Articles | Volume 20, issue 23
https://doi.org/10.5194/acp-20-15401-2020
© Author(s) 2020. 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-20-15401-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Dec 2020
Research article |
| 11 Dec 2020
| 11 Dec 2020
Constraining the relationships between aerosol height, aerosol optical depth and total column trace gas measurements using remote sensing and models
Shuo Wang
School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519000, China
School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519000, China
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519000, China
Chuyong Lin
School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519000, China
Weizhi Deng
School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519000, China
Related authors
No articles found.
Fan Lu, Kai Qin, Jason Blake Cohen, Qin He, Pravash Tiwari, Wei Hu, Chang Ye, Yanan Shan, Qing Xu, Shuo Wang, and Qiansi Tu
Atmos. Chem. Phys., 25, 5837–5856, https://doi.org/10.5194/acp-25-5837-2025, https://doi.org/10.5194/acp-25-5837-2025, 2025
Short summary
Short summary
This work describes a field campaign and new fast emissions estimation approach to attribute methane from a large known and previously unknown coal mine in Shanxi, China. The emissions computed are shown to be larger than known oil and gas sources, indicating that methane from coal mines may play a larger role in the global methane budget. The results are found to be slightly larger than or similar to satellite observational campaigns over the same region.
Bo Zheng, Jason Blake Cohen, Lingxiao Lu, Wei Hu, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Hui Su, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1446, https://doi.org/10.5194/egusphere-2025-1446, 2025
Short summary
Short summary
This study provides TROPOMI with a new methane emission estimation method that can accurately identify emission sources. Our results generate non-negative emission datasets using objective selection and filtering methods. The results include lower minimum emission thresholds for all power grids and fewer false positives. The new method provides more robust emission quantification in the face of data uncertainty, going beyond traditional plume identification and background subtraction.
Lingxiao Lu, Jason Blake Cohen, Kai Qin, Xiaolu Li, and Qin He
Atmos. Chem. Phys., 25, 2291–2309, https://doi.org/10.5194/acp-25-2291-2025, https://doi.org/10.5194/acp-25-2291-2025, 2025
Short summary
Short summary
This study applies an approach that assimilates NO2 vertical column densities from TROPOMI in a mass-conserving manner and inverts daily NOx emissions, presented over rapidly changing regions in China. Source attribution is quantified by the local thermodynamics of the combustion temperature (NOx/NO2). Emission results identify sources which do not exist in the a priori datasets, especially medium industrial sources located next to the Yangtze River.
Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Pravash Tiwari, and Jason Blake Cohen
Earth Syst. Sci. Data, 16, 5287–5310, https://doi.org/10.5194/essd-16-5287-2024, https://doi.org/10.5194/essd-16-5287-2024, 2024
Short summary
Short summary
Satellites have brought new opportunities for monitoring atmospheric NO2, although the results are limited by clouds and other factors, resulting in missing data. This work proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms of traditional methods as well as variance maximization and demonstrate a good ability to reproduce known polluted and clean areas around the world.
Qiansi Tu, Frank Hase, Kai Qin, Jason Blake Cohen, Farahnaz Khosrawi, Xinrui Zou, Matthias Schneider, and Fan Lu
Atmos. Chem. Phys., 24, 4875–4894, https://doi.org/10.5194/acp-24-4875-2024, https://doi.org/10.5194/acp-24-4875-2024, 2024
Short summary
Short summary
Four-year satellite observations of XCH4 are used to derive CH4 emissions in three regions of China’s coal-rich Shanxi province. The wind-assigned anomalies for two opposite wind directions are calculated, and the estimated emission rates are comparable to the current bottom-up inventory but lower than the CAMS and EDGAR inventories. This research enhances the understanding of emissions in Shanxi and supports climate mitigation strategies by validating emission inventories.
Kai Qin, Wei Hu, Qin He, Fan Lu, and Jason Blake Cohen
Atmos. Chem. Phys., 24, 3009–3028, https://doi.org/10.5194/acp-24-3009-2024, https://doi.org/10.5194/acp-24-3009-2024, 2024
Short summary
Short summary
We compute CH4 emissions and uncertainty on a mine-by-mine basis, including underground, overground, and abandoned mines. Mine-by-mine gas and flux data and 30 min observations from a flux tower located next to a mine shaft are integrated. The observed variability and bias correction are propagated over the emissions dataset, demonstrating that daily observations may not cover the range of variability. Comparisons show both an emissions magnitude and spatial mismatch with current inventories.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Xiaolu Li, Jason Blake Cohen, Kai Qin, Hong Geng, Xiaohui Wu, Liling Wu, Chengli Yang, Rui Zhang, and Liqin Zhang
Atmos. Chem. Phys., 23, 8001–8019, https://doi.org/10.5194/acp-23-8001-2023, https://doi.org/10.5194/acp-23-8001-2023, 2023
Short summary
Short summary
Remotely sensed NO2 and surface NOx are combined with a mathematical method to estimate daily NOx emissions. The results identify new sources and improve existing estimates. The estimation is driven by three flexible factors: thermodynamics of combustion, chemical loss, and atmospheric transport. The thermodynamic term separates power, iron, and cement from coking, boilers, and aluminum. This work finds three causes for the extremes: emissions, UV radiation, and transport.
Qiansi Tu, Frank Hase, Zihan Chen, Matthias Schneider, Omaira García, Farahnaz Khosrawi, Shuo Chen, Thomas Blumenstock, Fang Liu, Kai Qin, Jason Cohen, Qin He, Song Lin, Hongyan Jiang, and Dianjun Fang
Atmos. Meas. Tech., 16, 2237–2262, https://doi.org/10.5194/amt-16-2237-2023, https://doi.org/10.5194/amt-16-2237-2023, 2023
Short summary
Short summary
Four-year TROPOMI observations are used to derive tropospheric NO2 emissions in two mega(cities) with high anthropogenic activity. Wind-assigned anomalies are calculated, and the emission rates and spatial patterns are estimated based on a machine learning algorithm. The results are in reasonable agreement with previous studies and the inventory. Our method is quite robust and can be used as a simple method to estimate the emissions of NO2 as well as other gases in other regions.
Cited articles
Achtemeier, G. L., Goodrick, S. A., Liu, Y., Garcia-Menendez, F., Hu. Y.,
and Odman, M. T.: Modeling Smoke Plume-Rise and Dispersion from Southern
United States Prescribed Burns with Daysmoke, Atmosphere, 2, 358–388,
https://doi.org/10.3390/atmos2030358, 2011.
Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A, R. J., Sneep, M., van den Oord, G. H. J., Levelt, P. F., Stammes, P., Gleason, J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric NO2 from OMI, Atmos. Chem. Phys., 7, 2103–2118, https://doi.org/10.5194/acp-7-2103-2007, 2007
Briggs, G. A.: A plume rise model compared with observations, Journal of the Air Pollution Control Association, 15, 433–438, https://doi.org/10.1080/00022470.1965.10468404, 1965.
Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015.
Chew, B. N., Campbell, J. R., Salinas, S. V., Chang, C., W., Reid, J. S.,
Welton, E. J., and Liew, S. C.: Aerosol particle vertical distributions and
optical properties over Singapore, Atmos. Environ., 79, 599–613,
https://doi.org/10.1016/j.atmosenv.2013.06.026, 2013.
Cohen, J. B.: Quantifying the occurrence and magnitude of the Southeast
Asian fire climatology, Environ. Res. Lett., 9, 114018,
https://doi.org/10.1088/1748-9326/9/11/114018, 2014.
Cohen, J. B. and Prinn, R. G.: Development of a fast, urban chemistry metamodel for inclusion in global models, Atmos. Chem. Phys., 11, 7629–7656, https://doi.org/10.5194/acp-11-7629-2011, 2011.
Cohen, J. B. and Wang, C.: Estimating global black carbon emissions using a
top-down Kalman Filter approach, J. Geophys. Res.-Atmos., 119, 307–323,
https://doi.org/10.1002/2013JD019912, 2014.
Cohen, J. B., Prinn, R. G., and Wang, C.: The Impact of detailed urban-scale processing on the composition, distribution, and radiative forcing of anthropogenic aerosols, Geophys. Res. Lett., 38, L10808, https://doi.org/10.1029/2011GL047417, 2011.
Cohen, J. B., Lecoeur, E., and Hui Loong Ng, D.: Decadal-scale relationship between measurements of aerosols, land-use change, and fire over Southeast Asia, Atmos. Chem. Phys., 17, 721–743, https://doi.org/10.5194/acp-17-721-2017, 2017.
Cohen, J. B., Ng, D. H. L., Lim, A. W. L., and Chua, X. R.: Vertical distribution of aerosols over the Maritime Continent during El Niño, Atmos. Chem. Phys., 18, 7095–7108, https://doi.org/10.5194/acp-18-7095-2018, 2018.
Damoah, R., Spichtinger, N., Servranckx, R., Fromm, M., Eloranta, E. W., Razenkov, I. A., James, P., Shulski, M., Forster, C., and Stohl, A.: A case study of pyro-convection using transport model and remote sensing data, Atmos. Chem. Phys., 6, 173–185, https://doi.org/10.5194/acp-6-173-2006, 2006.
Deeter, M. N., Edwards, D. P., Francis, G. L., Gille, J. C., Martínez-Alonso, S., Worden, H. M., and Sweeney, C.: A climate-scale satellite record for carbon monoxide: the MOPITT Version 7 product, Atmos. Meas. Tech., 10, 2533–2555, https://doi.org/10.5194/amt-10-2533-2017, 2017.
DeWitt, H. L., Gasore, J., Rupakheti, M., Potter, K. E., Prinn, R. G., Ndikubwimana, J. D. D., Nkusi, J., and Safari, B.: Seasonal and diurnal variability in O3, black carbon, and CO measured at the Rwanda Climate Observatory, Atmos. Chem. Phys., 19, 2063–2078, https://doi.org/10.5194/acp-19-2063-2019, 2019.
Field, R. D., van der Werf, G. R., and Shen S. P. P.: Human amplification of
drought-induced biomass burning in Indonesia since 1960, Nat. Geosci., 2, 185–188, https://doi.org/10.1038/ngeo443, 2009.
Flower, V. J. B. and Kahn, R. A.: Assessing the altitude and dispersion of
volcanic plumes using MISR multi-angle imaging from space: Sixteen years of
volcanic activity in the Kamchatka Peninsula, Russia, J. Volcanol. Geoth.
Res., 337, 1–15, https://doi.org/10.1016/j.jvolgeores.2017.03.010, 2017.
Freeborn, P. H., Wooster, M. J., Roy, D. P., and Cochrane, M. A.:
Quantification of MODIS fire radiative power (FRP) measurement uncertainty
for use in satellite-based active fire characterization and biomass burning
estimation, Geophys. Res. Lett., 41, 1988–1994, https://doi.org/10.1002/2013GL59086,
2014.
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.
Generoso, S., Bey, I., Atti, J.-L., and Bron, F.-M.: A satellite- and
model-based assessment of the 2003 Russian fires: Impact on the Arctic
region, J. Geophys. Res., 112, D15302, https://doi.org/10.1029/2006JD008344, 2007.
Giglio, L., Csiszar, I., and Justice, C. O.: Global distribution and
seasonality of active fires as observed with the Terra and Aqua Moderate
Resolution Imaging Spectroradiometer (MODIS) sensors, J. Geophys.
Res., 111, G02016, https://doi.org/10.1029/2005JG000142, 2006.
GMAO (Global Modeling and Assimilation Office): MERRA-2 inst3_3d_aer_Nv: 3d, 3-Hourly, Instantaneous, Model-Level, Assimilation, Aerosol Mixing Ratio V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/LTVB4GPCOTK2, 2015.
Gonzalez-Alonso, L., Val Martin, M., and Kahn, R. A.: Biomass-burning smoke heights over the Amazon observed from space, Atmos. Chem. Phys., 19, 1685–1702, https://doi.org/10.5194/acp-19-1685-2019, 2019.
Grandey, B. S., Rothenberg, D., Avramov, A., Jin, Q., Lee, H.-H., Liu, X., Lu, Z., Albani, S., and Wang, C.: Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG, Atmos. Chem. Phys., 18, 15783–15810, https://doi.org/10.5194/acp-18-15783-2018, 2018.
Gunturu, U. B.: Aerosol-Cloud Interactions: A New Perspective in Precipitation Enhancement, PhD thesis, Massachusetts Institute of Technology, 2010.
Guo, J., Deng, M., Lee, S. S., Wang, F., Li, Z., Zhai, P., Liu, H., Lv, W.,
Yao W., and Li X.: Delaying precipitation and lightning by air pollution
over the Pearl River Delta. Part I: Observational analyses, J. Geophys.
Res., 121, 6472–6488, https://doi.org/10.1002/2015JD023257, 2016.
Guo, J., Li, Y., Cohen, J. B., Li, J., Chen, D., Xu, H., Liu, L., Yin, J.,
Hu, K., and Zhai, P.: Shift in the temporal trend of boundary layer height
trend in China using long-term (1979–2016) radiosonde data, Geophys.
Res. Lett., 46, 6080–6089, https://doi.org/10.1029/2019GL082666, 2019.
He, Q., Qin, K., Cohen, J. B., Loyola, D., Li, D., Shi, J., and Xue, Y.: Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements, Environ. Res. Lett., online first, https://doi.org/10.1088/1748-9326/abc7df, 2020
Heald, C. L., Jacob, D. J., Jones, D. B. A., Palmer, P. I., Logan, J. A., Streets, D. G., Sachse, G. W., Gille, J. C., Hoffman, R. N., and Nehrkorn, T.: Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P) observations to estimate Asian sources of carbon monoxide, J. Geophys. Res., 109, D23306, https://doi.org/10.1029/2004JD005185, 2004.
Husar, R. B., Prospero, J. M., and Stowe, L. L.: Characterization of
tropospheric aerosols over the oceans with the NOAA advanced very high
resolution radiometer optical thickness operational product, J. Geophys.
Res., 102, 16889–16909, https://doi.org/10.1029/96jd04009, 1997.
Ichoku, C. and Ellison, L.: Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements, Atmos. Chem. Phys., 14, 6643–6667, https://doi.org/10.5194/acp-14-6643-2014, 2014
Ichoku, C., Giglio, L., Wooster, M., and Remer, L.: Global characterization
of biomass-burning patterns using satellite measurements of fire radiative
energy, Remote Sens. Environ., 112, 2950–2962, https://doi.org/10.1016/j.rse.2008.02.009, 2008.
Jost, H., Drdla, K., Stohl, A., Pfister, L., Loewenstein, M., Lopez, J. P.,
Hudson, P. K., Murphy, D. M., Cziczo, D. J., Fromm, M., Bui, T. P.,
Dean-Day, J., Gerbig, C., Mahoney, M. J., Richard, E. C., Spichtinger, N.,
Pittman, V. J., Weinstock, E. M., Wilson, J. C., and Xueref, I.: In-situ
observations of mid-latitude forest fire plumes deep in the stratosphere,
Geophys. Res. Lett., 31, L11101, https://doi.org/10.1029/2003GL019253, 2004.
Kahn, R.: A Global Perspective on Wildfires, Eos, 101, https://doi.org/10.1029/2020EO138260, 2020.
Kahn, R. A., Li, W. H., Moroney, C., Diner, D. J., Martonchik, J. V., and
Fishbein, E.: Aerosol source plume physical characteristics from space-based
multiangle imaging, J. Geophys. Res., 112, D11205, https://doi.org/10.1029/2006JD007647, 2007.
Kahn, R. A., Chen, Y., Nelson, D. L., Leung, F. Y., Li, Q., Diner, D. J.,
and Logan, J. A..: Wildfire smoke injection heights: Two perspectives from
space, Geophys. Res. Lett., 35, L04809, https://doi.org/10.1029/2007GL032165, 2008.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: NCEP/NCAR 40-year
reanalysis project, B. Am. Meteorol. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kauffman, J. B., Steele, M. D., Cummings D. L., and Jaramillo V. J.: Biomass
dynamics associated with deforestation, fire, and, conversion to cattle
pasture in a Mexican tropical dry forest, Forest Ecol. Manag., 176, 1–12,
https://doi.org/10.1016/s0378-1127(02)00227-x, 2003.
Kim, D., Wang, C., Ekman, A. M. L., Barth, M. C., and Rasch, P.:
Distribution and direct radiative forcing of carbonaceous and sulfate
aerosols in an interactive size-resolving aerosol-climate model, J. Geophys.
Res., 113, D16309, https://doi.org/10.1029/2007JD009756, 2008.
Labonne, M., Breìon, F.-M., and Chevallier, F.: Injection height of biomass
burning aerosols as seen from a spaceborne lidar, Geophys. Res. Lett., 34, L11806, https://doi.org/10.1029/2007GL029311, 2007.
Lamsal, L. N., Martin, R. V., Padmanabhan, A., van Donkelaar, A., Zhang, Q.,
Sioris, C. E., Chance, K., Kurosu, T. P., and Newchurch, M. J.: Application
of satellite observations for timely updates to global anthropogenic NOx emission inventories, Geophys. Res. Lett., 38, L05810, https://doi.org/10.1029/2010GL046476, 2011.
Lamsal, L. N., Krotkov, N. A., Marchenko, S. V., Joiner, J., Oman, L., Vasilkov, A., Fisher, B., Qin, W., Yang, E.-S., Fasnacht, Z., Choi, S., Leonard, P., and Haffner, D.: OMI/Aura NO2 Tropospheric, Stratospheric & Total Columns MINDS Daily L3 Global Gridded 0.25 degree x 0.25 degree, NASA Goddard Space Flight Center, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/MEASURES/MINDS/DATA301, 2020.
Leung, F. Y. T., Logan, J. A., Park, R., Hyer, E., Kasischke, E., Streets,
D., and Yurganov, L.: Impacts of enhanced biomass burning in the boreal
forests in 1998 on tropospheric chemistry and the sensitivity of model
results to the injection height to emissions, J. Geophys. Res., 112, D10313,
https://doi.org/10.1029/2006JD008132, 2007.
Levelt, P. F., van den Oord, G. H. J., Dobber, M. R., Malkki, A., Visser,
H., de Vries, J., Stammes, P., Lundell, J. O. V., and Saari, H.: The ozone
monitoring instrument, IEEE T. Geosci. Remote, 44, 1093–1101,
https://doi.org/10.1109/TGRS.2006.872333, 2006.
Lin, C. Y., Cohen, J. B., Wang, S., and Lan, R. Y.: Application of a
combined standard deviation and mean based approach to MOPITT CO column data, and resulting improved representation of biomass burning and urban air pollution sources, Remote Sens. Environ., 241, 11720,
https://doi.org/10.1016/j.rse.2020.111720, 2020a.
Lin, C. Y., Cohen, J. B., Wang, S., Lan, R. Y., and Deng W. Z.: A new
perspective on the spatial, temporal, and vertical distribution of biomass
burning: quantifying a significant increase in CO emissions, Environ. Res. Lett., 15 104091, https://doi.org/10.1088/1748-9326/abaa7a, 2020b.
Lin, N. H., Sayer, A. M., Wang, S. H., Loftus, A. M., Hsiao, T. C., Sheu, G.
R., 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.
Mims, S. R., Kahn, R. A., Moroney, C. M., Gaitley, B. J., Nelson, D. L., and
Garay, M. J.: MISR stereo-heights of grassland fire smoke plumes in
Australia, IEEE T. Geosci. Remote, 48, 25–35,
https://doi.org/10.1109/TGRS.2009.2027114, 2010.
Ming, Y., Ramaswamy, V., and Persad, G.: Two opposing effects of absorbing
aerosols on global-mean precipitation, Geophys. Res. Lett., 37, L13701,
https://doi.org/10.1029/2010GL042895, 2010.
NASA/LARC/SD/ASDC: MOPITT CO gridded daily means (Near and Thermal Infrared Radiances) V008, NASA Langley Atmospheric Science Data Center DAAC, https://doi.org/10.5067/TERRA/MOPITT/MOP03J_L3.008, 2000.
Nelson, D. L., Garay M. J., Kahn R. A., and Dunst B. A.: Stereoscopic Height
and Wind Retrievals for Aerosol Plumes with the MISR INteractive eXplorer
(MINX), Remote Sens., 5, 4593–4628, https://doi.org/10.3390/rs5094593, 2013.
Nelson, D., Val, S.,Kahn, R., Koeberlein, E., Tosca, M., Diner, D., and Lawshe, C.: MISR plume height project, available at: https://misr.jpl.nasa.gov/getData/accessData/ (last access: 1 September 2019), 2015.
NOAA/OAR/ESRL PSL: NCEP/NCAR Reanalysis 1: Summary, NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, available at: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html (last access: 1 September 2019), 1996.
Palacios-Orueta, A., Chuvieco, E., Parra, A., and Carmona-Moreno, C.: Biomass Burning Emissions: A Review of Models Using Remote-Sensing Data, Environ. Monit. Assess., 104, 189–209, https://doi.org/10.1007/s10661-005-1611-y, 2005.
Paugam, R., Wooster, M., Freitas, S., and Val Martin, M.: A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models, Atmos. Chem. Phys., 16, 907–925, https://doi.org/10.5194/acp-16-907-2016, 2016.
Petersen, W. and Rutledge, S.: Regional variability in tropical convection:
observations from TRMM, J. Climate, 14, 3566–3586,
https://doi.org/10.1175/1520-0469(1989)046<0037:OOLFOI>2.0.CO;2, 2001.
Petrenko, M., Kahn, R. A., Chin, M., Soja, A. J., Kucsera, T., and
Harshvardhan: The use of satellite-measured aerosol optical depth to
constrain biomass burning emissions source strength in the global model
GOCART, J. Geophys. Res., 117, D18212, https://doi.org/10.1029/2012JD017870, 2012.
Pfister, G. G., Wiedinmyer, C., and Emmons, L. K.: Impacts of the fall 2007
California wildfires on surface ozone: Integrating local observations with
global model simulations, Geophys. Res. Lett., 35, L19814, https://doi.org/10.1029/2008GL034747, 2008.
Ramanathan, V., Ramana, M. V., Roberts, G., Kim, D., Corrigan, C., Chung, C., and Winker, D.: Warming trends in Asia amplified by brown cloud solar absorption, Nature, 448, 575–578, https://doi.org/10.1038/nature06019, 2007.
Randles, C., Da Silva, A., Buchard, V., Colarco, P., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., and Hair, J.: The MERRA-2 aerosol reanalysis, 1980 onward. Part I: system description and data assimilation evaluation, J. Climate, 30, 6823–6850, 2017.
Reid, J. S., Hyer, E. J., Johnson, R. S., Holben, B. N., Yokelson, R. J., Zhang, J., Campbell, J. R., Christopher, S. A., Di Girolamo, L., Giglio, L., Holz, R. E., Kearney, C., Miettinen, J., Reid, E. A., Turk, F. J., Wang, J., Xian, P., Zhao, G., Balasubramanian, R., Chew, B. N., Janjai, S., Lagrosas, N., Lestari, P., Lin, N.-H., Mahmud, M., Nguyen, A. X., Norris, B., Oanh, N. T. K., Oo, M., Salinas, S. V., Welton, E. J., and Liew, S. C.: Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program, Atmos. Res., 122, 403–468, https://doi.org/10.1016/j.atmosres.2012.06.005, 2013
Rogers, R. R., Hostetler, C. A., Hair, J. W., Ferrare, R. A., Liu, Z., Obland, M. D., Harper, D. B., Cook, A. L., Powell, K. A., Vaughan, M. A., and Winker, D. M.: Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar, Atmos. Chem. Phys., 11, 1295–1311, https://doi.org/10.5194/acp-11-1295-2011, 2011.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, New York, 1326 pp., ISBN: 0-471-17815-2, 1998.
Singh, N., Banerjee, T., Raju, M. P., Deboudt, K., Sorek-Hamer, M., Singh, R. S., and Mall, R. K.: Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain, Atmos. Chem. Phys., 18, 14197–14215, https://doi.org/10.5194/acp-18-14197-2018, 2018
Sofiev, M., Ermakova, T., and Vankevich, R.: Evaluation of the smoke-injection height from wild-land fires using remote-sensing data, Atmos. Chem. Phys., 12, 1995–2006, https://doi.org/10.5194/acp-12-1995-2012, 2012.
Spracklen, D. V., Mickley, L. J., Logan, J. A., Hudman, R. C., Yevich, R.,
Flannigan, M. D., and Westerling A. L.: Impacts of climate change from 2000
to 2050 on wildfire activity and carbonaceous aerosol concentrations in the
western United States, J. Geophys. Res.,114, D20301,
https://doi.org/10.1029/2008JD010966, 2009.
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols
on convective clouds and precipitation, Rev. Geophys., 50, RG2001,
https://doi.org/10.1029/2011RG000369, 2012.
Tosca, M. G., Randerson, J. T., Zender, C. S., Nelson, D. L., Diner, D. J.,
and Logan, J. A.: Dynamics of fire plumes and smoke clouds associated with
peat and deforestation fires in Indonesia, J. Geophys. Res., 116, D08207,
https://doi.org/10.1029/2010JD015148, 2011.
Trentmann, J., Luderer, G., Winterrath, T., Fromm, M. D., Servranckx, R., Textor, C., Herzog, M., Graf, H.-F., and Andreae, M. O.: Modeling of biomass smoke injection into the lower stratosphere by a large forest fire (Part I): reference simulation, Atmos. Chem. Phys., 6, 5247–5260, https://doi.org/10.5194/acp-6-5247-2006, 2006.
Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P. J., Artaxo, P., Bahadur, R., Balkanski, Y., Bauer, S. E., Bellouin, N., Benedetti, A., Bergman, T., Berntsen, T. K., Beukes, J. P., Bian, H., Carslaw, K. S., Chin, M., Curci, G., Diehl, T., Easter, R. C., Ghan, S. J., Gong, S. L., Hodzic, A., Hoyle, C. R., Iversen, T., Jathar, S., Jimenez, J. L., Kaiser, J. W., Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y. H., Lin, G., Liu, X., Luo, G., Ma, X., Mann, G. W., Mihalopoulos, N., Morcrette, J.-J., Müller, J.-F., Myhre, G., Myriokefalitakis, S., Ng, N. L., O'Donnell, D., Penner, J. E., Pozzoli, L., Pringle, K. J., Russell, L. M., Schulz, M., Sciare, J., Seland, Ø., Shindell, D. T., Sillman, S., Skeie, R. B., Spracklen, D., Stavrakou, T., Steenrod, S. D., Takemura, T., Tiitta, P., Tilmes, S., Tost, H., van Noije, T., van Zyl, P. G., von Salzen, K., Yu, F., Wang, Z., Wang, Z., Zaveri, R. A., Zhang, H., Zhang, K., Zhang, Q., and Zhang, X.: The AeroCom evaluation and intercomparison of organic aerosol in global models, Atmos. Chem. Phys., 14, 10845–10895, https://doi.org/10.5194/acp-14-10845-2014, 2014.
Turquety, S., Logan, J. A., Jacob, D. J., Hudman, R. C., Leung, F. Y.,
Heald, C. L., Yantosca, R. M., Wu, S., Emmons, L. K., Edwards, D. P., and
Sachse, G. W.: Inventory of boreal fire emissions for North America in 2004:
Importance of peat burning and pyroconvective injection, J. Geophys.
Res., 112, D12S03, https://doi.org/10.1029/2006JD007281, 2007.
Urbanski, S.: Wildland fire emissions, carbon, and climate: Emission
factors, Forest Ecol. Manage., 317, 51–60, https://doi.org/10.1016/j.foreco.2013.05.045, 2014.
Val Martin, M., Logan, J. A., Kahn, R. A., Leung, F.-Y., Nelson, D. L., and Diner, D. J.: Smoke injection heights from fires in North America: analysis of 5 years of satellite observations, Atmos. Chem. Phys., 10, 1491–1510, https://doi.org/10.5194/acp-10-1491-2010, 2010.
Val Martin, M., Kahn, R. A., Logan, J. A., Paugam, R., Wooster, M., and
Ichoku, C.: Space-based observational constraints for 1-D fire smoke
plume-rise models, J. Geophys. Res., 117, D22204, https://doi.org/10.1029/2012JD018370,
2012.
Val Martin, M., Kahn, R. A., and Tosca, M.: A Global Analysis of Wildfire
Smoke Injection Heights Derived from Space-Based Multi-Angle Imaging, Remote
Sens., 10, 1609, https://doi.org/10.3390/rs10101609, 2018.
Vernon, C. J., Bolt, R., Canty, T., and Kahn, R. A.: The impact of MISR-derived injection height initialization on wildfire and volcanic plume dispersion in the HYSPLIT model, Atmos. Meas. Tech., 11, 6289–6307, https://doi.org/10.5194/amt-11-6289-2018, 2018.
Wang, C.: The sensitivity of tropical convective precipitation to the direct radiative forcings of black carbon aerosols emitted from major regions, Ann. Geophys., 27, 3705–3711, https://doi.org/10.5194/angeo-27-3705-2009, 2009.
Wang, S. and Cohen, J.: model results for PRM and RM, Dataset, figshare, https://doi.org/10.6084/m9.figshare.10252526.v1, 2019.
Wang, S. and Cohen, J.: MERRA data, Dataset, figshare, https://doi.org/10.6084/m9.figshare.12386135.v1, 2020.
Winker, D. M., Tackett, J. L., Getzewich, B. J., Liu, Z., Vaughan, M. A., and Rogers, R. R.: The global 3-D distribution of tropospheric aerosols as characterized by CALIOP, Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, 2013.
Yu, P. F., Toon, O. B., Bardeen, C. G., Zhu, Y. Q., Rosenlof, K. H.,
Portmann, R. W., Thornberry, T. D., Gao, R. S., Davis, S. M., Wolf, E. T.,
Gouw, J., Peterson, D. A., Fromm, M. D., and Robock, A.: Black carbon lofts
wildfire smoke high into the stratosphere to form a persistent plume,
Science, 365, 587–590, https://doi.org/10.1126/science.aax1748, 2019.
Zhu, L., Val Martin, M., Gatti, L. V., Kahn, R., Hecobian, A., and Fischer, E. V.: Development and implementation of a new biomass burning emissions injection height scheme (BBEIH v1.0) for the GEOS-Chem model (v9-01-01), Geosci. Model Dev., 11, 4103–4116, https://doi.org/10.5194/gmd-11-4103-2018, 2018
Short summary
We analyze global measurements of aerosol height from fires. A plume rise model reproduces measurements with a low bias in five regions, while a statistical model based on satellite measurements of trace gasses co-emitted from the fires reproduces measurements without bias in eight regions. We propose that the magnitude of the pollutants emitted may impact their height and subsequent downwind transport. Using satellite data allows better modeling of the global aerosol distribution.
We analyze global measurements of aerosol height from fires. A plume rise model reproduces...
Altmetrics
Final-revised paper
Preprint