The registration is free, but limited. If interested in registering, please do so at: http://harvestuq.eventbrite.co.uk
9:50 Welcome (Serge Guillas & Chris Brierley, UCL)
10:00 round table: "Uncertainty Quantification Pipeline for Climate Models", Harvest team.
11:00 Edzer Pebesma (University of Münster, Germany): The uncertainty-enabled model web: concepts and tools
11:30 Peter Challenor (National Oceanography Centre, Southampton, UK): Two million years and counting: the RAPIT experiment.
12:00 Rick Archibald (Oak Ridge National Laboratory, USA): Applying uncertainty quantification to the CESM
13:30 Jenny Brynjarsdottir (Statistical and Applied
Mathematical Sciences Institute, USA): The importance of model discrepancy
14:00 Rodrigo Caballero (University of Stockholm, Sweden): Can deep-time paleoclimates help narrow the uncertainty in climate sensitivity?
14:30 Matthew Collins (University of Exeter, UK): The headache of using big models - is there a solution?
15:00 coffee/tea break
15:30 Alexander T. Archibald (Cambridge University, UK): Challenges in tropospheric chemistry under a changing climate
16:00 Jonathan (Jonty) Rougier (University of Bristol, UK): Two modes of science: The challenge of uncertainty assessment in complex systems
16:30 David Sexton (Met Office, UK): Challenges in quantifying uncertainty using state of the art climate models
Support comes from PASCAL2 (Pattern Analysis, Statistical Modelling and Computational Learning), a Network of Excellence funded by the European Union, through the Harvest project: "Uncertainty Quantification Pipeline for Climate Models". It enables month-long invitations of experts:
- University of Texas at Austin, USA (Michael Tobis, Charles Jackson)
- Illinois Institute of Technology at Chicago, USA (Lulu Kang)
- Carroll University in Wisconsin, USA (Christopher Kuster)
- US Naval Postgraduate School, USA (Robin Tokmakian)
- Research institute INRIA in France (Vivien Mallet)
These experts will collaborate with UCL researchers and together take advantage of UCL's High Performance
Computing resources for their simulations. This will lead to the release of
efficient open-source code for uncertainty quantification of complex computer