The diffuse aurora: A significant source of ionization in the middle atmosphere

Frahm, R. A.; Winningham, J. D.; Sharber, J. R.; Link, R.; Crowley, G.; Gaines, E. E.; Chenette, D. L.; Anderson, B. J.; Potemra, T. A.

doi:10.1029/97jd02430

Cited by 80 publications

(89 citation statements)

References 64 publications

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“…While Codrescu et al (1997) describe the high energy precipitation with an additional Maxwellian distribution, Sharber et al (1998) suggest that based on UARS/PEM observations during the November 1993 storm, a kappa distribution is more appropriate. Likewise, Frahm et al (1997) (also see the comment by Callis, 2000, and the reply by Frahm et al, 2000) have found that a kappa distribution fits spectra over diffuse aurora. CNA observations are sensitive to these high energy tails of the auroral electrons, and with the analysis of riometer data in this paper we support the importance of the high energy precipitation.…”

Section: Introductionmentioning

confidence: 87%

Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

et al. 2004

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Abstract. This study is a statistical analysis on energy distribution of precipitating electrons, based on CNA (cosmic noise absorption) data obtained from the 256-element imaging riometer in Poker Flat, Alaska (65.11 • N, 147.42 • W), and optical data measured with an MSP (Meridian Scanning Photometer) over 79 days during the winter periods from 1996 to 1998. On the assumption that energy distributions of precipitating electrons represent Maxwellian distributions, CNA is estimated based on the observation data of auroral 427.8-nm and 630.0-nm emissions, as well as the average atmospheric model, and compared with the actual observation data. Although the observation data have a broad distribution, they show systematically larger CNA than the model estimate. CNA determination using kappa or double Maxwellian distributions, instead of Maxwellian distributions, better explains the distribution of observed CNA data. Kappa distributions represent a typical energy distribution of electrons in the plasma sheet of the magnetosphere, the source region of precipitating electrons. Pure kappas are more likely during quiet times -and quiet times are more likely than active times. This result suggests that the energy distribution of precipitating electrons reflects the energy distribution of electrons in the plasma sheet.

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Section: Introductionmentioning

confidence: 87%

Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

et al. 2004

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“…This calculation considered MEE (30-1000 keV) with maximum energy deposition at altitudes between about 60 and 90 km (van de Kamp et al, 2016). Note that the ionization parameterization does not consider the contribution of Bremsstrahlung, which could be significant only at altitudes below 50 km (Frahm et al, 1997). Figure 14 shows examples of solar-cycle variability of the modeled atmospheric MEE ionization rates at ≈ 80 km altitude.…”

Section: Mid-energy Electrons (Mees) From the Radiation Beltsmentioning

confidence: 99%

Solar forcing for CMIP6 (v3.2)

et al. 2017

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Abstract. This paper describes the recommended solar forcing dataset for CMIP6 and highlights changes with respect to CMIP5. The solar forcing is provided for radiative properties, namely total solar irradiance (TSI), solar spectral irradiance (SSI), and the F10.7 index as well as particle forcing, including geomagnetic indices Ap and Kp, and ionization rates to account for effects of solar protons, electrons, and galactic cosmic rays. This is the first time that a recommendation for solar-driven particle forcing has been provided for a CMIP exercise. The solar forcing datasets are provided at daily and monthly resolution separately for the CMIP6 preindustrial control, historical (1850CMIP6 preindustrial control, historical ( -2014, and future (2015-2300) simulations. For the preindustrial control simulation, both constant and time-varying solar forcing components are provided, with the latter including variability on 11-year and shorter timescales but no long-term changes. For the future, we provide a realistic scenario of what solar behavior could be, as well as an additional extreme Maunderminimum-like sensitivity scenario. This paper describes the forcing datasets and also provides detailed recommendations as to their implementation in current climate models.For the historical simulations, the TSI and SSI time series are defined as the average of two solar irradiance models that are adapted to CMIP6 needs: an empirical onePublished by Copernicus Publications on behalf of the European Geosciences Union. A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361.0 W m −2 . The slight negative trend in TSI over the three most recent solar cycles in the CMIP6 dataset leads to only a small global radiative forcing of −0.04 W m −2 . In the 200-400 nm wavelength range, which is important for ozone photochemistry, the CMIP6 solar forcing dataset shows a larger solar-cycle variability contribution to TSI than in CMIP5 (50 % compared to 35 %).We compare the climatic effects of the CMIP6 solar forcing dataset to its CMIP5 predecessor by using timeslice experiments of two chemistry-climate models and a reference radiative transfer model. The differences in the long-term mean SSI in the CMIP6 dataset, compared to CMIP5, impact on climatological stratospheric conditions (lower shortwave heating rates of −0.35 K day −1 at the stratopause), cooler stratospheric temperatures (−1.5 K in the upper stratosphere), lower ozone abundances in the lower stratosphere (−3 %), and higher ozone abundances (+1.5 % in the upper stratosphere and lower mesosphere). Between the maximum and minimum phases of the 11-year solar cycle, there is an increase in shortwave heating rates (+0.2 K day −1 at the stratopause), temperatures (∼ 1 K at the stratopause), and ozone (+2.5 % in the upper stratosphere) in the tropical upper stratosphere using the CMIP6 forcing dataset. This solar-cycle response is slightly larger, but not statistically significantly different from that for the CMIP5 forcing dataset.CMIP6 models wi...

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“…Fuller-Rowell and Evans [1987] is based on the Television Infrared Observation Satellite Program and National Oceanic and Atmospheric Administration measurements and assumes a Maxwellian energy distribution. The ion production rate due to auroral electron precipitation is derived from the formulation described by Frahm et al [1997]. In this study, GITM is run with a 1°× 5°r esolution (longitude × latitude).…”

Section: Gitmmentioning

confidence: 99%

Ionization due to electron and proton precipitation during the August 2011 storm

Huang

et al. 2014

JGR Space Physics

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The parameterizations of monoenergetic particle impact ionization in Fang et al. (2010) (Fang2010) and Fang et al. (2013 are applied to the complex energy spectra measured by DMSP F16 satellite to calculate the ionization rates from electron and ion precipitations for a Northern Hemisphere pass from 0030 UT to 0106 UT on 6 August 2011. Clear enhancement of electron flux is found in the polar cap. The mean electron energy in the polar cap is mostly above 100 eV, while the mean energy in the auroral zone is typically above 1 keV. At the same time, F16 captures a strong Poynting flux enhancement in the polar cap, which is comparable to those in the auroral zone. The particle impact ionization rates using Fang2010 and Fang2013 parameterizations show clear enhancement at F region altitudes mainly due to the low-energy precipitating electrons, peaking probably in the cusp but also showing enhanced levels throughout most of the polar cap region. The general circulation models (GCMs), National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model, and Global Ionosphere-Thermosphere Model, using their default empirical formulations of particle impact ionization, do not capture the observed features shown in the total particle ionization rate applying the Fang2010 and Fang2013 parameterizations to DMSP measurements. The difference between GCM simulations and Fang2010 and Fang2013 applied to DMSP data is due to the difference of both the inputs to the models and the parameterization of the ionization rates.

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The diffuse aurora: A significant source of ionization in the middle atmosphere

Cited by 80 publications

References 64 publications

Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

Solar forcing for CMIP6 (v3.2)

Ionization due to electron and proton precipitation during the August 2011 storm

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