The behaviour of modern climate system simulators is controlled by numerous parameters. By matching model outputs with observed data we can perform inference on such parameters. This is a calibration problem that usually requires the ability to evaluate the computer code at any given configuration of the parameters. As the climate system simulator attempts to describe very complex physical phenomena, the task of running the model is very computationally demanding. Thus, a statistical model is required to approximate the model output. In this work, we use output from the Massachusetts Institute of Technology two-dimensional climate model (MIT2DCM), historical records and output from a three-dimensional climate model, to obtain estimates of the climate sensitivity, the effective thermal diffusivity in the deep ocean and the net aerosol forcing that control MIT2DCM. We use a Bayesian approach that allows for the use of scientifically based information on the climate parameters to be used in the calibration process. The model tackles the problem of dealing with multivariate computer model output and incorporates all estimation uncertainties into the posterior distributions of the climate parameters. Additionally we obtain estimates of the correlation structure of the unforced variability of temperature change patterns. These results are critical for understanding uncertainty in future climate change and provide an independent check that the information that is contained in recent climate change is robust to statistical treatment. These results include uncertainties in the estimation of the multivariate covariance matrices.
|Original language||English (US)|
|Number of pages||19|
|Journal||Journal of the Royal Statistical Society. Series C: Applied Statistics|
|State||Published - Sep 1 2009|
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty