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

[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]

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]

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

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]

Minimal substorm model P 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]

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