Response of the magnetosphereand ionosphere

to solar wind drivers (including complexity)

Mervyn FreemanBritish Antarctic Survey

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

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]

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]

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]

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

)2/(sin 3/83/23/4 TBvdtd

[Newell et al., J. Geophys. Res., 2007]

Not just about the solar wind

• magnetosphere and ionosphere produce the impact on satellites, power grids, etc, from the solar wind input

Add some physics – models

• Prediction is no better than assuming the average value of the observations over the event, PE = 0 (- - -)

2

2mod

1obs

obs xxPE

• PE = 1 is perfect prediction

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

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

3 challenges

• Non-linearity – chaos

• Memory – substorms

• Turbulence – intermittency

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?

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

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

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

LFM/Wind

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

LFM/THC

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

Ampere

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

Memory – substorms

• Auroral electrojet index, AE, is influenced by past history of the IMF– 3-hour timescale, comparable to that of the substorm cycle

)2/(sin 3/83/23/4 TBvdtd

1

0

1 n

ii

i dtdwn

dtd

[Newell et al., J. Geophys. Res., 2007]

Memory – substorms

• Simple integrate-and-fire model explains substorm timing statistically

• But not so well individually due to non-linearity

fff

E

Time

Onsets

[Freeman and Morley, Geophys. Res. Lett., 2004]

Minimal substorm model

1. Solar wind power input at magnetopause P accumulates energy in magnetotail E.

2. Unique minimum energy state for magnetosphere F exists for given solar wind state P.

3. Magnetotail can only move to lower energy state F when energy threshold C is exceeded.

),( BvPdt

dE

)(PgCF

CEFE when

P

E

F

Time

[Freeman and Morley, Geophys. Res. Lett., 2004]

Turbulence – intermittency

• Wilder fluctuations on short timescales

• Dependent on large-scale state[Consolini and de Michelis, Geophys. Res. Lett., 1998]

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]

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

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