Policymaking often involves different parties such as policymakers, stakeholders, and analysts each with distinct roles in the process. To assist policymakers, policy analysts help in structuring the problem, designing, and evaluating policy alternatives. Analysts face many challenges, like complexity and uncertainty in a system of interest, while supporting the policymaking process. Frequently, analysts rely on mathematical models that represent the key features of the system. Assumptions made during modelling introduce a significant level of uncertainty in the models, and forecasting based on models is therefore always bound by this uncertainty. Instead of focusing on limited best-estimate predictions under uncertainty, exploring a plethora of plausible futures by using mathematical models can help supporting decision-making. In current practice, uncertainty analysis for decision-making is mostly limited to technical and shallow uncertainties but not focused on deep uncertainty. This thesis contributes to a solution for enhanced handling of deep uncertainty to support policymaking. We have developed a new methodological approach for improving analytical support for policymaking under deep uncertainty, and demonstrated each analytical advancement stage with case studies. This thesis proposes to improve analytical support for policymaking to better handle deep uncertainty. Building upon the existing pragmatic practice, a systematic approach for designing adaptive policies under uncertainty is developed. The Adaptive Robust Design (ARD) approach in combination with multi-objective robust optimization will improve the support for policymaking under deep uncertainty. The effectiveness of ARD for developing adaptive robust policies under deep uncertainty is shown by illustrative case studies. @en