Global Sensitivity Analysis for Solar-Wind Simulations in the Space Weather Modelling Framework

The Space Weather Modelling Framework (SWMF) offers efficient and flexible sun-to-earth simulations based on coupled first principles and/or empirical models. This encompasses computing the quiet solar wind, generating a coronal mass ejection (CME), propagating the CME through the heliosphere, and calculating the magnetospheric impact via geospace models. The predictions from these different steps and models are affected by uncertainty and variation of many model inputs and parameters, such as the Poynting flux emanating from the photosphere and driving and heating the solar wind. In this presentation, as part of the NextGen SWMF project funded by NSF, we perform uncertainty quantification (UQ) for the quiet solar wind simulations produced by our Alfven Wave Solar atmosphere Model (AWSoM). We first catalogue the various sources of uncertainty and their distributions, and then propagate the uncertainty to key predictive quantities of interest, the in-situ solar wind and magnetic field at 1 au, through space-filling designs of high-fidelity simulations. Using this dataset, we then build polynomial chaos surrogate models that offer a convenient route to global sensitivity analysis, which quantifies the contribution of each input parameters uncertainty towards the variability of the QoIs. The resulting Sobol sensitivity index allows us to rank and retain only the most impactful parameters going forward, thereby achieving dimension-reduction of the stochastic space. We have performed this UQ analysis for both solar maximum and solar minimum conditions, and we will summarize our findings in this presentation.