Answers are grounded in this paper and cite their sources.
Geochemical synthesis reveals sediment provenance and transport pathways in circum-Arctic surface sediments, highlighting benchmarks for reconstructing paleoceanographic and glacial dynamics.
Arctic amplification is fundamentally reshaping the cryosphere, leading to accelerated sea-ice retreat, permafrost thaw, and intensified riverine discharge. These shifts collectively modify sediment source-to-sink dynamic processes in the Arctic Ocean. While surface sediments in this semi-enclosed basin integrate complex signals from diverse Eurasian and North American source regions, disentangling these provenance signatures requires a robust, multi-proxy framework that has historically been hampered by fragmented, heterogeneous datasets. Here, we present CASPROD (Circum-Arctic Sediment PROvenance Database), a standardized and high-resolution mineralogical and geochemical synthesis of Arctic surface sediments. The dataset integrates multi-proxy records from a broad spatial network, comprising 4308 sampling stations, including bulk sediment Sr-Nd isotopes ( n =175 stations), detrital zircon U-Pb ages ( n =4671 grains from 21 key stations), clay mineral assemblages ( n =1647 stations), and detrital mineral proportions ( n =2465 stations). These integrated proxies provide cross-validated sediment provenance constraints: Sr-Nd isotopes discriminate between ancient cratonic shields and juvenile orogenic belts; detrital zircon geochronology yields diagnostic age spectra distinguishing Eurasian versus North American crustal affinities; and clay and detrital mineralogy reflect different circum-Arctic sediment provenances, lithologies and transport processes. By synthesizing these diverse datasets, CASPROD delineates robust pan-Arctic spatial provenance domains and transport pathways. This database thus provides a critical benchmark for reconstructing palaeoceanographic, glacial, and sedimentary dynamics over geological timescales. CASPROD is freely available online (https://doi.org/10.6084/m9.figshare.31926927; Yao et al., 2026) in multiple machine-readable formats (e.g., tabular tables, GIS shapefiles, and GEOTIFF).
No takes yet. Share an insight, caveat, or question.
Yao et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: