Deterministic whole system multi-stage optimisation frameworks provide valuable insights into the cost effective design and operation of CO 2 capture and storage (CCS) systems. However commercial deployment of CCS faces significant technical and economic uncertainties, which necessitate flexibility in system development strategies as well as coordination of all aspects of a CCS system across both time and space. This paper builds on a whole system dynamic CCS optimisation tool developed at Imperial College and presents a mixed integer linear programming approach for multi-stage multi-scenario stochastic optimisation of a spatially explicit integrated CCS system under uncertainty. The model provides great advantages through flexible strategies for all potential system state changes at every stage and early one-fit-for-all investment solutions that minimise financial loss and offer operational flexibility. The model is showcased through a case study set in the UK between 2015-2050 focusing on the techno-economic performance of the CCS value chain and considering uncertainties in the financial market and the storage capacity within a portfolio of Southern North Sea saline aquifer and depleted oil and gas fields.