Nuggets of MIST science, summarising recent MIST papers in a bitesize format.
By Shannon Jones (University of Reading)
Coronal Mass Ejections (CMEs), or solar storms, are huge eruptions of particles and magnetic field from the Sun. With the help of 4,028 citizen scientists, we found that the appearance of CMEs changes over the solar cycle, with CMEs appearing more visually complex towards solar maximum.
We created a Zooniverse citizen science project in collaboration with the UK Science Museum, where we showed pairs of images of CMEs from the Heliospheric (wide-angle white-light) Imagers on board the twin STEREO spacecraft, and asked participants to decide whether the left or right CME looked most complicated, or complex. We used these data to rank 1,110 CMEs in order of their relative visual complexity. Figure 1 shows three example storms from across the ranking (see figshare for an animation with all CMEs).
Figure 1. Example images showing three example CMEs in ranked order of subjective complexity increasing from low (left-hand image) through to high (right-hand image).
Figure 2 shows the relative complexity of all 1,110 CMEs, with CMEs observed by STEREO-A shown by pink dots, and CMEs observed by STEREO-B shown by blue dots. This shows that the annual average complexity values follow the solar cycle, and that the average complexity of CMEs observed by STEREO-B is consistently lower that the complexity of CMEs observed by STEREO-A.
These results suggest that there is some predictability in the structure of CMEs, which may help to improve future space weather forecasts.
Figure 2. Top panel: relative complexity of every CME in the ranking plotted against time. Pink points represent STEREO-A images, while blue points represent STEREO-B images. Annual means and standard deviations are over plotted for STEREO-A (red dashed line) and STEREO-B (blue dashed line) CMEs. Bottom panel: Daily total sunspot number from SILSO shown in yellow, with annual means over plotted (orange dashed line).
See the paper for more details:
Jones, S. R., C. J. Scott, L. A. Barnard, R. Highfield, C. J. Lintott and E. Baeten (2020): The visual complexity of coronal mass ejections follows the solar cycle. Space Weather, https://doi.org/10.1029/2020SW002556.
By Ned Staniland (Imperial College London)
The presence of an internal plasma source (the moon Enceladus) coupled with the rapid rotation rate of Saturn (~10 hrs) results in an equatorially confined layer of plasma that stretches the dipolar planetary magnetic field into what is known as a magnetodisc. This structure is found at both gas giants and so understanding its formation and how it responds to different drivers reveals the dynamics of these magnetospheres and how the geologically active moons affect them. We explore the thickness of the equatorial current sheet that is associated with the stretched field geometry. We use 66 fast, high inclination crossings of the current sheet made by Cassini, where a clear signature in the magnetic field data (Figure 1a shows a sharp reversal in the radial field during the crossing) allows for a direct determination of its thickness and offset.
We find that the current sheet is thinner than previously calculated but identify several sources of spatial and temporal variability. For instance, the current sheet is 50% thicker in the nightside inner magnetosphere compared to the dayside (Figure 1b). This is consistent with the presence of a noon‐midnight convection electric field at Saturn that produces a hotter plasma population on the nightside, resulting in a thicker current sheet. However, the current sheet becomes thinner with radial distance on the nightside, while staying approximately constant on the dayside (Figure 1b), reflecting the solar wind compression of the magnetosphere and the stretching of the field in the tail. Some of the variability is well characterized by the planetary period oscillations (PPOs). But we also find evidence for non‐PPO drivers of variability, highlighting the interplay between different drivers that shape the Saturnian system.
This work shows the necessity for considering the variable structure of the largest current system in the Saturnian magnetosphere, which is essential particularly for future modelling efforts.
Figure 1a) shows Cassini magnetic field data during a current sheet crossing. We determine the current sheet boundaries by identifying spikes in the variance of the cylindrical radial field component (green line, top panel). Figure 1b) shows box plots calculated from the 66 crossings that highlight the radial profile and day-night asymmetry of the current sheet thickness.
For more information, please see:
Staniland, N. R., Dougherty, M. K., Masters, A., & Bunce, E. J. (2020). Determining the nominal thickness and variability of the magnetodisc current sheet at Saturn. Journal of Geophysical Research: Space Physics, 125, e2020JA027794. https://doi.org/10.1029/2020JA027794
By Sarah Bentley (Northumbria University)
Parameterised (statistical) models are being increasingly used in space physics, both as an efficient way to use large amounts of data and as an important step in real-time modelling, to capture physics on scales not incorporated in numerical modelling. We have used machine learning techniques to create a model for the power in ultra low frequency (1-15mHz, ULF) waves throughout Earth’s magnetosphere. Capturing the power in these global-scale waves is necessary to determine the energisation and transport of high energy electrons in Earth’s radiation belts, and the model can also be used to test how individual wave driving processes combine throughout the magnetosphere.
The model is constructed using ensembles of decision trees (i.e. a random forest). Each decision tree iteratively partitions the given parameter space into variable size bins to reduce the error in the predicted output values. These variable bins mitigate several difficulties inherent to space physics data (sparseness, interdependent driving parameters, nonlinearity) to produce an approximation of ULF wave power in our chosen parameter space: physical driving parameters (solar wind speed vsw, magnetic field component Bz and variance in proton number density var(Np)) and spatial parameters of interest (magnetic local time MLT, magnetic latitude and frequency band).
[frequency, latitude, component, MLT, vsw, Bz, var(Np)] → ULF wave power
It is not always possible to extract all physical processes from parameterised models such as this. Instead we suggest a hypothesis testing framework to examine the physics driving ULF wave power. This formalises the approach taken in full statistical surveys, beginning with dominant driving processes, testing how they manifest in the model, and then examining remaining power.
Figure 1: Variation of ULF wave power at one station, 5mHz. Model-predicted power spectral density is shown by magnetic local time at quantiles of (a) speed (for median Bz < 0 and var(Np)), (b) Bz < 0 (for median speed and var(Np)) and (c) var(Np) (for median speed and Bz < 0). Median values for speed, Bz < 0 and var(Np) are 421 km s−1, −1.8 nT and var(Np) = −0.716 log10(cm−3) respectively. (d)-(f) also show variation of wave power with speed, Bz and var(Np) but for Bz > 0 (with a median value of Bz = 1.7 nT held constant for (d) and (f)). Radius of each quantile corresponds to the power spectral density in log10(nT2/Hz) predicted for those solar wind values, at that station, frequency and magnetic local time.
In the paper we demonstrate how this method of iteratively considering smaller scale driving processes applies to magnetic local time asymmetries in ULF wave power. In Figure 1 we can see the wave power predicted by the model when we change one driving parameter and keep the others constant, for Bz<0 and Bz>0 separately. The MLT asymmetries in power clearly change with both driving parameter and there are two separate behaviour regimes for Bz>0, Bz<0. Digging deeper into these results using the framework, we conclude that
We also found significant remaining uncertainty with mild solar wind driving, suggesting that the internal state of the magnetosphere should be included in future models.
Please see the paper for full details:
Bentley, S. N., Stout, J., Bloch, T. E., & Watt, C. E. J. (2020). Random forest model of ultra‐low frequency magnetospheric wave power. Earth and Space Science, 7, e2020EA001274. https://doi.org/10.1029/2020EA001274
By Rhys Thompson (University of Reading)
The Van Allen outer radiation belt is a region in near‐Earth space containing mostly high‐energy electrons trapped by the Earth's geomagnetic field. It is a region populated by satellites that are vulnerable to damage from the high‐energy environment. Many modern outer radiation belt models simulate the long‐time behaviour of high‐energy electrons by solving a three‐dimensional Fokker‐Planck equation for the drift‐ and bounce‐averaged electron phase space density that includes radial, pitch‐angle, and energy diffusion.
Radial diffusion is an important process, driven by ultralow frequency (ULF) waves, where electrons are drawn from the outer boundary and accelerated toward Earth, or pushed away from the outer radiation belt and lost to interplanetary space. All of the physics is contained in the radial diffusion coefficient, DLL, often deterministically parameterized to providea single output from the specified inputs which does not allow for any variability in the underlying ULF wave power.
We perform idealized numerical ensemble experiments on radial diffusion, introducing temporal and spatial variability to a widely used DLL, based on the median of statistical ultralow frequency (ULF) wave power for a particular geomagnetic index Kp, through stochastic parameterization constrained by statistical properties of its underlying observations. Results for one of the experiments is shown below in Figure 1. Our results demonstrate the sensitivity of radial diffusion over a long time period to the full distribution of the radial diffusion coefficient, highlighting that information is lost when only using median ULF wave power. A better understanding of temporal and spatial variations of ULF wave interactions with electrons, and being able to characterize these variations to a good level of accuracy, is vital to produce a robust description of radial diffusion over long timescales in the outer radiation belt.
Figure 1: Ensemble results for the electron phase space density (PSD) at the end of a 2 day radial diffusion experiment, where ensemble DLL time series over the duration of the experiment are formulated by applying (lognormal) variability to a constant deterministic DLL (Kp=3) over a range of temporal variability scales (1, 3, 6, 12, and 24 hr, respectively). When variability is applied it persists until to the next hour of variability (relative to the temporal variability scale) where the process is repeated. The median (dashed), mean (dash‐dot) ensemble profiles are shown, as well as the initial PSD profile (dotted) and the deterministic solution with constant deterministic DLL (solid). Ensemble kernel density estimates of the resulting electron PSD are also shown.
Please see the paper for full details:
, 2020). Accounting for variability in ULF wave radial diffusion models. Journal of Geophysical Research: Space Physics, 125, e2019JA027254. , & (https://doi.org/10.1029/2019JA027254
By Jade Reidy (University of Southampton & British Antarctic Survey)
Polar cap arcs (auroral arcs occurring at high latitudes) have been under debate since they were first discovered over 100 years ago. Although reports present conflicting evidence of the arcs forming on open field lines whilst others argue they are formed on closed field lines, recent work suggests that more than one polar cap arc formation mechanism potentially exists (e.g. Reidy et al., 2017, 2018).
Two events containing polar cap arcs occurring over Svalbard have been investigated using multiscale ground‐based and spacecraft instrumentation. Figures 1a and 2a show UV images from each event from the Special Sensor Ultra-Violet Imager (SSUSI) on board low-orbiting spacecraft (DMSP). These auroral images have been projected onto magnetic local time grids with noon at the top and dawn to the right. On both SSUSI images, we have projected an all sky camera image from Svalbard; this demonstrates how the ground-based and global-scale observations are related and allowed us to find an interval where the arc passes through the small field of view of the Auroral Structure and Kinetics (ASK) instrument (shown in Figures 1b and 2b for each event). Key features of each event are summarised below:
Event 1 – A Closed Event
Figure 1: Observations of the polar cap arc occurring on 04 February 2016. (a) SSUSI and the all sky imager observations. (b) ASK instrument observations of the auroral arc.
Event 2 – An Open Event
Figure 2: Observations of the polar cap arc occurring on 15 December 2015, in the same format as Figure 1.
In the full paper we investigate the different formation mechanisms further by comparing to observations from different instrumentation (including a ground-based spectrograph, located on Svalbard, and the Super Dual Auroral Radar Network). We conclude both events to be consistent with different and distinct formation mechanisms and that this is reflected in the small scale observations.
Please see the paper for full details:
Reidy, J. A., Fear, R. C., Whiter, D. K., Lanchester, B. S., Kavanagh, A. J., Price, D. J., et al. (2020). Multi‐scale observation of two polar cap arcs occurring on different magnetic field topologies. Journal of Geophysical Research: Space Physics, 125, e2019JA027611. https://doi.org/10.1029/2019JA027611