Quantifying Uncertainties in the Quiet-Time Ionosphere-Thermosphere Using WAM-IPE

This study presents a data-driven approach to quantify uncertainties in the ionosphere-thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM-IPE) driven by synthetic solar wind drivers generated through a multi-channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low-density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance-based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system. Plain Language Summary The ionosphere-thermosphere (IT) is affected by several different drivers, including solar irradiance, geomagnetic activity, and tides and waves from the lower atmosphere. These drivers can interact with each other through multiple dynamic or electrodynamics processes and lead to day-to-day variability in the IT system. Quantifying the variability of the IT system due to these different external drivers and their relative importance is key to making a probabilistic prediction of the IT condition. We present a novel framework for uncertainty quantification and sensitivity analysis. Our results show that the solar wind drivers (IMF Bz, solar wind speed, and solar wind density) mainly lead to larger uncertainty in the low-latitude and equatorial IT at nighttime. The sensitivity analysis shows the dominant role of IMF Bz polarity in the uncertainty of the IT system.