Climate Model Initialization and Improvement using Particle Filters CLIMIP

Policymakers require predictions of regional and local climate for the near-term (the next 1-30 years) in order to plan effective adaptation strategies. Such predictions exist, but are subject to considerable uncertainties, firstly because our global climate models (GCMs) are imperfect, and because of the internal variability of climate, which can mask or enhance the changes due to external forcings, especially on regional scales (e.g. Hawkins and Sutton, 2009). Progress in climate science to improve GCMs and reduce uncertainty, and to use them more effectively to predict some of the internal variability component would therefore be extremely valuable. This project addresses both of these areas by providing a novel new method for initialising the GCMs, comparing that method with the state-of-the-art DePreSys system, and by identifying the processes responsible for model biases, and hence suggesting methods for GCM improvement.

The outcome of this research would benefit the whole climate community. Furthermore, operational centers like the Met Office, ECMWF, and other meteorological centers, coastal management, air pollution management, oil industry, and insurance companies will all benefit from this research on the long run. All these communities use climate predictions and some form of data assimilation and encounter highly nonlinear problems that need sophisticated data-assimilation methods. Particle filters are among the few methodologies that can handle truly nonlinear problems, and have the ability to bring climate prediction forward.
Active 'workfloor' collaboration with the Met Office and ECMWF ensures a quick dissemination of the results in the meteorological and oceanographic communities.
Through the NCEO, of which the PI is co theme leader of the Data Assimilation theme, efficient exchange of results within the NCEO community, including the climate theme, is guaranteed. This is strongly enhanced by the contact with the climate community via the NCAS link. Furthermore, the NCEO Knowledge Exchange manager Andy Shaw will organize workshops to promote NCEO achievements outside NCEO, and results from this project will contribute to that activity.
All results will be published in scientific journals and discussed in scientific meetings all over the world to ensure maximal exposure to the scientific community. Finally, a workshop on 'Climate model improvement through data assimilation' will be organised half-way the project to bring in scientific expertise from elsewhere, and to expose our results and ideas to the wider community.