Chapter 13Atmospheric Ionization Due to PrecipitatingCharged Particles

Jan Maik Wissing, Jan Philipp Bornebusch, and May-Britt Kallenrode

Abstract Precipitating charged particles contribute to the natural variations in theEarth’s atmosphere such as ionization, electron density, and composition of e.g.NOx and Ozone. Precipitating solar energetic and magnetospheric particles showa highly dynamic behavior in space and time. We present a 3D ionization modelconsidering the relevant particle species (electrons, protons, and alpha particles) aswell as precipitation areas (polar cap and auroral oval): the Atmospheric IonizationModel OSnabrück AIMOS, and discuss some of the atmospheric consequences ofprecipitating particles. We present the limitations of direct comparisons betweenEISCAT and precipitating particles and give comparisons between incoherent scat-ter measurements and a combination of AIMOS and the HAMMONIA GCM todemonstrate the consistency in both methods.

13.1 Introduction

The most obvious effect of energetic charged particles on the atmosphere is theaurora: precipitating magnetospheric particles excite and ionize e.g. oxygen in theupper atmosphere. Speculations about an aurora related production of NOx dateback into the second half of the 20th century, however, only the studies followingthe 1972 solar energetic particle (SEP) event led Heath et al. [1977] and Crutzenet al. [1975] to develop the relevant chemical models.

Studies of particle precipitation into the atmosphere in general were limited tosolar protons precipitating into the polar cap [e.g. Jackman et al., 2001, 2005; Ran-dall et al., 2007; Rohen et al., 2005; Verronen et al., 2002] with the contributionof solar electrons thought to be less than 10 % [McPeters and Jackman, 1985]. Theenergy/height range under study was defined by the particle instrument being in use;combinations of instruments such as in Mewaldt et al. [2005] are rarely used. Other

With contributions from Jens Kieser, Hauke Schmidt, Miriam Sinnhuber, Holger Winkler, NadineWieters.

J.M. Wissing · J.P. Bornebusch · M.-B. Kallenrode (�)University of Osnabrück, Barbarastr. 7, 49076 Osnabrück, Germanye-mail: mkallenr@uos.de

F.-J. Lübken (ed.), Climate and Weather of the Sun-Earth System (CAWSES),Springer Atmospheric Sciences,DOI 10.1007/978-94-007-4348-9_13, © Springer Science+Business Media Dordrecht 2013

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studies considered magnetospheric electrons precipitating into the auroral oval ei-ther uniformly [Callis et al., 1996a, 1996b, 1998] or in a spatial pattern determinedby parametrization of a geomagnetic index [Fang et al., 2008; Marsh et al., 2007].Magnetospheric protons were mostly neglected and again, the energy range of theparticle detectors defined the height range under study.

The limitation to selected particle species or particle sources as in these previousmodeling approaches can be considered as incomplete for two reasons: (1) solarenergetic as well as magnetospheric particles consist of different particle species:protons and electrons and to a lesser extend alpha particles and heavier atoms. (2)Precipitating particles of solar and magnetospheric origin can coexist in time: acoronal mass ejection (CME) might accelerate SEPs close to the Sun and in inter-planetary space. On hitting the magnetosphere, it can trigger a geomagnetic distur-bance, which causes precipitation of magnetospheric particles. Thus atmosphericconsequences are more complex than indicated in a simple solar protons vs. mag-netospheric electron paradigm; even within this simple paradigm, it is not alwayssimple to disentangle the atmospheric effects of both particle sources in the NOxdata [Seppälä et al., 2007].

13.2 Atmospheric Ionization Model OSnabrück AIMOS

AIMOS is designed to give a more complete picture of precipitating particles. Thebasic idea behind the Atmospheric Ionization Module OSnabrück AIMOS [Wiss-ing and Kallenrode, 2009] is to create 3D ionization rates over a wide range ofheights/particle energies. The model is based on satellite observations of energeticparticles and consists of two parts: a sorting algorithm creating a 2D horizontalpattern of the particle spectra on the top of the atmosphere and a Monte-Carlo simu-lation to determine the vertical pattern of ionization within each horizontal bin. Theflow chart of AIMOS is shown in Fig. 13.1.

The model’s vertical extent is based on (a) the model atmosphere and (b) theavailable particle data. Since it uses the atmosphere of the HAMMONIA GCM[Schmidt et al., 2006] it extends from ground up to 1.7 × 10−5 Pa, corresponding toan upper boundary between 250 and 600 km; it is spaced into 67 in pressure loga-rithmically equidistant vertical layers. The horizontal grid is 3.6° × 3.6°. Since datafrom one Geostationary Operational Environmental Satellite (GOES) and two PolarOrbiting Environmental Satellites (POES) are used, the energy range is determinedby the particle detectors on these satellites. The GOES satellite measures protonsfrom 4 to 500 MeV and Alpha particles from 4 to 500 MeV/particle using the SEMinstrument [NASA, 1996]. The POES satellites are flying at 850 km altitude. And asbeing equipped with MEPED and TED instruments [Evans and Greer, 2004], theenergy range of protons enlarges down to 150 eV. In addition, electrons are detectedbetween 150 eV and 2.5 MeV (which is extrapolated to 5 MeV in AIMOS). In total24 energy channels on 3 satellites are used.

AIMOS data are available at http://aimos.physik.uos.de. Examples for the use ofAIMOS data within the HEPPA initiative are given in Chap. 15; energetic particle

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Fig. 13.1 Flow chart of the Atmospheric Ionization Module OSnabrück AIMOS [Wissing andKallenrode, 2009]. Reprinted with kind permission from American Geophysical Union

precipitation impacts on the middle atmosphere are also discussed in Chaps. 8, 16,and 17.

The derivation of the horizontal precipitation pattern is the main challenge be-cause it requires some assumptions on how to distinguish between a rather uni-formly filled polar cap [Bornebusch et al., 2010] and the auroral oval with particlefluxes showing a strong dependence on local time [Wissing et al., 2008]. In addi-tion, the transition from cap to oval depends on energy and is highly variable withgeomagnetic activity [Leske et al., 2001]. A rather complex retrieval mechanism isrequired to extract the horizontal patterns from the satellite date: since particle dataare in-situ measurements, only particle fluxes along the satellite orbit can be mea-sured as indicated on the left hand side of Fig. 13.2; snapshots of particle spectraat the top of the atmosphere are not available. The combined data from at least twopolar orbiting spacecraft can be used to derive a map of the relative flux of magneto-spheric particles, leading to a horizontal precipitation pattern: the globe is split into14 regions with similar precipitation patterns with four subdivisions depending onlocal time [Wissing et al., 2008].

The maps depend on particle rigidity because the geomagnetic cutoff depends onrigidity. They vary in time because polar cap and auroral oval expand equatorward

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Fig. 13.2 Particle fluxes of 30–80 keV protons measured by two POES satellites in roughly per-pendicular orbits (left) are combined to give a precipitation matrix (right) [Wissing and Kallenrode,2009]. Reprinted with kind permission from American Geophysical Union

with increasing geomagnetic activity. The maps give patterns: the actual flux ofmagnetospheric particles can be obtained by scaling the flux measured along theorbit with the corresponding matrix. Precipitating solar energetic particles insidethe polar cap are taken from geostationary satellites or spacecraft in interplanetaryspace; the polar cap as the void in the matrices described above is filled uniformlywith these particles. An example for the resulting map of both particle species isshown in the right hand side of Fig. 13.2. In the flow chart this corresponds to therectangle labeled “Precipitating particles sorted by horizontal cell”.

The interaction between the precipitating particles and the atmosphere—and thusthe third dimension of the model—is described by a Monte-Carlo simulation us-ing the GEANT 4 toolkit (Agostinelli et al., 2003). A Monte-Carlo simulation isused instead of the simpler Bethe–Bloch or Berger–Seltzer algorithms for three rea-sons: (a) secondaries and their interaction with the atmosphere can be considered,(b) multiple scattering of electrons can be considered, and (c) the generation ofBremsstrahlung by electrons can be taken into account. The first two processes areimportant in particular in the rarefied upper atmosphere, the latter leads to ioniza-tion of the atmosphere well beyond the range of the primary electron. The simula-tion used within AIMOS is an extended version of the one introduced by Schroeteret al. [2006], in particular because it is based on an atmosphere taken from theHAMMONIA GCM [Schmidt et al., 2006]. Runs were performed for four differentgeographic latitudes, the four seasons, and three different levels of solar activity.

Processes considered in the Monte-Carlo simulation include continuous en-ergy loss, multiple scattering, generation of Bremsstrahlung by primaries as well

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as secondaries, photoelectric effect, Compton scattering and pair production—although the latter is extremely rare because of the rather low energies under study.Hadronic interactions are not considered for the same reason. The algorithm hasbeen tested against the results of Berger et al. [1970], Callis et al. [1998], Jackmanet al. [2005] and Schröter et al. [2006].

The Monte-Carlo simulation is performed with 40 logarithmically equidistantmonoenergetic particle beams with 9 different equally spaced directions of inci-dence to take into account the angular distribution of the particles. The ionizationprofiles than are derived as a convolution with the incident particle spectrum on topof the atmosphere.

Since AIMOS is the first model to combine solar and magnetospheric particlesand to consider all relevant particle species, it can be used to study the influenceof different particle populations and species at different levels of solar and or ge-omagnetic activity. Wissing and Kallenrode [2009] applied AIMOS at quiet times,during a simple solar energetic particle event and during the complex series of SEPsand shocks in October/November 2003. Although spatial and temporal variations inionization rates are rather complex, the authors identified some general features.

During quiet times (no SEPs, no geomagnetic disturbance), ionization inside bothpolar cap and auroral oval is mainly by electrons; the ionization inside the auroraloval exceeds that inside the polar cap. Thus for quiet times, a limitation to magne-tospheric electrons is a reasonable approximation.

During a simple solar particle event (no shock, no strong geomagnetic activ-ity) the ionization in the meso- and stratosphere mainly is due to protons while inthe (lower) thermosphere the ionization by electrons accelerated in the flare dom-inates. But even in the mesosphere, solar electrons can contribute up to 30 % tothe ionization. While solar electrons correctly have been neglected in the study ofstratospheric effects of precipitating particles their influence should not be ignoredat higher altitudes [Wissing and Kallenrode, 2009].

Now we consider a more complex situation, when after a solar event a shock hitsthe magnetosphere. Here the relative importance of particle precipitation inside thepolar cap and the auroral oval depends on geomagnetic activity and the spectrum ofthe solar energetic particles. At least in the lower thermosphere, both particle popu-lations have comparable influence on ionization rates. In the mesosphere, ionizationby magnetospheric electrons often exceeds that by solar electrons; it also can exceedthat by solar protons. Consequently, the consideration of both particle populationswith all particle species modifies the estimated ionization rates. These modifica-tions are not large enough to change the essential results of earlier studies but theyare large enough to be considered in comparisons between atmospheric models andmeasurements of atmospheric constituents.

The main vertical advantage of AIMOS compared to a previous study by Jack-man et al. [2005] is given in Fig. 13.4. Showing a 2 hour interval within the Octoberevent 2003 both models agree around 50 km altitude, but below 35 km the smallerupper energy range of the Jackman model results in a decreasing ionization rate.Above 65 km the AIMOS model again shows higher ionization rates due to lowenergetic particles and which are neglected in the Jackman model.

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Fig. 13.3 Total electron production (TEP) rate for a quiet time period (top) and a geomagneticactive period (bottom) for electrons (left) and protons (right) [Wissing et al., 2010]. Reprinted withkind permission from American Geophysical Union

Fig. 13.4 Comparison of anionization profile due toproton precipitation fromAIMOS and Jackman et al.[2005]. A longer time seriesof both models as well as amore detailed discussion canbe found in Wissing andKallenrode [2009] andJackmanet al. [2005]

The second main advantage of AIMOS is the horizontal pattern which can beseen e.g. in the relative contributions of electrons and protons to the total electronproduction TEP, that is the height-integrated ion pair production rate. TEP is shownin Fig. 13.3: the contribution of electrons to TEP rates exceeds that of protons atalmost all locations, that is within the auroral oval as well as within the polar cap.This holds for quiet times (top) as well as for times of high geomagnetic activity

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(bottom). Only during the early phase of a solar energetic particle event and insidethe polar cap the contribution of protons to TEP exceeds that of electrons [Wissinget al., 2010].

To illustrate the consequences of the consideration of the total particle inven-tory atmospheric chemistry modeling, Wissing et al. [2010] applied a combinationof AIMOS and the Bremen 3D chemistry and transport model [Sinnhuber et al.,2003a, 2003b; Winkler et al., 2008] to the October/November 2003 event. This studyshows that the consideration of electrons in addition to protons leads to a significantincrease in atmospheric ionization in the mesosphere and less so in the stratosphere.This is reflected in changes in the chemical composition, such as NOx productionand ozone depletion. Some of the results are discussed in Chap. 16.

One may argue that the horizontal ionization pattern in the upper atmosphere willbe quickly blurred by transport processes. According to Bhattacharya and Gerrard[2009] the mean wind speed at 85 km altitude is about 130 km/h; the maximumdaily mean during the observation time was less than 290 km/h. As particle precip-itation takes place in circles around the geomagnetic pole along the geomagneticlatitudes, zonal (East-West) transport will not impact the atmospheric ionization(electron density) significantly. Meridional (North-South) transport however mayhave a significant impact. The grid resolution in meridional direction is 400 km. Incombination with the 2 h AIMOS model time resolution the “smearing out” effectis limited to one meridional cell at maximum and even less in general.

13.3 Comparison to EISCAT Results

While in the previous DFG-project (ARTOS-AIMOS, described above) the conse-quences of particle precipitation were modeled with respect to atmospheric chem-istry and also can be compared to observations as demonstrated e.g. in Chap. 15, inthis DFG-project (MAIONO) we will compare particle precipitation to electron den-sities as inferred from incoherent scatter radar. Two approaches are chosen, a directcomparison and a comparison using the combined AIMOS/HAMMONIA modelchain.

The European Incoherent Scatter Radar (EISCAT) near Tromsø works with fre-quencies far above the plasma frequency. Therefore the emitted signal is not totallyreflected but scattered by single electrons in the ionospheric plasma. The backscat-tered signal spectrum is analyzed by using plasma theory, which allows to determineplasma parameters Farley [1996] e.g. electron density.

For the October/November 2003 event data coverage at EISCAT is excellent.In the comparison between precipitating solar energetic particles and electron den-sities as measured by EISCAT we used one energy channel of POES (or GOES)at a time, determined the corresponding stopping height and compared to EISCATmeasurements at this height. We tried to take into account in the EISCAT data thediurnal effects, solar flare effects due to increased hard electromagnetic radiation,and shocks/geomagnetic disturbances. Detailed analysis shows that electron densi-ties observed by EISCAT show a pronounced diurnal variation below 75 km, are not

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Fig. 13.5 Direct comparisonbetween EISCAT (red) andPOES (blue) data for protonswith energies 2.6–6.9 MeVlimited to times when POESis magnetically connected tothe vicinity of the EISCATstation. Curves are shifted inlog intensity to allow for abetter comparison. The blackdots give all POESmeasurements in a crossingwhile the blue dot indicates amean

correlated to the fluxes of precipitating solar protons, do not react directly to shocksbut show some correlation with substorms, at least at heights above about 90 km.This implies that our originally planned research design had been too optimistic.Nonetheless, the relation to substorms gives a clue on how to proceed.

To gain a better understanding on the relation between precipitating particlesand electron densities we dropped the source related approach (limitation to solarenergetic particles) and choose a more local one: EISCAT electron densities werecompared to the particle fluxes obtained when POES was magnetically connected tothe vicinity of the radar station. This means the foot-of-fieldline coordinates shouldbe within a rectangle defined by ±2.5° in latitude and ±10° in longitude around theradar station. Owing to the fast motion of POES, a smaller rectangle would not leadto any data coverage. This approach has the advantage that precipitating particles areconsidered independent of their origin. Again, the comparison is done by comparingfluxes in energy ranges to electron densities at the corresponding stopping heights.

Figure 13.5 shows a time series for this comparison. Although there is no per-fect correlation between the electron densities and the proton fluxes, similaritiesin the temporal pattern are apparent in this example of energy-height match. Suchcorrelations strongly depend on energy/height. In addition, Fig. 13.5 suggests thata correlation, if it exists, certainly would not be a simple linear one. To get a bet-ter impression of the correlation we decided to perform a rank correlation betweenall energy channels and height-averaged electron densities in 10 km height bins.We found that correlation coefficients increase with increasing particle energy anddecreasing height and tend to be higher during night. However, any correlation at60 km during nighttime disappears.

In sum, apparently it is more sensible to correlate EISCAT measurements withgeographically adjusted fluxes of precipitating energetic particles than with precip-itating solar energetic particles.

The horizontal resolution of AIMOS provides the necessary accuracy to allow acomparison with local measurements such as EISCAT. A direct comparison never-theless is inaccurate as already indicated in Fig. 13.5: AIMOS determines ioniza-tion rates while EISCAT measures electron densities. Thus a chemistry model is

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Fig. 13.6 Comparison of asimulation of electrondensities with and withoutparticle forcing compared toincoherent scatter radarmeasurements [Wissing et al.,2011]. Below 90 km theGCM uses parameterizationinstead of full ion chemistry,therefore this is the lowerboundary of the comparison.Reprinted with permissionfrom American GeophysicalUnion

required to consider recombination and other losses. To model electron densities forthe comparison to EISCAT observations we used the combination of AIMOS andthe HAMMONIA GCM.

The HAMburg MOdel of the Neutral and Ionized Atmosphere (HAMMONIA)[Schmidt et al., 2006] is a superset of the middle atmosphere version of theECHAM5 general circulation model (GCM) [e.g., Manzini et al., 2006] interac-tively coupled to the MOZART3 chemistry scheme [Kinnison, 2007]. In the versionused here, HAMMONIA includes e.g. photochemical reactions as well as reactionsassociated due to particle precipitation. The reaction rates are given by the ionizationrates from AIMOS combined with branching ratios from Roble and Ridley [1987]and Verronen [2006]. The new chemistry module is limited to altitudes above 90 km.

Figure 13.6 shows a comparison of the simulated electron densities to the onesmeasured by EISCAT with and without particle forcing. As day-time electron den-sities are affected by photoionization—which is comparable in size—we focus onnighttime here. From the figure it is evident that at least during night particle forc-ing must be considered to simulate reliable electron densities with the HAMMONIAGCM as it gives a benefit in the order of a factor of 100 to 1000. A more detailedstudy by Wissing et al. [2011] shows that this AIMOS/HAMMONIA model chainis able to produce useful electron density fields in terms of absolute values as wellas their dependence on geomagnetic latitude, geomagnetic activity and local time.Since interpretation of incoherent scatter radar data relies on assumptions this com-parison cannot be used as a validation for AIMOS, however, the consistency in theresults at least lends confidence to AIMOS and in particular to the sorting algorithmbecause in this comparison the spatial resolution of AIMOS has been tested.

13.4 Summary

AIMOS is a combined empirical/Monte-Carlo model to simulate the 3D ioniza-tion patterns of the atmosphere for solar and magnetospheric particles over a widerange of energies. Parametric studies show that the inclusion of all relevant particlesyields not only different ionization rates but also affects the modeling of atmospheric

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chemistry. A combination of AIMOS and the HAMMONIA GCM allows to deter-mine electron densities which are in reasonable agreement with the observationsfrom incoherent scatter radar; although this does not validate AIMOS it increasesour confidence in the AIMOS sorting algorithm.

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