Indian Network Project on Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts (COALESCE) C

Supplement Indian 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

https://doi.org/10.1175/BAMS-D-19-0030.2 Corresponding author: Chandra Venkataraman, [email protected] This document is a supplement to https://doi.org/10.1175/BAMS-D-19-0030.1 In final form 3 January 2020 ©2020 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E257 Details of survey methodology and locations This project with 22 institutions (Fig. ES1) involves participation of 40 investigators (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 communication), 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 Fig. ES1. COALESCE organization structure. 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 campaigns Field 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 instruments (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,

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E258 Table ES1. List of participating institutions and investigators. Names of principal investigators of the respective institutions are in bold.

Sr. No Name Affiliation Institute Chandra Venkataraman Professor, Department of Chemical Engineering; Associate faculty, 1 (National Coordinator) IDP in Climate studies Professor, Department of Chemical Engineering; Associate faculty, 2 Mani Bhushan IIT Bombay IDP in Climate studies Assistant Professor, Centre for Environmental Science and Engineering; 3 Harish Phuleria Associate faculty, IDP in Climate studies 5 Tarun Gupta Professor, Department of Civil Engineering 6 Debajyoti Paul Professor, Department of Earth Sciences IIT Kanpur 7 Anubha Goel Associate Professor, Department of Civil Engineering 8 Gazala Habib Associate Professor, Department of Civil Engineering 9 S.K. Dash Professor, Centre for Atmospheric Science IIT Delhi 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 14 S. M. Shiva Nagendra Professor, Department of Civil Engineering IIT Madras 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 Sciences PRL Ahmedabad 19 Harish Gadhavi Scientist-SE, Space and Atmospheric Sciences Division 20 T.K. Mandal Principal Scientist, Radio and Atmospheric Sciences 21 S.K.Sharma Scientist, Radio and Atmospheric Sciences NPL Delhi 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 Group NARL 28 Vikas Singh Scientist-SD, Weather and Climate Research Group 29 R. Naresh Kumar Assistant Professor, Department of Civil and Environmental Engineering BITS 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 33 Sanjay K Ghosh Professor, Department of Physics Bose Institute, Darjeeling 34 Sibaji Raha Professor, Department of Physics 35 Binoy K Saikia Scientist, Coal Chemistry Division CSIR-NEIST, Jorhat 36 Prasenjit Saikia Scientist, Coal Chemistry Division 37 S. Anand Scientist, Health Safety and Environment Group BARC, 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 Maharaja Ganga Singh University, 40 Anil Kumar Chhangani Head, Department of Environment Science 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 43 Udhayashankar T.H. Professor, Department of Environmental Engineering Engineering, Mysuru

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E259 Table ES2. Mapping of cities for vehicle survey. COALESCE institutes State Cities as per tier classification I II III IV V VI (100,000 (50,000 (20,000 (10,000 (5,000 (population > < < < < < No. of (Population population population population population population 5,000) surveys > ≤ < < < < (transport/ S1 1,000,000) 1,000,000) 99,999) 49,999) 19,999) 9,999) nontransport) University J&K Srinagar Jammu Anantnag Bandipore Gulmarg Achabal Banihal 670 of Kashmir IISER Punjab Chandigarh Amritsar Kapurthala Jalalabad Majitha Maloud Sansarpur 670 Mohali Rajasthan Jaipur Ajmer Balotra Bhadra Bhusawar Bhalariya Govindgarh 670 IIT Delhi Delhi Ghazaibad East Delhi West Delhi North Delhi South Delhi Central Delhi 670 Charkhi NPL Delhi Haryana Panipat Ambala Narnaul Bawal Farakhpur Rewari 670 Dadri Uttar IIT Kanpur kanpur Kanpur Khurja Mahrajganj Manikpur Mohanpur Amila 670 Pradesh BOSE Bihar Nalanda Patna Samastipur Ramnagar Thakurganj Asarganj N/A 670 Institute NEIST Assam Nagaland Dibrugarh Karimganj Nalbari Udalguri Amguri Howraghat 670 Jorhat IISER Madhya Bhopal Vidisha Jaora Multai Shahgarh Tirodi Badra 670 Bhopal Pradesh IIT Telangana Telangana Hyderabad Nirmal Naspur Utnur Tangapur Ratnapur 670 Hyderabad Mysore Karnataka Banglore Mandya Hunsur Pandavapura Arasinakunte Kadakola N/A 670 IIT Tamil Nadu Chennai Vellore Arakonam Lalgudi Pudur Puvalur Unjalur 670 Madras IIT Maharashtra Thane Ghatkopar Kharghar Uran Murbad Kharbav Saphale 670 Bombay BIT Jharkhand Ranchi Hazaribagh Rajrappa Churi Muri Bharno Topa 670 Mesra IIT West Bengal Kolkata Kharagpur Jhargram Kolaghat Mandarmani Digha Hariatara 670 Kharagpur IITM Pune Maharashtra Pune Lavasa Talegaon Keshavnagar Panchgani Dehu Adhale kh 670 Kshetra Wai Mahabaleswar Birwadi 670

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):

[]X C 11S C S EFx D TD 3 T CF, V E CO2 CO U E 0.4905kg Cm U

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

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E260 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 Anantnag Chandrapur Badgam Jalgaon Maharashtra J&K Baramula Satara Pulwama Latur Ganderbal Dhanbad Solan Hazaribagh Himachal Pradesh Jharkhand Una Ranchi Uttarakhand Udham Singh Nagar Fategarh sahib Maldah Firozpur Koch bihar West Bengal Sangrur Hugli Punjab Pathankot Bardhman SAS Nagar Ropar Thrissur Kerala Wayanad Sonipat Ambala Haryana Jhajjar Nagaon Panchkula Assam Golpara Nalbari Bikaner Sikar Meghalaya West Garo Hills Rajasmand Rajasthan Sri Ganganagar Nagaland Dimapur Kota Dhaulpur Baleshwar Balangir Gorakhpur Orissa Cuttack Bijnor Sundargarh Bairach Hardoi Uttar Pradesh Kushi nagar Nalgonda Ghaziabad Warangal Telangana Kanpur Medak Varanasi Sangareddy Janjgir-champa Krishna Chhattisgarh Raigarh Andhra Pradesh Chittoor SPSR Nellore Jhabua Hoshangabad Dakshin Kannada Madhya Pradesh Sehore Belgaum Karnataka Datia Mandya Rajgarh Kolar Junagadh Theni Tamil Nadu Vadodara Dharmapuri Gujarat Bhavnagar Ahmedabad Araria Bihar Surat Bhagalpur

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E261 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.

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

CCCO CO DR 22undiluted amb , CC CO22diluted CO amb

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

1 []XAducte x t DR EFx gkg , FDor

−1 −3 where EFX emission factor of pollutant X [gx (kgfuel) ], [X] is concentration of species X (g m ), 2 –1 Aduct is area of duct (m ), υex is exhaust velocity X (m s ), t is sampling time (s), F is fuel used (kg), and D is distance traveled by vehicle (km).

Methodologies for the ambient observational network Selecting 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,

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E262 Table ES4. Description and technical specifications of monitoring instruments used for source sampling.

Monitoring Measurement Measurement Sample Accuracy instruments technique Measurement range interval (resolution) Resolution Reference Heated Iso-kinetic (TSI) As inlet 0–20 m s−1 — — — — sampling probe Portable (Polltech Ins. Jaiprakash For dilution 1:100 — — — dilution tunnel Pvt. Ltd, India) et al. (2016) Velocity Pitot tube (Testo, Sparta, NJ) 0–100 m s−1 1 s ±0.2 m s−1 0.1 m s−1 — measurement

O2 0%–25% ±0.1%–0.8% 0.01%

CO2 (NDIR) 0%–50% ±0.3% to 0.5% 0.1% CO 0–10,000 ppm ±5 ppm 1 ppm Flue gas Electrochemical SO 0–5,000 ppm ±5 ppm 1 ppm x Jaiprakash emission sensor/NDIR (350, 1 s NO 0–4,000 ppm ±5 ppm 1 ppm et al. (2016) analyzer Testo, Sparta, NJ) NO2 0–500 ppm ±5 ppm 0.1 ppm ±10% for HC (NDIR) 100–40,000 ppm 10 ppm >4,000 ppm Temperature 0°–1,000°C ±0.5°C 0.1°C Temperature Sensor (Testo, Jaiprakash Temperature 0°–70°C 1 s ±0.2°C 0.1°C probe Sparta, NJ) et al. (2016) Relative Sensor (Testo, Relative Jaiprakash 0%–100% RH 1 s ±2% 0.7% humidity Sparta, NJ) humidity et al. (2016) ±100 ppm of Diluted CO Sensor (Testo, Jaiprakash 2 Diluted CO 0–10,000 ppm 1 s CO2 ± 3% 1 ppm analyzer Sparta, NJ) 2 et al. (2016) value Zero air (Polltech Ins. For dilution air 30 LPM — — — — assembly Pvt. Ltd, India) Multistream Cyclone (URG PM2.5 filter Jaiprakash et al. PM2.5 Cyclone 10 LPM — — — Corporation, USA) mass (2016) (URG Based) Rechargeable battery+ DC Sony Power supply 1.5 V — — — — adapter Speed 0.2 km h−1 0.01 km h−1 GPS + Vehicle testing VBOX Mini Distance 0.2–150 km h−1 1 s <50 cm 1 cm datalogger (Racelogic, U.K.) user guide Acceleration ±1 m s−2 0.01 1 m s−2 Number 0–3,000 cm−3 concentration Laser light Aerosol 0.001– 5% at scattering 1 s 0.01% — spectrometer 275,000 µg m−3 0.5 µm (TSI 3330) Number size distribution 0.3–10 µm (16 bins) Filter based Black Magee attenuation carbon and <0.1 to 1 s or Aethalometer 1 ng m−3 — Scientific User (Magee Scientific absorption >100 µg m−3 1 min Manual AE 33) coefficients Laser light Aerosol Integrating 0–20,000 Mm−1 Automatic Air Photon scattering scattering — — nephlometer (−30° to +45°C) scanning User Manual (Air Photon IN 102) coefficients

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E263 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 atmospheric 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. Analytical Instrument model/ Channel Filter Analyte method make

1 PM2.5 Mass, Elements (Al, Si, P, S, Cl, K, Ca, Sartorius microbalance Sc,Ti, V,Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Gravimetry, ED-XRF CP5, PAN alytical Epsilon 4 Teflon Rb, Sr, Mo, Cd, Sn, Sb, I, Ba, Hg, Pb, Bi, Ga, and ICP OES and Analytik Jena Plasma Ge, Y, Zr, In, Te, Cs) Quant PQ 9000 2 Water soluble inorganic ions Nylon with Thermo Dionex Dual Cation (Li+, Na+, NH +, K+, Ca+2, Mg+2) Ion chromatography denuder 4 ICS-Aquion − − − − − 3− 2− Anion (F , Cl , NO2 , Br , NO3 , PO4 , SO4 ) 3 Organic and elemental carbon fraction Quartz behind Thermal-optical DRI-2015 Multi-Wavelength (OC1, OC2, OC3, OC4, EC1, EC2, EC3) and quartz analysis Thermal Carbon Analyzer brown carbon 4 Volatile organics and organic molecular Agilent 7890B Gas Quartz behind GC-MS and IRMS/ markers for secondary organic aerosols Chromatograph–Mass quartz MC-ICPMS (SOA), C-13 isotope Spectrophotometer 5 Teflon Archival

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E264 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 locations 4) 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 RCMs Participating RCMs include WRF-CHEMERE, WRF-Chem, WRF-CMAQ, RegCM, and GEOS-Chem, which have differences in atmospheric chemistry mechanisms, aerosol physics, and meteorological 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.

Meteorological parameterizations: Aerosol module (AER); gas-phase land surface model (LSM); cumulus chemistry (GC); photolysis (PL); Model/ parameterization (CP); surface layer cloud microphysics coupled to aero- S. No. research group (SL); planetary boundary layer (PBL) sols (CM); radiation schemes (RAD) 1 WRF-Chem LSM: Noah LSM; CP: Grell 3D scheme; SL: AER: MADE; GC: RADM2; PL: Fast J Monin–Obukhov similarity theory; PBL: photolysis; CM:Thompson scheme; RAD: IIT Bombay Mellor–Yamada–Janjić Rapid Radiative Transfer Model 2 WRF-Chem LSM: Noah LSM; CP: Grell 3D scheme; SL: AER: MOSAIC; GC: CBM-Z; PL: Fast J IISER Bhopal and Monin–Obukhov similarity theory; PBL: photolysis; CM: Thompson scheme; RAD: NARL, Gadanki Mellor–Yamada–Janjić Rapid Radiative Transfer Model 3 RegCM LSM: BATS; CP: Emanuel scheme; PBL: AER: AERO (complete aerosol); RAD: NCAR IIT Delhi Holtslag scheme Community Climate Model Version3 4 WRF-CHIMERE LSM: Noah LSM; CP: Grell 3D ensemble AER: Aerosol module; GC: MELCHIOR scheme; SL: MM5 Monin–Obukhov scheme; reduced; PL: Fast-JX; CM: Lin et al. scheme; IIT Kharagpur PBL: Yonsei University (YSU) scheme RAD: Rapid Radiative Transfer Model (RRTM) 5 GEOS-Chem LSM: NASA Catchment Land Surface AER: ISORRPIA II thermodynamic module; (0.5° × 0.625°) Model; CP: relaxed Arakawa–Schubert; SL: GC: GEOS-Chem chemistry mechanisms; (IIT Madras) sigma/hybrid vertical grid system; PBL RAD: Rapid Radiative Transfer Model 6 WRF-CMAQ LSM: Noah LSM; CP: Grell 3D scheme; SL: AER: AERO5; GC: CB05; PL: CMAQ; RAD: Pune University Monin–Obukhov similarity theory; PBL: Rapid Radiative Transfer Model and NARL, Gadanki Mellor–Yamada–Janjić

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E265 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, Fig. ES3. Data assimilation protocol for the regional climate model intercomparsion. 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 aerosols 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.

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E266 Table ES7. Participating general circulation model (GCM) description.

Meteorological parameterizations: Aerosol module (AER); gas-phase land surface model (LSM); cumulus chemistry (GC); photolysis (PL); cloud Model/research parameterization (CP); surface layer microphysics coupled to aerosols (CM); S. No. group (SL); planetary boundary layer (PBL) radiation schemes (RAD) ECHAM6-HAM2 LSM: JSBACH; CP: Tiedtke scheme; SL: AER: Hamburg Aerosol Module; CM: Lohmann 1 Monin–Obukhov theory; PBL: Mellor– scheme; RAD: PSRAD IIT Bombay Yamada scheme CAM5 LSM: CLM4.5;CP: Zhang and McFarlane AER: 3-modal MAM; CM: Morrison two-moment 2 scheme ; SL: Similarity theory; PBL: moist (coupled to aerosol module) ;RAD: Rapid Radia- IIT Delhi turbulence scheme tive Transfer Model for GCMs (RRTMG) ECHAM6-HAM2 with cus- LSM: JSBACH; CP: Tiedtke scheme; SL: AER: Hamburg Aerosol Module; CM: Lohmann 3 tomized optical properties Monin–Obukhov theory; PBL: Mellor– scheme; RAD: PSRAD PRL, Ahmedabad Yamada scheme CESM1.1 LSM: CLM4.5; CP: Zhang and McFarlane AER: 7-modal MAM; CM: Morrison two-moment 4 scheme; SL: similarity theory; PBL: moist (coupled to aerosol module); RAD: Rapid Radia- CSIR-4PI and dry turbulence scheme tive Transfer Model for GCMs (RRTMG) NICAM-SPRINTARS LSM: MATSIRO; CP: A-S and Prognostic A-S AER: SPRINTARS; GC: Takemura sulfate 5 scheme; SL: Monin–Obukhov theory; PBL: chemistry; CM: Lin scheme; RAD: MSTRN-X BARC, Mumbai Mellor–Yamada scheme IITM-ESM LSM: Noah LSM, CP: modified simplified Prescribed optical properties for Aerosols, RAD: 6 Arakawa–Schubert (SAS) scheme; PBL: Han RRTM; CM: Zhao and Carr scheme IITM, Pune and Pan scheme

Table ES8. Observational data sources for model evaluation.

Observation data Global/regional/ Resolution S. No. Parameters source Period station data 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 Mar 2000– Global 3-hourly, daily, 4 Precipitation TRMM (TMPA-RT) × present (60°N to 60°S) 0.25° 0.25° Monthly Aerosol (AOD, SSA, Station 5 AERONET 2001–present — Daily, monthly size distribution) (10 in India) Aerosol and cloud 40 km × 40 km, Instantaneous, 6 CALIPSO 2006–present Global (vertical profiles, others) 30 m vertical daily, monthly Aerosol and cloud 7 MODIS 1999–present Global × Daily, monthly (various parameters) 1.0° 1.0° Aerosol and cloud 8 MISR 1999–present Global × Daily, monthly (various parameters) 0.5° 0.5° 9 Aerosol and cloud ISCCP 2000–15 Global — Monthly × 10 Cloud (various parameters) CloudSat 2006–present Global 2.0° 2.0°, Daily, monthly 480 m vertical At least 10 levels Pressure, temperature, RH, wind WMO-IGRA Station 11 2000–present between 1,000 Daily, monthly direction, and wind speed. (radiosonde Data) (62 in India) and 100 hPa 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 Published literature Station 14 BC surface concentration 2000–present — Daily, monthly (aethalometer data) (at least 10) Selected field Station (Kanpur, Published literature 15 BC vertical profile campaign during Bangalore, and — Daily (aircraft measurements) 2000–present Hyderabad)

AMERICAN METEOROLOGICAL SOCIETY JULY 2020 E267 References

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