Data Mining Inspired Localized Resistivity in Global MHD Simulations of
the Magnetosphere
Abstract
Recent advances in reconstructing Earth’s magnetic field and associated
currents by utilizing data mining of in situ magnetometer observations
in the magnetosphere have proven remarkably accurate at reproducing
observed ion diffusion regions. We investigate the effect of placing
regions of localized resistivity in global simulations of the
magnetosphere at specific locations inspired by the data mining results
for the substorm occurring on July 6, 2017. When explicit resistivity is
included, the simulation forms an x-line at the same time and location
as the MMS observation of an ion diffusion region at 15:35 UT on that
day. Without this explicit resistivity, reconnection forms later in the
substorm and far too close to Earth ($\gtrsim-15R_E$),
a common problem with global simulations of Earth’s magnetosphere. A
consequence of reconnection taking place farther down the tail due to
localized resistivity is that the reconnection outflows transport
magnetic flux Earthward and thus prevent the current sheet from thinning
enough for reconnection to take place nearer Earth. As these flows
rebound tailward from the inner magnetosphere, they can temporarily and
locally (in the dawn-dusk direction) stretch the magnetic field allowing
for small scale x-lines to form in the near Earth region. Due to the
narrow cross-tail extent of these x-lines
($\lesssim5R_E$) and their short lifespan
($\lesssim5$min), they would be difficult to observe
with in situ measurements. Future work will explore time-dependent
resistivity using 5 minute cadence data mining reconstructions.