Pollen-based climate reconstruction: Calibration of the vegetation-pollen processes

摘要

Palaeoclimate reconstructions are based on the relationship between climate and sediment pollen assemblages. This model is called the transfer function (TF). Process-based TF emerge as an opportunity to better quantify past climate changes. For example, when a process-based model of vegetation dynamics is part of the TF it allows to include atmospheric CO _2 concentration and plant-plant interactions as factors affecting the reconstruction. We propose the missing piece for a fully process-based TF: the model linking, at a continental scale, vegetation model outputs and pollen sampled in sediments. We perform its calibration and we explore the quality of fit.The model represents the error of the vegetation model LPJ-GUESS and four main processes: pollen production, dispersal, accumulation and sampling. Accumulation and sampling processes are either modelled using a multinomial-Poisson (MP) or a multinomial-negative binomial (MNB) model, both models allowing for overdispersion and structural zeros in the sense of null multinomial probabilities. We perform inference for a European pollen dataset by parallelising a Monte Carlo Markov Chain algorithm.Model fitness diagnostics indicate that MP model is not supported by the European dataset. The MNB model is also detected inconsistent, but with a p-value of 0.014 and without stationarity nor overdispersion problems. At this stage, the MNB model is considered as a robust alternative to more complex models. We finally discuss the challenge of the TF inversion for palaeoclimate reconstruction and vegetation model re-calibration.