NAO: a code for reconstructing North Atlantic Oscillation from XRF data
Más información: https://github.com/andrewcparnell/NAO Contacto: Andrew Parnell, andrew.parnell@mu.ie : R code for a Bayesian model for reconstructing North Atlantic Oscillation from X-Ray Fluorescence (XRF) data. This code follows a Bayesian modelling approach to produce a reconstruction of the NAO...
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ftdatacite:10.20350/digitalcsic/13848 2023-05-15T17:29:27+02:00 NAO: a code for reconstructing North Atlantic Oscillation from XRF data Parnell, Andrew Cahill, Niamh Sánchez-López, Guiomar Hernández, Armand Giralt, Santiago 2018 R programming language https://dx.doi.org/10.20350/digitalcsic/13848 https://digital.csic.es/handle/10261/239385 en eng Digital.CSIC https://dx.doi.org/10.1038/s41598-020-71372-5 https://dx.doi.org/10.1111/rssc.12065 https://www.gnu.org/licenses/gpl-3.0.html openAccess Other CreativeWork software article 2018 ftdatacite https://doi.org/10.20350/digitalcsic/13848 https://doi.org/10.1038/s41598-020-71372-5 https://doi.org/10.1111/rssc.12065 2021-11-05T12:55:41Z Más información: https://github.com/andrewcparnell/NAO Contacto: Andrew Parnell, andrew.parnell@mu.ie : R code for a Bayesian model for reconstructing North Atlantic Oscillation from X-Ray Fluorescence (XRF) data. This code follows a Bayesian modelling approach to produce a reconstruction of the NAO’s impact on the central Iberian Peninsula. The relationship between proxy and climate is derived from a training data set for the instrumental/proxy calibration period and is expressed through a likelihood function. This function is combined with a prior probability density function containing parameter information in order to obtain a posterior probability distribution of the reconstructed NAO values using Bayes’ theorem. Whilst Parnell et al. (2015) based their framework on reconstructing multivariate temperature and moisture measurements from raw pollen data, this method is easily adaptable to other proxies and climate variables. Indeed, Cahill et al. (2016) used a similar approach to reconstruct sea level from foraminifera. In all cases the measurements/counts of the proxy are required for a set of sediment layers (depths) in a core. Other/Unknown Material North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) Cahill ENVELOPE(-71.231,-71.231,-74.880,-74.880) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
Más información: https://github.com/andrewcparnell/NAO Contacto: Andrew Parnell, andrew.parnell@mu.ie : R code for a Bayesian model for reconstructing North Atlantic Oscillation from X-Ray Fluorescence (XRF) data. This code follows a Bayesian modelling approach to produce a reconstruction of the NAO’s impact on the central Iberian Peninsula. The relationship between proxy and climate is derived from a training data set for the instrumental/proxy calibration period and is expressed through a likelihood function. This function is combined with a prior probability density function containing parameter information in order to obtain a posterior probability distribution of the reconstructed NAO values using Bayes’ theorem. Whilst Parnell et al. (2015) based their framework on reconstructing multivariate temperature and moisture measurements from raw pollen data, this method is easily adaptable to other proxies and climate variables. Indeed, Cahill et al. (2016) used a similar approach to reconstruct sea level from foraminifera. In all cases the measurements/counts of the proxy are required for a set of sediment layers (depths) in a core. |
format |
Other/Unknown Material |
author |
Parnell, Andrew Cahill, Niamh Sánchez-López, Guiomar Hernández, Armand Giralt, Santiago |
spellingShingle |
Parnell, Andrew Cahill, Niamh Sánchez-López, Guiomar Hernández, Armand Giralt, Santiago NAO: a code for reconstructing North Atlantic Oscillation from XRF data |
author_facet |
Parnell, Andrew Cahill, Niamh Sánchez-López, Guiomar Hernández, Armand Giralt, Santiago |
author_sort |
Parnell, Andrew |
title |
NAO: a code for reconstructing North Atlantic Oscillation from XRF data |
title_short |
NAO: a code for reconstructing North Atlantic Oscillation from XRF data |
title_full |
NAO: a code for reconstructing North Atlantic Oscillation from XRF data |
title_fullStr |
NAO: a code for reconstructing North Atlantic Oscillation from XRF data |
title_full_unstemmed |
NAO: a code for reconstructing North Atlantic Oscillation from XRF data |
title_sort |
nao: a code for reconstructing north atlantic oscillation from xrf data |
publisher |
Digital.CSIC |
publishDate |
2018 |
url |
https://dx.doi.org/10.20350/digitalcsic/13848 https://digital.csic.es/handle/10261/239385 |
long_lat |
ENVELOPE(-71.231,-71.231,-74.880,-74.880) |
geographic |
Cahill |
geographic_facet |
Cahill |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://dx.doi.org/10.1038/s41598-020-71372-5 https://dx.doi.org/10.1111/rssc.12065 |
op_rights |
https://www.gnu.org/licenses/gpl-3.0.html openAccess |
op_doi |
https://doi.org/10.20350/digitalcsic/13848 https://doi.org/10.1038/s41598-020-71372-5 https://doi.org/10.1111/rssc.12065 |
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