Lower thermosphere – ionosphere (LTI) quantities: Current status of measuring techniques and models

Abstract

The lower-thermosphere–ionosphere (LTI) system consists of the upper atmosphere and the lower part of the ionosphere, and as such comprises a complex system coupled to both the atmosphere below and space above. The atmospheric part of the LTI is dominated by laws of continuum fluid dynamics and chemistry, while the ionosphere is a plasma system controlled by electromagnetic forces driven by the magnetosphere and solar wind. The LTI is hence a domain controlled by many different physical processes. However, systematic in situ measurements within this region are severely lacking, although the LTI is located only 80 to 200 km above the surface of our planet. This paper reviews the current state of the art in measuring the LTI, either directly or by several different remote-sensing methods. We begin by outlining the open questions within the LTI requiring high-quality in situ measurements, before reviewing directly observable parameters and their most important derivatives. The motivation for this review has arisen from the recent retention of the Daedalus mission as one among three competing mission candidates within the European Space Agency (ESA) Earth Explorer 10 Programme. However, this paper intends to cover the LTI parameters such that it can be used as a background scientific reference for any mission targeting in situ observations of the LTI.

Full text

Lower thermosphere - ionosphere (LTI) quantities: Current status
of measuring techniques and models
Minna Palmroth1,2, Maxime Grandin1, Theodoros Sarris3, Eelco Doornbos4, Stelios Tourgaidis3,5 ,
Anita Aikio6, Stephan Buchert7, Mark A. Clilverd8, Iannis Dandouras9, Roderick Heelis10,
Alex Hoffmann11, Nickolay Ivchenko12, Guram Kervalishvili13, David J. Knudsen14, Anna Kotova9,
Han-Li Liu15, David M. Malaspina16,17, Günther March18, Aurélie Marchaudon9, Octav Marghitu19,
Tomoko Matsuo20, Wojciech J. Miloch21, Therese Moretto-Jørgensen22, Dimitris Mpaloukidis3,
Nils Olsen23, Konstantinos Papadakis1, Robert Pfaff24, Panagiotis Pirnaris3, Christian Siemes18,
Claudia Stolle13,25, Jonas Suni1, Jose van den IJssel18, Pekka T. Verronen2,26, Pieter Visser18, and
Masatoshi Yamauchi27
1University of Helsinki, Department of Physics, Helsinki, Finland
2Space and Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
3Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
4Royal Netherlands Meteorological Institute KNMI, Utrecht, The Netherlands
5Space Programmes Unit, Athena Research & Innovation Centre, Athens, Greece
6Space Physics and Astronomy Research Unit, University of Oulu, Oulu, Finland
7Swedish Institute of Space Physics (IRF), Uppsala, Sweden
8British Antarctic Survey (UKRI-NERC), Cambridge, UK
9Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse, CNRS, CNES, Toulouse, France
10Center for Space Sciences, University of Texas at Dallas, Dallas, USA
11European Space Research and Technology Centre, European Space Agency, Noordwijk, The Netherlands
12Royal Institute of Technology KTH, Stockholm, Sweden
13GFZ Potsdam, German Research Centre for Geosciences, Potsdam, Germany
14Department of Physics and Astronomy, University of Calgary, Calgary, Canada
15National Center for Atmospheric Research, Boulder, Boulder, USA
16Astrophysical and Planetary Sciences Department, University of Colorado, Boulder, USA
17Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, USA
18Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands
19Institute for Space Sciences, Bucharest, Romania
20Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado at Boulder, Boulder, USA
21Department of Physics, University of Oslo, Oslo, Norway
22University of Bergen, Institute of Physics and Technology, Bergen, Norway
23DTU Space – Technical University of Denmark, Copenhagen, Denmark
24Heliophysics Science Division, NASA/Goddard Space Flight Center, Greenbelt, USA
25University of Potsdam, Faculty of Science, Potsdam, Germany
26Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland
27Swedish Institute of Space Physics (IRF), Kiruna, Sweden
Correspondence: Minna Palmroth (minna.palmroth@helsinki.fi)
Abstract.
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The lower-thermosphere–ionosphere (LTI) system consists of the upper atmosphere and the lower part of the ionosphere,
and as such comprises a complex system coupled to both the atmosphere below and space above. The atmospheric part of the
LTI is dominated by laws of continuum fluid dynamics and chemistry, while the ionosphere is a plasma system controlled by
electromagnetic forces driven by the magnetosphere and solar wind. The LTI is hence a domain controlled by many different5
physical processes. However, systematic in situ measurements within this region are severely lacking, although the LTI is
located only 80 to 200 km above the surface of our planet. This paper reviews the current state of the art in measuring the
LTI, either directly or by several different remote-sensing methods. We begin by outlining the open questions within the
LTI requiring high-quality in situ measurements, before reviewing directly observable parameters and their most important
derivatives. The motivation for this review has arisen from the recent retention of the Daedalus mission as one among three10
competing mission candidates within the European Space Agency (ESA) Earth Explorer 10 Programme. However, this paper
intends to cover the LTI parameters such that it can be used as a background scientific reference for any mission targeting in
situ observations of the LTI.
1 Introduction15
The region where the atmosphere meets space, called the Lower Thermosphere–Ionosphere (LTI), is sometimes also termed as
the ignorosphere because it is markedly difficult to make measurements of it directly. This region, spanning from about 80 km to
200 km in altitude, exhibits a relatively high atmospheric density, making systematic satellite in situ measurements impossible
from circular orbits. This is the region where de-orbiting spacecraft and orbital debris start to burn up while re-entering the
atmosphere. Hence sporadic rocket campaigns are currently the main source of in situ observations (e.g., Burrage et al., 1993;20
Brattli et al., 2009). Remote optical observations require measurable emissions reaching the remote detector; however, there is
a significant gap in ultraviolet, infrared and optical emissions (for Fabry-Perot interferometers) at approximately 100–140 km
altitude, allowing only a part of the LTI to be measured remotely. Ground-based radar measurements are also inherently remote,
but are indispensable especially in characterising the ionised part of the LTI, the ionosphere. Due to the lack of systematic
measurements, this region still yields discoveries and surprises; for instance, as recently reported by Palmroth et al. (2020),25
even Citizen Scientist pictures of the aurora may be relevant in obtaining new information on the LTI.
A few comprehensive reviews of the LTI have been published in the recent years. Vincent (2015) concentrates in the at-
mospheric dynamics within the region. Laštoviˇ
cka (2013) and Laštoviˇ
cka et al. (2014) review the trends in the observational
state-of-the-art within the upper atmosphere and ionosphere. Sarris (2019) reviews the characterisation status and presents the
key open questions especially in terms of measurement gaps within the LTI, while also highlighting the discrepancies between30
observations and models. A recently accepted review article by Heelis and Maute (2020) describes the challenges within the
understanding of the LTI in terms of coupling to the lower atmosphere, the LTI as a source of currents, its coupling to regions
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above, and the response of the LTI to different drivers. Apart from these recent reviews, one of the most thorough introductions
to the LTI dates back to a 1995 book within the American Geophysical Union Geophysical Monograph Series, reviewing,
among other aspects, the dynamics of the lower thermosphere (Fuller-Rowell, 2013). These reviews and scientific studies pub-35
lished in the literature explain that the LTI is essentially a transition region with steep gradients in altitude: the dominance of
the neutral atmosphere decreases within the LTI as evidenced by the decrease of the neutral density and the drastic increase of
the temperature due to absorption of solar EUV radiation that occurs within the thermosphere. On the other hand, this is also
the region where near-Earth space, controlled by electromagnetic effects, starts to influence the overall dynamics as part of the
neutrals are dissociated and the medium has the characteristics of a plasma system. First and foremost, this suggests that the40
LTI is a region where the underlying physical processes change in nature, warranting understanding both from the atmospheric
perspective as well as in terms of space plasma physics.
In the Earth’s denser lower atmospheric regions, up to the mesopause around 90 km altitude, the motion of the atmosphere
is driven by both solar irradiance and waves. The dynamics is typically described as a flow governed by the laws of continuum
fluid dynamics, for a gaseous fluid that is electrically neutral. In the continuum assumption, averaging is performed over45
sampling volumes, such that the fluid particles are normally distributed and can thus be described in terms of local bulk
macroscopic properties, notably pressure, temperature, density and flow velocity. The continuum assumption requires the
sampling volume to be in thermodynamic equilibrium, which implies a high frequency of collisions between atmospheric
particles. Atmospheric flow is then predicted by solving the fundamental conservation equations including the conservation of
mass, momentum, and energy, and a thermodynamic equation of state. The energy from solar irradiance is mostly deposited50
as sensible and latent heat fluxes, and via direct absorption of shortwave (solar) radiative energy, for instance by ozone in the
ozone layer and of re-radiated energy, typically by greenhouse gases and clouds.
Above the mesopause, neutral densities become so low that collisions gradually become less important, while the density of
the electrically charged ionospheric plasma increases. In contrast to the atmospheric material, near-Earth space plasmas cannot
be represented by a similar continuum assumption due to the scarcity of collisions. The laws controlling plasma motion need55
to be incremented by electromagnetic forces, and thus the forcing from the magnetosphere needs to be taken into account. At
high latitudes, the ionosphere is coupled via the magnetic field to the magnetosphere, and even further into the solar wind.
Further, plasma particles are typically not normally distributed, implying that plasmas cannot be described by, e.g., a single
temperature. In the transition region between the atmosphere described by the continuum dynamics, and geospace described
by plasma kinetic theory, at altitudes roughly between 80 and 200 km, the atmosphere starts to be significantly affected by the60
presence of the ionosphere. The neutral particles and plasmas interact through collisions and charge exchange, which maximise
at altitudes between 100 and 200 km but remain important up to around 500 km altitude, the nominal base of the exosphere,
beyond which collisions are practically non-existent.
Even though the LTI is characterised as a transition region between the atmosphere and space, it is also markedly a region
having characteristics of its own. This is particularly true in terms of the energy sink that the region represents. From the65
atmospheric perspective, the energy of upward propagating atmospheric waves, such as planetary waves, tides, and gravity
waves (for a review, see Vincent, 2015) is deposited into the LTI. These waves can drive plasma instabilities, which in turn lead
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to small-scale variations that can cause disruption of radio signals (e.g., Xiong et al., 2016). On the other hand, at polar latitudes,
the LTI is a major sink of energy transferred from the solar wind by processes within the magnetosphere and ionosphere, which
are not well understood. In particular, during times of very large solar and geomagnetic activity, for example as a response to70
interplanetary coronal mass ejections (ICMEs, e.g., Richardson and Cane, 2010) and stream interaction regions (SIRs) followed
by high-speed streams (HSSs, e.g., Grandin et al., 2019a), this energy input increases substantially, and can represent a larger
energy source than that provided by solar irradiance. Thus, the energetics, dynamics, and chemistry of the LTI result from a
complex interplay of processes with coupling both to the magnetosphere above and to the atmosphere below.
The neutral–plasma interactions and dynamics within the LTI are poorly understood, mainly due to a lack of systematic75
observations of the key parameters in the region. In the case of scarce observations, the solution is usually to build a model
which can be used to obtain information on the region. However, in the case of the LTI, this approach has been markedly difficult
due to the complexity of the system: the atmospheric models solving general circulation, chemistry, or the climate system (e.g.,
Gettelman et al., 2019) normally do not take into account electromagnetic forces. On the other hand, the magnetosphere
models using a first-principles plasma approach, either using the magnetohydrodynamics description (MHD; e.g., Janhunen80
et al., 2012; Glocer et al., 2013) or the (hybrid-)kinetic description (e.g., Omidi et al., 2011; Palmroth et al., 2018), have
to be coupled to the ionosphere and neutral atmosphere. The ionospheric first-principles (e.g., Marchaudon and Blelly, 2015;
Verronen et al., 2005) or (semi-)empirical models (e.g., Bilitza and Reinisch, 2008) require coupling both to the magnetosphere
and to the atmosphere. In the recent years, the different modelling communities have started to integrate the dedicated models
towards new regimes, as, e.g., the Whole Atmosphere Community Climate Model (WACCM) has been extended to cover85
the thermosphere and ionosphere to about 500 km altitude (WACCM-X Liu et al., 2018a). The MHD-based magnetospheric
models have been coupled to the ionosphere and neutral atmosphere (Tóth et al., 2005). However, even though the models may
currently be the main tool used to provide information on the coupled system, they can only be trusted after careful validation
and verification. Hence, ultimately the only way to understand the LTI holistically is by acquiring systematic measurements of
the system.90
There is a growing recognition that the Earth needs to be studied and understood as a coupled system of its various com-
ponents. The European Space Agency’s (ESA) Living Planet Programme embraces this need, calling for studies of the many
linkages within the system. From this viewpoint, it follows that our understanding is only ever as good as the weakest link. One
such weak link currently is the connection between the Earth and space. For example, there are considerable changes caused
by currents and energetic particles from outer space impinging on the atmosphere, and some of these changes are not well95
sampled and quantified at all, leading to significant (and maybe even critical) uncertainties. ESA’s Earth Explorer 10 candidate
mission Daedalus (Sarris et al., 2019) has been designed to explore the LTI systematically for the first time in situ to address
the challenges within the LTI described above.
This paper introduces the science behind the Daedalus candidate mission. First, we list the three main outstanding topics
under research, related to the LTI energy balance, LTI variability and dynamics, and LTI chemistry. The logic of the paper is to100
present the outstanding science questions first with a short summarising background. These science topics lead to the need of
observing the key LTI parameters, which are divided into those that can be observed directly, and those that need to be derived
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from several other parameters. The bulk of the review concentrates into these direct and derived observables, while the science
questions are on purpose concise, giving only a few central literature references. An important choice made in this paper is
related to the most important energy deposition mechanism driven by the solar wind and magnetospheric forcing, called Joule105
heating. This is such a vast topic that it requires a review of its own. However, here the emphasis is on the parameters required
to assess Joule heating accurately.
In the two most recent review papers of the LTI, Sarris (2019) emphasises the main gaps in the current understanding of this
key atmospheric region and discusses the related roadmaps and statements made by several agencies and other international
bodies, whereas Heelis and Maute (2020) provide a detailed review of the physical processes and couplings within the LTI. The110
purpose of this paper, in turn, is to systematically list and discuss the parameters that can be observed or derived from in situ
measurements, underlining the state-of-the-art in observations and numerical models. The intention is to give a background for
the measurement setup of any given future mission within the LTI, from the viewpoint of the major outstanding questions. The
paper is organised as follows: Section 2 presents the outstanding science questions related to the LTI. Sections 3 and 4 review
the current understanding of the LTI observed and derived parameters, respectively, which are key to improve the understanding115
of the region and required to close the outstanding questions. Section 5 ends the paper with concluding remarks.
2 Open questions in LTI energetics, dynamics, and chemistry
To assess the role of the LTI as a crucial component within the Earth’s atmospheric system, it is important to understand the
dominant processes involved in determining the energetics, dynamics, and chemistry of the LTI. Such knowledge is also critical
to develop capabilities to specify and forecast space weather phenomena that occur, originate and are modified in this region.120
This section summarises these broad topics to give the background for the required observed and derived parameters outlined
later. Figures 1 and 2 illustrate some of the crucial parameters in terms of energetics (temperatures on Fig. 1a and 2a), dynamics
(neutral winds and ion drifts on Fig. 1b–c and 2b), and chemistry (electron density on Fig. 1d and 2c, and neutral density on
Fig. 2c), both over global scales in Fig. 1 and as altitude profiles at selected latitudes on the duskside in Fig. 2. In particular,
Fig. 2 reminds the usual nomenclature used in atmospheric and ionospheric studies, indicating the names of the atmospheric125
layers alongside the temperature profiles (Fig. 2a) and the D,E,Fregions (or layers) in the ionosphere alongside the electron
density profiles (Fig. 2c).
2.1 LTI energetics
In the following, LTI energetics refers to the energy input, deposition, dissipation, and in general the energy balance within
the LTI. Energetics is driven on the one hand by the solar radiative flux and on the other hand by energy deposition into the130
LTI from above (near-Earth space) and below (lower atmospheric regions). The solar radiative flux is mostly controlled by
the inclination of the planet’s rotation axis with respect to the Sun-Earth line, as well as by the distance from the Sun. The
energy input from below mainly consists of atmospheric waves propagating upwards. The energy input from above is extracted
from the solar wind and processed by the magnetosphere, and it affects, e.g., the motion of the ionospheric charged particles
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Figure 1. Overview of some key atmosphere/ionosphere parameters from the WACCM-X model: a) neutral temperature, b) neutral wind
magnitude and streamlines, c) ion drift magnitude and streamlines and d) electron density. The model output is from a simulation of the
2015 St. Patrick’s Day storm, showing the simulated state of the atmosphere on 17 March 2015, 18:00 UTC, during a period of significant
high-latitude energy input. Slices through the model are shown at the top pressure level, at 0◦and –90◦longitude and at –1◦latitude. The
meridional slice over the Greenwich meridian (top right of each sphere) shows a dusk profile, while the 90◦west slice shows a noon profile.
Pressure level geopotential heights from the model have been exaggerated by 50 times to show vertical detail.
0
100
200
300
400
500
Height (km)
0 200 400 600 800 1000 1200 1400
Temperature (K)
Electron temperature
Neutral temperature
a)
−600−400−200 0 200 400 600
Meridional wind/drift velocity (m/s)
b)
1031061091012 1015 1018
Number density (cm-3)
30°
c)
60°
75°
Ion drift
Neutral wind
Electron density
Neutral density
30°
60°
75°
Troposphere
Stratosphere
Mesosphere
Lower
thermosphere 30°
60°
75°
D-layer
E-layer
F-layer
60°
30°30°
75° 75°
60°
Upper thermosphere
Figure 2. Altitude profiles from the WACCM-X model of some key atmospheric parameters at three different geographic latitudes (30, 60
and 75◦N), along the Greenwich meridian at 18:00 UTC on 17 March 2015, consistent with the vertical/meridional slice through this model
at that location in Figure 1.
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and electromagnetic fields within the LTI (e.g., Palmroth et al., 2004). There are two primary energy sinks which deposit135
magnetospheric energy into the ionosphere: Joule heating (JH), and particle (electron and proton) precipitation, of which the
current understanding suggests that JH represents the larger sink (e.g., Knipp et al., 1998; Lu et al., 1998). However, currently
the energy deposited per unit volume at LTI altitudes via JH and particle precipitation is not known. Furthermore, the influence
of this energy deposition on the local transport, thermal structure, and composition within LTI altitudes is also poorly known.
2.1.1 Joule heating140
Joule heating is in general terms caused by electric currents flowing through a resistive medium, which causes heating within
the medium. In the geospace, the current system consists of field-aligned currents (FACs, see Sect. 4.2), which find their closure
through ionospheric horizontal currents in the ionosphere (e.g., Sergeev et al., 1996), which is a resistive medium as neutral
and charged particles undergo collisions. Ultimately, the power density dissipated by JH is according to Poynting’s theorem
j·Ewhere jis the electric current density, and Ethe electric field in the frame of the neutral gas. The electric field in other145
reference frames with the neutral gas velocity U6=0is E0=E+U×Bwith Bthe magnetic field (Kelley, 2009).
In this primary energy deposition mechanism, the additional energy from the magnetosphere forces the plasma to advect
relative to the neutral gas, leading to ion–neutral frictional (or Joule) heating. During geomagnetic storms, current knowledge
indicates, albeit with large uncertainties, that this energy sink is on a par with the heat created by absorption of solar radiation,
which otherwise is the major driver of atmospheric dynamics (Knipp et al., 2005). The effects of moderate to strong geomag-150
netic activity can be significant in mid and equatorial latitudes, as auroral JH can launch travelling ionospheric disturbances
which can have measurable effects down to equatorial latitudes (e.g., Zhou et al., 2016; de Jesus et al., 2016). By enhancing the
ion temperature, JH modifies the chemical reaction rates and thus the local chemical equilibrium and ion and neutral composi-
tion. The way by which neutral winds, ion drifts and electric fields interplay to generate heating is largely unknown, primarily
due to the lack of co-located measurements of all key parameters involved. Since the topic of JH is vast, it is left as a subject155
of a subsequent paper, while we cover some of the knowledge and open questions of JH in Sect. 4.7.
To understand this energy deposition mechanism, it is imperative to explore the energy deposited into the LTI through JH, by
simultaneously measuring the comprehensive set of variables determining JH in the auroral latitudes and 100–200 km altitude
regions where it maximises, sampled over a broad range of atmospheric and geomagnetic conditions, and at a resolution that
captures the key scales associated with this heating process. In particular, since the quantification of JH is significantly affected160
by the measurements of the Pedersen conductivity (e.g., Palmroth et al., 2005), it is necessary to characterise how collision
cross-sections, frequencies, and the resulting conductivities vary with altitude and conditions in the LTI, by simultaneously
measuring the comprehensive set of variables determining these parameters over the relevant altitudes and for a range of
atmospheric and geomagnetic conditions.
2.1.2 Precipitation-driven energy input165
The second most important energy deposition mechanism is caused by particle precipitation, which is typically divided into
two categories: lower-energy auroral (∼0.01–20 keV, mostly electron) precipitation, depositing energy within ∼100–300 km
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altitude, and energetic (>30 keV) particle precipitation (EPP), including relativistic (>1 MeV) energies, consisting of ener-
getic electrons and ions depositing energy below ∼90 km altitude (Berger et al., 1970). Auroral ion precipitation occurs, with
the specificity of its own that precipitating protons can undergo multiple charge-exchange interactions with atmospheric con-170
stituents on their way down, leading to a spreading of the affected area (see special section by Galand, 2001). The sources
for auroral precipitation and EPP are particles both directly coming from the Sun or accelerated by various processes in the
magnetosphere. Broadly speaking, auroral precipitation comprises larger number fluxes (Newell et al., 2009), while EPP con-
sists of higher energies. Hence both affect the energy deposition within the LTI, the former through larger areas and the latter
through higher energies.175
The energy input from particle precipitation is given by the energy of the incoming particles deposited via either dynamical
or chemical processes at the altitude of dissipation, for example through electron temperature enhancement, ionisation of
neutrals, excitation of neutrals or ions, and dissociation of molecular species producing chemical components (see also Sect.
2.3). The altitude of maximum energy deposition by precipitation is determined by particle energies (e.g., Turunen et al.,
2009, Figure 3): Relativistic ions (E > 30 MeV) and electrons (E > 1MeV) penetrate down to the stratosphere, energetic180
ions (1< E < 30 MeV) and electrons (30 < E < 1000 keV) deposit their energy through ionisation into the mesosphere,
while the lower-energy ions (E < 1MeV) and auroral electrons (E < 30 keV) impact the thermosphere. The local values
of precipitation-induced heating are however largely unknown, as its quantification proves challenging due to the scarcity of
suitable observations. Detailed measurements of the energy spectrum and flux of particles passing through the thermosphere as
a function of solar/geomagnetic conditions are key to accurately quantifying the impact of precipitation on the climate system185
(see Sect. 2.3.1).
To quantify the energy deposited into the LTI through particle precipitation, it is necessary to measure the energy spectrum
and flux of precipitating particles at the auroral latitudes in regions where it maximises, sampled over a broad range of ge-
omagnetic conditions, and at resolutions in energy and pitch-angle that capture the characteristic scales associated with the
heating, ionisation, and dissociation processes of interest. To assess the local response and relative importance of JH and EPP190
in the LTI, it is necessary to simultaneously measure the comprehensive set of corresponding changes in composition, flows,
and temperatures with adequate temporal resolution to capture the involved processes.
2.1.3 Energetics driven by the neutral atmosphere
There are several types of waves within the lower atmosphere which travel vertically towards the LTI and are expected to
dissipate there. These waves couple with the neutral wind, temperature field, and density in the LTI. They are also believed195
to seed plasma instabilities, especially in the low-latitude region, and the upward propagating waves can produce large shears
that may affect the overall circulation within the LTI. In particular, gravity waves (see Sect. 4.9) contribute significantly to the
LTI energetics. From a high-resolution Whole Atmosphere Community Climate Model (WACCM) simulation (with horizontal
resolution of ∼25 km, Liu et al., 2014), it has been calculated that the total upward energy flux by resolved waves at 100 km
is 100–150 GW (Liu, 2016), which is comparable to the daily average JH power input (Knipp et al., 2004). This is likely an200
underestimation of the actual energy flux by gravity waves, since waves with horizontal scales less than 200 km are poorly
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resolved due to numerical dissipation (or not resolved at all) in the model. The energy deposition rate is also estimated based on
parametrised gravity waves: total wave energy deposition rate at 100 km is 35 GW, and 75% of that comes from parametrised
and resolved gravity waves (Becker, 2017). The role of the neutral atmosphere forcing for LTI energetics can only be estimated,
because there are no comprehensive and systematic observations of the coupling between neutral and ions in the LTI.205
2.2 LTI variability and dynamics
This section summarises typical phenomena of spatial and temporal variability in the LTI region and mentions dynamical
processes that lead to reorganisation of, e.g., neutral or electron density, conductivity, or wind. We consider forcing from above,
defined as variations driven by magnetospheric dynamics (Sect. 2.2.1). In this topic, the current key scientific questions related
to LTI variability and dynamics are to understand the ways in which the magnetosphere drives plasma motion in the high-210
latitude LTI, and how this motion affects the motion of the neutrals. We also consider forcing from below through atmospheric
waves (Sect. 2.2.2). In this topic, the current key research question is to understand how large shears, sharp gradients, and
small-scale plasma instabilities develop in the LTI in response to driving from below. LTI variability and dynamics take a
special form at the low geomagnetic latitudes, summarised in Sect. 2.2.3. In low latitudes, the current key scientific question
is to quantify the relative contributions of magnetospheric, solar and atmospheric forcing influencing LTI fluid dynamics and215
electrodynamics.
2.2.1 LTI forcing from above
Magnetospheric driving of the LTI can take the form of electromagnetic driving due to rapid variations in the geomagnetic
field, and wave-particle interactions within the magnetosphere. Both processes involve the geomagnetic field (Sect. 3.6) and
FACs (Sect. 4.2). The geomagnetic field variations are chiefly due to substorms which are often defined as periods of solar wind220
energy loading and subsequent magnetospheric unloading (e.g., McPherron, 1979). While there is still much debate about the
sequence of events that lead to a substorm onset (e.g., Angelopoulos et al., 2008; Lui, 2009), from the phenomenological
perspective it is agreed that substorms involve magnetotail reconnection (e.g., Angelopoulos et al., 2008), a FAC system
connecting the tail plasma sheet to the ionosphere called substorm current wedge (e.g., Keiling et al., 2009), fast tail plasma
flows (e.g., Angelopoulos et al., 1994), dipolarisation of the tail magnetic field (e.g., Runov et al., 2011), plasmoids launched225
tailwards (e.g., Ieda et al., 2001), and rapidly northward propagating bright auroral emissions (e.g., Frey et al., 2004). It is not
within the scope of this paper to review all the substorm-related subtleties; rather our purpose here is to emphasise the role
of substorms as one of the chief magnetospheric drivers of LTI energetics and dynamics. This driving is mostly manifested as
increased precipitating particle fluxes as well as intensified FACs.
Another broad category of magnetospheric drivers of the LTI consists of the various waves which modify the proton and230
electron pitch angles such that the particles precipitate into the LTI. These waves have a multitude of drivers, and their charac-
teristics and role in driving the LTI vary greatly. For example, Alfén waves, driven by solar wind-magnetosphere interactions,
propagate into the LTI, transferring energy and momentum, as well as modifying LTI local plasma properties such as the den-
sity, temperature and conductance via the total electron content (e.g., Pilipenko et al., 2014; Belakhovsky et al., 2016). Various
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wave modes, primarily ultra-low frequency (ULF), very-low frequency (VLF) and electromagnetic ion cyclotron (EMIC) waves235
drive energetic particle precipitation (Thorne, 2010, see also Sect. 3.1), which drives chemistry changes in the LTI (Sect. 2.3).
The characteristics and propagation of these waves is an important unsolved problem; however, they are well-measured only on
the ground (e.g., Sciffer and Waters, 2002; Engebretson et al., 2018; Graf et al., 2013; Manninen et al., 2020) or above the LTI
around 400 km altitude (e.g., Li and Hudson, 2019). To build a complete picture of wave propagation through the LTI, direct
in situ measurements of these waves are required simultaneously with the plasma and neutral gas parameters, which determine240
the wave propagation in this region.
Downward LTI forcing is not limited to processes originating from the magnetosphere. Solar flares are also known to
enhance electron density and hence JH in the LTI (e.g., Pudovkin and Sergeev, 1977; Sergeev, 1977; Curto et al., 1994;
Yamazaki and Maute, 2017). While the solar-flare-driven ionospheric current, or crochet current, near the subsolar region has
been intensively studied (e.g., Annadurai et al., 2018), its couterpart at high latitudes has been poorly understood for 40 years,245
although it significantly enhances pre-existing JH at the auroral electrojets (Pudovkin and Sergeev, 1977). The modification of
the auroral electrojets by solar flares can be more than a mere enhancement, as it can also lead to a change in the direction of
the electrojet, and the resulting geomagnetic deviation can exceed 200 nT. It is quite possible that the altitude of the ionospheric
current also changes, but no measurement method to prove this has been proposed.
To understand the forcing from above, it is necessary to explore the momentum transfer between the plasma and the neutral250
fluid in the LTI, by simultaneously measuring the comprehensive set of variables determining the forces globally, sampling a
broad range of atmospheric and geomagnetic conditions, and over timescales that capture the involved processes.
2.2.2 LTI forcing from below
Ionised gas under the influence of the geomagnetic field affects greatly the overall dynamics of the LTI, which makes it distinct,
but not decoupled, from the atmosphere. In addition to Joule heating, the electromagnetic coupling asserts the Lorentz force255
acting on the ionised gas, providing geospace with a lever on the atmosphere, and also providing a lever between hemispheres
connected by the dipolar geomagnetic field. Furthermore, the electromagnetic forcing affects and is affected by atmospheric
variations and disturbances, e.g., by planetary (Rossby) waves, gravity waves, and solar or lunar tides, originating from below
the LTI and propagating upwards. Many outstanding issues remain in our understanding of the complex large-scale and global
interactions between these processes and forces that act together to determine LTI dynamics. Especially the occurrence of260
strong flow shears, steep gradients or rapid variations in the LTI parameters have been observed but not been studied systemat-
ically due to a lack of consistent measurements of the relevant parameters. Consequently, the effects of such structures on the
LTI dynamics are not well known. The physics of the different atmospheric waves is reviewed in Sect. 4.9.
To understand the driving from below, it is necessary to simultaneously measure all the variables defining not only the neutral
dynamics but also the electrodynamics as well as the corresponding local changes in composition, densities, and temperatures265
at the relevant latitudes and altitudes, sampled over a range of atmospheric and geomagnetic conditions, and at temporal
scales that capture the key processes involved, including gravity waves, planetary waves and tides originating from the lower
atmosphere.
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2.2.3 Variability and dynamics in the low-latitude LTI
At low latitudes, the dynamics of the LTI, comprising the ionosphere Eregion and lower Fregion, determines significant parts270
of the variability of the entire thermosphere and ionosphere through global electric field variations (Scherliess and Fejer, 1999)
and related large- to medium-scale plasma transport, the most important phenomenon being known as the Equatorial Ionisation
Anomaly (e.g., Walker et al., 1994; Stolle et al., 2008b). The Eregion dynamo which results from charged particles transported
by thermospheric winds through the nearly horizontal magnetic field (e.g., Heelis, 2004) is understood to play a key role in
driving the electric fields and the equatorial electrojet, the latter being a ribbon of strong eastward dayside current flowing along275
the magnetic equator. While the general principles are described, the significant day-to-day variability of their magnitudes is
still subject of investigation (e.g., Yamazaki and Maute, 2017). A special category of the LTI variability and dynamics within
the low latitudes are post-sunset F-region equatorial plasma irregularities, in which the LTI and lower Fregion are believed to
play an important role. Suggested initial perturbations for these plasma irregularities are the variability of the vertical plasma
drift at sunset hours (e.g., Huang, 2018; Wu, 2015; Stolle et al., 2008a) and the role of upward propagating gravity waves280
(e.g., Krall et al., 2013; Hysell et al., 2014; Yokoyama et al., 2019). The resulting F-region plasma irregularities cause severe
effects on trans-ionospheric radio wave signal propagation (e.g., Xiong et al., 2016), and are thus an important source for space
weather disturbances.
To understand the LTI behaviour within low latitudes, it is imperative to reveal the morphology of flow shears and sharp
gradients in the LTI and their role in driving plasma instabilities by simultaneously measuring the comprehensive set of vari-285
ables that fully describe the LTI, including the ionospheric plasma density at a horizontal resolution that captures the relevant
processes, sampled over a wide range of latitudes and altitudes, with sufficient time resolution, and over appropriate temporal
scales for these processes.
2.3 LTI chemistry
The chemical composition of the LTI may change in response to particle precipitation (Sect. 2.3.1), temperature increase290
associated with frictional/Joule heating (Sect. 2.3.2), and through chemical heating (Sect. 2.3.3) resulting from exothermic
reactions. The current key science questions in upper atmospheric chemistry are related to the chemical effects of EPP within
the mesosphere (and the stratosphere below) as a function of geomagnetic driving conditions. Further, the role of driving
conditions from below, including the upward-propagating gravity waves, in influencing the LTI chemistry is not known. Finally,
it is not known whether the current model boundary conditions (see below) provide a good representation of the LTI physics295
as a function of LTI conditions and solar activity. This section is dedicated to summarise the background to these topics.
2.3.1 Precipitation-driven chemistry
Electron and ion precipitation ionise and dissociate neutrals through collisions (Sinnhuber et al., 2012). This has a direct
effect in the atmospheric chemical composition via ion chemistry which leads to production of odd hydrogen (HOx) and
nitrogen (NOx) (e.g., Codrescu et al., 1997; Seppälä et al., 2015). Considering the LTI coupling to lower atmosphere, odd300
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nitrogen (NOx= NO + NO2) is particularly important, because it has a long (∼months) chemical lifetime in polar winter
conditions, and it descends to mesospheric and stratospheric altitudes down to ∼35 km (Randall, 2007; Funke et al., 2014;
Päivärinta et al., 2016) and catalytically destroys ozone (Damiani et al., 2016; Andersson et al., 2018). Ozone is an effective
absorber of solar ultraviolet radiation, and its variability modulates the thermal balance of the middle atmosphere and polar
vortex dynamics (Brasseur and Solomon, 2005). These perturbations can propagate to surface levels and modulate regional305
patterns of temperatures and pressures (Gray et al., 2010; Seppälä et al., 2014). Investigation of atmospheric reanalysis datasets
and coupled-climate model runs have shown that NOxand HOxhave the potential to modify regional winter-time surface
temperatures by as much as ±5K, through re-distributing annular mode patterns at mid to high latitudes in both northern and
southern hemispheres (Seppälä et al., 2009; Baumgaertner et al., 2011). To understand these questions, it is necessary to make
simultaneous observations of the EPP flux, energy spectral gradients, ion composition, and NOx, and measure EPP fluxes with310
good resolution in the energy and pitch angle. Further, the involved energy spectral gradients need to be described, along with
the energy ranges that cover the deposition altitudes from the lower thermosphere to the mesosphere down to the stratopause.
These measurements need to be sampled at rates fast enough to resolve different precipitation mechanisms and boundaries on
scales of 10 km or smaller.
The LTI region chemistry is recognised to be important for long-term climate simulations due to its role in solar-driven315
NOxproduction and ozone impact (Matthes et al., 2017). However, there are substantial differences between simulated and
observed distributions of polar NOx, owing partly to an incomplete representation of electron precipitation (Randall et al.,
2015). Further, adequate climate simulations require a NOxupper boundary condition as well as a representation for the
dynamical-chemical coupling between thermospheric NOxand stratospheric ozone. For so-called high-top models, with upper
boundary in the thermosphere, the boundary conditions can be defined by empirical models based on satellite data (e.g., Marsh320
et al., 2004), which depend on geomagnetic indices, day of the year, and solar flux. However, current models are based on
temporally limited data and do not cover full solar cycles and/or differences between solar cycles, and recent studies indicate
a need for improvements (Hendrickx et al., 2018; Kiviranta et al., 2018). To improve the model boundary conditions, it is
necessary to make observations of NOxin the polar lower mesosphere below 150 km to characterise the NO reservoir and
variability. Preferably, the measurements should be carried out long enough to cover the solar cycle, and different EPP events325
to improve understanding of the drivers for the climate model boundary conditions.
2.3.2 Heating-driven chemistry
Changes in the ion and neutral temperatures, for instance associated with ion–neutral frictional heating, affect the chemical
reaction rates in the LTI and can consequently modify the LTI composition. Grandin et al. (2015) found that during high-
speed-stream-driven geomagnetic storms the auroral-oval F-region peak electron density can decrease by up to 40% in the330
evening magnetic local time (MLT) sector, especially around the equinoxes. The suggested mechanism to account for this
electron density decrease is that ion–neutral frictional heating associated with substorm activity may increase the ion and
neutral temperatures on timescales much less than an hour, resulting in an enhancement of the electron loss rate by increasing
both the chemical reaction rates (functions of the ion temperature) and the molecular densities by upwelling of the neutral
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atmosphere associated with the neutral temperature increase. A subsequent study by Marchaudon et al. (2018) confirmed that335
this mechanism, especially through the latter process, can account for the long-lasting F-region peak reduction. Heating-driven
composition changes in the LTI have also been revealed in association with subauroral polarisation streams (SAPS; e.g., Wang
et al., 2012) and solar proton events (e.g., Roble et al., 1987). However, not many studies discuss heating-driven chemistry in
the LTI, indicating a lack of systematic measurements.
2.3.3 LTI Chemistry and Chemical Heating340
Chemical heating is one of the main energy sources in the LTI, together with Joule heating, EUV radiation and particle
precipitation heating, resulting from the storage in latent chemical form and subsequent release of energy (Beig, 2003; Beig
et al., 2008). Chemical heating influences the upper atmosphere in a variety of ways, including the formation of mesospheric
inversion layers (Ramesh et al., 2013). Chemical energy is deposited in the LTI through the exothermic reactions typically
involving oxygen (atomic and molecular) and ozone (e.g., Singh and Pallamraju, 2018). Neutral species, namely O3, H2O,345
CO2, OH, and aerosols are believed to play a role both in the chemistry of the LTI and in the radiative balance of the mesosphere
(Mlynczak, 2000). On the other hand, CO2molecules can induce radiative cooling in the LTI through their emission at 15 µm.
Especially between 75 and 110 km altitude, this emission is the only significant cooling mechanism (e.g., Fomichev et al.,
1986), while below, radiative cooling by ozone and H2O is also important (e.g., Bi et al., 2011). Quantifying the contribution
of chemical heating to the changes in the LTI composition is vital in order to understand the full radiative balance of the upper350
atmosphere. Furthermore, the spatial and temporal distributions of neutral species could be used as tracers of wave and tidal
phenomena (Solomon and Roble, 2015), which affect the overall dynamics of the LTI. Therefore it is important to obtain
measurements of the chemical composition and heating in the LTI.
3 Observed LTI parameters: Current understanding
3.1 Precipitating particle fluxes and energies355
Particle precipitation is very much connected to the overall electrodynamic coupling within the LTI. Precipitation leads to
increased ionospheric conductivities (Aksnes et al., 2004) and creates field-aligned currents (FACs, see Sect. 4.2). FACs close
in the Eregion of the ionosphere, leading to ion-neutral frictional heating (Millward et al., 1999; Redmon et al., 2017, see
Sect. 4.7). Since it plays such a leading role in the electrodynamic coupling, we discuss precipitation first.
Particles (electrons and ions) precipitate into the LTI when they are scattered into the bounce loss cone. Pitch-angle scatter-360
ing can be due to the magnetic field curvature radius being close to the particle gyroradius (Sergeev and Tsyganenko, 1982) or
to wave–particle interactions. For instance, lower-band chorus waves, often present in the morningside and dayside magneto-
sphere, can lead to energetic (E > 30 keV) electron precipitation (Thorne et al., 2010), whereas electromagnetic ion cyclotron
(EMIC) waves can be efficient in scattering kiloelectronvolt protons and megaelectronvolt electrons into the bounce loss cone
(Rodger et al., 2008; Yahnin et al., 2009). Other suggested pitch-angle scattering waves include the plasmaspheric hiss, which365
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may contribute to the precipitation of subrelativistic electrons (He et al., 2018). Phenomena such as pulsating aurora have been
found to be associated with precipitating electrons across a wide range of energies (e.g., Grandin et al., 2017b; Tsuchiya et al.,
2018), which suggests interaction with whistler chorus waves (Miyoshi et al., 2015) or electrostatic electron cyclotron har-
monic waves (Fukizawa et al., 2018). Evaluating the relative contribution of each scattering process to the global precipitation
budget is challenging; obtaining particle measurements at multiple pitch angle values in the bounce loss cone with good energy370
resolution across the energy range could prove decisive in this endeavour.
Precipitating particles can have energies ranging from tens of electronvolt to tens of megaelectronvolt. While low-energy
(E≈0.1−30 keV) electrons and protons primarily precipitate at high latitudes, in the polar cusps and in the nightside
auroral oval (which is usually above ∼65◦geomagnetic latitude), relativistic electrons from the outer radiation belt (E≈
0.1−10 MeV) mostly precipitate at subauroral latitudes, i.e., equatorwards from the auroral oval. Solar energetic particles375
(E > 10 MeV protons), on the other hand, precipitate directly from the solar wind into the polar region (geomagnetic latitudes
above ∼60◦); however, the largest of those events are rare and typically occur only a few times per solar cycle (Neale et al.,
2013). Energetic neutral atoms (1–1000 keV, principally within the 100 keV range; Orsini et al., 1994; Roelof, 1997; Goldstein
and McComas, 2013) are produced via charge exchange when energetic ions interact with background neutral atoms such as
Earth’s geocorona. They can play a role in mass and energy transfer to lower latitudes beyond the auroral zone (Fok et al.,380
2003) and become strongly coupled to precipitating energetic ions in the lower thermosphere (Roelof, 1997).
To date the most comprehensive measurements of particle distributions in the near-Earth environment have been made
by flagship spacecraft missions such as DEMETER (Sauvaud et al., 2006), Cluster (Escoubet et al., 2001), Magnetospheric
Multiscale (MMS; Burch et al., 2016), Arase (Miyoshi et al., 2018), and the Van Allen Probes (Mauk et al., 2013). However,
at high altitudes, bounce loss cone angles have values of the order of a few degrees only, which is too small to be resolved by385
most particle instruments carried by those spacecraft. On the other hand, at altitudes where low-Earth orbit (LEO) satellites
fly, the bounce loss cone at auroral latitudes has its edges at an angle of about 60◦from the magnetic field direction (Rodger
et al., 2010a); it is therefore possible to resolve it with particle detectors. A large number of LEO spacecraft missions have
flown particle detectors measuring differential and integral precipitation fluxes. The Solar, Anomalous, and Magnetospheric
Particle Explorer (SAMPEX; Baker et al., 1993) mission (1992–2012) produced megaelectronvolt electron precipitation data390
that have been used in scientific studies (e.g., Blum et al., 2015). The SSJ experiment aboard Defense Meteorological Satellite
Program (DMSP) satellites has provided precipitating proton and electron observations in up to 20 channels covering the
lower energies (30 eV–30 keV) since 1974 (e.g. Hardy et al., 1984; Redmon et al., 2017); Figure 3a gives an example of
differential number flux of precipitating electrons measured by DMSP-F18 on 20 January 2016 in the evening sector of the
northern auroral oval. Higher-energy (>30 keV) precipitation observations have on the other hand been routinely provided by395
NOAA Polar-orbiting Operational Environmental Satellite Space Environment Monitor (POES/SEM) instrument suite since
1979, although measurements have suffered from contamination issues that were corrected by Asikainen and Mursula (2013).
Particle detectors can nowadays even be included in nanosatellite missions; one example of upcoming CubeSat missions aimed
to measure particle precipitation is FORESAIL-1 (Palmroth et al., 2019) which is expected to measure energetic and relativistic
electrons and protons.400
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Figure 3. (a) Auroral electron precipitation differential number flux measured by the DMSP-F18 spacecraft on 20 January 2016 at
18:08:11 UT. (b) Map of diffuse auroral electron energy flux in the northern hemisphere given by the OVATION-Prime model at the same
time. The radial coordinate is geomagnetic latitude, and the angular coordinate is MLT. The orange star indicates the position of the DMSP-
F18 spacecraft.
Indirect observations of particle precipitation can be achieved through various types of observations. Balloon experiments
flying in the stratosphere can detect Bremsstrahlung emission produced by precipitating particles interacting with neutrals in
the atmosphere, as is done during BARREL campaigns (Woodger et al., 2015). Energetic electron precipitation is routinely
monitored from the ground using riometers, which measure the cosmic noise absorption in the D region of the ionosphere
associated with particle precipitation (e.g., Hargreaves, 1969; Rodger et al., 2013; Grandin et al., 2017a). Phase and amplitude405
perturbations to subionospheric man-made narrow-band transmitter signals propagating over long distances are also routinely
used to identify energetic electron precipitation (Clilverd et al., 2009). Incoherent scatter radar observations can be used to
retrieve precipitating electron energy spectra (Virtanen et al., 2018), and to monitor the ionospheric impact of particle precipi-
tation (Verronen et al., 2015).
Empirical models have been developed by deriving statistical patterns of particle precipitation as a function of geomagnetic410
activity based on several years of spacecraft observations. The Hardy model (Hardy et al., 1985, 1989) was established by com-
piling two years of DMSP measurements of precipitation, and provides differential number fluxes of precipitating electrons
and protons as a function of the Kpindex. More recently, the OVATION-Prime model (Newell et al., 2014) was developed to
predict auroral power as a function of solar wind parameters. This model separates auroral precipitation into four types (dif-
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fuse, monoenergetic, broadband and ion); Figure 3b gives an example of output of the diffuse auroral precipitation, obtained415
during the conditions when the differential flux shown in Figure 3a was observed. For higher energies, while the AE-8 (elec-
trons) and AP-8 (protons) maps provide trapped fluxes in the radiation belts (Vette, 1992), the models developed by van de
Kamp et al. (2016) and van de Kamp et al. (2018) predict 30–1000 keV electron precipitation fluxes as a function of the Ap
index based on analysing energetic electron precipitation observed by POES satellites during 1998–2012. Such climatologies
prove particularly useful for space weather predictions and can be used as inputs to ionospheric models, such as the IRAP420
Plasmasphere-Ionosphere Model (IPIM; Marchaudon and Blelly, 2015) or the Whole Atmosphere Community Climate Model
(WACCM3; Kinnison et al., 2007). Finally, a few attempts to model particle precipitation in global, first-principle simulations
of the near-Earth environment have been made, in magnetohydrodynamics models (e.g., Palmroth et al., 2006a), in some cases
coupled with a test-particle code (e.g., Connor et al., 2015), as well as in hybrid-particle-in-cell simulations (e.g., Omidi and
Sibeck, 2007) and more recently using a hybrid-Vlasov model (Grandin et al., 2019b).425
3.2 Temperatures
The LTI temperature is a key background parameter, not only because it is a state parameter for the thermosphere itself, but
it is also key in ultimately driving neutral winds and atmospheric expansion, as well as determining conditions for chemical
reactions. While ion and electron temperatures, Tiand Te, can exceed the neutral temperature Tnby thousands of kelvin (see
Figure 2a showing neutral and electron temperature profiles at selected latitudes obtained from a WACCM-X simulation), the430
largest thermal energy reservoir in the LTI is in the neutral gas simply because of the low degree of ionisation in the LTI (see
Figure 4). The largest heat production is by absorption of solar EUV and UV radiation which is ionising and dissociating
molecules. This process accounts for the well-known basic vertical structure of Tnand the thermospheric chemical composi-
tion.
Reliable measurements of Tnhave been difficult and less abundant compared to those of the neutral density itself where435
especially the analysis of drag on satellite orbits has boosted the available data in the recent decades. In diffusive equilibrium
(for each gas component) the profiles of density and Tnare not independent. Early models of the thermosphere were based on
this assumption and an empirical formula, sometimes called the Bates profile:
Tn(z) = T∞−(T∞−Tz0) exp−z−z0
H(1)
with T∞the exospheric temperature, Tz0the temperature at the base, z0the height of the base, and Ha scale height (Bates,440
1959). Sources of Tnmeasurements include mass spectrometers on sounding rockets, which naturally are relatively sparse, on
satellites, which do not cover well the lower parts of the LTI, and by optical methods like UV occultations observed in space
and ground-based Fabry-Perot interferometers.
Incoherent scatter radars (ISRs) can reliably measure Tiwhen the mean ion mass is known or assumed. ISR measurements
of Tiare a core resource for the construction of empirical models, particularly the widely used NRLMSIS-00 (Picone et al.,445
2002). Below about 160 km altitude, the molecular ions O+
2, NO+, and N+
2with very similar masses are dominant, and in
the topside ionosphere the main ion is O+. In these altitude regions, the Tiestimation by ISR is based on relatively reliable
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knowledge of the mean ion mass. During geomagnetically quiet times, after sunset, before sunrise, and preferably at mid and
low latitudes, ion-neutral frictional heating is not expected to be significant. During geomagnetic activity, ion-neutral frictional
(Joule) heating (see Sect. 4.7), particle precipitation (see Sect. 3.1), and magnetic forcing (j×B; see Sect. 4.1) increase,450
leading to atmospheric expansion and satellite drag (e.g., Liu and Lühr, 2005) and a general upwelling of the thermosphere.
While diffusive equilibrium certainly cannot be assumed for a quantitative analysis in such dynamic situations, the upwelling
must still be supported by substantial increases of Tn, as simulations have confirmed (Lei et al., 2010).
The thermospheric temperature can be increased significantly during large geomagnetic storms. In numerical simulations of
a major storm (November 8-10, 2004), Tnwas shown to increase from 750 K to up to about 1200 K at high latitudes, whereas455
at the equator the increase of Tnover the quiet-time value, ≈1000 K at 400 km height, never exceeded 30%, and was about
15% on average over the duration of the storm (Lei et al., 2010). The results and observations imply that the energy input into
the thermosphere during this geomagnetic storm was invested for one part into geopotential energy, for another part into strong
winds reaching a good fraction of the thermal velocity, and for a third part directly into heating of the neutral gas. Both the
potential and kinetic wind energy are eventually converted into heat, the latter by molecular viscosity which is important for460
the dynamics of the thermosphere. The relative contribution of each of these energy sinks during a strong geomagnetic storm
requires further investigations to be determined in a quantitative way.
Compared to the solar-cycle-induced variation of Tn, the storm-induced changes seem to be still somewhat smaller. Typically
T∞varies between 750 and 1350 K over a solar cycle, with the power by solar EUV getting converted into both geopotential
energy of the atmosphere and directly into heat. The transition between solar-EUV-heated and dark regions is relatively smooth465
compared to the horizontal temperature gradients that are created by strong, localised Joule and particle precipitation heating.
Therefore the latter probably also generate substantial “available potential energy” in the sense of Lorenz (1955).
3.3 Neutral and ion composition and densities
3.3.1 Neutral and ion composition
The LTI is the region where the neutral atmosphere and the ionosphere are strongly coupled, and the exchange between470
neutrals and ions is continuous. This exchange occurs through ionisation and recombination, and is modulated by the solar UV
flux, particle precipitation and the electrojets. The neutral and ion constituents have however very different scale heights and
responses to the drivers as electrodynamic energy input, electric field, solar UV, atmospheric forcing, etc. (Schunk and Nagy,
1980). Figure 4 provides an example of density as a function of altitude for each of the major neutral and ion species in the
terrestrial upper atmosphere, for a given position and time. The ion densities are from the International Reference Ionosphere475
(IRI) model (Bilitza et al., 2014) and the neutral densities from the NRLMSISE-00 atmosphere model (Picone et al., 2002).
Composition observations are based on measuring the density of each species, ion or neutral, separately. The in situ composi-
tion measurements are performed by ion and neutral mass spectrometers, most notably onboard the AE-B and AE-C spacecraft
(1966–1985, PI: H. C. Brinton) and onboard Dynamics Explorer-2 (1981–1983; Carignan et al., 1981). These spacecraft had
perigees in the 300–400 km range. A few measurements have also been obtained onboard sounding rockets (Grebowsky and480
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Figure 4. Typical example of the density altitude profiles of the major ion and neutral species obtained from the NRLMSISE-00 and IRI-2012
models. Compare with Figure 2c.
Bilitza, 2000). Ion and neutral mass spectrometry technique has been systematically used also for the study of other planetary
upper atmospheres in our solar system (Waite et al., 2004; Balsiger et al., 2007; Wurz et al., 2012; Mahaffy et al., 2015).
However, after Dynamics Explorer-2 mission in 1983 no other successful neutral mass spectrometer measurements have been
obtained in the terrestrial thermosphere (Dandouras et al., 2018; Sarris et al., 2020).
For selected ion or neutral species, densities can be obtained also by remote-sensing optical measurements (e.g., Emmert485
et al., 2012; Qin and Waldrop, 2016). The NASA GOLD (Global-scale Observations of the Limb and Disk) mission, launched
in 2018, consists of a UV imaging spectrograph on a geostationary satellite providing remotely measured densities and tem-
peratures in the Earth’s thermosphere for O and N2(https://gold.cs.ucf.edu/). Similarly, the NASA ICON (Ionospheric Con-
nection Explorer) mission, launched in October 2019, includes an EUV and an FUV imager pointing at the Earth’s limb
(http://icon.ssl.berkeley.edu/).490
ISR measurements allow in theory to infer the ion composition in the ionosphere, as the ISR spectra depend on the mean
ion mass. However, this proves very difficult in practice (Kofman, 2000), and the ion composition is generally assumed when
analysing ISR data. On the other hand, assuming an incorrect ion composition when analysing ISR data can lead to large errors
in the retrieved parameters (in particular the ion temperature), which is why in several studies the assumed ion composition was
corrected using simulations from numerical models (e.g., Blelly et al., 2010; Pitout et al., 2013). A few studies have besides495
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made use of ISR observations to estimate the densities of some major neutral species, such as atomic oxygen and hydrogen
(Blelly et al., 1992).
The scarcity of composition measurements at Earth’s LTI region is thus replaced, to a certain extent, by numerical upper
atmosphere models. The NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension
(WACCM-X) simulates the entire atmosphere and thermospheric ionosphere, from the Earth’s surface up to ∼700 km alti-500
tude, and reproduces thermospheric composition, density, and temperatures in good correspondence with measurements and
empirical models (Liu et al., 2018a). Besides WACCM-X, the IRAP plasmasphere-ionosphere model (IPIM) describes the
transport of the multispecies ionospheric plasma from one hemisphere to the other along convecting and corotating magnetic
field lines, taking into account source processes at low altitudes such as photoproduction, chemistry, and energisation (Mar-
chaudon and Blelly, 2015). It is particularly suited to the study of the Eand Fregions. D-region studies require a model taking505
into account ion and neutral species in the mesosphere as well, including cluster ions and negatively charged ions. The recently
developed WACCM-D (Verronen et al., 2016) combines photoionisation by solar ultraviolet and X-ray radiation, ionization by
particle precipitation and galactic cosmic rays, and a detailed chemistry scheme of 307 reactions of 20 positive ions and 21
negative ions. Particularly aimed for particle precipitation studies, WACCM-D allows for simulations of NOxproduction in
the mesosphere–lower-thermosphere–ionosphere (MLTI), dynamical connections to the stratosphere, and the impact on ozone510
(Andersson et al., 2016; Kyrölä et al., 2018; Verronen et al., 2020). The Sodankylä Ion-neutral Chemistry (SIC) model is an-
other D-region photochemical model which has been used in studies of various phenomena in the MLTI (e.g., Verronen et al.,
2005; Kero et al., 2008; Seppälä et al., 2018).
3.3.2 Neutral and ion densities
Neutral densities can be derived from a number of observation techniques. Tracking the orbital decay of space objects from515
the ground is one of the first techniques still applied today (Storz et al., 2005; Doornbos et al., 2008; Bruinsma, 2015). It is
based on the orbit decay induced by the atmospheric drag. While tracking data are available from the 1960s onwards, their
resolution ranges from one orbit to several days. A more accurate observation technique is GNSS tracking of satellites, which
can provide a resolution along the orbit of up to 10 minutes, depending on the tracking accuracy and the altitude. As opposed to
tracking techniques, accelerometers provide instantaneous measurements of the non-gravitational acceleration. The first multi-520
year accelerometer measurements were performed by the Atmospheric Explorer missions and the Castor satellite in the 1970s
(Beaussier et al., 1977).
A new era began in the year 2000 with the launch of the Challenging Minisatellite Payload (CHAMP) satellite, which carried
a precise 3-axis accelerometer, star cameras and a GPS receiver as part of the scientific payload. The combination of the GPS
tracking and the accelerometer measurements allowed to obtain well-calibrated accelerations that could be used to derive525
accurate neutral density data at a high resolution along the orbit. The same combination of observation techniques is employed
by the GRACE, Gravity Field and Steady-State Ocean Circulation Explorer (GOCE), Swarm and GRACE-FO satellites, which
were launched in 2002, 2009, 2013 and 2018, respectively. All of these satellites have provided a wealth of neutral density
observations in the altitude range from 200 km to 500 km.
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Deriving neutral density from acceleration measurements requires knowledge of the neutral composition of the atmosphere530
to accurately model the gas-surface interactions that influence the aerodynamic coefficients of the satellites. That knowledge
is based on neutral mass spectrometer data collected in the 1960s, 1970s and 1980s. As indicated in Sect. 3.3.1, since the end
of the Dynamics Explorer-2 mission in 1983, no successful neutral mass spectrometer measurements have been obtained. Like
accelerometers, neutral mass spectrometers need to be calibrated to transform the precise relative composition measurements
into accurate absolute number densities. The derivation of the neutral density and wind from the accelerometer observations,535
when the accelerometer is located in the centre-of-mass of the satellite, is based on the measurement of the total linear non-
gravitational acceleration by the instrument. For a 3-axis accelerometer, the raw accelerometer observation vector aobs typically
needs to be calibrated by applying a 3×3 diagonal scale factor matrix Sand by adding a bias vector b(Doornbos, 2011):
acal =Saobs +b(2)
Typically, accelerometer scale factors are considered to be nearly constant (Tapley et al., 2007), whereas biases are typically540
estimated on a daily basis. Both the scale factors and biases can be estimated precisely from tracking by the Global Positioning
System (GPS) (Helleputte and Visser, 2009). It is anticipated that spaceborne multi-GNSS receivers will make this estimation
even more robust and precise.
The calibrated accelerometer observations acal include the aerodynamic accelerations aaero, but also need to be reduced first
by removing other contributions:545
aaero =acal −asrp −aalb −aIR −arem,(3)
where asrp,aalb ,aIR represent the accelerations caused by solar radiation pressure, Earth albedo and Earth infrared radia-
tion, respectively. The remaining accelerations arem are assumed to be negligible. The aerodynamic acceleration is typically
modelled as (Doornbos, 2011):
aaero =Ca
Aref
m
1
2ρvr2(4)550
where Caare dimensionless force coefficients (Anderson, 2010), Aref represents a reference area, mthe satellite mass, ρ
the neutral density and vrthe velocity of the atmosphere relative to the spacecraft body. This velocity includes the neutral
wind. Doornbos (2011) proposed and implemented an iterative scheme for successfully deriving neutral density and wind from
accelerometer observations for low-flying satellites such as CHAMP and GOCE.
The neutral density can also be derived by adding the number densities of the individual species composing the neutral555
atmosphere as measured by a neutral (or neutral and ion) mass spectrometer. This technique has been systematically used for
the study of planetary upper atmospheres (Waite et al., 2004; Balsiger et al., 2007; Wurz et al., 2012; Mahaffy et al., 2015).
Similarly, the thermal ion density can be derived by adding the number densities of the individual ion species composing the
ionosphere (Hoffman et al., 1974; Chappell, 1988; Welling et al., 2015). A low-Earth-orbiting satellite mission comprising
well-calibrated instruments such as a GPS receiver, an accelerometer, and a neutral and ion mass spectrometer could allow for560
the first time to measure simultaneously neutral and ion densities and compositions to determine the accuracy of the summing
method.
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3.4 Neutral winds
In the LTI, neutral winds are strongly influenced by many external drivers like geomagnetic and solar activity and tidal and
gravity waves (Rees, 1989). Figures 1b and 2b show the global distribution and selected altitude profiles, respectively, of the565
neutral winds during the St Patrick Day geomagnetic storm on 17 March 2015, obtained from a WACCM-X simulation. These
figures show in particular that large magnitudes of several hundred metres-per-second can be reached at polar latitudes.
The characterisation of neutral winds across a wide range of altitudes is critical to correctly quantify processes such as Joule
heating (e.g., Kosch et al., 2011) or F-region dynamics (e.g., Billett et al., 2020). In the lower altitude range of the LTI, the
neutral wind characteristics are poorly known. At higher altitudes, thermospheric neutral winds have been in the last decades570
retrieved by, e.g., accelerometers (Doornbos, 2011) onboard many satellite missions like Dynamics Explorer, CHAMP, GOCE,
and Upper Atmosphere Research Satellite embedding a Wind Imaging Interferometer (UARS/WINDII). The accelerometer
data can be further processed with a high-fidelity geometry and aerodynamic modelling to obtain thermospheric products
(March et al., 2019a, b). The availability of cross-track accelerations has led to a large amount of horizontal cross-wind data
(Sutton et al., 2005; Cheng et al., 2008; Doornbos et al., 2010), while the vertical acceleration was generally assumed too small575
to obtain reliable wind measurements (Visser et al., 2019). Vertical winds are more difficult to retrieve; however with the help
of linear and angular accelerations this was recently done with the latest release of the GOCE thermospheric data, which are
available on the ESA GOCE virtual archive (http://eo-virtual-archive1.esa.int/GOCE-Thermosphere.html).
Besides in situ measurements by spacecraft, various ground-based instruments enable neutral wind observations by remote
sensing. Wide-field Fabry-Perot interferometers, or scanning Doppler imagers (SDIs), measuring the Doppler shift of the580
airglow/auroral red (630.0 nm) and/or green (557.7 nm) emission lines allow to retrieve the F-region and E-region neutral
winds. One example of SDI is SCANDI (Aruliah et al., 2010), which observes the red line to measure neutral winds at around
250 km altitude within a large field of view in multiple horizontal bins giving a spatial resolution of the order of 100–300 km,
with a time resolution of about 8 min. Narrow-field Fabry-Perot interferometers (NFPIs) use the same principle to observe
neutral winds within smaller spatial bins (<10 km) with a high precision in the pointing direction (Shiokawa et al., 2012).585
The downside of those ground-based optical instruments is that they require clear and dark skies to provide neutral wind
measurements. A cross comparison of SDI and NFPI measurements can be found in Dhadly et al. (2015). Finally, incoherent
scatter radars can also allow to estimate neutral winds using a method called stochastic inversion (Nygrén et al., 2011). While
they provide a lower time resolution and larger uncertainties, on the other hand they allow to retrieve altitude profiles in the E
region (95–135 km altitude in 10 km bins) and are not affected by cloud cover nor daylight.590
Various empirical models of neutral winds have been built by combining large data sets consisting of observations from satel-
lites, rockets and ground-based instruments. The prime example of neutral wind climatologies is the Horizontal Wind Model
(HWM) series (Drob et al., 2008, 2015). The HWM model is constantly under development at the Naval Research Lab and its
latest edition is the HWM-14 (Drob et al., 2015). Neutral winds are also studied using first-principle models, wherein equa-
tions describing dynamics, as well as photochemical, transport, electrodynamical, thermodynamical and radiative processes are595
solved self-consistently. Examples of such models include, e.g., the Thermosphere Ionosphere Electrodynamics General Cir-
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culation Model (TIE-GCM; Richmond et al., 1992), WACCM (Liu et al., 2010), the Global Ionosphere Thermosphere Model
(GITM; Ridley et al., 2006), and the Magnetosphere-Thermosphere-Ionosphere Electrodynamics General Circulation Model
(MTIE-GCM; Peymirat et al., 1998).
3.5 Ion drift speed and Electric fields600
Ionospheric convection corresponds to the plasma drift relative to the neutral medium, being typically from dayside to night-
side through the midnight meridian and back towards the dayside at auroral latitudes. Convection is an important ionospheric
parameter which reflects the complex coupling between the solar wind and the magnetosphere as well as internal magneto-
spheric processes such as reconnection in the magnetotail (Dungey, 1961; Cowley and Lockwood, 1992). The high-latitude
flows generally form two cells, with anti-sunward flow over the polar cap and return sunward flows at lower latitudes in the au-605
roral zones, both in the evening and morning sectors. However, both the spatial extent of the flow system and the magnitude of
the flows vary and are related to the solar wind parameters, in specific to the north-south (Bz) and east-west (By) components
of the interplanetary magnetic field (IMF, e.g., Thomas and Shepherd, 2018).
Ionospheric ion drifts commonly refer to the Fregion above 200 km, where collisions between ions and neutrals are scarce,
and the relationship between ion velocity viand electric field Eis given by vi=E×B/B2, where Bis the Earth’s magnetic610
field and Bits magnitude. Therefore, strong ion flows correspond to strong electric fields. This is illustrated in the global
distribution and example profiles of the ion drift speed given in Fig. 1c and 2b, obtained from a WACCM-X simulation of the
St Patrick’s Day storm and revealing that ion drifts take place at high latitudes only, where strong electric fields are present.
Because magnetic field lines are equipotentials due to high parallel conductivities, plasma convection is almost perfectly
projected from the magnetosphere to the ionosphere (e.g., Weimer et al., 1985; Marchaudon et al., 2004), when taking into615
account the magnetic field convergence towards the surface, as can be seen in Fig. 2b through the fact that ion drift speeds do not
exhibit a significant altitude variability above ∼150 km. However, ionospheric convection, or ion drift, displays rapid variations
of the order of a few minutes, which directly reflect the variable solar wind-magnetosphere coupling. Ion drift measurements
have been developed in the 1970s-80s with the building of ground-based facilities (high-frequency (HF) coherent radars,
ionosondes and incoherent scatter radars) and the launch of satellites flying in the ionosphere or at higher altitudes. For clarity,620
this section is divided into subsections reviewing the different techniques.
3.5.1 HF coherent radars and SuperDARN
HF coherent radars transmit oblique waves and use ionospheric refraction of the signal to reach very large distances from the
radar. They generally cover a large field-of-view (50–60◦) by sounding several successive beams with a high-temporal resolu-
tion (1–2 min). A backscattered signal on ionospheric density irregularities aligned with the magnetic field allows retrieving625
the Doppler shift of the echoes, giving access to the ion drift at successive distances along the beam (15–45 km resolution)
(Greenwald et al., 1985; Villain et al., 1985). The main limitation of this measurement technique is the access to only one com-
ponent of the ion drift, called line-of-sight velocity. To overcome this, HF radars have been paired such as to have a common
volume of sounding where full ion horizontal velocity vectors can be reconstructed.
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At the beginning of the 1990s, a network of HF radars called SuperDARN was developed and pairs of radars were built630
first along the auroral zones of the northern and southern hemispheres in order to reconstruct the global convection pattern
at high latitudes (Greenwald et al., 1995). However, the often incomplete coverage of measured echoes in the field of view
of each radar did not allow good combined measurements coming from pairs of radars. To overcome this problem, statistical
maps of convection were first built from one radar, gridded in geomagnetic latitude and MLT and binned with respect to IMF
(Ruohoniemi and Greenwald, 1996; Ruohoniemi and Baker, 1998). These maps were then combined with real line-of-sight635
velocities of each radar to enhance the realistic representation of the ion drift. This method allows a continuity of coverage
in each polar hemisphere, but is only fully representative in regions where real fitted vectors are reconstructed from radars
measurements. More recently, the radar network has been extended in the polar cap and mid-latitude regions (Nishitani et al.,
2019) allowing for a better coverage during perturbed periods. New versions of statistical maps have also been proposed
using all radars in each hemisphere over a larger time period and with different types of binning to take into account seasonal640
variations, mid-latitudes echoes, IMF variations or directly geomagnetic activity levels (Ruohoniemi and Greenwald, 2005;
Pettigrew et al., 2010; Cousins and Shepherd, 2010; Thomas and Shepherd, 2018).
The strength of the SuperDARN radars is their capability to follow large-scale and meso-scale convection with an excellent
spatio-temporal resolution, whose patterns are often similar to the statistical maps obtained for equivalent IMF and/or tail
conditions (e.g., Provan et al., 1999; Wild et al., 2003; Huang et al., 2000; Senior et al., 2002; Imber et al., 2006). Complete645
reviews of SuperDARN radars can be found in Chisham et al. (2007) and Nishitani et al. (2019).
3.5.2 Ion drift measurements onboard satellites
Ion drift has also been systematically recorded with satellites, originally through the combination of ion-drift metre (IDM) and
retarding potential analyser (RPA) measurements. The IDM measures velocities in the direction perpendicular to the satellite
velocity vector; the RPA measures along-track velocities, and provides estimates of ion composition and ion temperature as650
well. The first satellites to make such measurements were AE-C in the 1970s (Hanson et al., 1973) and DE-2 in the 1980s
(Heelis et al., 1981). To this day the AE-C measurements remain the only published satellite-based measurements of the LTI
below 200 km altitude. The AE-C observations were followed by the series of DMSP satellites, which operate around 800 km
altitude on polar orbits with orbital periods spanning from 90 to 120 min. The DMSP spacecraft generally cross the auroral
zone and the polar cap region close to the dawn-dusk plane in about 10 to 15 min, which makes a full reconstruction of655
the instantaneous convection pattern impossible. However, these different satellites have been used to study specific localised
phenomena with great success, such as the convection in the polar cusp, cleft and cap (e.g., Heelis et al., 1976; Heelis, 1984;
Burch et al., 1985; Heelis et al., 1986), and the properties of enhanced convection at subauroral latitudes known as subauroral
ion drift (SAID) or subauroral polarisation stream (SAPS) (e.g., Spiro et al., 1979; Anderson et al., 1991). Moreover, the DMSP
data have successfully been used to build statistics of high-latitude convection patterns with respect to IMF conditions, seasons660
and hemispheres, from which well-known models have been derived (e.g., Heelis et al. (1982) and Hairston and Heelis (1990)
with DE-2 data, or Rich and Hairston (1994) and Papitashvili and Rich (2002) with DMSP data).
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The Swarm mission (Friis-Christensen et al., 2008), launched in 2013, consists of three satellites in polar circular orbits at
altitudes of ∼450–500 km. Swarm incorporated a new method of measuring ion drift and temperature known as thermal ion
imaging, or TII (Knudsen et al., 2017). TII sensors produce 2-D images of the low-energy ion distribution at rates as high as665
125 s−1, and can be used to determine higher-order features of the ion distribution functions such as ion temperature anisotropy
(Archer et al., 2015). Lomidze et al. (2019) showed that the Swarm TII cross-track ion velocity measurements are consistent
with a DE-2-based convection model (Weimer, 2005). TII measurements from Swarm have revealed the existence of intense
flow channels at the boundary of the nightside R1/R2 FAC systems (Archer et al., 2017), and in association with sub-auroral
"STEVE" arcs in which the ion flow velocity can exceed 5 km/s (MacDonald et al., 2018). Swarm ion flow measurements have670
been used in conjunction with magnetic field measurements in a number of studies of low-frequency electrodynamics including
measurements of quasi-static Poynting flux (see Sect. 4.3), and magnetosphere–ionosphere–thermosphere (MIT) coupling via
Alfvén waves (Park et al., 2017a; Miles et al., 2018; Pakhotin et al., 2018, 2020).
3.5.3 Incoherent Scatter Radars
Other ground-based instruments such as ISRs can also yield the ion drift (e.g., Caudal and Blanc, 1983; Rishbeth and Williams,675
1985) and dynasondes (Wright and Pitteway, 1982). ISRs measure the spectrum of ion acoustic waves, which gives information
on several plasma parameters, including one component of the ion velocity. By either pointing the radar beam to nearby
positions in a cycle or by using additional receivers (the tri-static capability of the European Incoherent Scatter (EISCAT)
radar), one can get the full 3D ion velocity vector, from which the electric field in the Fregion can be derived. The advantage
is that spatially small-scale features with relatively high time resolution (typically from a few tens of seconds to a few minutes)680
can be studied, but the measurements cover only a localised volume. However, by changing the beam elevation and azimuth,
larger latitudinal coverage can also be obtained. Those measurement modes have been used to build empirical models at low,
middle and high latitudes (e.g., Richmond et al., 1980; Foster, 1983; Holt et al., 1987; Senior et al., 1990).
The small-scale variability in electric fields at high latitudes is typically related to the electrodynamics of auroral arcs or
magnetospheric processes during substorms. By using the EISCAT ISR measurements, it has been established that auroral685
arcs are often associated with narrow intense electric fields just outside of the auroral arcs and related increased electron
densities due to auroral electron precipitation (Aikio et al., 2002). Cluster satellite measurements showed that those electric
fields develop rapidly in a time scale of minutes (Marklund et al., 2001; Aikio et al., 2004). Additionally, intense flow channels
of ionospheric plasma have been found on the dayside in the cusp region (Oksavik et al., 2004), in the polar cap (Nishimura
et al., 2014), and at high latitudes on the nightside in association with magnetospheric bursty bulk flows (Pitkänen et al., 2013).690
ISR measurements are also ideal for extreme velocities. Aikio et al. (2018) reported extremely high ion speeds reaching over
3000 m s−1(about ten times higher than the normal convection velocities) and verified the observation by three independent
measurements, the EISCAT UHF and VHF radar electric field and ion temperature measurements as well as the Swarm satel-
lite Electric Field Instruments (EFI). Aikio et al. (2018) suggested that the observed flow channel accommodates increased
nightside plasma flows during the substorm expansion phase as a result of reconnection in the near-Earth magnetotail. These695
narrow regions of high ion speeds facilitate strong ion-neutral frictional heating. No global convection models produce these
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features. The next generation EISCAT_3D ISR will be able to address this small-scale variability by conducting volumetric
measurements of plasma parameters including vector ion drifts in Northern Fennoscandia starting from 2022 (McCrea et al.,
2015).
3.5.4 Numerical simulations700
Several global models simulate the plasma convection around the Earth and the consecutive ion drift pattern within the iono-
sphere. The system can be modelled based on first principles, e.g., using a magnetohydrodynamic (MHD) model (e.g., Wilt-
berger et al., 2004; Honkonen et al., 2013; Gordeev et al., 2015). In this approach, the general plasma circulation within the
magnetosphere is mapped into the ionosphere where it is used to determine the ionospheric electric field and the plasma drift
pattern (e.g., Janhunen et al., 2012). While all global MHD simulations provide the ionospheric plasma drift pattern and the705
electric field, often the resulting polar cap potential can be overestimated (e.g., Haiducek et al., 2017), or underestimated (e.g.,
Palmroth et al., 2005) compared to best available measurements. A more measurement-based method to model ionospheric
electric fields and plasma drifts is to assimilate ion drift data coming from either SuperDARN convection maps or DMSP
observations into an electrodynamics coupling model such as Assimilative Mapping of Ionospheric Electrodynamics (AMIE)
technique (Richmond and Kamide, 1988; Cousins et al., 2015). SuperDARN and/or DMSP data can also feed ionospheric710
models such as the IRAP Plasmasphere-Ionosphere Model (IPIM; Marchaudon and Blelly, 2015; Marchaudon et al., 2018).
All these modelling techniques require accurate measurements of the ionospheric parameters such as conductivities, which can
be used to restrict the models to give more realistic results. This is important, because many of these models use the ionospheric
solution as a boundary condition within the magnetosphere, thus emphasising the role of the ionosphere to provide forcing for
above (e.g., Ridley et al., 2004).715
3.6 Magnetic fields
Measurements of the magnetic field vector Bprovide a key parameter for studying ionospheric electrodynamics. Magnetic
field variations allow to detect in situ and distant electric current density, j, through Ampère’s law, ∇ ×B=µ0j. The largest
contributions to the Earth’s magnetic field are due to sources within Earth’s core and crust, and from large-scale magnetospheric
currents (e.g., Olsen and Stolle, 2012). After removal of these contributions (e.g., as provided by geomagnetic field models)720
magnetic residuals are particularly valuable for studying ionospheric currents sources (e.g., Stolle et al., 2017). Especially in
target are the FACs (see Sect. 4.2) connecting the magnetosphere to the ionospheric Eregion. At high latitudes, the magnetic
residuals δBand the derived FACs, together with electric field observations are crucial to quantify significant parts of energy
deposition into the upper atmosphere by magnetic forcing (Sect. 4.1), Poynting flux (Sect. 4.3) or Joule heating (Sect. 4.7).
Magnetic signatures due to ionospheric currents are of much lower amplitude at middle and low latitudes. Nonetheless,725
satellite-based magnetic field observations are indispensable in understanding the global distribution of currents. Special at-
tention is given to currents which are connected to E-region and F-region dynamos. These are, for example, inter-hemispheric
currents connected to midlatitude Sq currents or low-latitude F-region dynamo currents (e.g., Olsen, 1997; Park et al., 2020;
Lühr et al., 2019), low-latitude gravity-driven and plasma-pressure-driven currents (e.g., Alken et al., 2017), variations of
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Figure 5. Left: a dayside orbital segment of magnetic field signatures of ionospheric currents from the Swarm mission. Middle: same orbital
segment but zoomed in magnitude. Right: low-latitude orbital segment of a nightside orbit. The location and time of the orbital segments are
provided in the panel title. The coordinate system is local and it is x-north, y-east, z-up.
equatorial electrojet currents in response to wave coupling from the middle and lower atmosphere (e.g., Yamazaki et al., 2017),730
or electromagnetic characterisation of post-sunset equatorial plasma irregularities (e.g., Rodríguez-Zuluaga and Stolle, 2019).
Figure 5 shows two examples of Swarm spacecraft orbital segments that reflect different ionospheric currents that may be mon-
itored with high precision magnetometers. Shown are differences between the magnetic data and predictions of the CHAOS-6
magnetic field model Finlay et al. (2016) to eliminate contributions from the core, crustal and large-scale magnetospheric field.
This day was geomagnetically quiet with Kp ≤1 the entire day. The strongest signals arise from auroral currents, and field-735
aligned currents are most pronounced in the eastward y-component. At the mid- and low latitudes day-side E-region currents
or currents associated to post-sunset plasma depletion in the Fregion dominate the signal.
High-precision magnetic field measurements such as those available by the CHAMP and Swarm satellites from altitudes
between 350 km and 500 km have tremendously improved our understanding of ionospheric phenomena from high to low
latitudes (e.g. Lühr et al., 2004; Alken and Maus, 2007; Alken, 2016; Park et al., 2017a; Park et al., 2020). However, similar740
magnetic field measurements taken at altitudes of 200 km and below, diving into the largely unexplored E-region dynamo,
would provide an invaluable key to understand the coupling between the atmosphere, solar radiation, and the geomagnetic field
through unprecedented data.
4 Derived LTI parameters: Current understanding
This section reviews the main LTI parameters which are not observed directly but rather derived from measurements of the745
parameters discussed in Sect. 3. For each of them, their current understanding and characterisation and their description in
numerical models are discussed. The derived parameters are ordered in this section starting from those associated with forcing
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of the LTI from above and moving on to those which characterise local properties of the LTI, and finally covering the forcing
from below.
4.1 Magnetic forcing and general energy circulation750
Forcing of the upper atmosphere is achieved essentially by energy and momentum transfer between charged and neutral parti-
cles. Energy deposition drives collisional heating of the neutrals, whereas the momentum imparted to the plasma by the Lorentz
force, j×B, is exchanged with the neutrals by collisional friction. The subject is a part of a broader concept called energy cir-
culation or energy transfer. At polar latitudes, both energy and momentum are primarily extracted from the solar wind (Axford
and Hines, 1961; Akasofu, 1981; Palmroth et al., 2003; Palmroth et al., 2006b) in a sequence of processes that include recon-755
nection at the dayside magnetopause (e.g., Crooker, 1979; Trattner et al., 2007; Hoilijoki et al., 2014) driving magnetospheric
convection (Dungey, 1961). The phenomena are mediated by magnetic field lines, which are essential also for FACs (Sect. 4.2)
that transfer momentum, and for the Poynting flux (Sect. 4.3) that transfers energy. Even though the strict meaning of magnetic
forcing is related to the momentum transfer and Lorentz forces, the term often covers (also) the effects of energy transfer and
Joule heating. For example, one of the goals of the Swarm mission (Friis-Christensen et al., 2006), namely quantification of760
magnetic forcing of the upper atmosphere, addresses variations in the neutral density and atmosperic upwelling in response
to Joule heating (e.g., Lühr et al., 2004; Prölss, 2011). In this section, we discuss magnetic forcing mainly as the transfer of
momentum via Lorentz forces, whereas Joule heating and heat transfer to the neutrals are discussed in sections 4.7 and 4.8,
respectively.
Since FACs are force-free (jkBhence j×B= 0), they are ideal transmitters of momentum between the high and low765
altitude ends of the MIT system, as part of the auroral current circuit (Boström, 1964). Lorentz forcing in the equatorial
magnetosphere, due to currents flowing across the magnetic field, is transferred by FAC tangential stress to Lorentz forcing in
the ionosphere due to the horizontal current that closes the FAC (Iijima, 2000). The capability of the ionosphere to carry an
electric current is a strong constraint for the seed region of the magnetic forcing, limiting it to a rather narrow range, essentially
the ionospheric E-layer, at ∼90–150 km altitude. In the lower part of this layer, the electrons become collisionless and their770
E×Bdrift provides the Hall component of the ionospheric current, jH=−σHE×B/B, perpendicular to the electric field,
with σHthe Hall conductivity. The ions become collisionless just at the upper side of the E-layer and their motion along the
electric field provides the Pedersen current, jP=σPE, with σPthe Pedersen conductivity (see Sect. 4.6). Above the E-layer,
electrons and ions drift together and the ionospheric current vanishes.
While Joule heating is related to the Pedersen current, the Lorentz force has contributions from the total ionospheric current,775
i.e., the sum of the Pedersen and Hall currents. Another way of writing the total current is to express it as the sum of a curl-free
and a divergence-free component (e.g., Vanhamäki and Juusola, 2018). The magnetic forcing is associated directly with the
curl-free component, that actually closes the FAC. The divergence-free component is not subject to local magnetic forcing
exerted by FAC closure, although it plays a key role in the inductive storage and release of energy, during transient MIT
coupling (Yoshikawa, 2002a, b). While such transients are essential for MIT dynamics, their time scales are typically short,780
between a few seconds and a few minutes, depending on the related spatial scales (Yoshikawa, 2002b). For the rest of the time,
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under quasi-static approximation, the curl-free component of the ionospheric current is dominated by the Pedersen current,
while the Hall current is largely divergence-free (Vanhamäki et al., 2012), therefore the Pedersen current appears to be the
main agent of magnetic forcing, be it related to Joule heating or the Lorentz force.
A simple example is provided by the quiet auroral arc (or oval, on a larger scale), which can be approximated with an ideal,785
1D structure, where the electric field is normal to the arc, the FAC is closed horizontally across the arc by the Pedersen current,
while the Hall current along the arc is divergence-free (e.g., Marghitu, 2012). In this case, the Lorentz force on the Pedersen
current is directed along the arc (or auroral zone) and drives plasma convection against the collisional drag of the neutrals. At
the same time, the Poynting flux carried by the FAC is in balance with the Joule heating of the Pedersen current that closes the
FAC and energy dissipation is essentially local, i.e., limited to the FAC closure area (Richmond, 2010; Vanhamäki et al., 2012).790
Not surprisingly, magnetic forcing is more intense during increased geomagnetic activity, when the FAC, particle precipitation,
conductivities, and electric field can be highly variable and non-uniform. At such times, smaller-scale enhancements can make a
significant contribution to the forcing (Codrescu et al., 1995). The relationship between driver and response is also considerably
more complex. For example, the Hall current may also contribute to the FAC closure, and dissipation can become non-local
(Fujii et al., 2011).795
The magnetic forcing described so far applies to the cases where the ionosphere–thermosphere system behaves like a load,
E·j>0, controlled by magnetospheric processes. The energy and momentum influx carried by FAC is thermally dissipated
(analogous to a resistance in an electric circuit), and can also feed the coherent convection of neutral wind (analogous to a
motor). Since the neutral atmosphere dominates the ionosphere at E-layer heights, its inertia is comparatively large and the
latter can become important only in the case of intense events whose duration is long enough, typically strong storms or800
substorms. In such cases, the opposite, flywheel effect (e.g., Deng et al., 1991, 1993; Paschmann et al., 2003), is observed as
well during the recovery stage, when the forcing on the ionosphere stops (or decreases) whereas the neutral atmosphere needs
a longer time to come to rest. At such times, the neutral wind is analogous to a dynamo and the ionosphere–thermosphere
system behaves like a generator, E·j<0, playing an active role in the interaction with the magnetosphere.
4.2 Field-aligned currents805
Field-aligned currents (FACs) were first suggested to connect the upper ionised atmosphere to the outer space by Birkeland
(1908), and their existence was confirmed some 60 years later by satellite observations (Zmuda et al., 1966; Cummings and
Dessler, 1967). Ever since, FACs have been one of the most central topics in space plasma research. Satellite observations of
magnetic field variations δBare often used to determine the in situ FAC density jk(e.g., Iijima and Potemra, 1978; Ritter
et al., 2013; Lühr et al., 2015; McGranaghan et al., 2016). The FAC density is often expressed using the infinite current sheet810
approximation:
jk=1
µ0
∂Bx
∂t
1
vn
(5)
where the Cartesian reference frame is such that xis parallel to the sheet direction (direction of maximum variance), yis normal
to the sheet plane (direction of intermediate variance), and zis along the mean magnetic field, while vnis the projection of
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the spacecraft velocity along the y-axis (Marchaudon et al., 2006). The three-dimensional current system can be derived from815
decomposition analysis of all vector components (e.g., Laundal et al., 2018). Of various topologies and scale sizes, FACs
connect together magnetospheric regions having different controlling parameters, like the plasma sheet or the low-latitude
boundary layer, to the auroral zone in the LTI. They also play an essential role in the magnetic forcing of the upper atmosphere
(see Sect. 4.1). FAC structures, consisting of upward and downward currents with planar or filamentary geometry (Boström,
1964), confine the transport of Poynting and momentum fluxes inside the current system, on large, meso-, and small scales.820
Reviews addressing the morphology and physics of FACs are available, e.g., in the AGU Geophysical Monographs edited by
Ohtani et al. (2000) and Keiling et al. (2018).
Figure 6 shows the typical large-scale high-latitude current system including FACs and horizontal currents, with the polar
cap convection cells under southward IMF driving shown with thin black lines. The most common FAC structures are the
so-called Region 1 (R1) and Region 2 (R2) currents (Iijima and Potemra, 1976). The R1 (poleward) / R2 (equatorward) current825
system consists of two thick current sheets, with the R1 current pair flowing downwards (in blue in the figure) and upwards
(in red) in the dawn and dusk sectors, respectively, while the R2 system flows in the opposite direction. Horizontal currents
in the high-latitude LTI consist of Hall (in orange) and Pedersen (in green) currents associated with the FACs. Large-scale
eastward/westward electrojets are associated with the convective electron motion on the dusk and dawn sides, respectively (e.g.,
Baumjohann, 1983). The midnight sector current distribution in Fig. 6 depicts the substorm current wedge (McPherron et al.,830
1973; Birn et al., 1999; Keiling et al., 2009) consisting of thick downward/upward current filaments on the dawn/dusk side,
connected in the ionosphere by the substorm (westward) electrojet. An additional FAC distribution (not shown in Fig. 6), called
NBZ (northward IMF Bz) or R0 current system, appears within the noon sector during strongly positive IMF Bzorientation
and manifests reconnection at the tail lobes and consequent sunward plasma flows. While Iijima and Potemra (1976, 1978)
needed several months of data to infer the large-scale FAC patterns, at present, the AMPERE network of in situ engineering835
magnetometers, operational on the Iridium satellites, is able to follow the large-scale FACs almost in real time (Anderson et al.
(2014); recent reviews by Milan et al. (2017) and Coxon et al. (2018)).
FAC current systems are also observed at smaller scales, of which auroral arcs provide the best example (Partamies et al.,
2010). A recent review is provided by Karlsson et al. (2020). Within this example, the upward FAC sheet (consisting mainly
of precipitating electrons) above the arc is paired with a downward FAC sheet (e.g., Elphic et al., 1998), similar to the scaled840
down R1/R2 system. Multiple arcs can consist of multiple pairs of upward and downward FACs, but are also observed to share
a unique current system, with all the arcs on the upward FAC leg (Wu et al., 2017). Another example of scaled-down current
system, similar to the substorm current wedge, is provided by the current circuit that connects magnetospheric bursty bulk
flows (Baumjohann et al., 1990; Angelopoulos et al., 1992) with ionospheric north–south auroral structures (Henderson et al.,
1998), also known as streamers. It has also been suggested that the substorm current wedge consists of several such wedgelets845
(Liu et al. (2015); recent review by Liu et al. (2018b)).
Planar and filamentary FAC structures, connected by meridional and zonal ionospheric current, respectively, correspond
to the two basic configurations of the auroral current circuit anticipated by Boström (1964), illustrated above with specific
examples. Mixed configurations are observed as well; for instance, event studies (Marghitu et al., 2009, 2011) and statistical
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Figure 6. Schematic view of the high-latitude ionospheric current system, showing the configuration of the driving field-aligned currents of
region 1 (along field lines closing via the outer magnetosphere) and region 2 (closing via the opposite hemisphere). Also shown schematically
are the locations and configuration of the ionospheric convection pattern, Pedersen and Hall currents, the substorm current wedge and auroral
electrojets.
evidence (Jiang et al., 2015) suggest that a current system consisting of planar FAC sheets and ionospheric FAC closure in the850
zonal direction may develop during the substorm growth phase. A different kind of mixed configuration is observed for active
Alfvénic arcs, where the planar FAC sheet can break into current filaments (Chaston et al., 2011). While sheets and filaments
provide simple geometries, convenient to organise FACs according to their scales, actual observations are rarely clear-cut and
reflect superpositions of geometries and scales, often more complex also than the two above examples of mixed configurations.
Various techniques have been developed to deal with actual FAC data, able to explore their multi-scale structure (e.g.,855
Bunescu et al., 2015), or to take advantage of multi-point in situ information, as provided, e.g., by the Cluster and Swarm
missions (e.g., Dunlop et al., 2002; Marchaudon et al., 2009; Ritter et al., 2013; Bl˘
ag˘
au and Vogt, 2019; Vogt et al., 2020). An
alternative option, which benefits from prior development and validation with ground-based data, is the spherical elementary
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current systems (SECS) technique (Amm, 1997; Amm and Viljanen, 1999), adapted also for Swarm data (Amm et al., 2015).
A key advantage of this approach is that, when used with LEO satellite data, it provides both the FACs and the ionospheric860
currents in a consistent manner (such that the FAC density is equal to the divergence of the curl-free ionospheric current). On the
other hand, its accuracy and resolution depend on the distance above the ionospheric current, in particular for the divergence-
free component (typically dominated by electrojet Hall current), whose effect is observed remotely. Statistical investigations
of FAC and ionospheric currents, based on Swarm data, were published, e.g., by Lühr et al. (2016), Huang et al. (2017), and
Workayehu et al. (2019).865
4.3 Poynting flux
The problem in assessing ionospheric energy deposition using ionospheric measurements only is that several elements need to
be evaluated simultaneously so that the total dissipation can be assessed. A possible way to overcome this problem is to evaluate
the total electromagnetic energy, i.e., the Poynting flux S=E×δB/µ0towards the ionosphere both using observations and
numerical simulations. If this assessment can be carried out on high enough orbits, the assumption is that the Poynting flux870
includes both the energy dissipated in Joule heating as well as the energy within particle precipitation. The main caveat in
this technique is to evaluate the magnetic field such that it does not contain contributions from the dipole, so that it represents
the extra electromagnetic energy towards the ionosphere. Therefore the field-aligned component of the Poynting vector can be
evaluated from the quasi-static electric and perturbation magnetic fields Eand δBmeasured above the ionosphere. In the limit
of quasi-static planar current sheets, the Poynting flux can be shown to be equal to the Joule dissipation J·Eintegrated along875
the magnetic field line below the spacecraft (Kelley et al., 1991).
Studies of high-latitude Poynting flux have been carried out with DE-2 (Gary et al., 1994) and, more recently, Swarm (Park
et al., 2017a; Pakhotin et al., 2018, 2020) missions. Rodríguez-Zuluaga et al. (2017) used Swarm to resolve magnetic-field-
aligned Poynting flux at low latitudes with a resolution of the order of 1 µW/m2. Waters et al. (2004) introduced a method to
characterise the high-latitude Poynting flux towards the ionosphere by combining electric field measurements of the Super Dual880
Auroral Radar Network (SuperDARN) with the Iridium constellation estimating magnetic perturbations. This technique has
the advantage of allowing the assessment of the net electromagnetic energy transferring to the ionosphere, including the energy
deposited to drive the neutral winds while not having to estimate the ionosphere conductivity. The technique presented in Waters
et al. (2004) agrees with the DMSP satellite in situ measurement to a few mW/m2. They estimate the total electromagnetic
energy flux of the order of 50 GW, maximising in the morning and afternoon sectors.885
The topic is also ideal for global numerical simulations covering the entire solar-wind–magnetosphere–ionosphere system.
These simulations can be used to assess the electric and magnetic fields within large volumes and map them to the ionospheric
plane. In fact, global MHD simulations have shown that the Poynting flux starts to focus towards the magnetosphere and
ionosphere already from the solar wind in regions where the open field lines are dragged towards the tail (Papadopoulos et al.,
1999; Palmroth et al., 2003; Palmroth et al., 2006c). Zhang et al. (2012) used a global MHD simulation and mapped the890
Poynting flux in the tail to the ionosphere, and confirmed that its ionospheric distribution reproduced the global morphology
of the Poynting flux measured by the Polar satellite. While these studies have been carried out using MHD simulations that
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have severe limitations in reproducing kinetic plasma physics within the magnetosphere, the results are indicative of processes
that need to be rigorously measured so that our magnetosphere–ionosphere system can be understood in terms of an energy
input/output system.895
4.4 Ion–neutral cross sections
Of the derived parameters characterising the local properties of the LTI, among the most critical ones are the ion–neutral cross
sections. Two types of ion–neutral cross sections will be briefly reviewed here: (i) the ion–neutral momentum-transfer cross
sections, and (ii) the ion–neutral chemical reaction cross sections.
Ion–neutral averaged momentum-transfer cross sections, which are denoted σcoll,in here, are needed to determine the ion–900
neutral collision frequencies (see sect. 4.5). They are functions of velocity-dependent momentum-transfer cross sections and
the relative velocity between particles. Using the velocity-dependent momentum-transfer cross section expression derived by
Dalgarno et al. (1958), Banks (1966) obtained the following formula for the averaged nonresonant ion–neutral momentum-
transfer cross section
σcoll,in =3√2
16 π3
24.88αe2
µ1
2kBTi
mi
+kBTn
mn−1
2
,(6)905
with αthe neutral gas atomic polarisability, ethe elementary charge, µthe ion–neutral reduced mass, kBBoltzmann’s constant,
Tiand mithe ion temperature and mass, respectively, and Tnand mnthe neutral temperature and mass, respectively. This
formula was derived under the assumption that the ion–neutral interaction is predominantly due to the polarisation force
arising from induced dipole attraction by the neutral gas, hence neglecting short-range quantum mechanical repulsion effects.
This assumption holds for temperatures below 300 K, which is the order of magnitude of the temperature in the LTI (see910
sect. 3.2). At higher altitudes, however, such as in the Fregion, the ion and neutral temperatures are well above this limit
and start diverging. The dominant interaction in the Fregion is the charge exchange of the O+ion with atomic oxygen
O. The momentum-transfer cross section for O+and O, σcoll,O+−Ois dominated by the charge-exchange process, whereas
the contribution from polarisation can be neglected in the Fregion (Banks, 1966). While the ion–neutral cross sections are
crucial, they have never been measured within the LTI, and laboratory measurements reproducing the conditions in the upper915
atmosphere are extremely challenging (Lindsay et al., 2001). Therefore, the estimates of σcoll,O+−Ohave been extrapolated
from measurements and theoretical calculations at lower temperature, which are summarised in the introduction of Joshi et al.
(2018).
The study of the ion-neutral interactions requires accurate measurements of plasma and neutral species in relevant partially
ionised media, including composition of the neutral and ion species, velocity distribution of ions and electrons, as well as920
ambient energy that is characterised by electric and magnetic fields, radiation, and temperature. Since such complex environ-
ments, particularly under the influence of various electromagnetic fields and with complicated composition, temperature, and
radiation fluxes, cannot easily be reproduced in a laboratory, the only way to understand the plasma-neutral gas interactions
in space is through in-situ observations in various environments in space (Yamauchi et al., 2019). Particularly, observations in
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low-density environments with substantial neutral particle content are needed, for example, in the upper ionosphere near the925
exobase of a planet or natural satellite, in comets, or in interstellar space.
Likewise, ion–neutral reaction cross sections, denoted σreac,in here, in the LTI are poorly known. They are also crucial as
they affect the chemical reaction rates, and hence are key parameters in upper-atmosphere models. Ion–neutral reaction cross
sections have been derived from laboratory measurements of ion–molecule reaction rate constants in drift tubes (e.g., Woo
and Wong, 1971), but those estimates suffer from two main problems: they can be energy-dependent, and simple ion-neutral930
relative speed distribution approximations do not hold in regions of the high-latitude LTI where ion convection speeds are high
(St.-Maurice and Torr, 1978). In such cases, the rate coefficient kof a given reaction is given by
k=
∞
Z
0
σreac,in(v)vf(v)dv, (7)
with f(v)the distribution of relative speeds vbetween the reactants. In this situation, σreac,in can be obtained by inverting
kwhen the laboratory relative speed distribution flab(v)is known (St.-Maurice and Torr, 1978). Lin and Bardsley (1977)935
proposed a Monte Carlo method to derive flab(v)to serve this purpose. Using this methodology, the reaction cross sections for
O+with the main molecular species in the LTI (N2, O2and NO) were published by Albritton et al. (1977) based on drift-tube
experiments in both helium and argon buffer gases.
4.5 Ion–neutral collision frequencies
The collision rates for all species in the LTI constitute a fundamental set of parameters in the coupling in the atmosphere–940
ionosphere–magnetosphere system. They depend on a number of terms as shown below. Under the assumption that the ion and
neutral populations in the LTI are separate Maxwellian distribution functions, the ion–neutral collision frequency νin can be
expressed as (Banks, 1966):
νin =4
3nn8kB
π1
2Ti
mi
+Tn
mn1
2
σcoll,in,(8)
with nnthe neutral density, kBBoltzmann’s constant, Tiand mithe ion temperature and mass, respectively, Tnand mn
945
the neutral temperature and mass, respectively, and σcoll,in the ion–neutral collision cross-section (see Sect. 4.4). From equa-
tion (6), one gets in the nonresonant case
νin = 2.21πnnαe2
µ1
2
,(9)
which is an expression that has been used for space physics applications such as the analysis of incoherent scatter radar
measurements (e.g., Virtanen et al., 2014). For a given ion–neutral pair, this can be further reduced as950
νin =Cinnn,(10)
with Cin a numerical coefficient whose value is given for relevant nonresonant ion–neutral pairs in the ionosphere by Schunk
and Nagy (1980, see Table 6). Resonant charge-exchange interactions occur when a neutral and an ion from the same species
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collide with each other; the corresponding ion–neutral collision frequencies, which are also given in Schunk and Nagy (1980,
see Table 5), depend on Tr= (Ti+Tn)/2.955
Besides laboratory experiment extrapolation to LTI conditions, ion–neutral collision frequencies have been estimated directly
in the ionosphere using incoherent scatter radars. Nygrén et al. (1989) developed a method using the EISCAT radar to determine
the ion–neutral collision frequency in the Eregion. It uses radar observations in the vertical, field-aligned and eastward-
tilted directions to obtain measurements of the ion velocity and the electric field vector, which are used to infer νin from
the momentum equation for the ions. This method was applied to estimate νin within 95–130 km altitude in Nygrén et al.960
(1989). It was used by Oyama et al. (2012) to study the temporal variations of the ion–neutral collision frequency within
106–135 km altitude during an ionospheric heating event. Alternative methods employing incoherent scatter radars to infer
the ion–neutral collision frequency in the LTI can be found in Kosch et al. (2011), who combined incoherent scatter radar
and optical observations to estimate νin in the vicinity of an auroral arc, as well as in Nicolls et al. (2014) who made use of
multifrequency radar measurements to retrieve νin and estimate the thermospheric neutral density.965
The ion–neutral collision frequency νin has been measured in situ only by sub-orbital rockets as they descended through the
LTI (see Fig. 8 in Sangalli et al., 2009), and the measurement has never been conducted from a satellite. The rocket technique
involves comparing the E×Bdrift velocity derived from the respective field measurements with direct measurements of the
horizontal drift speed of ions (Watanabe et al., 1991). In the LTI, the two quantities differ by an amount that depends on the
ratio κ=νin/Ωiwhere Ωiis the ion gyro-frequency. Accurate determination of κalso requires an independent measurement970
of the neutral wind velocity (Sangalli et al., 2009; Burchill et al., 2012). As such rocket measurements provide only a handful
of observations, the ion–neutral collision frequencies remain poorly characterised, and their inferred values likely suffer from
uncertainties. Collision cross-sections and collision rates are among the largest sources of errors in empirical models, General
Circulation Models and magnetosphere–ionosphere coupling simulations, for which they are key inputs. Furthermore, they
represent the largest source of uncertainty in estimating the ionospheric conductivities, which are key parameters in current975
coupled models of the LTI.
4.6 Ionospheric conductivities
Ionospheric conductivities are also particularly important derived local parameters, as they are required especially in modelling,
and further required to understand Joule heating. The ionospheric conductivity tensor is specified with the parallel conductivity
σk(along the magnetic field), the Pedersen conductivity σP(associated with ionospheric currents perpendicular to the geomag-980
netic field and parallel to the electric field defined in the reference frame moving with the conductive air at the speed of ambient
neutral air in the bottomside ionosphere) and the Hall conductivity σH(associated with ionospheric currents perpendicular to
both the electric and magnetic fields). Specifically, σPand σHare expressed as
σP=nee2νin
mi(ν2
in + Ω2
i)+νen
me(ν2
en + Ω2
e),(11)
σH=nee2−Ωi
mi(ν2
in + Ω2
i)+Ωe
me(ν2
en + Ω2
e),(12)985
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with nethe electron density, ethe elementary charge, νin the ion–neutral collision frequency (see Sect. 4.5), νen the electron–
neutral collision frequency, miand methe ion and electron masses, respectively, and Ωiand Ωethe ion and electron gy-
rofrequencies, respectively. Due to high electrical conductivity in the direction of the magnetic field, σkis very large, up to
tens of S m−1(e.g., Yamazaki and Maute, 2017). This means that the geomagnetic field lines are nearly equipotential in the
ionosphere, facilitating the approximation of the ionospheric parameters in the height-integrated form. As a consequence, the990
Pedersen and Hall conductivities are often presented as their height-integrated forms (i.e., conductances) as
ΣP=ZσP(h)dh, (13)
ΣH=ZσH(h)dh. (14)
The Pedersen and Hall conductivities and conductances can be estimated locally using incoherent scatter radar observations.
Using the measured electron density profiles and expressions for the ion–neutral and electron–neutral collision frequencies such995
as given in Brekke and Hall (1988), Hall and Pedersen conductivity profiles can be derived and integrated across altitudes to
obtain the corresponding conductances. Using this methodology, Aikio and Selkälä (2009) used the EISCAT radar to determine
local conductances during a one-month campaign, and found that the conductances are larger in the morning sector than in
the evening sector. The ionospheric conductivities can also be determined from spacecraft measurements using UV- and X-ray
imaging of the aurora onboard polar-orbiting spacecraft (Aksnes et al., 2002; Aksnes et al., 2005).1000
In local numerical models, σPand σHare computed according to their definitions (eq. 11–12). However, large uncertainties
exist regarding the values of the collision frequencies (νin and νen), and if electron density profiles are inaccurately reproduced,
this can result in significant errors in the calculated conductivities. Ionospheric conductivities have been calculated using the
TRANSCAR couple kinetic/fluid transport code (e.g., Lilensten et al., 1996), more recently making use of the AMIE procedure
for multi-instrument data assimilation into the model (Blelly et al., 2005). In global magnetosphere–ionosphere coupling sim-1005
ulations, the conductivities are absolute key parameters and can have far-reaching effects if reproduced erroneously. Often the
conductances consist of two parts, an empirically derived F10.7-dependent dayside part (e.g., Moen and Brekke, 1993), while
the nightside conductances are often empirical formulations based on electron precipitation (e.g., Robinson et al., 1987). Even
small changes in the conductances and, e.g., adding seasonal variations can have tremendous effects in the overall modelling
results (Ridley, 2007), and therefore their estimation in situ is of great importance.1010
4.7 Heating
The largest source of heating in the LTI is the absorption of solar EUV and UV radiation with an average rate of roughly
1 mW m−2(Peterson et al., 2012) or globally a few hundred GW. The solar cycle modulates these values by about ±50% (e.g.,
Lean et al., 2003). Furthermore, the heat flux maximises at the subsolar point and decreases away from it, in darkness to almost
zero (except for a small contribution of Lyman αradiation originating from the geocorona; e.g., Maeda, 1969; Waldrop and1015
Paxton, 2013).
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Heating by electric currents, alternatively named frictional and Joule heating, is another important source. The Joule heating
rate can be expressed locally as
qJH =σP(E+u×B)2(15)
with σPthe Pedersen conductivity, Ethe electric field, uthe neutral wind speed and Bthe magnetic field. The generated heat1020
flux varies from insignificant to about 60 mW m−2, the estimated global power between about 1 GW and up to roughly 1 TW
(Buchert, in review; Fedrizzi et al., 2012; Sarris et al., 2020), although different methods suggest either large underestimations
(Codrescu et al., 1995) or overestimations (Palmroth et al., 2005) within the measurements, while the exact values are not
known. The ion–neutral frictional heating flux peaks during large geomagnetic storms (Lu et al., 2016). In such cases, JH
surpasses the thermospheric heating by solar radiation, although on average the solar input is larger. However, JH rates, their1025
spatial distribution and temporal variations are relatively poorly determined, preventing more quantitative assessments.
The third source of heat is associated with particle precipitation which produces the aurora. The total heat flux produced by
particle precipitation can range between 50% and 100% of that produced through Joule heating (Vickrey et al., 1982). Large
particle precipitation energy fluxes tend to occur on small scales (visible as structures in the aurora); consequently, the energy
flux associated with auroral particle precipitation can often surpass that of Joule heating locally. Virtanen et al. (2018) estimated1030
corresponding values of up to 160 mW m−2.
Figure 7 shows the electron density, Pedersen conductivity and Joule heating rate in a TIE-GCM simulation of the LTI on
17 March 2015. The top row shows views above the North Pole while the bottom row shows latitude–altitude cuts (in the
direction indicated with red lines in the upper panels). Fig. 7e and Fig. 7f indicate that both the Pedersen conductivity and
the Joule heating rate maximise at about 120 km altitude, whereas Fig. 7b and Fig. 7c indicate that the global maxima of each1035
parameter occurs at different local times. This illustrates the dependence of Joule heating not only on the Pedersen conductivity
but also on the electric fields which can be very localised. The white stripes centered on 90◦latitude in the bottom-row figures
correspond to the region of 87.5–90◦latitude where TIE-GCM does not give output.
4.8 Heat transfer to the neutral gas by ion and electron cooling
When the ion and electron temperatures, Tiand Te, respectively, are increased compared to the neutral temperature Tn, heat is1040
transferred to the neutral gas by ion and electron cooling, and also between ions and electrons. The corresponding steady-state
heat transfer rate from ions to neutrals can be expressed as (e.g., Killeen et al., 1984)
q∆T,i =Neνin
mi
mn+mi
3kB(Ti−Tn),(16)
with Nethe electron density, νin the ion–neutral collision frequency, mnthe neutral mass, mithe ion mass, and kBBoltz-
mann’s constant. Neglecting heat transfer from and to the light electrons, this heat transfer would (for similar ion and neutral1045
compositions) amount to about half of the JH rate (15), while the other half of the JH rate heats the neutrals directly.
The analogous expression for the heat transfer rate to the neutral gas by electron cooling, q∆T,e , is complicated by the
numerous inelastic collisions between electrons and neutrals which are important for energy transfer. Comprehensive tables
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Figure 7. Maps of (a) the electron density, (b) the Pedersen conductivity, and (c) the Joule heating rate at ∼120 km altitude on 17 March 2015
18:00:00 (UTC) based on TIE-GCM. (d) Electron density, (e) Pedersen conductivity and (f) Joule heating rate as a function of geographic
latitude and altitude in the plane indicated with a red line in the above panels.
of relevant cross sections can be found in Schunk and Nagy (2009). Finally, above roughly 200 km altitude, ion-electron
Coulomb collisions provide for significant heat exchange between ions and electrons when Ti6=Te. Contrary to the external1050
heating mechanisms discussed in Sect. 4.7, heat transfer between neutrals, ions and electrons is an internal process in the LTI;
hence, it is not associated with a heat source from the LTI perspective.
In practice, estimating the amount of heat transferred to the neutral gas requires coordinated measurements and subsequent
modelling, and can thus only be carried out in fortuitous conditions at specific locations, where measurements are available.
Such conditions were met for instance in Marchaudon et al. (2018), who used simultaneous measurements from SuperDARN1055
and EISCAT radars, ionosondes, the CHAMP satellite (Reigber et al., 2006), and subsequent modelling with the IPIM model
(Marchaudon and Blelly, 2015) to investigate the mechanism behind the ionosphere F-peak electron density decrease at high
latitudes during a high-speed stream event. They concluded that neutrals in the thermosphere were heated by up to 160 K
during the studied event as a consequence of enhanced high-latitude convection, in agreement with earlier results by Gardner
et al. (2012). However, Marchaudon et al. (2018) showed that the long-lasting ionospheric effects were due to the fact that1060
this heating led to the expansion of the thermosphere and a change in the [O]/[N2] ratio at the F-region peak altitude, which
ultimately resulted in the extinction of the ionospheric F2layer.
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4.9 Atmospheric waves
Forcing from below can also affect the derived parameters within the LTI. The most important type of forcing from below
is presented by gravity waves, which are excited by many meteorological processes (Fritts and Alexander, 2003), and which1065
become increasingly prevalent at higher altitudes due to decreasing air density. The gravity waves, especially the ones with
fast vertical speed, can penetrate into the LTI region and up to ∼200 km before they are dissipated by molecular damping.
These waves perturb the neutral winds and neutral and plasma temperatures and densities. Travelling ionospheric disturbances
(TIDs) have been observed in connection with tornados, deep convections, and earthquakes (Tsugawa et al., 2011; Nishioka
et al., 2013; Azeem et al., 2015, 2018). Gravity waves may seed plasma instabilities, leading to the formation of equatorial1070
Spread-F (Kelley et al., 1981; Hysell et al., 1990; Palmroth et al., 2000). High-resolution numerical simulations also suggest
that gravity waves can produce large vertical wind shears above the mesopause, where the atmosphere is statistically the
most stable (Liu, 2007, 2017). These wind shears can have important implications for E-region electrodynamics, including
sporadic Elayers (Mathews, 1998; Haldoupis, 2012).
Gravity wave dissipation, either due to wave breaking or molecular damping, causes heat and species transport (Walterscheid,1075
1981; Liu, 2000; Becker, 2004; Garcia et al., 2007). This affects the transport within the LTI, its mass exchange with the
mesosphere, and the compositional structure of the entire thermosphere. In numerical models, such as WACCM-X and TIE-
GCM, the thermospheric density, the O/N2ratio, and ionospheric plasma density are sensitively dependent on the effective
eddy diffusion parametrised or specified in the mesosphere and lower thermosphere. For example, rather realistic semi-annual
variation in thermospheric density was obtained by adjusting the eddy diffusion at the lower boundary of TIE-GCM (at ∼97 km)1080
(Qian et al., 2009).
On global scales, tidal waves and fast-propagating planetary waves (e.g., ultra fast Kelvin waves) and their variability can be
an important cause of LTI variability. Like gravity waves, propagating tides can reach up to ∼200 km before being largely dis-
sipated by molecular damping, and can cause large perturbations in wind, temperature and composition in the LTI. Propagating
tides are quantified by satellites, as outlined in a comprehensive review by Liu (2016). Recent studies have provided further1085
evidence that the ionospheric day-to-day variability could be closely tied to the tidal day-to-day variability. For example, the
pre-reversal enhancement of equatorial vertical E×Bdrift shows strong day-to-day variability. A recent analysis demonstrates
that, through the E-region dynamo at midlatitudes in the summer hemisphere, the day-to-day variability of tidal winds plays
a central role (Liu, 2020). It is also found that tidal winds determine the day-to-day variability of the equatorial vertical drift
(Zhou et al., 2020).1090
Contrary to tides, the quantification of gravity waves and of their effects in the LTI remains a challenge for both obser-
vations and numerical modelling, because of the scarcity of LTI observations in general, the multiscale nature of gravity
waves (10s–1000s km), and their global distribution. The altitude range of most ground-based and satellite techniques for
measuring perturbations associated with gravity waves is limited to below 100 km. A notable exception is lidar measure-
ments of metal layers, which can sometimes reach up to ∼170 km (Chu et al., 2016, and references therein). However,1095
the occurrence of such metal layers is sporadic, and it is challenging to obtain horizontal information, long-term variation,
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and global distribution of the waves from these measurements. Two new NASA satellite missions, Global-scale Observa-
tions of the Limb and Disk (GOLD; Eastes et al., 2017) and Ionosphere Connection Explorer (ICON; Immel et al., 2018)
make remote measurements in the LTI region, around 160 km and 100–160 km, respectively. LTI gravity wave information
can potentially be extracted from their measurements. With the scarcity of direct gravity wave measurements in LTI, low-1100
altitude in situ measurements would be extremely valuable. At the same time, global measurements of gravity waves in the
mesosphere, for example by the upcoming NASA Atmosphere Wave Explorer (AWE, https://www.nasa.gov/press-release/
nasa-selects-mission-to-study-space-weather-from-space-station), will be highly complementary in linking the waves in the
LTI to lower atmospheric sources.
4.10 Total electron content and derived quantities1105
The total electron content (TEC) refers to the integrated electron density nealong the line-of-sight between a receiver and a
satellite-borne transmitter: T EC =Rneds. It can be derived from the measurements of two carrier wave frequencies (f1,f2)
transmitted by Global Navigation Satellite System (GNSS) satellites:
sT EC =f2
1f2
2
f2
1−f2
2
L1−L2
K+DCBt+DCBr+ ∆, (17)
where L1and L2are carrier phase observations, Kis a constant, DCBtand DCBrare differential code biases of the trans-1110
mitter and receiver, respectively, and ∆accounts for ambiguities due to cycle slip corrections and remaining errors. sT E C is
the slant TEC related to the actual number of electrons between the transmitter and receiver. It depends on the elevation angle
of the GNSS satellite as the ray path through ionospheric layers with higher electron density gets longer for lower elevations.
By using an adequate mapping function, the vertical TEC, vT E C, can be obtained (e.g., Noja et al., 2013; Zhang et al., 2016;
Montenbruck and Rodríguez, 2020; Jin et al., 2019). For ground-based receivers, it is often assumed that the pierce point of1115
the ionosphere is at 350 km, and a thin, shell-like ionospheric model is used for obtaining vT E C. However, for a receiver on-
board another satellite above the pierce point, often a more advanced mapping function, assuming thick ionosphere should be
used (Noja et al., 2013). More complex approaches for vT E C are also typically used in tomography techniques (e.g., Meggs
et al., 2004). Note that additional biases in TEC measurements, such as receiver temperature, may contribute to errors in TEC
estimations (Coster et al., 2013). The concepts of ground-based sT EC and its mapping to v T EC are shown in Figure 8a.1120
TEC is nowadays routinely used for monitoring electron density variations in the ionosphere. It is generally displayed as 2D
maps based on networks of ground-based receivers (Coster et al., 1992). Over the years, TEC has successfully been used for
studies of the response of the ionosphere to geomagnetic storms (Danilov, 2013; Mendillo, 2006), but also to study dynamics of
ionospheric structures such as polar cap patches or travelling ionospheric disturbances (Tsugawa et al., 2004; Durgonics et al.,
2017). In numerical simulations which model the LTI, TEC is naturally obtained by integrating the computed electron density1125
along a (slanted or vertical) path. Similarly, from tomographic reconstructions of the ionosphere (e.g., Norberg et al., 2015),
TEC can be obtained by numerical integration of the inverted electron density. In such cases, additional measurements than
solely from GNSS, such as incoherent scatter radar or ionosonde observations, can be used to feed the tomographic inversion
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Figure 8. (a) Schematics of obtaining sT EC and vT E C: T - transmitter, R - receiver, Ne - electron density profile in the ionosphere, ipp -
ionospheric pierce point. (b) Example of ROT and electron density variations dne/dt (absolute values) as measured by Swarm A during one
hour on September 8, 2017.
system and therefore contribute to the TEC derivation. One example of such an assimilative tomography network is Tomoscand
in Fennoscandia (e.g., Norberg et al., 2018).1130
Ground-based networks used for TEC calculations are limited in coverage, in particular in the polar regions and over oceans.
This limitation can be addressed by GNSS receivers onboard LEO-satellites. Such satellite-based measurements of the topside
ionosphere TEC have recently provided an additional highly valuable contribution, by characterising structures, density gradi-
ents and irregularities in ionospheric plasma (e.g., Chartier et al., 2018; Jin et al., 2019). They further support in situ plasma
density measurements, as they do not only follow similar characteristics in the context of structuring (Zakharenkova et al.,1135
2016; Xiong et al., 2016; Jin et al., 2019), but they also add additional information about the directional extent of plasma
density variations (e.g., Park et al., 2017b; Follestad et al., 2020). Thus TEC, together with in situ plasma density measure-
ments, gives a detailed insight into scales of plasma density structures, gradients, and the overall plasma density variations. A
large number of tracked GNSS satellites by a receiver onboard LEO-satellites can even provide insight into the spatial extent
of structures in the ionosphere, such as polar cap patches, equatorial Spread-F, auroral blobs, when using inversion methods1140
(Follestad et al., 2020).
Derived TEC parameters, such as the rate of change of TEC (ROT), being the temporal derivative of TEC, and the ROT
index (ROTI), being the standard deviation of ROT in a given time interval, provide important characterisation of plasma
irregularities in the topside ionosphere. It has been shown that satellite-based TEC variations are correlated with the in situ
measurements of plasma density variations (Xiong et al., 2016). Indeed, due to the shape of ionospheric density profiles, the1145
largest contribution to TEC comes from plasma in the vicinity of the LEO satellite. Thus, ROT and ROTI are closely related
to the variability in the local ionospheric plasma density (Jin et al., 2019; Xiong et al., 2016), see also Fig. 8b. As such, they
can also be related to the quality of trans-ionospheric radio signals, and since TEC is derived from the measurements of GNSS
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signals, these measurements can also give insight into processes behind ionospheric scintillations, especially if the receiver
provides high-frequency data.1150
TEC and other GNSS-related measurements depend on the electron density variations in the topside ionosphere, and hence
on the satellite’s orbit. The peak of the ionospheric Flayer, which gives the largest contribution to the variations in the signal
propagation, is below the altitudes of most of LEO-satellites. A low-perigee satellite mission embedding a high-frequency
GNSS receiver onboard could allow for the first time the probing of different contributions to TEC, in particular from different
parts of the Fregion and upper Eregion of the ionosphere or from the plasmasphere, and related variability, and as such it1155
could provide a unique opportunity to investigate the effect of plasma irregularities on the GNSS signals at different altitudes.
5 Concluding remarks
There are at least four viewpoints from which understanding of the LTI is crucial: scientific, policy-based, technological, and
from the perspective of the society at large. First and foremost, humans explore – not only our own planet but also celestial and
astronomical bodies within and beyond our Solar system. It is astonishing that we can find a region so near to the surface of our1160
planet – 80 to 200 km altitude – which is still called the ignorosphere due to the lack of systematic in situ measurements. Since
the LTI is shown to influence atmospheric and climate systems, it should be understood as part of our planetary system. In fact,
from this perspective the entire near-Earth space linking to the LTI belongs to the Earth system. Even so, the LTI and the near-
Earth space are often regarded as fall-betweens as the understanding of their phenomena requires knowledge from atmospheric
physics, chemistry and space physics. This multitude of fields should not hinder us in exploring the LTI; on the contrary the1165
inherent interdisciplinary nature should challenge us to think about the region from many different scientific perspectives.
Policymakers are continually interested in the climate system due to climate change and its anthropogenic nature. From the
climate change perspective, as described in Sect. 2.3, it is imperative to understand the role of electron precipitation within the
natural polar climate variability to accurately quantify the contribution due to human activities. Understanding the LTI is a key
element in this endeavour. However, policymakers have recently also been pushing towards getting prepared for possible large1170
space weather events, as, e.g., the European Union, the United Kingdom and the United States of America have issued reports
in this matter (see, e.g., Cabinet Office, 2017; Executive Office of the President of the United States, 2019). The goal is to
develop mitigation strategies to protect human infrastructure from the potentially devastating impact that a major geomagnetic
storm could have. The current understanding of geospace still contains some gaps that preclude predictive simulations of the
effects to be anticipated from a given solar storm. One such gap is related to the LTI, whose complexity comes from the1175
intertwined behaviours of its neutral and ionised components which are governed by distinct physical processes and from its
couplings to below and above. A major difficulty lies in that it proves extremely challenging to measure physical parameters at
LTI altitudes, both through in situ instruments and via remote observations.
From a technological perspective, the reason why the LTI is called the ignorosphere is because the exploration of the region
is so difficult. Spacecraft on circular low-Earth orbits experience thermal problems and return to Earth due to the increased1180
drag. Remote sensing methods require some emission to be gathered by the remote instrument, but there are regions within
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the LTI which do not emit these signals (Sarris, 2019). Ground-based measurements, such as the incoherent scatter radars
are invaluable in describing the ionised part of the LTI. However, due to the coupled nature of the LTI, it is necessary to
explore several other regions such as the magnetotail simultaneously, which is of course extremely challenging to achieve in
a systematic way. Therefore the exploration of the LTI needs to be understood as a coordinated effort from the beginning.1185
However, due to the difficulties involved, a mission obtaining systematic in situ measurements of the LTI would be a great
technological achievement comparable to exploring the deepest seas and the furthest galaxies.
In this review, open questions related to the LTI have been divided according to three viewpoints: energetics, variability and
dynamics, and chemistry. From the perspective of energetics, large uncertainties exist in estimating the relative contributions
of the various sources of energy (solar radiation flux, Joule heating, particle precipitation, atmospheric waves, exothermic1190
chemical reactions) under various contexts (latitude, local time, season, geomagnetic activity, solar cycle), which makes the
determination of the LTI energy budget very challenging. Regarding variability and dynamics, the understanding of the complex
couplings of the LTI with the magnetosphere and the underlying neutral atmosphere is still limited. This is not only true
at high latitudes, where substorms create large effects, but also at low latitudes where the equatorial electrojet and plasma
irregularities play a large role. Finally, the LTI chemistry involves a great number of species, whose densities are determined1195
by temperature-dependent photochemical reactions, and by production rates associated with particle precipitation. However,
many of the critical parameters, such as the ion–neutral cross sections and collision rates, remain poorly characterised.
To address the unanswered science questions pertaining to the LTI, additional observations of its key parameters, ideally
in situ, are needed in various regions of interest. In the ionospheric Eregion, interest is in the equatorial electrojet region,
corresponding to geomagnetic latitudes (MLAT) comprised within, roughly, ±7◦, and the auroral latitudes, comprised in most1200
situations within 60–75◦. In the Fregion, the areas of interest encompass all latitudes and include, in particular: equatorial
plasma bubble region (within ±30◦MLAT, 18–4 MLT), midlatitude Sqcurrents (within ±60◦MLAT, 6–19 MLT), auroral
latitudes, the polar cusp regions on the dayside (70–80◦MLAT, 10–14 MLT), and the polar cap region (>70◦MLAT). Ideally,
systematic observations in these regions should cover a wide range of seasons and geomagnetic conditions to enable the study
of their effects on the LTI energetics, variability, dynamics, and chemistry.1205
Systematic in situ observations of the key LTI parameters (precipitating particle fluxes and energy spectra, ion and neu-
tral temperatures, compositions and densities, neutral winds, ion drift speeds and electric fields, and magnetic fields) could
allow the derivation of physical quantities whose knowledge is crucial to the correct modelling of the LTI and its external
drivers. For instance, the forcing from above consists not only of particle precipitation but also of the Lorentz force exerted
onto the ions, of field-aligned currents, and of the Poynting flux which all need to be taken into account while developing1210
boundary conditions for ionospheric models. Microscopic parameters such as the ion–neutral cross sections, collision frequen-
cies, and heat transfer coefficients are also needed in the kinetic transport and photochemical modules of ionospheric models.
Furthermore, supercomputing centres are becoming increasingly accessible to research groups, and hence space environment
modelling is expected to make significant steps towards understanding and forecasting space weather. The increased amount
of available computing power also enables assimilative schemes to incorporate more data, as is currently done using the AMIE1215
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technique for ground-based radar and satellite observations (Cousins et al., 2015). All these models benefit from systematic in
situ observations, leading to a reduction of uncertainties and errors in numerical simulations.
Finally, a phenomenon that continually interests society at large is the beautiful and vivid auroral displays that take place
in the LTI. For example, a recent Citizen Science auroral discovery, named the dunes, reached 2.5 billion people all around
the world in just two weeks after publication (Palmroth et al., 2020). This discovery followed a similar one by a collaboration1220
between Canadian amateur photographers and space physicists, which revealed the phenomenon nowadays known as STEVE
(MacDonald et al., 2018; Gallardo-Lacourt et al., 2018), and which has reached even larger numbers describing public interest.
All above-mentioned features pertaining to the LTI – its role as a region to be explored, the technological challenges it poses,
and its role within world-wide policies combined with the fascinating aurora and other optical features – makes it an ideal topic
for a wide range of stories to engage the public at large, generating increased interest in natural sciences. For these reasons, a1225
dedicated satellite mission providing regular observations of the key LTI parameters is highly desirable and timely.
Code and data availability. The DMSP data used to make Figure 3a are provided by NOAA and can be downloaded from the DMSP/SSJ
archives at https://satdat.ngdc.noaa.gov/dmsp/data/. Figure 3b was made using OvationPyme, the Python implementation of Ovation Prime
2010, which is available at https://github.com/lkilcommons/OvationPyme. The NRLMSISE-00 and IRI-2012 models used to produce Fig-
ure 4 are available online through NASA’s Community Coordinated Modeling Center (CCMC) at https://ccmc.gsfc.nasa.gov/modelweb/1230
models/nrlmsise00.php and https://ccmc.gsfc.nasa.gov/modelweb/models/iri2012_vitmo.php, respectively. Data from the Swarm mission
visualised in Figures 5 and 8(b) are provided by ESA via http://swarm-diss.eo.esa.int or ftp://swarm-diss.eo.esa.int. TIE-GCM model run
data used to generate Figure 7 were produced at the Democritus University of Thrace, with AMIE inputs provided by Gang Lu, NCAR/HAO.
WACCM-X model run data used to generate Figures 1 and 2 were produced at NCAR/HAO, provided by Federico Gasperini. Both data sets
are available from the author TS on request, while run requests of TIE-GCM can be made via the CCMC at https://ccmc.gsfc.nasa.gov/1235
models/modelinfo.php?model=TIE-GCM and the WACCM-X code can be downloaded as part of the Community Earth System Model
version 2 http://www.cesm.ucar.edu/models/cesm2/.
Author contributions. The writing of this article was led by MP and coordinated by MG, with contributions from all co-authors. ED made
Figures 1, 2, and 6. MG made Figure 3, RP made Figure 4, CSt and NO made Figure 5, TS and ST made Figure 7, WM made Figure 8.
Significant contributions to the text by section are as follows: 1: MP; 2.1: MP, SB, HLL; 2.2: MP, DM, CSt, MY; 2.3: MP, PTV, MAC, MG;1240
3.1: MG, AK; 3.2: PV, GM, MG; 3.3: ID, PV, MG; 3.4: GM, GK, MG, ED; 3.5: AM, AA, DK, MP; 3.6: CSt, NO; 4.1: OM, MP, TMJ, DK;
4.2: DK, TMJ, OM, MP, MG; 4.3: MP, DK; 4.4: MG, MY; 4.5: MG, DK; 4.6: TM, MG, AA; 4.7: SB, MG; 4.8: SB, MG; 4.9: HLL, MP;
4.10: WM, CSi, JvdIJ, MG; 5: MP, MG.
Competing interests. The authors declare that they have no competing interests.
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Acknowledgements. This work was funded under the European Space Agency (ESA/ESTEC) contract number 4000127346/19/NL/IA with1245
the Democritus University of Thrace for the Daedalus science and requirements consolidation study, in the framework of the Earth Ex-
plorer 10 Phase-0 feasibility studies (PI: TS). MP and MG are supported by European Research Council Consolidator grant 682068-
PRESTISSIMO, and Academy of Finland grants 309937 and 312351. Work at the Democritus University of Thrace is supported under
project KE82324. National Center for Atmospheric Research is a major facility sponsored by the National Science Foundation under Co-
operative Agreement No. 1852977. HLL’s work is in part supported by NSF grants OPP-1443726 and AGS-1552153 and NASA grant1250
NNX16AB82G. AM thanks the Programme National Soleil-Terre de l’Institut des Sciences de l’Univers (PNST/INSU) for the scientific
support of her activities. WJM is supported in part by the Research Council of Norway grants 267408 and 275653. OM acknowledges sup-
port by ESA contracts 4000127660 MAGICS and 4000118383 SIFACIT. The WACCM-X model output visualised in Figures 1, 2 and 9 was
provided by Federico Gasperini (NCAR/HAO) and the model simulations made use of AMIE high-latitude forcing input provided by Gang
Lu (NCAR/HAO).1255
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