The Arctic is responding more rapidly to global warming than most other areas on our planet. Northward-flowing Atlantic Water is the major means of heat advection toward the Arctic and strongly affects the sea ice distribution. Records of its natural variability are critical for the understanding of feedback mechanisms and the future of the Arctic climate system, but continuous historical records reach back only ~150 years. Here, we present a multidecadal-scale record of ocean temperature variations during the past 2000 years, derived from marine sediments off Western Svalbard (79°N). We find that early-21st-century temperatures of Atlantic Water entering the Arctic Ocean are unprecedented over the past 2000 years and are presumably linked to the Arctic amplification of global warming.

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology.Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields.The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.

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Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of 0.78 on the classification and detection task, and 0.80 on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to 0.84 and 0.86, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifera.

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Core‐top sediment samples from Kongsfjorden, Svalbard, and adjacent fjord and shelf areas were collected in order to investigate a potential relationship between Mg/Ca‐ratios of Arctic benthic foraminifera and the ambient bottom water temperatures (BWT). The area is influenced by large seasonal variation in factors such as light and temperature, which is further strengthened by oceanographic shifts, including inflow of relatively warm Atlantic water. Four hydrological seasons have been defined. The studied samples were collected during the years 2005–2010 and comprise data from three hydrological seasons: spring, summer, and autumn. Five common species of cold‐water benthic foraminifera were investigated: Islandiella helenae/norcrossi, Buccella frigida, Nonionellina labradorica, Elphidium clavatum, and Cassidulina reniforme. For E. clavatum and C. reniforme, the investigations failed. For the remaining three species, the Mg/Ca‐temperature correlations initially appeared stochastic holding correlation coefficients between 0.01 and 0.15. However, grouping the data based on seasons gave stronger Mg/Ca‐temperature correlations, indicating specific growing seasons for the three species. The equations represent a starting point for a discussion on seasonality rather than robust, “ready‐to‐use” equations. I. helenae/norcrossi seems to reproduce and grow during summer (July/August) in outer Kongsfjorden. For B. frigida, a Mg/Ca‐temperature correlation is seen both in summer (July/August) and autumn (October/November) samples, indicative of a continuous reproduction/growth‐season lasting from July to November. N. labradorica appears to reproduce and grow during autumn (October/November). The results indicate that temperature reconstructions based on these benthic foraminifera reproduce seasonal temperatures rather than annual average temperatures.