Nuggets of MIST science, summarising recent MIST papers in a bitesize format.
By Beatriz Sánchez-Cano (University of Leicester)
Several instrument operations, as well as communication systems with rovers at the surface, depend on radio signals that propagate throughout the atmosphere of Mars. This is the case for two radars currently operational in Mars’ orbit, sounding the ionosphere, surface and subsurface of the planet: The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) on board Mars Express, which operates between 0.1 and 5.5 MHz, and the Shallow Radar (SHARAD) onboard the Mars Reconnaissance Orbiter, which operates at 20 MHz. However, both radars typically suffer from complete blackouts for several days (and even weeks) when solar storms hit Mars. It is thought that an increase in the electron density of the lower ionosphere below 100 km occurs, where even a small enhancement in ionization significantly increases the signal attenuation. In analogy with Earth, some works suggest that solar protons of tens of MeV can cause these absorption layers. However, at Mars, the current origin andlong duration is not known.
Sánchez-Cano et al. (2019) focused on both the MARSIS and SHARAD radar performances during a powerful solar storm that hit Mars in September 2017. The space weather event consisted of a X8.2-class flare emitted by the Active Region 12673 at the western limb of the solar disk on 10 September 2017 (Figure 1a). This was followed by solar energetic particles (ions and electrons) that arrived at Mars few hours later, as recorded by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission (see Figure 1b,c). Based on MAVEN observations and numerical simulations of energetic electron precipitation, Sánchez-Cano et al. (2019) found that high energy electrons (and not protons) were the main ionization source, creating a dense layer of ions and electrons of magnitude ~1010 m-3 at ~90 km on the Martian nightside. For frequencies between 3 and 20 MHz, the peak absorption level is found at 70 km altitude, and the layer was composed mainly of O2+, the main Martian ionosphere component. This layer attenuated radar signals continuously for 10 days, preventing the radars from receiving any HF signals from the planetary surface across a planetary scale (Figure 1d). This contrasts with the typical few hour durations that these phenomena have at Earth.
This work highlights the need for careful assessments of radar performances for future operational systems, especially during space weather events. During these events, a good characterization of the low ionosphere is necessary for radar operations (and other instruments that use HF radio links), operational planning, as well as for communications with the Martian surface in the HF range.
For more information please see the paper below:
Sánchez‐Cano, B., Blelly, P.‐L., Lester, M., Witasse, O., Cartacci, M., Orosei, R., et al ( 2019). Origin of the extended Mars radar blackout of September 2017. Journal of Geophysical Research: Space Physics, 124. https://doi.org/10.1029/2018JA026403
Figure 1: (a) MAVEN-EUV irradiance observations of wavelength 0.1-7 nm. (b) MAVEN-SEP ion differential flux spectra. (c) MAVEN-SEP electron differential flux spectra. (d) Each symbol denotes when MARSIS and SHARAD were in operation. Empty symbols designate the cases when the surface was observed, and filled symbols when was not observed. The exception are green diamonds that indicate the times when SHARAD observed a highly blurry surface.
by Homayon Aryan (University of Sheffield)
Numerical codes modelling the evolution of the radiation belts often account for wave-particle interaction with magnetosonic waves. The diffusion coefficients incorporated in these codes are generally estimated based on the results of statistical surveys of the occurrence and amplitude of these waves. These statistical models assume that the spectrum of the magnetosonic waves can be considered as continuous in frequency space, however, this assumption can only be valid if the discrete nature of the waves satisfy the Chirikov resonance overlap criterion.
The Chirikov resonance overlap criteria describes how a particle trajectory can move between two resonances in a chaotic and unpredictable manner when the resonances overlap, such that it is not associated with one particular resonance [Chirikov, 1960]. It can be shown that the Chirikov resonance overlap criterion is fulfilled if the following equation is satisfied:
δθ = (vl / tanθm) / (1 - (ω2/vΩce2))
where θm is the mean angle between the propagation direction and the external magnetic field, δθ is the standard deviation of the wave propagation angles , l is the harmonic number, v=me/mp is the electron to proton mass ratio, and Ωce is the electron gyro-frequency [Artemyev et al., 2015].
Here we use Cluster observations of magnetosonic wave events to determine whether the discrete nature of the waves always satisfy the Chirikov resonance overlap criterion, extending a case study by Walker et al. . An example of a magnetosonic wave event is shown in panels a-c of the Figure. Panel d shows that the Chirikov overlap criterion is satisfied for this case. However, a statistical analysis shows that most, but not all, discrete magnetosonic emissions satisfy the Chirikov overlap criterion. Therefore, the use of the continuous spectrum, assumed in wave models, may not always be justified. We also find that not all magnetosonic wave events are confined very close to the magnetic equator as it is widely assumed. Approximately 75% of wave events were observed outside 3° and some at much higher latitudes ~21° away from the magnetic equator. This observation is consistent with some past studies that suggested the existence of low-amplitude magnetosonic waves at high latitudes. The results highlight that the assumption of a continuous frequency spectrum could produce erroneous results in numerical modelling of the radiation belts.
For more information please see the paper below:
2019). Equatorial magnetosonic waves observed by Cluster satellites: The Chirikov resonance overlap criterion. Journal of Geophysical Research: Space Physics, 124. https://doi.org/10.1029/2019JA026680, , , & (
Figure: Observation of a magnetosonic wave event measured by Cluster 2 on 16 November 2006 at around 02:08 to 02:33~UT. The top three panels (a, b, and c) show the dynamic wave spectrogram (Bx, By, and Bz respectively) measured by STAFF search coil magnetometer. Panel d shows the analysis of the Chirikov resonance overlap criterion outlined in equation shown on top-left of the panel. The blue and red dots represent 10~s averaged values of dqand the ratio on the right hand side of equation respectively.
by Mai Mai Lam (University of Southampton)
The substorm cycle comprises the loading and explosive release of magnetic energy into the Earth system, causing complex and brilliant auroral light displays as large as a continent. Within one substorm, over 50% of the total solar wind energy input to the Earth system is estimated to be converted to Joule heating of the atmosphere.Such Joule heating is highly variable, and difficult to measure for individual substorms. One quantity that we need to measure in order to calculate the Joule heating is the distribution of Pedersen conductance. Ideally this should be done across the very large range of latitudes and local times that substorms expand into. Pedersen conductance can be examined with high accuracy by exploiting ground-based incoherent scatter radar data, but only on the scale of a few kilometres.
The THEMIS all-sky imagers form a network of nonfiltered cameras that spans North America. Previous results have shown that the optical intensity of a single ground camera with a green filter can be used to find a reasonable estimate of Pedersen conductance. Therefore we asked whether THEMIS white-light cameras could measure the conductance as precisely as radars can, but at multiple locations across a continent. We found that the conductance estimated by one THEMIS camera has an uncertainty of 40% compared to the radar estimates on a spatial scale of 10 – 100 km and a timescale of 10 minutes. In addition, our results indicate that the THEMIS camera network could identify regions of high and low Pedersen conductance on even finer spatio-temporal scales. This means we can use the THEMIS network, and its data archive, to learn more about how much substorms heat up the atmosphere and how complicated and changeable this behaviour is.
For more information please see the paper below:
“How well can we estimate Pedersen conductance from the THEMIS white‐light all‐sky cameras?”, M. M. Lam , M. P. Freeman, C. M. Jackman, I. J. Rae, N. M. E. Kalmoni, J. K. Sandhu, C. Forsyth. Journal of Geophysical Research. https://doi.org/10.1029/2018JA026067
Figure caption: (a) Absolute difference between camera- and radar-derived 1 min Pedersen conductance (black solid) and the effect of different temporal smoothing (coloured broken). (b) As for a, but for the relative difference between camera- and radar-derived Pedersen conductance (normalised to the radar conductance). (c) Comparison of camera-derived and radar-derived Pedersen conductance values for days with different geomagnetic conditions as indicated by Kp: 1 min radar values (blue crosses), 1 min radar values smoothed over 10 min (red diamonds), and 1 min values derived from camera intensity (black squares).
by Martin Archer (Queen Mary University of London)
The abrupt boundary between a magnetosphere and the surrounding plasma, the magnetopause, has long been known to support surface waves which travel down the flanks. However, just like a stone thrown in a pond causes ripples which spread out in all directions, impulses acting on our magnetopause should also cause waves to travel towards the magnetic poles. It had been proposed that the ionosphere might result in a trapping of surface wave energy on the dayside as a standing wave or eigenmode of the magnetopause surface. This mechanism should act as a global source of magnetopause dynamics and ultra-low frequency waves that might then drive radiation belt and auroral interactions.
While many potential impulsive drivers are known, no direct observational evidence of this process had been found to date and searches for indirect evidence had proven inconclusive, casting doubt on the theory. However, Archer et al. (2019) show using all five THEMIS spacecraft during their string-of-pearls phase that this mechanism does in fact occur.
Figure: THEMIS observations and a schematic of the magnetopause standing wave.
They present observations of a rare isolated fast plasma jet striking the magnetopause. This caused motion of the boundary and ultra-low frequency waves within the magnetosphere at well-defined frequencies. Through comparing the observations with the theoretical expectations for several possible mechanisms, they concluded that the jet excited the magnetopause surface eigenmode – like how hitting a drum once reveals the sounds of its normal modes.
Hear the signals as audible sound here: https://www.youtube.com/watch?v=mcG03NBJf-s
For more information please see the paper below:
‘Direct Observations Of A Surface Eigenmode Of The Dayside Magnetopause’. M.O. Archer, H. Hietala, M.D. Hartinger, F. Plaschke, V. Angelopoulos. Nature Communications. | https://doi.org/10.1038/s41467-018-08134-5
by Samuel J. Wharton (University of Leicester)
The Earth’s magnetosphere is constantly being disturbed by ultralow frequency (ULF) waves. These waves transport energy and momentum through the system and can form standing waves on magnetospheric field lines. These standing waves have a resonant frequency which depends on the magnetic field strength and plasma distribution along the field line. The waves result in perturbations in the magnetic field and plasma in the ionosphere. These occur at the resonant frequency and can be directly observed with instruments on the ground. Being able to measure the resonant frequency can provide valuable information about the state of the magnetosphere.
Traditionally, this can be done by applying a cross-phase spectral technique to ground-based magnetometers. It works by finding the frequency where the phase change with latitude is most rapid. This occurs at the local resonant frequency.
The Super Dual Auroral Radar Network (SuperDARN) is a global consortium of 35 radars that observe radio waves backscattered from the ionosphere. The radars detect ULF waves by observing the movements of ionospheric plasma.
For the first time, we have applied the cross-phase technique to SuperDARN. These radars have a much greater spatial resolution and coverage and provide more detailed information than can be achieved with magnetometers alone. In this study, we have used some notable techniques, such as developing a Lomb-Scargle cross-phase technique for uneven data and exploiting an improved fitting procedure Reimer et al. (2018).
We have been able to apply these methods to several examples and validate the results with ground magnetometer estimations. When available, ionospheric heaters can be used to reduce the uncertainty in the backscatter location. However, the majority of SuperDARN data does not have a heater in the field of view and observes ‘natural scatter’. Figure 1 shows an example of the technique applied to natural scatter. The red band in Figure 1e lies at the resonant frequency. Hence, we can measure the resonant frequencies with and without an ionospheric heater.
This study demonstrates that SuperDARN can be used as a tool to monitor resonant frequencies and therefore the plasma distribution of the magnetosphere. This opens up a new application for the SuperDARN radars.
For more information, please see the paper below:
Wharton, S. J., Wright, D. M., Yeoman, T. K., & Reimer, A. S. (2019). Identifying ULF wave eigenfrequencies in SuperDARN backscatter using a Lomb-Scargle cross-phase analysis. Journal of Geophysical Research: Space Physics, 124. https://doi.org/10.1029/2018JA025859
Figure 1: This shows an example of the local resonant frequency being measured by SuperDARN. (a) and (b) show range-time-intensity plots for beams 12 and 15 of the Þykkvibær radar. (c) shows filtered line-of-sight velocities for range gates 10 and 9 on those beams respectively. (d) The cross-phase spectrum for data in (c). (e) The cross-phase spectrum from (d) smoothed.