A method for climate and vegetation reconstruction through the inversion of a dynamic vegetation model
Author
Summary, in English
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.
Department/s
Publishing year
2010
Language
English
Pages
371-389
Publication/Series
Climate Dynamics
Volume
35
Issue
2-3
Document type
Journal article
Publisher
Springer
Topic
- Physical Geography
Keywords
- LPJ-GUESS
- Pollen sample
- vegetation model
- Dynamic
- Particle filter
- Palaeoclimate reconstruction
- Model inversion
Status
Published
ISBN/ISSN/Other
- ISSN: 1432-0894