1. Uncertainties in Predictions of Arctic Climate Peter Challenor, Bablu Sinha (NOC) Myles Allen (Oxford), Robin Tokmakian (NPS)

2. Uncertainty Uncertainty in climate modelling comes from: Model parameters Initial conditions Forcing fields Model structure

3. Estimating uncertainty Set probability distributions on inputs Simulate Propagate through model Distribution of output Expensive

4. Emulators Statistical model of the climate model Build the emulator from a designed set of model runs Validate Calibrate (model discrepancy) Estimate uncertainty/ sensitivity (Monte Carlo)

5. An Example

6. Climate prediction in the Arctic Look at coupled GCMs over the 21st century HADCM3 Link to CHIME, CCM and HADGEM Use climateprediction.net to run large ensembles of HADCM3 Use sea ice properties (volume/extent) as our output

7. Emulation of Sea Ice Expand the sea ice edge as a Fourier series and emulate the Fourier coefficients Produce maps of the probability distribution of sea ice extent through the 21 st Century

8. Summary Use newly developed emulator technology to investigate uncertainty in Arctic climate Look at uncertainty in model parameters + initial conditions Use a variety of models (mainly HADCM3 but modified by better models) Builds on work being done in RAPID- WATCH