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Foraminifera play an important role in oceanographic and paleoceanographic research. The test morphology and chemistry within species, as well as the presence or absence of certain species, are affected by environmental conditions. Classification of different species of foraminifera is a crucial yet tedious task for researchers. Deep-learning approaches can help with morphological studies and aid in species classification; however, they require large-scale datasets that are challenging to obtain and annotate because of the extremely small size and delicate handling of these microorganisms. In this work, we expand on an existing mathematical model for foraminifera shell growth to generate 3D synthetic models to aid in these studies. We define parameter spaces for the model which are intended to approximate seven randomly chosen foraminifera taxa. Along with providing an open-source code base to support other researchers in generating models and studying growth patterns, we further extend the synthetic data generation to include a rendering component that mimics two existing robotic imaging systems. We provide two use cases for our synthetic dataset. First, we show how orientation can affect the automated classification of different species and how incorporating aleatoric uncertainty indicators can help select the next views of the samples to significantly improve classification accuracy from 82% to 89%. Next, we show how a sparse set of synthetic 2D images can be used to extract 3D morphology of foraminifera using Neural Radiance Fields (NeRFs). 

We present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation. 

The high rate of biological productivity in the North Atlantic is stimulated by the advective supply of nutrients into the region via the Gulf Stream (nutrient stream). It has been proposed that the projected future decline in the Atlantic Meridional Overturning Circulation (AMOC) will cause a reduction in nutrient supply and resulting productivity. In this work, we examine how the nutrient stream changed over the Younger Dryas climate reversal that marked the transition out of the last ice age. Gulf Stream nutrient content decreased, and oxygen content increased at the Florida Straits during this time of weakened AMOC. The decreased nutrient stream was accompanied by a reduction in biological productivity at higher latitudes in the North Atlantic, which supports the link postulated in theoretical and modeling studies. 

Earth system models suggest that anthropogenic climate change will influence marine phytoplankton over the coming century with light-limited regions becoming more productive and nutrient-limited regions less productive. Anthropogenic climate change can influence not only the mean state but also the internal variability around the mean state, yet little is known about how internal variability in marine phytoplankton will change with time. Here, we quantify the influence of anthropogenic climate change on internal variability in marine phytoplankton biomass from 1920 to 2100 using the Community Earth System Model 1 Large Ensemble (CESM1-LE). We find a significant decrease in the internal variability of global phytoplankton carbon biomass under a high emission (RCP8.5) scenario and heterogeneous regional trends. Decreasing internal variability in biomass is most apparent in the subpolar North Atlantic and North Pacific. In these high-latitude regions, bottom-up controls (e.g., nutrient supply, temperature) influence changes in biomass internal variability. In the biogeochemically critical regions of the Southern Ocean and the equatorial Pacific, bottom-up controls (e.g., light, nutrients) and top-down controls (e.g., grazer biomass) affect changes in phytoplankton carbon internal variability, respectively. Our results suggest that climate mitigation and adaptation efforts that account for marine phytoplankton changes (e.g., fisheries, marine carbon cycling) should also consider changes in phytoplankton internal variability driven by anthropogenic warming, particularly on regional scales. 

Fossil single-celled marine organisms known as foraminifera are widely used in oceanographic research. The identification of species is one of the most common tasks when analyzing ocean samples. One of the primary criteria for species identification is their morphology. Automatic segmentation of images of foraminifera would aid on the identification task as well as on other morphological studies. We pose this problem as an edge detection task for which capturing the correct topological structure is essential. Due to the presence of soft edges and even unclosed segments, state-of-the-art techniques have problems capturing the correct edge structure. Standard pixel-based loss functions are also sensitive to small deformations and shifts of the edges penalizing location more heavily than actual structure. Hence, we propose a homology-based detector of local structural difference between two edge maps with a tolerable deformation. This detector is employed as a new criterion for the training and design of data-driven approaches that focus on enhancing these structural differences. Our approaches demonstrate significant improvement on morphological segmentation of foraminifera when considering region-based and topology-based metrics. Human ranking of the quality of the results by marine researchers also supports these findings. 

Abstract During the last deglaciation Earth’s climate experienced strong and abrupt variations, resulting in major changes in global temperature, sea level, and ocean circulation. Although proxy records have significantly improved our understanding of climate during this period, questions remain regarding the connection between ocean circulation evolution and resulting geotracer distributions, including those of deep waters in the Pacific. Here we use the C‐iTRACE simulation, a transient ocean‐only, isotope‐enabled version of the Community Earth System Model, to better understand deglacial deep Pacific radiocarbon evolution in the context of circulation and reservoir age changes. Throughout the deglaciation, the Pacific Ocean circulation in C‐iTRACE responds strongly to glacial meltwater forcing, leading to large changes in deep Pacific Δ¹⁴C age. A multi‐millennial weakening of the overturning circulation from 20 to 15 ka BP leads to increases in deep Pacific Δ¹⁴C ages, but from 20 to 18 ka BP, nearly half (40%–60%) of this aging is controlled by changing surface reservoir age, corroborating previous studies showing that Δ¹⁴C is not solely a circulation age tracer. As the deglaciation proceeds, circulation change controls progressively more of the Δ¹⁴C age, accounting for more than 75% of it across the deep Pacific from 15 to 8 ka BP.