Inverse modeling of the 2021 spring  super dust storms in East Asia

Jin, Jianbing; Pang, Mijie; Segers, Arjo; Han, Wei; Li, Fang; Li, Baojie; Feng, Haochuan; Lin, Hai; Liao, Hong

doi:10.5194/acp-22-6393-2022

Cited by 26 publications

(28 citation statements)

References 58 publications

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“…The dust plume formed at 12:00 CST on 14 March is triggered by the strong wind associated with the movement of the convective system. These findings are consistent with the results of Jin et al (2022) using inverse modeling. They revealed that windblown dust emissions originating from both China and Mongolia contribute to the SDS events that occur in spring 2021.…”

Section: Dust Source Region and Emissionsupporting

confidence: 92%

Record-breaking dust loading during two mega dust storm events over northern China in March 2021: aerosol optical and radiative properties and meteorological drivers

et al. 2022

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Abstract. Although a remarkable reduction in the frequency of sand and dust storms (SDSs) in the past several decades has been reported over northern China (NC), two unexpected mega SDSs occurred on 15–20 and 27–29 March 2021 (abbreviated as the “3.15” and “3.27” SDS events), which has reawakened widespread concern. This study characterizes the optical, microphysical, and radiative properties of aerosols and their meteorological drivers during these two SDS events using the Sun photometer observations in Beijing and a comprehensive set of multiple satellite (including MODIS, VIIRS, CALIOP, and Himawari-8) and ground-based observations (including the CMA visibility network and AD-Net) combined with atmospheric reanalysis data. Moreover, a long-term (2000–2021) dust optical depth (DOD) dataset retrieved from MODIS measurements was also utilized to evaluate the historical ranking of the dust loading in NC during dust events. During the 3.15 and 3.27 events, the invasion of dust plumes greatly degraded the visibility over large areas of NC, with extreme low visibility of 50 and 500 m recorded at most sites on 15 and 28 March, respectively. Despite the shorter duration of the 3.27 event relative to the 3.15 event, sun photometer and satellite observations in Beijing recorded a larger peak AOD (∼2.5) in the former than in the latter (∼2.0), which was mainly attributed to the short-term intrusion of coarse-mode dust particles with larger effective radii (∼1.9 µm) and volume concentrations (∼2.0 µm3 µm−2) during the 3.27 event. The shortwave direct aerosol radiative forcing induced by dust was estimated to be −92.1 and −111.4 W m−2 at the top of the atmosphere, −184.7 and −296.2 W m−2 at the surface, and +92.6 and +184.8 W m−2 in the atmosphere in Beijing during the 3.15 and 3.27 events, respectively. CALIOP observations show that during the 3.15 event the dust plume was lifted to an altitude of 4–8 km, and its range of impact extended from the dust source to the eastern coast of China. In contrast, the lifting height of the dust plume during the 3.27 event was lower than that during the 3.15 event, which was also confirmed by ground-based lidar observations. The MODIS-retrieved DOD data registered these two massive SDS events as the most intense episode in the same period in history over the past 2 decades. These two extreme SDS events were associated with both atmospheric circulation extremes and local meteorological anomalies that favored enhanced dust emissions in the Gobi Desert (GD) across southern Mongolia and NC. Meteorological analysis revealed that both SDS events were triggered by an exceptionally strong Mongolian cyclone generated at nearly the same location (along the central and eastern plateau of Inner Mongolia) in conjunction with a surface-level cold high-pressure system at the rear, albeit with differences in magnitude and spatial extent of impact. In the GD, the early melting of spring snow caused by near-surface temperature anomalies over dust source regions, together with negative soil moisture anomalies induced by decreased precipitation, formed drier and barer soil surfaces, which allowed for increased emissions of dust into the atmosphere by strongly enhanced surface winds generated by the Mongolian cyclone.

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Section: Dust Source Region and Emissionsupporting

confidence: 92%

Record-breaking dust loading during two mega dust storm events over northern China in March 2021: aerosol optical and radiative properties and meteorological drivers

et al. 2022

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“…The analysis of global dust emission using inversion model constrained by available observation data reveals that current dust models on average underestimate the Asian dust emission (Kok et al, 2021). Consistently, a recent study applying data assimilation to derive dust emission suggests > 15 million tons of dust emitted from the Chinese and Mongolian Gobi Desert during the 2021 East Asia dust storm event (Jin et al, 2022). This is much higher than that in FLEXDUST-update, MERRA-2 and CAMS-F, suggesting that the dust emission schemes used in these models may miss important processes that can invigorate extreme dust emission during the dust storm event.…”

Section: Underestimation Of Dust Emission and Concentration In Flexdu...mentioning

confidence: 52%

“…It has been found that the barer, drier and more loosened soil due to the anomalous early snow melting and a lack of precipitation in spring over the dust source region, together with the exceptionally strong Mongolian cyclone developed over the source region (i.e., central and eastern Mongolia), had triggered the emission and transport of enormous amounts of dust to East Asia during this event (Gui et al, 2022;Yin et al, 2022). The 2021 East Asia sandstorm has provided a unique testbed for dust modelling, particularly in regard to dust emission schemes which has the largest uncertainties (Jin et al, 2022;Wang et al, 2022). The ability of dust models in reproducing such extreme dust events will be critical for understanding the causes and projecting the occurrences of such extreme events in the future.…”

Section: Introductionmentioning

confidence: 99%

Modelling the 2021 East Asia super dust storm using FLEXPART and FLEXDUST and its comparison with reanalyses and observations

Tang

Haugvaldstad²,

Stordal³

et al. 2023

Front. Environ. Sci.

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The 2021 East Asia sandstorm began from the Eastern Gobi desert steppe in Mongolia on March 14, and later spread to northern China and the Korean Peninsula. It was the biggest sandstorm to hit China in a decade, causing severe air pollution and a significant threat to human health. Capturing and predicting such extreme events is critical for society. The Lagrangian particle dispersion model FLEXPART and the associated dust emission model FLEXDUST have been recently developed and applied to simulate global dust cycles. However, how well the model captures Asian dust storm events remains to be explored. In this study, we applied FLEXPART to simulate the recent 2021 East Asia sandstorm, and evaluated its performance comparing with observation and observation-constrained reanalysis datasets, such as the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and CAMS global atmospheric composition forecasts (CAMS-F). We found that the default setting of FLEXDUST substantially underestimates the strength of dust emission and FLEXPART modelled dust concentration in this storm compared to that in MERRA-2 and CAMS-F. An improvement of the parametrization of bare soil fraction, topographical scaling, threshold friction velocity and vertical dust flux scheme based on Kok et al. (Atmospheric Chemistry and Physics, 2014, 14, 13023–13041) in FLEXDUST can reproduce the strength and spatio-temporal pattern of the dust storm comparable to MERRA-2 and CAMS-F. However, it still underestimates the observed spike of dust concentration during the dust storm event over northern China, and requires further improvement in the future. The improved FLEXDUST and FLEXPART perform better than MERRA-2 and CAMS-F in capturing the observed particle size distribution of dust aerosols, highlighting the importance of using more dust size bins and size-dependent parameterization for dust emission, and dry and wet deposition schemes for modelling the Asian dust cycle and its climatic feedbacks.

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“…At the same time we see rapid advances in sensor technologies and the availability of aerosol measurements from large-scale network activities that can complement the modeling activities. Those measurements are preferably used to calibrate models and to perform model error corrections through the application of data assimilation techniques (Kalnay, 2002). Examples of popular aerosol measurements used for this purpose are groundbased lidar data (Yumimoto et al, 2008), surface particular matter (PM) concentration observations (Lin et al, 2008;Jin et al, 2018Jin et al, , 2019a, polar-orbiting satellite observations (Schutgens et al, 2012;Khade et al, 2013;Yumimoto et al, 2016a;Di Tomaso et al, 2017;Jin et al, 2022) and geostationary remote sensing data (Yumimoto et al, 2016b;Jin et al, 2019bJin et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%

“…Those measurements are preferably used to calibrate models and to perform model error corrections through the application of data assimilation techniques (Kalnay, 2002). Examples of popular aerosol measurements used for this purpose are groundbased lidar data (Yumimoto et al, 2008), surface particular matter (PM) concentration observations (Lin et al, 2008;Jin et al, 2018Jin et al, , 2019a, polar-orbiting satellite observations (Schutgens et al, 2012;Khade et al, 2013;Yumimoto et al, 2016a;Di Tomaso et al, 2017;Jin et al, 2022) and geostationary remote sensing data (Yumimoto et al, 2016b;Jin et al, 2019bJin et al, , 2020. Among available measurements, satellite aerosol products provide valuable information through their high spatial coverage: a single instrument is used to observe a large spatial area making additional harmonization efforts unnecessary.…”

Section: Introductionmentioning

confidence: 99%

How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent

Jin

Henzing²,

Segers³

2023

Atmos. Chem. Phys.

Self Cite

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Abstract. Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that, to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (hybrid AOD assimilation) that includes two sequentially conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of Ångström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (standard AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur due to either the standard AOD assimilation analysis or the hybrid AOD assimilation analysis are evaluated against independent ground PM2.5 observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.

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Inverse modeling of the 2021 spring super dust storms in East Asia

Cited by 26 publications

References 58 publications

Record-breaking dust loading during two mega dust storm events over northern China in March 2021: aerosol optical and radiative properties and meteorological drivers

Record-breaking dust loading during two mega dust storm events over northern China in March 2021: aerosol optical and radiative properties and meteorological drivers

Modelling the 2021 East Asia super dust storm using FLEXPART and FLEXDUST and its comparison with reanalyses and observations

How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent

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