Articles | Volume 21, issue 6
https://doi.org/10.5194/acp-21-4319-2021
© Author(s) 2021. 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-21-4319-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2021
Research article |
| 22 Mar 2021
| 22 Mar 2021
A revised mineral dust emission scheme in GEOS-Chem: improvements in dust simulations over China
Rong Tian
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD)/Key Laboratory for
Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing
University of Information Science & Technology, Nanjing 210044, China
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD)/Key Laboratory for
Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing
University of Information Science & Technology, Nanjing 210044, China
Jianqi Zhao
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD)/Key Laboratory for
Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing
University of Information Science & Technology, Nanjing 210044, China
Related authors
Kun Wang, Xiaoyan Ma, Rong Tian, and Fangqun Yu
Atmos. Chem. Phys., 23, 4091–4104, https://doi.org/10.5194/acp-23-4091-2023, https://doi.org/10.5194/acp-23-4091-2023, 2023
Short summary
Short summary
From 12 March to 6 April 2016 in Beijing, there were 11 typical new particle formation days, 13 non-event days, and 2 undefined days. We first analyzed the favorable background of new particle formation in Beijing and then conducted the simulations using four nucleation schemes based on a global chemistry transport model (GEOS-Chem) to understand the nucleation mechanism.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Tong Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2555, https://doi.org/10.5194/egusphere-2025-2555, 2025
Short summary
Short summary
We use the WRF-Chem-SBM model to investigate how the meteorological conditions impact aerosol-cloud interactions (ACI) under different pollution regimes for marine liquid-phase clouds. Our findings highlight the changes in aerosol effects on clouds and precipitation with thermodynamic conditions, as well as the sensitivity of ACI to meteorological conditions under different pollution regimes, which help to advance the understanding of ACI.
Jianqi Zhao, Xiaoyan Ma, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3662, https://doi.org/10.5194/egusphere-2024-3662, 2024
Preprint archived
Short summary
Short summary
We conduct a comparative analysis of aerosol-cloud responses in liquid-phase clouds under different aerosol and meteorological conditions based on simulations using the WRF-Chem-SBM model. Our findings highlight the different effects of aerosols on clouds and precipitation, as well as variations in the roles of aerosol and meteorological factors influencing aerosol-cloud interactions, in different environment.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
Atmos. Chem. Phys., 24, 9101–9118, https://doi.org/10.5194/acp-24-9101-2024, https://doi.org/10.5194/acp-24-9101-2024, 2024
Short summary
Short summary
We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean in winter based on the WRF-Chem–SBM model, which couples a spectral-bin microphysics scheme and an online aerosol module. Our study highlights the differences in aerosol–cloud interactions between land and ocean and between precipitation clouds and non-precipitation clouds, and it differentiates and quantifies their underlying mechanisms.
Tong Sha, Siyu Yang, Qingcai Chen, Liangqing Li, Xiaoyan Ma, Yan-Lin Zhang, Zhaozhong Feng, K. Folkert Boersma, and Jun Wang
Atmos. Chem. Phys., 24, 8441–8455, https://doi.org/10.5194/acp-24-8441-2024, https://doi.org/10.5194/acp-24-8441-2024, 2024
Short summary
Short summary
Using an updated soil reactive nitrogen emission scheme in the Unified Inputs for Weather Research and Forecasting coupled with Chemistry (UI-WRF-Chem) model, we investigate the role of soil NO and HONO (Nr) emissions in air quality and temperature in North China. Contributions of soil Nr emissions to O3 and secondary pollutants are revealed, exceeding effects of soil NOx or HONO emission. Soil Nr emissions play an important role in mitigating O3 pollution and addressing climate change.
Kun Wang, Xiaoyan Ma, Rong Tian, and Fangqun Yu
Atmos. Chem. Phys., 23, 4091–4104, https://doi.org/10.5194/acp-23-4091-2023, https://doi.org/10.5194/acp-23-4091-2023, 2023
Short summary
Short summary
From 12 March to 6 April 2016 in Beijing, there were 11 typical new particle formation days, 13 non-event days, and 2 undefined days. We first analyzed the favorable background of new particle formation in Beijing and then conducted the simulations using four nucleation schemes based on a global chemistry transport model (GEOS-Chem) to understand the nucleation mechanism.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
EGUsphere, https://doi.org/10.5194/egusphere-2023-331, https://doi.org/10.5194/egusphere-2023-331, 2023
Preprint archived
Short summary
Short summary
We improve the ability of WRF-Chem model to simulate aerosol-cloud physical and chemical processes by coupling a spectral-bin cloud microphysics scheme and online aerosol module, and consequently explore the aerosol-cloud interactions over eastern China and its adjacent ocean in boreal winter. Our study highlights the differences in aerosol-cloud interactions between land and ocean, precipitation clouds and non-precipitation clouds, and differentiates and quantifies their underlying mechanisms.
Yiwen Hu, Zengliang Zang, Xiaoyan Ma, Yi Li, Yanfei Liang, Wei You, Xiaobin Pan, and Zhijin Li
Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, https://doi.org/10.5194/acp-22-13183-2022, 2022
Short summary
Short summary
This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
Short summary
Short summary
Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Cited articles
Alfaro, S. C. and Gomes, L.: Modelling mineral aerosol production by wind
erosion: Emission intensities and aerosol size distributions in source
areas, J. Geophys. Res., 106, 18075–18084,
https://doi.org/10.1029/2000JD900339, 2001.
Astitha, M., Lelieveld, J., Abdel Kader, M., Pozzer, A., and de Meij, A.: Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties, Atmos. Chem. Phys., 12, 11057–11083, https://doi.org/10.5194/acp-12-11057-2012, 2012.
Bonan, G. B.: A land surface model (LSM version 1.0) for ecological,
hydrological, and atmospheric studies: Technical description and user's
guide, Tech. Rep. NCAR/TN-417+STR, Natl. Cent. for Atmos. Res., Boulder,
Colo., USA, 1996.
Chen, D., Wang, Y., McElroy, M. B., He, K., Yantosca, R. M., and Le Sager, P.: Regional CO pollution and export in China simulated by the high-resolution nested-grid GEOS-Chem model, Atmos. Chem. Phys., 9, 3825–3839, https://doi.org/10.5194/acp-9-3825-2009, 2009.
Chen, L., Gao, Y., Zhang, M., Fu, J. S., Zhu, J., Liao, H., Li, J., Huang, K., Ge, B., Wang, X., Lam, Y. F., Lin, C.-Y., Itahashi, S., Nagashima, T., Kajino, M., Yamaji, K., Wang, Z., and Kurokawa, J.: MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia, Atmos. Chem. Phys., 19, 11911–11937, https://doi.org/10.5194/acp-19-11911-2019, 2019.
Chen, S., Zhao, C., Qian, Y., Leung, L. R., Huang, J., Huang, Z., Bi, J.,
Zhang, W., Shi, J., Yang, L., Li, D., and Li, J.: Regional modeling of dust
mass balance and radiative forcing over East Asia using WRF-Chem, Aeolian
Res., 15, 15–30, https://doi.org/10.1016/j.aeolia.2014.02.001, 2014.
Chen, S., Huang, J., Qian, Y., Zhao, C., Kang, L., Yang, B., Wang, Y., Liu,
Y., Yuan, T., Wang, T., Ma, X., and Zhang, G.: An overview of mineral dust
modeling over East Asia, J. Meteorol. Res., 31, 633–653, https://doi.org/10.1007/s13351-017-6142-2, 2017.
Cheng, T., Peng, Y., Feichter, J., and Tegen, I.: An improvement on the dust emission scheme in the global aerosol-climate model ECHAM5-HAM, Atmos. Chem. Phys., 8, 1105–1117, https://doi.org/10.5194/acp-8-1105-2008, 2008.
Cheng, Y., Zheng, G., Wei, C., Mu, Q., Zheng, B., Wang, Z., Gao, M., Zhang,
Q., He, K., and Carmichael, G.: Reactive nitrogen chemistry in aerosol water
as a source of sulfate during haze events in China, Science Advances, 2,
e1601530, https://doi.org/10.1126/sciadv.1601530, 2016.
Chin, M., Diehl, T., Tan, Q., Prospero, J. M., Kahn, R. A., Remer, L. A., Yu, H., Sayer, A. M., Bian, H., Geogdzhayev, I. V., Holben, B. N., Howell, S. G., Huebert, B. J., Hsu, N. C., Kim, D., Kucsera, T. L., Levy, R. C., Mishchenko, M. I., Pan, X., Quinn, P. K., Schuster, G. L., Streets, D. G., Strode, S. A., Torres, O., and Zhao, X.-P.: Multi-decadal aerosol variations from 1980 to 2009: a perspective from observations and a global model, Atmos. Chem. Phys., 14, 3657–3690, https://doi.org/10.5194/acp-14-3657-2014, 2014.
Darmenova, K., Sokolik, I. N., Shao, Y., Marticorena, B., and Bergametti,
G.: Development of a physically based dust emission module within the
Weather Research and Forecasting (WRF) model: Assessment of dust emission
parameterizations and input parameters for source regions in Central and
East Asia, J. Geophys. Res., 114, D14201, https://doi.org/10.1029/2008JD011236, 2009.
DeMott, P. J., Sassen, K., Poellot, M. R., Baumgardner, D., Rogers, D. C.,
Brooks, S. D., Prenni, A. J., and Kreidenweis, S. M.: African dust aerosols
as atmospheric ice nuclei, Geophys. Res. Lett., 30, 1732,
https://doi.org/10.1029/2003gl017410, 2003.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D.,
Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rodgers, D. C.:
Predicting global atmospheric ice nuclei distributions and their impacts on
climate, P. Natl. Acad. Sci. USA, 107, 11217–11222,
https://doi.org/10.1073/pnas.0910818107, 2010.
Fairlie, T. D., Jacob, D. J., and Park, R. J.: The impact of transpacific
transport of mineral dust in the United States, Atmos. Environ., 41,
1251–1266, https://doi.org/10.1016/j.atmosenv.2006.09.048, 2007.
FAO: The Digitized Soil Map of the World Including Derived Soil Properties (version 3.6), FAO, Rome, Italy, 1995.
FAO: The Digitized Soil Map of the World Including Derived Soil Properties (version 3.6), FAO, Rome, Italy, 2003.
Fécan, F., Marticorena, B., and Bergametti, G.: Parametrization of the
increase of the aeolian erosion threshold wind friction velocity due to soil
moisture for arid and semi-arid areas, Ann. Geophys., 17, 149–157,
https://doi.org/10.1007/s00585-999-0149-7, 1999.
Foroutan, H., Young, J., Napelenok, S., Ran, L., Appel, K. W., Gilliam, R.
C., and Pleim, J. E.: Development and evaluation of a physics-based windblown
dust emission scheme implemented in the CMAQ modeling system: WINDBLOWN DUST
IN CMAQ, J. Adv. Model. Earth Sy., 9, 585–608,
https://doi.org/10.1002/2016MS000823, 2017.
Gherboudj, I., Beegum, S. N., Marticorena, B., and Ghedira, H.: Dust emission
parameterization scheme over the MENA region: Sensitivity analysis to soil
moisture and soil texture: Dust emission over Mena regiopn., J. Geophys. Res.
Atmos., 120, 10915–10938, https://doi.org/10.1002/2015JD023338, 2015.
Giannadaki, D., Pozzer, A., and Lelieveld, J.: Modeled global effects of airborne desert dust on air quality and premature mortality, Atmos. Chem. Phys., 14, 957–968, https://doi.org/10.5194/acp-14-957-2014, 2014.
Gillette, D. A., Fryrear, D. W., Gill, T. E., Ley, T., Cahill, T. A., and
Gearhart, E. A.: Relation of vertical flux of particles smaller than 10 µm to total aeolian horizontal mass flux at Owens Lake, J. Geophys.
Res.-Atmos., 102, 26009–26015, https://doi.org/10.1029/97JD02252, 1997.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O.,
and Lin, S.-J.: Sources and distributions of dust aerosols simulated with
the GOCART model, J. Geophys. Res., 106, 20255–20273,
https://doi.org/10.1029/2000JD000053, 2001.
Ginoux, P., Prospero, J. M., Torres, O., and Chin, M.: Long-term simulation
of global dust distribution with the GOCART model: correlation with North
Atlantic Oscillation, Environ. Model. Softw., 19, 113–128,
https://doi.org/10.1016/S1364-8152(03)00114-2, 2004.
Gomes, L., Arrúe, J. L., López, M. V., Sterk, G., Richard, D.,
Gracia, R., Sabre, M., Gaudichet, A., and Frangi, J. P.: Wind erosion in a
semiarid agricultural area of Spain: the WELSONS project, Catena, 52,
235–256, https://doi.org/10.1016/S0341-8162(03)00016-X, 2003.
Goudie, A. S.: Desert dust and human health disorders, Environ. Int., 63,
101–113, https://doi.org/10.1016/j.envint.2013.10.011, 2014.
Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., Bauer, S., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Fillmore, D., Ghan, S., Ginoux, P., Grini, A., Horowitz, L., Koch, D., Krol, M. C., Landing, W., Liu, X., Mahowald, N., Miller, R., Morcrette, J.-J., Myhre, G., Penner, J., Perlwitz, J., Stier, P., Takemura, T., and Zender, C. S.: Global dust model intercomparison in AeroCom phase I, Atmos. Chem. Phys., 11, 7781–7816, https://doi.org/10.5194/acp-11-7781-2011, 2011.
Iversen, J. D. and White, B. R.: Saltation threshold on Earth, Mars and
Venus, Sedimentology, 29, 111–119, https://doi.org/10.1111/j.1365-3091.1982.tb01713.x, 1982.
Ju, T., Li, X., Zhang, H., Cai, X., and Song, Y.: Comparison of two
different dust emission mechanisms over the Horqin Sandy Land area: Aerosols
contribution and size distributions, Atmos. Environ., 176, 82–90,
https://doi.org/10.1016/j.atmosenv.2017.12.017, 2018.
Kang, J.-Y., Yoon, S.-C., Shao, Y., and Kim, S.-W.: Comparison of vertical
dust flux by implementing three dust emission schemes in WRF/Chem, J.
Geophys. Res., 116, D09202, https://doi.org/10.1029/2010JD014649, 2011.
Klingmüller, K., Metzger, S., Abdelkader, M., Karydis, V. A., Stenchikov, G. L., Pozzer, A., and Lelieveld, J.: Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch), Geosci. Model Dev., 11, 989–1008, https://doi.org/10.5194/gmd-11-989-2018, 2018.
Kok, J. F.: Does the size distribution of mineral dust aerosols depend on the wind speed at emission?, Atmos. Chem. Phys., 11, 10149–10156, https://doi.org/10.5194/acp-11-10149-2011, 2011a.
Kok, J. F.: A scaling theory for the size distribution of emitted dust
aerosols suggests climate models underestimate the size of the global dust
cycle, P. Natl. Acad. Sci. USA, 108, 1016–1021, https://doi.org/10.1073/pnas.1014798108, 2011b.
Kok, J. F., Mahowald, N. M., Fratini, G., Gillies, J. A., Ishizuka, M., Leys, J. F., Mikami, M., Park, M.-S., Park, S.-U., Van Pelt, R. S., and Zobeck, T. M.: An improved dust emission model – Part 1: Model description and comparison against measurements, Atmos. Chem. Phys., 14, 13023–13041, https://doi.org/10.5194/acp-14-13023-2014, 2014a.
Kok, J. F., Albani, S., Mahowald, N. M., and Ward, D. S.: An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions, Atmos. Chem. Phys., 14, 13043–13061, https://doi.org/10.5194/acp-14-13043-2014, 2014b.
Kontos, S., Liora, N., Giannaros, C., Kakosimos, K., Poupkou, A., and Melas,
D.: Modeling natural dust emissions in the central Middle East:
Parameterizations and sensitivity, Atmos. Environ., 190, 294–307,
https://doi.org/10.1016/j.atmosenv.2018.07.033, 2018.
Ku, B. and Park, R. J.: Comparative inverse analysis of satellite (MODIS)
and ground (PM10) observations to estimate dust emissions in East Asia,
Asia-Pac. J. Atmos. Sci., 49, 3–17, https://doi.org/10.1007/s13143-013-0002-5, 2013.
Kumar, R., Barth, M. C., Pfister, G. G., Naja, M., and Brasseur, G. P.: WRF-Chem simulations of a typical pre-monsoon dust storm in northern India: influences on aerosol optical properties and radiation budget, Atmos. Chem. Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, 2014.
Latimer, R. N. C. and Martin, R. V.: Interpretation of measured aerosol mass scattering efficiency over North America using a chemical transport model, Atmos. Chem. Phys., 19, 2635–2653, https://doi.org/10.5194/acp-19-2635-2019, 2019.
Laurent, B., Marticorena, B., Bergametti, G., Chazette, P., Maignan, F., and
Schmechtig, C.: Simulation of the mineral dust emission frequencies from
desert areas of China and Mongolia using an aerodynamic roughness length map
derived from the POLDER/ADEOS-1 surface products, J. Geophys. Res., 110,
D18S04, https://doi.org/10.1029/2004JD005013, 2005.
Laurent, B., Marticorena, B., Bergametti, G., and Mei, F.: Modeling mineral
dust emissions from Chinese and Mongolian deserts, Global Planet. Changes,
52, 121–141, https://doi.org/10.1016/j.gloplacha.2006.02.012,
2006.
Laurent, B., Marticorena, B., Bergametti, G., Leon, J.-F., and Mahowald, N.:
Modeling mineral dust emissions from the Sahara desert using new surface
properties and soil database, J. Geophys. Res., 113, D14218,
https://doi.org/10.1029/2007JD009484, 2008.
Li, K., Jacob, D. J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K. H.,
Zhang, Q., and Zhai, S.: A two-pollutant strategy for improving ozone and
particulate air quality in China, Nat. Geosci., 12, 906–910, https://doi.org/10.1038/s41561-019-0464-x, 2019.
Lin, J., Xin, J., Che, H., Wang, Y., and Donkelaar, A. V.: Clear-sky aerosol
optical depth over East China estimated from visibility measurements and
chemical transport modeling, Atmos. Environ., 95, 258-267, https://doi.org/10.1016/j.atmosenv.2014.06.044, 2014.
Liu, D., Ishizuka, M., Mikami, M., and Shao, Y.: Turbulent characteristics of saltation and uncertainty of saltation model parameters, Atmos. Chem. Phys., 18, 7595–7606, https://doi.org/10.5194/acp-18-7595-2018, 2018.
Liu, H., Jacob, D. J., Bey, I., and Yantosca, R. M.: Constraints from 210Pb
and 7Be on wet deposition and transport in a global three-dimensional
chemical tracer model driven by assimilated meteorological fields, J.
Geophys. Res., 106, 12109–12128, https://doi.org/10.1029/2000JD900839, 2001.
Lu, H. and Shao, Y.: A new model for dust emission by saltation bombardment,
J. Geophys. Res., 104, 16827–16842, https://doi.org/10.1029/1999JD900169, 1999.
Ma, S., Zhang, X., Gao, C., Tong, D. Q., Xiu, A., Wu, G., Cao, X., Huang, L., Zhao, H., Zhang, S., Ibarra-Espinosa, S., Wang, X., Li, X., and Dan, M.: Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1), Geosci. Model Dev., 12, 4603–4625, https://doi.org/10.5194/gmd-12-4603-2019, 2019.
Macpherson, T., Nickling, W. G., Gillies, J. A., and Etyemezian, V.: Dust
emissions from undisturbed and disturbed supply-limited desert surfaces, J.
Geophys. Res., 113, F02S04, https://doi.org/10.1029/2007JF000800, 2008.
Mahowald, N. and Kiehl, L.: Mineral aerosol and cloud interactions, Geophys.
Res. Lett., 30, 1475, https://doi.org/10.1029/2002GL016762, 2003.
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1.
Design of a soil-derived dust emission scheme, J. Geophys. Res., 100,
16415, https://doi.org/10.1029/95JD00690, 1995.
Menut, L., Pérez, C., Haustein, K., Bessagnet, B., Prigent, C., and
Alfaro, S.: Impact of surface roughness and soil texture on mineral dust
emission fluxes modeling: IMPACT OF ROUGHNESS AND SOIL TEXTURE ON MINERAL
DUST, J. Geophys. Res.-Atmos., 118, 6505–6520, https://doi.org/10.1002/jgrd.50313,
2013.
Mokhtari, M., Gomes, L., Tulet, P., and Rezoug, T.: Importance of the surface size distribution of erodible material: an improvement on the Dust Entrainment And Deposition (DEAD) Model, Geosci. Model Dev., 5, 581–598, https://doi.org/10.5194/gmd-5-581-2012, 2012.
Nagashima, K., Suzuki, Y., Irino, T., Nakagawa, T., Tada, R., Hara, Y.,
Yamada, K., and Kurosaki, Y.: Asian dust transport during the last century
recorded in Lake Suigetsu sediments, Geophys. Res. Lett., 43, 2835–2842,
https://doi.org/10.1002/2015GL067589, 2016.
Owen, P. R.: Saltation of uniform grains in air, J. Fluid Mech., 20,
225–242, 1964.
Panebianco, J. E., Mendez, M. J., and Buschiazzo, D. E.: PM10 Emission,
Sandblasting Efficiency and Vertical Entrainment During Successive
Wind-Erosion Events: A Wind-Tunnel Approach, Bound.-Lay. Meteorol.,
161, 335–353, https://doi.org/10.1007/s10546-016-0172-7, 2016.
Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L.: Predicting the mineral composition of dust aerosols – Part 1: Representing key processes, Atmos. Chem. Phys., 15, 11593–11627, https://doi.org/10.5194/acp-15-11593-2015, 2015a.
Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L.: Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations, Atmos. Chem. Phys., 15, 11629–11652, https://doi.org/10.5194/acp-15-11629-2015, 2015b.
Prigent, C., Tegen, I., Aires, F., Marticorena, B., and Zribi M.: Estimation
of the aerodynamic roughness length in arid and semiarid regions over the
globe with the ERS scatterometer, J. Geophys. Res., 110, D09205,
https://doi.org/10.1029/2004JD005370, 2005.
Prigent, C., Jiménez, C., and Catherinot, J.: Comparison of satellite microwave backscattering (ASCAT) and visible/near-infrared reflectances (PARASOL) for the estimation of aeolian aerodynamic roughness length in arid and semi-arid regions, Atmos. Meas. Tech., 5, 2703–2712, https://doi.org/10.5194/amt-5-2703-2012, 2012.
Rajot, J. L., Alfaro, S. C., Gomes, L., and Gaudichet, A.: Soil crusting on
sandy soils and its influence on wind erosion, Catena, 53, 1–16,
https://doi.org/10.1016/S0341-8162(02)00201-1, 2003.
Rice M. A., Willetts, B. B. and McEwan, I. K.: Wind erosion of crusted soil
sediments, Earth Surf. Proc. Land., 21, 279–293,
1996a.
Ridley, D. A., Heald, C. L., and Ford, B.: North African dust transport and
deposition: a satellite and model perspective, J. Geophys. Res., 117,
D02202, https://doi.org/10.1029/2011JD016794, 2012.
Ridley, D. A., Heald, C. L., Kok, J. F., and Zhao, C.: An observationally constrained estimate of global dust aerosol optical depth, Atmos. Chem. Phys., 16, 15097–15117, https://doi.org/10.5194/acp-16-15097-2016, 2016.
Roney, J. A. and White, B. R.: Estimating fugitive dust emission rates using
an environmental boundary layer wind tunnel, Atmos. Environ.,
40, 7668–7685, https://doi.org/10.1016/j.atmosenv.2006.08.015, 2006.
Saidou Chaibou, A. A., Ma, X., and Sha, T.: Dust radiative forcing and its
impact on surface energy budget over West Africa, Sci. Rep.-UK, 10, 12236,
https://doi.org/10.1038/s41598-020-69223-4, 2020a.
Saidou Chaibou, A. A., Ma, X., Kumar, K. R., Jia, H., Tang, Y., and Sha, T.:
Evaluation of dust extinction and vertical profiles simulated by WRF-Chem
with CALIPSO and AERONET over North Africa, J. Atmos.
Sol.-Terr. Phy., 199, 105213, https://doi.org/10.1016/j.jastp.2020.105213, 2020b.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data
set for earth system modeling, J. Adv. Model. Earth Sy., 6, 249–263,
https://doi.org/10.1002/2013MS000293, 2014.
Shao, Y.: A model for mineral dust emission, J. Geophys. Res., 106,
20239–20254, https://doi.org/10.1029/2001JD900171, 2001.
Shao, Y.,: Simplification of a dust emission scheme and comparison with
data, J. Geophys. Res., 109, D10202, https://doi.org/10.1029/2003JD004372,
2004.
Shao, Y., Ishizuka, M., Mikami, M., and Leys, J. F.: Parameterization of
size-resolved dust emission and validation with measurements, J. Geophys.
Res., 116, D08203, https://doi.org/10.1029/2010JD014527, 2011.
Shao, Y. P., Raupach, M. R., and Leys, J. F.: A model for predicting aeolian
sand drift and dust entrainment on scales from paddock to region, Austr. J.
Soil Res., 34, 309–342, https://doi.org/10.1071/SR9960309, 1996.
Su, L. and Fung, J. C. H.: Sensitivities of WRF-Chem to dust emission
schemes and land surface properties in simulating dust cycles during
springtime over East Asia: Simulated Dust Cycles Over East Asia, J. Geophys.
Res.-Atmos., 120, 11215–11230, https://doi.org/10.1002/2015JD023446, 2015.
Tegen, I.: Modeling the mineral dust aerosol cycle in the climate system,
Quaternary Sci. Rev., 22, 1821–1834, 2003.
Tegen, I., Andrew, A. L., and Fung, I.: The influence on climate forcing of
mineral aerosols from disturbed soils, Nature, 380, 419–422,
https://doi.org/10.1038/380419a0, 1996.
Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and
Heimann, M.: Impact of vegetation and preferential source areas on global
dust aerosol: Results from a model study, J. Geophys. Res.-Atmos., 107,
4576, https://doi.org/10.1029/2001JD000963, 2002.
Tian, R., Ma, X., Jia, H., Yu, F., Sha, T., and Zan, Y.: Aerosol radiative
effects on tropospheric photochemistry with GEOS-Chem simulations,
Atmos. Environ., 208, 82–94, https://doi.org/10.1016/j.atmosenv.2019.03.032,
2019.
Todd, M. C., Bou Karam, D., Cavazos, C., Bouet, C., Heinold, B., Baldasano,
J. M., Cautenet, G., Koren, I., Perez, C., Solmon, F., Tegen, I., Tulet, P.,
Washington, R., and Zakey, A.: Quantifying uncertainty in estimates of
mineral dust flux: an intercomparison of model performance over the Bodele
Depression, Northern Chad, J. Geophys. Res., 113, D24107,
https://doi.org/10.1029/2008JD010476, 2008.
Tong, D. Q., Wang, J. X. L., Gill, T. E., Lei, H., and Wang, B.: Intensified
dust storm activity and Valley fever infection in the southwestern United
States, Geophys. Res. Lett., 44, 4304–4312,
https://doi.org/10.1002/2017GL073524, 2017.
Uno, I., Wang, Z., Chiba, M., Chun, Y. S., Gong, S. L., Hara, Y., Jung, E.,
Lee, S.-S., Liu, M., Mikami, M., Music, S., Nickovic, S., Satake, S., Shao,
Y., Song, Z., Sugimoto, N., Tanaka, T., and Westphal, D. L.: Dust model
intercomparison (DMIP) study over Asia: Overview, J. Geophys. Res.,
111, D12213, https://doi.org/10.1029/2005JD006575, 2006.
Wang, C. Z., Niu, S. J., and Zhou, Y.: Recent progress on the observation
study of wind erosion in China, Meteorol. Mon., 11107–11116, 2009
(in Chinese).
Wang, Y., Zhang, Q. Q., He, K., Zhang, Q., and Chai, L.: Sulfate-nitrate-ammonium aerosols over China: response to 2000–2015 emission changes of sulfur dioxide, nitrogen oxides, and ammonia, Atmos. Chem. Phys., 13, 2635–2652, https://doi.org/10.5194/acp-13-2635-2013, 2013.
Wang, Y., Zhang, Q., Jiang, J., Zhou, W., Wang, B., He, K., Duan, F., Zhang,
Q., Philip, S., and Xie, Y.: Enhanced sulfate formation during China's severe
winter haze episode in January 2013 missing from current models: Modeling
Winter Haze Formation in China, J. Geophys. Res.-Atmos., 119,
10425-10440, https://doi.org/10.1002/2013JD021426, 2014.
Wang, Y. X., Mcelroy, M. B., Jacob, D. J., and Yantosca, R. M.: A nested
grid formulation for chemical transport over Asia: Applications to CO, J.
Geophys. Res.-Atmos., 109, D22307, https://doi.org/10.1029/2004JD005237, 2004.
Wang, Z., Pan, X., Uno, I., Li, J., Wang, Z., Chen, X., Fu, P., Yang, T.,
Kobayashi, H., Shimizu, A., Sugimoto, N., and Yamamoto, S.: Significant
impacts of heterogeneous reactions on the chemical composition and mixing
state of dust particles: A case study during dust events over northern
China, Atmosp. Environ., 159, 83–91, https://doi.org/10.1016/j.atmosenv.2017.03.044, 2017.
Wu, C.-L.: Improvements of Dust Emission Processes in CESM Model and Its
Application, doctoral dissertation, University of Chinese Academy of
Sciences, 2013 (in Chinese).
Wu, C. L. and Lin Z. H.: Impact of two different dust emission schemes on
the simulation of a severe dust storm in East Asia using the WRF/Chem model,
Clim. Environ. Res., 19, 419-436, 2014 (in Chinese).
Wu, M., Liu, X., Yang, K., Luo, T., Wang, Z., Wu, C., Zhang, K., Yu, H., and
Darmenov, A.: Modeling Dust in East Asia by CESM and Sources of Biases, J.
Geophys. Res.-Atmos., 124, 8043–8064, https://doi.org/10.1029/2019JD030799, 2019.
Xi, X. and Sokolik, I. N.: Seasonal dynamics of threshold friction velocity
and dust emission in Central Asia, J. Geophys. Res.-Atmos., 120,
1536–1564, https://doi.org/10.1002/2014JD022471, 2015.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and
Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys.
Res., 108, 4416, https://doi.org/10.1029/2002JD002775, 2003.
Zeng, Y., Wang, M., Zhao, C., Chen, S., Liu, Z., Huang, X., and Gao, Y.: WRF-Chem v3.9 simulations of the East Asian dust storm in May 2017: modeling sensitivities to dust emission and dry deposition schemes, Geosci. Model Dev., 13, 2125–2147, https://doi.org/10.5194/gmd-13-2125-2020, 2020.
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle
dry deposition scheme for an atmospheric aerosol module, Atmos. Environ.,
35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001.
Zhang, L., Kok, J. F., Henze, D. K., Li, Q., and Zhao, C.: Improving
simulations of fine dust surface concentrations over the western United
States by optimizing the particle size distribution: Improving Simulated
Dust Over Western US, Geophys. Res. Lett., 40, 3270–3275, https://doi.org/10.1002/grl.50591, 2013.
Zhang, L., Liu, L., Zhao, Y., Gong, S., Zhang, X., Henze, D. K., Capps, S.
L., Fu, T.-M., Zhang, Q., and Wang, Y.: Source attribution of particulate
matter pollution over North China with the adjoint method, Environ.
Res. Lett., 10, 084011, https://doi.org/10.1088/1748-9326/10/8/084011, 2015.
Zhang, J., Teng, Z., Huang, N., Guo, L., and Shao, Y.: Surface renewal as a significant mechanism for dust emission, Atmos. Chem. Phys., 16, 15517–15528, https://doi.org/10.5194/acp-16-15517-2016, 2016.
Zhao, C., Liu, X., and Leung, L. R.: Impact of the Desert dust on the summer monsoon system over Southwestern North America, Atmos. Chem. Phys., 12, 3717–3731, https://doi.org/10.5194/acp-12-3717-2012, 2012.
Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J. F., Zaveri, R. A., and Huang, J.: Uncertainty in modeling dust mass balance and radiative forcing from size parameterization, Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, 2013.
Zhao, J., Ma, X., Wu, S., and Sha, T.: Dust emission and transport in
Northwest China: WRF-Chem simulation and comparisons with multi-sensor
observations, Atmos. Res., 241, 104978, https://doi.org/10.1016/j.atmosres.2020.104978, 2020.
Zheng, B., Zhang, Q., Zhang, Y., He, K. B., Wang, K., Zheng, G. J., Duan, F. K., Ma, Y. L., and Kimoto, T.: Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China, Atmos. Chem. Phys., 15, 2031–2049, https://doi.org/10.5194/acp-15-2031-2015, 2015.
Short summary
We improve the treatment of the dust emission process in GEOS-Chem by considering the effect of geographical variation of aerodynamic roughness length, smooth roughness length and soil texture, as well as the Owen effect and a more physically based formulation of sandblasting efficiency, which improve estimated threshold friction velocity and dust concentrations over China. Our study highlights the importance of incorporation of realistic land-surface properties into the dust emission scheme.
We improve the treatment of the dust emission process in GEOS-Chem by considering the effect of...
Altmetrics
Final-revised paper
Preprint