Impact of dynamical and microphysical schemes on black carbon prediction in a regional climate model over India
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Aerosol concentrations and their properties strongly depend on dynamics of atmosphere. Effects of physical and dynamical parameterizations on meteorology and black carbon (BC) mass in Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) are investigated over India. Simulations are performed in ten experiments considering two boundary layer, three cumulus parameterization, and five microphysics schemes during winter and monsoon of 2008. Morrison double-moment physical parameterization, Yonsei University boundary layer parameterization with Kain-Fritsch and Grell-Freitas cumulus parameterization schemes are found suitable to simulate meteorology and BC mass over India. BC mass is found to be underestimated in almost all experiments during winter; while, BC mass is overestimated in monsoon over Ahmedabad, Delhi, and Kanpur, which suggests inefficient wet scavenging of BC in monsoon, while lower emission rate may cause differences in winter. The results will be useful in understanding parameterizations and their impact on aerosols.
KeywordsDynamical parameterization Meteorology Aerosols Black carbon Regional climate model
Meteorological data for initial and boundary conditions are downloaded from http://rda.ucar.edu/datasets/ds083.2/. The initial and boundary conditions for chemical field, biogenic emissions, biomass burnings, anthropogenic emissions and programs used to process the data sets are obtained from https://www2.acom.ucar.edu/wrf-chem. The Radiosonde data are obtained from University of Wyoming. Authors are grateful to Rajesh Kumar and his team (UCAR, USA) for their support in WRF-Chem installation and simulations. Authors are thankful to anonymous reviewers for their valuable comments and suggestions.
The funding for the study is provided by Department of Science and Technology (DST), Government of India (SR/S4/AS-107/2012).
- Chen Y, Zhao C, Zhang Q, Deng ZZ, Huang MY, Ma XC (2009) Aircraft study of mountain chimney effect of Beijing. China J Geophys Res 114(D8), https://doi.org/10.1029/2008JD010610
- Emmons LK, Walters S, Hess PG, Lamarque JF, Pfister GG, Fillmore D, Granier C, Guenther A, Kinnison D, Laepple T, Orlando J, Tie X, Tyndall G, Wiedinmyer C, Baughcum SL, Kloster S (2010) Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4). Geosci Model Dev 3(1):43–67CrossRefGoogle Scholar
- Fast JD, Gustafson WI, Easter RC, Zaveri RA, Barnard JC, Chapman EG, Grell GA, Peckham SE (2006) Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of houston using a fully coupled meteorology-chemistry-aerosol model. J Geophys Res 111(D21), https://doi.org/10.1029/2005JD006721
- Grell GA, Dévényi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Letts 29(14, 1693) https://doi.org/10.1029/2002GL015311
- Pai DS, Latha S, Rajeevan M, Sreejith O P, Satbhai NS, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25∘ × 0.25∘) long period (1901 - 2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65:1–18Google Scholar
- Pruppacher HR, Klett JD, Wang PK (eds) (1997) Microphysics of clouds and precipitation. Kluwer Academic Publishers, DordrechtGoogle Scholar
- Ramachandran S, Kedia S (2010) Black carbon aerosols over an urban region: radiative forcing and climate impact. J Geophys Res 115, https://doi.org/10.1029/2009JD013560
- Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2008) A description of the advanced research wrf version 2. Technical report, DTIC DocumentGoogle Scholar
- Stull RB (ed) (1988) An introduction to boundary layer meteorology. Kluwer Academic Publishers, DordrechtGoogle Scholar
- Tewari M, Chen F, Wang W, Dudhia J, LeMone M, Mitchell K, Ek M, Gayno G, Wegiel J, Cuenca R (2004) Implementation and verification of the unified noah land surface model in the WRF modelGoogle Scholar