Advanced

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

Garreta, Vincent; Miller, Paul LU ; Guiot, Joel; Hely, Christelle; Brewer, Simon; Sykes, Martin LU and Litt, Thomas (2010) In Climate Dynamics 35(2-3). p.371-389
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
LPJ-GUESS, Pollen sample, vegetation model, Dynamic, Particle filter, Palaeoclimate reconstruction, Model inversion
in
Climate Dynamics
volume
35
issue
2-3
pages
371 - 389
publisher
Springer
external identifiers
  • wos:000280237900008
  • scopus:77954957900
ISSN
1432-0894
DOI
10.1007/s00382-009-0629-1
project
MERGE
BECC
language
English
LU publication?
yes
id
dd843c36-8178-400a-8770-aad90460f1e7 (old id 1656073)
date added to LUP
2010-08-30 13:56:14
date last changed
2018-05-29 10:08:12
@article{dd843c36-8178-400a-8770-aad90460f1e7,
  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.},
  author       = {Garreta, Vincent and Miller, Paul and Guiot, Joel and Hely, Christelle and Brewer, Simon and Sykes, Martin and Litt, Thomas},
  issn         = {1432-0894},
  keyword      = {LPJ-GUESS,Pollen sample,vegetation model,Dynamic,Particle filter,Palaeoclimate reconstruction,Model inversion},
  language     = {eng},
  number       = {2-3},
  pages        = {371--389},
  publisher    = {Springer},
  series       = {Climate Dynamics},
  title        = {A method for climate and vegetation reconstruction through the inversion of a dynamic vegetation model},
  url          = {http://dx.doi.org/10.1007/s00382-009-0629-1},
  volume       = {35},
  year         = {2010},
}