The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth
Open accessAbstract
We have implemented a module for tropospheric aerosols (GOCART) online in the NASA Goddard Earth Observing System version 4 model and simulated global aerosol distributions for the period 2000–2006. The new online system offers several advantages over the previous offline version, providing a platform for aerosol data assimilation, aerosol‐chemistry‐climate interaction studies, and short‐range chemical weather forecasting and climate prediction. We introduce as well a methodology for sampling model output consistently with satellite aerosol optical thickness (AOT) retrievals to facilitate model‐satellite comparison. Our results are similar to the offline GOCART model and to the models participating in the AeroCom intercomparison. The simulated AOT has similar seasonal and regional variability and magnitude to Aerosol Robotic Network (AERONET), Moderate Resolution Imaging Spectroradiometer, and Multiangle Imaging Spectroradiometer observations. The model AOT and Angstrom parameter are consistently low relative to AERONET in biomass‐burning‐dominated regions, where emissions appear to be underestimated, consistent with the results of the offline GOCART model. In contrast, the model AOT is biased high in sulfate‐dominated regions of North America and Europe. Our model‐satellite comparison methodology shows that diurnal variability in aerosol loading is unimportant compared to sampling the model where the satellite has cloud‐free observations, particularly in sulfate‐dominated regions. Simulated sea salt burden and optical thickness are high by a factor of 2–3 relative to other models, and agreement between model and satellite over‐ocean AOT is improved by reducing the model sea salt burden by a factor of 2. The best agreement in both AOT magnitude and variability occurs immediately downwind of the Saharan dust plume.
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Cites
Summarizing multiple aspects of model performance in a single diagram
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Aerosols, Cloud Microphysics, and Fractional Cloudiness
Albrecht, B. A. (1989) · Science · doi:10.1126/science.245.4923.1227 · Cited by 4,422
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Fully coupled “online” chemistry within the WRF model
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Cited by
MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., et al. (2011) · Journal of Climate · doi:10.1175/jcli-d-11-00015.1 · Cited by 4,852
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Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19
Liu, F., Page, A., Strode, S. A., Yoshida, Y., Choi, S., Zheng, B., Lamsal, L. N., Li, C., Krotkov, N. A., Eskes, H., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc2992 · Cited by 348
Gueymard, C. A., & Yang, D. (2020) · Atmospheric Environment · doi:10.1016/j.atmosenv.2019.117216 · Cited by 292
Climate Forcing and Trends of Organic Aerosols in the Community Earth System Model (CESM2)
Tilmes, S., Hodzic, A., Emmons, L. K., Mills, M. J., Gettelman, A., Kinnison, D. E., Park, M., Lamarque, J. ‐., Vitt, F., Shrivastava, M., et al. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001827 · Cited by 246
Metadata
- Authors
- Colarco, P., da Silva, A., Chin, M., & Diehl, T.
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 2010-07-27
- Volume
- 115
- Issue
- D14
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W2001981020
- Cited by
- 668
- References
- 85
- FWCI
- 8.00
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Colarco, P., da Silva, A., Chin, M., & Diehl, T. (2010). Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2009jd012820
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
