I work with Gareth Chisham and Mervyn Freeman at the British Antarctic Survey, in the S W th ithi th Cli t PSpace Weather group within the Climate Programme.

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1. There are a number of responses seen, but I will be focusing on one called the M ff tMansurov effect.

2. This is a correlation between polar surface pressure anomalies and the interplanetary magnetic field, first observed in the 1970s.

3. I will present our latest results just published in GRL (Volume 41, Issue 18) which shed light on the cause of the Mansurov effect by examining its signature in the atmospheric column.

4. The role of clouds in atmospheric electricity-related phenomena has been proposed p y p p pand I briefly outline these

5. I end with a summary and some conclusions.

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1. Let me first briefly introduce the global atmospheric electric circuit.

2. The world’s thunderstorms maintain a vertical electrical potential difference, Vi, of about 250 kV between the ground and the ionosphere.

3. This drives current throughout the atmosphere, including a vertically-downwards current density, Jz, which away from thunderstorms is referred to as the fair-weather or semi-fair-weather current, in clear and moderately cloudy conditions, respectively.

4. There are many different causes of perturbations to Jz that are due to variability in solar activity and the solar-wind. y

5. Different solar wind perturbations to Jz correlated to different large-scale, day-to-day atmospheric variations .

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1. Just to remind you, the ever-present, but ever-varying solar wind is formed as the S ' t t l bl ff i tSun's topmost layer blows off into space.

2. Carried with the plasma is the embedded interplanetary magnetic field still attached to the sun which interacts with the Earth’s plasma and magnetic field.

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The solar wind plasma and interplanetary magnetic field interact with the Earth’s t h d i h t d i ti i th i h t dmagnetosphere and ionosphere to produce variations in the ionosphere-to-ground

potential difference and therefore in the vertical current density Jz , in the atmosphere at high latitudes.

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1. The Mansurov effect is the very highly-statistically-significant correlation of the daily-f IMF B ith th l f lmean of IMF By with the polar surface pressure anomaly.

2. It is strongest and simplest in Antarctica, probably due to topography.

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1. What is not established is the mechanism for the Mansurov effect.

2. Day-to-day correlations are useful for examining these Jz effects, since we can exclude mechanisms such as the solar UV/stratospheric ozone effect, and the solar-wind-driven energetic particle precipitation/stratospheric ozone effect which operate on quite different time scales.

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1. The Mansurov effect is a correlation of polar surface pressure anomaly with the d il f t f th IMF i f t th ‘d t d k’ t fdaily-mean of a component of the IMF, in fact, the ‘dawn-to-dusk’ component of the interplanetary magnetic field IMF By in the geocentric solar magnetospheric(GSM) co-ordinate system in near-Earth space.

(In the GSM system, the X-axis is from the Earth to the Sun, the Y-axis is defined to be perpendicular to the Earth’s magnetic dipole so that the X-Z plane contains the dipole axis, the positive Z-axis is chosen to be in the same sense as the northern magnetic pole, and positive Y is from dawn to dusk)

2. At high latitudes, this is a proxy for the daily mean of the solar-wind-induced, ionosphere-to-ground, vertical electric potential on which the vertical current Jz will depend.

3. The relationship between, say, the 12 UT values of pressure anomaly and the daily-mean IMF By is somewhat linear.

4. It has a positive slope in the Antarctic and a negative one in the Arctic which reflects the fact that Jz decreases with increasing By at one pole increases with increasingthe fact that Jz decreases with increasing By at one pole increases with increasing By at the other pole.

5. The persistence of ~10 days reflects a typical timescale for the persistence in IMF By.

6. The time lag between surface pressure and solar wind of the order of 2 days or less.

7. There is a well-established periodicity of about 27 days, reflecting the solar rotation rate.

8. The amplitude of the effect is small but highly-statistically-significant (field sig. ~ 0.3% in Antarctic; Lam et al. Environmental Research Letters, 8, 2013).

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1. One of the many related phenomena is the Burns effect (Burns et al., J. Geophysical R h 2007 2008)Research, 2007; 2008)

2. This has been observed as a correlation of polar surface pressure anomaly with variations in the thunderstorm-generated ionosphere-to-ground potential

3. The relationship between the 12 UT values of pressure anomaly and the daily-mean IMF By is somewhat linear in polar regions

4. The sign of the slope is expected to have the same sign and similar slope in the two hemispheres because Jz increases with p

increases in the internal generator in both polar regions.

5 . In fact, if the pressure response to IMF By and the internal generator are plotted on the same graph, as a function of potential difference rather than IMF or electric field, then the Burns and the Mansurov effects have similar slopes. There is the same atmospheric response to the same size of driver regardless of the source of the driver. See Burns et al., JGR, 2008.

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1. Just a comment about why we look at By rather than Bz since I am always asked b t thiabout this.

2. This high-latitude phenomenon is dependent on By rather than Bz which is an indication of the timescales involved.

3. These are plots (missing from this upload) of the dependence of the daily-averagedsolar-wind-induced potential difference at Vostok on daily-averaged IMF By and IMF Bz(2001 to 2009).

4. The key message here is that ~87% of the modelled solar-wind-induced potential y g pdifference at Vostok is controlled by IMF By

while less than 1% is associated with Bz.

5. This is consistent with the observed dependence of surface pressure on By rather than Bz, on daily timescales.

6. Although Bz has a strong influence on the electric potential perturbation, it is equal and approximately opposite on the

dawnside and duskside of the polar cap This means that at a given geographicaldawnside and duskside of the polar cap. This means that at a given geographical location, this influence largely cancels

out over a day [Burns et al., JGR, 2007].

7. The lack of correlation between IMF Bz and surface pressure is thus consistent with a mechanism driven by the day-to-day

variability in the daily average ionosphere-to-ground potential difference.

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1. Let’s now move on to our new results

2. We extend the previous IMF By-polar surface pressure correlation studies to a study throughout the troposphere and lower stratosphere using NCEP/NCAR reanalysis data

3. The is for 1999 – 2002, when we see significant correlations in both Antarctica and the Arctic.

4. We use 12 UT values of geopotential height with the seasonal cycle removed, at 17 constant pressure levelsp

5. I call this h, on a latitude, longitude, pressure level, time grid (lambda, phi, p, t).

6. Daily averages of IMF By are calculated from hourly OMNIWeb data, freely available on the web (see acknowledgements).

7. We see how h correlates with IMF By when the datasets are displaced in time with respect to to each other by tau days. Positive tau is defined so that the solar wind data leads the IMF data.

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1. We find the mean h for all days in the 1999-2002 interval when IMF By is greater th l t 3 t l (352 d )than or equal to 3 nanoteslas (352 days)

2. And likewise for the other state of the solar wind, when IMF By is less than or equal to minus 3 nanoteslas (273 days)

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1. I will only present two plots.

2. One will be of their difference, Delta h bar.

3. The field mean of this quantity over the Antarctic polar cap to be precise.

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1. ...the other plot will be of zonal means.

2. Little z just denotes the zonal means of the three quantities we have already defined.

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1. I conduct a Wilcoxon Rank-Sum test which is a non-parameteric test of the null h th i th t t l ti h th di t ib ti i t thhypothesis that two populations have the same mean distribution against the hypothesis that they differ.

2. An example of two populations is h bar plus and h bar minus (in the green circle).

3. I obtain a test statistic Z and a probability of obtaining that value or greater by chance.

4. The smaller the probability then the more likely the null hypothesis is wrong. 5% is a typical level to choose below which there is deemed a significant difference between yp gthe two populations.

5. I’m going to be even more stringent in my plots and use 1%.

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1. For simplicity, let’s start with the zonal mean results.

2. In these plots, the vertical axis is from the south pole to 60 S and the horizontal axis is for time lags between – 15 and + 15 days.

3. The plots are of zonal 4-yr means of the difference between the By positive and Bynegative mean geopotential height anomalies.

4. Blue is a negative anomaly, orange is a positive one, and black is zero.

5. I have masked out results which have a probability of a difference between the two populations by chance above 1%populations by chance above 1%

6. The top panel is for 1000 hPa (near surface), the middle 600 hPa (4 km), and the bottom 200 hPa (12 km).

7. Most difference between By positive and By negative populations occurs poleward of 70 S so let’s focus on that region

8. At each height, there is an increase in the zonal means from around zero to a peak.

9. For 1000 hPa level, the peak is centred about time lag zero or day 1, with a width of something like 10 days, identical to previous studies using station data (Burns et al. JGR, 2008).

10. The key result is that at 80-90S, the time lag between of the peak value increases as the altitude increases.

11. The temporal width in the correlation is due to the strong autocorrelation in the IMF.

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1. Fields of atmospheric data typically exhibit strong spatial autocorrelations, so to t l th f l di t I th t i t t t f fi ld t ti fcontrol the false discovery rate, I now use the more stringent test of field testing of

grids of these probabilities for the region poleward of 70 S*.

2. The left hand vertical axis is the log of pressure from 1000 hPa to 10 hPa and the right hand axis is approximate height.

3. Time lag is on the horizontal axis, from -50 to +50 days.

4. The panel shows the mean of the delta h bar values for the region poleward of 70 S, with no statistics applied.pp

*using Walker minimum local p value method described in Wilks, 2006.

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1. In the next panel I have masked out probabilities of obtaining a difference between th B iti d B ti l b h th t d 1%the By positive and By negative mean values by chance that exceed 1%.

2. 230 hPa is the dashed line, it marks a winter (maximum) value for the polar tropopause.

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1. In the bottom panel, I give cross sections of the results at standard pressure levels, ff t th lt toffset so the results are easy to see.

2. Starting at 1000 hPa, at the bottom of the panel, and going up to 250 hPa near the top of the panel.

3. Black lines show results achieving 1% significance, and orange lines shows less significant results.

4. The bottom 2 panels are evidence of a correlation between the solar-wind-induced ionospheric electric potential and anomalies in the geopotential height. This is for p p g p gAntarctica, in troposphere and very base of the stratosphere.

5. The significant correlation is for ~ 10 days interval, with a peak within 1-7 days of zero time lag.

6. Again, the results show that the time lag of the peak amplitude systematically increases with altitude, consistent with the initiation of a disturbance near the surface which is then propagated upwards.

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1. It has been proposed that the link between Jz and surface pressure anomalies for th M ff t i th ff t f h t f l d i h ithe Mansurov effect is the effect of charge transfer on cloud microsphysics.

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1. Such a mechanism requires a very large amplification of energy fluctuations in the l t hlower troposphere.

2. …from the ergs cm-2 sec-1 provided by solar wind modulations*, to the watts cm-

2sec-1 of the correlated atmospheric dynamical changes.

3. However, very small energy inputs, via Jz, can divert energy flow e.g. between thermal, gravitational, and dynamical energies and latent heat, rather like a transistor switch.

4. Two proposed processes are (a) invigoration in storm clouds and (b) changes on p p p ( ) g ( ) gradiation balance on layer clouds.

*1 erg = 0.0000001 Joules

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