Joint palaeoclimate reconstruction from pollen data via forward models and climate histories

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dc.contributor.author Parnell, Andrew C.
dc.contributor.author Haslett, John
dc.contributor.author Sweeney, James
dc.contributor.author et al.
dc.date.accessioned 2016-11-30T15:48:36Z
dc.date.available 2016-11-30T15:48:36Z
dc.date.copyright 2016 Elsevier en
dc.date.issued 2016-11-01
dc.identifier.citation Quaternary Science Reviews en
dc.identifier.uri http://hdl.handle.net/10197/8167
dc.description.abstract We present a method and software for reconstructing palaeoclimate from pollen data with a focus on accounting for and reducing uncertainty. The tools we use include: forward models, which enable us to account for the data generating process and hence the complex relationship between pollen and climate; joint inference, which reduces uncertainty by borrowing strength between aspects of climate and slices of the core; and dynamic climate histories, which allow for a far richer gamut of inferential possibilities. Through a Monte Carlo approach we generate numerous equally probable joint climate histories, each of which is represented by a sequence of values of three climate dimensions in discrete time, i.e. a multivariate time series. All histories are consistent with the uncertainties in the forward model and the natural temporal variability in climate. Once generated, these histories can provide most probable climate estimates with uncertainty intervals. This is particularly important as attention moves to the dynamics of past climate changes. For example, such methods allow us to identify, with realistic uncertainty, the past century that exhibited the greatest warming. We illustrate our method with two data sets: Laguna de la Roya, with a radiocarbon dated chronology and hence timing uncertainty; and Lago Grande di Monticchio, which contains laminated sediment and extends back to the penultimate glacial stage. The procedure is made available via an open source R package, Bclim, for which we provide code and instructions. en
dc.language.iso en en
dc.publisher Elsevier en
dc.rights This is the author’s version of a work that was accepted for publication in Quaternary Science Reviews. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Quaternary Science Reviews (VOL 151, ISSUE 2016, (2016)) DOI: 10.1016/j.quascirev.2016.09.007. en
dc.subject Machine learning en
dc.subject Statistics en
dc.subject Palaeoclimate reconstruction en
dc.subject Statistical modelling en
dc.subject Forward models en
dc.subject Climate histories en
dc.subject Joint inference en
dc.subject Palynology en
dc.subject Chronological uncertainty en
dc.title Joint palaeoclimate reconstruction from pollen data via forward models and climate histories en
dc.type Journal Article en
dc.status Peer reviewed en
dc.identifier.volume 151 en
dc.identifier.startpage 111 en
dc.identifier.endpage 126 en
dc.identifier.doi 10.1016/j.quascirev.2016.09.007
dc.neeo.contributor Parnell|Andrew C.|aut|
dc.neeo.contributor Haslett|John|aut|
dc.neeo.contributor Sweeney|James|aut|
dc.neeo.contributor et al.||aut|
dc.date.updated 2016-11-08T12:52:36Z


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