Electric Field Variability and Impact on the Thermosphere

1
Electric Field Variability and Impact on the
Thermosphere

• Yue Deng1,2, Astrid Maute1,
• Arthur D. Richmond1 and Ray G. Roble1
• HAO National Center for Atmospheric Research
• CIRES University of Colorado and SWPC NOAA

2
Joule heating calculation
Codrescu et al., 1995

• The quantitative application of GCMs for
  predictive purposes is limited by uncertainties
  in the energy inputs
• How big is the E-field variability and whats the
  effect to the energy input? (Codrescu et al.,
  1995, 2000, Crowley Hackert, 2001, Matsuo
  et al., 2003, Matsuo Richmond 2008 and so
  on.)

3
Dynamic Explorer 2 Data Set

• Time period August 1981-March 1983
• Ion Drift Meter (IDM) g cross-track ion drift
• Retarding Potential Analyzer (RPA) g along-track
  ion drift
• Fluxgate Magnetometer (MAGB) g magnetic field
• Low Altitude Plasma Instrument (LAPI) g ion /
  electron energy flux
• IGRF for geomagnetic main field
• IMF conditions hourly averaged
• Number of passes 2895

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Empirical Model

• Empirical model of the high latitude forcing
• Electric potentialb
• Magnetic Potentialb
• Poynting fluxb
• Small scale electric field variabilityb
• Auroral particle precipitation
• a Input to general circulation models

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Poynting flux empirical Model
Diff
Bt 5 nT, Equinox, IMF_angle 1800

• Point measurements of E-field and B-field data
  from the DE-2 satellite.
• Poynting gt ExB Weimer05

6
Standard deviation s of E-Field
where E electric field (here Ed1 and Ed2
components) N number of trips EDE2 electric field
from DE2 data set Emodel electric field from
empirical model
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Energy distribution (Equinox)
E
EvarE
Poynting

• Altitude integrated Joule heating and Poynting
  flux from the topside.
• E-field variability increases JH significantly.
• Total Joule heating has a similar distribution
  as Poynting flux, with some detailed difference
  at the polar cap, cusp and nightside.

8
Comparison of energy input into GCM
Total energy input GW
By 0 Bz -5nT SW400km/s HP30GW

• The E-field variability increases the energy
  input by gt 100.
• The total Joule heating has a good agreement
  with Poynting flux.
• The inconsistent particle precipitation makes
  the JH higher than Poynting flux in the solstice.

9
Temperature response

• Polar average (Lat gt 47.50) at equinox.
• E-field variation causes gt100 K temperature
  increase above 300 km.
• Temperature difference 62 K, 250 K.

10
Density response

• Percentage difference compared with the average
  E-field case.
• The difference is close to 30 at 400 km
  altitude.

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Conclusion

• The electric field variability increases the
  Joule heating by more than 100, and
  significantly improves the agreement between the
  Joule heating and Poynting flux.
• E-field variation causes gt100 K temperature
  increase at 400 km, and the corresponding
  percentage difference of density is close to 30.

12
Future Work

• Develop a consistent particle precipitation
  model.
• Improve the similarity of the total Joule heating
  and the Poynting flux distributions.
• Comparison with observations to evaluate the
  Poynting flux and E-field variability in the
  model.

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Thanks!
14
Questions?

• Q1 Why there was no E-var empirical model before
  when the idea has been proposed since 1995 and
  the DE-2 data are there?
• A. Just a matter of time, funding.
• Q2 Why there are no dependence on solar wind
  velocity and density?
• A. Maybe in the future, it will be parameterized
  to IEF instead of IMF. IEF is close to VxB and
  the effect of solar wind will be taken into
  account indirectly.
• Q3 Why 50 lat resolution for Poynting model and
  20 for others? How about horizontal resolution?
• A. Possibly Poynting flux needs both E and B.
  The available data are less. Check with Astrid.
• Horizontally, the Fourier function has been used
  for the MLT fit. The latitudinal dependence is
  presented by the Spherical Cap Function.
• Q4 Is the E-var from the empirical model
  sub-grid? Is it temporally and spatially
  correlated?
• A. E-var just shows the difference between the
  DE-2 observation and empirical average model, and
  can include both sub-grid and large scale
  variation.
• When I implement the E-field variability by
  switching the sign of the sigma-E every time
  step, this means it is not temporally correlated.
  When we the sign in the whole polar region
  simultaneously, it means it is spatially
  full-correlated. When I set some phase difference
  between different latitude and longitude, in some
  way it is spatially uncorrelated.

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Questions? (Cond.)

• Q5 Does the E-var from the empirical model
  represent more like spatial variability or
  temporal variability?
• A. Technically, it should be both. From the
  methodology of the processing the data, it
  represents more about the temporal variability
  between different satellite orbits. When run this
  model for a real case, hourly IMF condition will
  be recommended to use to drive the model, since
  the average model is binned based on the hourly
  IMF conditions and the E-field variability model
  is referred to that average model. If higher
  frequent IMF data (10 min average) have been used
  to drive the model, the E-var model should
  subtract the temporal component between 10min and
  1 hour, which has been shown in the average
  model.
• Q6 Why the E-var is maximum in the winter
  season?
• A. Usually, the E-var is largest when the
  conductance is small from the observation.
  EJ/sigma. When sigma is small, sigma and J are
  variable, the E can be very variable.