Maritime transportation makes significant contribution to economic prosperity and suffers much from various risks, including classical (e.g. ship collisions) and emerging ones such as those relating to climate change risks (e.g. flooding, heatwaves, storms). Data uncertainty represents an important challenge for safety science in general climate risk analysis in specific given the insufficiency of historical failure data, compared to classical risks. Broadly data uncertainty is categorised into three groups: fuzziness, incompleteness and randomness, hence triggers risk studies using the uncertainty theories such as fuzzy logic, Dempster-Shafer (D-S) theory of evidence and Bayesian probabilistic inference. This chapter aim is to introduce the newest study on the development of climate risk modelling using the three uncertainty theories, individually and collectively within the maritime transport context. It will help understand the state of the art of the relevant research and inspire new ideas.