Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4249-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4249-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Jul 2021
Development and technical paper |
| 06 Jul 2021
| 06 Jul 2021
Grid-independent high-resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (12.5.0)
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
now at: Department of Atmospheric & Oceanic Sciences, University of California, 520 Portola Plaza, Los Angeles, California 90095, USA
Randall V. Martin
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
Paul Ginoux
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey 08540, USA
Melanie Hammer
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
Melissa P. Sulprizio
School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
David A. Ridley
California Environmental Protection Agency, Sacramento, CA 95814, USA
Aaron van Donkelaar
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
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Cited
23 citations as recorded by crossref.
- Transpacific Transport of Asian Peroxyacetyl Nitrate (PAN) Observed from Satellite: Implications for Ozone S. Zhai et al. 10.1021/acs.est.4c01980
- Cooling from aerosol–radiation interaction of anthropogenic coarse particles in China X. Wang et al. 10.1038/s41612-024-00773-4
- Development and evaluation of processes affecting simulation of diel fine particulate matter variation in the GEOS-Chem model Y. Li et al. 10.5194/acp-23-12525-2023
- Exploring the processes controlling secondary inorganic aerosol: evaluating the global GEOS-Chem simulation using a suite of aircraft campaigns O. Norman et al. 10.5194/acp-25-771-2025
- Ice nucleating properties of airborne dust from an actively retreating glacier in Yukon, Canada Y. Xi et al. 10.1039/D1EA00101A
- Vertical profiles of global tropospheric nitrogen dioxide (NO2) obtained by cloud slicing the TROPOspheric Monitoring Instrument (TROPOMI) R. Horner et al. 10.5194/acp-24-13047-2024
- Why is ozone in South Korea and the Seoul metropolitan area so high and increasing? N. Colombi et al. 10.5194/acp-23-4031-2023
- Impact of Legislated and Best Available Emission Control Measures on UK Particulate Matter Pollution, Premature Mortality, and Nitrogen‐Sensitive Habitats E. Marais et al. 10.1029/2023GH000910
- A new process-based and scale-aware desert dust emission scheme for global climate models – Part II: Evaluation in the Community Earth System Model version 2 (CESM2) D. Leung et al. 10.5194/acp-24-2287-2024
- Influence of Oil and Gas End-Use on Summertime Particulate Matter and Ozone Pollution in the Eastern US K. Vohra et al. 10.1021/acs.est.4c10032
- Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling A. Chappell et al. 10.1029/2023JD038584
- Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning S. Shen et al. 10.1021/acsestair.3c00054
- Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter D. Zhang et al. 10.1021/acsestair.4c00084
- Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth H. Zhu et al. 10.5194/acp-24-11565-2024
- Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP) R. Martin et al. 10.5194/gmd-15-8731-2022
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
- 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
- Climate and air quality impact of using ammonia as an alternative shipping fuel A. Wong et al. 10.1088/1748-9326/ad5d07
- A Global‐Scale Mineral Dust Equation X. Liu et al. 10.1029/2022JD036937
- Variable effects of spatial resolution on modeling of nitrogen oxides C. Li et al. 10.5194/acp-23-3031-2023
- Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South D. Zhang et al. 10.1021/acs.est.2c07253
- Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties H. Zhu et al. 10.5194/acp-23-5023-2023
- Sources of Air Pollution Health Impacts and Co‐Benefits of Carbon Neutrality in Santiago, Chile M. Nawaz et al. 10.1029/2023JD038808
23 citations as recorded by crossref.
- Transpacific Transport of Asian Peroxyacetyl Nitrate (PAN) Observed from Satellite: Implications for Ozone S. Zhai et al. 10.1021/acs.est.4c01980
- Cooling from aerosol–radiation interaction of anthropogenic coarse particles in China X. Wang et al. 10.1038/s41612-024-00773-4
- Development and evaluation of processes affecting simulation of diel fine particulate matter variation in the GEOS-Chem model Y. Li et al. 10.5194/acp-23-12525-2023
- Exploring the processes controlling secondary inorganic aerosol: evaluating the global GEOS-Chem simulation using a suite of aircraft campaigns O. Norman et al. 10.5194/acp-25-771-2025
- Ice nucleating properties of airborne dust from an actively retreating glacier in Yukon, Canada Y. Xi et al. 10.1039/D1EA00101A
- Vertical profiles of global tropospheric nitrogen dioxide (NO2) obtained by cloud slicing the TROPOspheric Monitoring Instrument (TROPOMI) R. Horner et al. 10.5194/acp-24-13047-2024
- Why is ozone in South Korea and the Seoul metropolitan area so high and increasing? N. Colombi et al. 10.5194/acp-23-4031-2023
- Impact of Legislated and Best Available Emission Control Measures on UK Particulate Matter Pollution, Premature Mortality, and Nitrogen‐Sensitive Habitats E. Marais et al. 10.1029/2023GH000910
- A new process-based and scale-aware desert dust emission scheme for global climate models – Part II: Evaluation in the Community Earth System Model version 2 (CESM2) D. Leung et al. 10.5194/acp-24-2287-2024
- Influence of Oil and Gas End-Use on Summertime Particulate Matter and Ozone Pollution in the Eastern US K. Vohra et al. 10.1021/acs.est.4c10032
- Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling A. Chappell et al. 10.1029/2023JD038584
- Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning S. Shen et al. 10.1021/acsestair.3c00054
- Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter D. Zhang et al. 10.1021/acsestair.4c00084
- Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth H. Zhu et al. 10.5194/acp-24-11565-2024
- Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP) R. Martin et al. 10.5194/gmd-15-8731-2022
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
- 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
- Climate and air quality impact of using ammonia as an alternative shipping fuel A. Wong et al. 10.1088/1748-9326/ad5d07
- A Global‐Scale Mineral Dust Equation X. Liu et al. 10.1029/2022JD036937
- Variable effects of spatial resolution on modeling of nitrogen oxides C. Li et al. 10.5194/acp-23-3031-2023
- Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South D. Zhang et al. 10.1021/acs.est.2c07253
- Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties H. Zhu et al. 10.5194/acp-23-5023-2023
- Sources of Air Pollution Health Impacts and Co‐Benefits of Carbon Neutrality in Santiago, Chile M. Nawaz et al. 10.1029/2023JD038808
Latest update: 20 Feb 2025
Short summary
Dust emissions in models, for example, GEOS-Chem, have a strong nonlinear dependence on meteorology, which means dust emission strengths calculated from different resolution meteorological fields are different. Offline high-resolution dust emissions with an optimized global dust strength, presented in this work, can be implemented into GEOS-Chem as offline emission inventory so that it could promote model development by harmonizing dust emissions across simulations of different resolutions.
Dust emissions in models, for example, GEOS-Chem, have a strong nonlinear dependence on...
