Estimating Age Model Uncertainties for the Last Interglaciation

Thursday, 18 December 2014
Jeremy S Hoffman1, Peter U Clark1, Nicklas G Pisias2, Shaun A Marcott3 and Jeremy D Shakun4, (1)Oregon State University, College of Earth, Ocean, and Atmospheric Sciences, Corvallis, OR, United States, (2)Coll Oceanic & Atmospheric Sci, Corvallis, OR, United States, (3)Oregon State University, Corvallis, OR, United States, (4)Boston College, Chestnut Hill, MA, United States
The last interglaciation (LIG; Marine Isotope Stage 5, ~129-116 ka) was the most recent period in Earth history with significantly higher-than-present global sea level (≥6 m) and as such is commonly used as an intercomparison target for global paleoclimate modeling efforts. However, the spatio-temporal expression and amplitude of temperature variability during the LIG remain poorly constrained, primarily confounded by spurious chronostratigraphic control across ocean basins and between hemispheres. In contrast to the relatively well-dated Holocene, age models for the LIG are problematic in that both the onset and duration of peak interglacial conditions are assigned different ages and lengths based on the record and/or chronometer considered (e.g., ice cores, speleothems, corals, marine sediment cores). Moreover, marine proxy records spanning the LIG are frequently aligned to reference time series targets and assigned some somewhat arbitrary chronological uncertainty. Building a spatially coherent time frame for the LIG must include a robust characterization of age-model uncertainty, as this greatly affects what climatic features and patterns can be confidently resolved in global and regional LIG temperature estimates. Here, we assess the total chronological uncertainty that results from developing an age model based on climatostratigraphic alignment of high-latitude Southern Ocean sea-surface temperature reconstructions to an Antarctic ice-core deuterium record using a modified version of a cross-correlation maximization algorithm. Combining this modified cross-correlation maximization algorithm with a Monte Carlo randomization scheme, we generate a coherent and robust quantification of climatostratigraphic LIG age-model uncertainty to refine regional and global estimates of LIG temperatures.