Modeling Dust in East Asia by CESM and Sources of Biases

Wu, Mingxuan; Liu, Xiaohong; Yang, Ki Yeon; Luo, Tao; Wang, Zhien; Wu, Chenglai; Zhang, Kai; Yu, Hongbin; Darmenov, Anton

doi:10.1029/2019jd030799

Cited by 33 publications

(38 citation statements)

References 80 publications

(151 reference statements)

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“…The three models also overestimate DOD over Northwest China due to the overestimation of dust emission in MAM, JJA, and SON. Wu et al (2019) showed that CESM1 with K14 dust emission scheme better agrees with CALIOP observations in Northwest China. Since the source functions used in the four models are all zeros north to 60°N, the four models don't produce any dust emissions in NH high-latitude regions, while ground observations indicate considerable local dust sources.…”

Section: Discussionmentioning

confidence: 61%

“…8d, h, and l. We note that there are considerable differences between satellite retrievals from L15 and Y15. Dust extinction from L15 is larger in the upper troposphere and lower in the lower troposphere than that from Y15, which is due to different dust identification and separation methods (Wu et al, 2019).…”

Section: Dust Extinctionmentioning

confidence: 94%

“…extinction in the lower troposphere over main source regions, similar as Yu et al (2010). Wu et al (2019) compared dust extinction modeled by the Community Earth System Model (CESM) with satellite retrievals from Luo et al (2015aLuo et al ( , 2015b L15, Yu et al (2015) Y15, and standard CALIOP level 3 (CAL-L3) product and found high model biases of dust extinction in the upper troposphere and large uncertainties in different CALIPSO products over East Asia.…”

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confidence: 99%

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Understanding Processes that Control Dust Spatial Distributions with Global Climate Models and Satellite Observations

Wu¹,

Liu²,

Yu³

et al. 2020

Preprint

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<p><strong>Abstract.</strong> Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the northern hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the southern hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions.</p>

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Section: Discussionmentioning

confidence: 61%

Section: Dust Extinctionmentioning

confidence: 94%

mentioning

confidence: 99%

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Understanding Processes that Control Dust Spatial Distributions with Global Climate Models and Satellite Observations

Wu¹,

Liu²,

Yu³

et al. 2020

Preprint

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“…Dust emission is the first and foremost process for model improvements of dust cycle (Shao, 2008;Shao et al, 2011). Improving dust emission not only lies in the development of dust emission scheme but also in its implementation into climate models (e.g., Shao, 2008;Wu et al, 2016;Wu et al, 2019). For example, different dust emission schemes with specific land cover datasets and criteria for the occurrence of dust emission are adopted in the models (Table 1 and references therein).…”

Section: Discussionmentioning

confidence: 99%

“…Chem. Phys., 19, 12025-12049, https://doi.org/10.5194/acp-19-12025-2019, 2019 Randles, C. A., Silva, A. M. d., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. 108, 4416, https://doi.org/10.1029/2002jd002775, 2003. https://doi.org/10.5194/acp-2020-179 Preprint. Discussion started: 3 April c Author(s) 2020.…”

Section: Discussionmentioning

confidence: 99%

Global dust cycle and uncertainty in CMIP5 models

Wu¹,

Lin²,

Liu³

2020

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Abstract. Dust cycle is an important component of the Earth system and have been implemented into climate models and Earth System Models (ESMs). An assessment of the dust cycle in these models is vital to address the strengths and weaknesses of these models in simulating dust aerosol and its interactions with the Earth system and enhance the future model developments. This study presents a comprehensive evaluation of global dust cycle in 15 models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The various models are compared with each other and with an aerosol reanalysis as well as station observations of dust deposition and concentrations. The results show that the global dust emission in these models ranges from 735 to 8186 Tg yr−1 and the annual mean dust burden ranges from 2.5 to 41.9 Tg, both of which scatter by a factor of about 10–20. The models generally agree with each other and observations in reproducing the dust belt that extends from North Africa, Middle East, Central and South Asia, to East Asia, although they differ largely in the spatial extent of this dust belt. The models also differ in other dust source regions such as North America and Australia, where the contributions of these sources to global dust emissions vary by a factor of more than 500. We suggest that the coupling of dust emission with dynamic vegetation can enlarge the range of simulated dust emission. For the removal process, all the models estimate that wet deposition is a smaller sink than dry deposition and wet deposition accounts for 12–39 % of total deposition. The models also estimate that most (77–91 %) of dust particles are deposited onto continents and 9–23 % of them are deposited into oceans. A linear relationship between dust burden, lifetime, and fraction of wet deposition to total deposition from these models suggests a general consistency among the models. Compared to the observations, most models reproduce the dust deposition and dust concentrations within a factor of 10 at most stations, but larger biases by more than a factor of 10 are also noted at specific regions and for certain models. These results cast a doubt on the interpretation of the simulations of dust-affected fields in climate models and highlight the need for further improvements of dust cycle especially on dust emission in climate models.

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Brown Carbon Fuel and Emission Source Attributions to Global Snow Darkening Effect

Brown¹,

Wang²,

Flanner³

et al. 2021

Preprint

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 Brown carbon forcing on snow is larger than in previous modeling studies and is comparable to the snow darkening effect of black carbon. Northern Asia, Southeast Asia, and Southern Africa are the largest source contributors to global brown carbon snow darkening effect. Seasonal brown carbon is closely correlated with snow processes in the model, indicating the importance of aerosol-snow feedbacks.

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Modeling Dust in East Asia by CESM and Sources of Biases

Cited by 33 publications

References 80 publications

Understanding Processes that Control Dust Spatial Distributions with Global Climate Models and Satellite Observations

Understanding Processes that Control Dust Spatial Distributions with Global Climate Models and Satellite Observations

Global dust cycle and uncertainty in CMIP5 models

Brown Carbon Fuel and Emission Source Attributions to Global Snow Darkening Effect

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