce the uncertainty and improve the understanding of the climatic impact of dust aerosols over

562

India and the surrounding regions.

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Acknowledgements:

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We thank the European Centre for Medium Range Weather Forecasting for ERA-Interim reanalysis products which have been used as initial and boundary condition. The data sets for initial and boundary conditions for chemical fields, biogenic emissions, biomass burning emissions and programs used to process these data are downloaded from http://www2.acd.ucar.edu/wrf-chem/. MODIS aerosol optical depth, AIRS derived OLR, OMI measured AI data used in the study are downloaded from http://disc.sci.gsfc.nasa.gov/giovanni. CALIPSO data sets used in the were obtained from the NASA Langley Research Centre Atmospheric Science Data Center. Thanks to the Central Pollution Control Board, New Delhi, India for providing the air quality monitoring data through their data portal. The National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation (NSF). PARAM Yuva supercomputer of National PARAM Supercomputing Facility at CDAC Pune has been used for model simulation.

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Ramanathan, V., P. J. Crutzen, J. T. Kiehl, D. Rosenfeld, 2001. Aerosols, Climate, and the Hydrological Cycle, Science, 294, 2119-2124. Ram, K., M. M. Sarin, S. N. Tripathi, 2012. Temporal Trends in Atmospheric PM2.5, PM10, Elemental Carbon, Organic Carbon, Water-Soluble Organic Carbon, and Optical Properties: Impact of Biomass Burning Emissions in the Indo Gangetic Plain, Environ. Sci. Technol., 46, 686-695.

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AC C

794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845

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Table 1: Selected parameterizations used in WRF-Chem for the model simulations.

Adopted scheme

Cloud microphysics Shortwave and Longwave radiation Land surface model Planetary boundary layer Cumulus Initial/Boundary condition Photolysis Surface layer Gas phase chemistry Aerosol chemistry Anthropogenic emissions Biogenic emissions Fire emissions Wet scavenging Vertical mixing Aerosol-cloud-radiation-interaction

Morrison double moment (Morrison et al., 2009) RRTM for GCM (Iacono et al., 2008) NOAH (Chen and Dudhia, 2001) YSU scheme (Hong et al., 2006) Grell-3d (Grell and Devenyi, 2002) ECMWF ERA (Dee et al., 2011) Fast-J photolysis (Wild et al., 2000) Monin-Obukhov scheme MOZART MOSAIC with aqueous reactions EDGAR-HTAP MEGAN online Fire Inventory from NCAR (FINN) (Wiedinmyer et al., 2011) On On On

849 850 851

855 856 857 858 859

SC

EP

854

AC C

853

TE D

852

RI PT

Physics/Chemistry

M AN U

846 847 848

860 861 862 863 864 28

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865

Table 2: Median values of various aerosol related parameters over 4 grid boxes (R1: 17-25oN, 60-

866

68oE; R2: 21-29oN, 69-77oE; R3:20-28oN, 80-88oE; R4:10-18oN, 80-88oE) over different regions of

867

study domain for PD (26-31 March 2015) and DD (02-07 April 2015) period.

868

871 872 873 874

R3

R4

R1

PM10 (µg m-3)

1077

646

354

144

1412

PM2.5 (µg m-3)

686

396

295

134

672

PM ratio

0.68

0.67

0.84

0.92

OLR (Wm-2)

266.3

266.0

259.1

271.1

PBLH (m)

228.6

795.1

677.2

563.6

SSA

0.97

0.95

0.93

ASY

0.68

0.670

Fluxsfc

279.3

279.6

NET TOA (Wm-2)

+ 1.39

NET SFC (Wm-2)

+ 1.62

R2

R3

R4

997

532

149

518

365

127

SC

R2

0.53

0.68

0.86

295.4

288.7

287.9

284.7

432.4

812.1

773.9

535.3

0.95

0.96

0.96

0.94

0.96

0.665

0.666

0.69

0.677

0.666

0.686

262.4

299.9

274.2

274.4

264.8

296.8

TE D

M AN U

0.53

+ 0.11

+ 0.01

+ 0.09

- 2.49

-0.45

- 0.68

- 0.34

+ 0.99

-0.02

+ 0.09

+0.09

+ 0.32

-0.62

+ 0.04

EP

870

DD (02-07 April 2015)

R1

AC C

869

PD (26-31 March 2015)

RI PT

Parameter

875

29

M AN U

SC

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876 877

Figure 1: Meteorological condition (air temperature and winds) at the surface, 850 hPa and 700 hPa

879

averaged for DD (02-07 April 2015) period as (a) simulated from WRF-Chem and (b) obtained

880

from ERA reanalysis. (c) Change in the model simulated average meteorological condition at the

881

surface, 850 hPa and 700 hPa calculated as the difference between PD (26-31 March 2015) and DD

882

period.

AC C

883

EP

878

884 885 886 887 888 30

TE D

M AN U

SC

RI PT

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EP

889

Figure 2: Aerosol optical depth (AOD) at 550 nm (a) simulated from WRF-Chem and (b) measured

891

by MODIS satellite and (c) OMI measured aerosol index (AI) averaged for DD (02-07 April 2015)

892

period.

893 894

AC C

890

895 896 897 898

31

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899

SC

900

Figure 3: CALIPSO satellite overpass over India on (a) 29th March before the dust storm and (b)

902

04th April after the dust storm. Vertical extinction profile at 532 nm in km-1 from CALIPSO and

903

WRF-Chem on (c) 29th March and (d) 04th April.

M AN U

901

904

908 909 910 911 912 913

EP

907

AC C

906

TE D

905

914 915 916 917 918 32

TE D

M AN U

SC

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919

Figure 4: (a) Vertical feature mask image from CALIPSO satellite showing vertical layer property

921

on 04 April 2015. Vertical profiles of (b) Angström exponent (α) and (c) PM ratio (PM2.5/PM10) on

922

04 April from WRF-Chem.

924 925 926

AC C

923

EP

920

927

33

RI PT

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928 929

Figure 5: Daily averaged PM10 concentration over two locations in Mumbai city (a) Bandra and (b)

931

Airoli from CPCB ground based monitoring network and WRF-Chem.

SC

930

M AN U

932 933 934 935

939 940 941 942 943 944

EP

938

AC C

937

TE D

936

34

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945

Figure 6: Change in the outgoing longwave radiation (OLR) in Wm-2 due to dust storm (calculated

947

as the difference in OLR for PD (26-31 March 2015) and DD (02-07 April 2015) period) as (a)

948

simulated using WRF-Chem and (b) obtained from AIRS over the study domain. Four sub-regions

949

used in the study (defined in Table 2) are shown by white boxes.

M AN U

SC

946

950 951

955 956 957 958 959 960

EP

954

AC C

953

TE D

952

961 962 963 964

35

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RI PT

965 966 967

SC

968

Figure 7: Change in the total columnar concentration (calculated as the difference PD (26-31

970

March 2015) and DD (02-07 April 2015) period) of (a) PM10 in µg m-3 (b) PM2.5 in µg m-3, and (c)

971

PM ratio (PM2.5/PM10) from WRF-Chem.

972 973

977 978 979 980 981

EP

976

AC C

975

TE D

974

M AN U

969

982

36

RI PT

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SC

983

M AN U

984

Figure 8: Vertical profile of median value of PM10 concentration a grid box of size 8 x 8 degree

986

centered over the (a) Arabian Sea (R1), (b) Northwest India (R2), (c) North India (R3) and (d) the

987

Bay of Bengal (R4) for PD (26-31 March 2017) and DD (02-07 April 2017) period from WRF-

988

Chem.

991 992 993 994 995 996

EP

990

AC C

989

TE D

985

997 998 999 1000 1001 37

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1002

RI PT

1003

1004

SC

1005

Figure 9: Spatial maps showing the change in the average aerosol optical properties (a) Single

1007

scattering albedo (SSA) and (b) Asymmetry parameter (ASY) averaged for a period of seven days

1008

during the dust storm (DD; 02-07 April 2017) when dust are considered (with dust) and not

1009

considered (without dust) in WRF-Chem simulation.

M AN U

1006

1013 1014

EP

1012

AC C

1011

TE D

1010

38

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SC

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1015

TE D

1016

Figure 10: Average Shortwave (SW), longwave (LW) and the NET (SW + LW) dust radiative

1018

perturbation at the top of the atmosphere (TOA), surface (SFC), and atmosphere (ATM) due to

1019

additional dust in the atmosphere by dust storm during DD (02-07 April 2015) period from WRF-

1020

Chem.

1022 1023

AC C

1021

EP

1017

1024 1025 1026 1027 1028 39

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1029

RI PT

1030

1031

SC

1032

Figure 11: Change in the air temperature (∆T calculated as the difference in temperature from

1034

simulations without and with dust aerosols) in oC due to dust at (a) 2m, (b) 850 hPa, and (c) 500 hpa

1035

from WRF-Chem.

M AN U

1033

1036 1037

1041 1042 1043 1044 1045

EP

1040

AC C

1039

TE D

1038

1046 1047

40

M AN U

SC

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1048 1049 1050

Figure 12: Atmospheric heating (cooling) rate in K/day due to dust aerosols in the SW (LW)

1052

averaged for DD (02-07 April 2015) period from WRF-Chem.

1055 1056 1057 1058 1059

EP

1054

AC C

1053

TE D

1051

1060

41

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Dust storm originated over the Arabian peninsula and transported toward India WRFChem model is used to estimate the radiative impact of dust

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PM concentration reveals the presence of dust aerosols from the surface to ~4 km

Dust storm induced a cooling effect at the surface in shortwave

SC

Significant impact of dust on temperature profile is observed

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Temperature increased at 500 hPa due to dust over India and the Arabian Sea