2020
DOI: 10.1029/2019je006122
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Simulation of the 2018 Global Dust Storm on Mars Using the NASA Ames Mars GCM: A Multitracer Approach

Abstract: Global dust storms are the most thermodynamically significant dust events on Mars. The most recent of these events occurred in 2018. Although it was monitored by several spacecraft in orbit and on the surface, many questions remain regarding its onset, expansion and decay. Here, we model the 2018 event with the National Aeronautics and Space Administration (NASA) Ames Mars Global Climate Model in order to better understand the evolution of the storm. Our results highlight a mechanism for the expansion of the s… Show more

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Cited by 44 publications
(77 citation statements)
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References 68 publications
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“…The Ames Mars Global Climate Model (MGCM) is based on the National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL) cubed-sphere finite volume (FV3) dynamical core and includes physical process routines developed at NASA Ames and NOAA/GFDL specifically for Mars conditions (Haberle et al 2019;Bertrand et al 2020). Planetary boundary layer physics are included based on the level-2 Mellor and Yamada (1982) parameterization, with turbulent fluxes computed using Monin-Obukhov similarity theory (Haberle et al 1993(Haberle et al , 1999.…”
Section: The Nasa Ames Mars Atmospheric Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…The Ames Mars Global Climate Model (MGCM) is based on the National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL) cubed-sphere finite volume (FV3) dynamical core and includes physical process routines developed at NASA Ames and NOAA/GFDL specifically for Mars conditions (Haberle et al 2019;Bertrand et al 2020). Planetary boundary layer physics are included based on the level-2 Mellor and Yamada (1982) parameterization, with turbulent fluxes computed using Monin-Obukhov similarity theory (Haberle et al 1993(Haberle et al , 1999.…”
Section: The Nasa Ames Mars Atmospheric Modelmentioning
confidence: 99%
“…A two-moment lognormal distribution of dust with a specified effective radiative particle size (2 µm here) is injected from into the lowest atmospheric model layer at each physics scheme timestep when the simulated dust column opacity is lower than that in the dust map for that location and time of year. The injected dust is then mixed by sub-gridscale processes, transported by model resolved winds, undergoes gravitational sedimentation, and is radiatively active (Haberle et al 2019;Bertrand et al 2020).…”
Section: Dust In the Nasa Ames Simulationsmentioning
confidence: 99%
“…Of particular note, Bertrand et al. (2020) use tracer tagging to watch how dust particles are lifted, transported, sedimented, and re‐lifted during their simulation of the MY34 GDS. They show that transfer of dust between the Tharsis region and Arabia Terra/Terra Sabeae may have helped precondition the Tharsis region for the “storm within the storm” that Montabone et al.…”
Section: New Phenomenamentioning
confidence: 99%
“…Here we use the NASA Ames Global Climate Model to investigate coagulation in 1D and in 3D during the 2018 Global Dust Storm. We will build our investigation upon the previous modeling of the GDS performed with a uniform lifted effective particle radius (Bertrand et al, 2020). That study revealed that the dust number density during the dust storm is 100 times higher than during non-storm conditions, and should thus favor coagulation processes.…”
mentioning
confidence: 95%
“…Recent modeling efforts of the 2018 GDS highlight that climate models do not simultaneously capture both the evolution of surface temperatures and the decay rate of global column dust opacities, which suggests that significant changes in dust particle sizes may occur during the dust storm (e.g. Bertrand et al, 2020, Montabone et al, 2020. These models typically assume a constant lifted dust particle size-with size evolution occurring in the atmosphere but only because of gravitational sedimentation.…”
mentioning
confidence: 99%