The Earth’s Magnetosphere: A Systems Science Overview and Assessment

Borovsky, Joseph E.; Valdivia, J. A.

doi:10.1007/s10712-018-9487-x

Cited by 95 publications

(78 citation statements)

References 253 publications

(275 reference statements)

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“…However, there also exist regions where the spatial separation of particle and electromagnetic energy inputs is not well preserved (Deng et al, 2015). This suggests that the magnetospheric mechanism that produces the field-aligned current and the precipitating particle flux requires further investigation (Borovsky & Valdivia, 2018) despite our ability to establish relationships between the electron energy and the field-aligned current intensity in some regions (Cran-McGreehin & Wright, 2005;Knight, 1973). In the I-T, energetic particle precipitation changes the ionospheric conductance, which is further modified by the transport of plasma associated with the closure of the field-aligned current.…”

Section: Magnetosphere-ionosphere Interactionsmentioning

confidence: 99%

Challenges to Understanding the Earth's Ionosphere and Thermosphere

Heelis¹,

Maute

2020

JGR Space Physics

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We discuss, in a limited way, some of the challenges to advancing our understanding and description of the coupled plasma and neutral gas that make up the ionosphere and thermosphere (I‐T). The I‐T is strongly influenced by wave motions of the neutral atmosphere from the lower atmosphere and is coupled to the magnetosphere, which supplies energetic particle precipitation and field‐aligned currents at high latitudes. The resulting plasma dynamics are associated with currents generated by solar heating and upward propagating waves, by heating from energetic particles and electromagnetic energy from the magnetosphere and by the closure of the field‐aligned currents applied at high latitudes. These three contributors to the current are functions of position, magnetic activity, and other variables that must be unraveled to understand how the I‐T responds to coupling from the surrounding regions of geospace. We have captured the challenges to this understanding in four major themes associated with coupling to the lower atmosphere, the generation and flow of currents within the I‐T region, the coupling to the magnetosphere, and the response of the I‐T region reflected in the neutral and plasma density changes. Addressing these challenges requires advances in observing the neutral density, composition, and velocity and simultaneous observations of the plasma density and motions as well as the particles and field‐aligned current describing the magnetospheric energy inputs. Additionally, our modeling capability must advance to include better descriptions of the processes affecting the I‐T region and incorporate coupling to below and above at smaller spatial and temporal scales.

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Section: Magnetosphere-ionosphere Interactionsmentioning

confidence: 99%

Challenges to Understanding the Earth's Ionosphere and Thermosphere

Heelis¹,

Maute

2020

JGR Space Physics

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“…To investigate this substorm hypothesis for S (1) (t) − S (1)pred (t), the variables D (1) (t), S (1) (t), and S (1) (t) − S (1)pred (t) are superposed-epoch averaged in Fig. 4 for a collection of 2155 substorm events; the collection is from Borovsky and Yakymenko (2017). The zero epoch in Fig.…”

Section: Creation Of Composite (Aggregate) Variables From the State Vmentioning

confidence: 99%

Compacting the description of a time-dependent multivariable system and its multivariable driver by reducing the state vectors to aggregate scalars: the Earth's solar-wind-driven magnetosphere

Borovsky

Osmane

2019

Nonlin. Processes Geophys.

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Using the solar-wind-driven magnetosphereionosphere-thermosphere system, a methodology is developed to reduce a state-vector description of a time-dependent driven system to a composite scalar picture of the activity in the system. The technique uses canonical correlation analysis to reduce the time-dependent system and driver state vectors to time-dependent system and driver scalars, with the scalars describing the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The methodology identifies independent modes of reaction of a system to its driver. The analysis of the magnetospheric system is demonstrated. Using autocorrelation analysis, Jensen-Shannon complexity analysis, and permutation-entropy analysis the properties of the derived aggregate scalars are assessed and a new mode of reaction of the magnetosphere to the solar wind is found. This state-vector-reduction technique may be useful for other multivariable systems driven by multiple inputs.

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“…Boundary conditions are particularly important in driven systems like the magnetosphere (e.g., Borovsky & Valdivia, ; Vassiliadis et al, ). Recently, Chen et al () studied an ensemble of inner magnetosphere simulations using the Rice Convection Model (RCM)‐Equilibrium (Lemon et al, ) where the electric field boundary condition was varied using a statistical model of errors in the cross‐polar cap potential drop.…”

Section: Introductionmentioning

confidence: 99%

Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

2018

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To assess the effect of uncertainties in solar wind driving on the predictions from the operational configuration of the Space Weather Modeling Framework, we have developed a nonparametric method for generating multiple possible realizations of the solar wind just upstream of the bow shock, based on observations near the first Lagrangian point. We have applied this method to the solar wind inputs at the upstream boundary of Space Weather Modeling Framework and have simulated the geomagnetic storm of 5 April 2010. We ran a 40‐member ensemble for this event and have used this ensemble to quantify the uncertainty in the predicted Sym‐H index and ground magnetic disturbances due to the uncertainty in the upstream boundary conditions. Both the ensemble mean and the unperturbed simulation tend to underpredict the magnitude of Sym‐H in the quiet interval before the storm and overpredict in the storm itself, consistent with previous work. The ensemble mean is a more accurate predictor of Sym‐H, improving the mean absolute error by nearly 2 nT for this interval and displaying a smaller bias. We also examine the uncertainty in predicted maxima in ground magnetic disturbances. The confidence intervals are typically narrow during periods where the predicted dBH/dt is low. The confidence intervals are often much wider where the median prediction is for enhanced dBH/dt. The ensemble also allows us to identify intervals of activity that cannot be explained by uncertainty in the solar wind driver, driving further model improvements. This work demonstrates the feasibility and importance of ensemble modeling for space weather applications.

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The Earth’s Magnetosphere: A Systems Science Overview and Assessment

Cited by 95 publications

References 253 publications

Challenges to Understanding the Earth's Ionosphere and Thermosphere

Challenges to Understanding the Earth's Ionosphere and Thermosphere

Compacting the description of a time-dependent multivariable system and its multivariable driver by reducing the state vectors to aggregate scalars: the Earth's solar-wind-driven magnetosphere

Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

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