The Effects of Biomass Burning Aerosols on Solar Radiation and Climate Over Northern Thailand: A Case Study of the 2013 Smoke-Haze Season
Abstract
This study aims to investigate the direct effects of aerosols on solar radiation and the indirect effects on climate variables in Northern Thailand using the WRF-Chem meteorology-chemistry coupled model during the 2013 smoke-haze season (February - April). The WRF-Chem model was run with two different cases, i.e., without any aerosol feedback, and with the direct and indirect effects of biomass burning aerosol. The study results finds that the biomass burning aerosols directly affect on reduction of solar radiation at the Earth’s surface. The areal averaged decreasing solar radiation in February, March and April are 1.0 W/m2, 1.0 W/m2, and 1.2 W/m2, respectively with the maximum decrease of 9.0-14.9 W/m2 at some specific area. The reduction in downward short-wave radiation flux did not obviously affect on the regional temperature. The difference between with and without the effect of aerosol on simulated temperature is ±0.1 oC. The biomass burning aerosols can act as cloud condensation nuclei (CCN), and thus an indirect effect to cloud property and formation. This study found that the simulation with aerosol effects produces the less cloud than normal. Nevertheless, during the dry season, the atmospheric water vapor is insufficient for precipitation potential. Moreover, the many aerosols during the smoke-haze season scatter solar radiation resulting in the decrease of energy to warm the Earth's surface. This situation results in the reduction of the height of mixing or PBLH layer and wind speed, led to decrease the potential of vertical dispersion of the smoke haze, and support to accumulated pollutants at the ground surface. The results of this study are expected to play an important role in enhancing the research and application of interactions between air quality and climate in Northern Thailand. Keywords: haze, aerosol, impact, solar radiation, climateReferences
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Storelvmo, T. (2012). Uncertainties in aerosol direct and indirect effects attributed to uncertainties in convective transport parameterizations. Atmospheric Research, 118, 357–369.
Wang, X. Y., Liang, X. Z., Jiang, W. M., Tao, Z. N., Wang, J. X. L., Liu, H. N., Han, Z. W., Liu, S. Y., Zhang, Y. Y., Grell, G. A., & Peckham, S. E. (2010). WRF-Chem simulation of East Asian air quality: Sensitivity to temporal and vertical emissions distributions. Atmospheric Environment, 44(5), 660–669.
Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., Balkanski, Y., Bellouin, N., Boucher, O., Christopher, S., DeCola, P., Kahn, R., Koch, D., Loeb, N., Reddy, M. S., Schulz, M., Takemura, T., & Zhou, M. (2006). A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmospheric Chemistry and Physics, 6(3), 613-666.
Zhang, Y., Wen, X. Y., & Jang, C. J. (2010). Simulating chemistry-aerosol-cloud-radiation-climate feedbacks over the continental US using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmospheric Environment, 44(29), 3568–3582.
Zhang, Y., Sartelet, K., Zhu, S., Wang, W., Wu, S. Y., Zhang, X., Wang, K., Tran, P., Seigneur, C., & Wang, Z. (2013). Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 2: Evaluation of chemical concentrations and sensitivity simulations. Atmospheric Chemistry and Physics, 13(14),
6845–6875.
Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., Coakley, J. A., Hanansen, J. E., & Hofmann, D. J. (1992). Climate forcing by anthropogenic aerosols. Science (New York, N.Y.). 255(5043), 423-430.
Chen, F., & Dudhia, J. (2001). Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation. Monthly Weather Review, 129(4), 587–604.
Chen, S.H., & Sun, W.-Y. (2002). A One-dimensional Time Dependent Cloud Model. Journal of the Meteorological Society of Japan. Ser. II, 80(1), 99–118.
Chotamonsak, C., Salathe, E. P., Kreasuwan, J., & Chantara, S. (2012). Evaluation of precipitation simulations over Thailand using a WRF regional climate model. Chiang Mai Journal of Science, 39(4), 623–628.
Chuang, C. C., Kelly, J. T., Boyle, J. S., & Xie, S. C. (2012). Sensitivity of aerosol indirect effects to cloud nucleation and autoconversion parameterizations in short-range weather forecasts during the May 2003 aerosol IOP. Journal of Advances in Modeling Earth Systems, 4(9).
Chung, C. E., Ramanathan, V., Kim, D., & Podgorny, I. A. (2005). Global anthropogenic aerosol direct forcing derived from satellite and ground-based observations. Journal of Geophysical Research Atmospheres, 110(24), 1-17.
Fan, J., Leung, L. R., Demott, P. J., Comstock, J. M., Singh, B., Rosenfeld, D., Tomlinson, J. M., White, A., Prather, K. A., Minnis, P., Ayers, J. K., & Min, Q. (2014). Aerosol impacts on California winter clouds and precipitation during CalWater 2011: Local pollution versus long-range transported dust. Atmospheric Chemistry and Physics, 14(1), 81–101.
Forkel, R., Werhahn, J., Hansen, A. B., McKeen, S., Peckham, S., Grell, G., & Suppan, P. (2012). Effect of aerosol-radiation feedback on regional air quality - A case study with WRF/Chem. Atmospheric Environment, 53, 202–211.
Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y. J. (2003). An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87(2–3), 273–282.
Gochis, D. J., Shuttleworth, W. J., & Yang, Z.-L. (2002). Sensitivity of the Modeled North American Monsoon Regional Climate to Convective Parameterization. Monthly Weather Review, 130(5), 1282–1298.
Grell, G. A., & Dévényi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters, 29(14), 38-1-38–4.
Guo, J. P., Deng, M. J., Fan, J. W., Li, Z. Q., Chen, Q., Zhai, P. M., Dai, Z. J., & Li, X. W. (2014). Precipitation and air pollution at mountain and plain stations in northern China: Insights gained from observations and modeling. Journal of Geophysical Research-Atmospheres, 119(8), 4793–4807.
Hong, S.Y., Noh, Y., & Dudhia, J. (2006). A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Monthly Weather Review, 134(9), 2318–2341.
Kaufman, Y. J., Koren, I., Remer, L. A., Rosenfeld, D., & Rudich, Y. (2005). The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean. In Proceedings of the National Academy of Sciences, 102(32), 11207–11212.
Kodros, J. K., Scott, C. E., Farina, S. C., Lee, Y. H., L’Orange, C., Volckens, J., & Pierce, J. R. (2015). Uncertainties in global aerosols and climate effects due to biofuel emissions. Atmospheric Chemistry and Physics, 15(15), 8577-8596.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research: Atmospheres, 102(D14), 16663–16682.
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., & Rosenfeld, D. (2001). Atmosphere: Aerosols, climate, and the hydrological cycle. Science. 294(5549), 2119-2124.
Rosenfeld, D. (2006). Aerosols, clouds, and climate. Science, 312(5778), 1323-1324.
Rosenfeld, D., Lohmann, U., Raga, G. B., O’Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., & Andreae, M. O. (2008). Flood or drought: How do aerosols affect precipitation? Science, 321(5894), 1309-1313.
Saide, P. E., Spak, S., Carmichael, G. R., Mena-Carrasco, M. A., Yang, Q., Howell, S., Leon, D. C., Snider, J. R., Bandy, A. R., Collett, J. L., Benedict, K. B., De Szoeke, S. p., Hawkins, L. N., Crosier, J., & Springston, S. R. (2012). Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx. Atmospheric Chemistry and Physics, 12(6), 3045–3064.
Satheesh, S. K., & Krishna Moorthy, K. (2005). Radiative effects of natural aerosols: A review. Atmospheric Environment, 39(11), 2089-2110.
Storelvmo, T. (2012). Uncertainties in aerosol direct and indirect effects attributed to uncertainties in convective transport parameterizations. Atmospheric Research, 118, 357–369.
Wang, X. Y., Liang, X. Z., Jiang, W. M., Tao, Z. N., Wang, J. X. L., Liu, H. N., Han, Z. W., Liu, S. Y., Zhang, Y. Y., Grell, G. A., & Peckham, S. E. (2010). WRF-Chem simulation of East Asian air quality: Sensitivity to temporal and vertical emissions distributions. Atmospheric Environment, 44(5), 660–669.
Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., Balkanski, Y., Bellouin, N., Boucher, O., Christopher, S., DeCola, P., Kahn, R., Koch, D., Loeb, N., Reddy, M. S., Schulz, M., Takemura, T., & Zhou, M. (2006). A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmospheric Chemistry and Physics, 6(3), 613-666.
Zhang, Y., Wen, X. Y., & Jang, C. J. (2010). Simulating chemistry-aerosol-cloud-radiation-climate feedbacks over the continental US using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmospheric Environment, 44(29), 3568–3582.
Zhang, Y., Sartelet, K., Zhu, S., Wang, W., Wu, S. Y., Zhang, X., Wang, K., Tran, P., Seigneur, C., & Wang, Z. (2013). Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 2: Evaluation of chemical concentrations and sensitivity simulations. Atmospheric Chemistry and Physics, 13(14),
6845–6875.
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2018-09-12
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