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)

Ma, Siqi; Zhang, Xuelei; Gao, Chao; Tong, Daniel; Xiu, Aijun; Wu, Guangjian; Cao, Xingzhong; Huang, Ling; Zhao, Hongmei; Zhang, Shichun; Ibarra, Sergio; Wang, Xin; Li, Xiaolan; Mo, Dan

doi:10.5194/gmd-12-4603-2019

Cited by 31 publications

(29 citation statements)

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“…Dust density is assumed to be 2500 kg m −3 for the first dust bin and 2650 kg m −3 for dust bins 2-5. In WRF-Chem, there are three dust emission schemes that can be used with GOCART: the original GOCART-WRF scheme (dust_opt=1) (Bagnold, 1941;Belly, 1964;Gillette and Passi, 1988), the Air Force Weather Dust 0.1-1.0 1.0-1.8 1.8-3.0 3.0-6.0 6.0-10.0 Sea salt 0.1-0.5 0.5-1.5 1.5-5.0 5.0-10.0 -Agency (AFWA) scheme (dust_opt=3) (Marticorena and Bergametti, 1995;Su and Fung, 2015;Wang et al, 2015), and the University of Cologne (UoC) scheme (dust_opt=4) (Shao, 2001(Shao, , 2004Shao et al, 2011). The detailed description of all schemes is provided in LeGrand et al (2019).…”

Section: Wrf-chem Experimental Setupmentioning

confidence: 99%

“…WRF-Chem is a popular open-source tool that is widely used to study atmospheric chemistry, air quality, and aerosols (Jish Prakash et al, 2015;Khan et al, 2015;Kalenderski et al, 2013;Kalenderski and Stenchikov, 2016;Parajuli et al, 2019;Anisimov et al, 2017;Osipov and Stenchikov, 2018). This model has been used extensively to study aerosols and their impact on air quality (Fast et al, 2006(Fast et al, , 2009Ukhov et al, 2020a, b;Parajuli et al, 2020), climate (Zhao et al, 2010(Zhao et al, , 2011Chen et al, 2014;Fast et al, 2006) and to analyze dust outbreaks (Bian et al, 2011;Chen et al, 2014;Fountoukis et al, 2016;Ma et al, 2019;LeGrand et al, 2019;Su and Fung, 2015;Eltahan et al, 2018;Bukowski and van den Heever, 2020) in the ME and north Africa (Zhang et al, 2015;Flaounas et al, 2016;Rizza et al, 2017;Karagulian et al, 2019;Rizza et al, 2018), North America (Zhao et al, 2012), India (Dipu et al, 2013;Kumar et al, 2014), and Australia (Nguyen et al, 2019). Many aforementioned studies utilized the WRF-Chem model coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol module (Chin et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…The GOCART module simulates major tropospheric aerosol components, including sulfate, dust, black and organic carbon, and sea salt, and includes algorithms for dust and sea salt emissions, dry deposition, and gravitational settling. The GOCART module is one of the most popular aerosol modules used in WRF-Chem (Bian et al, 2011;Dipu et al, 2013;Kumar et al, 2014;Chen et al, 2014;Su and Fung, 2015;Zhang et al, 2015;Flaounas et al, 2016;Fountoukis et al, 2016;Rizza et al, 2017;Flaounas et al, 2017;Nabavi et al, 2017;Chen et al, 2018;Rizza et al, 2018;Ma et al, 2019;LeGrand et al, 2019;Parajuli et al, 2019;Yuan et al, 2019;Ukhov et al, 2020a;Eltahan et al, 2018;Nguyen et al, 2019;Bukowski and van den Heever, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module

et al. 2021

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Abstract. In this paper, we rectify inconsistencies that emerge in the Weather Research and Forecasting model with chemistry (WRF-Chem) v3.2 code when using the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol module. These inconsistencies have been reported, and corrections have been implemented in WRF-Chem v4.1.3. Here, we use a WRF-Chem experimental setup configured over the Middle East (ME) to estimate the effects of these inconsistencies. Firstly, we show that the old version underestimates the PM2.5 diagnostic output by 7 % and overestimates PM10 by 5 % in comparison with the corrected one. Secondly, we demonstrate that submicron dust particles' contribution was incorrectly accounted for in the calculation of optical properties. Therefore, aerosol optical depth (AOD) in the old version was 25 %–30 % less than in the corrected one. Thirdly, we show that the gravitational settling procedure, in comparison with the corrected version, caused higher dust column loadings by 4 %–6 %, PM10 surface concentrations by 2 %–4 %, and mass of the gravitationally settled dust by 5 %–10 %. The cumulative effect of the found inconsistencies led to the significantly higher dust content in the atmosphere in comparison with the corrected WRF-Chem version. Our results explain why in many WRF-Chem simulations PM10 concentrations were exaggerated. We present the methodology for calculating diagnostics we used to estimate the impacts of introduced code modifications. We share the developed Merra2BC interpolator, which allows processing Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) output for constructing initial and boundary conditions for chemical species and aerosols.

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Section: Wrf-chem Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module

et al. 2021

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“…Dust aerosols emitted into the atmosphere can affect the climate system directly by modifying the radiation budget (Sokolik and Toon 1996;Huang et al 2006;Fu et al 2009), indirectly by altering cloud microphysical properties (Twomey et al 1984;Ackerman et al 2000;Sassen et al 2003;Andreae and Rosenfeld 2008;Wang et al 2010), and possibly by suppressing rainfall processes (Rosenfeld et al 2001;Solmon et al 2008;Creamean et al 2013). Dust can be transported thousands of kilometers from source regions to downwelling areas (Duce et al 1980;Hsu et al 2012;Huang et al 2015;Guo et al 2017;Chen et al 2018;Ma et al 2019). Once large amounts of dust aerosols are deposited into the oceans, soluble or bioavailable iron from dust aerosols can affect biogeochemical processes in the ocean (Jickells et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Impact of Arctic amplification on declining spring dust events in East Asia

Jun

Liu

et al. 2019

Clim Dyn

Self Cite

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Dust aerosols play key roles in affecting regional and global climate through their direct, indirect, and semi-direct effects. Dust events have decreased rapidly since the 1980s in East Asia, particularly over northern China, primarily because of changes in meteorological parameters (e.g. surface wind speed and precipitation). In this study, we found that winter (December–January–February) Arctic amplification associated with weakened temperature gradients along with decreased zonal winds is primarily responsible for the large decline in following spring (March–April–May) dust event occurrences over northern China since the mid-1980s. A dust index was developed for northern China by combining the daily frequency of three types of dust event (dust storm, blowing dust, and floating dust). Using the empirical orthogonal function (EOF) analysis, the first pattern of dust events was obtained for spring dust index anomalies, which accounts for 56.2% of the variability during 1961–2014. Moreover, the enhanced Arctic amplification and stronger Northern Hemisphere annular mode (NAM) in winter can result in the anticyclonic anomalies over Siberia and Mongolia, while cyclonic anomalies over East Europe in spring. These results are significantly correlated with the weakened temperature gradients, increased precipitation and soil moisture, and decreased snow cover extent in the mid-latitude over Northern Hemisphere. Based on the future predictions obtained from the Fifth Climate Models Intercomparison Project (CMIP5), we found that the dust event occurrences may continually decrease over northern China due to the enhanced Arctic amplification in future climate.

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“…Some studies (Kang et al, 2011;Foroutan et al, 2017;Ma et al, 2019) have implemented this formulation in the model and proposed the proper range of these parameters over different soil types. Many measurements from laboratory experiments and field observations have demonstrated the close relationship between α and u * (Gillette et al, 1997;Gomes et al, 2003;Rajot et al, 2003;Roney and White, 2006;Macpherson et al, 2008;Panebianco et al, 2016;Zhang et al, 2016). To improve the original scheme, we extract α from these measurements over different soil types, based on the expression of LS99, as depicted in Fig.…”

Section: Sandblasting Efficiency αmentioning

confidence: 99%

A revised mineral dust emission scheme in GEOS-Chem: improvements in dust simulations over China

Tian

Zhao

2021

Atmos. Chem. Phys.

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Abstract. Mineral dust plays a significant role in climate change and air quality, but large uncertainties remain in terms of dust emission prediction. In this study, we improved treatment of the dust emission process in a global 3-D chemical transport model (GEOS-Chem v12.6.0), by incorporating the geographical variation of aerodynamic roughness length (Z0), smooth roughness length (Z0s) and soil texture and by introducing the Owen effect and the formulation of the sandblasting efficiency α by Lu and Shao (1999). To investigate the impact of the modifications incorporated in the model, several sensitivity simulations were performed for a severe dust storm during 27 March to 2 April 2015 over northern China. Results show that simulated threshold friction velocity is very sensitive to the updated Z0 and Z0s field, with the relative difference ranging from 10 % to 60 % compared to the original model with a uniform value. The inclusion of the Owen effect leads to an increase in surface friction velocity, which mainly occurs in the arid and semi-arid regions of northwest China. The substitution of a fixed value of α assumed in the original scheme with one varying with friction velocity and soil texture based on observations reduces α by 50 % on average, especially over regions with sand texture. Comparisons of sensitivity simulations and measurements show that the revised scheme with the implementation of updates provides more realistic threshold friction velocities and PM10 mass concentrations. The performance of the improved model has been evaluated against surface PM10 observations as well as MODIS aerosol optical depth (AOD) values, showing that the spatial and temporal variation of mineral dust are better captured by the revised scheme. Due to the inclusion of the improvement, average PM10 concentrations at observational sites are more comparable to the observations, and the average mean bias (MB) and normalized mean bias (NMB) values are reduced from −196.29 µg m−3 and −52.79 % to −47.72 µg m−3 and −22.46 % respectively. Our study suggests that the erodibility factor, sandblasting efficiency and soil-related properties which are simply assumed in the empirical scheme may lack a physical mechanism and spatial–temporal representativeness. Further study and measurements should be conducted to obtain a more realistic and detailed map of these parameters in order to improve dust representation in the model.

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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)

Cited by 31 publications

References 98 publications

Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module

Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module

Impact of Arctic amplification on declining spring dust events in East Asia

A revised mineral dust emission scheme in GEOS-Chem: improvements in dust simulations over China

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