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Climate Dynamics

, Volume 37, Issue 11–12, pp 2235–2251 | Cite as

A climate-isotope regression model with seasonally-varying and time-integrated relationships

  • Matt J. Fischer
  • Lisa M. Baldini
Article

Abstract

This study investigates multivariable and multiscalar climate-δ18O relationships, through the use of statistical modeling and simulation. Three simulations, of increasing complexity, are used to generate time series of daily precipitation δ18O. The first simulation uses a simple local predictor (daily rainfall amount). The second simulation uses the same local predictor plus a larger-scale climate variable (a daily NAO index), and the third simulation uses the same local and non-local predictors, but with varying seasonal effect. Since these simulations all operate at the daily timescale, they can be used to investigate the climate-δ18O patterns that arise at daily-interannual timescales. These simulations show that (1) complex links exist between climate-δ18O relationships at different timescales, (2) the short-timescale relationships that underlie monthly predictor-δ18O relationships can be recovered using only monthly δ18O and daily predictor variables, (3) a comparison between the simulations and observational data can elucidate the physical processes at work. The regression models developed are then applied to a 2-year dataset of monthly precipitation δ18O from Dublin and compared with event-scale data from the same site, which illustrates that the methodology works, and that the third regression model explains about 55% of the variance in δ18O at this site. The methodology introduced here can potentially be applied to historic monthly δ18O data, to better understand how multiple-integrated influences at short timescales give rise to climate-δ18O patterns at monthly-interannual timescales.

Keywords

Time series analysis Multivariate regression Oxygen isotopes GNIP NAO Characteristic timescale 

Notes

Acknowledgments

The authors wish to thank Dr. Simon Poulson and the UNR isotope laboratory for analyzing the Dublin rainwater samples. The research was funded by Science Foundation Ireland and a University College Dublin (UCD) Seed Funding grant while LB was a graduate student at University College Dublin, Ireland.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Institute for Environmental ResearchAustralian Nuclear Science and Technology OrganisationMenaiAustralia
  2. 2.Department of Earth SciencesDurham University, Science LabsDurhamUK

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