Articles | Volume 22, issue 6
https://doi.org/10.5194/acp-22-4129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-4129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2022
Research article |
| 31 Mar 2022
| 31 Mar 2022
Radiative and microphysical responses of clouds to an anomalous increase in fire particles over the Maritime Continent in 2015
Azusa Takeishi
CORRESPONDING AUTHOR
Laboratoire d'Aérologie, UPS/CNRS, 14 avenue Edouard Belin, 31400 Toulouse, France
Chien Wang
Laboratoire d'Aérologie, UPS/CNRS, 14 avenue Edouard Belin, 31400 Toulouse, France
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Cited articles
Ackermann, I. J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F. S., and
Shankar, U.: Modal aerosol dynamics model for Europe: Development and first
applications, Atmos. Environ., 32, 2981–2999,
https://doi.org/10.1016/S1352-2310(98)00006-5, 1998. a
Darmenov, A. S. and da Silva, A.: The Quick Fire Emissions Dataset (QFED):
Documentation of versions 2.1, 2.2 and 2.4, Technical Report Series on
Global Modeling and Data Assimilation, NASA/TM-2015-104606/Vol. 38, https://gmao.gsfc.nasa.gov/pubs/docs/Darmenov796.pdf (last access: 25 March 2022), 2015. a
Fan, J., Leung, L. R., Rosenfeld, D., Chen, Q., Li, Z., Zhang, J., and Yan, H.:
Microphysical effects determine macrophysical response for aerosol impacts on
deep convective clouds, P. Natl. A. Sci. USA, 110,
E4581–E4590, https://doi.org/10.1073/pnas.1316830110, 2013. a
Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado,
L. A. T., Martin, S. T., Yang, Y., Wang, J., Artaxo, P., Barbosa, H. M. J.,
Braga, R. C., Comstock, J. M., Feng, Z., Gao, W., Gomes, H. B., Mei, F.,
Pöhlker, C., Pöhlker, M. L., Pöschl, U., and de Souza, R. A. F.:
Substantial convection and precipitation enhancements by ultrafine aerosol
particles, Science, 359, 411–418, https://doi.org/10.1126/science.aan8461, 2018. a
Freitas, S. R., Longo, K. M., Chatfield, R., Latham, D., Silva Dias, M. A. F., Andreae, M. O., Prins, E., Santos, J. C., Gielow, R., and Carvalho Jr., J. A.: Including the sub-grid scale plume rise of vegetation fires in low resolution atmospheric transport models, Atmos. Chem. Phys., 7, 3385–3398, https://doi.org/10.5194/acp-7-3385-2007, 2007. a
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell, G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC – 1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models, Geosci. Model Dev., 4, 419–433, https://doi.org/10.5194/gmd-4-419-2011, 2011. a
Ge, C., Wang, J., and Reid, J. S.: Mesoscale modeling of smoke transport over the Southeast Asian Maritime Continent: coupling of smoke direct radiative effect below and above the low-level clouds, Atmos. Chem. Phys., 14, 159–174, https://doi.org/10.5194/acp-14-159-2014, 2014. a
Giglio, L. and Justice, C.: MOD14A2 MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD14A2.006, 2015a. a
Giglio, L. and Justice, C.: MYD14A2 MODIS/Aqua Thermal Anomalies/Fire 8-Day L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MYD14A2.006, 2015b. a
Graham, N. E. and Barnett, T. P.: Sea Surface Temperature, Surface Wind
Divergence, and Convection over Tropical Oceans, Science, 238, 657–659,
https://doi.org/10.1126/science.238.4827.657, 1987. a
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF
model, Atmos. Environ., 39, 6957–6975,
https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Guenther, A., Zimmerman, P., and Wildermuth, M.: Natural volatile organic
compound emission rate estimates for U.S. woodland landscapes, Atmos. Environ., 28, 1197–1210, https://doi.org/10.1016/1352-2310(94)90297-6, 1994. a
Hodzic, A. and Duvel, J. P.: Impact of Biomass Burning Aerosols on the Diurnal
Cycle of Convective Clouds and Precipitation Over a Tropical Island, J. Geophys. Res.-Atmos., 123, 1017–1036,
https://doi.org/10.1002/2017JD027521, 2018. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., and Akimoto, H.: Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019–11058, https://doi.org/10.5194/acp-13-11019-2013, 2013. a
Lebo, Z.: A Numerical Investigation of the Potential Effects of
Aerosol-Induced Warming and Updraft Width and Slope on Updraft Intensity in
Deep Convective Clouds, J. Atmos. Sci., 75, 535–554,
https://doi.org/10.1175/JAS-D-16-0368.1, 2018. a
Lee, D., Sud, Y. C., Oreopoulos, L., Kim, K.-M., Lau, W. K., and Kang, I.-S.: Modeling the influences of aerosols on pre-monsoon circulation and rainfall over Southeast Asia, Atmos. Chem. Phys., 14, 6853–6866, https://doi.org/10.5194/acp-14-6853-2014, 2014. a
Lee, H.-H. and Wang, C.: The impacts of biomass burning activities on convective systems over the Maritime Continent, Atmos. Chem. Phys., 20, 2533–2548, https://doi.org/10.5194/acp-20-2533-2020, 2020. a
Lenssen, N. J. L., Goddard, L., and Mason, S.: Seasonal Forecast Skill of ENSO
Teleconnection Maps, Weather Forecast., 35, 2387–2406,
https://doi.org/10.1175/WAF-D-19-0235.1, 2020. a
Lin, N.-H., Tsay, S.-C., Maring, H. B., Yen, M.-C., Sheu, G.-R., Wang, S.-H.,
Chi, K. H., Chuang, M.-T., Ou-Yang, C.-F., Fu, J. S., Reid, J. S., Lee,
C.-T., Wang, L.-C., Wang, J.-L., Hsu, C. N., Sayer, A. M., Holben, B. N.,
Chu, Y.-C., Nguyen, X. A., Sopajaree, K., Chen, S.-J., Cheng, M.-T., Tsuang,
B.-J., Tsai, C.-J., Peng, C.-M., Schnell, R. C., Conway, T., Chang, C.-T.,
Lin, K.-S., Tsai, Y. I., Lee, W.-J., Chang, S.-C., Liu, J.-J., Chiang, W.-L.,
Huang, S.-J., Lin, T.-H., and Liu, G.-R.: An overview of regional experiments
on biomass burning aerosols and related pollutants in Southeast Asia: From
BASE-ASIA and the Dongsha Experiment to 7-SEAS, Atmos. Environ., 78,
1–19, https://doi.org/10.1016/j.atmosenv.2013.04.066, 2013. a
Lin, N.-H., Sayer, A. M., Wang, S.-H., Loftus, A. M., Hsiao, T.-C., Sheu,
G.-R., Hsu, N. C., Tsay, S.-C., and Chantara, S.: Interactions between
biomass-burning aerosols and clouds over Southeast Asia: Current status,
challenges, and perspectives, Environ. Pollut., 195, 292–307,
https://doi.org/10.1016/j.envpol.2014.06.036, 2014. a, b
Liu, T., Mickley, L. J., Marlier, M. E., DeFries, R. S., Khan, M. F., Latif,
M. T., and Karambelas, A.: Diagnosing spatial biases and uncertainties in
global fire emissions inventories: Indonesia as regional case study, Remote
Sens. Environ., 237, 111557, https://doi.org/10.1016/j.rse.2019.111557, 2020. a
Madronich, S.: Photodissociation in the atmosphere: 1. Actinic flux and the
effects of ground reflections and clouds, J. Geophys. Res.-Atmos., 92, 9740–9752, https://doi.org/10.1029/JD092iD08p09740, 1987. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137,
991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a, b
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model: Its
Numerical Stability and Application to a Regional Prediction of Advection
Fog, Bound.-Lay. Meteorol., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006. a
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure
Model for the Atmospheric Boundary Layer, J. Meteorol. Soc. Jpn., 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a
NASA: AERONET, https://aeronet.gsfc.nasa.gov/, last access: 25 March 2022. a
NCEP, NWS, NOAA, U.S. DOC: NCEP FNL Operational Model Global Tropospheric
Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO, U.S.A., NCEP, NWS, NOAA, U.S. DOC [data set], https://doi.org/10.5065/D6M043C6, 2000. a, b
NIES: Regional Emission inventory in ASia (REAS) Data Download Site, https://www.nies.go.jp/REAS/, last access: 25 March 2022. a
NOAA: Cold and Warm Episodes by Season, https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php, last access: 25 March, 2022. a
Olson, J. B., Kenyon, J. S., Angevine, W. A., Brown, J. M., Pagowski, M., and
Sušelj, K.: A Description of the MYNN-EDMF Scheme and the Coupling to
Other Components in WRF–ARW, NOAA Technical Memorandum OAR GSD; 61,
https://doi.org/10.25923/n9wm-be49, 2019. a
Pan, X., Ichoku, C., Chin, M., Bian, H., Darmenov, A., Colarco, P., Ellison, L., Kucsera, T., da Silva, A., Wang, J., Oda, T., and Cui, G.: Six global biomass burning emission datasets: intercomparison and application in one global aerosol model, Atmos. Chem. Phys., 20, 969–994, https://doi.org/10.5194/acp-20-969-2020, 2020. a
Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Monthly Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, U.S.A. [data set], https://doi.org/10.5067/MODIS/MOD08_M3.061 (Terra) and https://doi.org/10.5067/MODIS/MYD08_M3.061 (Aqua), 2015a. a
Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Daily Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, U.S.A. [data set], https://doi.org/10.5067/MODIS/MOD08_D3.061 (Terra) and https://doi.org/10.5067/MODIS/MYD08_D3.061 (Aqua), 2015b. a
Rosenfeld, D.: TRMM observed first direct evidence of smoke from forest fires
inhibiting rainfall, Geophys. Res. Lett., 26, 3105–3108,
https://doi.org/10.1029/1999GL006066, 1999. a
Sabin, T. P., Babu, C. A., and Joseph, P. V.: SST–convection relation over
tropical oceans, Int. J. Climatol., 33, 1424–1435,
https://doi.org/10.1002/joc.3522, 2013. a
Schell, B., Ackermann, I. J., Hass, H., Binkowski, F. S., and Ebel, A.:
Modeling the formation of secondary organic aerosol within a comprehensive
air quality model system, J. Geophys. Res.-Atmos., 106,
28275–28293, https://doi.org/10.1029/2001JD000384, 2001. a
Schneider, T., Bischoff, T., and Haug, G. H.: Migrations and dynamics of the
intertropical convergence zone, Nature, 513, 45–53,
https://doi.org/10.1038/nature13636, 2014. a
Schultz, M. G., Heil, A., Hoelzemann, J. J., Spessa, A., Thonicke, K.,
Goldammer, J. G., Held, A. C., Pereira, J. M. C., and van het Bolscher, M.:
Global wildland fire emissions from 1960 to 2000, Global Biogeochem. Cy., 22, GB2002, https://doi.org/10.1029/2007GB003031, 2008. a
Simpson, D., Guenther, A., Hewitt, C. N., and Steinbrecher, R.: Biogenic
emissions in Europe: 1. Estimates and uncertainties, J. Geophys. Res.-Atmos., 100, 22875–22890, https://doi.org/10.1029/95JD02368, 1995. a
Skamarock, W. C., Klemp, J. B, Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, NCAR Technical Note, NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH, 2008. a
Stockwell, W. R., Middleton, P., Chang, J. S., and Tang, X.: The second
generation regional acid deposition model chemical mechanism for regional air
quality modeling, J. Geophys. Res.-Atmos., 95,
16343–16367, https://doi.org/10.1029/JD095iD10p16343, 1990. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M.,
Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification of
the unified NOAH land surface model in the WRF model, in: 20th conference on weather analysis and forecasting, 16th conference on numerical weather
prediction, American Meteorological Society, Seattle, WA, U.S.A., 12–16 January 2004, 11–15, https://www2.mmm.ucar.edu/wrf/users/physics/phys_refs/LAND_SURFACE/noah.pdf (last access: 25 March 2022), 2004. a
Thompson, G. and Eidhammer, T.: A Study of Aerosol Impacts on Clouds and
Precipitation Development in a Large Winter Cyclone, J. Atmos. Sci., 71, 3636–3658, https://doi.org/10.1175/JAS-D-13-0305.1, 2014. a
Tompkins, A. M.: On the Relationship between Tropical Convection and Sea
Surface Temperature, J. Climate, 14, 633–637,
https://doi.org/10.1175/1520-0442(2001)014<0633:OTRBTC>2.0.CO;2, 2001. a
Tropical Rainfall Measuring Mission (TRMM): TRMM (TMPA/3B43) Rainfall Estimate L3 1 month 0.25∘ × 0.25∘ V7, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, U.S.A., https://doi.org/10.5067/TRMM/TMPA/MONTH/7, 2011. a
Tsay, S.-C., Hsu, N. C., Lau, W. K.-M., Li, C., Gabriel, P. M., Ji, Q., Holben,
B. N., Welton, E. J., Nguyen, A. X., Janjai, S., Lin, N.-H., Reid, J. S.,
Boonjawat, J., Howell, S. G., Huebert, B. J., Fu, J. S., Hansell, R. A.,
Sayer, A. M., Gautam, R., Wang, S.-H., Goodloe, C. S., Miko, L. R., Shu,
P. K., Loftus, A. M., Huang, J., Kim, J. Y., Jeong, M.-J., and Pantina, P.:
From BASE-ASIA toward 7-SEAS: A satellite-surface perspective of boreal
spring biomass-burning aerosols and clouds in Southeast Asia, Atmos. Environ., 78, 20–34, https://doi.org/10.1016/j.atmosenv.2012.12.013, 2013. a
UCAR: WRF Source Codes and Graphics Software Downloads, UCAR [code], https://www2.mmm.ucar.edu/wrf/users/download/get_source.html (last access: 25 March 2022), 2022a. a
UCAR: Fire Emission Factors and Emission Inventories, UCAR [data set], https://www.acom.ucar.edu/Data/fire/, last access: 25 March 2022, 2022b. a
Wang, C., Deser, C., Yu, J.-Y., DiNezio, P., and Clement, A.: El Niño and
Southern Oscillation (ENSO): A Review, in: Coral Reefs of the Eastern Tropical Pacific, 8, edited by: Glynn, P. W., Manzello, D. P., and Enochs, I. C., Springer, 8, 85–106, https://doi.org/10.1007/978-94-017-7499-4_4, 2017. a
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a
Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) Modeling
System, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010,
2010. a
Wei, J., Malanotte-Rizzoli, P., Eltahir, E. A. B., Xue, P., and Xu, D.:
Coupling of a regional atmospheric model (RegCM3) and a regional oceanic
model (FVCOM) over the maritime continent, Clim. Dynam., 43, 1575–1594,
https://doi.org/10.1007/s00382-013-1986-3, 2014. a
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011. a
Woolnough, S. J., Slingo, J. M., and Hoskins, B. J.: The Relationship between
Convection and Sea Surface Temperature on Intraseasonal Timescales, J. Climate, 13, 2086–2104,
https://doi.org/10.1175/1520-0442(2000)013<2086:TRBCAS>2.0.CO;2, 2000.
a
Xavier, P., Rahmat, R., Cheong, W. K., and Wallace, E.: Influence of
Madden-Julian Oscillation on Southeast Asia rainfall extremes: Observations
and predictability, Geophys. Res. Lett., 41, 4406–4412,
https://doi.org/10.1002/2014GL060241, 2014. a
Zhang, Y., Fan, J., Logan, T., Li, Z., and Homeyer, C. R.: Wildfire Impact on
Environmental Thermodynamics and Severe Convective Storms, Geophys. Res. Lett., 46, 10082–10093, https://doi.org/10.1029/2019GL084534,
2019a. a
Zhang, Y., Wang, K., Jena, C., Paton-Walsh, C., Guérette, É.-A., Utembe, S.,
Silver, J. D., and Keywood, M.: Multiscale Applications of Two
Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in
Australia, Part II: Comparison of WRF/Chem and WRF/Chem-ROMS and Impacts of
Air-Sea Interactions and Boundary Conditions, Atmosphere, 10, 210, https://doi.org/10.3390/atmos10040210, 2019b. a
Zhu, A., Xu, H., Deng, J., Ma, J., and Li, S.: El Niño–Southern Oscillation (ENSO) effect on interannual variability in spring aerosols over East Asia, Atmos. Chem. Phys., 21, 5919–5933, https://doi.org/10.5194/acp-21-5919-2021, 2021. a
Short summary
Nanometer- to micrometer-sized particles in the atmosphere, namely aerosols, play a crucial role in cloud formation as cloud droplets form on aerosols. This study uses a weather forecasting model to examine the impacts of a large emission of aerosol particles from biomass burning activities over Southeast Asia. We find that additional cloud droplets brought by fire-emitted particles can lead to taller and more reflective convective clouds with increased rainfall.
Nanometer- to micrometer-sized particles in the atmosphere, namely aerosols, play a crucial role...
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