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This repository contains the bremla package (Bayesian Regression Modeling of Layer-counted archives) for uncertainty quantification and synchronization for layer-counted proxy archives, including the data and code to reproduce the results for
Myrvoll-Nilsen, E., Riechers, K., Rypdal, M. & Boers, N. (2022). Comprehensive uncertainty estimation of the timing of Greenland warmings of the Greenland Ice core records. Climate of the Past, 18, 1275-1294. doi.org/10.5194/cp-18-1275-2022
and
Myrvoll-Nilsen, E., Riechers, K. & Boers, N. (202x). Tie-point paper (title TBD)
The simplest way to install the package is to install the remotes package and run
#install.packages("remotes")
remotes::install_github("eirikmn/bremla")To get started, see the documentation '?bremla' and the associated example.
The package includes the NGRIP/GICC05 data set 'NGRIP_d18O_and_dust_5cm.xls' downloaded from Centre for Ice and Climate at the Niels Bohr Institute of the University of Copenhagen, Denmark, and the table of stadial-interstadial events presented by Rasmussen et al. (2014). Also includes the tie-point presented in Adolphi et al. (2018) and Muscheler et al. (2020). Other data and tie-points should work as well.
Warning: The provided real data example generates several thousand simulated chronologies with individual lengths exceeding 18,000. More if both synchronized and unsynchronized chronologies computed. This will require a significant amount of memory. If needed, reduce the number of samples generated or free up memory before use.
This is a real data example for the GICC05 chronology of the NGRIP ice core record which produces 5000 simulations from a time scale synchronized using the Adolphi tie-points.
require(INLA)
library(bremla)
data("event_intervals")
data("events_rasmussen")
data("NGRIP_5cm")
age = NGRIP_5cm$age
depth = NGRIP_5cm$depth
depth2 = depth^2/depth[1]^2 #normalize for stability
d18O = NGRIP_5cm$d18O
data = data.frame(age=age,dy=c(NA,diff(age)),depth=depth,depth2=depth2,d18O=d18O)
formula = dy~-1+depth2+d18O
events=list(locations = events_rasmussen$depth,
locations_unit="depth",degree=1)
results = bremla(formula,data,reference.label="GICC05",
nsims=5000,
events=events,
synchronization=list(method="adolphi"),
control.fit=list(method="inla"),
control.sim=list(synchronized=TRUE) )
summary(results)
#>
#> Call:
#> knit("README.Rmd", "README.md")
#>
#> Time used:
#> Model fitting Chron. sampling Total
#> 9.7505 93.6226 106.2326
#>
#> The fixed component is explained by linear predictor:
#> dy ~ -1 + depth2 + d18O + psi_fill(degree=1, n_events=69)
#>
#> The noise component is explained by an ar1 process.
#>
#> The model is fitted using INLA, with following estimates for the hyperparameters:
#> mean sd quant0.025 quant0.25 quant0.5 quant0.75 quant0.975
#> sigma_epsilon 0.4444 0.0025 0.4396 0.4428 0.4444 0.4461 0.4493
#> phi 0.2639 0.0072 0.2498 0.2590 0.2639 0.2687 0.2779
#>
#> Simulating 5000 chronologies, using GICC05 as reference.
#>
#> 5000 synchronized chronologies sampled using 4 tie-point distributions (Adolphi).
#> Tie-points are fixed at GICC05 ages (yb2k):
#> 12050.3, 13050.62, 22050, 42049.59.
This code is associated and written for the papers Myrvoll-Nilsen et al. (2022) and Myrvoll-Nilsen et al. (202x) mentioned above. Feel free to use the code, but please cite the accompanying papers.
The code in this repository is made available under the terms of the GNU (version >=2) License. For details, see LICENSE.md file.