The data collected by the SSUSI instrument is composed of signals from the aurora and dayglow in the atmosphere. In Fig.  the distributions shown in each panel are the residual counts, which are the total counts minus the dayglow predicted by the model. This is the default data product provided in the SSUSI files. This data product does not include measurement uncertainties; the measurements are therefore assumed to be independent and are given equal weight in all of the fits and parameter estimates presented in this study.

The probability density function (PDF) for the Gaussian distribution is given by 1fG(x)=1σG2πe-12x-μGσG2 for real values of x. Here, μG and and σG are respectively the location parameter (mean value) and scale parameter (standard deviation) of the Gaussian distribution. We use a Gaussian distribution for the dayglow (DG) residual due to its similar shape, as shown in Fig. a.

The probability density function for the Moyal distribution is given by 2fM(k)=e-k+e-k2/2π for real values of k. A description of the distribution can be found in and Chap. 26 in . We can introduce a scale parameter associated with the width of the distribution (σM), and a location parameter (μM) by making the standardized variable k=(y-μM)σM, from which the the distribution in variable y is given by 3gM(y)=1σMfMy-μMσM.

This PDF might be useful in describing the intensity distribution of the aurora because it was invented to describe the energy loss of charged relativistic particles due to the ionization of the medium. This is similar to the process of creating aurora from energetic particles, cascading energy into secondary electrons that produce the aurora. Figure b shows how the Moyal distribution compares with the distribution of auroral intensities on the nightside with little to no dayglow. Such a good fit is typically seen in regions of strong aurora towards the nightside, motivating its application on describing the auroral part of the observed intensity distribution.

The distribution function in SciPy uses two parameters: location and scale. These parameters shift the distribution center and determine the width of the distribution. The Moyal distribution also has defined moments, and analytical formulas for each. We use the Moyal mean given by 4Moyal mean=μM+σM[γ+ln⁡(2)] where γ≈0.5772 is Euler's constant (also known as Euler Gamma) and ln⁡(2) is the natural logarithm of 2 .

To perform the fitting, we use a Python package called LMFIT Non-Linear Least-Squares Minimization and Curve-Fitting for Python; seefor more information and the convoluted probability distribution function 5fZ(z)=∫-∞∞fX(x)fY(z-x)dx, where we take fX(x)=fG(x;μG,σG) and fY(y)=gM(y;μM,σM). The fitting routine then returns the parameter estimates that provide the least-squares best fit. We use these best estimates to calculate the Moyal mean (Eq. ) and to plot the best-fit distributions of X, Y, and Z=X+Y to visually verify that the fitted distribution is a good match to the observed distribution (see, e.g., Figs. , , and ).

We validated the fitting procedure with randomly sampled data by generating Gaussian- and Moyal-distributed random values, summing them, and creating histograms as shown in Figs.  and .

One of the challenges posed by the convolution integral () is that it is not possible to estimate μG and μM separately; it is only possible to estimate the sum μ=μM+μG. This can be seen via the change of variables v=x+μM: fZ(z)=∫-∞∞fX(x-μG)fY(z-x-μM)dx=∫-∞∞fX(v-(μG+μM))fY(z-v)dv. First, we attempted to simultaneously estimate all four parameters (μM, σM, μG, and σG). However, this turns out to not be a uniquely defined problem. This is because several different values of μG and μM yield the same result. The convoluted distribution may appear similar for different locations of the underlying distributions. Hence, the convoluted PDF is not suitable for constraining the locations of both the DG-residual and aurora, and we must assume one of the locations is known. It makes sense to fix μG because it is easier to estimate from the data in regions outside the auroral oval than to estimate the location parameter of the auroral part of the signal. With μG fixed we can estimate the three remaining parameters (μM,σM and σG).

Figures  and show results of the fits (solid lines). The fit and parameter extraction in Fig.  is successful when using the known μG. Here, the correct μM, σM and σG are extracted. The location of the plotted Gaussian distribution is taken from the input values to show the result of the fit.

Since we do not know the exact value that μG should have, it represents a source of uncertainty. Figure  illustrates with synthetic data how the extracted parameters related to the Moyal distribution differ from the actual distribution parameters when the assumed μG differs from the true location. This must be taken into consideration when interpreting the modeled intensities from our separation method. From Fig.  we can see that the error in the estimated value of μG (its deviation from the true value) and the corresponding error in the fitted μM is such that their sum is preserved.

Equation () shows how the Moyal mean is calculated. The fitting function returns the correct values of σG and σM in all cases; only the estimated value of μM is affected by our choice of μG. Consequently, the error in the DG-residual estimate causes an equal error in the Moyal mean estimate, as discussed above. From manual examination of individual fits we find that typical fit parameter uncertainties are 0.5 %–2 %.

When applying the separation method to the data, we interpret the Moyal mean as a proxy for auroral intensity. From this, we can apply the method to real data from the SSUSI auroral dataset.

To apply the method to the data, we first collect the data in bins based on the grid, and create histograms, as shown in Fig. . The histograms are limited in intensity from -2000–5000 R to save on computation time. Truncating the window like this does not significantly affect estimation of either the mean or Moyal mean. The estimates are saved for each bin, and plotted as a polar plot.

In Fig. a shows the performance of the fit on a bin with little to no aurora. As expected, the Moyal mean estimates a larger value than the regular mean. This is also the case in panel (b), where we have more aurora than in panel (a), but it is still sunlit, being on the dayside during local summer. In panel (c) the regular mean estimates a value larger than the Moyal mean, and this is because of the long tail in this bin that the Moyal distributions does not capture. As can be seen in these panels, the convoluted distribution fits well to the real data in these bins. Therefore we argue that this method is a fair representation of statistical auroral intensities.

We use the method to estimate auroral boundaries. The code can be found at https://github.com/aohma/fuvpy (last access: 18 October 2023). The boundary method is a two-step process. First, the initial boundaries are identified by dividing the plot into slices and then estimating the threshold of the aurora in that slice. The model is then fitted to the initial boundaries found in the slices. This model is based on a Fourier series and uses three terms for equatorward boundaries and six terms for poleward boundaries. We have made minor alterations to fit our use, such as not considering the temporal evolution of the boundaries, as we look at statistics. We also modified the detection range for auroral boundaries to match the variations in our statistical maps, while visually being the best fit. During local summer, these threshold values are: 300–450 R in 5 R increments. During local winter, they are: 170–250 R in 5 R increments. To give an indication of the level of variation, or spread, of the estimated boundaries, we plot each set of boundary estimates individually. The boundary estimation method uses regularization to be even in the east-west direction unless the data suggests otherwise. Thus, there is little variation in the areas without data, primarily on the nightside below ∼72° MLat where there are few or no measurements.

We utilized the Swarm Ionospheric Polar Electrodynamics (Swipe) empirical model to represent high-latitude ionospheric electrodynamics . The SWIPE model is parameterized by the interplanetary magnetic field (IMF) components Bz and By, solar wind velocity Vx, dipole tilt angle, and the solar flux index in the F10.7 cm band, and provides electric field maps for both hemispheres based on the specified input parameters. The model is constructed using data collected by the Swarm and Challenging Mini-satellite Payload (CHAMP) satellites, and was developed to closely mirror the Swarm-based Average Magnetic field and Polar current System (AMPS) model, which characterizes large-scale ionospheric currents. For further details on the Swarm mission and its instrumentation, see . The source code is available at 10.5281/zenodo.10148940, ). In our implementation, we modified several functions within the code to generate maps of the same hemisphere under varying IMF By conditions.

Figure  shows our results for the Northern Hemisphere during local summer (dipole tilt angle >15°) and IMF By>5 nT. The Moyal mean and the total mean are respectively shown in the left and middle column. To facilitate comparison of the two means, the difference between the two is shown in the column at right. The number on the label for each row indicates the dataset represented by the row. In the difference plots, we can see that our method is usually around 100–150 R brighter than the total mean, with the difference being smaller in areas with stronger aurora. The boundary model detects different boundaries for the total mean and the Moyal mean. For example, the boundary is narrower for the total mean, particularly in the dayside area of the oval. The typical latitudinal range of both the equatorward and poleward boundaries generally agree well with auroral boundaries identified in other recent studies .

In all the rows, the total mean estimates of the oval extent are much smaller around the pre- and post-noon sectors at approximately 80° MLAT. This can be seen in the estimated boundaries, as this sector has a much narrower band than the boundaries estimated by the Moyal mean. Generally, for these rows, the polar cap estimated by the Moyal mean is smaller than that estimated by the total mean.

The bright spot located in the polar cap between 11 and 15 MLT is called the high-latitude dayside aurora (HiLDA). This spot is visible in statistics for both methods of calculating the mean. More detailed descriptions of HiLDA can be found elsewhere .

Figure  is the same as Fig. , but the data selected is for IMF By<-5 nT. We observe mostly the same patterns in the difference plots. The boundaries in the total mean plots seem very narrow on the dayside compared to the Moyal mean boundaries. There appear to be considerable differences inside the polar cap, and the Moyal mean estimates are much higher (∼100–250 R) than the total mean.

A dawn-dusk shift appears in the polar cap location in Figs.  and . In particular the dusk sector in Fig.  appears wider and stronger than in Fig. , while the dawn sector in Fig.  appears wider and stronger than in Fig. . Both of these features can be seen in boundaries derived from both the Moyal mean and regular mean statistics. The sign of IMF By does not appear to affect the overall intensities in the auroral oval.

Figure  (Fig. ) shows the Moyal mean, total mean, and the difference between them for dipole tilt angle <-15° and IMF By>5 nT (IMF By<-5 nT). The data selection represents local winter conditions in the Northern Hemisphere.

In Fig.  the Moyal mean-based estimates of auroral intensity is overall larger than the mean-based estimates outside the oval, as well as in the part of the oval in the noon sector. In the night side of the oval, which is completely dark, the total mean-based estimates are much larger than the Moyal mean-based estimates. In the area around noon, the Moyal mean tends to estimate slightly wider boundaries, whereas in the pre- and post-midnight sectors, the total mean estimates lead to wider boundaries than those associated with the Moyal mean. All the rows seem to follow similar patterns, with stronger intensity and wider auroral oval on the dawn side.

In Fig.  the differences between Moyal mean-based statistics and those based on the total mean are approximately the same as in Fig. . The intensities and boundaries follow similar patterns, with the Moyal mean-based estimates of auroral intensity within the oval (except for the noon sector) overall lower than those based on the total mean. The spot found in the polar cap in the second row of Fig.  does not appear in the second row of this plot.

The mirror asymmetry in the dawn-dusk shift of the polar cap location and difference in intensities between dawn and dusk found in Figs.  and  is not evident in these plots. The polar cap appears to be in the same place and the dawn sector appears to be the widest and strongest for both signs of IMF By. During negative IMF By, the polar cap appears to be elongated compared with a more circular polar cap during positive IMF By. As in the summer case, the sign of IMF By does not appear to affect the auroral intensities overall in the auroral oval.

Because of the inclination of the Sun synchronous orbit of the DMSP (Defense Meteorological Satellite Program) satellites, the coverage of the SSUSI instrument is more towards the nightside in the Southern Hemisphere, especially during local summer (tilt <-15°). However, we can still perform the boundary-estimation and carry out a limited comparison with the Northern Hemisphere.

Figures  and show that in the Southern Hemisphere, DMSP coverage is almost exclusively confined to the nightside, and coverage of the dayside is extremely limited. Despite the poor coverage on the dayside, the summer plots suggest that there is a shift in the polar cap location with a dependence on IMF By that is opposite the dependence observed for the Northern Hemisphere. This is most clearly observed in Datasets 3–5. The dawn sector appears to be brighter and wider during positive IMF By, whereas the dusk sector appears to be brighter and wider for negative IMF By. This sign dependence is likewise opposite the dependence observed for the Northern Hemisphere.

In Figs.  and we see that we have good data coverage during local winter in the Southern Hemisphere and that the boundaries derived from the Moyal mean are significantly narrower than the boundaries derived from total mean statistics. This is similar to the results obtained for local winter in the Northern Hemisphere (Figs.  and ). As in the Northern Hemisphere, the IMF By dependent shift in polar cap location is absent during local winter. In these figures, it appears that the auroral oval is the widest and strongest in the post-midnight sector for both IMF By polarities, which is consistent with the Northern Hemisphere. The intensities are also similar for ± IMF By when looking at the same dataset, except for the midnight sector in Dataset 3. The systematic IMF By-dependent deformation of the polar cap during local winter is also present in the Southern Hemisphere.

Figures  and show the seasonal dependence of the shift in the polar cap found in our results for the Northern and Southern Hemispheres respectively. Previous studies mention an IMF By-dependent displacement in the boundaries and center of the polar cap . However, these studies did not consider seasonal effects. For negative IMF By the polar cap is displaced towards dusk, and for positive IMF By the polar cap is displaced towards dawn during local summer in the Northern Hemisphere (opposite polarity for the Southern Hemisphere).

The center of the polar caps was determined using the Haversine formula at a mean emission height of 130 km above the Earth's surface (shown as crosses in Figs.  and ). The formula was first used to determine the distance between each MLT step in the estimated boundaries. The center was then calculated using the weighted average of all the points in the estimated boundaries, using the previously calculated distance between MLT–steps as weights. Using the center values obtained from Datasets 1 to 5, we found a mean distance of 492 km with a standard deviation of 16 km between the centers of the polar caps for positive IMF By and negative IMF By in the Northern Hemisphere. The corresponding numbers for the Southern Hemisphere using Datasets 3–5 is a mean displacement of 430 km with a standard deviation of 50 km. The larger standard deviation in the Southern Hemisphere is caused by the displacement in Dataset 5, which is 100 km smaller than for Datasets 3 and 4.

During local winter in the both hemispheres, there is no significant systematic shift in the center of the polar caps. For negative IMF By, the polar cap boundaries in the Northern Hemisphere appear to take on an elongated shape, whereas the polar cap boundaries remain mostly circular for positive IMF By. The cause of this elongation will not be further explored in this study.

During local summer in the Northern Hemisphere, there is a clear difference in the width and intensity of the dawn and dusk sectors of the auroral oval depending on the sign of IMF By. For positive IMF By, the dusk sector of the auroral oval is wider and brighter than the dawn sector. This is opposite for negative IMF By. This may simply be related to the shift in polar cap location, making the auroral oval wider in the section opposite of the shift direction, as the equatorward boundaries remain largely unaffected by the sign of IMF By. These effects are also present in the Southern Hemisphere, but with the sign of IMF By reversed.

The choice of distribution can significantly influence estimation of auroral intensity . Selecting a different distribution with a shape similar to the Moyal distribution would yield different modeled mean values. We chose the Moyal distribution because it performs well under sunlit conditions and for aurora with little to no dayglow during local summer. However, as shown in Fig. , the Moyal distribution often yields a poorer fit to the data for distributions from local winter. It only succeeds in certain cases, such as in the post-midnight sector during strong auroral activity during local winter (panel (b)). A likely explanation for these failures is that the assumption Z=X+Y where X and Y are Gaussian and Moyal distributed, respectively, is an oversimplification. In reality, auroral intensity at any given location within the auroral oval varies depending on, for example, the level of geomagnetic activity. In terms of distributions, it may be more conceptually appropriate to think of the distribution of auroral intensities Y itself as a combination of several Moyal distributions having different means and spreads. Implementing this would be a significant effort that is outside the scope of the present study.

In certain areas, the fit performs poorly due to the shape of the data distribution. Figure  shows normalized histograms of data from four bins in the auroral oval during local winter in the Northern Hemisphere. In panels (a) and (c), the fitted Moyal distribution falls off too quickly, failing to capture the tails of the data. Conversely, in panel (b), the data closely follows the shape of a pure Moyal distribution. Panels (b) and (c) represent bins at the same MLAT but approximately 4 h MLT apart (on either side of midnight), yet they exhibit very different distributions.

A trend emerges in the post-midnight auroral oval around 70° MLAT during local winter, where the distribution of intensities often takes on a Moyal-like shape. Pre-midnight, however, the data tends to exhibit a steeper distribution, as seen in panels (a) and (c). Additionally, spikes are present in panels (a) and (c), typically between -100 and 0 R, which adversely affect the fit. These spikes may cause the fitting function to overemphasize narrow features, resulting in fits that are too narrow overall. This challenge is one reason why we rely on the total mean to estimate boundaries during local winter. The failure of the fitting function is evident in the blue regions of the difference plots in Figs.  and . This approach is reasonable, as significant dayglow is not expected in these regions during local winter, and our method was originally designed to isolate the auroral signal from a dayglow residual. However, the cause of the negative tails in the distributions remains unclear. In panel (d), the distribution is narrow with a spike in the same range as observed during local summer (-50 to -60 R). While this spike is not visible due to the range of the histogram's y axis, it suggests the presence of some dayglow, which is plausible given the bin's location. The fit is nevertheless a relatively poor representation of the intensity distribution. We therefore deem the total mean to yield a more appropriate measure of aurora during local winter.

Figure  shows the distributions for the same bins as in Fig. , but during local summer in the Northern Hemisphere. Here, the distributions are dominated by a symmetrical component near the center, which corresponds to the DG-residual. Under these conditions, the fit error is significantly lower. However, in panel (d), a small but notable leftward tail introduces a discrepancy, causing the total mean to take on a more negative value than is likely realistic. The origin of this left tail is unknown, however it may be related to the counting errors left from the dayglow-removal routine. The tail is relatively flat and extends into very negative values. During local summer, a spike is also consistently present in the range of -50 to -60 R across nearly all bins. Unlike during winter, this spike is narrow and does not significantly affect the fit.

One of the primary sources of uncertainty in our method stems from the SSUSI EDR Auroral dataset and the Scanning Imaging Spectrograph (SIS) instrument. The dayglow-removal model often overestimates the subtraction, resulting in a dataset with a negative bias. The negative values are caused by data processing: removing scatters from bright emissions (H121.6 nm, O130.4 nm and O135.6 nm) into the LBH bands and counting errors. The isolated spikes are due to glints and MeV particle noise. Ideally, the dayglow residual (DG-residual) should be centered around 0 R, but this is rarely the case. The value of μG=-150 R that we assume for local summer is only an average value and varies slightly across the MLT/MLAT grid.

To improve the precision of estimates of average auroral intensity a more precise model of this offset is needed, assigning a unique offset to each bin prior to fitting since our fitting procedure cannot simultaneously determine both location parameters (see Sect. ). Within the auroral zone, the presence of aurora makes it difficult to determine offsets. Because of this, assumptions based on bins located north and south of the auroral zone were used in the fitting in the auroral zone. Figures  and in Sect.  highlight how DG-residual locations influence the extracted Moyal parameters and modeled intensities. Given these dataset-related challenges, differences of ≲100 R across the MLT/MLAT grid should be viewed as noise. However, variations over smaller regions, such as within the auroral oval, are less impacted by this negative baseline issue.

During local summer, small regions on the nightside exhibit total mean estimates that exceed the Moyal mean, as shown in the figures in Sect.  and . This discrepancy may result from a combination of reduced sunlight on the nightside and stronger auroral activity due to magnetotail reconnection. It is likely that the distribution deviates from the one assumed in the convolution, which leads to suboptimal fits as seen in the winter example in Fig.  where the Moyal distribution declines too rapidly relative to the observed intensity distribution. During summer, this issue is fortunately confined to a few small areas, and the Moyal mean appears to constitute an overall good estimate of the average auroral intensity.

In the Northern Hemisphere’s local winter, our method tends to underestimate auroral intensities compared to the total mean across much of the auroral oval, as shown in panels (a) and (c) of Fig. . However, in the dayside region (typically between 10–14 MLT and 70–80° MLAT), our method estimates stronger auroral intensities than the total mean. This is likely due to sufficient solar illumination in this region during local winter, which affects the total mean through DG-residuals. Despite this, the fit is not perfect, as the fitted distribution declines too rapidly (see panels (a) and (c) of Fig. ). For this reason, we rely on the boundaries and values provided by the total mean during local winter.

In the polar cap, our method also returns higher values than the total mean. This may occur because our method avoids producing negative values in areas without aurora, unlike the total mean, which can include negative tails. Panel (d) of Fig.  demonstrates how negative tails in the distribution are avoided during fitting, even though this example pertains to local summer. This issue is particularly prevalent on the dayside during summer for Datasets 3–5. By concentrating on the main shape of the distribution, our method avoids these negative counts.

The nonlinear fitting software (LMFIT) returns the uncertainties of the fitted parameters. These are usually less than 10 % with successful fits during the local summer, with the vast majority of the fits returning uncertainties of 0.5 %–2 %. The main source of uncertainty during summer appears to be the observation that could be considered faulty, based on the large negative counts in certain bins, and the prescribed shape of the distributions we have chosen, as discussed above.

During reconnection between the IMF and the magnetosphere with a significant IMF By component, forces and stresses in the dawn/dusk directions are applied to the Earth's magnetic field. Figure 2 in shows how the IMF By component changes the position of the geomagnetic field lines, as viewed from the Sun. In the authors suggested that the displacements observed in the geomagnetic field lines can be explained by a “dipole plus uniform magnetic field” model. In the Northern Hemisphere, the field lines lying in the dawn-dusk meridian experience a shift in the direction opposite to that of the added magnetic field.

According to , the aurora mainly occurs at the boundaries of open and closed flux, as shown in Fig. 1 in . Thus, forces that change the location of the boundaries also affect the location of the aurora.

During northward IMF, reconnection occurs on lobe field lines poleward of the cusp . During single-lobe reconnection, the IMF reconnects with lobe field lines in one hemisphere. This occurs when a significant IMF By component exists . These opened field lines drape over the dayside . Figure 19 in shows lobe reconnection with northward IMF and the draping across the dayside magnetopause. When an IMF By component is introduced, suggest that the draped field line may drift duskward for IMF By<0 or dawnward for IMF By>0. This is due to a kink in the field line after reconnection, pointing in the opposite direction of the IMF By, which causes a tension force that moves the field line in the direction opposite to IMF By .

In the Northern Hemisphere for positive IMF By, the polar cap is shifted towards dawn, whereas for negative IMF By it is shifted towards dusk (opposite in the Southern Hemisphere). Figure  shows that the location of the polar cap varies substantially more (∼500 km) for positive dipole tilt than for negative dipole tilt in the Northern Hemisphere. To our knowledge, this dipole tilt dependence of the dawn-dusk shift of the polar cap has not been reported previously.

Lobe reconnection appears to be the most likely explanation for the seasonally dependent dawn-dusk shift in polar cap location. This is because lobe reconnection becomes more efficient in the hemisphere that is tilted towards the Sun . The fact that this polar cap shift is seen in both hemispheres for the opposite sign of IMF By strengthens our hypothesis that this is due to lobe reconnection. The seasonal and IMF By polarity-dependent widening and brightening of the dawn/dusk sector of the auroral oval might also be related to the seasonal change in lobe reconnection efficiency. This shift in polar cap location may be interpreted as a direct influence of the IMF forcing on open field lines. The striking differences between local summer and local winter may therefore relate to the vastly different forcing from the IMF on the open field lines. This effect is likely exaggerated during northward IMF as little tail reconnection takes place, allowing the magnetosphere to retain a highly asymmetric shape .

Magnetic reconnection at the Earth's magnetopause serves as the primary driver of ionospheric convection. When the IMF is oriented southward, the resulting convection pattern typically consists of two cells, commonly referred to as the Dungey cycle. In contrast, under northward IMF conditions with a substantial IMF By component, the convection patterns can exhibit either two or three cells, with one cell generally dominating the others. This dominant cell frequently extends across the magnetic pole , is commonly referred to as the lobe convection cell, and is believed to be comprised exclusively of open lobe magnetic field lines .

Figures  and display the auroral boundaries derived from Dataset 4, which are presented in the third rows of Figs.  and , together with ionospheric potential patterns taken from the SWIPE model (see Sect. ). In these figures, the left column corresponds to IMF By>5 nT, while the right column represents IMF By<-5 nT. Figure  illustrates local summer conditions in the Northern Hemisphere, and Fig.  depicts local winter conditions in the Northern Hemisphere. The mean values for solar wind velocity, By, Bz, dipole tilt angle, and F10.7 are indicated in the lower left corner of each panel. These values are collected from the average solar wind conditions.

During local summer, the presence of IMF By causes one of the convection cells to become circular, while the other takes on a crescent shape. For positive IMF By, both the polar cap boundaries and the circular convection cell are displaced toward dawn; for negative IMF By, they are shifted toward dusk. In local summer, convection in the dawn-dusk direction primarily occurs within the polar cap boundaries, whereas in local winter, the convection inside the polar cap boundaries is oriented more directly anti-sunward.

reported a single convection cell in the dayside polar cap, with clockwise flow driven by reconnection on open field lines primarily within the polar cap, while a second, nightside cell with counterclockwise flow circulates across the polar cap boundary. This is consistent with our results. The tilt-related convection differences also align with . During local winter, convection is predominantly across the polar cap from dayside to nightside, whereas during local summer, the cells are shifted more along the dawn–dusk direction, indicating enhanced circulation within the polar cap. They suggested that the seasonal differences may arise from a combination of altered reconnection topologies due to dipole tilt, such as lobe reconnection, and variations in ionospheric conductance driven by differing solar illumination. However, the formation of the lobe convection cell appears to be the most dominant effect.

In , similar convection patterns were found. During local winter, anti-sunward flow occurs inside the polar cap boundaries, while the return flow is mostly equatorward of those boundaries. In local summer, a larger portion of the return flow occurs within the polar cap boundaries. This is attributed to seasonal differences in lobe reconnection, because lobe reconnection imposes tension forces on the field lines, thereby producing seasonal asymmetries.