Search

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8ĝ€ ̄% classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented.

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented.

During the Late Pleistocene–Holocene, the Ross Sea Ice Shelf exhibited strong spatial variability in relation to the atmospheric and oceanographic climatic variations. Despite being thoroughly investigated, the timing of the ice sheet retreat from the outer continental shelf since the Last Glacial Maximum (LGM) still remains controversial, mainly due to a lack of sediment cores with a robust chronostratigraphy. For this reason, the recent recovery of sediments containing a continuous occurrence of calcareous foraminifera provides the important opportunity to create a reliable age model and document the early deglacial phase in particular. Here we present a multiproxy study from a sediment core collected at the Hallett Ridge (1800m of depth), where significant occurrences of calcareous planktonic and benthic foraminifera allow us to document the first evidence of the deglaciation after the LGM at about 20.2 ka. Our results suggest that the co-occurrence of large Neogloboquadrina pachyderma tests and abundant juvenile forms reflects the beginning of open-water conditions and coverage of seasonal sea ice. Our multiproxy approach based on diatoms, silicoflagellates, carbon and oxygen stable isotopes on N. pachyderma, sediment texture, and geochemistry indicates that abrupt warming occurred at approximately 17.8 ka, followed by a period of increasing biological productivity. During the Holocene, the exclusive dominance of agglutinated benthic foraminifera suggests that dissolution was the main controlling factor on calcareous test accumulation and preservation. Diatoms and silicoflagellates show that ocean conditions were variable during the middle Holocene and the beginning of the Neoglacial period at around 4 ka. In the Neoglacial, an increase in sand content testifies to a strengthening of bottom-water currents, supported by an increase in the abundance of the tycopelagic fossil diatom Paralia sulcata transported from the coastal regions, while an increase in ice-raft