A comparison of the probability distribution of observed  substorm magnitude with that predicted by a minimal substorm model

Morley, S.; Freeman, M. P.; Tanskanen, Eija

doi:10.5194/angeo-25-2427-2007

Cited by 11 publications

(16 citation statements)

References 43 publications

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“…Given that the autocorrelation e ‐folding time of the solar wind velocity is 32 h compared to 5–10 h for the solar wind magnetic field [ Borovsky et al, ] and that the solar wind ram pressure is approximately constant during individual substorms [ Kistler et al, ], we determine a functional relationship between solar wind driving and plasma sheet pressure assuming that pressure variations in the plasma sheet during the growth phase result from the addition of magnetic flux to the tail lobes. Morley et al [] determined a functional form of the polar cap potential with respect to solar wind driving (their equation (10)):

normalΦ = 2 \times 10^{4} {(π p \times 10^{6})}^{\frac{1}{3}} + 1.4 \times 10^{3} ((), π p \times 10^{6})

where

p = ε true/ 4 π l_{0}^{2}

is the solar wind power per unit area. Taking this equation and assuming no loss of lobe flux during the growth phase, the increase in magnetic flux in the lobes is then

F = normalΦ t

where t is an integration period, taken in this case to be an hour.…”

Section: Resultsmentioning

confidence: 99%

“…Intervals during which dSML/dt was less (more) than the median negative (positive) change were labeled as expansion (recovery) phase times. The main difference between our method and that of Juusola et al [2011] is that we take all nonexpansion or recovery phase intervals to be growth phases (Juusola et al [2011] only considered southward IMF intervals) since solar wind power input functions, such as the ε function [Perreault and Akasofu, 1978;Akasofu, 1979;Morley et al, 2007], are nonzero for all but purely northward IMF.…”

Section: Datamentioning

confidence: 99%

“…Given that the autocorrelation e-folding time of the solar wind velocity is 32 h compared to 5-10 h for the solar wind magnetic field [Borovsky et al, 1998] and that the solar wind ram pressure is approximately constant during individual substorms [Kistler et al, 2006], we determine a functional relationship between solar wind driving and plasma sheet pressure assuming that pressure variations in the plasma sheet during the growth phase result from the addition of magnetic flux to the tail lobes. Morley et al [2007] determined a functional form of the polar cap potential with respect to solar wind driving (their equation (10)):…”

Section: Figures 2a and 2bmentioning

confidence: 99%

“…This energy is stored as magnetic energy in the magnetotail lobes until released by reconnection in the magnetotail, being roughly equally partitioned between enhanced particle precipitation and Joule heating in the ionosphere; energization of the ring current; and loss in plasmoids and the postplasmoid plasma sheet [ Richardson et al, ; Ieda et al, ]. The timing of release, the amount of available energy released, how and why it is partitioned into different energy forms and pathways, and the factors controlling these properties are significant unknowns in our understanding of substorm dynamics [ Koskinen and Tanskanen , ; Freeman and Morley , ; Morley et al, ].…”

Section: Introductionmentioning

confidence: 99%

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Increases in plasma sheet temperature with solar wind driving during substorm growth phases

Forsyth¹,

Watt

Rae³

et al. 2014

Geophysical Research Letters

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During substorm growth phases, magnetic reconnection at the magnetopause extracts ∼1015 J from the solar wind which is then stored in the magnetotail lobes. Plasma sheet pressure increases to balance magnetic flux density increases in the lobes. Here we examine plasma sheet pressure, density, and temperature during substorm growth phases using 9 years of Cluster data (>316,000 data points). We show that plasma sheet pressure and temperature are higher during growth phases with higher solar wind driving, whereas the density is approximately constant. We also show a weak correlation between plasma sheet temperature before onset and the minimum SuperMAG AL (SML) auroral index in the subsequent substorm. We discuss how energization of the plasma sheet before onset may result from thermodynamically adiabatic processes; how hotter plasma sheets may result in magnetotail instabilities, and how this relates to the onset and size of the subsequent substorm expansion phase.

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normalΦ = 2 \times 10^{4} {(π p \times 10^{6})}^{\frac{1}{3}} + 1.4 \times 10^{3} ((), π p \times 10^{6})

where

p = ε true/ 4 π l_{0}^{2}

is the solar wind power per unit area. Taking this equation and assuming no loss of lobe flux during the growth phase, the increase in magnetic flux in the lobes is then

F = normalΦ t

where t is an integration period, taken in this case to be an hour.…”

Section: Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Figures 2a and 2bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Increases in plasma sheet temperature with solar wind driving during substorm growth phases

Forsyth¹,

Watt

Rae³

et al. 2014

Geophysical Research Letters

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show abstract

“…DP1 is the least well‐explained mode set (at 17%) based on the IMF, which is expected since it represents the equivalent current response to substorm onset. This is an impulsive response to a time‐integrated property of the IMF and arguably (Freeman & Morley, ; Morley et al, ) also to an additional trigger from upstream IMF variations in around half of cases (Milan et al, ). It is likely that more of the variability of the B z ‐controlled modes would be explained by a nonlinear function of the solar wind parameters (e.g., Boynton et al, ; Finch & Lockwood, ; Newell et al, ; Spencer et al, ), but we do not investigate this here.…”

Section: Discussionmentioning

confidence: 99%

Interplanetary Magnetic Field Control of Polar Ionospheric Equivalent Current System Modes

2019

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We analyze the response of different ionospheric equivalent current modes to variations in the interplanetary magnetic field (IMF) components By and Bz. Each mode comprises a fixed spatial pattern whose amplitude varies in time, identified by a month‐by‐month empirical orthogonal function separation of surface measured magnetic field variance. Here we focus on four sets of modes that have been previously identified as DPY, DP2, NBZ, and DP1. We derive the cross‐correlation function of each mode set with either IMF By or Bz for lags ranging from −10 to +600 mins with respect to the IMF state at the bow shock nose. For all four sets of modes, the average correlation can be reproduced by a sum of up to three linear responses to the IMF component, each centered on a different lag. These are interpreted as the statistical ionospheric responses to magnetopause merging (15‐ to 20‐min lag) and magnetotail reconnection (60‐min lag) and to IMF persistence. Of the mode sets, NBZ and DPY are the most predictable from a given IMF component, with DP1 (the substorm component) the least predictable. The proportion of mode variability explained by the IMF increases for the longer lags, thought to indicate conductivity feedbacks from substorms. In summary, we confirm the postulated physical basis of these modes and quantify their multiple reconfiguration timescales.

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An Empirical Orthogonal Function Reanalysis of the Northern Polar External and Induced Magnetic Field During Solar Cycle 23

2018

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We apply the method of data‐interpolating empirical orthogonal functions (EOFs) to ground‐based magnetic vector data from the SuperMAG archive to produce a series of month length reanalyses of the surface external and induced magnetic field (SEIMF) in 110,000 km2 equal‐area bins over the entire northern polar region at 5 min cadence over solar cycle 23, from 1997.0 to 2009.0. Each EOF reanalysis also decomposes the measured SEIMF variation into a hierarchy of spatiotemporal patterns which are ordered by their contribution to the monthly magnetic field variance. We find that the leading EOF patterns can each be (subjectively) interpreted as well‐known SEIMF systems or their equivalent current systems. The relationship of the equivalent currents to the true current flow is not investigated. We track the leading SEIMF or equivalent current systems of similar type by intermonthly spatial correlation and apply graph theory to (objectively) group their appearance and relative importance throughout a solar cycle, revealing seasonal and solar cycle variation. In this way, we identify the spatiotemporal patterns that maximally contribute to SEIMF variability over a solar cycle. We propose this combination of EOF and graph theory as a powerful method for objectively defining and investigating the structure and variability of the SEIMF or their equivalent ionospheric currents for use in both geomagnetism and space weather applications. It is demonstrated here on solar cycle 23 but is extendable to any epoch with sufficient data coverage.

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A comparison of the probability distribution of observed substorm magnitude with that predicted by a minimal substorm model

Cited by 11 publications

References 43 publications

Increases in plasma sheet temperature with solar wind driving during substorm growth phases

Increases in plasma sheet temperature with solar wind driving during substorm growth phases

Interplanetary Magnetic Field Control of Polar Ionospheric Equivalent Current System Modes

An Empirical Orthogonal Function Reanalysis of the Northern Polar External and Induced Magnetic Field During Solar Cycle 23

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