Polar magnetic variations beyond the quiet daily variations (QDC) are predominantly caused by the horizontal and field-aligned currents related to the convection systems sketched in Fig. 1. The horizontal currents are equivalent to oppositely directed ionospheric drift motions. The DP2 (forward) and DP3 (reverse) convection modes could be considered the basic modes for the transpolar convection and currents while DP1 (substorm) and DP4 (DPY) convection modes may generate perturbations of the two basic transpolar convection systems.

The basic definition of the Polar Cap (PC) index could be found in Troshichev et al. (2006). In summary (cf. Stauning, 2013b), the PC index is based on an assumed linear relation between EM, the “geo-effective” (or “merging”) electric field in the solar wind encountering the Earth, and ΔFPROJ, the polar cap horizontal magnetic variation (at ground) projected to the so-called “optimum direction”.

The linear relation is ΔFPROJ=αEM+β. The optimum direction is perpendicular to the average DP2 transpolar equivalent current direction (see Fig. 1) and makes an angle φ to the dawn–dusk direction. The projection enhances the coupling of the PC index to the dominant DP2 forward convection mode.

The “geo-effective” (or “merging”) electric field (Kan and Lee, 1979) is defined through EM=VSWBTsin2(θ/2), where VSW is the solar wind velocity, BT is the transverse component of the interplanetary magnetic field (BT=(By2+Bz2), while θ is the polar angle between the Z axis of the geocentric solar-magnetospheric (GSM) coordinate system and the transverse IMF component.

Equation (1) is now inverted to give a definition of the PC index by equivalence with the merging electric field measured in mV m-1: PC=(ΔFPROJ-β)/α(∼EM). The scaling parameters comprise the optimum direction angle, φ, derived to give optimum correlation between the solar wind intensities and the projected magnetic variations, while the coefficients, α (slope) and β (intercept), are found from Eq. (1) through regression analyses based on an ensemble of concurrent values of the merging electric field, EM, and the polar cap horizontal magnetic variation vector, ΔF, counted from the quiet level, FQL.

The initial concept of the reference quiet day curve (QDC) for Polar Cap (PC) index calculations was defined in Troshichev et al. (2006) (hereinafter TJS2006) by the sentence: “Magnetic deviations δD and δH are calculated from a certain level, `curve of quiet day', which presents the daily magnetic variation, observed at the particular station during extremely quiescent days.”

The initial QDC procedure described in Janzhura and Troshichev (2008) (hereinafter JT2008) is based on the above principle. Each element of the two components of an initial QDC value is derived from quiet data values recorded at the same time of day within an interval of 30 days. The date for the calculated QDC is found as the weighted average of all dates with quiet segments. Successive displacements of the 30-day interval provide a series of QDC data sets from which non-linear interpolation provides QDC values for each day. The selection relies on quiescence criteria based on limits regarding the variability and the gradient of the respective component data within 20 min intervals. The concept from TJS2006 is violated in the new index procedure by the addition of an IMF By-related solar wind sector (SS) term derived from the daily median of all data, quiet as well as disturbed, to the basic QDC derived from quiet data samples only.

In the initial (JT2008) QDC procedure the separations within the 30-day interval between the dates of the quiet samples and the actual QDC date are not considered. Thus, the possible modulation of the basic QDC values with the phase of the solar 27-day rotation cycle, in particular with the solar wind sector-related phase of systematic variations in the IMF By component, is lost. This is seen clearly in Fig. 4 of JT2008 where the displayed variations in QDC amplitudes through November months during all years 1998–2002 are almost perfectly linear to indicate slow seasonal changes only.

The IAGA-endorsed QDC procedure, which includes a solar wind sector (SS) contribution, is described briefly in the notes found in the PC index documentation (Appendix_AD).

The “Note on calculation 2 (sector structure)” reads:

“The sector structure is determined for each minute by a two-step smoothing process from the THL (Thule) X and Y component daily median values, respectively. In the first step, a 7-day running mean is produced, which in the next step is again smoothed by a 7-day “Robust Loess” (quadratic fit).”

The solar wind sector (SS) contributions, ΔHSS, determined from the daily median values of the H-components, are presented in Fig. 6a, b of Janzhura and Troshichev, 2011 (hereinafter JT2011) for June 1996 and 2001. Examples of smoothing and processing over various intervals are shown in the figures. The variations in the final ΔHSS term through any single day are generally small (here less than ±5 nT) compared to the amplitude of the total variation (-35 to +65 nT) through June 2001.

A similar presentation could be made for the geomagnetic D-component. Examples of the solar wind sector terms for the D-component are provided in JT2011. Note, however, that there are inconsistencies between the scaling of the SS effects in Figs. 3, 6, and 7 vs. Figs. 4 and 8 in JT2011 to be discussed in Sect. 5.2 below.

A quite new feature in the IAGA-endorsed QDC derivation procedure is the subtraction of the SS terms from the measured component data values thereby changing the data base used to derive the “basic” QDC. This feature is included neither in JT2011 nor in the “Polar Cap (PC) index” documentation available at http://pc-index.org, but found only by analysing the computer procedure “qday_db.m“ in Appendix_AF that reads % Calculate COMP-SS     by_x = var_x - ss_x;     by_y = var_y - ss_y; % Calculate QDC for every component The effects of this modification of the data basis for QDC calculations depend on the relative position of quiet intervals with respect to the IMF By phase. They are possibly small but uncertain, which stresses (again) the need for a comprehensive and updated description of the IAGA-endorsed PC index procedure. Moreover, the files holding the solar sector terms (ss_x, ss_y) and the derived QDC values (qdc_x, qdc_y) are not available in Appendix_AF for independent examination.

The IAGA-endorsed “effective” QDC is defined in “Note on calculation 5 (geomagnetic disturbances in observatory data)” found in Appendix_AD: “Subtract sector structure and QDC from THL X and THL Y and make 5 min averages”. The corresponding computer procedure “dist5m.m” found in Appendix_AF reads % substract SS and QDC variations     dist_xs = x - ss_x - qdc_x;     dist_ys = y - ss_y - qdc_y; Thus, an “effective” quiet day level is composed of a “basic” QDC (TJS2006; JT2008) with an added solar wind sector (SS) contribution as explained in JT2011. It is important to note that all elements of the basic QDC are derived from quiet samples recorded at corresponding times of the day while the added SS term is based on daily median values of all data whether quiet or disturbed. The main problem for the “effective” QDC now defined in the IAGA-endorsed procedure relates to the solar wind sector contributions as pointed out in the critical commentary by Stauning (2013a).

Here, we resolve the horizontal geomagnetic terms in orthogonal (H,D) components in order to use the examples of the IMF By-related vector, ΔFSS, provided in JT2011. The “effective” QDC values for the geomagnetic H-component are produced by adding IMF By-related terms, ΔHSS (presented for June 2001 in Fig. 6b of JT2011) to the initial basic QDCs determined (approximately) by the method described in JT2008. The result is presented in Fig. 1 of JT2011 for days 145 through 245 of the summer of 2001 with the QDC curve in black superimposed on the magnetic variation data in faint grey or in Fig. 4.10 (in colour, more readable) of Troshichev and Janzhura (2012) (hereinafter TJ2012).

When adding the ΔHSS terms to the smoothly varying basic QDC-H values, the ΔHSS variation patterns are transferred to become modulations of the “effective” QDC-H component patterns as seen most easily in the upper envelope corresponding to night values, and in the lower envelope for midday values of the QDC-H curves shown in Fig. 1 of JT2011 (or in Fig. 4.10 of TJ2012) The QDC-D component including a ΔDSS term is handled correspondingly.

Note from these figures that the upper and lower envelopes of the “effective” QDC-H traces are almost congruent such that the in-between daily range of the “effective” QDC-H series only shows slow seasonal variations with maximum amplitude at midsummer. When ΔHSS varies monotonically (with constant slope), the modulation patterns of the upper and lower envelopes are identical apart from half a day's phase shift. When the ΔHSS slope changes, then small differences between the upper and lower envelopes may result. The display in Fig. 1 of JT2011 (Fig. 4.10 of TJ2012) presents these characteristics.

There are two improper features in the “effective” QDCs presented in these figures: A.

The top of the QDC-H traces present calculated night values of the quiet magnetic field, but the real night values are not significantly affected by the IMF By-variations (cf. Figs. 2 and 3c here). Hence, the upper envelope of the “effective” QDC should be rather flat and not strongly modulated by IMF By variations.

The addition of the solar wind sector term to the basic QDC in the new IAGA-endorsed PC-procedure is not justified since the day and night changes in the magnetic components with IMF By are quite different and, therefore, could not be compensated for by just adding a slowly varying daily term. Statistically, the difference between IMF By effects at day and night is easily seen in Fig. 2 (cf. Fig. 5 of JT2011), which displays the mean daily variation in the H-component recorded at Thule during the summer months for three gradations of the IMF By: By < -3 nT, -2 < By < 2 nT, and By > 3 nT. With magnetic local time (MLT) noon located at 15:00 UT and local time (LT) noon at 16:00 UT it is easy to see that there is hardly any IMF By-related difference between the three gradations through local night (∼ 00:00–12:00 UT) while there are substantial differences at local daytime (∼ 12:00–24:00 UT).

Note also from Fig. 2 that the amplitude range in the mean daily variation varies with the IMF By parameter. For summer 2001, from midnight (∼ 04:00 UT) to noon (∼ 16:00 UT) the amplitudes in the daily variation for the three cases are ∼ 70, 120 and 230 nT for positive, near-zero, and negative IMF By gradations, respectively. Corresponding large variations should appear in the daily amplitudes of the “effective” QDC-H component displayed for the same period in Fig. 1 of JT2011 (or Fig. 4.10 of TJ2012) but are not seen.

The display in Fig. 2 relates to all conditions and not just the quiet cases, but the trends are the same for just the quiet cases. Thus, imposing on the basic QDC-H series the (almost) same ΔHSS shift at night and at day is not in agreement with the statistics presented in Fig. 2. It could be noted from Fig. 1 of JT2011 (or Fig. 4.10 of TJ2012) that the amplitude of the variations in both the upper and lower envelope of the “effective” QDCs through June (days 152–181) are ∼ 100 nT, which corresponds to the range in the ΔHSS variation (-35 to +65 nT) through June 2001 shown by the magenta line in Fig. 6b of JT2011.

These objections were published in Ann. Geophys. in a commentary, Stauning (2013a), to the paper JT2011. The commentary paper was submitted in 2012 and forwarded at that time to the authors of JT2011, but no reply has been published yet.

Magnetic data from Thule have been examined for a closer inspection of the different day and night response in the H- and D-components to IMF By variations. Plots were made to present summer data from mid-May to mid-August (days 135 to 235). For each year the component data have been divided into all-hour (00:00–24:00 UT), daytime (13:00–23:00 UT), and night-time (01:00–11:00 UT) groups. The H-component data from these groups are displayed in separate plots in the example for 2002 in Fig. 3.

In order to reduce the ordinary daily variations, the regular quiet day variations have been suppressed by subtracting the basic QDC values from the recorded data using QDC files (without the solar wind sector contributions) calculated by the JT2008 procedure and supplied from AARI. Furthermore, from the displayed H-components the daily median values have been derived. With a weighted smoothing over 5 days these median values are shown in heavy red line. For comparison, the related IMF By values (smoothed over 7 days) are presented in magenta line in the lower part of the diagrams with their scale values indicated to the right.

From Fig. 3 it is seen that the IMF By variations displayed by the magenta line in the bottom of each field are reproduced to some extent in the all-hour plot of the H-component in Fig. 3a. However, it is clear that the strong IMF By-related variations are only reflected in the daytime values displayed in Fig. 3b. For the night-time values of the H-component displayed in Fig. 3c there is no correspondence between the variations in the H-component and the IMF By changes. Corresponding results appear in similar diagrams for further years (1997–2009) looked at.

To derive PC index values, the magnetic variation vector, ΔF, corrected for the “effective” QDC (QDCeff) should be projected to the optimum direction characterized by the angle, φ. Then the intercept value, β, should be subtracted, and the result should be divided by the slope, α. Thus, for the magnetic variation vector: ΔF=FRAW-FQDCeff=FRAW-FQDC-ΔFSS. Here, for Thule (77.47 N, 290.77 E), with declination (year 2001) = 297.33∘, the optimum angle and slope values presented at http://pc-index.org have been used in combination with ΔFSS vectors comprising the components ΔHSS and ΔDSS presented in JT2011 (SS(HTHL) and SS(DTHL)). For the projection of the SS contributions to the optimum direction we have used ΔFSS,PROJ=ΔHSSsin(VPROJ)-ΔDSScos(VPROJ), where VPROJ=longitude-declination+UThr×15∘+φ. Using the relation in Eq. (3) the SS-related contribution to the PC index is then ΔPCNSS=-ΔFSS,PROJ/α. The optimal direction at noon is close to perpendicular to the IMF By-related contributions. Hence the projection of the SS vector, ΔFSS= (ΔHSS, ΔDSS), to the optimum direction is small at noon. At other local hours the projection angle changes and the projected values of the (almost constant) SS vector become more significant in magnitude and shift sign twice. The diminished slope values, α, away from midday cause further enhancement (cf. Eq. 7) of the effects from the SS contribution to the PC index values during local night and morning hours.

The next question is now: what are the consequences of using the “effective” QDC procedure (Appendix_AF) for the PC index calculations? Here, we use the data published by the authors of the procedure to derive the additions to the PCN index values from the solar wind sector terms. The solar wind sector effects are mainly associated with IMF By-related currents (DP4 convection mode, cf. Fig. 1) in and around the daytime Cusp region at latitudes equatorward of Thule, the current direction being related to the sign of By. This current system is located at and close to local noon and its effects on magnetic recordings from Thule are very clear for the daytime sector in the data plots in Fig. 3b as well as in the statistical daytime values presented in Fig. 2. It should be noted that since the solar wind sector-related disturbance vectors, ΔFSS, are derived from smoothed daily medians, they keep an almost constant value and direction in the local (rotating) reference frame through day and night and from one day to the next.

Since we need both the ΔHSS and the ΔDSS terms to calculate ΔPCNSS, example values could have been extracted from Fig. 4b in JT2011, where the peak values seen on 22 June 2001 are also the peak values in the displays in their Fig. 8. There is a problem, however. The example values, counted from the levels at IMF By = 0, would then be ΔHSS= 150 nT , ΔDSS= 90 nT. Control calculations of the 5-day sliding averages of the median ΔHSS and ΔDSS terms for June 2001 agree with the concept that the values in Fig. 6b of JT2011 are correct while the values presented in their Figs. 4 (with the scale factor 10) and in Fig. 8 are around twice the correct values. Hence, for calculations of the IMF By modulation of the PCN index we use here: ΔHSS=65nT;ΔDSS=40nT(on22June2001atIMFBy∼ 4nT). Using these values for the present case from 22 June 2001 where IMF By ∼ 4 nT, the solar wind sector (SS) related changes, ΔPCNSS, in the index values have been calculated and are displayed in Fig. 4. The contributions, ΔPCNSS, related to the ΔHSS and the ΔDSS terms, which change little during the day or from day to day (see Fig. 6b of JT2011), come on top of the PCN values calculated from the actual magnetic variations corrected for the basic QDC.

Table 1 displays the SS contributions to the PCN index from the solar wind sector terms given in Eq. (8) for some selected times through the day. Like for Fig. 4, the coefficients, optimum angle and slope values are taken from the IAGA-endorsed files of coefficients provided at the web site http://pc-index.org.

Thus, although the IMF By-related variations in the polar cap magnetic fields maximize at noon, the ΔPCNSS values at this local time are quite small due to the projection to the optimum direction in combination with the high slope values. The real IMF By-related variations in the polar cap magnetic fields are almost absent during the night and morning hours as seen clearly in Fig. 3c as well as in the statistical values in Fig. 2. Hence, it is not appropriate that the unjustified solar wind sector-related ΔPCNSS values are so large in the night and morning hours, here almost 2.5 mV m-1 in the index values through 03:00–09:00 UT, for a case with IMF By ∼ 4 nT, which is a moderate and common value.

For comparison of specific data sets, OMNI solar wind data referred to the magnetospheric nose have been downloaded from the omniweb site (ftp://omniweb.gsfc.nasa.gov/omni/) while PCN index values derived according to the new IAGA-adopted PC index procedure have been downloaded from the http://pc-index.org (2014) website.

In order to get a clear view of the effects of the solar wind sector terms, values of the northern Polar Cap (PCN) index and the geo-effective electric field (EM) shifted in time to apply to the polar cap have been contrasted in plots of which some examples are shown here. Both quantities have fluctuations. Hence, for a fair comparison some averaging is needed.

From the diagram in Fig. 6b of JT2011 a period of positive excursion in the ΔHSS term could be the interval from 18 to 25 June 2001 with peak value on 22 June, the date used for Fig. 4 here. For this interval the series of EM as well as PCN values have been averaged over corresponding times through the 8 days. The 8-day mean values are displayed in Fig. 5a. From Fig. 6b of JT2011 a corresponding interval for negative excursions in ΔHSS could be from 3 to 10 June, 2001. The 8-day average values for this interval are presented in Fig. 5b.

In Fig. 5a it is seen that between 03:00 and 09:00 UT (local night at Thule) the PCN index values are much lower, by around 1.0 to 1.5 mV m-1, than the corresponding EM values (in mV m-1). This agrees well with the values of the anticipated depression, ΔPCNSS, seen within this time interval in Fig. 4 bearing in mind that the values on 22 June represent the peak excursion with ΔHSS ∼ 65 nT in Fig. 6b of JT2011. The ΔDSS values also needed for calculation of PCN index values vary similarly to ΔHSS as seen in Fig. 4 of JT2011.

For the night hours of the second interval, 3–10 June 2001, the data displayed in Fig. 5b indicate the opposite trend compared to Fig. 5a for the relation between PCN and EM. Now, the average PCN values are larger than the corresponding EM values by 0.5 to 1.0 mV m-1. Considering that the ΔHSS values range from ∼ -20 to -35 nT during this interval and that the ΔDSS values are also lower than in the foregoing interval there is again agreement with the expectations provided by Fig. 4.

There are also some systematic differences in the relations between PCN and EM during daytime in the two cases (cf. Fig. 12 in Sect. 7.3 on reverse convection). The important point here is the documentation that during night hours the handling of the solar wind sector terms (ΔHSS and ΔDSS) used in the “effective” QDC derivation in new IAGA-endorsed procedure has adverse consequences. The solar wind sector terms, which give small contributions at the dayside, may give quite substantial, but unjustified changes in the PC index values when used at the night side.

Figure 7 presents the statistics for the occurrence frequencies and strengths of strong reverse convection cases through the data interval from 1997 to 2009 used in the recent IAGA-endorsed PC index procedure. Here, the strong reverse convection cases are defined as those where the (negative) value of the horizontal magnetic term projected to the optimum direction, FPROJ, is more than 50 nT below the projected quiet level. The figure presents the monthly sums (smoothed over 2 months) of the product of the QDC-corrected and projected field values and their duration [nT × h] for Thule (blue line) and Vostok (red line).

For Thule and for Vostok the occurrence frequency of reverse convection cases peaks in the local summer months. Furthermore, from Fig. 7 it is clear that the occurrence frequency and intensity of reverse convection cases are much larger at Thule compared to Vostok. The summations over the entire span of years (excluding year 2003, no data from Vostok) give 3 times the intensity × duration value at Thule (-221 369 nT × h) compared to Vostok (-73 955 nT × h).

For comparison, Fig. 8 presents the corresponding display for strong forward convection cases (FPROJ more than 50 nT above the projected quiet level) and for the same span of years (1997–2009). From Fig. 8 it is seen that the intensities of forward convection cases are about the same for Thule and Vostok. The summations over the entire span of years (excluding year 2003) give an intensity × duration value at Thule of 2 486 796 nT × h slightly less than that for Vostok of 2 627 511 nT × h.

Including reverse convection cases adds to the “noise level” in the calculations of the optimum angle determined by the bulk of forward convection cases but the changes in angles are small. For the regression coefficients, however, including reverse convection events gives substantial increases in the slope values and negative increases in the intercept values. The effects from reverse convection events on the regression are illustrated in Fig. 11a and b of Stauning (2013b). The effects are particularly strong at daytime in the summer season and much larger for Thule than for Vostok due to the larger frequency and strength of reverse convection events in the northern polar cap compared to the southern. In addition to the direct consequences, in particular for the PCN index values, including the reverse convection cases causes strong imbalance between PCN and PCS coefficients and values.

The effects of including reverse convection cases in the data base for the regression calculations are illustrated in Fig. 9. In each of the fields for slopes (upper field) and intercepts (bottom field) the diagram holds a section for each of the 12 months of a year. The curves within 1 monthly section define the coefficient values through the 24 h of a day averaged over the month in question. They are shown in different line colours for the three versions. The IAGA-endorsed version (from http://pc-index.org) in red line is based on data from 1997 to 2009. The AARI#3 version in green line is based on data from 1998 to 2001 (TJS2006). Both versions include reverse convection cases. The DMI version in blue line is based on data from 1997 to 2009 excluding strong reverse convection events. A similar figure without the IAGA-endorsed parameters and using a different epoch for the DMI coefficients is provided in Stauning (2013b).

The extended data base interval for the IAGA version compared to the AARI#3 version now includes the less active years 1997 and 2002–2009. Referring to Fig. 7 it is easy to see that the abundance of reverse convection cases is much smaller in these years than in the very active years from 1998 to 2001. Hence, the relative importance of reverse convection cases is reduced from the AARI#3 (green lines) to the IAGA version (red lines) in Fig. 9, and the consequences are reduced slopes and less negative intercept values particularly at midday in the summer months. These changes are in strong contrast to the general conclusion in Troshichev et al. (2011a) on the invariability of PC index coefficients regardless of epoch. The PCS coefficients may remain about the same but the PCN coefficients change substantially with changing data epoch.

For illustration of the inter-hemispherical balance, Fig. 10 presents the PCN and PCS regression parameters, slopes and intercepts, derived from the present IAGA-endorsed coefficient files (http://pc-index.org). For easy comparison, the regression parameters have now been plotted vs. local solar time by advancing the PCN coefficients by 4 h and delaying the PCS coefficients by 7 h corresponding to the different longitudes of the two stations. Furthermore, the seasonal axis for the PCS index (in red) has been shifted by 6 months to make the seasonal variations in the PCS traces correspond to the equivalent variations in the PCN traces. It is now easy to see that the correspondence between PCN and PCS coefficients is not good.

Figure 11 presents the corresponding diagrams for the PCN and PCS coefficients derived by the DMI procedure omitting strong reverse convection events, but using the same span of years (1997–2009, ex. 2003), the same geomagnetic data from Thule and Vostok, respectively, and the same OMNI data as those used in the IAGA-endorsed procedure. The traces are again plotted vs. local time and season like in Fig. 10. A similar diagram based on data from the epoch 1995–2005 was published in Stauning (2013b).

From a comparison between the plots in Figs. 10 and 11, two features emerge. Firstly, the DMI procedure provides much better agreement between PCN and PCS index coefficients than the IAGA-endorsed procedure. Secondly, the sets of PCS coefficients agree fairly well between the IAGA-endorsed version and the DMI version, whereas there are large differences between the two sets of slope and intercept coefficients for the PCN index. The main reason for this discrepancy is the inclusion of strong reverse convection cases in the IAGA-endorsed procedure in combination with the much larger frequency and intensity of strong reverse convection events at Thule compared to Vostok.

Table 2 provides a summary of the effects from strong reverse convection on the peak values of the slope and intercept coefficients. For the AARI#3 version (epoch 1998–2001) and the IAGA-endorsed (http://pc-index.org) version (epoch 1997–2009) the ratios of strong reverse to strong forward convection intensities have been calculated from events with projected deviation from QDC in the horizontal component exceeding 100 nT. The events were extracted within local summer months (June–July for Thule, December–January for Vostok) and within local midday hours (12:00–20:00 UT for Thule, 04:00–12:00 UT for Vostok). For the DMI version for epoch 1997–2009, the strong reverse convection cases have been screened away in the regression procedure.

Note the strong decrease in the peak slope parameter with the decreasing relative amount of reverse convection events and the corresponding decrease in the numerical value of the (negative) intercept parameter. Also note the improved match between slope and intercept parameters for the northern (PCN) and southern (PCS) indices obtained by omitting the strong reverse convection cases in the DMI version.

Referring to the defining equation (Eq. 3) for the PC index, a larger slope gives reduced PC index values for the strong events where the intercept contribution is relatively small compared to the disturbance. Conversely, for weak events, where the projected disturbance, ΔFPROJ, is small, an increased negative intercept value gives larger PC index values. With the present IAGA-endorsed regression coefficients, the large intercept values for Thule give PCN index values that on the average are around 0.5–1.0 mV m-1 too large compared to the related geoeffective electric field values during quiet summer daytime conditions.

An example of unjustified PC index enhancements for a quiet interval (17–24 June 2008) is presented in Fig. 12. The blue line indicates the (weak) average geoeffective electric field through 24 h determined from OMNI data while the red line presents the mean IAGA-endorsed PCN values taken from http://pc-index.org. The two quantities should, on the average, be equal according to the definition of the PC index. During midday hours (12:00–22:00 UT) the average PCN index values are up to 1 mV m-1 larger than the corresponding electric field values. The increased PCN level is not justified in the magnetic variations, which are very small at this time, but just the result of the large negative intercept regression parameter that was derived with the inclusion of strong reverse convection cases.

At the date and time at the middle of the interval of enhanced index values (20 June, 16:00 UT) the IAGA-endorsed coefficients according to http://pc-index.org are α=65.61, β= -40.89 (cf. Fig. 10). Hence, the addition to the PC index coming just from the coefficients, notably the large (negative) intercept value, is Δ PC =-β/α= 0.62 mV m-1 which is in agreement with the display in Fig. 12.

Live PC index values and PCN and PCS index series are now made available through the new web site: http://pc-index.org. Furthermore, it holds PCN and PCS index coefficients derived by the IAGA-endorsed procedure. QDC values are not included. The web site includes the document “Polar Cap (PC) Index” written by O. A. Troshichev.

The Space-DTU (2014) ftp web site for PC indices: ftp://ftp.space.dtu.dk/WDC/indices/pcn/PC_index_IAGA_endorsement_documentation/ includes the documents: PC_index_description_main_document.pdf and PC_index_description_Appendix_A.pdf, and a directory, PC_index_description_Appendix_A___file_archive, with program transcripts and data files (neither including QDC values nor solar wind sector terms).