[Science_Parameters: Science_Category='EARTH SCIENCE', Science_Topic='ATMOSPHERE', Science_Term='ATMOSPHERIC TEMPERATURE', Science_Variable_Level_1='SURFACE TEMPERATURE']
Bayesian Algorithm for Reconstructing Climate Anomalies in Space and TimeEntry ID: BARCAST
Abstract: Reconstructing the spatial pattern of a climate field through time from a data set of overlapping instrumental and climate proxy time series is a non-trivial statistical problem. The need to transform the proxy observations into estimates of the climate field, and the fact that the observed time series are not uniformly distributed in space, further complicate the analysis. Current leading ... approaches to this problem are based on estimating the full covariance matrix between the proxy time series and instrumental time series over a 'calibration' interval, and then using this covariance matrix in the context of a linear regression to predict the missing instrumental values from the proxy observations for years prior to instrumental coverage.
A fundamentally different approach to this problem is formulated by specifying parametric forms for the spatial covariance and temporal evolution of the climate field, as well as 'observation equations' describing the relationship between the data types and the corresponding true values of the climate field. A hierarchical Bayesian model is used to assimilate both proxy and instrumental data sets, and estimate the probability distribution of all model parameters and the climate field through time on a regular spatial grid. The output from this approach includes an estimate of the full covariance structure of the climate field and model parameters, as well as diagnostics that estimate the utility of the different proxy time series.
This methodology is demonstrated using an instrumental surface temperature data set, after corrupting a number of the time series to mimic proxy observations. The results are compared to those achieved using the Regularized Expectation-Maximization algorithm of Schneider (2001), and in these experiments the Bayesian algorithm produces reconstructions with greater skill.
Access Constraints None
Distribution Media: Online
Fees: No fees
Role: TECHNICAL CONTACT
Email: tingley at fas.harvard.edu
Department of Earth and Planetary Sciences Harvard University 20 Oxford St.
Province or State: MA
Postal Code: 02138
Role: SERF AUTHOR
Email: Tyler.B.Stevens at nasa.gov
5700 Rivertech Court
Province or State: MD
Postal Code: 20737
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