1. The limited contribution of model uncertainty to the uncertainty in observationally-constrained estimates of anthropogenic warming Nathan Gillett, Canadian Centre for Climate Modelling and Analysis, Environment Canada Peter Stott Met Office Hadley Centre, UK

2. Introduction Simulated future GHG warming may be scaled by a regression coefficient of observed GHG warming against simulated GHG warming using the ASK approach. Huntingford et al. (2005) propose that model uncertainties (μ i ) should be accounted for in such a regression as well as internal variability in the observations (ν 0 ) and simulations (ν i ): The covariance of μ i is estimated from inter- model differences in a multi-model ensemble. How important is this model uncertainty term?

3. Data and method We follow the approach of Huntingford et al. (2005) to estimate natural and anthropogenic contributions to 20 th century global mean warming using output from four GCMs: HadCM3, CCSM, PCM, and MIROC3, and HadCRUT3 observations. We use decadal mean T4 spherical harmonics. We repeat the analysis after setting inter- model covariance to zero.

4. 20 th century attributable warming

5. Contributions to uncertainty

6. Attributable warming over ocean regions

7. Conclusions Model uncertainty makes only a small contribution to the uncertainty in global mean and ocean basin warming attributable to anthropogenic influence in the Huntingford approach. Thus model uncertainty makes only a small contribution to uncertainty in projected warming derived in this way. Internal variability in the observations is the dominant source of uncertainty. Any common error in models is not accounted for here.