Using the solar-wind-driven
magnetosphere–ionosphere–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.
Introduction
In this report a methodology is described that can produce a compact
description of the behavior of a time-dependent, multivariable system driven
by a time-dependent, multivariable driver or by multiple drivers. The system
used to develop this methodology is the Earth's
magnetosphere–ionosphere–thermosphere system driven by the time-dependent
solar wind. The spatial domain wherein the Earth's magnetic field dominates
over the solar wind is known as the magnetosphere. The interaction between
the solar wind, and the magnetosphere is surprisingly complex and the
magnetosphere's evolution in response to the time-varying solar wind is rich
and diverse. The magnetospheric system is characterized by multiple
subsystems that interact with each other (cf. Lyon, 2000; Otto, 2005;
Siscoe, 2011; Eastwood et al., 2015; Borovsky and Valdivia, 2018): almost 6
orders of magnitude of spatial scales are involved in the global behavior of
the magnetosphere, from ∼1 to ∼6×105 km. This system is highly coupled, dynamic, with memory and with
feedback loops. Multiple physical processes act to couple the various
subsystems, with the strength of the couplings evolving with time as the
subsystems evolve owing to the couplings. Even after a half of a century of
measurements and analysis, its subsystems and the couplings between its
subsystems are not fully understood (Stern, 1989, 1996; Denton et al.,
2016). It has been argued that the system adjectives “adaptive”,
“nonlinear”, “dissipative”, and “complex” apply to the magnetospheric
system (Borovsky and Valdivia, 2018). (See also the earlier systems analyses
by Horton et al., 1999, Chapman et al., 2004, Valdivia et al., 2005,
2013, and Sharma, 2010.) The magnetospheric system is well measured: there
are hundreds of thousands of hours of simultaneous measurements of various
aspects of the magnetospheric system and its solar-wind driver over the five
decades of the “space age” (cf. Stern, 1989, 1996; King and Papitashvili,
2005).
The methodology developed here creates a compact description of a
time-dependent, multivariable system driven by a time-dependent,
multivariable driver. The diverse variables describing the system may be
intercorrelated, and the variables describing the driving may be
intercorrelated. The methodology was developed to gain an understanding of
the Earth's magnetosphere–ionosphere–thermosphere system as driven by the
solar wind. To utilize the methodology the system and its driver are
conceptualized by a time-dependent, multidimensional system state vector
S(t) and a time-dependent, multidimensional driver state vector
D(t), with the assumption that the driver vector D
affects the system vector S, but not vice versa, written
D→S. The individual time-dependent scalar
variables making up the state vector S(t) are time-dependent
measures of various forms of activity in the system and various properties
of the system, and the individual time-dependent scalar variables making up
the driver state vector D(t) are various time-dependent
measures of the properties of the drivers of the system. We will utilize the
correlation properties between the components (individual time-dependent
variables) of S and the components of D. Canonical
correlation analysis (CCA) will be used to derive scalar projections (dot
products) of S(t) and scalar projections of D(t)
that have the highest Pearson linear correlation coefficient between them.
The derived scalar projections S(1)(t), S(2)(t), S(3)(t),
… of the vector S(t) will be composite (aggregate)
measures of activity in the system, and the derived scalar projections
D(1)(t), D(2)(t), D(3)(t), … of the vector
D(t) will be the composite drivers of S(1)(t),
S(2)(t), S(3)(t), …, respectively. In essence, the
aggregate variables S(1)(t), S(2)(t), S(3)(t), …
are “latent variables” of the system constructed from the “manifest
variables” in the system state vector. This reduced scalar picture
D(i)→S(i) of the system driven by the driver focuses on the
time-dependent properties of the system that react to the driver. By
maximizing the correlations, the predictability of the system from
knowledge of the state of the driver is also maximized.
The solar-wind-driven magnetospheric system very cleanly follows the
D→S picture where the driver affects the
system, but the system does not affect the driver. The Earth's magnetosphere
has no influence whatsoever on the properties of the solar wind that passes
the Earth. Measurements of this magnetospheric system will be used in
Sects. 2 and 3 to explore the mathematical reduction of the state-vector
D(t)→S(t) picture to the composite-scalar
D(i)(t)→S(i)(t) picture. Table 1 lists the nine time-dependent
measurements of the magnetosphere in the system state vector S
and the eight time-dependent measurements of the solar wind in the driver state
vector D. The individual variables in the system state vector
and in the driver state vector are described in the Appendix.
The nine time-dependent variables going into the system state vector
S(t) of the magnetosphere and the eight time-dependent variables
going into the driver state vector D(t) of the solar wind.
System (magnetospheric) variablesDriver (solar-wind) variablesAuroral lower index ALSolar wind speed vswAuroral upper index AUSolar wind number density nswPolar cap index PCISolar 10.7 cm radio flux F10.7Planetary K index KpNorth–south-component magnetic field – BzGeomagnetic range index amMach-number function f(M)Time derivative of disturbance storm-time index DstMagnetic-field clock angle θclockHemispheric electron precipitation power mPeMagnetic-field Sun–Earth angle θBnHemispheric ion precipitation power mPiMagnetic-field-vector fluctuation amplitude |ΔB|Pressure of the ion plasma sheet Pips
This report is organized as follows. In Sect. 2 the CCA approach is
applied to the magnetospheric system driven by the solar wind to derive the
first three time-dependent sets of composite variables S(1)(t) and
D(1)(t), S(2)(t) and D(2)(t), and S(3)(t) and
D(3)(t) from the state vectors S(t) and D(t). In Sect. 3 the three sets of composite variables S(i) and
D(i) for the magnetospheric system are explored, and the
complexity–entropy properties of the aggregate variable S(1)(t) are
analyzed. In Sect. 4 the advantages of the reduced D(i)→S(i) scalar description are examined: these advantages include (a) a
compact description of global system-wide reactions to variations in the
driver, (b) increased predictability of the system from knowledge of the
driver, (c) linearity in the description of the system's response to the
driver, and (d) lower noise in correlations between the system variables and
the driver variables. The reduced scalar picture can also reveal independent
modes of reaction of the system to the driver, providing insight into the
behavior of the system in reaction to complexities in the driver. The
variables of the magnetospheric and solar-wind state vectors are described
in the Appendix.
Creation of composite (aggregate) variables from the state
vectors
Using the Earth's magnetosphere–ionosphere–thermosphere system as driven by
the solar wind, the reduction of a time-dependent state-vector picture
D(t)→S(t) to the time-dependent
composite-variable-pair picture D(i)(t)→S(i)(t) will be
performed. The nine measured variables chosen for the nine-dimensional
magnetospheric system state vector S appear in the first column
of Table 1, and the eight measured variables chosen for the eight-dimensional
solar-wind driver state vector D appear in the second column of
Table 1, with explanations of those measures deferred to the Appendix.
One-hour averages of all magnetospheric and solar-wind variables are used in
the years 1991–2007. No time lags are used between the solar-wind
measurements and the magnetospheric measurements: most expected time lags
will be about 1 h (e.g., Clauer et al., 1981; Smith et al., 1999), which is
the time resolution of the data set.
Canonical correlation analysis (CCA) is applied to the time-dependent state
vectors S(t) and D(t). CCA finds correlation
patterns between two multivariable data sets (Nimon et al., 2010; Hair et
al., 2010). It yields pairs of composite (aggregate) variables (a) that are
linear combinations of the variables of the two data sets and (b) that have
maximal correlations with each other. Each pair of composite variables is
called the “Nth canonical correlation”. From the data sets of S(t) and D(t) the first pair of composite variables yielded
(the first canonical variates) is S(1)(t) and D(1)(t): these two
variables are projections of S and D given by
S(1)(t)=CS1⋅S(t) and
D(1)(t)=CD1⋅D(t), where
CS1 and CD1 are time-independent
coefficient (weight) vectors. S(1) and D(1) are the composite
variables from S and D that have the highest
Pearson linear correlation coefficient with each other. Here, CCA is in a
sense creating the system function S(1)(t) that is most reactive to the
driver vector D(t) and creating the driver scalar function
D(1)(t) that describes that driving. CCA then yields other pairs of
composite variables S(2) and D(2) (the second canonical
correlation), S(3) and D(3) (the third canonical correlation),
etc. S(2) and D(2) are the projections of S and
D that have the highest correlation with each other, provided
that S(2) and D(2) are uncorrelated with S(1) and D(1).
S(3) and D(3) are the projections of S and D that have the highest correlations with each other, provided they are
uncorrelated with S(1), S(2), D(1), and D(2).
S(1)(t), S(2)(t), and S(3)(t) represent three independent
modes of reaction of the global system to the driver D(t). The
CCA process will identify these modes (and their respective drivers).
CCA is a matrix equation solution, non-iterative, that yields a single
unique solution (Johnson and Wichern, 2007). CCA operates on standardized variables (with the
mean value subtracted and the values then divided by the standard
deviation), denoted with an asterisk. (For each variable the mean value
and standard deviation are calculated for the entire data set.) CCA operates
most efficiently on variables that are Gaussian distributed: hence the
logarithms of some variables are used to yield more-Gaussian-like
distributions. All standardized variables v* have a mean value of zero, a
standard deviation unity, and no units.
When CCA is applied to the 1991–2007 S(t) and D(t)
data sets (see Table 1), the first canonical pair of time-dependent
variables is
1aS1=0.0260log101+|AL|*+0.1151log101+|AU|*+0.2160|PCI|*+0.1451Kp*+0.2881log101+am*+0.0201d|Dst|/dt*+0.0492log100.01+mPe*+0.2531log100.01+mPi*+0.0854log100.01+Pips*1bD1=0.8378log10vsw*+0.6876log10nsw*+0.1018log10F10.7*-0.1676-Bz*+0.3547fM*+0.3844sin2θclock/23*+0.0960θBn3*+0.0943log100.1+|ΔB|*.S(1) and D(1) have mean values of zero and standard deviations of
unity. The derived composite variables given by expressions (1) are robust
and reproducible: applying the CCA process to various subsets of the full
1991–2007 data set, the CCA process repeatedly yields essentially the same
coefficients that are in Eqs. (1a) and (1b) (cf. Borovsky and Denton,
2018).
CCA applied to the time-dependent state vectors S(t) and
D(t) for the 1991–2007 data set yields the second canonical
pair of time-dependent scalar variables as
2aS2=-0.2628log101+|AL|*-0.0874log101+|AU|*-0.1302|PCI|*-0.0556Kp*+0.1928log101+am*+0.0028d|Dst|/dt*-0.8506log100.01+mPe*+0.9218log100.01+mPi*+0.3493log100.01+Pips*2bD2=0.1195log10vsw*+0.8874log10nsw*+0.1202log10F10.7*-0.1138-Bz*+0.2669fM*-0.5079sin2θclock/23*-0.0186θBn3*+0.0260log100.1+|ΔB|*.
For the 1991–2007 data set CCA yields the third canonical pair of
time-dependent scalar variables as
3aS3=-0.1796log101+|AL|*-0.2220log101+|AU|*-1.0351|PCI|*+0.8265Kp*+0.5809log101+am*-0.2169d|Dst|/dt*+0.3856log100.01+mPe*-0.6100log100.01+mPi*+0.1064log100.01+Pips*3bD3=0.4241log10vsw*-0.1985log10nsw*-0.1404log10F10.7*-0.6704-Bz*-0.1008fM*+0.0572sin2θclock/23*-0.3134θBn3*+0.3055log100.1+|ΔB|*.
The properties of S(1)(t) and D(1)(t), S(2)(t) and
D(2)(t), and S(3)(t) and D(3)(t) as given by Eqs. (1)–(3) are explored in Sect. 3.
Properties of the scalar reduced picture for the magnetospheric
system
The three sets of composite variables S(1) and D(1), S(2) and
D(2), and S(3) and D(3) for the magnetospheric system are
explored and the advantages of the reduced D(i)→S(i) scalar
description are investigated.
The primary mode of system response as represented by
D(1)→S(1)
In Fig. 1 the composite system variable S(1) (as given by Eq. 1a) is plotted for the years 1991–2007 as a function of the composite
driver variable D(1) (as given by Eq. 1b). Each black point in
Fig. 1 represents 1 h of data. The Pearson linear correlation
coefficient between S(1) and D(1) for the 1991–2007 data set is
rcorr=0.921. Accordingly, rcorr2=84.8 % of the
variance of the system function S(1)(t) is described by the driver
function D(1)(t), and so 15.2 % of the variance of S(1) is
unaccounted for by D(1). The blue line in Fig. 1 is a
linear-regression fit to S(1), and the red curve is a 50-point vertical
running average of the black points. Note the approximate linearity of
system variable S(1) as a function of driver variable D(1),
indicated by the manner in which the running average tracks the
linear-regression line.
The aggregate system scalar S(1) is plotted as a function of
the driver scalar D(1) for the 1 h resolution 1991–2007 data set. Each
black point is 1 h of data.
Note that whereas the correlation coefficient between S(1)(t) and
D(1)(t) is rcorr=0.921, the maximum correlation coefficient
between any single variable in the system state vector S(t) and
any single variable in the driver state vector D(t) is only
rcorr=0.586 (between sin2(θclock/2)3 and log10(1+|AL|)).
As a further note, the Pearson linear correlation coefficients between
S(1) and various “physics-based” solar-wind driver functions from the
literature are the following: +0.378 for -vswBz, +0.557 for
vswBsouth (Eq. 2 of Holzer and Slavin, 1979), +0.679 for the
Newell function dΦ/dt (Eq. 1 of Newell et al., 2007), +0.723 for
the quick reconnection function Rq (Eq. 8 of Borovsky and Birn,
2014), and +0.761 for the nonlinear reconnection-coupled MHD generator
with Bohm viscosity (Eq. 65 of Borovsky, 2013). All of these driver
functions have poor correlations with S(1) in comparison with the
+0.921 correlation of D(1) with S(1).
Plots of the nine components of the coefficient vectors used to
project the system state vector S into the aggregate variables
S(1)(a), S(2)(c), and S(3)(e) and plots
of the eight components of the coefficient vectors used to project the driver
state vector D into the driver scalar variables D(1)(b), D(2)(d), and D(3)(f).
In the six panels of Fig. 2 the coefficients of the six vectors CS1,
CD1, CS2, CD2, CS3, and CD3 are plotted. (These are
the coefficients in Eqs. 1–3.) Examining these six panels
enables the reaction modes represented by S(1), S(2), and
S(3) to be interpreted as well as their drivers D(1), D(2),
and D(3). Figure 2a indicates that all coefficients of S(1) are
positive: this indicates a mode of the magnetospheric system in which all
measures of activity in the system vector S increase or
decrease in unison, with S(1) representing a “global activity index”.
Figure 2b indicates that all of the coefficients of D(1) are positive.
The variables in the driver state vector D (Table 1) and their
signs were all chosen so that a positive increase in each variable would
result in a generally accepted increase in magnetospheric activity. The
individual variables on the right-hand side of Eq. (1b) have all been
correlatively associated with the driving of magnetospheric activity
(Berthelier, 1976; Borovsky and Funsten, 2003; Newell et al., 2007; Borovsky
and Denton, 2014; Borovsky and Birn, 2014; Osmane et al., 2015). S(1)
is selected by the CCA process to have highest correlation with solar-wind
variability: S(1) is focused on activity that reacts to the solar-wind
driver.
Using the linear-regression curve in Fig. 1 as a “prediction” of the
value of S(1) from knowledge of the value of D(1) yields
S(1)pred=0.9209D(1)-4.4×10-5.
In Fig. 3 the autocorrelation functions of S(1)(t) (red curve),
D(1)(t) (blue curve), and S(1)(t)-S(1)pred(t) (green curve)
are plotted. In Fig. 3a it is seen that the autocorrelation functions of
S(1) and D(1) are very similar, with 1/e autocorrelation times of
23.3 h for S(1) and 22.7 h for D(1). In Fig. 3b the three
autocorrelation functions are plotted for time shifts up to 40 d. Note
the 27 d peak in the autocorrelation functions of D(1)(t) and
S(1)(t): this is associated with the 27 d rotation period of the Sun
as viewed from the Earth and the persistence of features on the solar
surface that give rise to solar wind with characteristic properties. This
causes the driver D(t) properties to have a 27 d periodicity,
which drives the system S(t) with a 27 d periodicity.
The autocorrelation functions for the system scalar S(1)
(red), the driver scalar D(1) (blue), and the unaccounted-for variance
E(1)E(1)pred (green) are plotted. In panel (a) the plot extends
to 50 h and in panel (b) the plot extends to 40 d.
The quantity S(1)-S(1)pred is the portion of S(1)(t) that is
not accounted for by D(1)(t), i.e., the unaccounted-for variance of
S(1)(t). S(1)(t)-S(1)pred(t) is completely uncorrelated with
D(1)(t). Further, S(1)(t)-S(1)pred(t) is completely
uncorrelated with each of the eight individual solar-wind variables on the
right-hand side of Eq. (1b). Since S(1) is so similar to
D(1), the standard analyses of the S(1)(t) time series (e.g.,
determining the correlation dimension, examining the state space, or Fourier
analyzing; Sharma et al., 2005a; Vassiliadis, 2006) would largely be an
analysis of the properties of the solar-wind time series D(1)(t) – not
so for S(1)(t)-S(1)pred(t), which is uncorrelated with D(1).
The autocorrelation function of S(1)(t)-S(1)pred(t) in Fig. 3a
is very different from the autocorrelation function of D(1): the 1/e
autocorrelation time of S(1)(t)-S(1)pred(t) is 2.4 h. Determining
what the unaccounted-for variance S(1)-S(1)pred originates from is
of great interest. Four suggestions of what contributes to
S(1)-S(1)pred are made here. First, some fraction of
S(1)-S(1)pred may be associated with noise in the various
measurements of the magnetospheric system and of the solar wind. Shot noise
(random noise in the values of the variables) would have an autocorrelation
time of less than 1 h, the autocorrelation function of the shot-noise going
from 1 to 0 in one data-resolution time shift (cf. Sect. 2.4 of Borovsky et
al., 1997). Second, some fraction of S(1)(t)-S(1)pred(t) may be
owed to errors in the measurement values in the state vectors S(t) and D(t). Errors in the values of the variables of
D could be caused by the spatial structure of the solar wind
and the measuring spacecraft upstream of the Earth not intercepting the
exact solar-wind structures that hit and drive the Earth (cf. Weimer et al.,
2003; Borovsky, 2018a): this could affect all of the variables of D. Extrapolating local measures to estimate global properties can also
lead to errors: this might affect the hemispheric particle-precipitation
variables mPe and mPi (Emery et al., 2008) in S and
also the magnetospheric pressure values Pips (Borovsky, 2017) in
S. Variables reacting to more than one physical process (such
as d|Dst|/dt and Pips) could also appear to have error
in the values when relating the values to D(1). Third, unaccounted-for
time lags between solar-wind variables and magnetospheric variables may be
resulting in weakened correlations: most time lags are 1 h or less, but
measurements of magnetospheric particle populations can have lags of several
hours (e.g., Denton and Borovsky, 2009; Borovsky, 2017). The fourth
suggestion is that some fraction of S(1)(t)-S(1)pred(t) might be
associated with system variations that are not directly associated with the
solar-wind driver as measured by D. The autocorrelation time of
S(1)(t)-S(1)pred(t) is approximately the 2–3 h time duration of a
magnetospheric substorm (Borovsky et al., 1993; Weimer, 1994; Chu et al.,
2015). Substorms are large transients in the reaction of the magnetospheric
system to solar-wind driving. (Substorms have been described as
self-organized criticality events in the driven magnetospheric system;
Klimas et al., 2000.) The occurrence of a substorm is notoriously
difficult to predict from solar-wind data (Freeman and Morley, 2004; Hsu and
McPherron, 2009; Newell and Liou, 2011). The timing of substorm occurrence
would be particularly difficult to infer from the 1 h resolution variables
going into D because of the 3 h smoothing used on the
clock-angle term sin2(θclock/2)3 in Eq. (1b) for D(1), with the clock angle being critical
for substorm occurrence (Newell and Liou, 2011). The occurrence of a
substorm would produce signatures in many of the variables used in
S(1), typically an enhancement in the variable's amplitude lasting 2–3 h (Weimer, 1994).
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. 4 is the onset time of each of the 2155 substorms. Substorms are
associated with intervals of driving of the magnetosphere (e.g., Caan et al.,
1977; Morley and Freeman, 2007); this is indicated by the increase in the
superposed average of D(1) beginning prior to the onset time in Fig. 4. However, substorms also represent a transient release of stored energy in
the magnetosphere (Birn et al., 2006); this is indicated in Fig. 4 by the
superposed average of S(1) exceeding the superposed average of
D(1) after the substorm onset and by the positive perturbation of the
S(1)-S(1)pred curve after onset. The S(1)-S(1)pred curve
indicates a transient in S(1) that is unaccounted for by D(1)
associated with the occurrence of substorms. The autocorrelation time of the
green superposed-average S(1)-S(1)pred time series plotted in
Fig. 4 is 2.6 h, similar to the Fig. 1 autocorrelation time of the full
1991–2007 time series of S(1)-S(1)pred.
Superposed epoch averages of S(1) (red), D(1) (blue),
and S(1)-S(1)pred (green) for 2155 substorms. The epoch time
(t=0) is the time of onset of each substorm.
Compacting the description of the system from a multidimensional state
vector to a few variables S(1), S(2), S(3), … is
a form of dimensional reduction to a small set of fundamental latent
variables: in that dimensional reduction a potential question is whether the
reduced (more-fundamental) variables themselves exhibit a reduction of their
embedding dimension from the embedding dimensions of the manifest variables
in the state vector. Additionally, it would be valuable to differentiate
S(1) from other indices commonly used to characterize magnetospheric
activity. In order to achieve this task we use the methodology of Rosso et
al. (2007) based on the combined use of permutation entropy (Bandt and
Pompe, 2002) and Jensen–Shannon complexity mapping. This mapping developed
by Rosso et al. (2007) is particularly useful to disentangle deterministic
and stochastic time series. Additionally, one can use the Jensen–Shannon
complexity alone to extract correlational structures in time series. In
other words, one can seek timescales upon which coherent
structures/fluctuations/modes arise. In some instances, coherent modes are
signatures of deterministic chaos (Maggs and Morales, 2013) or a reduction
to the number of degrees of freedom (Osmane et al., 2019). Readers with
little familiarity with these two information theoretic measures can consult
the reviews of Riedl et al. (2013) and Zanin et al. (2012) or the pedestrian
methodology section found in Osmane et al. (2019). In Figs. 5 and 6, we
map the values of AL (red), am (blue), S(1) (black) and D(1)
(pink) on the complexity–entropy plane for an interval of 500 h duration
with no data gaps, embedding dimensions of d=4, and embedding delay T
ranging between 2 and 40 h. Because there are a total of d!=24
ordinal patterns we are limited to embedding delays of the order of 2 d. For embedding delays greater than T=48 h the number of segments
N-(d-1)*T becomes too small to ascertain the likelihood of forbidden ordinal
patterns. Error bars for the Jensen–Shannon complexity, shown for the zoomed
panels of the complexity–entropy planes, are computed as the square root of
the number of ordinal patterns divided by the number of segments available.
Hence, larger embedded dimensions d, require a larger number of segments
N-(d-1)*T to determine whether the Jensen–Shannon complexity lies
significantly above the stochastic boundary (see below).
Jensen–Shannon complexity and permutation entropy, AL (red) and am
(blue) for 500 points with no gaps sampled at 1 h interval, embedding
dimension d=4, and embedding delays ranging between 2 and 40 h.
Jensen–Shannon complexity and permutation entropy for S(1)
(black), D(1) (pink) for 500 points with no gaps sampled at 1 h
interval, embedding dimension d=4, and embedding delays ranging between 2
and 40 h.
The bottom left panels in Figs. 5 and 6 show the value of the permutation
entropy for AL, am, D(1) and S(1) as a function of embedding
delay. Similarly, the bottom right panels show the value of the
Jensen–Shannon complexity for AL, am, D(1) and S(1) as a function
of embedding delay. What we notice is that all four signals are highly
stochastic since the normalized permutation entropy is very close to 1.
However, we see that the Jensen–Shannon complexity for S(1) is of
comparable magnitude as for am, and it is significantly larger than for
AL. This is not a surprise because the construction of S(1) was based
on am, and the Jensen–Shannon complexity indicates that the former
preserved the correlated structures of the latter on timescales ranging
between a few hours to a few days. The top left panel of Figs. 5 and 6
shows the complexity–entropy plane, and the top right panel is a zoom of the
right corner where most of the data for AL, am, and S(1) lies. In both figures the blue line curves represent the maximum and
minimum value of complexity for a fixed entropy value, and the dashed curve
represents the complexity–entropy mapping of fractional Brownian motion
(fBm) with Hurst exponent ranging between 0 and 1, which is a stochastic
process that also contains correlated structures. The fBm curve is a
boundary between deterministic (above) and stochastic (below) fluctuations.
We note that AL is effectively stochastic, whereas am and S(1) lie
above the fBm boundary for a few tens of hours. The explanation for this
behavior from am lies in its construction: it is repeated for 3 h
at a time. Hence, ordinal patterns of size d=4 and embedding delays of a
few hours will register the repetition as correlated structures. Since
S(1) is constructed in part with am, it also contains part of its
correlated structure.
For longer embedding delays of the order of seasonal variations ranging from
27 to 45 d (not shown), all four time series overlap and are
indistinguishable from stochastic fluctuations. In terms of
complexity–entropy plane it translates into a permutation entropy of
approximately 1 and a Jensen–Shannon complexity of almost 0. Hence, the
system variable S(1), based on various magnetospheric indices,
preserves the stochastic and correlational structures of its individual
components. The comparable values of the permutation entropy (and therefore
Jensen–Shannon complexity) with the system variable with the indices for
long times are not fortuitous. The permutation entropy is invariant under any
monotonic transformations (for instance, if one scales the time series by a
positive real number, or if one takes the logarithm). However, if one used a
linear combination of non-monotonic functions, for instance trigonometric
functions, then the permutation entropy would not be invariant. Since the
Jensen–Shannon complexity is a function of the permutation entropy, it is
also invariant under monotonic transformations. Additionally, if one takes
an average around the mean of some time series over a time TAU, one will
reduce the noise level for fluctuations with timescales less than TAU. Thus,
the stochastic nature of the signal will be reduced, and the permutation
entropy and Jensen–Shannon complexity would move up in the plane towards the
chaotic and/or periodic regions. The equivalence mapping of the information
theoretic measures for the system variables and geomagnetic indices is a
consequence of the monotonic transformation linking the former to the latter
and the absence of coarse-graining of the indices.
The secondary modes of reaction represented by
S(2) and S(3)
In Fig. 7a and b the second and third scalar pairs are plotted,
S(2) as a function of D(2) and S(3) as a function of
D(3), respectively (Eqs. 2 and 3). The correlation
coefficient for the second pair is still quite high (rcorr=0.775)
but lower than that of the first pair (Fig. 1). This correlation
coefficient rcorr=0.775 for the secondary mode is better than
correlations obtained in most studies of solar-wind–magnetosphere coupling
using single measures of the magnetospheric system (e.g., Table 3 of Newell
et al., 2007; Table 1 of Borovsky, 2013). D(2) describes
rcorr2=60.0 % of the variance of S(2). In Fig. 7b the
correlation coefficient for the third pair is low (rcorr=0.456);
D(3) only describes rcorr2=20.8 % of the variance of
S(3). Canonical pairs beyond the third pair have even weaker
correlations.
For the 1 h resolution 1991–2007 data set, the aggregate system
scalar S(2) is plotted as a function of the driver scalar D(2) in
panel (a) and the aggregate system scalar S(3) is plotted as a function
of the driver scalar D(3) in panel (b). Each black point is 1 h of
data.
Figure 2c shows that mode S(2) (Fig. 7a) is dominated by
opposite-signed coefficients for mPi and mPe, which respectively
are measures of the global ion precipitation into the atmosphere versus the
global electron precipitation into the atmosphere. In this S(2) mode
the intensity of ion and electron precipitation reacts oppositely. Figure 2d
shows that D(2) (the driver of S(2)) is dominated by the solar
wind number density nsw opposite to the clock angle sin2(θclock/2) of the solar-wind magnetic field, with the solar wind density
increasing while the clock angle decreases resulting in more ion
precipitation and less electron precipitation. This ion-versus-electron
precipitation mode is a newly uncovered mode of reaction of the magnetosphere–ionosphere–thermosphere system
to the solar wind.
Figure 2e shows that S(3) (Fig. 7b) is characterized by the polar cap index (PCI) and
mPi acting oppositely to Kp and am. PCI is a measure of high-latitude
electrical currents in the magnetosphere, and mPi is a measure of
high-latitude ion precipitation; Kp and am are measures of global
magnetospheric convection. This S(3) mode is very similar to a
high-latitude versus convection mode uncovered by Borovsky (2014) and by
Holappa et al. (2014). Figure 2f indicates that the driver D(3) for
this mode is the solar wind velocity acting oppositely to the magnetic field
clock angle: the wind velocity increasing while the clock angle is reduced
producing more convection and less high-latitude activity, or the wind
slowing down while the clock angle increases producing less convection and
more high-latitude activity.
Advantages of the reduced (aggregate-variable) representation of
the system
The aggregate variable S(1) acts as a global activity index for the
magnetospheric system: S(1) is new and unfamiliar, and experience using
S(1) is needed to gain an understanding of the full usefulness of this
measure. S(1) could be thought of as a next-generation magnetospheric
index. In Earth systems science global aggregate variables are familiar: the
global warming index (Hasselman, 1997; Haustein et al., 2016), the global
mean sea level (Vermeer and Rahmstorf, 2009), the mean global temperature
(Hansen et al., 2006), the Palmer Drought Severity Index (Wells et al.,
2004), and Sea Surface Temperature indices (Kaplan et al., 1998). And
stock-market aggregate indices are well known and are more-meaningful gauges
of an economy than the price of a single stock (e.g., Pan and Mishra,
2018). Here the aggregate variable S(1) is mathematically derived. The
individual variables of S that go into the definition of
S(1) represent familiar and identifiable aspects of activity in the
magnetospheric system. The composite variable S(1) is a mix of these
understood measurements, the mix reflecting some global properties of the
system's reaction to the solar wind. Unfamiliar as it is, the
composite-scalar D(1)→S(1) reduction of the state-vector
D→S picture exhibits some outright advantages
for the magnetospheric system. This is particularly true in comparison with
the standard method of analysis of magnetospheric driving by the solar wind
that uses only a single measurement of magnetospheric activity and a single
function of solar-wind variables. Four advantages are discussed in the
following four paragraphs.
Linearity. The plotted points in Fig. 1 indicate that there is a linear response of
the composite system variable S(1)(t) to the composite driver
D(1)(t). Usually, single measures of the magnetosphere tend to have a
nonlinear response to the solar wind (e.g., Voros, 1994; Valdivia et al.,
1996; Sharma et al., 2005b; Borovsky, 2013; Stepanova and Valdivia, 2016),
with the individual activity variables saturating (becoming anomalously
weak) when solar-wind driving becomes strong (e.g., Fig. 3 of Reiff and
Luhmann, 1986; Fig. 17 of Lavraud and Borovsky, 2008; Fig. 6 of Borovsky,
2013). Such a saturation is not seen for S(1) driven by D(1).
Undoubtedly, the linearity of the result is in part owed to the maximizing
of the “linear” correlation coefficient in the CCA process. The linearity
of the S(1)-vs.-D(1) relation has a great advantage: the same
mathematical relationship between S(1) and D(1) (i.e., Eq. 4) holds for weak driving of the system (small D(1)) (e.g., Kerns and
Gussenhoven, 1990) and for strong driving of the system (large D(1))
(e.g., Sharma and Veeramani, 2011).
Low noise. The high correlation between S(1) and D(1) (cf. Fig. 1)
indicates that there is a relatively low level of noise in the
linear-regression fit to S(1): the activity in the system as described
by S(1) responds directly to the solar-wind driving as described by
D(1). For example, the unaccounted-for variance of S(1) is only
15.2 %. Single measures of the magnetospheric system have much weaker
Pearson linear correlation coefficients with solar-wind variables than
S(1) and D(1) do. Examples can be found in Table 3 of Newell et al. (2007) and Table 1 of Borovsky (2013): the maximum correlation coefficient
in those tables is 0.860 (for the Dst index), but usually it is much lower.
The lower noise is also confirmed by the Jensen–Shannon complexity analysis
of S(1): the points for S(1) and D(1) sit closer to the
maximum complexity curve than AL and other indices. The lower noise (and
higher rcorr) reduces “regression dilution bias” (Bock and Petersen,
1975; Hutcheon et al., 2010) when the system activity is fit by the driver
strength. Regression dilution bias can lead to spurious interpretation of
trends in the data when subsets of the data are compared, particularly when
a subset with systematically weaker driving is compared with a subset with
systematically stronger driving.
High prediction efficiency. In magnetospheric physics, predicting what the reaction of the
magnetospheric system will be to measured upstream solar-wind conditions is
very important: i.e., the prediction of “space weather” (Singer et al.,
2001). The high correlation between S(1) and D(1) means that there
will be a high prediction efficiency when the value of S(1) is
predicted from knowledge of the value of D(1). Note that this is a high
prediction of S(1)(t) without using past values of S(1), just
using the present value of D(1)(t). By optimizing the Pearson linear
correlation coefficient between S and D, S(1)
was designed to focus on aspects of the magnetospheric system that are
responsive to the conditions of the solar wind. Internal dynamics of the
system that are not dependent on the time-varying state of the driver are
de-emphasized in S(1).
Compactness of the description. Reductionist analysis has concluded that the
magnetosphere–ionosphere–thermosphere system is extremely complex (e.g.,
Siscoe, 2011; Eastwood et al., 2015; Borovsky and Valdivia, 2018). As
it is driven by the solar wind, there are major outstanding issues as to how the
system functions (e.g., Denton et al., 2016). Having a single scalar variable
S(1)(t) that describes a universal global reaction of the system to
its driver promises to yield insight as to how the combined system operates.
Uncovering new modes of reaction. In the CCA analysis of the system and driver state vectors, two additional
aggregate variables S(2)(t) and S(3)(t) were generated
(Eqs. 2a and 3a). Analysis in Sect. 3.2 showed these two
variables to represent two modes of reaction of the system to the driver
that are independent of (uncorrelated with) the global-activity mode
represented by S(1)(t). The mode represented by S(3) is known
(having been independently discovered by this CCA methodology in Borovsky,
2014, and by a principle-components methodology in Holappa et al., 2014),
but the mode represented by S(2) has until now been unknown. The CCA
methodology used here also identifies the aggregate driver variable that
drives each of the independent modes. In the future, expanding the system state
vector to include a larger number of measurements in the diverse
magnetospheric system should enable this state-vector-reduction methodology
to uncover more unknown modes of reaction of the system to the driver. Once
a reaction and its driver are uncovered, reductionist analysis can be
applied to determine the physical reasons why the mode arises.
For a system measured by multiple time-dependent variables (which are
collected into a time-dependent system state vector S(t)), with
that system driven by multiple time-dependent factors (inputs) (which are
collected into a time-dependent driver state vector D(t)),
canonical correlation analysis (CCA) can be used to reduce the D(t)→S(t) state-vector picture to a D(i)(t)→S(i)(t) composite-scalar picture. The reduction will work, even if
there is influence on the driver by the system (i.e., D(t)↔S(t)). The advantageous properties of
this reduction that were examined for the magnetospheric system should apply
to systems in general.
Future developments of this methodology will focus on the introduction of
time lags between the driver and the system, on the introduction of
integro-differential correlations rather than algebraic correlations (e.g.,
Borovsky, 2017), and on the use of dynamic canonical correlation analysis
(e.g., Dong and Qin, 2018a, b).
Data availability
The 1991–2007 data set of hourly values of S(1), S(2), S(3),
D(1), D(2), and D(3) has been made available at
10.5281/zenodo.1560686 (Borovsky, 2018b) and at 10.17605/OSF.IO/QYTNJ (Borovsky, 2018c) as a tab-delimited
text file.
The variables comprising the magnetospheric system state
vector and the solar-wind driver state vector
The time-dependent variables of the magnetospheric system state vector and
the solar-wind driver state vector are listed in Table 1.
The magnetospheric variables measure various aspects of activity in the
magnetosphere. The auroral upper index AU (Davis and Sugiura, 1966) measures
the electrical current in the high-latitude ionosphere: this variable is taken
to be a measure of electrical currents in the dayside magnetosphere (Goertz
et al., 1993). The auroral lower index AL (Davis and Sugiura, 1966) measures
the electrical current in the high-latitude nightside ionosphere: this variable
is taken to be a measure of auroral activity in the nightside magnetosphere
(Goertz et al., 1993). The polar cap index PCI is a measure of the strength
of cross-polar-cap electrical current in the ionosphere (Troshichev et al.,
1988). The planetary K index Kp is a measure of the strength of global
convection in the magnetosphere (Thomsen, 2004). The range index am (Mayaud,
1980) is another measure of the strength of global magnetospheric
convection. The disturbance storm-time index Dst measures plasma pressure in
the inner magnetosphere (Dessler and Parker, 1959); Dst also reacts to the
currents on the dayside boundary of the magnetosphere and to the
cross-magnetotail currents in the nightside magnetosphere. The time
derivative of the magnitude of the Dst index d|Dst|/dt is a
compound measure of magnetospheric activity: when d|Dst|/dt
is positive, hot plasma is being convected from the magnetotail into the
dipolar portion of the magnetosphere, and when d|Dst|/dt is
negative, convection has recently subsided. The variables mPe and
mPi are estimates of the full-Earth power in magnetospheric electron
precipitation into the atmosphere and magnetospheric ion precipitation into
the atmosphere (Emery et al., 2008, 2009), with these estimates coming from
observations on only a few spacecraft in orbit around the Earth. The average
of the ion-plasma-sheet particle pressure Pips around the Earth
(Borovsky, 2017) is obtained from three to five spacecraft.
The variables going into the solar-wind driver state vector are various
measures of the time-dependent solar wind at Earth. The solar wind speed
vsw ranges from 244 to 1045 km s-1 in the 1991–2007 data set. The
solar wind number density ranges from 0.3 to 98.2 particles cm-3 in the data set. Bz is the magnetic-field component
in the solar-wind plasma that is approximately aligned with the Earth's
magnetic-dipole orientation. The function f(M) (Borovsky and Birn, 2014) is
a function of the solar-wind Mach number M that accounts for the properties
of the bow shock that forms upstream of the Earth in the supersonic
solar-wind flow. The clock angle θclock measures the angular
alignment of the solar-wind magnetic-field vector with the Earth's
magnetic-dipole orientation. The angle θBn measures the
orientation of the solar-wind magnetic-field vector with respect to the
Sun–Earth line. F10.7 is the 10.7 cm radio flux from the Sun, a proxy
for the ionization of the upper atmosphere of the Earth by solar photons.
Author contributions
JEB devised this study and performed the CCA analysis. AO performed the
complexity and entropy analysis. Both authors are responsible for the
interpretation of the results and for the writing of the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Mick Denton and Juan Alejandro
Valdivia for helpful discussions.
Financial support
This work was supported by the NSF GEM
Program via award AGS-1502947, by the NASA Heliophysics LWS TRT program via
grants NNX16AB75G and NNX14AN90G, by the NSF Solar-Terrestrial Program via
grant AGS-12GG13659, by the NASA Heliophysics Guest Investigator Program via
grant NNX17AB71G, by the NSF SHINE program via award AGS-1723416, and by the
Academy of Finland via grant no. 297688/2015.
Review statement
This paper was edited by A. Surjalal Sharma and reviewed by Marina Stepanova and one anonymous referee.
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