We present a proof of concept for the probabilistic emulation of the Ring current-Atmosphere interactions Model with Self-Consistent magnetic field (RAM-SCB) particle flux. We extend the workflow developed by Licata and Mehta (2023) by applying it to the ring current and further developing its uncertainty quantification methodology. We introduce a novel approach for sampling over 20 years of solar and geomagnetic activity to identify 30 simulation periods, each one week long, to generate the training, validation, and test datasets. Large-scale physics-based simulation models for the ring current can be computationally expensive. This work aims at creating an emulator that is more efficient, capable of forecasting, and provides an estimate on the uncertainty of its predictions, all without requiring large computational resources. We demonstrate the emulation process on a subset of particle flux: a single energy channel of omnidirectional flux. A principal component analysis (PCA) is used for the dimensionality reduction into the reduced-space, and the dynamic modeling is performed with a recurrent neural network. A hierarchical ensemble of Long-Short Term Memory (LSTM) neural networks provides the statistics needed to produce a probabilistic output, resulting in a reduced-order probabilistic emulator (ROPE) that performs time-series forecasting of the ring current’s particle flux with an estimate on its uncertainty distribution. The resulting ROPE from this smaller subset of RAM-SCB particle flux provides dynamic predictions with errors less than 11% and calibration scores under 10%, demonstrating that this workflow can provide a probabilistic emulator with a robust and reliable uncertainty estimate when applied to the ring current.