Atmospheric rivers can provide as much as 50% of the total annual rainfall to the U.S. West Coast via orographic precipitation. Dust is thought to enhance orographic precipitation via the “seeder-feeder”; mechanism, in which ice particles from a high cloud fall through a lower orographic cloud, seeding precipitation in the low cloud. Using the Weather Research and Forecasting model, we vary dust concentrations in simulations of two-dimensional flow over a mountain. This idealized framework allows us to test the sensitivity of the precipitation-dust response to a variety of different dust concentrations and initial conditions. The model is run using an ensemble of 60 radiosondes collected from Bodega Bay, CA in 2017-2018, clustered based on their vertical moisture profile into “deep moist”, “shallow moist”, and “subsaturated” clusters. The principle impact on precipitation is to increase the ratio of precipitation falling as snow. This produces a “spillover” effect, decreasing precipitation upwind of the peak and increasing precipitation downwind of the peak. The largest impacts on the snow/rain ratio occur at the end of the event, during cold front passage. The ensemble mean does not produce a significant seeder-feeder response, however in individual cases with favorable initial conditions there is a significant increase in precipitation throughout the domain due to dust effects on the seeder-feeder mechanism. These findings afford an opportunity to build a more comprehensive understanding for the conditions under which dust aerosol can have a significant impact on precipitation during atmospheric rivers, with implications for future developments in forecasting.