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This study is based on multiproxy data gained from a 14C-dated 6.5 m long sediment core and a 210Pb-dated 23 cm short core retrieved from Lake Rauchuagytgyn in Chukotka, Arctic Russia. Our main objectives are to reconstruct the environmental history and ecological development of the lake during the last 29 kyr and to investigate the main drivers behind bioproduction shifts. The methods comprise age-modeling, accumulation rate estimation, and light microscope diatom species analysis of 74 samples, as well as organic carbon, nitrogen, and mercury analysis. Diatoms have appeared in the lake since 21.8 ka cal BP and are dominated by planktonic Lindavia ocellata and L. cyclopuncta. Around the Pleistocene–Holocene boundary, other taxa including planktonic Aulacoseira, benthic fragilarioid (Staurosira), and achnanthoid species increase in their abundance. There is strong correlation between variations of diatom valve accumulation rates (DARs; mean 176.1×109 valves m2 a1), organic carbon accumulation rates (OCARs; mean 4.6 g m−2 a−1), and mercury accumulation rates (HgARs; mean 63.4 µg m−2 a−1). We discuss the environmental forcings behind shifts in diatom species and find moderate responses of key taxa to the cold glacial period, postglacial warming, the Younger Dryas, and the Holocene Thermal Maximum. The short-core data likely suggest recent change of the diatom community at the beginning of the 20th century related to human-induced warming but only little evidence of atmospheric deposition of contaminants. Significant correlation between DAR and OCAR in the Holocene interglacial indicates within-lake bioproduction represents bulk organic carbon deposited in the lake sediment. During both glacial and interglacial episodes HgAR is mainly bound to organic matter in the lake associated with biochemical substrate conditions. There were only ambiguous signs of increased HgAR during the industrialization period. We conclude that if increased short-term emissions are neglected, pristine Arctic lake systems can potentially serve as long-term CO2 and Hg sinks during warm climate episodes driven by insolation-enhanced within-lake primary productivity. Maintaining intact natural lake ecosystems should therefore be of interest to future environmental policy.

Age–depth relationships 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 relationships 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 relationships. We primarily focused on the development of age determination data from a data collection of high-latitude lake systems (50 to 90∘ N, 55 sediment cores, and a total of 602 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 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 continuously deposited succession of dating points (CS1), for one sediment core with scattered dating points (CS2), and for multiple sediment cores (CS3). 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 multiple sediment cores (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 dating and modeling process, as well as the local variability in catchment settings affecting the accumulation rates of the sediment cores within the collection near the glacial–interglacial transition.

Refined dating techniques and high-precision radiocarbon dating have enabled more accurate age controls for paleoenvironmental reconstruction of lake systems. However, low bioproductivity and the influence of old carbon have a profound impact on radiocarbon dating series of non-varved sediment records from Arctic lakes. Geochronological tools such as software systems for age-depth modeling provide sophisticated justifications for age-depth relationships. But because there are many different tools available with varying underlying mathematical methods and models, the model output can show diverging results, e.g., for problematic sediment cores with scatter age dating points. A detailed comparison of the results of individual modeling system is therefore often tedious and potentially error-prone. Due to time constraints and a lack of alternative options, users typically only select and apply one modeling system to provide a geochronological timeframe for paleoenvironmental interpretation. Therefore, we introduce our “Linked age and depth modeling” (LANDO) approach that links five modeling systems (Bacon, Bchron, clam, hamstr, Undatable) in a single multi-language Jupyter Notebook. LANDO reduces the effort of using established modeling systems for both single and multiple dating series and makes the results directly comparable. In addition, we introduce an ensemble age-depth model that uses the output from all models to create a data-driven, semi-informed age-depth relationship. In our talk we will highlight our adapted fuzzy change point method, in which we used independent proxy data to evaluate the performance of each modeling system in representing lithological changes. LANDO is already publicly available on GitHub: https://github.com/GPawi/LANDO.

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.

Increasing air and sea surface temperatures at high latitudes lead to accelerated thaw, destabilization, and erosion of perennially frozen soils (i.e., permafrost), which are often rich in organic carbon. Coastal erosion leads to an increased mobilization of organic carbon into the Arctic Ocean that can be converted into greenhouse gases and may therefore contribute to further warming. Carbon decomposition can be limited if organic matter is efficiently deposited on the seafloor, buried in marine sediments and thus removed from the short-term carbon cycle. Basins, canyons and troughs near the coastline can serve as sediment traps and potentially accommodate large quantities of organic carbon along the Arctic coast. Here we use biomarkers (source-specific molecules), stable carbon isotopes (δ13C) and radiocarbon (Δ14C) to identify the sources of organic carbon in the nearshore zone of the southern Canadian Beaufort Sea. We use an end-member model based on the carbon isotopic composition of bulk organic matter to identify sources of organic carbon. Monte Carlo simulations are applied to quantify the contribution of coastal permafrost erosion to the sedimentary carbon budget. The models suggest that 40% of all carbon released by coastal erosion is efficiently trapped and sequestered in the nearshore zone. We conclude that permafrost coastal erosion releases huge amounts of sediment and organic matter into the nearshore zone. Rapid burial removes large quantities of carbon from the carbon cycle in depositional settings.

When performing spatial-temporal investigations of multiple lake systems, geoscientists face the challenge of dealing with complex and heterogeneous data of different types, structure, and format. To support comparability, it is necessary to transform such data into a uniform format that ensures syntactic and semantic comparability. This paper presents a data science approach for transforming research data from different lake sediment cores into a coherent framework. For this purpose, we collected published and unpublished data from paleolimnological investigations of Arctic lake systems. Our approach adapted methods from the database field, such as developing entity-relationship (ER) diagrams, to understand the conceptual structure of the data independently of the source. We demonstrated the feasibility of our approach by transforming our ER diagram into a database schema for PostgreSQL, a popular database management system (DBMS). We validated our approach by conducting a comparative analysis on a set of acquired data, hereby focusing on the comparison of total organic carbon and bromine content in eight selected sediment cores. Still, we encountered serious obstacles in the development of the ER model. Heterogeneous structures within collected data made an automatic data integration impossible. Additionally, we realized that missing error information hampers the development of a conceptual model. Despite the strong initial heterogeneity of the original data, our harmonized dataset leads to comparable datasets, enabling numerical inter-proxy and inter-lake comparison.