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Age-depth correlations are the key elements in paleoenvironmental studies to place proxy measurements into a temporal context. However, potential influencing factors of the available radiocarbon data and the associated modeling process can cause serious divergences of age-depth correlations from true chronologies, which is particularly challenging for paleolimnological studies in Arctic regions. This paper provides geoscientists with a tool-assisted approach to compare outputs from age-depth modeling systems and to strengthen the robustness of age-depth correlations. We primarily focused in the development on age determination data from a data collection of high latitude lake systems (50° N to 90° N, 62 sediment cores, and a total of 661 dating points). Our approach used five age-depth modeling systems (Bacon, Bchron, clam, hamstr, Undatable) that we linked through a multi-language Jupyter Notebook called LANDO (“Linked age and depth modeling”). Within LANDO we have implemented a pipeline from data integration to model comparison to allow users to investigate the outputs of the modeling systems. In this paper, we focused on highlighting three different case studies: comparing multiple modeling systems for one sediment core with a continuous, undisturbed succession of dating points (CS1 - “Undisturbed sequence”), for one sediment core with scattered dating points (CS2 - “Inconsistent sequence”), and for multiple sediment cores (CS3 - “Multiple cores”). For the first case study (CS1), we showed how we facilitate the output data from all modeling systems to create an ensemble age-depth model. In the special case of scattered dating points (CS2), we introduced an adapted method that uses independent proxy data to assess the performance of each modeling system in representing lithological changes. Based on this evaluation, we reproduced the characteristics of an existing age-depth model (Lake Ilirney, EN18208) without removing age determination data. For the multiple sediment core (CS3) we found that when considering the Pleistocene-Holocene transition, the main regime changes in sedimentation rates do not occur synchronously for all lakes. We linked this behavior to the uncertainty within the modeling process as well as the local variability of the sediment cores within the collection.

Lake systems play a central role in broadening our knowledge about future trends in the Arctic, as their sediments store information on interactions between climate change, lake ontogeny, external abiotic sediment input, and biodiversity changes. In order to make reliable statements about future lake trajectories, we need sound multi-proxy data from different lakes across the Arctic. Various studies using data from repositories already showed the effectiveness of multi-proxy, multi-site investigations (e.g., Kaufman et al., 2020; PAGES 2k Consortium, 2017). However, there are still datasets from past coring expeditions to Arctic lake systems that are neither included in any of these repositories nor subject to any particular standard. When working with such data from heterogeneous sources, we face the challenge of dealing with data of different format, type, and structure. It is therefore necessary to transform such data into a uniform format to ensure semantic and syntactic comparability. In this talk, we present an interdisciplinary approach by transforming research data from different lake sediment cores into a coherent framework. Our approach adapts methods from the database field, such as developing entity-relationship (ER) diagrams, to understand the conceptual structure of the data independently of the source. Based on this knowledge, we developed a conceptual data model that allows scientists to integrate heterogeneous data into a common database. During the talk, we present further steps to prepare datasets for multi-site statistical investigation. To test our approach, we compiled and transformed a collection of published and unpublished paleolimnological data of Arctic lake systems into our proposed format. Additionally, we show our results from conducting a comparative analysis on a set of acquired data, hereby focusing on comparing total organic carbon and bromine content. We conclude that our harmonized dataset enables numerical inter-proxy and inter-lake comparison despite strong initial heterogeneity. [1] D. S. Kaufman et al., “A global database of Holocene paleotemperature records,” Sci. Data, vol. 7, no. 115, pp. 1–34, 2020.[2] PAGES 2k Consortium, “A global multiproxy database for temperature reconstructions of the Common Era,” Sci. Data, vol. 4, no. 170088, pp. 1–33, 2017.