Data


Statistical reconstruction of global vegetation for the last glacial maximum

Abstract

We provide an estimate of global vegetation density for the Last Glacial Maximum (LGM) using a simple statistic model. For today's climate, vegetation is divided into 11 vegetation types plus bare soil, for each of whichempirical relationship between the probability of its occurrence and climate controls is derived. The relationships are then used to reconstruct the glacial vegetation patterns with and without considering CO2 modifications. For the LGM, the climate drivers are estimated from an ensemble-average of global paleo-climate simulations. The reconstruction suggests that vegetation types existing in today's cooler and drier regimes prevailed during the LGM and today's desert areas had more vegetation then. The vegetation patterns of the Amazon and Sahara are examined in detail. In the Amazon, tropical rainforest cover is reduced from 80% in today's climate40% in the LGM climate. The Sahara was partly covered by shrubs and grassland, with bare ground fraction reduced from 80% today to 30% in the LGM. The reconstructed vegetation patterns are compared with available biome data.

Resources

1-s2.0-S0921818117306148-main.pdf Request access Accessed 0 times | Last updated 13.08.2018

Bibliography

Shao, Y., Anhäuser, A., Ludwig, P., Schlüter, P., Williams, E. (2018): Statistical reconstruction of global vegetation for the last glacial maximum. Elsevier – In: Global and Planetary Change, Vol. 168, p: 67 - 77, DOI: https://doi.org/10.1016/j.gloplacha.2018.06.002

Authors Shao, Yaping and Anhäuser, Andreas and Ludwig, Patrick and Schlüter, Philipp and Williams, Ehimen
Type article
Title Statistical reconstruction of global vegetation for the last glacial maximum
URL https://www.sciencedirect.com/science/article/pii/S0921818117306148
DOI https://doi.org/10.1016/j.gloplacha.2018.06.002
Journal Global and Planetary Change
Year 2018
Volume 168
Pages 67 - 77
Publisher Elsevier
Month 09
Export BibTeX

Additional Metadata

Spatial {"type":"Polygon","coordinates":[[[-180,90],[180,-90],[180,-90],[-180,-90]]]}
Export RDF
Back to dataset list