A method for climate and vegetation reconstruction through the inversion of a dynamic vegetation model

Abstract

Climate reconstructions from data sensitive to past climates provide estimates of what these climates were like. Comparing these reconstructions with simulations from climate models allows to validate the models used for future climate prediction. It has been shown that for fossil pollen data, gaining estimates by inverting a vegetation model allows inclusion of past changes in carbon dioxide values. As a new generation of dynamic vegetation model is available we have developed an inversion method for one model, LPJ-GUESS. When this novel method is used with high-resolution sediment it allows us to bypass the classic assumptions of (1) climate and pollen independence between samples and (2) equilibrium between the vegetation, represented as pollen, and climate. Our dynamic inversion method is based on a statistical model to describe the links among climate, simulated vegetation and pollen samples. The inversion is realised thanks to a particle filter algorithm. We perform a validation on 30 modern European sites and then apply the method to the sediment core of Meerfelder Maar (Germany), which covers the Holocene at a temporal resolution of approximately one sample per 30 years. We demonstrate that reconstructed temperatures are constrained. The reconstructed precipitation is less well constrained, due to the dimension considered (one precipitation by season), and the low sensitivity of LPJ-GUESS to precipitation changes.

A few prospective ideas on climate reconstruction: from a statistical single proxy approach towards a multi-proxy and dynamical approach

“…been done for 30 sites in Europa and Garreta et al (2009) have shown that the mean bias was <1 • C and 3% in absolute value. Thus, the method seems to be unbiased.…”

“…Either PFT (Sitch et al, 2003) or species (Hickler et al, 2004) may be simulated. Garreta et al (2009) used the species version which includes 18 species. LPJ-GUESS has standard inputs, i.e.…”

“…MCMC algorithm are then not applicable. Garreta et al (2009) have proposed to use a particle filter technique more adapted to time-series and stochastic processes. We do not use here PFT's scores but a restricted vector of 14 arboreal pollen taxa (Abies, Alnus, Betula, Carpinus, Corylus, Fagus, Fraxinus, Picea, Pinus, evergreen Quercus, deciduous Quercus, Tilia, Ulmus and Populus) and a 15th herbaceous taxon, summing all the herbaceous taxa.…”

“…This choice has the maximum of coherency with the 18 species defined in LPJ-GUESS. Garreta et al (2009) applied their method to a fossil core (Meerfelder maar) ), but we just present here the validation of the method with modern samples. The monthly temperature and precipitation were deduced from a 6-dimensional climate parameter vector: C=(T Jan , T Jul , P win , P spr , P sum , P aut ), which is slightly different on what has been done in the previous sections.…”

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

“…In this approach, several most likely climate scenarios are estimated for the time period in question, the vegetation model is run with each scenario and the simulated vegetation types are compared with the fossil pollen data to estimate the most coherent simulations. The past climate is obtained by inverting the climate input from the vegetation model (Guiot et al 1999(Guiot et al , 2009Garreta et al 2010). Wu et al (2007a) used an inverse modelling approach with modern and glacial CO 2 concentrations to investigate the impact of changing CO 2 concentration on the reconstruction results from Africa and Eurasia.…”