Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-473-2021
© 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/gmd-14-473-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Jan 2021
Development and technical paper |
| 25 Jan 2021
| 25 Jan 2021
Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module
Alexander Ukhov
CORRESPONDING AUTHOR
Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Ravan Ahmadov
CIRES, University of Colorado, Boulder, CO, USA
NOAA Earth System Research Laboratory, Boulder, CO, USA
Georg Grell
NOAA Earth System Research Laboratory, Boulder, CO, USA
Georgiy Stenchikov
Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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The article presents novelties in characterizing fine particles suspended in the air by means of combining various measurements that observe light propagation in atmosphere. Several non-coincident observations (some of which require sunlight, while others work only at night) could be united under the assumption that aerosol properties do not change drastically at nighttime. It also proposes how to describe particles' composition in a simplified manner that uses new types of observations.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Illia Shevchenko, Oleg Dubovik, and Anton Lopatin
Atmos. Chem. Phys., 20, 16089–16116, https://doi.org/10.5194/acp-20-16089-2020, https://doi.org/10.5194/acp-20-16089-2020, 2020
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Both natural (dust, sea salt) and anthropogenic (sulfate, organic and black carbon) aerosols are common over the Red Sea coastal plains. King Abdullah University of Science and Technology (KAUST), located on the eastern coast of the Red Sea, hosts the only operating lidar system in the Arabian Peninsula, which measures atmospheric aerosols day and night. We use these lidar data and high-resolution WRF-Chem model simulations to study the potential effect of dust aerosols on Red Sea environment.
Klaus Klingmüller, Vlassis A. Karydis, Sara Bacer, Georgiy L. Stenchikov, and Jos Lelieveld
Atmos. Chem. Phys., 20, 15285–15295, https://doi.org/10.5194/acp-20-15285-2020, https://doi.org/10.5194/acp-20-15285-2020, 2020
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Particulate air pollution cools the climate and partially masks the greenhouse warming by reflecting sunlight and enhancing the reflection by clouds. The intensity of this cooling depends on interactions between pollution and desert dust within the atmosphere. Our simulations with a global atmospheric chemistry-climate model indicate that these interactions significantly weaken the cooling.
Cited articles
Alghamdi, M. A., Almazroui, M., Shamy, M., Redal, M. A., Alkhalaf, A. K.,
Hussein, M. A., and Khoder, M. I.: Characterization and elemental composition
of atmospheric aerosol loads during springtime dust storm in western Saudi
Arabia, Aerosol Air Qual. Res., 15, 440–453, 2015. a
Anisimov, A., Tao, W., Stenchikov, G., Kalenderski, S., Prakash, P. J., Yang, Z.-L., and Shi, M.: Quantifying local-scale dust emission from the Arabian Red Sea coastal plain, Atmos. Chem. Phys., 17, 993–1015, https://doi.org/10.5194/acp-17-993-2017, 2017. a, b
Bagnold, R.: The physics of blown sand and desert dunes, William
Morrow & Company N.D., New York, USA, 1941. a
Bangalath, H. K. and Stenchikov, G.: Role of dust direct radiative effect on
the tropical rain belt over Middle East and North Africa: A high-resolution
AGCM study, J. Geophys. Res.-Atmos., 120, 4564–4584,
https://doi.org/10.1002/2015JD023122,
2015. a
Banks, J. R., Brindley, H. E., Stenchikov, G., and Schepanski, K.: Satellite retrievals of dust aerosol over the Red Sea and the Persian Gulf (2005–2015), Atmos. Chem. Phys., 17, 3987–4003, https://doi.org/10.5194/acp-17-3987-2017, 2017. a
Barnard, J. C., Fast, J. D., Paredes-Miranda, G., Arnott, W. P., and Laskin, A.: Technical Note: Evaluation of the WRF-Chem “Aerosol Chemical to Aerosol Optical Properties” Module using data from the MILAGRO campaign, Atmos. Chem. Phys., 10, 7325–7340, https://doi.org/10.5194/acp-10-7325-2010, 2010. a
Belly, P.: Sand movement by wind, Tech. Mem. 1, US Army Coastal Eng. Res.
Cent., Washington, D.C., USA, 1964. a
Bosilovich, M., Lucchesi, R., and Suarez, M.: MERRA-2: File specification GMAO Office Note No. 9 (Version 1.1), available at: https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf (last access: 20 January 2021), 2016. a
Bukowski, J. and van den Heever, S. C.: Convective distribution of dust over the Arabian Peninsula: the impact of model resolution, Atmos. Chem. Phys., 20, 2967–2986, https://doi.org/10.5194/acp-20-2967-2020, 2020. a, b
Chen, S., Yuan, T., Zhang, X., Zhang, G., Feng, T., Zhao, D., Zang, Z., Liao,
S., Ma, X., Jiang, N., Zhang, J., Yang, F., and Lu, H.: Dust modeling over East Asia during the summer
of 2010 using the WRF-Chem model, J. Quant. Spectrosc.
Ra., 213, 1–12, 2018. a
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N.,
Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric
aerosol optical thickness from the GOCART model and comparisons with
satellite and Sun photometer measurements, J. Atmos.
Sci., 59, 461–483, 2002. a
Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S.,
Andrae, U., Balmaseda, M., Balsamo, G., Bauer, D. P., Bechtold, P., Beljaars, A. C. M., Van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim
reanalysis: Configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc., 137, 553–597, 2011. a
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of
aerosol optical properties from Sun and sky radiance measurements, J.
Geophys. Res.-Atmos., 105, 20673–20696, 2000. a
Eltahan, M., Shokr, M., and Sherif, A. O.: Simulation of severe dust events
over Egypt using tuned dust schemes in weather research forecast (WRF-Chem),
Atmosphere, 9, 246, https://doi.org/10.3390/atmos9070246, 2018. a, b, c, d
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010. a
Farahat, A.: Air pollution in the Arabian Peninsula (Saudi Arabia, the United
Arab Emirates, Kuwait, Qatar, Bahrain, and Oman): causes, effects, and
aerosol categorization, Arab. J. Geosci., 9, 196, https://doi.org/10.1007/s12517-015-2203-y, 2016. a
Fast, J., Gustafson Jr, W., Easter, R., Zaveri, R., Barnard, J., Chapman, E.,
Grell, G., and Peckham, S.: Evolution of ozone, particulates, and aerosol
direct forcing in an urban area using a new fully-coupled meteorology,
chemistry, and aerosol model, J. Geophys. Res, 111, D21305, https://doi.org/10.1029/2005JD006721, 2006. a, b, c
Fast, J., Aiken, A. C., Allan, J., Alexander, L., Campos, T., Canagaratna, M. R., Chapman, E., DeCarlo, P. F., de Foy, B., Gaffney, J., de Gouw, J., Doran, J. C., Emmons, L., Hodzic, A., Herndon, S. C., Huey, G., Jayne, J. T., Jimenez, J. L., Kleinman, L., Kuster, W., Marley, N., Russell, L., Ochoa, C., Onasch, T. B., Pekour, M., Song, C., Ulbrich, I. M., Warneke, C., Welsh-Bon, D., Wiedinmyer, C., Worsnop, D. R., Yu, X.-Y., and Zaveri, R.: Evaluating simulated primary anthropogenic and biomass burning organic aerosols during MILAGRO: implications for assessing treatments of secondary organic aerosols, Atmos. Chem. Phys., 9, 6191–6215, https://doi.org/10.5194/acp-9-6191-2009, 2009. a
Flaounas, E., Kotroni, V., Lagouvardos, K., Klose, M., Flamant, C., and Giannaros, T. M.: Assessing atmospheric dust modelling performance of WRF-Chem over the semi-arid and arid regions around the Mediterranean, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2016-307, 2016. a, b
Flaounas, E., Kotroni, V., Lagouvardos, K., Klose, M., Flamant, C., and Giannaros, T. M.: Sensitivity of the WRF-Chem (V3.6.1) model to different dust emission parametrisation: assessment in the broader Mediterranean region, Geosci. Model Dev., 10, 2925–2945, https://doi.org/10.5194/gmd-10-2925-2017, 2017. a, b, c, d, e
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.,
Haywood, J., Lean, J., Lowe, D., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Van Dorland, R.: Climate change 2007: the
physical science basis, Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, p. 212,
2007. a
Fountoukis, C., Ackermann, L., Ayoub, M. A., Gladich, I., Hoehn, R. D., and
Skillern, A.: Impact of atmospheric dust emission schemes on dust production
and concentration over the Arabian Peninsula, Modeling Earth Systems and
Environment, 2, 115, https://doi.org/10.1007/s40808-016-0181-z, 2016. a, b
Ghan, S. J. and Zaveri, R. A.: Parameterization of optical properties for
hydrated internally mixed aerosol, J. Geophys. Res.-Atmos., 112, D10201, https://doi.org/10.1029/2006JD007927, 2007. a
Gillette, D. A. and Passi, R.: Modeling dust emission caused by wind erosion,
J. Geophys. Res.-Atmos., 93, 14233–14242, 1988. a
Gong, S.: A parameterization of sea-salt aerosol source function for sub- and
super-micron particles, Global Biogeochem. Cy., 17, 1097, https://doi.org/10.1029/2003GB002079, 2003. 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, 2005. a
Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J., Setzer, A.,
Vermote, E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A
federated instrument network and data archive for aerosol characterization,
Remote Sens. Environ., 66, 1–16, 1998. a
Huang, Q., Marsham, J. H., Parker, D. J., Tian, W., and Grams, C. M.:
Simulations of the effects of surface heat flux anomalies on stratification,
convective growth, and vertical transport within the Saharan boundary layer,
J. Geophys. Res.-Atmos., 115, D05201, https://doi.org/10.1029/2009JD012689, 2010. a
Jish Prakash, P., Stenchikov, G., Kalenderski, S., Osipov, S., and Bangalath, H.: The impact of dust storms on the Arabian Peninsula and the Red Sea, Atmos. Chem. Phys., 15, 199–222, https://doi.org/10.5194/acp-15-199-2015, 2015. a, b
Kalenderski, S., Stenchikov, G., and Zhao, C.: Modeling a typical winter-time dust event over the Arabian Peninsula and the Red Sea, Atmos. Chem. Phys., 13, 1999–2014, https://doi.org/10.5194/acp-13-1999-2013, 2013. a, b
Karagulian, F., Temimi, M., Ghebreyesus, D., Weston, M., Kondapalli, N. K.,
Valappil, V. K., Aldababesh, A., Lyapustin, A., Chaouch, N., Al Hammadi, F.,
and Abdooli, A.: Analysis of a severe dust storm and its impact on air quality
conditions using WRF-Chem modeling, satellite imagery, and ground
observations, Air Qual. Atmos. Hlth., 12, 1–18, 2019. a
Khan, B., Stenchikov, G., Weinzierl, B., Kalenderski, S., and Osipov, S.: Dust plume formation in the free troposphere and aerosol size distribution during the Saharan Mineral Dust Experiment in North Africa, Tellus B, 67, 27170, https://doi.org/10.3402/tellusb.v67.27170, 2015. a, b
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. a, b, c, d
LeGrand, S. L., Polashenski, C., Letcher, T. W., Creighton, G. A., Peckham, S. E., and Cetola, J. D.: The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3.8.1, Geosci. Model Dev., 12, 131–166, https://doi.org/10.5194/gmd-12-131-2019, 2019. a, b, c
Lihavainen, H., Alghamdi, M., Hyvärinen, A.-P., Hussein, T., Aaltonen, V., Abdelmaksoud, A., Al-Jeelani, H., Almazroui, M., Almehmadi, F., Al Zawad, F., Hakala, J., Khoder, M., Neitola, K., Petäjä, T., Shabbaj, I. I., and Hämeric, K.: Aerosols physical properties at Hada Al Sham, western Saudi Arabia, Atmos. Environ., 135, 109–117, 2016. a
Liu, S., McKeen, S., Hsie, E.-Y., Lin, X., Kelly, K., Bradshaw, J., Sandholm,
S., Browell, E., Gregory, G., Sachse, G., Bandy, A., Thornton, D., Blake, D., Rowland, F., Newell, R., Heikes, B., Singh, H., and Talbot, R.: Model study of tropospheric trace species distributions during PEM-West A, J. Geophys. Res.-Atmos., 101, 2073–2085, 1996. a
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. a, b, c
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1.
Design of a soil-derived dust emission scheme, J. Geophys.
Res.-Atmos., 100, 16415–16430, 1995. a
Miller, R. and Tegen, I.: Climate response to soil dust aerosols, J. Climate, 11, 3247–3267, 1998. a
Nguyen, H. D., Riley, M., Leys, J., and Salter, D.: Dust storm event of
February 2019 in Central and East Coast of Australia and evidence of
long-range transport to New Zealand and Antarctica, Atmosphere, 10, 653, https://doi.org/10.3390/atmos10110653,
2019. a, b
O'Neill, N., Eck, T., Smirnov, A., Holben, B., and Thulasiraman, S.: Spectral
discrimination of coarse and fine mode optical depth, J. Geophys.
Res.-Atmos., 108, 4559, https://doi.org/10.1029/2002JD002975, 2003. a
Osipov, S. and Stenchikov, G.: Simulating the regional impact of dust on the
Middle East climate and the Red Sea, J. Geophys. Res.-Oceans,
123, 1032–1047, 2018. a
Osipov, S., Stenchikov, G., Brindley, H., and Banks, J.: Diurnal cycle of the dust instantaneous direct radiative forcing over the Arabian Peninsula, Atmos. Chem. Phys., 15, 9537–9553, https://doi.org/10.5194/acp-15-9537-2015, 2015. a, b
Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Shevchenko, I., Dubovik, O., and Lopatin, A.: Aerosol vertical distribution and interactions with land/sea breezes over the eastern coast of the Red Sea from lidar data and high-resolution WRF-Chem simulations, Atmos. Chem. Phys., 20, 16089–16116, https://doi.org/10.5194/acp-20-16089-2020, 2020. a, b
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R.,
Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: The weather research and forecasting model: Overview, system efforts, and future directions, B. Am. Meteorol. Soc., 98, 1717–1737,
2017. a
Rizza, U., Barnaba, F., Miglietta, M. M., Mangia, C., Di Liberto, L., Dionisi, D., Costabile, F., Grasso, F., and Gobbi, G. P.: WRF-Chem model simulations of a dust outbreak over the central Mediterranean and comparison with multi-sensor desert dust observations, Atmos. Chem. Phys., 17, 93–115, https://doi.org/10.5194/acp-17-93-2017, 2017. a, b, c, d
Shao, Y.: A model for mineral dust emission, J. Geophys. Res.-Atmos., 106, 20239–20254, 2001. a
Shao, Y.: Simplification of a dust emission scheme and comparison with data,
J. Geophys. Res.-Atmos., 109, D10202, https://doi.org/10.1029/2003JD004372, 2004. a
Shao, Y., Ishizuka, M., Mikami, M., and Leys, J.: Parameterization of
size-resolved dust emission and validation with measurements, J.
Geophys. Res.-Atmos., 116, D08203, https://doi.org/10.1029/2010JD014527, 2011. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A description of the advanced research WRF version 2,
Tech. rep., National Center For Atmospheric Research Boulder Co Mesoscale and
Microscale Meteorology Div, Boulder, CO, USA, 2005. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M., Huang, X. Y., Wang, W., and Powers, J. G.: A description of the Advanced Research (WRF) model, Version 3, Natl. Ctr. Atmos. Res., Boulder, CO, USA, available at: https://github.com/wrf-model/WRF (last access: 20 January 2021), 2008. a, b
Sulaiman, S. A., Singh, A. K., Mokhtar, M. M. M., and Bou-Rabee, M. A.:
Influence of Dirt Accumulation on Performance of PV Panels, Energ. Proc.,
50, 50–56, https://doi.org/10.1016/j.egypro.2014.06.006, 2014. a
Ukhov, A., Mostamandi, S., da Silva, A., Flemming, J., Alshehri, Y., Shevchenko, I., and Stenchikov, G.: Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations, Atmos. Chem. Phys., 20, 9281–9310, https://doi.org/10.5194/acp-20-9281-2020, 2020a. a, b, c, d, e, f
Ukhov, A., Mostamandi, S., Krotkov, N., Flemming, J., da Silva, A., Li, C.,
Fioletov, V., McLinden, C., Anisimov, A., Alshehri, Y. M., and Stenchikov, G.: Study of
SO Pollution in the Middle East Using MERRA-2, CAMS Data Assimilation
Products, and High-Resolution WRF-Chem Simulations, J. Geophys.
Res.-Atmos., 125, e2019JD031993, https://doi.org/10.1029/2019JD031993, 2020b. a
Wang, K., Zhang, Y., Yahya, K., Wu, S.-Y., and Grell, G.: Implementation and
initial application of new chemistry-aerosol options in WRF/Chem for
simulating secondary organic aerosols and aerosol indirect effects for
regional air quality, Atmos. Environ., 115, 716–732, 2015. a
Watson, A. J., Bakker, D., Ridgwell, A., Boyd, P., and Law, C.: Effect of iron supply on Southern Ocean CO2 uptake and implications for glacial atmospheric CO2, Nature, 407, 730–733, https://doi.org/10.1038/35037561, 2000. a
Yuan, T., Chen, S., Huang, J., Zhang, X., Luo, Y., Ma, X., and Zhang, G.:
Sensitivity of simulating a dust storm over Central Asia to different dust
schemes using the WRF-Chem model, Atmos. Environ., 207, 16–29, 2019. a
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for
simulating aerosol interactions and chemistry (MOSAIC), J.
Geophys. Res.-Atmos., 113, D13204, https://doi.org/10.1029/2007JD008782, 2008. a
Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson Jr., W. I., Fast, J. D., and Easter, R.: The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments, Atmos. Chem. Phys., 10, 8821–8838, https://doi.org/10.5194/acp-10-8821-2010, 2010.
a
Zhao, C., Liu, X., Ruby Leung, L., and Hagos, S.: Radiative impact of mineral dust on monsoon precipitation variability over West Africa, Atmos. Chem. Phys., 11, 1879–1893, https://doi.org/10.5194/acp-11-1879-2011, 2011. a
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. a
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. a
Zhu, X., Prospero, J., and Millero, F. J.: Diel variability of soluble Fe (II) and soluble total Fe in North African dust in the trade winds at Barbados, J. Geophys. Res.-Atmos., 102, 21297–21305, 1997. a
Short summary
We discuss and evaluate the effects of inconsistencies found in the WRF-Chem code when using the GOCART module. First, PM surface concentrations were miscalculated. Second, dust optical depth was underestimated by 25 %–30 %. Third, an inconsistency in the process of gravitational settling led to the overestimation of dust column loadings by 4 %–6 %, PM10 by 2 %–4 %, and the rate of gravitational dust settling by 5 %–10 %. We also presented diagnostics that can be used to estimate these effects.
We discuss and evaluate the effects of inconsistencies found in the WRF-Chem code when using the...
