Project Details
Description
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.
Status | Finished |
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Effective start/end date | 8/15/04 → 7/31/08 |
Funding
- National Science Foundation: $106,466.00