Articles | Volume 18, issue 16
https://doi.org/10.5194/acp-18-12491-2018
© Author(s) 2018. 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-18-12491-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How reliable are CMIP5 models in simulating dust optical depth?
Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey 08544, USA
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
08540, USA
Paul Ginoux
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
08540, USA
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- Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models J. Liu et al. 10.1038/s41612-023-00550-9
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60 citations as recorded by crossref.
- Can Climate Models Reproduce the Decadal Change of Dust Aerosol in East Asia? C. Wu et al. 10.1029/2018GL079376
- Multi-model ensemble projection of the global dust cycle by the end of 21st century using the Coupled Model Intercomparison Project version 6 data Y. Zhao et al. 10.5194/acp-23-7823-2023
- Evaluation of Machine-Learning Models for Predicting Aeolian Dust: A Case Study over the Southwestern USA Y. Aryal 10.3390/cli10060078
- A new process-based and scale-aware desert dust emission scheme for global climate models – Part I: Description and evaluation against inverse modeling emissions D. Leung et al. 10.5194/acp-23-6487-2023
- A New Satellite-Based Global Climatology of Dust Aerosol Optical Depth K. Voss & A. Evan 10.1175/JAMC-D-19-0194.1
- Evaluation of CMIP6 model simulations of PM2.5 and its components over China F. Ren et al. 10.5194/gmd-17-4821-2024
- The shift of decadal trend in Middle East dust activities attributed to North Tropical Atlantic variability G. Liu et al. 10.1016/j.scib.2023.05.031
- Changes in Dust Activity in Spring over East Asia under a Global Warming Scenario Q. Zong et al. 10.1007/s13143-021-00224-7
- On the Geomorphic, Meteorological, and Hydroclimatic Drivers of the Unusual 2018 Early Summer Salt Dust Storms in Central Asia X. Xi 10.1029/2022JD038089
- Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0) B. Pu et al. 10.5194/acp-20-55-2020
- What drives historical and future changes in photovoltaic power production from the perspective of global warming? R. Constantin Scheele & S. Fiedler 10.1088/1748-9326/ad10d6
- Estimating the Spread in Future Fine Dust Concentrations in the Southwest United States S. Brey et al. 10.1029/2019JD031735
- Predominant Type of Dust Storms That Influences Air Quality Over Northern China and Future Projections J. Li et al. 10.1029/2022EF002649
- Mineral dust aerosol impacts on global climate and climate change J. Kok et al. 10.1038/s43017-022-00379-5
- Aerosol characteristics in CMIP6 models' global simulations and their evaluation with the satellite measurements J. Bharath et al. 10.1002/joc.8324
- Modeling Dust in East Asia by CESM and Sources of Biases M. Wu et al. 10.1029/2019JD030799
- Two Typical Synoptic-Scale Weather Patterns of Dust Events over the Tibetan Plateau X. Feng et al. 10.1007/s13143-023-00325-5
- Dust‐Drought Nexus in the Southwestern United States: A Proxy‐Model Comparison Approach S. Arcusa et al. 10.1029/2020PA004046
- Temporal coherence in particulate matter in East Asian outflow regions: fingerprints of ENSO and Asian dust M. Kueh et al. 10.1038/s41612-023-00530-z
- Understanding day–night differences in dust aerosols over the dust belt of North Africa, the Middle East, and Asia J. Tindan et al. 10.5194/acp-23-5435-2023
- Parameterization schemes on dust deposition in northwest China: Model validation and implications for the global dust cycle X. Zhang et al. 10.1016/j.atmosenv.2019.04.017
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- Size-resolved dust direct radiative effect efficiency derived from satellite observations Q. Song et al. 10.5194/acp-22-13115-2022
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- The emission, transport, and impacts of the extreme Saharan dust storm of 2015 B. Harr et al. 10.5194/acp-24-8625-2024
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- How well do the CMIP6 models simulate dust aerosols? A. Zhao et al. 10.5194/acp-22-2095-2022
- Major Changes in Extreme Dust Events Dynamics Over the Arabian Peninsula During 2003–2017 Driven by Atmospheric Conditions H. Gandham et al. 10.1029/2020JD032931
- Enhanced dust emission following large wildfires due to vegetation disturbance Y. Yu & P. Ginoux 10.1038/s41561-022-01046-6
- Weakened dust activity over China and Mongolia from 2001 to 2020 associated with climate change and land-use management S. Wang et al. 10.1088/1748-9326/ac3b79
- Improved representation of the global dust cycle using observational constraints on dust properties and abundance J. Kok et al. 10.5194/acp-21-8127-2021
- Global Dust Variability Explained by Drought Sensitivity in CMIP6 Models Y. Aryal & S. Evans 10.1029/2021JF006073
- Spatiotemporal regionalization of atmospheric dust based on multivariate analysis of MACC model over Iran K. Mohammadpour et al. 10.1016/j.atmosres.2020.105322
- Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models J. Liu et al. 10.1038/s41612-023-00550-9
- Aerosol absorption in global models from AeroCom phase III M. Sand et al. 10.5194/acp-21-15929-2021
- Dust emission response to precipitation and temperature anomalies under different climatic conditions Y. Aryal & S. Evans 10.1016/j.scitotenv.2023.162335
Latest update: 03 Nov 2024
Short summary
Biases in dust modeling may result in biases in simulating energy budget and regional climate. Output of seven Coupled Model Intercomparison Project Phase 5 (CMIP5) models is examined. Seasonal cycle and spatial pattern of dust optical depth (DOD) in very dusty regions are largely captured by multi-model mean. But observed connections between DOD and local controlling factors such as bareness are not well represented. Future projections by CMIP5 models and a regression model are also analyzed.
Biases in dust modeling may result in biases in simulating energy budget and regional climate....
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