Summary
Climate change is a threat to the societies in which we live so it is no surprise that policy and decision makers look to climate change science to provide the foundation and justification for the decisions they need to make. Yet nonlinearities in many components of the climate system, limited observational timeseries, and the extrapolatory nature of the climate prediction problem, raise substantial obstacles to the generation of robust information. This impacts the search for both scientific understanding and policy guidance. In this talk I will touch briefly on the physical simplicity of the basis for identifying climate change as a threat and the importance of this simplicity when communicating with many audiences. I will then discuss some issues relating to i) the extraction of robust information from observational timeseries, for adaptation planning, ii) the role of physical science in the modelling of global economic impacts, and iii) the implications of nonlinearity for the design of climate ensembles for use by both policy makers and scientists. These examples will highlight the value of, indeed the requirement for, multi-disciplinary approaches in the science of climate change.
Biography
Dr David Stainforth is a Senior Research Fellow in the Grantham Research Institute. He is a physicist by training and has many years’ experience of climate modelling. While a researcher at Oxford University, he co-founded and was chief scientist of the climateprediction.net project, the world’s largest climate modelling experiment. He has been both a NERC Research Fellow and a Tyndall Research Fellow at Oxford University. His current research interests focus on how we can extract robust and useful information about future climate, and climate related phenomena, from science and from modelling experiments. This includes issues of how to design climate modelling experiments and how to link climate science to real-world decision making in such a way as to be of value to industry, policy makers and wider society.