The investigators are developing Bayesian statistical models for the study of the distribution of climate system properties. The study is based on output from the MIT 2DLO climate model as well as an estimation of the natural climate variability from atmosphere-ocean general circulation models (AOGCM). The statistical models account for all uncertainties by focusing on the estimation of the main patterns of natural variability. This is achieved by building prior distributions for the covariance matrix from ensemble runs of AOGCMs. Particular attention is paid to the spectral decomposition of the covariance matrix. In addition the statistical models are hierarchical in order to consider all sources of errors in a comprehensive way. These errors include the interpolation error due to the impossibility of evaluating climate models in a time short enough to embed it within a Monte Carlo iterative estimation method. The proposed research falls clearly into the ``Representing uncertainty in geosystems'' theme of the NSF Program for Collaborations in Mathematical Geosciences. The main focus is to improve the estimates of parameters that govern the large-scale behavior of the climate system. The resulting analysis will include an assessment of the uncertainty of those estimates. The research is a collaborative effort between climate scientists and statisticians as it requires the use of climate system models as well as analyzing climate observational datasets. The broader aspect of this project is that the estimated uncertainties in climate system behavior can be used for uncertainty analysis of climate change projections. By enhancing the ability to analyze the risks of climate change on society, this research will provide valuable input to policymakers.
|Effective start/end date||8/15/04 → 7/31/08|
- National Science Foundation: $106,466.00