SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data

Boyall, Laura; Parnell, Andrew C.; Lincoln, Paul; Ojala, Antti; Hernández, Armand; Martin-Puertas, Celia

doi:https://doi.org/10.5194/cp-21-1465-2025

Articles | Volume 21, issue 8

https://doi.org/10.5194/cp-21-1465-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/cp-21-1465-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 8

Research article

|

29 Aug 2025

Research article |

| 29 Aug 2025

SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data

Laura Boyall, Andrew C. Parnell, Paul Lincoln, Antti Ojala, Armand Hernández, and Celia Martin-Puertas

Supplement

https://doi.org/10.5194/cp-21-1465-2025-supplement

Data sets

Diss Mere XRF Data L. Boyall and C. Martin-Puertas https://doi.org/10.5281/zenodo.15168266

μ-XRF and varve data from Lake Nautajärvi (NAU-23) Paul Lincoln https://doi.org/10.5281/zenodo.14645779

NOAA/WDS Paleoclimatology - Global Holocene Mean Surface Temperature Reconstructions D. S. Kaufman et al. https://doi.org/10.25921/vzys-1280

Holocene temperature reconstruction using paleoclimate data assimilation M. P. Erb et al. https://doi.org/10.5281/zenodo.6426332

NOAA/WDS Paleoclimatology - Globally Resolved Surface Temperatures Since the Last Glacial Maximum M. B. Osman et al. https://doi.org/10.25921/njxd-hg08

Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling (https://atmos.washington.edu/~hakim/lmr/) R. Tardif et al. https://doi.org/10.5194/cp-15-1251-2019

Monthly Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0 Met Office https://doi.org/10.5285/35fb8318798e437ba5b108e5eca6e92d

rnoaa: NOAA weather data S. Chamberlain et al. https://github.com/ropensci/rnoaa

Model code and software

LauraBoyall/SCUBIDO: v1.0.0 L. Boyall et al. https://doi.org/10.5281/zenodo.16883480

Short summary

We present a new approach to reconstructing annual mean temperature using geochemical data from lake sediments. This paper uses Bayesian inference, a type of statistical approach, and creates a model called Simulating Climate Using Bayesian Inference with proxy Data Observations (SCUBIDO), which takes the high-resolution geochemical data and transforms them into quantitative climate information at an annual resolution. We show the results from two lakes in England and Finland to produce temperature reconstructions for the past 8000 years with data every year.