CMG COLLABORATIVE RESEARCH: Improved Bayesian Estimators for Uncertainty in Climate System Properties

Project: Research project

Project Details


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 date8/15/047/31/08


  • National Science Foundation: $106,466.00


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