1. Mervyn Freeman British Antarctic Survey
Response of the magnetosphere and ionosphere to solar wind drivers (including complexity)
Mervyn Freeman
British Antarctic Survey

2. The importance of Bz
IMF Bz is a strong influence on many properties of the magnetosphere and ionosphere
(and their space weather impacts)
electrical currents (GIC)
and electric field -> Joule heating (satellite drag)
particle precipitation (GNSS)
auroral oval location
geosynchronous magnetic field and energetic particles (satellite anomalies)
etc

3. The importance of Bz - currents
IMF Bz is a strong influence on auroral electrojet index, AE
peak magnitude of large-scale currents
hourly averages
[Newell et al., J. Geophys. Res., 2007]

4. The importance of Bz - location
IMF Bz is a strong influence on the latitude of auroral currents
cusp = poleward edge of auroral oval at noon (instantaneous)
Bs = Bz when Bz < 0, Bs = 0 otherwise (hourly average)
[Newell et al., J. Geophys. Res., 2007]

5. Bz – not even half the answer
IMF Bz explains only 37% of variance of the auroral electrojet index, AE
hourly averages
similarly for other quantities
[Newell et al., J. Geophys. Res., 2007]

6. Not only Bz IMF By, B, and solar wind v (and n) also important
still explains only 69% of variance of the auroral electrojet index, AE
hourly averages
[Newell et al., J. Geophys. Res., 2007]

7. Not just about the solar wind
magnetosphere and ionosphere produce the impact on satellites, power grids, etc, from the solar wind input

8. Add some physics – models
PE = 1 is perfect prediction
Prediction is no better than assuming the average value of the observations over the event, PE = 0 (- - -)
[Pulkkinen et al., J. Geophys. Res., 2010]

9. [Pulkkinen et al., J. Geophys. Res., 2010]

10. 3 challenges Non-linearity – chaos Memory – substorms
Turbulence – intermittency

11. Non-linearity – chaos
What is the sensitivity of the M-I response to uncertainties in the solar wind driver?
How big and how quickly do errors grow?

12. [Merkin et al., J. Geophys. Res., 2013]

13. [Merkin et al., J. Geophys. Res., 2013]

14. LFM/Wind
[Merkin et al., J. Geophys. Res., 2013]

15. LFM/THC
[Merkin et al., J. Geophys. Res., 2013]

16. Ampere
[Merkin et al., J. Geophys. Res., 2013]

17. [Merkin et al., J. Geophys. Res., 2013]

18. Memory – substorms
Auroral electrojet index, AE, is influenced by past history of the IMF
3-hour timescale, comparable to that of the substorm cycle
[Newell et al., J. Geophys. Res., 2007]

19. Memory – substorms
fff E
Time
Onsets
Simple integrate-and-fire model explains substorm timing statistically
But not so well individually due to non-linearity
[Freeman and Morley, Geophys. Res. Lett., 2004]

20. Minimal substorm modelP
Solar wind power input at magnetopause P accumulates energy in magnetotail E.
Unique minimum energy state for magnetosphere F exists for given solar wind state P.
Magnetotail can only move to lower energy state F when energy threshold C is exceeded. E F
Time
[Freeman and Morley, Geophys. Res. Lett., 2004]

21. Turbulence – intermittency
Wilder fluctuations on short timescales
Dependent on large-scale state
[Consolini and de Michelis, Geophys. Res. Lett., 1998]

22. Turbulence – intermittency
Similar properties in space as well as time
Wild fluctuations vary with spatial scale (and time scale)
[Consolini and de Michelis, Geophys. Res. Lett., 1998]

23. 3 solutions? Non-linearity, Memory, Turbulence challenges need:
Models and Ensemble forecasting
represent evolving uncertainties from Sun to Earth
Observations and Data assimilation
update prediction with latest information
Scaling schemes
to handle unresolved scales and extremes

24. Summary Bz is important but ... it’s not even half the answer
magnetosphere and ionosphere produce the impact from the solar wind input
M-I observations, models, and research are just as vital