Modelling the Thermosphere-Ionosphere Response to Space Weather Effects: the Problem with the Inputs Alan Aylward, George Millward, Alex Lotinga Atmospheric Physics Laboratory University College London

Using Global Circulation Models as a Forecasting Tool: First you need to develop a global model of the upper atmosphere Then you need to drive it by external inputs in a realistic way If the physics is right it should simulate the “real” atmosphere and any transmitted effects From this grew the idea of forecasting - or at least “nowcasting”. Can you input data from, say, a solar wind monitor and predict the ionospheric response? This takes “Space Weather” into the realm of “Space Weather Forecasting” with many of its concomitant conditions We enter the world of data assimilation: the inputs define our accuracy

CTIP/CTIM Properties 3-dimensional, time-dependent Solves equations of momentum, energy and continuity for ions and neutrals km thermosphere, ,000km for the ionosphere and plasmasphere Resolution 2 degs latitude, 18 degrees longitude by 1 scale height altitude seconds time resolution 3 neutral constituents (O, O 2, N 2 ) and 2 ions (H +, O + ) Wave forcing at the lower boundary(80km) Self-consistent dynamo calculations

“Standard” input of magnetosphere-ionosphere coupling An empirical model of high-latitude convection gives the polar cap electric field pattern. Many exist - Heppner and Maynard, Foster, Weimar, Heelis, Rich and Maynard We use magnetospheric inputs based on statistical models of auroral precipitation and electric fields from Tiros and Foster (Fuller- Rowell 1987 and Foster 1986). These inputs are linked to a power index based on TIROS/NOAA auroral particle measurements.

Coupled Thermosphere Ionosphere Plasmasphere model (CTIP) Atmospheric temperature changes due to dynamic Auroral forcing (i.e., Magnetic Storm) Global gravity wave propagation green/red +20K, blue -20K

April 1997 Storm event Dusk effect (neutral winds) TEC enhancement (particle precipitation) Total Electron Content (TEC) change Negative phase (neutral gas composition) But complications continually arise: the response to storms is not simple :

But how realistic are the inputs?

SuperDARN Super Dual Auroral Radar Network Northern Hemisphere Southern Hemisphere

Joule heating from CTIP model runs Empirical electric fields Electric field input derived from SuperDARN

So what else is needed? We can input SUPERDARN fields at 2 minutes resolution But there is a precipitation pattern on top of this Where can we get that from? Getting matched precipitation and electric field has long beena problem for GCMs

OVATION datasets ( OVATION model Predicts location of auroral oval and maps magnetospheric boundaries onto the ionosphere Uses: a)DMSP satellite particle data b)SuperDARN convection patterns c)All-sky imaging camera

However even given these we still need a dense network of stations to constrain the empirical inputs

MIRACLE: Magnetometers, Ionospheric Radars, All-sky Cameras Large Experiment Combined ASC images from Kilpisjarvi and Muonio showing an auroral arc, projected at 110km altitude. Kurihara et al., Annales, 2006

Experience from US studies: A supposedly “operational” nowcasting system has been delivered to the US Air Force using GPS inputs assimilated into an ionosphere model However this is without a self-consistent thermosphere Contrast the density of TEC/Ne measurements with those of neutral atmosphere composition and winds The northern US continent is well covered but even for electron density/TEC coverage outside this is poor. Does this matter??

Global model of Joule heating for moderate conditions (Thayer, 1995) Including neutral wind dynamo No neutral wind dynamo U n =0

The Auroral zone inputs are not the only problem The equatorial ionosphere is notoriously difficult to model Its scale sizes do not match easily with GCMs It is part of a general problem that there are aspects of modelling the ionospheric/thermospheric behaviour which can only be solved globally …..And you can’t ignore the lower atmosphere

V = E x B ( m/s)

Conclusions On the whole we know the physics, much as we do with tropospheric meteorology The problem with taking this to “nowcasting” and forecasting is with resolution and inputs Whereas some data might be available at a high enough resolution (electron densities) it is unlikely we will ever get neutral atmosphere data at the same density “Average” and low resolution behaviour we can simulate well already, but “local” forecasts or specific features is not what you should expect from GCMs