What Features of Field Aligned Currents Might Global Scale Fits Miss?

Much of the nomenclature for field aligned current systems is based on analyses of satellite magnetometer data consisting of visual inspection of time series traces. Recent statistical analyses take advantage of digital data processing and nearly continuous solar wind data coverage. These advances allow more mathematically sophisticated techniques using multiple parameter regression with upstream parameters and various global fitting techniques. For several reasons the new methods do not necessarily provide a better basis for understanding the current systems. First, because of dynamics in both solar wind/IMF and the magnetosphere itself statistical analysis over many passes, will suffer from temporal and spatial smoothing even when binned by solar wind/IMF conditions. Second, the solar wind/IMF measurements do not provide a perfect system state monitor due to variability in the solar wind and the associated time history effects on the magnetosphere, uncertainties in the convection time from L1 and dynamics in conditions actually imposed on the magnetosphere resulting from phenomena at the bow shock. Third, global fitting procedures have inherent limitations in spatial resolution determined by the spatial density of the input data which in turn is related to the resolution with which the `governing' parameters are binned. By using data from the Iridium constellation we obtain a global `snapshot' representation of the FAC system during stable IMF conditions to minimize problems with the first two effects. By comparing these results with Oersted we identify the features that are most likely to be missed in similar global-scale fit analyses. We find that currents near or at the polar cap boundary, often near the cusp, are the most likely to suffer from poor fidelity in large scale fitting analyses. These currents have densities typically ten times larger than the nominal large scale currents and comparable net current. These results illustrate the need to make sure that any new nomenclature reflects both the results of large-scale fitting analyses and the finer spatial resolution afforded by the time series data used historically to characterize the current systems.