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

Abstract. Quantification of proxy records obtained from geological archives is key for extending the observational record to estimate the rate, strength, and impact of past climate changes, but also to validate climate model simulations, improving future climate predictions. SCUBIDO (Simulating Climate Using Bayesian Inference with proxy Data Observations), is a new statistical model for reconstructing palaeoclimate variability and its uncertainty using Bayesian inference on multivariate non-biological proxy data. We have developed the model for annually laminated (varved) lake sediments as they provide a high-temporal resolution to reconstructions with precise chronologies. This model uses non-destructive X-Ray Fluorescence core scanning (XRF-CS) data (chemical elemental composition of the sediments) because it can provide multivariate proxy information at a near continuous, sub-mm resolution, and when applied to annually laminated (varved) lake sediments or sediments with high accumulation rates, the reconstructions can be of an annual resolution.

SCUBIDO uses a calibration period of instrumental climate data and overlapping XRF-CS data to learn about the direct relationship between each geochemical element (reflecting different depositional processes) and climate, but also the covariant response between the elements and climate. The understanding of these relationships is then applied down core to transform the qualitative proxy data into a posterior distribution of palaeoclimate with quantified uncertainties. In this paper, we describe the mathematical details of this Bayesian approach and show detailed walk-through examples that reconstruct Holocene annual mean temperature in central England and southern Finland. The mathematical details and code have been synthesised into the R package SCUBIDO to encourage others to use this modelling approach. Whilst the model has been designed and tested on varved sediments, XRF-CS data from other types of sediment records which record a climate signal could also benefit from this approach.