Supporting Data for the GFDL Variable-Resolution Global Chemistry-Climate Model for Research at the Nexus of US Climate and Air Quality Extremes

Lin, Meiyun
Issue date: 2023
Rights:
Creative Commons 1.0 Universal - Public Domain Dedication (CC0)
Cite as:
Lin, Meiyun. (2023). Supporting Data for the GFDL Variable-Resolution Global Chemistry-Climate Model for Research at the Nexus of US Climate and Air Quality Extremes [Data set]. Geophysical Fluid Dynamics Laboratory, Princeton University. https://doi.org/10.34770/azw8-7g66
@electronic{lin_meiyun_2023,
  author      = {Lin, Meiyun},
  title       = {{Supporting Data for the GFDL Variable-Re
                solution Global Chemistry-Climate Model
                for Research at the Nexus of US Climate
                and Air Quality Extremes}},
  publisher   = {{Geophysical Fluid Dynamics Laboratory, P
                rinceton University}},
  year        = 2023,
  url         = {https://doi.org/10.34770/azw8-7g66}
}
Description:

Data can be accessed at this URL https://www.google.com/url?q=https://app.globus.org/file-manager?origin_id%3D5ac03700-685a-4845-ab82-4a5771b58426%26origin_path%3D%252F1%252Fusers%252FMeiyun.Lin%252FJAMES2023%252FAM4VR%252F&sa=D&source=docs&ust=1702930359304167&usg=AOvVaw3Ea4S0D2HRtyV4ufeTCZjI The data were produced by the GFDL Variable-Resolution Global Chemistry-Climate Model (AM4VR) running in AMIP (Atmospheric Model Intercomparison Project) mode, driven by observed sea surface temperature (SST) and sea ice distributions, historical anthropogenic emissions, land use and atmospheric radiative forcing agents over 1988-2020. Please refer to Lin et al. (JAMES, 2023) for the details of the experiment design. Meiyun Lin, Larry W. Horowitz, Ming Zhao, Lucas Harris, Paul Ginoux, John Dunne, Sergey Malyshev, Elena Shevliakova, Hamza Ahsan, Steve Garner, Fabien Paulot, Arman Pouyaei, Steven J. Smith, Yuanyu Xie, Niki Zadeh, Linjiong Zhou. The GFDL Variable-Resolution Global Chemistry-Climate Model for Research at the Nexus of US Climate and Air Quality Extremes. Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2023MS003984

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