Dynamic Magnetosphere Ionosphere Thermosphere coupling

ongoing

DynaMIT addresses a fundamental misunderstanding of how Earth is coupled to space in the polar ionosphere. Here, the neutral atmosphere collides with charged particles influenced by electromagnetic fields, profoundly impacting the dynamics of the atmosphere and its surroundings. Despite being the best instrumented region of space, our understanding of Earth’s ionosphere is severely limited. The current paradigm contains two crippling assumptions: that the ionosphere is 1) only 2D, and 2) in a steady state. This conceptualisation obscures the complex interplay of forces that change the fluids and electromagnetic fields in both regions. This prevents us from accurately understanding phenomena, such as the aurora polaris, that have been observed and studied from ground for centuries. To advance beyond the state of the art we must transition to a dynamic view of space-atmosphere coupling. This project will apply first principles to model how the neutral atmosphere, plasma, and electromagnetic fields interact. To do this, we will build and combine the DynaMIT model with novel measurements using an innovative data assimilation technique developed in-house. This ground-breaking combination of multi-instrument data with full 3D numerical simulations will create a radical new platform from which we will interrogate fundamental outstanding issues in space physics: 1) how ionospheric dynamics disturbs Earth’s magnetic field; 2) how energy flows between regions, and how it dissipates in the atmosphere; and 3) how space-atmosphere coupling shapes near-Earth space. These questions are critical for understanding how Earth interacts with space, and the influence on technology, climate, and circulation in the lower atmosphere. If successful, DynaMIT will be a paradigm change that transforms our conceptual understanding of how the atmosphere is coupled with space, provides language to explain ionospheric observations, and paves the way for improvements in space weather predictions.