A climate-isotope regression model with seasonally-varying and time-integrated relationships |
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| Authors: | Matt J Fischer Lisa M Baldini |
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| Institution: | 1. Institute for Environmental Research, Australian Nuclear Science and Technology Organisation, Menai, NSW, 2234, Australia
2. Department of Earth Sciences, Durham University, Science Labs, Durham, DH1 3LE, UK
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| Abstract: | This study investigates multivariable and multiscalar climate-??18O relationships, through the use of statistical modeling and simulation. Three simulations, of increasing complexity, are used to generate time series of daily precipitation ??18O. 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-??18O patterns that arise at daily-interannual timescales. These simulations show that (1) complex links exist between climate-??18O relationships at different timescales, (2) the short-timescale relationships that underlie monthly predictor-??18O relationships can be recovered using only monthly ??18O 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 ??18O 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 ??18O at this site. The methodology introduced here can potentially be applied to historic monthly ??18O data, to better understand how multiple-integrated influences at short timescales give rise to climate-??18O patterns at monthly-interannual timescales. |
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