A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part 2: Comparison with the Regularized Expectation-Maximization Algorithm

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A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part 2: Comparison with the Regularized Expectation-Maximization Algorithm

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dc.contributor.author Tingley, Martin Patrick
dc.contributor.author Huybers, Peter John
dc.date.accessioned 2012-11-27T19:54:10Z
dc.date.issued 2010
dc.identifier.citation Tingley, Martin and Peter Huybers. 2010. A Bayesian algorithm for reconstructing climate anomalies in space and time. Part 2: Comparison with the regularized expectation-maximization algorithm. Journal of Climate 23(10): 2782-2800. en_US
dc.identifier.issn 0894-8755 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:9949280
dc.description.abstract Part I presented a Bayesian algorithm for reconstructing climate anomalies in space and time (BARCAST). This method involves specifying simple parametric forms for the spatial covariance and temporal evolution of the climate field as well as ``observation equations?? describing the relationships between the data types and the corresponding true values of the climate field. As this Bayesian approach to reconstructing climate fields is new and different, it is worthwhile to compare it in detail to the more established regularized expectation? maximization (RegEM) algorithm, which is based on an empirical estimate of the joint data covariance matrix and a multivariate regression of the instrumental time series onto the proxy time series. The differing as- sumptions made by BARCAST and RegEM are detailed, and the impacts of these differences on the analysis are discussed. Key distinctions between BARCAST and RegEM include their treatment of spatial and temporal covariance, the prior information that enters into each analysis, the quantities they seek to impute, the end product of each analysis, the temporal variance of the reconstructed field, and the treatment of uncertainty in both the imputed values and functions of these imputations. Differences between BARCAST and RegEM are illustrated by applying the two approaches to various surrogate datasets. If the assumptions inherent to BARCAST are not strongly violated, then in scenarios comparable to practical applications BARCAST results in reconstructions of both the field and the spatial mean that are more skillful than those produced by RegEM, as measured by the coefficient of efficiency. In addition, the uncertainty intervals produced by BARCAST are narrower than those estimated using RegEM and contain the true values with higher probability. en_US
dc.description.sponsorship Earth and Planetary Sciences en_US
dc.language.iso en_US en_US
dc.publisher American Meterological Union en_US
dc.relation.isversionof doi:10.1175/2009JCLI3016.1 en_US
dash.license LAA
dc.title A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part 2: Comparison with the Regularized Expectation-Maximization Algorithm en_US
dc.type Journal Article en_US
dc.description.version Accepted Manuscript en_US
dc.relation.journal Journal of Climate en_US
dash.depositing.author Huybers, Peter John
dc.date.available 2012-11-27T19:54:10Z

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  • FAS Scholarly Articles [6868]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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