In light of the magnitude and pace of the environmental changes in the northern permafrost zone (NPZ) and their feedbacks to climate, contemporary, accurate and quantitative ecological forecasting has never been so paramount to the development of climate change adaptation and mitigation strategies. Yet, uncertainties associated with carbon (C) projections in the NPZ remain the largest to projections of global C budget and climate. While there are persisting lacks of data documenting important and emerging soil and vegetation dynamics in the NPZ, the volume, variety and accessibility of observational data in the NPZ has grown exponentially over the past decades and significantly improved our understanding of terrestrial C dynamic. Yet, a lag persists between large availability of historical, new and iterative data collections and the capacity of terrestrial biosphere models to fully incorporate this information, limiting advances in reducing the uncertainty of ecological forecasting in the NPZ. In this new project, we are developing the Arctic Carbon Monitoring and Prediction System (ACMPS), a data assimilation system that will use the information from field observations from ecological networks, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the NPZ. The ACMPS will be coupling model development and testing, data-assimilation techniques and near-term forecasting capacity to improve the accuracy of historical and future simulations of ecosystem permafrost and C dynamics across the NPZ. We will present the structure and workflow of the ACMPS, as well as preliminary assessment of model sensitivity and uncertainty analysis of soil and vegetation carbon fluxes, using a terrestrial biosphere model specifically developed to represent permafrost, vegetation and carbon dynamics in arctic and boreal ecosystems. Plain-language Summary We are presenting the Arctic Carbon Monitoring and Prediction System, a data assimilation system that uses field observation, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the northern permafrost zone, and to better inform development of climate change adaptation and mitigation strategies.