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Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies

·2007·The Geological Society of London on behalf of The Micropalaeontological Society eBooks

Abstract

This book unites climate modelling, palaeoceanography and palaeontology to address fundamental events in the climate history of Earth over the past 600 million years. Understanding the ‘tipping points’ that have led to rapid changes in the Earth's climate is vitally important with the realization that humans modify global climate. In an effort to better understand past and future climate change, general circulation models have become the forerunners of attempts to simulate future climate. Although extraordinarily sophisticated, they remain imperfect tools that require ‘grounding’ in geological data. In this, the study of past major climate transitions like the Palaeozoic icehouse worlds and the extreme greenhouse of the Cretaceous are invaluable. Both the mechanisms that forced changes in the Earth's climate as well as the proxies that track these changes are discussed. The central message of the book is that general circulation models tested with geological data in an iterative ‘ground truth’ process provide the best estimates of the Earth's ancient climate.

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Metadata

Venue
The Geological Society of London on behalf of The Micropalaeontological Society eBooks
Published
2007-12-05
OpenAlex
W1854715208
Cited by
489
References
0
FWCI
26.95
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies. https://app.cochat.ai/papers/10.1144/tms002
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Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies. (2007). The Geological Society of London on behalf of The Micropalaeontological Society eBooks. https://doi.org/10.1144/tms002

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.