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EDITORIAL article

Front. Environ. Sci., 21 November 2022
Sec. Atmosphere and Climate
Volume 10 - 2022 | https://doi.org/10.3389/fenvs.2022.1058052

Editorial: Atmospheric dust: How it affects climate, environment and life on Earth?

  • 1King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 2The University of Kansas, Lawrence, KS, United States
  • 3Khalifa University, Abu Dhabi, United Arab Emirates

1 Introduction

Mineral dust particles, which are mainly emitted from deserts and agricultural areas (Shao et al., 2011; Ginoux et al., 2012), are the second largest constituents of atmospheric aerosols by mass. They block the sunlight by scattering and absorption, which in turn affects the Earth’s physical and biological processes (e.g., Mahowald et al., 2009; Bangalath and Stenchikov, 2015; Parajuli et al., 2022). Dust aerosols directly affect air quality (e.g., Ukhov et al., 2020), provide nutrients (e.g., phosphorous, iron) to marine and terrestrial ecosystems (e.g., Kellogg and Griffin, 2006), darken snow and ice surface thus have impact on cryosphere (Francis et al., 2018; Francis et al., 2022), and can also change rainfall distribution by modifying circulation patterns and cloud properties (Shao et al., 2011; Choobari et al., 2014; Jin et al., 2017; Francis et al., 2021a; Parajuli et al., 2022). Dust bowls of the 1930s, caused by severe droughts coupled with poor agricultural practices over the American and Canadian prairies made extensive crop damage, affected daily lives, and contributed to the economic downturn during the Great Depression (Bolles et al., 2017). Prevailing air pollution in populated cities around the world clearly highlights the impact of these tiny atmospheric particles on human health. In this context, in this paper, we highlight some known challenges of dust observation and modeling, in an attempt to identify and guide future research direction.

1.1 Dust emissions

Our understanding of atmospheric aerosols has grown significantly in terms of their types, physical properties, chemical composition, radiative effects, and hygroscopic properties during the past decades due to advances in observational techniques and model development. Global and regional weather/climate models are used to simulate the emission of dust and its interactions with the climate (Chen et al., 2014; Kok et al., 2014; Jin et al., 2015; Francis et al., 2021a; Singh et al., 2021; Parajuli et al., 2022). However, the large spatiotemporal heterogeneity of dust sources, from giant sand dunes to small ridge and furrows of the agricultural field, from short-lived dust devils for several minutes to global dust transport for several weeks (Uno et al., 2009; Francis et al., 2020), makes it extremely challenging to represent the dust cycle (emission, transport, and deposition) in climate models. Recent studies show that most global climate models used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013) fail to reproduce the key aspects of the dust cycle when compared to satellite and ground-based observations (Evan et al., 2014; Singh et al., 2018), partially due to poor simulation of circulations. Since these models are increasingly being used for conducting historical as well as future climate simulations, there is a pressing need for improving the overall representation of dust cycle and dust-climate interactions in the models. Figure 1 summarizes our present understanding of the multi-faceted aspects of dust-climate interactions.

FIGURE 1
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FIGURE 1. The dust cycle and its impacts.

One of the most pressing challenges in dust modeling is to represent anthropogenic dust sources (Ginoux et al., 2012; Webb and Pierre, 2018). Anthroposphere is a changing space and thus it is challenging to identify and represent new dust sources that keep emerging with urbanization, desertification (Jin et al., 2017), and changes in agricultural practices. For example, the Tigris-Euphrates region is one of the most active anthropogenic dust sources, which continues to expand in its strength because of increased or bad agricultural practices (Parajuli et al., 2019). It will not be surprising if it becomes the center of a modern “Dust Bowl” in the future under a prolonged drought scenario because it shares many similarities with the high plains of the United States. Such a disaster will be very costly in terms of the regional economy, ecology, and air quality. Therefore, given their regional effects, regional cooperation among all nations is essential to avoid such occurrences in the future.

One of the main uncertainties of dust–climate interactions comes from the poor representation of dust sources, emissions, and mineral components. Dust parameterizations currently used in most dust modeling schemes were either derived from wind-tunnel or field experiments, which produce unrealistic emission fluxes in their global application because of their scale dependency. Unfortunately, there has been no solid progress in terms of improving dust emission fluxes given the large diversity of natural as well as anthropogenic dust sources. A few notable attempts have emerged recently in this regard (e.g., Chappell et al., 2021; Kok et al., 2021; Leung et al., 2022) but reasonable representation of dust fluxes remains a big challenge in climate models.

1.2 Dust/aerosol optical properties

Dust optical depth (DOD) is a measure of how much solar radiation is blocked by dust aerosols within the entire atmospheric column. In most dust modules used in climate models, dust emission fluxes are calculated using simple equations, originally derived from wind tunnel or field experiments (Marticorena and Bergametti, 1995). These equations calculate how much dust is emitted into the atmosphere (vertical flux of dust) mainly as a function of surface winds, soil particle size distribution, soil moisture, soil erodibility, and clay content. When such fine-scale parameterizations are adapted for calculating grid-scale emissions in global/regional models, they must be constrained using appropriate observations. Otherwise, the calculated dust fluxes will not be realistic. This is one reason why different dust models currently in use give a range of global dust emission estimates, from 500 to 5000 Tg/year (Zender et al., 2004; Huneeus et al., 2011).

To deal with the issue discussed above, it is a usual practice to constrain dust emissions in the models against some ground-based observations or satellite retrievals (Zhao et al., 2010; Parajuli et al., 2019), by utilizing a tuning parameter obtained iteratively until the desired match is obtained between predicted and observed AOD (Aerosol Optical Depth). This tuning parameter is highly sensitive to spatial and temporal resolution; therefore, it is important to determine this tuning parameter for each model set-up independently rather than using the prescribed values in the default model configuration. Inadequately tuned models, when used for conducting historic, present or future climate simulations, can yield unrealistic AODs. Consequently, the subsequent processes involving dust–radiation interactions as well as dust–cloud interactions can be ill-simulated, which in turn can misrepresent the impacts of dust aerosols on climate. Another important issue is that while tuning the dust model to get the desired AOD, we are essentially assuming that the loadings of other aerosol species are well simulated by the model or they are less important. While it is true that dust is the major contributor to AOD in desert areas (Parajuli et al., 2019; Francis et al., 2021b), it may not be the case in some regions where sea salt, biomass burning, organic, and other anthropogenic aerosols may contribute significantly to the total AOD. Although it is possible to get the proportion of DOD to total AOD from satellite retrievals, e.g., CALIOP (Winker et al., 2013), this data suffers from coarse resolution, incomplete sampling, cloud contamination, and an assumption of a constant dust LIDAR ratio. A more effective method to determine the contribution of different aerosol types to total AOD is yet to be invented and is a key topic for further research.

Recent field and laboratory measurements show that the dust refractive index in the longwave band varies regionally, particularly the imaginary part (Di Biagio et al., 2017; Kok et al., 2021). Another large uncertainty regarding dust optical depth is the mineral components of dust aerosols, which determines both physical and chemical properties of dust aerosols, in particular the absorption of solar radiation by dust aerosols. Observational data demonstrate large spatial variations in dust mineral components (e.g., iron oxides) on a global scale (Perlwitz et al., 2015; Di Biagio et al., 2019). However, most of the current climate models assume that all dust particles in the atmosphere have the same mineral components and thus utilize a globally-constant value for the imaginary part of the dust refractive index, which is determined by dust mineral components (Sand et al., 2021). Therefore, in future model development, dust aerosols should be treated as various mineral components to better estimate dust total optical depth as well as dust absorptive optical depth, the latter of which plays a significant role in the dust–climate interactions (Green et al., 2020; Li et al., 2021).

1.3 Particle size distribution

Dust particle size distributions (PSD) shows what range of sizes of dust particles are there in the atmosphere. PSDs of atmospheric aerosols determine the amount of light scattered or heat absorbed as well as the formation of cloud condensation nuclei (CCN) or Ice Nuclei (IN), which ultimately govern the aerosol-cloud interactions and associated rainfall processes (Mahowald et al., 2014). Therefore, it is important to ensure that the predicted PSD is realistic in the model. Although the availability of PSD observations is limited, size distributions obtained by inversion from the AERONET network are sufficient to obtain a reasonable PSD (Parajuli et al., 2020). AERONET is a global network of more than 400 stations retrieving key aerosol properties including optical depth and size distributions (Holben et al., 1998). However, near major dust source regions, these observations are scarce (Francis et al., 2019) which induces uncertainties in regional and global models. Adebiyi and Kok. (2020) have shown that most models miss the coarse dust particles near emission regions due to a fast deposition of large particles. This induces a bias in dust aerosol interaction with the longwave radiation where large particles are associated with a warming effect (Francis et al., 2021a; Francis et al., 2022).

1.4 Aerosol vertical distribution

Dust vertical distribution is another important aspect that is often overlooked while conducting global/regional climate simulations (Parajuli et al., 2020), which indicates how much dust load is present at different altitudes in the atmosphere. The vertical distribution of aerosols affects physical processes near the surface as well as in the upper atmosphere. For example, if the vertical distribution of aerosol concentration is not realistic, the model will show unrealistic surface PM10/PM2.5 even if the total columnar AOD is right. This could lead to serious consequences in air quality applications. Similarly, if the aerosol concentrations are not realistic in the upper atmosphere, CCN/IN number concentrations will not be accurate and consequently, cloud properties such as cloud depths, cloud heights, and mixing ratios of hydrometeors (e.g., rain, graupel, snow, and ice) will be affected.

In the above context, Lidar data, which are available from several ground-based sites, aircraft, and satellite platforms across the globe (Winker et al., 1996; Welton et al., 2000), can be instrumental in constraining the vertical distribution of aerosols in the models. The Micro-Pulse Lidar Network (MPLNET), maintained by the NASA Goddard Space Flight Center (GSFC), is a network of MPL lidars and a huge volume of data on vertical profile are available across the globe (Welton et al., 2002). Other coordinated lidar networks include the European Aerosol Research Lidar Network EARLINET (Pappalardo et al., 2014), German Aerosol Lidar Network (Bosenberg, 2001), the Latin American Lidar Network LALINET (Guerrero-Rascado et al., 2016), the Asian dust and aerosol lidar observation network AD-Net (Shimizu et al., 2016), and the Commonwealth of Independent States Lidar Network CIS-LiNet (Chaikovsky et al., 2006). These data can be used to verify or constrain the model-predicted aerosol vertical distributions.

1.5 Dust direct, semi-direct, and indirect effects

When dust scatters or absorbs shortwave radiation, it affects the radiation budget at Earth’s surface and in the atmosphere thus directly changing Earth’s surface and atmospheric temperature, respectively. Such temperature change brings about a wide range of consequences, from local circulation changes to changes in convective activity. The effects of aerosols on climate are generally classified into three categories, which are direct, semi-direct, and indirect effects (Lohmann and Feichter, 2001; Forkel et al., 2012). Aerosol directly affects Earth’s radiative budget by scattering and absorbing shortwave radiation, which is generally known as the “direct aerosol effect.” Dust effects on radiation in turn lead to changes in temperature, wind speed, relative humidity, and atmospheric stability. These consequential effects are called aerosol “semi-direct effects” (Hansen et al., 1997). Additionally, aerosols also affect Earth’s climate through clouds as they interact with clouds in multiple ways. These effects that occur through clouds are classified as indirect effects (Twomey, 1991). The indirect effects are further classified into two categories. Aerosols form cloud condensation nuclei (CCN) or ice nuclei (IN) (Stull, 2000) and thus change the cloud optical properties including cloud albedo—the “first indirect effect” (Kravitz et al., 2014). The subsequent changes in other cloud properties including cloud cover, cloud lifetime, and rainfall are called the “second indirect effect” (Lohmann and Feichter, 2001).

Several studies have shown that dust particles, especially near source regions, induce a warming effect at the surface (Francis et al., 2020; Francis et al., 2021a; Francis et al., 2021b) which is not well represented in regional models (Francis et al., 2021a; Kok et al., 2021). This misrepresentation may be due to the fact that models tend to deposit large particles faster than in reality (Kok et al., 2021). Therefore, dust size particles, their deposition, and interaction in the longwave band need to be revisited in current models.

Atmospheric dust aerosols are also known to affect regional atmospheric circulation by modifying the radiative balance both at the surface and throughout the atmosphere, i.e., the semi-direct effects. To date, large uncertainties remain with regard to the impact of aerosols on atmospheric stability, convection and wind patterns (Francis et al., 2021a; Francis et al., 2021b). Additionally, the impacts of dust on the radiative budget, and hence on the circulation, can differ from over land to over water due to the difference in surface albedo between the two locations. The spatio-temporal heterogeneity in dust semi-direct effect makes it even more complex to account for in climate models.

Dust particles can further affect the rainfall patterns through various direct and indirect pathways (Koren et al., 2005; Jin et al., 2015; Parajuli et al., 2022). Changes in rainfall patterns can have broader and long-term consequences because the entire biosphere directly depends upon rainfall for its survival. Unequal distribution of rainfall can affect the frequency and intensity of floods and droughts and affect the distribution of regional water resources. Changes in the prevailing monsoon system and rainfall pattern can affect agricultural production, limit access to drinking water supply, and affect daily life activities.

Dust aerosols emitted from the Middle East have been shown to strengthen the Indian summer monsoonal circulations and thus precipitation (Jin et al., 2014; Vinoj et al., 2014; Jin et al., 2015; Jin et al., 2016). Dust aerosols accumulated over the Arabian Sea can induce a strong warming effect in the middle troposphere, which increases the meridional thermal contrast and thus enhances the southwest monsoon flow, pumping more moisture from the northern Indian Ocean and the Arabian Sea to the Indian subcontinent and ultimately resulting in more precipitation. Beyond Asia, similar dust–monsoon interactions are also identified in North Africa (Zhao et al., 2011) and North America (Zhao et al., 2012).

The direct and indirect effects of dust on rainfall was quantified recently by Parajuli et al. (2022). The study which used WRF-Chem simulations over the Red Sea coastal region showed different responses of dust on rainfall for extreme and normal rain events. Dust enhanced net rainfall for extreme events (∼6%) but suppressed (∼−1%) for normal rain events. For extreme rainfall events, the net effect was primarily driven by indirect effects, which enhanced rainfall by about +4.6%. This is because extreme rain events are usually caused by synoptic features and the high dust concentration facilitates raindrops to grow in the presence of sufficient moisture. For normal rainfall events, the net effect was governed by direct effects, which suppressed rainfall by about −5.8%. The result was suppression because the dust’s direct effect depends upon the local circulation, the sea breezes in their case. Dust scatters shortwave radiation, cools the land surface and thus weakens the sea breeze circulation, which brings more moisture to the land ultimately suppressing the rainfall.

Considering the fact that aerosols are essential for rain formations in warm clouds, these effects are perhaps smaller than what happens in the real world. The small effects observed are in part due to the modeling challenges. It is difficult or even impossible to know the actual effect of dust on rainfall in a modeling framework because we do not know which backgrounds aerosols precede in seeding the clouds in the real world.

2 The way forward

In summary, more in-situ measurements and remote sensing observations from satellites are needed to better understand the dust effect on climate. Dust parameterizations should be improved based on currently available ground-based and satellite observations. High-resolution simulations accounting for direct and indirect effects of dust can unravel the various physical mechanisms of dust-climate interactions. We urge the scientific community to pay attention to the above details in global/regional climate models and make attempts to improve them so that the models can realistically represent the effect of dust on climate in past, present or future climate simulations. Although we illustrated the above aspects with regards to dust, they are equally important for all other aerosol types, including natural (e.g., sea salt, biogenic emissions) and anthropogenic (e.g., sulfate, black and organic carbon).

Collection summary

In this Research Topic, we have presented articles focusing on the effect of dust on health, climate, as well as ways to improve dust-forecasting capability with data assimilation. An article by Wang et al. showed how dust affects Indian Summer Monsoon both in the short-term (fast response) and long-term (slow response), through its effect on the atmosphere and ocean, respectively. Plocoste et al. presented a novel statistical technique to calculate PM10 thresholds using aerosol concentration in the areas where mineral dust contributes significantly to total atmospheric aerosol loading. Huo et al. highlighted that the presence of sand dunes and other similar geographic features in desert regions affect the horizontal sand flux and thus their presence should be accounted in dust parameterizations. Kunin et al. demonstrated how continuous data assimilation of meteorological variables for the first 18 h eliminates the spin-up issues and helps in improving the forecasting skills of WRF-Chem. Chen et al. highlighted the reduction in pollutant concentrations due to the COVID-19 lockdown in Eastern China. Finally, Yang et al. showed that about 22% of the global PM2.5-attributable deaths are caused by desert dust and the newly proposed PM2.5 guideline by the World Health Organization from 10 to 5 μg/m3 would potentially save one million lives.

We elaborate this Research Topic further to provide extended insights into the broader aspects of dust-climate interactions.

Author contributions

SP prepared the first draft of the manuscript. QJ and DF further expanded and edited the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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References

Adebiyi, A. A., and Kok, J. F. (2020). Climate models miss most of the coarse dust in the atmosphere. Sci. Adv. 6, eaaz9507. doi:10.1126/sciadv.aaz9507

PubMed Abstract | CrossRef Full Text | Google Scholar

Bangalath, H. K., and Stenchikov, G. (2015). 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. doi:10.1002/2015JD023122

CrossRef Full Text | Google Scholar

Bolles, K., Forman, S. L., and Sweeney, M. (2017). Eolian processes and heterogeneous dust emissivity during the 1930s Dust Bowl Drought and implications for projected 21st-century megadroughts. Holocene 27, 1578–1588. doi:10.1177/0959683617702235

CrossRef Full Text | Google Scholar

Bösenberg, J. (2001). The German aerosol lidar network: Methodology, data, analysis. Report.

Google Scholar

Chaikovsky, A., Ivanov, A., Balin, Y., Elnikov, A., Tulinov, G., Plusnin, I., et al. (2006). Lidar network CIS-LiNet for monitoring aerosol and ozone in CIS regions. Twelfth Jt. Int. Symposium Atmos. Ocean Optics/Atmospheric Phys., 6160, 833–841. doi:10.1117/12.675920

CrossRef Full Text | Google Scholar

Chappell, A., Webb, N., Hennen, M., Zender, C., Ciais, P., Schepanski, K., et al. (2021). Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere. Geosci. Model. Dev. Discuss. doi:10.5194/gmd-2021-337

CrossRef Full Text | Google Scholar

Chen, S., Zhao, C., Qian, Y., Leung, L. R., Huang, J., Huang, Z., et al. (2014). Regional modeling of dust mass balance and radiative forcing over East Asia using WRF-Chem. Aeolian Res. 15, 15–30. doi:10.1016/j.aeolia.2014.02.001

CrossRef Full Text | Google Scholar

Choobari, O. A., Zawar-Reza, P., and Sturman, A. (2014). The global distribution of mineral dust and its impacts on the climate system: A review. Atmos. Res. 138, 152–165. doi:10.1016/j.atmosres.2013.11.007

CrossRef Full Text | Google Scholar

Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. Atmos. Chem. Phys. 19, 15503–15531. doi:10.5194/acp-19-15503-2019

CrossRef Full Text | Google Scholar

Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: A new dataset of in situ measurements for climate modeling and remote sensing. Atmos. Chem. Phys. 17, 1901–1929. doi:10.5194/acp-17-1901-2017

CrossRef Full Text | Google Scholar

Evan, A. T., Flamant, C., Fiedler, S., and Doherty, O. (2014). An analysis of aeolian dust in climate models. Geophys. Res. Lett. 41, 5996–6001. doi:10.1002/2014GL060545

CrossRef Full Text | Google Scholar

Forkel, R., Werhahn, J., Hansen, A. B., McKeen, S., Peckham, S., Grell, G., et al. (2012). Effect of aerosol-radiation feedback on regional air quality – a case study with WRF/Chem. Atmos. Environ. X. 53, 202–211. doi:10.1016/j.atmosenv.2011.10.009

CrossRef Full Text | Google Scholar

Francis, D., Alshamsi, N., Cuesta, J., Gokcen Isik, A., and Dundar, C. (2019). Cyclogenesis and density currents in the Middle East and the associated dust activity in september 2015. Geosciences 9, 376. doi:10.3390/geosciences9090376

CrossRef Full Text | Google Scholar

Francis, D., Chaboureau, J. P., Nelli, N., Cuesta, J., Alshamsi, N., Temimi, M., et al. (2021a). Summertime dust storms over the Arabian Peninsula and impacts on radiation, circulation, cloud development and rain. Atmos. Res. 250, 105364. doi:10.1016/j.atmosres.2020.105364

CrossRef Full Text | Google Scholar

Francis, D., Eayrs, C., Chaboureau, J.-P., Mote, T., and Holland, D. M. (2018). Polar jet associated circulation triggered a Saharan cyclone and derived the poleward transport of the African dust generated by the cyclone. J. Geophys. Res. Atmos. 123 (11), 11 899–911 917. doi:10.1029/2018JD029095

CrossRef Full Text | Google Scholar

Francis, D., Fonseca, R., Nelli, N., Bozkurt, D., Picard, G., and Guan, B. (2022). Atmospheric rivers drive exceptional Saharan dust transport towards Europe. Atmos. Res. 266, 105959. doi:10.1016/j.atmosres.2021.105959

CrossRef Full Text | Google Scholar

Francis, D., Fonseca, R., Nelli, N. R., Cuesta, J., Evan, A., Temimi, M., et al. (2020). The atmospheric drivers of the major saharan dust storm in june 2020. Geophys. Res. Lett. 47, e2020GL090102. doi:10.1029/2020GL090102

CrossRef Full Text | Google Scholar

Francis, D., Nelli, N., Fonseca, R., Weston, M., Flamant, C., and Cherif, C. (2021b). The dust load and radiative impact associated with the June 2020 historical Saharan dust storm. Atmos. Environ. 268 (2022), 118808. doi:10.1016/j.atmosenv.2021.118808

CrossRef Full Text | Google Scholar

Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M. (2012). Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev. Geophys. 50, RG3005. doi:10.1029/2012RG000388

CrossRef Full Text | Google Scholar

Green, R. O., Mahowald, N., Ung, C., Thompson, D. R., Bator, L., Bennet, M., et al. (2020). “The Earth surface mineral dust source investigation: An Earth science imaging spectroscopy mission,” in IEEE Aerospace Conference, Big Sky, MT, USA, 07-14 March 2020 (IEEE), 1–15.

Google Scholar

Guerrero-Rascado, J. L., Landulfo, E., Antuña, J. C., Barbosa, H. D. M. J., Barja, B., Bastidas, Á. E., et al. (2016). Latin American Lidar Network (LALINET) for aerosol research: Diagnosis on network instrumentation. J. Atmos. Solar-Terrestrial Phys. 138, 112–120. doi:10.1016/j.jastp.2016.01.001

CrossRef Full Text | Google Scholar

Hansen, J., Sato, M., and Ruedy, R. (1997). Radiative forcing and climate response. J. Geophys. Res. 102, 6831–6864. doi:10.1029/96JD03436

CrossRef Full Text | Google Scholar

Holben, B. N., Eck, T. F., Slutsker, I. A., Tanre, D., Buis, J. P., Setzer, A., et al. (1998). AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 66 (1), 1–16. doi:10.1016/s0034-4257(98)00031-5

CrossRef Full Text | Google Scholar

Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., et al. (2011). Global dust model intercomparison in AeroCom phase I. Atmos. Chem. Phys. 11, 7781–7816. doi:10.5194/acp-11-7781-2011

CrossRef Full Text | Google Scholar

IPCC (2013). The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.

Google Scholar

Jin, Q., Wei, J., Yang, Z.-L., and Lin, P. (2017). Irrigation-induced environmental changes around the aral sea: An integrated view from multiple satellite observations. Remote Sens. 9, 900. doi:10.3390/rs9090900

CrossRef Full Text | Google Scholar

Jin, Q., Wei, J., and Yang, Z.-L. (2014). Positive response of Indian summer rainfall to Middle East dust. Geophys. Res. Lett. 41, 4068–4074. doi:10.1002/2014GL059980

CrossRef Full Text | Google Scholar

Jin, Q., Wei, J., Yang, Z.-L., Pu, B., and Huang, J. (2015). Consistent response of Indian summer monsoon to Middle East dust in observations and simulations. Atmos. Chem. Phys. 15, 9897–9915. doi:10.5194/acp-15-9897-2015

CrossRef Full Text | Google Scholar

Jin, Q., Yang, Z.-L., and Wei, J. (2016). Seasonal responses of Indian summer monsoon to dust aerosols in the Middle East, India, and China. J. Clim. 29, 6329–6349. doi:10.1175/jcli-d-15-0622.1

CrossRef Full Text | Google Scholar

Kellogg, C. A., and Griffin, D. W. (2006). Aerobiology and the global transport of desert dust. Trends Ecol. Evol. 21 (11), 638–644. doi:10.1016/j.tree.2006.07.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Kok, J. F., Adebiyi, A. A., Albani, S., Balkanski, Y., Checa-Garcia, R., Chin, M., et al. (2021). Improved representation of the global dust cycle using observational constraints on dust properties and abundance. Atmos. Chem. Phys. 21, 8127–8167. doi:10.5194/acp-21-8127-2021

CrossRef Full Text | Google Scholar

Kok, J. F., Albani, S., Mahowald, N. M., and Ward, D. S. (2014). 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. doi:10.5194/acp-14-13043-2014

CrossRef Full Text | Google Scholar

Koren, I., Kaufman, Y. J., Rosenfeld, D., Remer, L. A., and Rudich, Y. (2005). Aerosol invigoration and restructuring of Atlantic convective clouds. Geophys. Res. Lett. 32, L14828. doi:10.1029/2005GL023187

CrossRef Full Text | Google Scholar

Kravitz, B., Wang, H., Rasch, P. J., Morrison, H., and Solomon, A. B. (2014). Process-model simulations of cloud albedo enhancement by aerosols in the Arctic. Phil. Trans. R. Soc. A 372, 20140052. doi:10.1098/rsta.2014.0052

PubMed Abstract | CrossRef Full Text | Google Scholar

Leung, D., Kok, J., Li, L., Mahowald, N., Prigent, C., Okin, G., et al. (2022). A new process-based and scale-respecting dust emission scheme for global climate models. Vienna, Austria: EGU General Assembly 2022.

Google Scholar

Li, L., Mahowald, N. M., Miller, R. L., Pérez García-Pando, C., Klose, M., Hamilton, D. S., et al. (2021). Quantifying the range of the dust direct radiative effect due to source mineralogy uncertainty. Atmos. Chem. Phys. 21, 3973–4005. doi:10.5194/acp-21-3973-2021

CrossRef Full Text | Google Scholar

Lohmann, U., and Feichter, J. (2001). Can the direct and semi-direct aerosol effect compete with the indirect effect on a global scale? Geophys. Res. Lett. 28, 159–161. doi:10.1029/2000GL012051

CrossRef Full Text | Google Scholar

Mahowald, N., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D. S., et al. (2014). The size distribution of desert dust aerosols and its impact on the Earth system. Aeolian Res. 15, 53–71. doi:10.1016/j.aeolia.2013.09.002

CrossRef Full Text | Google Scholar

Mahowald, N. M., Engelstaedter, S., Luo, C., Sealy, A., Artaxo, P., Benitez-Nelson, C., et al. (2009). Atmospheric iron deposition: Global distribution, variability, and human perturbations. Ann. Rev. Mar. Sci. 1, 245–278. doi:10.1146/annurev.marine.010908.163727

PubMed Abstract | CrossRef Full Text | Google Scholar

Marticorena, B., and Bergametti, G. (1995). Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme. J. Geophys. Res. 100 (D8), 16415–16430. doi:10.1029/95JD00690

CrossRef Full Text | Google Scholar

Pappalardo, G., Amodeo, A., Apituley, A., Comeron, A., Freudenthaler, V., Linné, H., et al. (2014). Earlinet: Towards an advanced sustainable European aerosol lidar network. Atmos. Meas. Tech. 7, 2389–2409. doi:10.5194/amt-7-2389-2014

CrossRef Full Text | Google Scholar

Parajuli, S. P., Stenchikov, G. L., Ukhov, A., and Kim, H. (2019). Dust emission modeling using a new high-resolution dust source function in WRF-Chem with implications for air quality. JGR. Atmos. 124, 10109–10133. doi:10.1029/2019JD030248

CrossRef Full Text | Google Scholar

Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Mostamandi, S., Kucera, P. A., Axisa, D., et al. (2022). Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations. Atmos. Chem. Phys. Discuss. 22, 8659–8682. doi:10.5194/acp-22-8659-2022

CrossRef Full Text | Google Scholar

Parajuli, S. P., Stenchikov, G. L., Ukhov, A., Shevchenko, I., Dubovik, O., and Lopatin, A. (2020). 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. doi:10.5194/acp-20-16089-2020

CrossRef Full Text | Google Scholar

Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L. (2015). Predicting the mineral composition of dust aerosols – Part 1: Representing key processes. Atmos. Chem. Phys. 15, 11593–11627. doi:10.5194/acp-15-11593-2015

CrossRef Full Text | Google Scholar

Sand, M., Samset, B. H., Myhre, G., Gliß, J., Bauer, S. E., Bian, H., et al. (2021). Aerosol absorption in global models from AeroCom phase III. Atmos. Chem. Phys. 21, 15929–15947. doi:10.5194/acp-21-15929-2021

CrossRef Full Text | Google Scholar

Shao, Y., Wyrwoll, K. H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H., et al. (2011). Dust cycle: An emerging core theme in Earth system science. Aeolian Res. 2 (4), 181–204. doi:10.1016/j.aeolia.2011.02.001

CrossRef Full Text | Google Scholar

Shimizu, A., Nishizawa, T., Jin, Y., Kim, S. W., Wang, Z., Batdorj, D., et al. (2016). Evolution of a lidar network for tropospheric aerosol detection in East Asia. Opt. Eng. 56 (3), 031219. doi:10.1117/1.OE.56.3.031219

CrossRef Full Text | Google Scholar

Singh, C., Ganguly, D., and Dash, S. K. (2018). On the dust load and rainfall relationship in south asia: An analysis from CMIP5. Clim. Dyn. 50, 403–422. doi:10.1007/s00382-017-3617-x

CrossRef Full Text | Google Scholar

Singh, C., Singh, S. K., Chauhan, P., and Budakoti, S. (2021). Simulation of an extreme dust episode using WRF-CHEM based on optimal ensemble approach. Atmos. Res. 249, 105296. doi:10.1016/j.atmosres.2020.105296

CrossRef Full Text | Google Scholar

Stull, R. (2000). Meteorology for scientists and engineers Brooks/Cole. Available at: https://www.eoas.ubc.ca/books/Practical_Meteorology/mse3.html (access July 4, 2022).

Google Scholar

Twomey, S. A. (1991). Aerosols, clouds and radiation. Atmos. Environ. Part A. General Top. 25, 2435–2442. doi:10.1016/0960-1686(91)90159-5

CrossRef Full Text | Google Scholar

Ukhov, A., Mostamandi, S., da Silva, A., Flemming, J., Alshehri, Y., Shevchenko, I., et al. (2020). 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. doi:10.5194/acp-20-9281-2020

CrossRef Full Text | Google Scholar

Uno, I., Eguchi, K., Yumimoto, K., Takemura, T., Shimizu, A., Uematsu, M., et al. (2009). Asian dust transported one full circuit around the globe. Nat. Geosci. 2, 557–560. doi:10.1038/ngeo583

CrossRef Full Text | Google Scholar

Vinoj, V., Rasch, P., Wang, H., Yoon, J. H., Ma, P. L., Landu, K., et al. (2014). Short-term modulation of Indian summer monsoon rainfall by West Asian dust. Nat. Geosci. 7, 308–313. doi:10.1038/ngeo2107

CrossRef Full Text | Google Scholar

Webb, N. P., and Pierre, C. (2018). Quantifying anthropogenic dust emissions. Earth's. Future 6, 286–295. doi:10.1002/2017EF000766

CrossRef Full Text | Google Scholar

Welton, E. J. (2002). Twenty-first international laser radar conference (ILRC21). Quebec City: Canada.

Google Scholar

Welton, E. J., Voss, K. J., Gordon, H. R., Maring, H., Smirnov, A., Holben, B., et al. (2000). Ground-based lidar measurements of aerosols during ACE-2: Instrument description, results, and comparisons with other ground-based and airborne measurements. Tellus B 52, 636–651. doi:10.1034/j.1600-0889.2000.00025.x

CrossRef Full Text | Google Scholar

Winker, D. M., Couch, R. H., and McCormick, M. (1996). An overview of LITE: NASA's lidar in-space technology experiment. Proc. IEEE 84 (2), 164–180. doi:10.1109/5.482227

CrossRef Full Text | Google Scholar

Winker, D. M., Tackett, J. L., Getzewich, B. J., Liu, Z., Vaughan, M. A., and Rogers, R. R. (2013). The global 3-D distribution of tropospheric aerosols as characterized by CALIOP. Atmos. Chem. Phys. 13, 3345–3361. doi:10.5194/acp-13-3345-2013

CrossRef Full Text | Google Scholar

Zender, C. S., Miller, R. L. L., and Tegen, I. (2004). Quantifying mineral dust mass budgets:Terminology, constraints, and current estimates. Eos Trans. AGU. 85 (48), 509–512. doi:10.1029/2004EO480002

CrossRef Full Text | Google Scholar

Zhao, C., Liu, X., and Leung, L. R. (2012). Impact of the Desert dust on the summer monsoon system over Southwestern North America. Atmos. Chem. Phys. 12, 3717–3731. doi:10.5194/acp-12-3717-2012

CrossRef Full Text | Google Scholar

Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson, W. I., et al. (2010). 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. doi:10.5194/acp-10-8821-2010

CrossRef Full Text | Google Scholar

Zhao, C., Liu, X., Ruby Leung, L., and Hagos, S. (2011). Radiative impact of mineral dust on monsoon precipitation variability over West Africa. Atmos. Chem. Phys. 11, 1879–1893. doi:10.5194/acp-11-1879-2011

CrossRef Full Text | Google Scholar

Keywords: dust-climate interaction, dust emission and deposition, air quality—climate interactions, dust radiative forcing, mineral dust aerosol

Citation: Parajuli SP, Jin Q and Francis D (2022) Editorial: Atmospheric dust: How it affects climate, environment and life on Earth?. Front. Environ. Sci. 10:1058052. doi: 10.3389/fenvs.2022.1058052

Received: 30 September 2022; Accepted: 11 November 2022;
Published: 21 November 2022.

Edited by:

Hong Liao, Nanjing University of Information Science and Technology, China

Reviewed by:

Jean-Baptiste Renard, UMR7328 Laboratoire de Physique et Chimie de l’Environnement et de l’Espace (LPC2E), France
Jingyi Chen, Pacific Northwest National Laboratory (DOE), United States

Copyright © 2022 Parajuli, Jin and Francis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sagar P. Parajuli, psagar@utexas.edu

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