Articles | Volume 22, issue 3
https://doi.org/10.5194/acp-22-2095-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-2095-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How well do the CMIP6 models simulate dust aerosols?
Department of Meteorology, University of Reading, Reading, UK
National Centre for Atmospheric Science, Reading, UK
Claire L. Ryder
Department of Meteorology, University of Reading, Reading, UK
Laura J. Wilcox
Department of Meteorology, University of Reading, Reading, UK
National Centre for Atmospheric Science, Reading, UK
Related authors
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
David S. Stevenson, Alcide Zhao, Vaishali Naik, Fiona M. O'Connor, Simone Tilmes, Guang Zeng, Lee T. Murray, William J. Collins, Paul T. Griffiths, Sungbo Shim, Larry W. Horowitz, Lori T. Sentman, and Louisa Emmons
Atmos. Chem. Phys., 20, 12905–12920, https://doi.org/10.5194/acp-20-12905-2020, https://doi.org/10.5194/acp-20-12905-2020, 2020
Short summary
Short summary
We present historical trends in atmospheric oxidizing capacity (OC) since 1850 from the latest generation of global climate models and compare these with estimates from measurements. OC controls levels of many key reactive gases, including methane (CH4). We find small model trends up to 1980, then increases of about 9 % up to 2014, disagreeing with (uncertain) measurement-based trends. Major drivers of OC trends are emissions of CH4, NOx, and CO; these will be important for future CH4 trends.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025, https://doi.org/10.5194/acp-25-8289-2025, 2025
Short summary
Short summary
The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. We review its contribution to AR6 (Sixth Assessment Report of the Intergovernmental Panel on Climate Change) and the wider understanding of the role of these species in climate and climate change. We identify challenges and provide recommendations to improve the utility and uptake of climate model data, detailed summary tables of CMIP6 models, experiments, and emergent diagnostics.
Feifei Luo, Bjørn H. Samset, Camilla W. Stjern, Manoj Joshi, Laura J. Wilcox, Robert J. Allen, Wei Hua, and Shuanglin Li
Atmos. Chem. Phys., 25, 7647–7667, https://doi.org/10.5194/acp-25-7647-2025, https://doi.org/10.5194/acp-25-7647-2025, 2025
Short summary
Short summary
Black carbon (BC) aerosol is emitted from the incomplete combustion of biomass and fossil fuels. We found that Asian BC leads to strong local cooling and drying. Reductions in precipitation primarily depend on the thermodynamic effects due to solar radiation absorption by BC. The combined thermodynamic and dynamic effects shape the spatial pattern of precipitation responses to Asian BC. These results help us further understand the impact of emissions of anthropogenic aerosols on Asian climate.
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-3189, https://doi.org/10.5194/egusphere-2025-3189, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This manuscript defines as a list of variables and scientific opportunities which are requested from the CMIP7 Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
Wah Kin Michael Lai, Jon Robson, Laura Wilcox, Nick Dunstone, and Rowan Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2025-2598, https://doi.org/10.5194/egusphere-2025-2598, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In a climate model at two different resolutions, anthropogenic aerosols induce a fast cooling followed by a delayed warming in the subpolar North Atlantic. The delayed warming is stronger at higher resolution due to a stronger Atlantic Meridional Overturning Circulation (AMOC) response. This difference is due to the lower resolution model having more sea ice which insulates the ocean. This result show that the North Atlantic response to external forcing is sensitive to regional differences.
Duncan Watson-Parris, Laura J. Wilcox, Camilla W. Stjern, Robert J. Allen, Geeta Persad, Massimo A. Bollasina, Annica M. L. Ekman, Carley E. Iles, Manoj Joshi, Marianne T. Lund, Daniel McCoy, Daniel M. Westervelt, Andrew I. L. Williams, and Bjørn H. Samset
Atmos. Chem. Phys., 25, 4443–4454, https://doi.org/10.5194/acp-25-4443-2025, https://doi.org/10.5194/acp-25-4443-2025, 2025
Short summary
Short summary
In 2020, regulations by the International Maritime Organization aimed to reduce aerosol emissions from ships. These aerosols previously had a cooling effect, which the regulations might reduce, revealing more greenhouse gas warming. Here we find that, while there is regional warming, the global 2020–2040 temperature rise is only +0.03 °C. This small change is difficult to distinguish from natural climate variability, indicating the regulations have had a limited effect on observed warming to date.
Alcide Zhao, Laura J. Wilcox, and Claire L. Ryder
Atmos. Chem. Phys., 24, 13385–13402, https://doi.org/10.5194/acp-24-13385-2024, https://doi.org/10.5194/acp-24-13385-2024, 2024
Short summary
Short summary
Climate models include desert dust aerosols, which cause atmospheric heating and can change circulation patterns. We assess the effect of dust on the Indian and east Asian summer monsoons through multi-model experiments isolating the effect of dust in current climate models for the first time. Dust atmospheric heating results in a southward shift of western Pacific equatorial rainfall and an enhanced Indian summer monsoon. This shows the importance of accurate dust representation in models.
Natalie G. Ratcliffe, Claire L. Ryder, Nicolas Bellouin, Stephanie Woodward, Anthony Jones, Ben Johnson, Lisa-Maria Wieland, Maximilian Dollner, Josef Gasteiger, and Bernadett Weinzierl
Atmos. Chem. Phys., 24, 12161–12181, https://doi.org/10.5194/acp-24-12161-2024, https://doi.org/10.5194/acp-24-12161-2024, 2024
Short summary
Short summary
Large mineral dust particles are more abundant in the atmosphere than expected and have different impacts on the environment than small particles, which are better represented in climate models. We use aircraft measurements to assess a climate model representation of large-dust transport. We find that the model underestimates the amount of large dust at all stages of transport and that fast removal of the large particles increases this underestimation with distance from the Sahara.
Catherine Anne Toolan, Joe Adabouk Amooli, Laura J. Wilcox, Bjørn H. Samset, Andrew G. Turner, and Daniel M. Westervelt
EGUsphere, https://doi.org/10.5194/egusphere-2024-3057, https://doi.org/10.5194/egusphere-2024-3057, 2024
Short summary
Short summary
Our research explores how well air pollution and rainfall patterns in Africa are represented in current climate models, by comparing model data to observations from 1981 to 2023. While most models capture seasonal air quality changes well, they struggle to replicate the distribution of non-dust pollutants and certain rainfall patterns, especially over east Africa. Improving these models is crucial for better climate predictions and preparing for future risks.
Claire L. Ryder, Clément Bézier, Helen F. Dacre, Rory Clarkson, Vassilis Amiridis, Eleni Marinou, Emmanouil Proestakis, Zak Kipling, Angela Benedetti, Mark Parrington, Samuel Rémy, and Mark Vaughan
Nat. Hazards Earth Syst. Sci., 24, 2263–2284, https://doi.org/10.5194/nhess-24-2263-2024, https://doi.org/10.5194/nhess-24-2263-2024, 2024
Short summary
Short summary
Desert dust poses a hazard to aircraft via degradation of engine components. This has financial implications for the aviation industry and results in increased fuel burn with climate impacts. Here we quantify dust ingestion by aircraft engines at airports worldwide. We find Dubai and Delhi in summer are among the dustiest airports, where substantial engine degradation would occur after 1000 flights. Dust ingestion can be reduced by changing take-off times and the altitude of holding patterns.
Zhen Liu, Massimo A. Bollasina, and Laura J. Wilcox
Atmos. Chem. Phys., 24, 7227–7252, https://doi.org/10.5194/acp-24-7227-2024, https://doi.org/10.5194/acp-24-7227-2024, 2024
Short summary
Short summary
The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model-simulated spatio-temporal variability in the climatological monsoon precipitation across Asia, which critically modulates the efficacy of aerosol–cloud–precipitation interactions, the predominant driver of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response.
Emmanouil Proestakis, Antonis Gkikas, Thanasis Georgiou, Anna Kampouri, Eleni Drakaki, Claire L. Ryder, Franco Marenco, Eleni Marinou, and Vassilis Amiridis
Atmos. Meas. Tech., 17, 3625–3667, https://doi.org/10.5194/amt-17-3625-2024, https://doi.org/10.5194/amt-17-3625-2024, 2024
Short summary
Short summary
A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer diameter) and coarse-mode (supermicrometer diameter) components of atmospheric pure dust is presented. The dataset is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and datasets, and air quality.
Jonathan Elsey, Nicolas Bellouin, and Claire Ryder
Atmos. Chem. Phys., 24, 4065–4081, https://doi.org/10.5194/acp-24-4065-2024, https://doi.org/10.5194/acp-24-4065-2024, 2024
Short summary
Short summary
Aerosols influence the Earth's energy balance. The uncertainty in this radiative forcing is large depending partly on uncertainty in measurements of aerosol optical properties. We have developed a freely available new framework of millions of radiative transfer simulations spanning aerosol uncertainty and assess the impact on radiative forcing uncertainty. We find that reducing these uncertainties would reduce radiative forcing uncertainty, but non-aerosol uncertainties must also be considered.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Jianyu Zheng, Zhibo Zhang, Hongbin Yu, Anne Garnier, Qianqian Song, Chenxi Wang, Claudia Di Biagio, Jasper F. Kok, Yevgeny Derimian, and Claire Ryder
Atmos. Chem. Phys., 23, 8271–8304, https://doi.org/10.5194/acp-23-8271-2023, https://doi.org/10.5194/acp-23-8271-2023, 2023
Short summary
Short summary
We developed a multi-year satellite-based retrieval of dust optical depth at 10 µm and the coarse-mode dust effective diameter over global oceans. It reveals climatological coarse-mode dust transport patterns and regional differences over the North Atlantic, the Indian Ocean and the North Pacific.
Eleni Drakaki, Vassilis Amiridis, Alexandra Tsekeri, Antonis Gkikas, Emmanouil Proestakis, Sotirios Mallios, Stavros Solomos, Christos Spyrou, Eleni Marinou, Claire L. Ryder, Demetri Bouris, and Petros Katsafados
Atmos. Chem. Phys., 22, 12727–12748, https://doi.org/10.5194/acp-22-12727-2022, https://doi.org/10.5194/acp-22-12727-2022, 2022
Short summary
Short summary
State-of-the-art atmospheric dust models have limitations in accounting for a realistic dust size distribution (emission, transport). We modify the parameterization of the mineral dust cycle by including particles with diameter >20 μm, as indicated by observations over deserts. Moreover, we investigate the effects of reduced settling velocities of dust particles. Model results are evaluated using airborne and spaceborne dust measurements above Cabo Verde.
Anthony C. Jones, Adrian Hill, John Hemmings, Pascal Lemaitre, Arnaud Quérel, Claire L. Ryder, and Stephanie Woodward
Atmos. Chem. Phys., 22, 11381–11407, https://doi.org/10.5194/acp-22-11381-2022, https://doi.org/10.5194/acp-22-11381-2022, 2022
Short summary
Short summary
As raindrops fall to the ground, they capture aerosol (i.e. below-cloud scavenging or BCS). Many different BCS schemes are available to climate models, and it is unclear what the impact of selecting one scheme over another is. Here, various BCS models are outlined and then applied to mineral dust in climate model simulations. We find that dust concentrations are highly sensitive to the BCS scheme, with dust atmospheric lifetimes ranging from 5 to 44 d.
Jie Zhang, Kalli Furtado, Steven T. Turnock, Jane P. Mulcahy, Laura J. Wilcox, Ben B. Booth, David Sexton, Tongwen Wu, Fang Zhang, and Qianxia Liu
Atmos. Chem. Phys., 21, 18609–18627, https://doi.org/10.5194/acp-21-18609-2021, https://doi.org/10.5194/acp-21-18609-2021, 2021
Short summary
Short summary
The CMIP6 ESMs systematically underestimate TAS anomalies in the NH midlatitudes, especially from 1960 to 1990. The anomalous cooling is concurrent in time and space with anthropogenic SO2 emissions. The spurious drop in TAS is attributed to the overestimated aerosol concentrations. The aerosol forcing sensitivity cannot well explain the inter-model spread of PHC biases. And the cloud-amount term accounts for most of the inter-model spread in aerosol forcing sensitivity.
Liang Guo, Laura J. Wilcox, Massimo Bollasina, Steven T. Turnock, Marianne T. Lund, and Lixia Zhang
Atmos. Chem. Phys., 21, 15299–15308, https://doi.org/10.5194/acp-21-15299-2021, https://doi.org/10.5194/acp-21-15299-2021, 2021
Short summary
Short summary
Severe haze remains serious over Beijing despite emissions decreasing since 2008. Future haze changes in four scenarios are studied. The pattern conducive to haze weather increases with the atmospheric warming caused by the accumulation of greenhouse gases. However, the actual haze intensity, measured by either PM2.5 or optical depth, decreases with aerosol emissions. We show that only using the weather pattern index to predict the future change of Beijing haze is insufficient.
Hongbin Yu, Qian Tan, Lillian Zhou, Yaping Zhou, Huisheng Bian, Mian Chin, Claire L. Ryder, Robert C. Levy, Yaswant Pradhan, Yingxi Shi, Qianqian Song, Zhibo Zhang, Peter R. Colarco, Dongchul Kim, Lorraine A. Remer, Tianle Yuan, Olga Mayol-Bracero, and Brent N. Holben
Atmos. Chem. Phys., 21, 12359–12383, https://doi.org/10.5194/acp-21-12359-2021, https://doi.org/10.5194/acp-21-12359-2021, 2021
Short summary
Short summary
This study characterizes a historic African dust intrusion into the Caribbean Basin in June 2020 using satellites and NASA GEOS. Dust emissions in West Africa were large albeit not extreme. However, a unique synoptic system accumulated the dust near the coast for about 4 d before it was ventilated. Although GEOS reproduced satellite-observed plume tracks well, it substantially underestimated dust emissions and did not lift up dust high enough for ensuing long-range transport.
Lixia Zhang, Laura J. Wilcox, Nick J. Dunstone, David J. Paynter, Shuai Hu, Massimo Bollasina, Donghuan Li, Jonathan K. P. Shonk, and Liwei Zou
Atmos. Chem. Phys., 21, 7499–7514, https://doi.org/10.5194/acp-21-7499-2021, https://doi.org/10.5194/acp-21-7499-2021, 2021
Short summary
Short summary
The projected frequency of circulation patterns associated with haze events and global warming increases significantly due to weakening of the East Asian winter monsoon. Rapid reduction in anthropogenic aerosol further increases the frequency of circulation patterns, but haze events are less dangerous. We revealed competing effects of aerosol emission reductions on future haze events through their direct contribution to haze intensity and their influence on the atmospheric circulation patterns.
Rei Kudo, Henri Diémoz, Victor Estellés, Monica Campanelli, Masahiro Momoi, Franco Marenco, Claire L. Ryder, Osamu Ijima, Akihiro Uchiyama, Kouichi Nakashima, Akihiro Yamazaki, Ryoji Nagasawa, Nozomu Ohkawara, and Haruma Ishida
Atmos. Meas. Tech., 14, 3395–3426, https://doi.org/10.5194/amt-14-3395-2021, https://doi.org/10.5194/amt-14-3395-2021, 2021
Short summary
Short summary
A new method, Skyrad pack MRI version 2, was developed to retrieve aerosol physical and optical properties, water vapor, and ozone column concentrations from the sky radiometer, a filter radiometer deployed in the SKYNET international network. Our method showed good performance in a radiative closure study using surface solar irradiances from the Baseline Surface Radiation Network and a comparison using aircraft in situ measurements of Saharan dust events during the SAVEX-D 2015 campaign.
Jonathan K. P. Shonk, Andrew G. Turner, Amulya Chevuturi, Laura J. Wilcox, Andrea J. Dittus, and Ed Hawkins
Atmos. Chem. Phys., 20, 14903–14915, https://doi.org/10.5194/acp-20-14903-2020, https://doi.org/10.5194/acp-20-14903-2020, 2020
Short summary
Short summary
We use a set of model simulations of the 20th century to demonstrate that the uncertainty in the cooling effect of man-made aerosol emissions has a wide range of impacts on global monsoons. For the weakest cooling, the impact of aerosol is overpowered by greenhouse gas (GHG) warming and monsoon rainfall increases in the late 20th century. For the strongest cooling, aerosol impact dominates over GHG warming, leading to reduced monsoon rainfall, particularly from 1950 to 1980.
David S. Stevenson, Alcide Zhao, Vaishali Naik, Fiona M. O'Connor, Simone Tilmes, Guang Zeng, Lee T. Murray, William J. Collins, Paul T. Griffiths, Sungbo Shim, Larry W. Horowitz, Lori T. Sentman, and Louisa Emmons
Atmos. Chem. Phys., 20, 12905–12920, https://doi.org/10.5194/acp-20-12905-2020, https://doi.org/10.5194/acp-20-12905-2020, 2020
Short summary
Short summary
We present historical trends in atmospheric oxidizing capacity (OC) since 1850 from the latest generation of global climate models and compare these with estimates from measurements. OC controls levels of many key reactive gases, including methane (CH4). We find small model trends up to 1980, then increases of about 9 % up to 2014, disagreeing with (uncertain) measurement-based trends. Major drivers of OC trends are emissions of CH4, NOx, and CO; these will be important for future CH4 trends.
Debbie O'Sullivan, Franco Marenco, Claire L. Ryder, Yaswant Pradhan, Zak Kipling, Ben Johnson, Angela Benedetti, Melissa Brooks, Matthew McGill, John Yorks, and Patrick Selmer
Atmos. Chem. Phys., 20, 12955–12982, https://doi.org/10.5194/acp-20-12955-2020, https://doi.org/10.5194/acp-20-12955-2020, 2020
Short summary
Short summary
Mineral dust is an important component of the climate system, and we assess how well it is predicted by two operational models. We flew an aircraft in the dust layers in the eastern Atlantic, and we also make use of satellites. We show that models predict the dust layer too low and that it predicts the particles to be too small. We believe that these discrepancies may be overcome if models can be constrained with operational observations of dust vertical and size-resolved distribution.
Laura J. Wilcox, Zhen Liu, Bjørn H. Samset, Ed Hawkins, Marianne T. Lund, Kalle Nordling, Sabine Undorf, Massimo Bollasina, Annica M. L. Ekman, Srinath Krishnan, Joonas Merikanto, and Andrew G. Turner
Atmos. Chem. Phys., 20, 11955–11977, https://doi.org/10.5194/acp-20-11955-2020, https://doi.org/10.5194/acp-20-11955-2020, 2020
Short summary
Short summary
Projected changes in man-made aerosol range from large reductions to moderate increases in emissions until 2050. Rapid reductions between the present and the 2050s lead to enhanced increases in global and Asian summer monsoon precipitation relative to scenarios with continued increases in aerosol. Relative magnitude and spatial distribution of aerosol changes are particularly important for South Asian summer monsoon precipitation changes, affecting the sign of the trend in the coming decades.
Cited articles
Adebiyi, A. A. and Kok, J. F.: Climate models miss most of the coarse dust in the atmosphere, Sci. Adv., 6, eaaz9507,
https://doi.org/10.1126/sciadv.aaz9507, 2020.
Albani, S., Mahowald, N. M., Perry, A. T., Scanza, R. A., Zender, C. S.,
Heavens, N. G., Maggi, V., Kok, J. F., and Otto-Bliesner, B. L.: Improved
dust representation in the Community Atmosphere Model, J. Adv. Model. Earth Syst., 6, 541–570, https://doi.org/10.1002/2013MS000279, 2014.
Bauer, S. E., Tsigaridis, K., Faluvegi, G., Kelley, M., Lo, K. K., Miller,
R. L., Nazarenko, L., Schmidt, G. A., and Wu, J.: Historical (1850–2014)
Aerosol Evolution and Role on Climate Forcing Using the GISS ModelE2.1
Contribution to CMIP6, J. Adv. Model. Earth Syst., 12, e2019MS001978,
https://doi.org/10.1029/2019MS001978, 2020.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., Lavergne, C. de, Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé,
C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, L., E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N.,
Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S.,
Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul,
L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P.,
Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P.,
Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle,
M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and
Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Syst., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020.
Cakmur, R. V., Miller, R. L., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P.,
Koch, D., Kohfeld, K. E., Tegen, I., and Zender, C. S.: Constraining the
magnitude of the global dust cycle by minimizing the difference between a
model and observations, J. Geophys. Res., 111, D06207, https://doi.org/10.1029/2005JD005791, 2006.
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F., Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell, G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe, S., Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, Future Gener. Comp. Sy., 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014 (data available at: https://esgf-data.dkrz.de/search/cmip6-dkrz/, last access: 10 February 2022).
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S.,
Kampenhout, L. van, Vertenstein, M., Bertini, A., Dennis, J., Deser, C.,
Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J.,
Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E.,
Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System
Model Version 2 (CESM2), J. Adv. Model. Earth Syst., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020.
Di Biagio, C., Balkanski, Y., Albani, S., Boucher, O., and Formenti, P.:
Direct Radiative Effect by Mineral Dust Aerosols Constrained by New
Microphysical and Spectral Optical Data, Geophys. Res. Lett., 47, e2019GL086186, https://doi.org/10.1029/2019GL086186, 2020.
Evan, A. T.: Surface Winds and Dust Biases in Climate Models, Geophys. Res. Lett., 45, 1079–1085, https://doi.org/10.1002/2017GL076353, 2018.
Evan, A. T., Flamant, C., Fiedler, S., and Doherty, O.: An analysis of
aeolian dust in climate models, Geophys. Res. Lett., 41, 5996–6001,
https://doi.org/10.1002/2014GL060545, 2014.
Evans, S., Dawson, E., and Ginoux, P.: Linear Relation Between Shifting ITCZ
and Dust Hemispheric Asymmetry, Geophys. Res. Lett., 47, e2020GL090499,
https://doi.org/10.1029/2020GL090499, 2020.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins,
W. J., Cox, P., Driouech, F., Emori, S., and Eyring, V.: Evaluation of
Climate Models, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University, 5, 741–866, ISBN 978-1-107-05799-1, 2013.
Gassó, S., Grassian, V. H., and Miller, R. L.: Interactions between
Mineral Dust, Climate, and Ocean Ecosystems, Elements, 6, 247–252,
https://doi.org/10.2113/gselements.6.4.247, 2010.
Gates, W. L., Boyle, J., Covey, C., Dease, C., Doutriaux, C., Drach, R., Fiorino, M., Gleckler, P., Hnilo, J., Marlais, S., Phillips, T., Potter, G., Santer, B., Sperber, K., Taylor, K., and Williams, D.: An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I), B. Am. Meteorol. Soc., 73, 1962–1970, 1998.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
Silva, A. M. da, Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O.,
and Lin, S.-J.: Sources and distributions of dust aerosols simulated with
the GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.:
Global-scale attribution of anthropogenic and natural dust sources and their
emission rates based on MODIS Deep Blue aerosol products, Rev. Geophys., 50, RG3005, https://doi.org/10.1029/2012RG000388, 2012.
Gkikas, A., Proestakis, E., Amiridis, V., Kazadzis, S., Di Tomaso, E., Tsekeri, A., Marinou, E., Hatzianastassiou, N., and Pérez García-Pando, C.: ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set, Atmos. Meas. Tech., 14, 309–334, https://doi.org/10.5194/amt-14-309-2021, 2021.
Gliß, J., Mortier, A., Schulz, M., Andrews, E., Balkanski, Y., Bauer, S. E., Benedictow, A. M. K., Bian, H., Checa-Garcia, R., Chin, M., Ginoux, P., Griesfeller, J. J., Heckel, A., Kipling, Z., Kirkevåg, A., Kokkola, H., Laj, P., Le Sager, P., Lund, M. T., Lund Myhre, C., Matsui, H., Myhre, G., Neubauer, D., van Noije, T., North, P., Olivié, D. J. L., Rémy, S., Sogacheva, L., Takemura, T., Tsigaridis, K., and Tsyro, S. G.: AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations, Atmos. Chem. Phys., 21, 87–128, https://doi.org/10.5194/acp-21-87-2021, 2021.
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., 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, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Huang, Y., Adebiyi, A. A., Formenti, P., and Kok, J. F.: Linking the
Different Diameter Types of Aspherical Desert Dust Indicates That Models
Underestimate Coarse Dust Emission, Geophys. Res. Lett., 48, e2020GL092054,
https://doi.org/10.1029/2020GL092054, 2021.
Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., Bauer, S., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Fillmore, D., Ghan, S., Ginoux, P., Grini, A., Horowitz, L., Koch, D., Krol, M. C., Landing, W., Liu, X., Mahowald, N., Miller, R., Morcrette, J.-J., Myhre, G., Penner, J., Perlwitz, J., Stier, P., Takemura, T., and Zender, C. S.: Global dust model intercomparison in AeroCom phase I, Atmos. Chem. Phys., 11, 7781–7816, https://doi.org/10.5194/acp-11-7781-2011, 2011.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Jin, Q., Wei, J., Lau, W. K. M., Pu, B., and Wang, C.: Interactions of Asian
mineral dust with Indian summer monsoon: Recent advances and challenges,
Earth-Sci. Rev., 215, 103562,
https://doi.org/10.1016/j.earscirev.2021.103562, 2021.
Kim, D., Chin, M., Yu, H., Diehl, T., Tan, Q., Kahn, R. A., Tsigaridis, K.,
Bauer, S. E., Takemura, T., Pozzoli, L., Bellouin, N., Schulz, M.,
Peyridieu, S., Chédin, A., and Koffi, B.: Sources, sinks, and
transatlantic transport of North African dust aerosol: A multimodel analysis
and comparison with remote sensing data, J. Geophys. Res.-Atmos., 119, 6259–6277, https://doi.org/10.1002/2013JD021099, 2014.
Kim, D., Chin, M., Yu, H., Pan, X., Bian, H., Tan, Q., Kahn, R. A.,
Tsigaridis, K., Bauer, S. E., Takemura, T., Pozzoli, L., Bellouin, N., and
Schulz, M.: Asian and Trans-Pacific Dust: A Multimodel and Multiremote
Sensing Observation Analysis, J. Geophys. Res.-Atmos., 124, 13534–13559,
https://doi.org/10.1029/2019JD030822, 2019.
Knippertz, P. and Stuut, J.-B. W. (Eds.): Mineral Dust: A Key Player in the
Earth System, Springer Netherlands, https://doi.org/10.1007/978-94-017-8978-3, 2014.
Kok, J. F.: A scaling theory for the size distribution of emitted dust
aerosols suggests climate models underestimate the size of the global dust
cycle, P. Natl. Acad. Sci. USA, 108, 1016–1021, 2011.
Kok, J. F., Ridley, D. A., Zhou, Q., Miller, R. L., Zhao, C., Heald, C. L.,
Ward, D. S., Albani, S., and Haustein, K.: Smaller desert dust cooling
effect estimated from analysis of dust size and abundance, Nat. Geosci.,
10, 274–278, https://doi.org/10.1038/ngeo2912, 2017.
Kok, J. F., Ward, D. S., Mahowald, N. M., and Evan, A. T.: Global and
regional importance of the direct dust-climate feedback, Nat. Commun., 9, 241, https://doi.org/10.1038/s41467-017-02620-y, 2018.
Kok, J. F., Adebiyi, A. A., Albani, S., Balkanski, Y., Checa-Garcia, R., Chin, M., Colarco, P. R., Hamilton, D. S., Huang, Y., Ito, A., Klose, M., Leung, D. M., Li, L., Mahowald, N. M., Miller, R. L., Obiso, V., Pérez García-Pando, C., Rocha-Lima, A., Wan, J. S., and Whicker, C. A.: Improved representation of the global dust cycle using observational constraints on dust properties and abundance, Atmos. Chem. Phys., 21, 8127–8167, https://doi.org/10.5194/acp-21-8127-2021, 2021.
Kramer, S. J., Alvarez, C., Barkley, A. E., Colarco, P. R., Custals, L., Delgadillo, R., Gaston, C. J., Govindaraju, R., and Zuidema, P.: Apparent dust size discrepancy in aerosol reanalysis in north African dust after long-range transport, Atmos. Chem. Phys., 20, 10047–10062, https://doi.org/10.5194/acp-20-10047-2020, 2020.
Li, L., Mahowald, N. M., Miller, R. L., Pérez García-Pando, C., Klose, M., Hamilton, D. S., Gonçalves Ageitos, M., Ginoux, P., Balkanski, Y., Green, R. O., Kalashnikova, O., Kok, J. F., Obiso, V., Paynter, D., and Thompson, D. R.: Quantifying the range of the dust direct radiative effect due to source mineralogy uncertainty, Atmos. Chem. Phys., 21, 3973–4005, https://doi.org/10.5194/acp-21-3973-2021, 2021.
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016.
Maharana, P., Dimri, A. P., and Choudhary, A.: Redistribution of Indian
summer monsoon by dust aerosol forcing, Meteorol. Appl., 26, 584–596,
https://doi.org/10.1002/met.1786, 2019.
Mahowald, N., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D.
S., and Flanner, M. G.: The size distribution of desert dust aerosols and
its impact on the Earth system, Aeolian Res., 15, 53–71,
https://doi.org/10.1016/j.aeolia.2013.09.002, 2014.
Mahowald, N. M., Kloster, S., Engelstaedter, S., Moore, J. K., Mukhopadhyay, S., McConnell, J. R., Albani, S., Doney, S. C., Bhattacharya, A., Curran, M. A. J., Flanner, M. G., Hoffman, F. M., Lawrence, D. M., Lindsay, K., Mayewski, P. A., Neff, J., Rothenberg, D., Thomas, E., Thornton, P. E., and Zender, C. S.: Observed 20th century desert dust variability: impact on climate and biogeochemistry, Atmos. Chem. Phys., 10, 10875–10893, https://doi.org/10.5194/acp-10-10875-2010, 2010.
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, https://doi.org/10.1029/95JD00690, 1995.
Miller, R. L., Cakmur, R. V., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P.,
Koch, D., Kohfeld, K. E., Prigent, C., Ruedy, R., Schmidt, G. A., and Tegen,
I.: Mineral dust aerosols in the NASA Goddard Institute for Space Sciences
ModelE atmospheric general circulation model, J. Geophys. Res.-Atmos. , 111, 6403–6444, https://doi.org/10.1029/2005JD005796, 2006.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E., Sellar, A., Turnock, S. T., Woodhouse, M. T., Abraham, N. L., Andrews, M. B., Bellouin, N., Browse, J., Carslaw, K. S., Dalvi, M., Folberth, G. A., Glover, M., Grosvenor, D. P., Hardacre, C., Hill, R., Johnson, B., Jones, A., Kipling, Z., Mann, G., Mollard, J., O'Connor, F. M., Palmiéri, J., Reddington, C., Rumbold, S. T., Richardson, M., Schutgens, N. A. J., Stier, P., Stringer, M., Tang, Y., Walton, J., Woodward, S., and Yool, A.: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations, Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, 2020.
N'Datchoh, E. T., Diallo, I., Konaré, A., Silué, S., Ogunjobi, K.
O., Diedhiou, A., and Doumbia, M.: Dust induced changes on the West African
summer monsoon features, Int. J. Climatol., 38, 452–466, https://doi.org/10.1002/joc.5187, 2018.
Pu, B. and Ginoux, P.: How reliable are CMIP5 models in simulating dust optical depth?, Atmos. Chem. Phys., 18, 12491–12510, https://doi.org/10.5194/acp-18-12491-2018, 2018.
Richter, D. and Gill, T.: Challenges and Opportunities in Atmospheric Dust Emission, Chemistry, and Transport, B. Am. Meteorol. Soc., 99, ES115–ES118, https://doi.org/10.1175/BAMS-D-18-0007.1, 2018.
Ridley, D. A., Heald, C. L., Kok, J. F., and Zhao, C.: An observationally constrained estimate of global dust aerosol optical depth, Atmos. Chem. Phys., 16, 15097–15117, https://doi.org/10.5194/acp-16-15097-2016, 2016.
Rind, D., Orbe, C., Jonas, J., Nazarenko, L., Zhou, T., Kelley, M., Lacis,
A., Shindell, D., Faluvegi, G., Romanou, A., Russell, G., Tausnev, N.,
Bauer, M., and Schmidt, G.: GISS Model E2.2: A Climate Model Optimized for
the Middle Atmosphere – Model Structure, Climatology, Variability, and
Climate Sensitivity, J. Geophys. Res.-Atmos., 125, e2019JD032204,
https://doi.org/10.1029/2019JD032204, 2020.
Ryder, C. L., Highwood, E. J., Walser, A., Seibert, P., Philipp, A., and Weinzierl, B.: Coarse and giant particles are ubiquitous in Saharan dust export regions and are radiatively significant over the Sahara, Atmos. Chem. Phys., 19, 15353–15376, https://doi.org/10.5194/acp-19-15353-2019, 2019.
Sanap, S. D., Ayantika, D. C., Pandithurai, G., and Niranjan, K.: Assessment
of the aerosol distribution over Indian subcontinent in CMIP5 models,
Atmos. Environ., 87, 123–137, https://doi.org/10.1016/j.atmosenv.2014.01.017, 2014.
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A.,
Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M.,
Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E.,
Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A.,
Rocher, M., Roehrig, R., Salas-y-Mélia, D., Sanchez, E., Terray, L.,
Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé,
C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role
of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Syst., 11, 4182–4227,
https://doi.org/10.1029/2019MS001791, 2019.
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.
Senior, C. A., Jones, C. G., Wood, R. A., Sellar, A., Belcher, S.,
Klein-Tank, A., Sutton, R., Walton, J., Lawrence, B., Andrews, T., and
Mulcahy, J. P.: U.K. Community Earth System Modeling for CMIP6, J. Adv. Model. Earth Syst., 12, e2019MS002004, https://doi.org/10.1029/2019MS002004, 2020.
Shao, Y., Wyrwoll, K.-H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H.,
Mikami, M., Tanaka, T. Y., Wang, X., and Yoon, S.: Dust cycle: An emerging
core theme in Earth system science, Aeolian Res., 2, 181–204,
https://doi.org/10.1016/j.aeolia.2011.02.001, 2011.
Skonieczny, C., McGee, D., Winckler, G., Bory, A., Bradtmiller, L. I.,
Kinsley, C. W., Polissar, P. J., De Pol-Holz, R., Rossignol, L., and
Malaizé, B.: Monsoon-driven Saharan dust variability over the past
240,000 years, Sci. Adv., 5, eaav1887,
https://doi.org/10.1126/sciadv.aav1887, 2019.
Sogacheva, L., Popp, T., Sayer, A. M., Dubovik, O., Garay, M. J., Heckel, A., Hsu, N. C., Jethva, H., Kahn, R. A., Kolmonen, P., Kosmale, M., de Leeuw, G., Levy, R. C., Litvinov, P., Lyapustin, A., North, P., Torres, O., and Arola, A.: Merging regional and global aerosol optical depth records from major available satellite products, Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, 2020.
Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner,
A., Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of
CMIP5 vs. CMIP3 simulations of the late 20th century, Clim. Dynam., 41,
2711–2744, https://doi.org/10.1007/s00382-012-1607-6, 2013.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019.
Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., and
Nakajima, T.: Global three-dimensional simulation of aerosol optical
thickness distribution of various origins, J. Geophys. Res.-Atmos., 105, 17853–17873, https://doi.org/10.1029/2000JD900265, 2000.
Tanaka, T. Y. and Chiba, M.: Global Simulation of Dust Aerosol with a
Chemical Transport Model, MASINGAR, 83A, 255–278,
https://doi.org/10.2151/jmsj.83A.255, 2005.
Volodin, E. and Gritsun, A.: Simulation of observed climate changes in 1850–2014 with climate model INM-CM5, Earth Syst. Dynam., 9, 1235–1242, https://doi.org/10.5194/esd-9-1235-2018, 2018.
Volodin, E. M. and Kostrykin, S. V.: The aerosol module in the INM RAS
climate model, Russ. Meteorol. Hydrol., 41, 519–528,
https://doi.org/10.3103/S106837391608001X, 2016.
Volodin, E. M., Mortikov, E. V., Kostrykin, S. V., Galin, V. Y., Lykossov,
V. N., Gritsun, A. S., Diansky, N. A., Gusev, A. V., Iakovlev, N. G.,
Shestakova, A. A., and Emelina, S. V.: Simulation of the modern climate
using the INM-CM48 climate model, Russ. J. Numer. Anal. M., 33, 367–374,
https://doi.org/10.1515/rnam-2018-0032, 2018.
Voss, K. K. and Evan, A. T.: A New Satellite-Based Global Climatology of
Dust Aerosol Optical Depth, J. Appl. Meteorol. Clim., 59, 83–102,
https://doi.org/10.1175/JAMC-D-19-0194.1, 2020.
WCRP: CMIP6, World Climate Research Programme, https://esgf-node.llnl.gov/search/cmip6/, last access: 10 February 2022.
Wilcox, L. J., Liu, Z., Samset, B. H., Hawkins, E., Lund, M. T., Nordling, K., Undorf, S., Bollasina, M., Ekman, A. M. L., Krishnan, S., Merikanto, J., and Turner, A. G.: Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions, Atmos. Chem. Phys., 20, 11955–11977, https://doi.org/10.5194/acp-20-11955-2020, 2020.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert,
D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P.,
Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S.
F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey,
D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A.,
Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0
and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model. Earth Syst., 10, 357–380, https://doi.org/10.1002/2017MS001115, 2018.
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of
mineral dust in the Hadley Centre climate model, J. Geophys. Res.-Atmos., 106, 18155–18166, https://doi.org/10.1029/2000JD900795, 2001.
Wu, C., Lin, Z., Liu, X., Li, Y., Lu, Z., and Wu, M.: Can Climate Models
Reproduce the Decadal Change of Dust Aerosol in East Asia?, Geophys. Res.
Lett., 45, 9953–9962, https://doi.org/10.1029/2018GL079376, 2018.
Wu, C., Lin, Z., and Liu, X.: The global dust cycle and uncertainty in CMIP5 (Coupled Model Intercomparison Project phase 5) models, Atmos. Chem. Phys., 20, 10401–10425, https://doi.org/10.5194/acp-20-10401-2020, 2020.
Wu, M., Liu, X., Yang, K., Luo, T., Wang, Z., Wu, C., Zhang, K., Yu, H., and
Darmenov, A.: Modeling Dust in East Asia by CESM and Sources of Biases, J.
Geophys. Res.-Atmos., 124, 8043–8064, https://doi.org/10.1029/2019JD030799,
2019.
Wu, M., Liu, X., Yu, H., Wang, H., Shi, Y., Yang, K., Darmenov, A., Wu, C., Wang, Z., Luo, T., Feng, Y., and Ke, Z.: Understanding processes that control dust spatial distributions with global climate models and satellite observations, Atmos. Chem. Phys., 20, 13835–13855, https://doi.org/10.5194/acp-20-13835-2020, 2020.
Xian, P., Klotzbach, P. J., Dunion, J. P., Janiga, M. A., Reid, J. S., Colarco, P. R., and Kipling, Z.: Revisiting the relationship between Atlantic dust and tropical cyclone activity using aerosol optical depth reanalyses: 2003–2018, Atmos. Chem. Phys., 20, 15357–15378, https://doi.org/10.5194/acp-20-15357-2020, 2020.
Yu, H., Chin, M., Yuan, T., Bian, H., Remer, L. A., Prospero, J. M., Omar,
A., Winker, D., Yang, Y., Zhang, Y., Zhang, Z., and Zhao, C.: The
fertilizing role of African dust in the Amazon rainforest: A first multiyear
assessment based on data from Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observations, Geophys. Res. Lett., 42, 1984–1991,
https://doi.org/10.1002/2015GL063040, 2015.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H.,
Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The
Meteorological Research Institute Earth System Model Version 2.0,
MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J. Meteorol. Soc. Jpn. Ser. II , 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys. Res.-Atmos., 108, D06208, https://doi.org/10.1029/2002JD002775, 2003.
Short summary
The CMIP6 models' simulated dust processes are getting more uncertain as models become more sophisticated. Of particular challenge are the links between dust cycles and optical properties, and we recommend more detailed output relating to dust cycles in future intercomparison projects to constrain such links. Also, models struggle to capture certain key regional dust processes such as dust accumulation along the slope of the Himalayas and dust seasonal cycles in North China and North America.
The CMIP6 models' simulated dust processes are getting more uncertain as models become more...
Altmetrics
Final-revised paper
Preprint