Quantitative palaeoclimate reconstruction as an inverse problem: A Bayesian inference of late-Holocene climate on the eastern Tibetan Plateau from a peat cellulose δ18O record

Abstract

There has been a sustained need to quantify past climate changes from proxy records to better understand the driving mechanisms and thus to improve the prediction of the future. Transfer function is an intuitive and frequently used method in this regard. However, this method is unable to provide interpretive and predictive results from proxy records, because physical processes are not included. The inverse proxy modelling method opened up a new avenue for quantitative palaeoclimate reconstructions through the integration of proxy records with deterministic models. It is fundamentally different from the statistical approach, representing a conceptual advance in quantitative palaeoclimatology. Here we demonstrate the potential of this method by placing a mechanistic model and a 6000 year long peat cellulose δ18O record obtained from the high-cold and monsoonal eastern Tibetan Plateau ( c. 3500 m a.s.l.) in a Bayesian paradigm. In this worked example, the marginal posterior probability distributions of palaeoclimate variables such as the δ18O of soil water, temperature, and relative humidity were inferred jointly through the solution to an ill-posed inverse problem using the Markov chain-Monte Carlo method. Our results indicate that the observed variation of the peat cellulose δ18O record in this monsoonal area essentially reflects the changes in the oxygen isotopic composition of soil water, which is closely linked to that of rainfall. Compared with hydrology, temperature and humidity have little influence on the oxygen isotope fractionation of leaf water.