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https://doi.org/10.1175/BAMS-D-19-0030.2 Corresponding author: Chandra Venkataraman, chandra@iitb.ac.inThis document is a supplement to https://doi.org/10.1175/BAMS-D-19-0030.1In final form 3 January 2020©2020 American Meteorological SocietyFor information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

SupplementIndian Network Project on Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts (COALESCE)C. Venkataraman, M. Bhushan, S. Dey, D. Ganguly, T. Gupta, G. Habib, A. Kesarkar, H. Phuleria, and R. Sunder Raman

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Details of survey methodology and locationsThis project with 22 institutions (Fig. ES1) involves participation of 40 investiga-tors (Table ES1) and most importantly, over 70 research students and staff. Sur-vey questionnaires were adapted from previously validated instruments for residential sector (Census 2011; Interna-tional Institute of Population Science, 2007, 2017; Balakrishnan et al. 2004), agricultural residue burning (Gupta 2014), brick kilns (Maithel et al. 2012; S. Maithel 2017, personal communica-tion), and on-road vehicles (Table ES2; Goel et al. 2015; S. K. Guttikunda 2016, personal communication). Selection of the survey districts/villages to capture the pan-India diversity in biomass fuels used for cooking, heating, and lighting in residential sector is based on district/village level data (Census 2011), along with agroclimatic information (Basu and Guha 1996) for residential cooking; that in agricultural residue burning practices is based on district-wise crop production data (OGDP 2015) of nine target crops (Pandey et al. 2014; Sahai et al. 2011; Jain et al. 2014), different key brick kiln technologies, and a variety of fuel mixes (Table ES3; TERI 2002; Development Alternatives 2012; Maithel et al. 2012; Verma and Uppal 2013; Weyant et al. 2014; SAMEEEKSHA 2018).

Details of field measurement campaignsField measurements of aerosol emissions are planned using a design of a portable source sampler adapted from previous work (Jaiprakash et al. 2016; Jaiprakash and Habib 2018a,b) using the carbon balance method. The design and performance of portable dilution sampler is detailed in Jaiprakash et al. (2016). The modified sampler for this project will consist of an inlet, a heated duct, a dilution tunnel of 3-L capacity (diameter = 10 cm and length = 40 cm) which provides maximum dilution ratio 1:100 at 3-s residence time to achieve complete gas-to-particle partitioning, clean air generation system, and power supply unit (Fig. ES2). For residential cookstove and open biomass burning a multiarm inlet will be used to withdraw the emissions mixed with background air that will be collected on filters and a fraction will enter into a dilution tunnel which would be connected to real-time measurement instru-ments (aethalometer, nephelometer, and optical particle spectrometer). In case of vehicular and brick kiln emission measurement, the emissions will be withdrawn using a heated particle sampling probe working on ejector technique and will be collected on filters after dilution in the primary dilution tunnel. Then a fraction of diluted exhaust will enter into the secondary dilution tunnel where further dilution will take place before the real-time measurement using aethalometer, nephelometer, and optical particle spectrometer. The source sampler will also include a PM sampler consisting of PM2.5 sharp cut cyclone and filter holders for particle, a flue gas analyzer for measurement of gaseous pollutants (CO,

Fig. ES1. COALESCE organization structure.

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Table ES1. List of participating institutions and investigators. Names of principal investigators of the respective institutions are in bold.

Sr. No Name Affiliation Institute

1 Chandra Venkataraman (National Coordinator)

Professor, Department of Chemical Engineering; Associate faculty, IDP in Climate studies

IIT Bombay2 Mani Bhushan Professor, Department of Chemical Engineering; Associate faculty, IDP in Climate studies

3 Harish Phuleria Assistant Professor, Centre for Environmental Science and Engineering; Associate faculty, IDP in Climate studies

5 Tarun Gupta Professor, Department of Civil Engineering

IIT Kanpur6 Debajyoti Paul Professor, Department of Earth Sciences

7 Anubha Goel Associate Professor, Department of Civil Engineering

8 Gazala Habib Associate Professor, Department of Civil Engineering

IIT Delhi9 S.K. Dash Professor, Centre for Atmospheric Science

10 Sagnik Dey Associate Professor, Centre for Atmospheric Science

11 Dilip Ganguly Assistant Professor, Centre for Atmospheric Science

12 Ramya Sunder Raman Associate Professor, Department of Earth and Environmental Sciences IISER Bhopal

13 R. Ravi Krishna Professor, Department of Chemical Engineering

IIT Madras14 S. M. Shiva Nagendra Professor, Department of Civil Engineering

15 Sachin S. Gunthe Associate Professor, Department of Civil Engineering

16 Shubha Verma Associate Professor, Department of Civil Engineering IIT Kharagpur

17 S. Sajani Senior Scientist, Multi-scale modeling Programme CSIR(4PI),Bangalore

18 S. Ramachandran Professor and Chairperson, Space and Atmospheric SciencesPRL Ahmedabad

19 Harish Gadhavi Scientist-SE, Space and Atmospheric Sciences Division

20 T.K. Mandal Principal Scientist, Radio and Atmospheric Sciences

NPL Delhi21 S.K.Sharma Scientist, Radio and Atmospheric Sciences

22 C. Sharma Sr. Principal Scientist, Radio and Atmospheric Sciences

23 S. Singh Principal Scientist, Radio and Atmospheric Sciences

24 G. Pandithurai Scientist F IITM Pune

25 Baerbel Sinha Assistant Professor, Environmental Science IISER Mohali

26 Arshid Jehangir Sr. Assistant Professor, Environmental Science University of Kashmir

27 Amit Kesarkar Scientist-SE, Weather and Climate Research GroupNARL

28 Vikas Singh Scientist-SD, Weather and Climate Research Group

29 R. Naresh Kumar Assistant Professor, Department of Civil and Environmental EngineeringBITS Mesra

30 Jawed Iqbal Assistant Professor, Department of Civil and Environmental Engineering

31 Asif Qureshi Assistant Professor, Department of Civil Engineering IIT Hyderabad

32 Abhijit Chatterjee Associate Professor, Environmental Science Section

Bose Institute, Darjeeling33 Sanjay K Ghosh Professor, Department of Physics

34 Sibaji Raha Professor, Department of Physics

35 Binoy K Saikia Scientist, Coal Chemistry DivisionCSIR-NEIST, Jorhat

36 Prasenjit Saikia Scientist, Coal Chemistry Division

37 S. Anand Scientist, Health Safety and Environment GroupBARC, Mumbai

38 Tanmay Sarkar Technical Officer, Health Safety and Environment Group

39 Rohini Bhawar Assistant Professor, Department of Atmospheric and Space Sciences University of Pune

40 Anil Kumar Chhangani Head, Department of Environment Science Maharaja Ganga Singh University, Bikaner

41 Jitender Singh Laura Head, Department of Environment Science Maharshi Dayanand University, Rohtak

42 K.S. Lokesh Professor, Department of Environmental Engineering Sri Jayachamarajendra College of Engineering, Mysuru43 Udhayashankar T.H. Professor, Department of Environmental Engineering

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CO2, NOx, SOx, and total hydrocarbons). The exhaust velocity will be measured in the duct using a pitot tube. The dilution ratio inside the dilution tunnel will be calculated using CO2 measurement of undiluted and diluted exhaust following Jaiprakash et al. (2016). Unlike earlier dilution samplers commercially available the present sampler can be operated for a range of dilution ratio 5:1 to 100:1 by varying the clean airflow and suction flow in particle sampling probe. Details of all components and instruments used in the dilution sampler are given in Table ES4.

Equation for the carbon balance method to estimate emission factors (Roden et al. 2006):

EFCO CO kg Cm

CF,xXV

� ��

�

��

�

����

��

�

����

[ ]

.

1 1

0 49052

3� �

where EFX is emission factors of species X [gx (kgfuel)−1], [X] is concentration of species X (g m−3), V is volume of air sampled (m3), ∆CO2 is concentration of CO2 above ambient (ppm), ∆CO is concentration of CO above ambient (ppm), and CF is carbon fraction in fuel [kg C (kgfuel)−1].

Table ES2. Mapping of cities for vehicle survey.

COALESCE institutes State Cities as per tier classification

S1

(Population >

1,000,000)

I (100,000

> population

≤ 1,000,000)

II (50,000

< population

< 99,999)

III (20,000

< population

< 49,999)

IV (10,000

< population

< 19,999)

V (5,000

< population

< 9,999)

VI (population

< 5,000)

No. of surveys

(transport/nontransport)

University of Kashmir J&K Srinagar Jammu Anantnag Bandipore Gulmarg Achabal Banihal 670

IISER Mohali Punjab Chandigarh Amritsar Kapurthala Jalalabad Majitha Maloud Sansarpur 670

IIT DelhiRajasthan Jaipur Ajmer Balotra Bhadra Bhusawar Bhalariya Govindgarh 670

Delhi Ghazaibad East Delhi West Delhi North Delhi South Delhi Central Delhi 670

NPL Delhi Haryana Panipat Ambala Narnaul Charkhi Dadri Bawal Farakhpur Rewari 670

IIT Kanpur Uttar Pradesh kanpur Kanpur Khurja Mahrajganj Manikpur Mohanpur Amila 670

BOSE Institute Bihar Nalanda Patna Samastipur Ramnagar Thakurganj Asarganj N/A 670

NEIST Jorhat Assam Nagaland Dibrugarh Karimganj Nalbari Udalguri Amguri Howraghat 670

IISER Bhopal

Madhya Pradesh Bhopal Vidisha Jaora Multai Shahgarh Tirodi Badra 670

IIT Hyderabad Telangana Telangana Hyderabad Nirmal Naspur Utnur Tangapur Ratnapur 670

Mysore Karnataka Banglore Mandya Hunsur Pandavapura Arasinakunte Kadakola N/A 670

IIT Madras Tamil Nadu Chennai Vellore Arakonam Lalgudi Pudur Puvalur Unjalur 670

IIT Bombay Maharashtra Thane Ghatkopar Kharghar Uran Murbad Kharbav Saphale 670

BIT Mesra Jharkhand Ranchi Hazaribagh Rajrappa Churi Muri Bharno Topa 670

IIT Kharagpur West Bengal Kolkata Kharagpur Jhargram Kolaghat Mandarmani Digha Hariatara 670

IITM Pune Maharashtra Pune Lavasa Talegaon Keshavnagar Panchgani Dehu Adhale kh 670

Kshetra Wai Mahabaleswar Birwadi 670

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Table ES3. State-wise field survey districts for residential (Re), agricultural residue burning (Ag), and brick sectors (Br).

State Districts Re Ag Br State Districts Re Ag Br

J&K

Anantnag

Maharashtra

Chandrapur

Badgam Jalgaon

Baramula Satara

Pulwama Latur

Ganderbal

Jharkhand

Dhanbad

Himachal PradeshSolan Hazaribagh

Una Ranchi

Uttarakhand Udham Singh Nagar

Punjab

Fategarh sahib

West Bengal

Maldah

Firozpur Koch bihar

Sangrur Hugli

Pathankot Bardhman

SAS Nagar

Ropar

Kerala

Thrissur

Wayanad

Haryana

Sonipat

Ambala

Jhajjar

Assam

Nagaon

Panchkula Golpara

Nalbari

Rajasthan

Bikaner

Sikar Meghalaya West Garo Hills

Rajasmand

Sri Ganganagar Nagaland Dimapur

Kota

Dhaulpur

Orissa

Baleshwar

Balangir

Uttar Pradesh

Gorakhpur Cuttack

Bijnor Sundargarh

Bairach

Hardoi

Kushi nagar

Telangana

Nalgonda

Ghaziabad Warangal

Kanpur Medak

Varanasi Sangareddy

ChhattisgarhJanjgir-champa

Andhra Pradesh

Krishna

Raigarh Chittoor

SPSR Nellore

Madhya Pradesh

Jhabua

Hoshangabad

Karnataka

Dakshin Kannada

Sehore Belgaum

Datia Mandya

Rajgarh Kolar

Gujarat

JunagadhTamil Nadu

Theni

Vadodara Dharmapuri

Bhavnagar

AhmedabadBihar

Araria

Surat Bhagalpur

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Equations for estimating emission factors using dilution sampler (Jaiprakash et al. 2016; Lipsky and Robinson 2005):

DRCO

undilutedCO

amb

COdiluted

COamb

�� � � � �� � � � �C C

C C2 2

2 2

,

where DR is dilution ratio, (CCO2)undiluted is concentration of CO2 in undiluted exhaust, (Cco)amb concentration of CO2 in ambient, and (CCO2)diluted is concentration of CO2 after undiluted exhaust

EF g kgDR

or

duct ex

xX A t

F D�� � � � � � �1 [ ]

,�

where EFX emission factor of pollutant X [gx (kgfuel)−1], [X] is concentration of species X (g m−3), Aduct is area of duct (m2), υex is exhaust velocity X (m s–1), t is sampling time (s), F is fuel used (kg), and D is distance traveled by vehicle (km).

Methodologies for the ambient observational networkSelecting regionally representative sites. A key objective of this study is to sample for fine particulate matter (PM2.5) that is representative of a given region and to apportion the sources of the measured PM mass.

The sites selected are such that the measurements made at these sites will normally be consistent with measurements made at locations separated by 100–500 km from each of these sites. All sites are located such that they capture regionally representative aerosol,

Fig. ES2. Schematic of the source sampling train for on-field measurements. A multiarm inlet (Roden et al. 2006) will be used when used for residential agricultural residue sector measurements and a nozzle inlet along with primary dilution tunnel (Lipsky and Robinson 2005) for vehicular and brick kiln sectors.

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Table ES4. Description and technical specifications of monitoring instruments used for source sampling.

Monitoring instruments

Measurement technique Measurement

Measurement range

Sample interval

Accuracy (resolution) Resolution Reference

Heated sampling probe

Iso-kinetic (TSI) As inlet 0–20 m s−1 — — — —

Portable dilution tunnel

(Polltech Ins. Pvt. Ltd, India)

For dilution 1:100 — — —Jaiprakash

et al. (2016)

Pitot tube (Testo, Sparta, NJ)Velocity

measurement0–100 m s−1 1 s ±0.2 m s−1 0.1 m s−1 —

Flue gas emission analyzer

Electrochemical sensor/NDIR (350, Testo, Sparta, NJ)

O2 0%–25%

1 s

±0.1%–0.8% 0.01%

Jaiprakash et al. (2016)

CO2 (NDIR) 0%–50% ±0.3% to 0.5% 0.1%

CO 0–10,000 ppm ±5 ppm 1 ppm

SOx 0–5,000 ppm ±5 ppm 1 ppm

NO 0–4,000 ppm ±5 ppm 1 ppm

NO2 0–500 ppm ±5 ppm 0.1 ppm

HC (NDIR) 100–40,000 ppm±10% for

>4,000 ppm10 ppm

Temperature 0°–1,000°C ±0.5°C 0.1°C

Temperature probe

Sensor (Testo, Sparta, NJ)

Temperature 0°–70°C 1 s ±0.2°C 0.1°CJaiprakash

et al. (2016)

Relative humidity

Sensor (Testo, Sparta, NJ)

Relative humidity

0%–100% RH 1 s ±2% 0.7%Jaiprakash

et al. (2016)

Diluted CO2 analyzer

Sensor (Testo, Sparta, NJ)

Diluted CO2 0–10,000 ppm 1 s±100 ppm of CO2 ± 3%

value1 ppm

Jaiprakash et al. (2016)

Zero air assembly

(Polltech Ins. Pvt. Ltd, India)

For dilution air 30 LPM — — — —

Multistream PM2.5 Cyclone (URG Based)

Cyclone (URG Corporation, USA)

PM2.5 filter mass

10 LPM — — —Jaiprakash et al.

(2016)

Rechargeable battery+ DC adapter

Sony Power supply 1.5 V — — — —

GPS + datalogger

Vehicle testing (Racelogic, U.K.)

Speed

0.2–150 km h−1 1 s

0.2 km h−1 0.01 km h−1

VBOX Mini user guide

Distance <50 cm 1 cm

Acceleration ±1 m s−2 0.01 1 m s−2

Aerosol spectrometer

Laser light scattering (TSI 3330)

Number concentration

0–3,000 cm−3

1 s 0.01%5% at 0.5 µm

—Number size distribution

0.001– 275,000 µg m−3

0.3–10 µm (16 bins)

Aethalometer

Filter based attenuation

(Magee Scientific AE 33)

Black carbon and absorption coefficients

<0.1 to >100 µg m−3

1 s or 1 min

1 ng m−3 —Magee

Scientific User Manual

Integrating nephlometer

Laser light scattering

(Air Photon IN 102)

Aerosol scattering

coefficients

0–20,000 Mm−1 (−30° to +45°C)

Automatic scanning

— —Air Photon

User Manual

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that is, outside urban centers and away from local pollution sources including but not lim-ited to diesel, cookstove/wood smoke, agricultural field burning, automobile, road dust, or construction dust emissions. Further, these sites are also not located in places with unusual/nonrepresentative meteorology (such as in a valley) for a given region. A weight of evidence approach (Lekinwala et al. 2020), including physical siting criteria, trajectory ensemble, and wind-rose approaches, along with a suite of statistical approaches has been used to identify “regional” sites.

Choice of sampler, filter substrates, and chemical analyses techniques. The Speciation Air Sampling System (SASS; Met One Instruments Inc., Oregon, United States) was deployed to collect samples for the chemical and gravimetric analysis of PM2.5 particles, from its use in earlier networks (in the U.S. EPA Speciation Trends Network and now Chemical Speciation Network). The sampler configuration (Table ES5) is consistent with collecting samples that are required to meet the project goals. Aerosol samples will be collected every other day for 2 years, from January 2019. Meteorological sensors (Met One Instruments Inc., model AIO 2) will also acquire data and will be operated in conjunction with the SASS during each sampling event.

The sampler is configured such that acidic gases are denuded prior to collection onto the nylon substrate (channel 2). Additionally, it is well established that measurement of atmo-spheric particulate matter organic carbon with quartz filter substrates is likely to result in positive artifacts (absorption of organic carbon gaseous species, onto filters) and negative artifacts (volatilization of particle phase semivolatile organic compounds after captured by filters; Turpin et al. 2000). It is proposed to correct for positive artifacts by sampling with two quartz filters in series (QbQ, channel 3). This backup filter will be used to correct for the absorption/adsorption of gaseous organic compounds on the front quartz filters (McDow and Huntzicker 1990; Turpin et al. 1994; Hart and Pankow 1994; Kim et al. 2001). A summary of the filter substrates, analytes, chemical analyses methods, and instruments is presented in Table ES5.

Quality assurance/quality control (QA/QC) plan. Data quality for any study has several dimensions, but the primary goal should be usefulness to data users and understanding of the dataset’s characteristics. All flow audits and performance checks for the SASS sampler

Table ES5. Summary of SASS configuration, filter type, and analytical method for quantification of different constituents present in the source and ambient aerosol samples.

Channel Filter AnalyteAnalytical method

Instrument model/make

1

Teflon

PM2.5 Mass, Elements (Al, Si, P, S, Cl, K, Ca, Sc,Ti, V,Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Cd, Sn, Sb, I, Ba, Hg, Pb, Bi, Ga,

Ge, Y, Zr, In, Te, Cs)

Gravimetry, ED-XRF and ICP OES

Sartorius microbalance CP5, PAN alytical Epsilon 4 and Analytik Jena Plasma

Quant PQ 9000

2Nylon with

denuder

Water soluble inorganic ions

Ion chromatographyThermo Dionex Dual

ICS-AquionCation (Li+, Na+, NH4

+, K+, Ca+2, Mg+2)

Anion (F−, Cl−, NO2−, Br−, NO3

−, PO43−, SO4

2−)

3Quartz behind

quartz

Organic and elemental carbon fraction (OC1, OC2, OC3, OC4, EC1, EC2, EC3) and

brown carbon

Thermal-optical analysis

DRI-2015 Multi-Wavelength Thermal Carbon Analyzer

4Quartz behind

quartz

Volatile organics and organic molecular markers for secondary organic aerosols

(SOA), C-13 isotope

GC-MS and IRMS/ MC-ICPMS

Agilent 7890B Gas Chromatograph–Mass

Spectrophotometer

5 Teflon Archival

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are being conducted in accordance with manufacturer recommended standard operating and maintenance procedures. Several metrics are considered for assessing the quality of the chemical species measurements. A few examples of the metrics/parameters that will be used in this study for QA/QC include:

1) Accuracy—All analyses will be standardized to reference values that are traceable to NIST or similar standards.

2) Precision—Measured both at individual laboratories and the whole network through regular QC replicates, results from multiple channels at the same site, and interlaboratory blind sample comparisons.

3) Completeness—Data completeness (>95%) will be monitored at all locations4) Sensitivity/detection—The method detection limits (MDLs) and limit of detection (LOD) will

be reported for every analyte measured at all of the chemical and gravimetric analyses laboratories.

Additionally, laboratory blanks, field blanks, spikes, and replicate samples will be used as a part of QA/QC of all analytes.

Details of participating GCMs and RCMsParticipating RCMs include WRF-CHEMERE, WRF-Chem, WRF-CMAQ, RegCM, and GEOS-Chem, which have differences in atmospheric chemistry mechanisms, aerosol physics, and meteo-rological physics schemes (Table ES6). Aerosol microphysics schemes include condensation, coagulation, transport, and deposition processes employing different mathematical approaches to treat aerosol dynamics. WRF-CMAQ and WRF-Chem (with the MADE scheme) adopt a “modal” approach, WRF-CHIMERE and WRF-Chem (with MOSAIC) use a “sectional” approach, while GEOS-Chem adopts a bulk approach following the GOCART model. The RegCM model uses a bulk scheme for sulfate, organic carbon, and black carbon with sectional schemes for

Table ES6. Participating regional climate model (RCM) description.

S. No.Model/ research group

Meteorological parameterizations: land surface model (LSM); cumulus parameterization (CP); surface layer (SL); planetary boundary layer (PBL)

Aerosol module (AER); gas-phase chemistry (GC); photolysis (PL); cloud microphysics coupled to aero-sols (CM); radiation schemes (RAD)

1 WRF-Chem LSM: Noah LSM; CP: Grell 3D scheme; SL: Monin–Obukhov similarity theory; PBL: Mellor–Yamada–Janjić

AER: MADE; GC: RADM2; PL: Fast J photolysis; CM:Thompson scheme; RAD: Rapid Radiative Transfer ModelIIT Bombay

2 WRF-Chem LSM: Noah LSM; CP: Grell 3D scheme; SL: Monin–Obukhov similarity theory; PBL: Mellor–Yamada–Janjić

AER: MOSAIC; GC: CBM-Z; PL: Fast J photolysis; CM: Thompson scheme; RAD: Rapid Radiative Transfer Model

IISER Bhopal and NARL, Gadanki

3 RegCM LSM: BATS; CP: Emanuel scheme; PBL: Holtslag scheme

AER: AERO (complete aerosol); RAD: NCAR Community Climate Model Version3IIT Delhi

4 WRF-CHIMERE LSM: Noah LSM; CP: Grell 3D ensemble scheme; SL: MM5 Monin–Obukhov scheme; PBL: Yonsei University (YSU) scheme

AER: Aerosol module; GC: MELCHIOR reduced; PL: Fast-JX; CM: Lin et al. scheme; RAD: Rapid Radiative Transfer Model (RRTM)

IIT Kharagpur

5 GEOS-Chem (0.5° × 0.625°)

LSM: NASA Catchment Land Surface Model; CP: relaxed Arakawa–Schubert; SL: sigma/hybrid vertical grid system; PBL

AER: ISORRPIA II thermodynamic module; GC: GEOS-Chem chemistry mechanisms; RAD: Rapid Radiative Transfer Model(IIT Madras)

6 WRF-CMAQ LSM: Noah LSM; CP: Grell 3D scheme; SL: Monin–Obukhov similarity theory; PBL: Mellor–Yamada–Janjić

AER: AERO5; GC: CB05; PL: CMAQ; RAD: Rapid Radiative Transfer ModelPune University

and NARL, Gadanki

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dust and sea salt, and includes the direct effect of all aerosol species, along with the sulfate indirect effect. The models have different approaches to calcu-late gas to particle conversion; WRF-CMAQ and GEOS-Chem models adopt ISORROPIA for inorganic species, while WRF-Chem uses MARS for inorganic and SORGAM for organic gas to particle conversion. Calculation of aerosol–radiation interac-tions requires the coupling of the aerosol scheme with the shortwave radiation scheme. The radiation transfer module in selected models uses aerosol optical properties (extinction optical depth, single scattering albedo, asymmetry parameter) varying across wavelength bands (e.g., WRF-Chem at 200, 400, 600, and 1,000 nm and WRF-CHIMERE single-scattering albedos and the asymmetry parameter at 400 and 600 nm along with the AOD at 300, 400, and 999 nm). Among the participating RCMs, most allow for the use of more than one scheme for planetary boundary layer and cloud physics in terms of the cumulus parameterization. Lateral boundary conditions for the RCMs will come from the ERA-Interim data for meteorology, with 3DVAR data assimilation every 12 h (Fig. ES3), and chemical boundary conditions will come from MOZART, except for WRF-CHEMERE and GEOS-Chem, which would be using output from LMDZ-INCA and GEOS-Chem global models, respectively.

All participating GCMs but one have interactive aerosol schemes with different levels of complexity (Table ES7). The GCMs with interactive aerosol schemes include all the significant processes influencing the aerosol life cycle, such as precursor gas and particle emissions, gas and aqueous-phase chemistry, nucleation, condensation, coagulation, aging, precipitation scavenging, and dry deposition. The aerosol module in ECHAM6-HAM2 predicts the time evolution of aerosol size distribution through a modal approach, using a superposition of seven lognormal modes, with internal mixing within modes. Aerosol dynamics uses a three-mode modal aerosol module in the CAM5 model, with aerosol species internally mixed within modes and externally mixed between Aitken, accumulation, and coarse modes, with distinct aerosol optical properties for each mode. SPRINTARS, the aerosol module used in NICAM-SPRINTARS, has a prognostic treatment of aerosols from major natural and anthropogenic sources. The NICAM-SPRINTARS model uses a single-moment cloud microphysics scheme, not coupled to aerosols, thus not including the indirect effect of aerosols. The IITM-ESMv2, a state-of-the-art Earth system model from India suitable for studies of long-term climate and particularly the Indian monsoon rainfall, uses prescribed spectral optical properties of aero-sols to estimated aerosol direct radiative forcing in the model simulations. Model simulated variables will be evaluated against observations (Table ES8) from the Indian region as well as observations that are being made at COALESCE network stations.

Fig. ES3. Data assimilation protocol for the regional climate model intercomparsion.

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Table ES7. Participating general circulation model (GCM) description.

S. No.Model/research group

Meteorological parameterizations: land surface model (LSM); cumulus parameterization (CP); surface layer (SL); planetary boundary layer (PBL)

Aerosol module (AER); gas-phase chemistry (GC); photolysis (PL); cloud microphysics coupled to aerosols (CM); radiation schemes (RAD)

1ECHAM6-HAM2 LSM: JSBACH; CP: Tiedtke scheme; SL:

Monin–Obukhov theory; PBL: Mellor–Yamada scheme

AER: Hamburg Aerosol Module; CM: Lohmann scheme; RAD: PSRADIIT Bombay

2CAM5 LSM: CLM4.5;CP: Zhang and McFarlane

scheme ; SL: Similarity theory; PBL: moist turbulence scheme

AER: 3-modal MAM; CM: Morrison two-moment (coupled to aerosol module) ;RAD: Rapid Radia-tive Transfer Model for GCMs (RRTMG)IIT Delhi

3ECHAM6-HAM2 with cus-tomized optical properties

LSM: JSBACH; CP: Tiedtke scheme; SL: Monin–Obukhov theory; PBL: Mellor–Yamada scheme

AER: Hamburg Aerosol Module; CM: Lohmann scheme; RAD: PSRAD

PRL, Ahmedabad

4CESM1.1 LSM: CLM4.5; CP: Zhang and McFarlane

scheme; SL: similarity theory; PBL: moist and dry turbulence scheme

AER: 7-modal MAM; CM: Morrison two-moment (coupled to aerosol module); RAD: Rapid Radia-tive Transfer Model for GCMs (RRTMG)CSIR-4PI

5NICAM-SPRINTARS LSM: MATSIRO; CP: A-S and Prognostic A-S

scheme; SL: Monin–Obukhov theory; PBL: Mellor–Yamada scheme

AER: SPRINTARS; GC: Takemura sulfate chemistry; CM: Lin scheme; RAD: MSTRN-XBARC, Mumbai

6IITM-ESM LSM: Noah LSM, CP: modified simplified

Arakawa–Schubert (SAS) scheme; PBL: Han and Pan scheme

Prescribed optical properties for Aerosols, RAD: RRTM; CM: Zhao and Carr schemeIITM, Pune

Table ES8. Observational data sources for model evaluation.

S. No. ParametersObservation data

source PeriodGlobal/regional/

station data

Resolution

Spatial Temporal

1 Precipitation, temperature IMD gridded 2000–15 India 0.25° × 0.25° Daily, monthly

2 Precipitation, temperature CRU TS3.23 2000–15 Global 0.5° × 0.5° Daily, monthly

3 Precipitation GPCP v2.2 2000–present Global 2.5° × 2.5° Monthly

4 Precipitation TRMM (TMPA-RT)Mar 2000–

presentGlobal

(60°N to 60°S) 0.25° × 0.25° 3-hourly, daily, Monthly

5Aerosol (AOD, SSA,

size distribution)AERONET 2001–present

Station (10 in India)

— Daily, monthly

6Aerosol and cloud

(vertical profiles, others)CALIPSO 2006–present Global

40 km × 40 km, 30 m vertical

Instantaneous, daily, monthly

7Aerosol and cloud

(various parameters)MODIS 1999–present Global 1.0° × 1.0° Daily, monthly

8Aerosol and cloud

(various parameters)MISR 1999–present Global 0.5° × 0.5° Daily, monthly

9 Aerosol and cloud ISCCP 2000–15 Global — Monthly

10 Cloud (various parameters) CloudSat 2006–present Global 2.0° × 2.0°, 480 m vertical

Daily, monthly

11Pressure, temperature, RH, wind

direction, and wind speed.WMO-IGRA

(radiosonde Data)2000–present

Station (62 in India)

At least 10 levels between 1,000

and 100 hPaDaily, monthly

12 Aerosol TOMS 2000–05 Global 1.0° × 1.25° Daily, monthly

13 Aerosol OMI/Aura 2004–present Global 1.0° × 1.0° Daily, Monthly

14 BC surface concentrationPublished literature (aethalometer data)

2000–presentStation

(at least 10)— Daily, monthly

15 BC vertical profilePublished literature

(aircraft measurements)

Selected field campaign during

2000–present

Station (Kanpur, Bangalore, and

Hyderabad)— Daily

A M E R I C A N M E T E O R O L O G I C A L S O C I E T Y J U LY 2 0 2 0 ES268

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