An assessment of biomarker-based multivariate classification methods versus the PIP25 index for paleo Arctic sea ice reconstruction. (November 2018) - Köseoğlu, Denizcan
Highlights: HBI-based spring sea ice reconstruction methods compared for the last 16 cal kyr BP. Multivariate classification is consistent with PIP25 -based sea ice concentrations. Periods of unstable climate potentially cause discrepancies between the two methods. Sources of discrepancy may be determined using basic data visualisation techniques. Abstract: The development of various combinative methods for Arctic sea ice reconstruction using the sympagic highly branched isoprenoid (HBI) IP25 in conjunction with pelagic biomarkers has often facilitated more detailed descriptions of sea ice conditions than using IP25 alone. Here, we investigated the application of the Phytoplankton-IP25 index (PIP25 ) and a recently proposed Classification Tree (CT) model for describing temporal shifts in sea ice conditions to assess the consistency of both methods. Based on biomarker data from three downcore records from the Barents Sea spanning millennial timescales, we showcase apparent and potential limitations in both approaches, and provide recommendations for their identification or prevention. Both methods provided generally consistent outcomes and, within the studied cores, captured abrupt shifts in sea ice regimes, such as those evident during the Younger Dryas, as well as more gradual trends in sea ice conditions during the Holocene. The most significant discrepancies occurred during periods of highly unstable climate change, such as those characteristic of the Younger Dryas–Holocene transition. Such intervals of increased discrepancy were identifiable by significant changes of HBI distributions and correlations to values not observed in proximal surface sediments. We suggest that periods of highly fluctuating climate that are not represented in modern settings may hinder the performance and complementary application of PIP25 and CT-based methods, and that data visualisation techniques should be employed to identify such occurrences in downcore records. Additionally, due to the reliance of both methods on biomarker distributions, we emphasise the importance of accurate and consistent biomarker quantification.