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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2025
Research article |
| 29 Aug 2025
| 29 Aug 2025
SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data
Laura Boyall
CORRESPONDING AUTHOR
Department of Geography, Royal Holloway University of London, Egham, TW20 0EX, UK
Andrew C. Parnell
School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
Paul Lincoln
Department of Geography, Royal Holloway University of London, Egham, TW20 0EX, UK
Antti Ojala
Department of Geography and Geology, University of Turku, 20014 Turku, Finland
Geological Survey of Finland, Vuorimiehentie 5, 02151 Espoo, Finland
Armand Hernández
GRICA Group, Centro Interdisciplinar de Química e Bioloxía (CICA), Faculty of Sciences, Universidade de Coruña, Coruña, Spain
Celia Martin-Puertas
Department of Geography, Royal Holloway University of London, Egham, TW20 0EX, UK
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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.
We present a new approach to reconstructing annual mean temperature using geochemical data from...
