2017
DOI: 10.1002/2017jd027143
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Meteoric Smoke Deposition in the Polar Regions: A Comparison of Measurements With Global Atmospheric Models

Abstract: The accumulation rate of meteoric smoke particles (MSPs) in ice cores—determined from the trace elements Ir and Pt, and superparamagnetic Fe particles—is significantly higher than expected from the measured vertical fluxes of Na and Fe atoms in the upper mesosphere and the surface deposition of cosmic spherules. The Whole Atmosphere Community Climate Model with the Community Aerosol and Radiation Model for Atmospheres has been used to simulate MSP production, transport, and deposition, using a global MSP input… Show more

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Cited by 26 publications
(32 citation statements)
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References 121 publications
(203 reference statements)
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“…Gao et al, 2007;Toohey et al, 2013). UM-UKCA for example includes meteoric smoke particles (Brooke et al, 2017) and an internally generated QBO. Model output is in the form of monthly means.…”
Section: Experiments Setupmentioning
confidence: 99%
“…Gao et al, 2007;Toohey et al, 2013). UM-UKCA for example includes meteoric smoke particles (Brooke et al, 2017) and an internally generated QBO. Model output is in the form of monthly means.…”
Section: Experiments Setupmentioning
confidence: 99%
“…Lanci et al (2012) demonstrated that wet deposition of the MSPs is more important than dry deposition: the deposition flux is about an order of magnitude higher in central Greenland than the eastern highlands of Antarctica, consistent with the relative snowfall at the two locations. It should be noted, however, that a recent study using a global circulation model to predict the deposition of MSPs over the Earth's surface has found a much lower deposition flux in these polar locations, by more than an order of magnitude, when using a cosmic dust input to the atmosphere of 43 t d −1 (Brooke et al 2017). …”
Section: Formation Of Meteoric Smoke Particlesmentioning
confidence: 91%
“…Lastly, Dhomse et al (2013) and Brooke et al (2017) have modelled the mass deposition flux of MSPs over the earth's surface, using two different global circulation models. The strongest deposition occurs over northern and southern mid-latitudes, reflecting the geographical distribution of deep stratosphere-troposphere exchange which is driven both by large mountain ranges and storm tracks over the North Atlantic, North Pacific and Southern Oceans.…”
Section: Siderophile and Isotope Contributions To The Earth's Surfacementioning
confidence: 99%
“…Simulations are performed in atmosphere-only mode, and we use CMIP6 recommended sea-surface temperatures and sea-ice concentration that are obtained from https://esgf-node.llnl.gov/projects/cmip6/. The main updates since Dhomse et al (2014) are: i) updated dynamical model (from HadGEM3-A r2.0 to HadGEM3 Global Atmosphere 4.0), hence improved vertical and horizontal resolution (N48L60 vs N96L85), ii) coupling between aerosol and radiation scheme (Mann et al, 2015), iii) additional sulphuric particle formation pathway via heterogeneous nucleation on transported meteoric smoke particle cores (Brooke et al, 2017). The atmosphere-only RJ4.0 UM-UKCA model applied here is the identical model to that applied in Marshall et al (2018) and Marshall et al (2019), with the former run in pre-industrial setting for the VolMIP interactive Tambora experiment (see Zanchettin et al, 2016) and the latter in year-2000 timeslice mode for the perturbed injection-source-parameter ensemble analysed there.…”
Section: Model Experimentsmentioning
confidence: 99%
“…The UM-UKCA experiments includes the online radiative effects from both tropospheric as well as stratospheric aerosol simulated with same interactive aerosol microphysics module. There several important improvements in aerosol microphysics module since the original Pinatubo analysis presented in Dhomse et al (2014), that are discussed in Brooke et al (2017); Marshall et al (2018Marshall et al ( , 2019; Yoshioka et al (2019). Section 3 provides the specifics of the model experiments, with section 4 describing the observational datasets.…”
mentioning
confidence: 99%