Environmental science has developed to the stage where there are simulators (numerical models), often involving the solutions of PDEs, that give good representations of the real world. In addition our data collection capabilities have increased in recent years so we can now collect very large amounts of data on the natural world. How can we best combine both data and models, both of which are uncertain, to make good decisions about environmental policy? In this paper I will look at ways that machine learning and statistical modelling can be used to bring uncertain data and models together to make better decisions. Examples will include the calibration of climate and other environmental models, modelling air pollution and climate prediction for future emission scenarios not covered by the RCPs.