Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 362848
Title Bayesian palaeoclimate reconstruction - Discussion on the paper by Haslett et al
Author(s) Buck, C.E.; Braak, C.J.F. ter; Millard, A.R.; Rougier, J.; O'Hagan, A.; Birks, H.J.B.; Telford, R.; Katz, R.W.; Murphy, T.B.; Gormley, I.C.; Sahu, S.K.; Mardia, K.V.; Scott, E.M.
Source Journal of the Royal Statistical Society. Series A, Statistics in Society 169 (2006)3. - ISSN 0964-1998 - p. 430 - 438.
DOI https://doi.org/10.1111/j.1467-985X.2006.00429.x
Department(s) Biometris (WU MAT)
PRI Biometris
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2006
Keyword(s) statistical calibration
Abstract Summary. We consider the problem of reconstructing prehistoric climates by using fossil data that have been extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise: the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by explicitly modelling uncertainty and reconstructing entire climate histories. The statistical issues that are raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include a spatial process model for compositional data, local modelling of lattice data, the use, as a prior, of a random walk with long-tailed increments, a two-stage implementation of the Markov chain Monte Carlo approach and a fast approximate procedure for cross-validation in inverse problems. We present some details, contrasting its reconstructions with those which have been generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome.
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