Time series modeling of paleoclimate data
This paper applies time series modeling methods to paleoclimate series for temperature, ice volume, and atmospheric concentrations of CO 2 and CH 4 . These series, inferred from Antarctic ice and ocean cores, are well known to move together in the transitions between glacial and interglacial periods...
| Published in: | Environmetrics |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2015
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1002/env.2373 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2373 https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2373 |
| Summary: | This paper applies time series modeling methods to paleoclimate series for temperature, ice volume, and atmospheric concentrations of CO 2 and CH 4 . These series, inferred from Antarctic ice and ocean cores, are well known to move together in the transitions between glacial and interglacial periods, but the dynamic relationship between the series is open to question. A further unresolved issue is the role of Milankovitch theory, in which the glacial/interglacial cycles are correlated with orbital variations. We perform tests for Granger causality in the context of a vector autoregression model. Previous work with climate series has assumed nonstationarity and adopted a cointegration approach, but in a range of tests, we find no evidence of integrated behavior. We use conventional autoregressive methodology while allowing for conditional heteroscedasticity in the residuals, associated with the transitional periods. Copyright © 2015 John Wiley & Sons, Ltd. |
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