Seasonal variations of atmospheric electricity measured at Amundsen-Scott South Pole Station

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi: /2004ja010536, 2004 Seasonal variations of atmospheric electricity measured at Amundsen-Scott South Pole Station B. D. Reddell, J. R. Benbrook, and E. A. Bering Department of Physics, University of Houston, Houston, Texas, USA E. N. Cleary 1 and A. A. Few Department of Space Physics and Astronomy, Rice University, Houston, Texas, USA Received 12 April 2004; revised 9 June 2004; accepted 7 July 2004; published 22 September [1] Recent studies have shown that the general understanding of the Earth s global circuit is not entirely complete. Electric current originates from thunderstorm cloud tops and travels to the ionosphere, where it then leaks back down to the Earth s surface. Superimposed on this current is the dynamo generated from the interaction of the solar wind with Earth s magnetosphere. This paper investigates seasonal variations of the vertical electric field and current density as measured at the South Pole between 1991 and After initial data reduction, a model approach was used to decouple the magnetospheric and atmospheric components of the measurements. This approach calculated and subtracted the polar cap ionospheric potential from the measured data to obtain a signal of global tropospheric origin, in principle. The diurnal variations of the resulting data were averaged as a function of UT. These averages were calculated for the data as a whole and for the date sorted and binned by season and by magnetic activity level. The seasonally binned average results are consistent with recent papers indicating that the electric field measurement show global convective electrical activity to be a minimum during the Northern Hemisphere winter, in contradiction to the original 1929 Carnegie data. Because the electric field was a maximum during the Northern Hemisphere summer season, the midlatitude regions must contribute more strongly than the tropics to global atmospheric electricity. This analysis supports the link of electrical activity to global temperature. The magnetic activity binned results suggest that the polar cap potential model used underestimates the cross polar cap potential when there is a high K p index. INDEX TERMS: 2427 Ionosphere: Ionosphere/atmosphere interactions (0335); 2463 Ionosphere: Plasma convection; 3304 Meteorology and Atmospheric Dynamics: Atmospheric electricity; 1610 Global Change: Atmosphere (0315, 0325); KEYWORDS: atmospheric electricity, magnetosphere/ionosphere interactions, plasma convection, cross-polar cap potential, global circuit Citation: Reddell, B. D., J. R. Benbrook, E. A. Bering, E. N. Cleary, and A. A. Few (2004), Seasonal variations of atmospheric electricity measured at Amundsen-Scott South Pole Station, J. Geophys. Res., 109,, doi: /2004ja Introduction [2] This paper presents a statistical analysis of atmospheric electricity parameters measured at the South Pole from 1991 through The data have been averaged both by season and K p index. The results are found to agree with prior studies. Minimum and maximum values for the electric field and their corresponding UT times are reported for each season. In addition, these data support the idea that atmospheric electrical parameters can be used as a diagnostic for global temperature change [Williams, 1992]. The data can also be used to study the validity of models of cross polar cap potential. 1 Now at Science Applications International Corporation, San Diego, California, USA. Copyright 2004 by the American Geophysical Union /04/2004JA [3] The Earth s fair weather electric field has long been a source of interest to the atmospheric science community. Lord Kelvin [1860] first proposed the idea of the global circuit in order to explain why the Earth s atmosphere is electrified in fair weather conditions. The global circuit is modeled as a spherical shell capacitor, with the inner shell being the Earth s surface and the outer shell being the average base of the ionosphere, at 100 km altitude. In reality, since most of the positive charge is stored as space charge primarily below 10 km, the outer shell begins at this point. There is essentially an electrical short between the ionosphere and stratosphere because the total resistance between the 10 km and 100 km is 10 times less than from ground to 10 km [Bering et al., 1998]. Between the two shells lies the Earth s atmosphere; there are regions of fair weather and regions of thunderstorms within these spherical shells. [4] To understand the circuit path, start with the area where most of the source current is believed to originate, in thunderstorms. The model postulates that the current sources are the 1of17

2 estimated active thunderstorms over the globe at any one time [Brooks, 1925]. It has been shown that thunderstorms occur approximately 10 times more frequently over land than ocean [Orville and Henderson, 1986; Price and Rind, 1992; Williams and Heckman, 1993]. Land area is distributed unevenly over the surface of the Earth with a greater percentage existing in the Northern Hemisphere. The land surface experiences more extreme diurnal temperature fluctuations and is more likely to produce thunderstorms when the surface temperatures are at or near their maximum values. As the Earth rotates, the relative fraction of land and ocean receiving solar radiation changes, creating temporal variations in thunderstorm development and the source current. This variation results in changes in the magnitude of the fair weather current density and electric field, that is, in the return current of the global electric circuit. [5] Within a thunderstorm, it is well known that thunderstorm dynamics creates charge separation such that clouds are positively charged at the top and negatively charged at their bottom. The tops of the thunderstorm clouds are at an altitude of approximately 10 km on average. Atmospheric conductivity increases exponentially with height. As a result, the conductivity at cloud top altitude is approximately 10 times greater than at the bottom. Therefore the positive charge on cloud tops drives current upward toward the ionosphere where it is distributed rapidly over the globe. This upward current is known as the Wilson current. Because of the high conductivity of the lower ionosphere, horizontal currents from individual thunderstorms do not greatly alter the potential at any point relative to the ground. [6] Providing local perturbations are not present (no nearby, i.e., closer than km, electrically active clouds), the local measurement of the atmospheric electric field or current density should be representative of the global average value of the Earth-ionosphere potential difference (V I ). Observational evidence comes from measurements of the vertical electric field profile obtained from aircraft that have been integrated to obtain V I [Markson, 1986]. These data exhibit diurnal variation in good agreement with data obtained by the research vessel Carnegie [Parkinson and Torreson, 1931; Torreson et al., 1946] and other experiments (see below). Other experiments have measured V I simultaneously at widely separated locations. Typically, such experiments observe potentials that agree within the 20% error bars [Mühleisen, 1971; Markson, 1985; Markson and Price, 1996]. At high geomagnetic latitudes the dynamo action of the solar wind acting on the magnetosphere imposes a crosspolar cap potential that can be a significant perturbation of V I. For reasons discussed below, this contribution must be subtracted from high-latitude observations to extract a globally representative value for the purely tropospheric component of the polar region Earth-ionosphere potential. [7] On the basis of this model the ionosphere can be treated as an equipotential surface at 100 km, where Ohm s law can be applied: V I ¼ IR: I is the net current flowing in the global circuit and is constant with altitude except on very small time scales. It has a value of approximately 1 ka [Israel, 1973]. R is the total atmospheric resistance. It is expected that R will ð1þ exhibit global diurnal variations due to dependence on cloud cover, boundary layer convection, aerosols, and water vapor. Aerosols, cloud cover, and water vapor all act to decrease conductivity by decreasing ion mobility. V I has an average value of approximately 250 kv [Markson and Kendra, 1992]. [8] The primary quantities measured in this experiment are E z, the vertical electric field at the local surface, and j z, the vertical current density. The columnar resistance, r s,is defined as the resistance of 1 m 2 from altitude s (the altitude of the local surface) to the ionosphere; it relates j z to V I, and the total global current I: j z ¼ V I =r s ¼ IðR=r s Þ: ð2þ [9] If local current generators do not exist, the mean air- Earth current density should be altitude invariant within a given column owing to charge conservation. The columnar resistance at the South Pole does not experience diurnal variations owing to the absence of diurnal sunrises/sunsets, regular diurnal wind variations, and low pollution levels. Therefore the current density measurements, j z, are directly proportional to V I and the globally integrated current I. Keeping only the vertical components, equation (1) can be rewritten as j z ¼ se z ; where s is the conductivity. Combining equations (2) and (3) leads to the following relationship for E Z : ð3þ E z ¼ ðir=r s sþ: ð4þ [10] The circuit is completed as current returns to the bottom of the thunderstorm clouds through lightning, conduction, and point discharge processes. The electric field and current are reversed beneath a thundercloud, pointing upward and opposite the direction of the fair weather electric field and current. The path described is the one of least resistance by at least an order of magnitude compared with all other possibilities except when intracloud lightning occurs. Finally, it is appropriate to mention the discharge time of this spherical capacitor model. For a spherical capacitor with a separation distance equal to a typical scale height of 5 7 km, the capacitance is approximately 1 Farad. Therefore the time constant of the circuit is T ¼ 2pRC ¼ 1200 s: [11] The first widely studied atmospheric data was obtained in the Carnegie expeditions in the 1920s. Analysis of that data provided initial support for the global circuit hypothesis. The initial findings concluded that the vertical electric field maximizes in the Northern Hemisphere winter and attributed the source of the electricity in the circuit to global thunderstorm activity [Ault and Mauchly, 1926; Whipple, 1929; Parkinson and Torreson, 1931; Torreson et al., 1946]. Subsequent analyses also suggested that electrical activity peaks in the Northern Hemisphere winter season [Paramanov, 1950; Frank-Kamenetsky et al., 2001]. The discrepancy between the amplitude ratios of the diurnal ð5þ 2of17

3 variation curve and of thunderstorm activity suggests that other processes are present. Point discharge and conduction currents from electrified, nonlightning producing clouds add to the baseline electric field, decreasing the relative strength of the peaks [Burke and Few, 1978; Price, 1994; Hendon, 1994]. More recent analysis has supported the idea that the electric field results from the overall convective electrical cloud activity, including point discharge currents [Williams and Heckman, 1993; Füllekrug et al., 1999]. Williams and Heckman [1993] argue that point discharge is the major contributor to the global circuit since calculated singlesource curves for point discharge have an amplitude ratio closer to that of the original Carnegie curve. Yet conduction currents over thunderstorms are correlated with the total lightning flash rate [Blakeslee et al., 1989], suggesting that the current output from various sources may often occur in parallel. The question of the sources of the global circuit continues to remain actively controversial [Dolezalek, 1972; Bering, 1995]. [12] Because the Carnegie data were primarily limited to the tropical and subtropical regions of the globe, a more globally representative data set is needed in order to understand the global circuit. Reanalyses of the Carnegie data, Mauna Loa data, and analysis of 1998 Vostok data have shown that electrical activity actually maximizes in the Northern Hemisphere summer season [Adlerman and Williams, 1996; Corney et al., 2003]. Furthermore, approximately 3 years of measurements of the Schumann resonances at Nagycenk station in Hungary show similar results [Märcz et al., 1997]. The South Pole data used in this analysis are a globally representative data set. Seasonal variations are calculated, presented, and compared with the above recent studies Relation to Global Warming [13] The Intergovernmental Panel on Climate Change (IPCC) has predicted that globally averaged surface temperatures are expected to rise by 1.4 to 5.8 degrees Celsius by the year 2100 owing to the buildup of greenhouse gases in the Earth s atmosphere [Intergovernmental Panel on Climate Change (IPCC), 2001]. Estimates of the Earth s surface temperatures have increased only a few tenths of a degree Celsius in the last century, largely because of the tremendous heat capacity of the oceans. This change is well within the expected variability in the temperature record. Therefore the seriousness of the situation and plans for environmental action are being hotly debated. Since surface temperatures are correlated with thunderstorm activity on diurnal, seasonal, and interannual time scales, global warming studies could be simplified by making single observations of the global circuit [Price, 1993]. [14] The dry bulb and wet bulb temperatures are quantities that take into account both the temperature and moisture content. Williams [1994] demonstrated that there is nonlinear response of atmospheric electrical parameters to slight changes in wet bulb temperature. Recent work by Williams et al. [2003] suggests that the dry bulb temperature is better correlated with the global circuit. Global warming would cause an increase in moisture flux from land and oceanic surfaces. This added heat and moisture would be reflected in an increase of convective activity and cloud complexes producing lightning, corona, and conduction currents. The fact that the tropical region has the most convective activity on the Earth and is the source of 2/3 of the world s lightning suggests that monitoring global temperature trends through the Earth s global circuit is not merely an option but may be the most effective and efficient means of making this measurement. It has also been suggested that measuring both the DC global circuit and the power level in the AC global circuit or Schumann resonance can provide a proxy measurement of both global temperature and global rainfall rates [Markson and Lane-Smith, 1994] Coupling of Ionospheric and Magnetospheric Convection to Surface Electric Measurements [15] The Earth s magnetosphere is a very large, optically transparent system composed of a rarefied plasma in a relatively strong magnetic field. It has proven very difficult to sense the flow of plasma in the magnetosphere remotely. Measurements at high latitude of ionospheric convection patterns have been made by Earth-orbiting satellites [Heppner, 1972a, 1972b; Heelis et al., 1983; Heppner and Maynard, 1987] and ground-based radars [Foster, 1983, 1984; Foster et al., 1981, 1986] and other techniques such as balloon measurements [Mozer and Serlin, 1969; Mozer and Manka, 1971; Bering et al., 1989a, 1989b]. One of the major effects of the flow of plasma in the magnetosphere is a substantial magnetospheric contribution to variations of ionospheric potential. Model studies have shown that electric fields with scale sizes the order of 500 km map down to the Earth s surface [Roble and Tzur, 1986]. Thus variations in the vertical electric field and current at the surface are produced by large-scale variations of ionospheric potential. Models have shown that ionospheric sources can change the air-earth current by ±20% at high latitudes during periods of geomagnetic quiet and by greater amounts during geomagnetic storms [Mühleisen et al., 1971; Park, 1976a, 1976b, 1979; Hays and Roble, 1979; Roble and Hays, 1979]. Recent analyses of observations have confirmed these predictions and found variations of V/m (i.e., up to 50%) under some circumstances [Bering et al., 1991a, 1991b, 1991c, 1991d, 1992, 1993a, 1993b, 1994a, 1994b; Byrne et al., 1993; Tinsley et al., 1998; Frank-Kamenetsky et al., 1999, 2001; Corney et al., 2003]. The most important consequence of the difficulties in measuring magnetospheric convection is that we do not yet know if the average patterns that have been inferred from various sampling techniques (radar, satellite, balloon) ever represent the instantaneous situation. Without knowledge of the actual potential function at any given time, it is impossible to be certain that any particular model makes sense or corresponds to reality [Heelis et al., 1983]. For example, recent work has challenged the main assumption often used to analyze satellite electric field data that the magnetosphere has a small number of fixed convection patterns which occur in response to a given interplanetary magnetic field [Lockwood et al., 1990; Lockwood, 1991]. The controversy that this suggestion has generated can only be resolved by a combination of experiments such as this one that make continuous measurements of parameters directly related to the ionospheric potential and of experiments that take snapshots of the convection pattern [Bering et al., 1991a; Michnowski, 1998]. [16] The eventual availability of simultaneous ionospheric potential and magnetic perturbation data from a 3of17

4 network of stations at high latitudes may begin to produce a database that will answer this question. There is certainly ample evidence that ground level electric fields respond to transients of magnetospheric origin [Cobb, 1967; Mühleisen and Reiter, 1973; Park, 1976b; Park and Dejnakarintra, 1977; Reiter, 1977; Markson, 1979; Olson, 1983; Ruhnke et al., 1983; Holzworth et al., 1987; Rusakov et al., 1988; Bering et al., 1992, 1994a, 1994b; Ruhnke, 1992; Byrne et al., 1993; Michnowski, 1998; Corney et al., 2003]. Atmospheric electricity observations correlated with geomagnetic variations and solar events suggest that extraterrestrial sources modulate the global circuit in the polar regions [Tinsley et al., 1998]. The newest results from Vostok station are particularly intriguing. [17] There are effects seen in these Vostok observations that have yet to be explained, for example, reductions of surface field for a few days at sector boundaries and the reduced amplitude of the diurnal variation of the electric field in the local winter. These effects may have to do with changes in conductivity in the ionosphere below the F region, for example, in polar mesospheric clouds, influenced by particle precipitation [Frank-Kamenetsky et al., 1999, 2001]. It has recently been shown that even a one point study can be used to confirm the direct influence of the polar cap potential on the surface electric field, find the coupling constant, and identify problems with existing models of the polar cap potential [Corney et al., 2003]. These newest observations have provided quantitative confirmation of the idea that multistation measurements can provide meaningful data on ionospheric potential patterns. [18] Thus it has become apparent that the Earth-ionosphere potential drop at high latitude results from the superposition of an atmospheric component and a magnetospheric component. The solar wind interaction with the Earth s magnetosphere creates convection patterns at the poles. Because of this contribution, the ionosphere cannot be considered an equipotential. Instead, there is a crosspolar cap potential drop from dawn to dusk of kv during quiet times and as high as kv during geomagnetic storms [Tinsley et al., 1998]. A key issue in the analysis of the tropospheric contribution to the atmospheric electric field was the application of an appropriate model to account for the magnetospheric component. There are several models of the cross-cap potential. Two recent ones are the IZMEM model [Papitashvili et al., 1994] and the model developed by Weimer [1995, 1996]. Both models were used to analyze the new vertical geoelectric field measurements from Vostok, Antarctica during 1998 [Corney et al., 2003] and to analyze the 1979 data from the same location [Frank-Kamenetsky et al., 1999]. Corney et al. [2003] concluded that the Weimer model had more hours of significant correlation with their data than the IZMEM model did for three reasons. First, the IZMEM model is based on magnetometer data, which means that a conductivity model must be assumed to infer potential. Second, the IZMEM model uses the x component of the interplanetary magnetic field (IMF B x ) instead of the solar wind velocity as its third input parameter, as the Weimer model does. Finally, the Weimer model uses the dipole tilt angle as a continuously variable season parameter, whereas the IZMEM introduced seasonal variations by splitting data into summer, winter, and equinox intervals [Corney et al., 2003]. For these reasons the Weimer model was used in this analysis. The final stage of the analysis attempts to validate the Weimer model. We have specifically asked if the data show that the model properly predicts the instantaneous cross-polar cap potential. [19] In summary, the results presented here attempt to answer the following questions regarding the Earth s global circuit: (1) What seasonal variations can be obtained from electric field and current density measured at the South Pole during ? (2) Does the analysis presented here support the link between atmospheric electricity and global warming? (3) What are the effects of calculating and subtracting a model value for the magnetospheric crosscap potential on the conclusion? (4) Do the data show an influence of the level of magnetic activity? (5) Is the model of cross-polar cap potential that we used valid? 2. Experiment Setup [20] Two identical sites, each with a current sensor, electric field mill, and buried vault box were established 600 m apart at Amundsen-Scott South Pole Station in mid- January of 1991 [Byrne et al., 1993]. The separation distance was based on estimates of the correlation length of turbulence in the planetary boundary layer. Ideally, these instruments should be placed above the local electrode layer. In fact, all measurements were made at a height of 3 m, which was determined by practical issues pertaining to mast construction, at 1-s time resolution. The wind at the South Pole is usually steady (7 15 knots) and boundary layer turbulence is confined to the first several centimeters above the surface owing to its smoothness. Blowing snow under these conditions is also a near-surface phenomenon. Higher wind speeds (>15 knots) can greatly alter the measurements when charged blowing snow reaches instrument levels. The other condition that impacts these measurements is fog, which causes icing on the electric field sensor and contact charging on the current sensor. Detailed descriptions of the setup and instrumentation can be found in earlier publications [Burke and Few, 1978; Byrne et al., 1993]. [21] Climatological conditions at Antarctica make it a preferred laboratory for the measurement of global circuit parameters. The ice sheet is almost 3 km thick, providing a very uniform electrical plane. The nearly flat, smooth surface reduces surface wind-driven turbulence and the small uniform slope of the ice sheet leads to a down-slope wind almost constant in direction and speed. Relatively few man-made aerosols and pollutants distort the conductivity. The experiment was set up in a sector upwind of South Pole Station, adjacent to the Clean Air Sector where manmade pollution and aerosol generation activities are banned. Diurnal heating, soil radioactivity, and convection effects do not influence the ground level conductivity. An inversion layer, in which temperature increases with height, leaves denser air at the surface. This then creates greater stability in the air and reduces the tendency for convection currents to disturb the measurements [Dalrymple, 1966]. At an altitude of 2835 m above sea level, models and our data indicate that the current density was approximately a factor of two larger 4of17

5 than the sea level value [Roble and Tzur, 1986; Byrne et al., 1993]. 3. Method [22] The approach used in this analysis was to perform a superimposed diurnal epoch analysis of the data. The experiment was deployed from January of 1991 to September of 1993, with the best data coming from December of For an average half-hour bin, a total of 564 days (16,905 min) of data were collected. The first step in the data reduction process was to decommutate zero-level checking and sign swapping and compute 1-min averages of the 1-s sample rate raw data [Byrne et al., 1993]. Owing to budgetary and time constraints, 189 days (5670 min) were not processed and therefore did not enter into this analysis. The second step was to remove as many of the various local perturbations to the data as possible. For these data the largest local perturbations are the contamination owing to local weather events. Review of surface weather records, visual inspection, and two computer algorithms were used to filter out any local perturbations owing to weather. Periods of overcast, blowing snow, fog, or precipitation were excluded by look-up table from the analysis. Intervals of saturated, excessively turbulent, or negative data were also eliminated by look-up table. This procedure removed 227 days (6796 min) of data from consideration, leaving 148 days (4439 min) available for subsequent analysis. The next step was to select the minimum and maximum allowed values for E z and j z. We also established a minimum total number of good minutes per 30-min block that had to be present before any data from that block were used. Two computer algorithms applied these criteria. The first computer algorithm would only allow electric field values between 20 and 600 V/m and current density values between 0.4 and 10.0 pa/m 2 to enter the averaging calculations. This algorithm removed approximately 2 days (75 min) of data. The second algorithm would only allow the processing of a 30-min bin provided there were at least 25 min contributing to that bin. By applying this last algorithm, another day (42 min) was removed, leaving 145 days (4322 min) of data available for magnetospheric correction. Figure 1 summarizes the data reduction in graphical form. [23] The next step was to remove the nontropospheric global perturbation from the superimposed cross-cap potential. Each data point was considered to be the sum of an atmospheric contribution and a magnetospheric contribution. The model developed by Weimer [1995, 1996] was used to estimate the magnetospheric effect. It is a leastsquares fit of the electric field measurements made by the DE 2 satellite using the spherical harmonic (terms up to l = 8 and m = 3) representation of the electric potential [Weimer, 1995, 1996]. Hourly IMF and solar wind data from the IMP 8 satellite and the K p index were obtained from the National Space Science Data Center (NSSDC) OMNIWeb database. During this interval, the value for the cross-cap potential at the South Pole was calculated. Next, a factor to scale the potential at an altitude of 100 km to an electric field value at ground level was calculated. The average of the electric field data was 171 V/m. The average Earth-ionosphere potential drop was taken to be 256 kv [Markson and Kendra, 1992]. Dividing these values gave a scaling factor of 0.67 Vm 1 /kv, which is in excellent agreement with values previously determined at both South Pole and Vostok, Antarctica [Tinsley et al., 1998; Corney et al., 2003]. The scaled model potential values were subtracted from the measured electric field values to estimate the corrected atmospheric values. Equations (2) and (3) were used to find the scale factor correction for the current density. Figure 2 displays the average magnetospheric correction as a function of UT time for the electric field and current density used in our analysis. Figures 2a and 2b show the Weimer model correction to the electric field and current density organized by season. Notice that the corrections have a sinusoidal shape, with the extremum corrections of +15 V/m for the electric field at 0700 UT and 25 V/m at 2100 UT. The current density extremum corrections are 0.5 pa/m 2 at 0700 UT and 0.8 pa/m 2 at 2100 UT. Figures 2c and 2d show the corrections for the electric field and current density organized by K p index. These extremum values and times are similar to those presented in Figures 2a and 2b. [24] The K p index is a planetary index that nominally measures the magnitude of magnetic disturbances caused by phenomena other than diurnal variation and the long-term components of the geomagnetic storm time variation [Kivelson and Russell, 1995]. The K p index is an arithmetic average of the K s values at 13 standard observatories spread around the Earth. The K s values are standardized indices quantized to units of 1/3 (0, 1/3, 2/3,..., 9) that give the level of magnetic disturbance at a particular observatory. The same levels can also be written as (0, 0+, 1, 1+, 2,...,9, 9). These local disturbance levels are essentially the difference between the highest and lowest disturbed horizontal magnetic components during a 3-hour interval. For the purposes of this paper, low magnetic activity is designated by K p < 2+, medium magnetic activity is designated by 2 < K p < 4, and high magnetic activity is designated by K p >4. [25] Owing to instrument development problems and inclement weather all good data from both arrays were used as a single dataset for the basis of this analysis. To understand the results, it is important to understand the number of data points remaining after removing local and global perturbations. Figure 3 shows the number of data points (in units of number of 1-min data points and in number of days) used in the analysis. The top half of Figure 3 displays the number of data points and days by season. The uncorrected data (Figures 3b, 3d, 3f, and 3h) are the total number and days of data points available for the analysis that passed computer code, weather quality, and visual inspection filters without requiring IMP 8 availability. The corrected data (Figures 3a, 3c, 3e, and 3g) are the number of good uncorrected data points and days for which IMP 8 satellite data were available for the magnetospheric correction. Each plot is subdivided into four panels, each representing a season. The top panel is the winter season, beneath that is the spring season, beneath that is the summer season, and finally in the bottom panel is the autumn season. On each panel are two curves. The solid line in each panel is scaled with the left vertical axis, showing the number of 1-min data points as a function of UT time. The dashed line in each panel is scaled with the 5of17

6 Figure 1. The number of diurnally averaged data points in minutes and days for (a) the vertical electric field, (b) the current density, and (c) the calculated conductivity. The solid line is the total data available. The dashed line represents the initially reduced data. The dashed and one-dot line is the data available after being filtered for good weather. The dashed and three-dot line is the data available after applying the first computer algorithm. The long-dashed line is the data available after applying the second computer algorithm. The dotted line is the remaining data for which the Weimer potential calculation can be applied and for which diurnal variations can be studied. right vertical axis, showing the same data as the solid line but in units of days versus UT time. The bottom half of Figure 3 (Figures 3e through 3f) are sorted by K p index instead of season. Therefore in each plot there are three panels, the top panel being data sorted by low K p index (K p < 2+), the middle panel being medium K p index (2 < K p <4), and the bottom panel being high K p index (K p >4 ). [26] Figure 3 shows that the number of data points was greatly reduced by performing the last data reduction step in the process, the magnetospheric correction. It is important to note that on average the data set was reduced to about one third to one fourth of its original size by the requirement of IMP 8 data availability. In several cases the amount of data was reduced to insignificance or even zero. [27] The last step in the process was to average the data as a function of UT for both the uncorrected and the corrected data sorted by season and by K p index. Conductivity was calculated from the E z and j z data on a point-wise basis prior to averaging. Since the season change occurs about threequarters of the way through the particular transitional month, the start of the given season was taken to be the first of the following month, e.g., 1 January as the start of the Northern Hemisphere winter rather than 22 December, 1 October as the first day of the Northern Hemisphere autumn, etc. Note that in all figures, the seasonal curves are referenced to Northern Hemisphere seasons. 4. Results [28] Figure 4 displays the uncorrected and corrected average daily variation for E z, j z, and s of the entire database, without any sorting criteria applied. The uncorrected and corrected electric field data have maximums of 205 V/m at 1400 UT and 210 V/m at 1830 UT, respectively. The minimum values of 145 and 138 V/m occur at 0230 and 0300 UT, respectively. Also, the uncorrected and corrected current densities have maximums at 1300 and 2030 UT, respectively. Their minimums occur at 0000 and 0500 UT, 6of17

7 Figure 2. The average magnetospheric correction as a function of UT time for the electric field and current density. (a) The average Weimer model correction to the electric field (by season). (b) The average Weimer model correction to the current density (by season). (c) The average Weimer model correction to the electric field (by Kp Index). (d) The average Weimer model correction to the current density (by Kp Index). respectively. More interesting trends appear when the data have been filtered for good weather days and displayed by season and Kp index Electric Field [29] Curves showing the diurnal variation of the electric field, current density, and conductivity for each of the four seasons are presented in Figure 5. For the corrected electric field data, Figure 5a shows that the lowest values for all seasons occurred at 0030, 0330, and 0600 UT. The same figure also shows the highest values for the corrected electric field data for all seasons occur at 1500, 1900, and 2100 UT. As mentioned earlier, the autumn season, which is represented as the solid line, was the data set with the highest quality. The corrected electric field had a peak value of 213 V/m at 1900 UT and a minimum value of 141 V/m at 0600 UT. Additionally, the corrected autumn electric field had a secondary maximum of 155 V/m at 0500 UT. The more statistically significant uncorrected data represented in Figure 5b show a more flattened curve. The peak minimum and maximum values of 135 and 191 V/m were shifted to 0200 and 1500 UT, respectively. The secondary maximum in the corrected autumn curve at 0500 UT was not present in the uncorrected data. The dashed lines in Figures 5a and 5b are the corrected and uncorrected winter data, respectively. The data from the winter months was of slightly poorer quality than the autumn months. For both the uncorrected and the corrected data, the winter season contains the lowest electric field values, with the corrected (uncorrected) data averaging 154 (162) V/m. The single lowest value of the corrected electric field was 97 V/m, which occurred in winter at 0330 UT. For both the corrected and uncorrected data, peak winter values of 200 V/m occurred at 1500 UT. It is interesting to note that the corrected maximum and minimum for the winter season were shifted 3 hours earlier than the peaks for the autumn season. The lines in Figures 5a and 5b containing alternating dashes and dots represent the spring season corrected and uncorrected data. The corrected spring electric field had a minimum value of 155 V/m at 0600 UT and a peak value of 224 V/m at 2100 UT. The minimum value of 165 V/m in the uncorrected data was at 0200 UT. There were three secondary maxima: the corrected electric field had values of 195, 206, and 224 V/m at 0900, 1400, and at 2100 UT. The peak 7of17

8 Figure 3. The total number data points used in the analysis in terms of 1-min averaged data points referenced to the left vertical axis (solid line) and in number of days referenced to the right vertical axis (dashed line). The top row of panels are sorted by season: from top to bottom, the subpanels represent winter, spring, summer, and then autumn. The bottom row of panels is sorted by K p index; from top to bottom, the subpanels represent low K p index, medium K p index, and high K p index. (a, e) Electric field data with Weimer model magnetospheric correction applied. (b, f ) Total uncorrected electric field data available for analysis. (c, g) Current density data with Weimer magnetospheric correction applied. (d, h) Total uncorrected current density data available for analysis. value of 201 V/m for the uncorrected spring data occurred at 1500 UT. The summer season is represented as the dotted line in Figures 5a and 5b. Of all seasons, the highest value of 240 V/m for the corrected electric field occurred during the summer season at 1900 UT. As mentioned earlier, the statistics for this season were much poorer than the others. This extremely sparse data set was a result of weather problems and associated equipment problems coupled with the unavailability of IMP 8 and solar wind data. The atmospheric data from this season appear to be similar to that of the spring season but with more peaks and valleys. [30] The parameter that most directly probes the global thermometer suggestion is the DC level or diurnal average of the data shown in Figure 5. The Weimer model is an electrostatic one; thus the 24-hour average of the Weimer model correction should be zero in the long run. Since the uncorrected data had three times the statistical weight of the uncorrected data, the averages of the uncorrected data are the best available estimates of these parameters. The values of the averages were, in sequence from highest to lowest, summer at 196 V/m, spring at 185 V/m, autumn at 168 V/m, and then winter at 162 V/m. Table 1 displays the summary of the corrected and uncorrected seasonal data. [31] Curves showing the diurnal variation of the data sorted by K p index are presented in Figure 6. Figures 6a and 6b are the corrected and uncorrected electric field data; low K p (K p < 2+) is represented by a solid line, medium K p (2 < K p < 4) is represented by a dashed line, and high K p (K p >4 ) is represented by a dashed and dotted line. The curves shown in Figure 6a, for the corrected data, have the characteristic shape with minima at 0400, 0330, and 0600 UT and maxima spread over a wide time range at 1900, 1900, and 1000 UT. The similarity in shape of all three curves is interesting; inspection of Figure 6a shows that similar trends can be seen in the corrected data. Between 1200 and 1700 UT all three curves trend upward with similar slopes, and after 2100 UT they trend downward with similar slopes. These three corrected data curves have a similar global shape, as expected. However, there are differences in the data. When the K p index was medium or low, the electric field maximized at 1900 UT, while it maximized at 1000 UT for high K p values. In addition, the corrected 8of17

9 Figure 4. Diurnal averages of the uncorrected and corrected (unsorted) data set plotted as functions of UT. (a) The vertical electric field. (b) The current density. (c) The calculated conductivity. high K p data show higher values in the morning hours and lower values in the afternoon hours than either the low or medium K p data. Table 2 displays the summary of the corrected and uncorrected data organized by K p. [32] For all subsets (by season and by K p ) the standard deviations were computed and found to be 30% of the mean. The errors in the mean are measures of the reliability of the measurements. They had a magnitude of less than 5 V/m in absolute terms or less than 3% as a fraction of the average values. The errors in the mean are represented as error bars in Figures 5 and 6. For each curve the bins with the lowest error and highest error have error bars plotted. In addition, there is a third bar that represents the average error over all bins. [33] Finally, Figure 7 displays a comparison of the data revealing the effects of the data reduction method used in this analysis. It displays the electric field for autumn and for high K p index. For both plots the solid line represents the raw data with no magnetospheric correction, the dashed line represents the raw data set where the magnetospheric correction can be applied, and the dotted line is the actual corrected data. As mentioned earlier, the statistics were reduced by internal filtering and by applying the Weimer model with associated IMP 8 satellite availability requirements. As a result of the correction, additional features have appeared. For example, secondary maxima appear at 0500 UT on the autumn curve and 1000 UT on the high K p index curve Current Density [34] The current density is the fundamental quantity measured in this experiment. However, it is much more difficult to measure than the electric field. The current density is on the order of pa/m 2 and is easily obscured by convection currents of charged blowing snow and space charge. Owing to the higher sensitivity to local conditions and equipment malfunctions, the best quality data sets are again during the Northern Hemisphere autumn and winter. The diurnal averages for both the corrected and uncorrected seasonal current density data are presented in Figures 5c and 5d, respectively. They are plotted in a fashion similar to the electric field data. For the autumn and winter seasons the current density data (both uncorrected and corrected) showed similar trends to each other. The corrected current density curves for both the autumn and winter seasons have similar shapes, except for a large separation at UT. The spring and summer seasons have a large percentage of their data missing and consequent statistical concerns, as mentioned earlier. Some agreement can be seen between the autumn and winter corrected current density data in Figure 5c with their corresponding corrected electric field displayed in Figure 5a. They both had decreasing slopes between 0100 and 0400 UT, increasing slopes between 0600 and 1600 UT, then relatively unchanging values between 1600 and 2100 UT. One difference, however, was that the percentage change from winter to autumn was about twice as high for the current density as it was for the electric field; the electric field showed a 12% average increase and the current density 21%. The values of the uncorrected averages were, in sequence from highest to lowest, autumn at 6.19 pa/m 2, winter at 5.29 pa/m 2, summer at 5.17 pa/m 2, and then spring at 4.14 pa/m 2.It is interesting to see that the sequence is the same as the electric field data but shifted forward by a season. Table 3 displays the summary of the corrected and uncorrected seasonal data. 9of17

10 Figure 5. Seasonally sorted and binned diurnal averages plotted as functions of UT. (a) Corrected electric field (b) Uncorrected electric field (c) Corrected current density (d) Uncorrected current density (e) Corrected conductivity (f ) Uncorrected conductivity. For each curve, error bars representing the lowest, highest, and average errors are provided. [35] The corrected and uncorrected diurnal averages for the current density as a function of K p index are presented in Figures 6c and 6d. Both the corrected and uncorrected current density data have similar variations for all three K p ranges between 0900 and 1800 UT. At all other times, there are large separations between the data values. Table 4 displays the summary of the corrected and uncorrected data organized by K p Calculated Conductivity [36] The calculated conductivity was computed by dividing the current density by the electric field. The division was performed point by point prior to averaging. Figures 5e and 5f display the diurnal averages for the corrected and uncorrected seasonal conductivities in a similar fashion as the electric field. Overall, the uncorrected autumn and winter data in Figure 5f show very flat curves averaging around Siemens/m. The corrected data in Figure 5e retain some of the flat structure as the corrections to j z and E z divide out. The additional spikiness was a result of maximum values for the current density passing the filters and the minimum values for the electric field passing the filters so that when the division was performed to obtain the conductivity extreme values resulted. Additionally, there were no corrected summer conductivity values between 0000 and 1900 UT because of the absence of corrected current density data at these times during summer. Table 1. Corrected (Uncorrected) Electric Field Data (by Season) a Season Average High Value Low Value Time of High Time of Low Summer b 161 (196) 240 (228) 95 (158) 1900 (1900) 0030 (0030) Spring 187 (185) 224 (201) 155 (165) 2100 (1500) 0600 (0200) Autumn 173 (168) 213 (193) 141 (135) 1900 (1500) 0600 (0200) Winter 154 (162) 200 (200) 97 (123) 1500 (1500) 0330 (0200) a Values have units of V/m and times are in UT. b Data set has poor statistics. 10 of 17

11 Figure 6. Kp index sorted and binned diurnal averages plotted as functions of UT. (a) Corrected electric field (b) Uncorrected electric field (c) Corrected current density (d) Uncorrected current density (e) Corrected conductivity (f) Uncorrected conductivity. For each curve, error bars representing the lowest, highest, and average errors are provided. [37] Curves showing the diurnal variation of the calculated conductivity sorted by K p index are presented in Figure 6. Figures 6e and 6f show the corrected and uncorrected calculated conductivity data, respectively. Both the corrected and uncorrected data show similar curves for all three K p ranges. 5. Discussion 5.1. Seasonal Variation of Data [38] Our results indicate that the atmospheric processes contributing to the fair weather electric field were most active during the Northern Hemisphere summer and spring, followed by autumn and then winter. The electrical activity had a diurnal maximum at 1900 UT. This result agrees with the Mauna Loa data showing extrema at the same times with the current density varying between 80% and 120% of its mean [Cobb, 1968]. As discussed above, the Weimer model correction should not change 24-hour averages. Therefore the uncorrected data, with its much better statistics, may be used the estimate the 24-hour averages. These data show that the maximum occurred in the summer, followed by spring, autumn, and then winter. A summer maximum of the electric field in the Northern Hemisphere summer is in contradiction to the original Carnegie and Maud analysis result [Parkinson and Torreson, 1931; Torreson et al., 1946] but consistent with the analysis of Mauna Loa data by Adlerman and Williams [1996]. The original Carnegie data analysis results apparently exhibited the opposite seasonal varia- Table 2. Corrected (Uncorrected) Electric Field Data (by Kp Index) a Kp Range Average High Value Low Value Time of High Time of Low Low 172 (165) 235 (200) 138 (134) 1900 (1500) 0400 (0200) Medium 167 (167) 213 (193) 118 (135) 1900 (1500) 0330 (0000) High 176 (175) 207 (201) 147 (138) 1000 (1000) 0600 (0000) a Values have units of V/m and times are in UT. 11 of 17

12 Figure 7. The effects of the data correction method used in this analysis. (a) The electric field for the autumn season. (b) The electric field for high Kp index. For both plots the solid line represents the raw data with no magnetospheric correction, the dashed line represents the raw data set to which the magnetospheric correction can be applied, and the dashed line is the actual corrected data. tions from our analysis results, a 30% greater winter value over that of the summer [Parkinson and Torreson, 1931; Markson, 1986]. This apparent paradox was thought to be explained by the tropical region being most active during the winter. The view that the Northern Hemisphere winter was dominant was apparently confirmed by several subsequent studies [Paramanov, 1950; Adlerman and Williams, 1996, and references therein]. Recent reexamination of these older data have found them to be influenced strongly by seasonal variation in Northern Hemisphere boundary layer aerosol particle densities [Adlerman and Williams, 1996]. Reanalysis of the Carnegie and Maud data and newly published data on air-earth current from Mauna Loa suggest that many of the previous potential gradient measurements were unreliable indicators of the true seasonal variation [Adlerman and Williams, 1996]. These new results agree with the results presented here in showing that electrical parameters peak in Northern Hemisphere summer. Additionally, the results presented here are in agreement with the analysis of the data measured at Vostok, Antarctica in 1998 and in agreement with the seasonal variations of the AC global circuit, or Schumann resonances, measured at Nagycenk station in Hungary from 1993 to 1996 [Märcz et al., 1997; Corney et al., 2003]. [39] The atmospheric electric field data ideally can be correlated with global meteorological activity. In Figure 5a there is a plateau in the spring and autumn electric field curves at UT. As this time corresponds to afternoon in Asia/Indonesia and the seasonal average includes September, a primary monsoon month for India, we attribute these features to the monsoons in that region. The spring data show peak values around UT. Major contributions to these peaks were probably thunderstorms in North America, as these months are the primary times for thunderstorm activity there. [40] Strong evidence that the electric field measurements are of a global nature comes from the calculated amplitude variations. For all seasons in Figure 6a, there exists a characteristic sinusoidal shape with the minimum centered between 0300 and 0600 UT and the maximum centered between 1800 and 2100 UT. The amplitude variation of the average diurnal curve has been an important parameter in atmospheric electricity research Table 3. Corrected (Uncorrected) Current Density Data (by Season) a Season Average High Value Low Value Time of High Time of Low Summer 8.26 (5.17) 10.4 (8.41) 5.61 (1.15) 2100 (1530) 0800 (1030) Spring 4.54 (4.14) 6.81 (5.53) 2.62 (2.99) 2230 (2030) 0500 (1830) Autumn 6.02 (6.19) 7.02 (6.84) 5.08 (5.01) 1630 (1400) 0300 (0000) Winter 5.02 (5.29) 6.69 (5.88) 2.46 (4.29) 2030 (1200) 0430 (0130) a Values have units of pa/m 2 and times are in UT. 12 of 17