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...
| Main Authors: | , , , , |
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| Format: | Other/Unknown Material |
| Language: | English |
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Digital.CSIC
2018
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.20350/digitalcsic/13848 https://digital.csic.es/handle/10261/239385 |
| Summary: | 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. |
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