Orbital-scale fluctuations in continental weathering flux and continental ice-volume during greenhouse and icehouse climate intervals : evidence from oxygen and neodymium isotopes

Rapid changes in global and local climate and weather, noticeable over our lifetimes, have spurred heated public and political debate over the cause of these climatic changes, whether or not we can reverse, stop or slow down this process, and how to create adequate models that can predict Earth\u27s changing climate. To create the most accurate models of future climate, many researchers look to past greenhouse intervals for insight; intervals of time identified by high modeled atmospheric CO2, a lack of glacial deposits, and geologic evidence of globally high sea-levels--all of which suggest little to no glacial ice and a relatively monotonous climate. However, recent stratigraphic and geochemical evidence from the Cretaceous \u27supergreenhouse\u27 interval document compelling evidence to support dynamic greenhouse climates. The goals of the studies hereafter described are to document and describe orbital-scale (104-105 yr) climate fluctuations recorded by δ18O as a proxy for glacio-eustasy and sea surface temperature and µNd as a proxy for continental weathering and wet/warm or dry/cool climate during two global greenhouse intervals: the Late Silurian and Late Devonian. The first study (Chapter 1) explores whether glacio-eustasy was the driver for orbital-scale shallowing-upward cycles developed in Late Silurian and early Late Devonian tropical and subtropical greenhouse climates. Two intra-cycle δ18O conodont apatite trends were observed: asymmetric trends demonstrating progressive δ18O increases coincident with facies shallowing, or symmetric trends demonstrating initially decreasing, then increasing δ18O values. These isotopic trends and intra-cycle magnitudes support the hypothesis that these cycles developed in response to glacio-eustasy during glacial stages, implying that Late Silurian and Late Devonian greenhouse climates were more dynamic than traditionally assumed. Relationships between orbital-scale continental weathering flux and glacial-interglacial marine cycles was first assessed comparing intra-cycle µNd and δ18O values from Middle Pennsylvanian icehouse cycles (Chapter 2). Observed conodont δ18O trends support previous interpretations that sampled cycles were generated by glacio-eustasy (30-50 m magnitudes) combined with \u3c1° sea surface temperature changes. µNd trends typically demonstrate lower µNd values during interglacial intervals and higher µNd during glacial intervals, supporting claims that increases in precipitation and/or air temperatures during interglacial intervals result in increased continental weathering rates and/or increased flux to marine basins. Using this initial continental weathering flux study and Pleistocene Nd-isotope studies as a model, we tested the phase-relationships of continental weathering flux and sea-level change in Upper Silurian and Upper Devonian greenhouse cycles (Chapter 3). Upper Silurian µNd demonstrates relatively uniform intra-cycle values, due to averaging out of far-field source variations in continental weathering flux, relatively uniform Late Silurian sub-tropical climate, and/or subdued continental weathering and flux due to the absence of upland and inland vascular land plants. Upper Devonian µNd demonstrate greater intra-cycle variation in µNd, which may be due to enhanced chemical weathering as a result of upland and inland colonization of land plants with large root systems and an intensified hydrologic cycle due to evapotranspiration. Observed µNd trends support the hypothesis that these greenhouse cycles record increases and decreases in continental weathering due to increases and decreases in precipitation and/or air temperature. µNd and 147Sm/144Nd values for Upper Silurian and Upper Devonian cycles support previous claims of far-field transport of Caledonian Mountain Belt material via trans-Laurentian fluvial systems

An Autonomous Agent Framework for Constellation Missions: A Use Case for Predicting Atmospheric CO\u3csub\u3e2\u3c/sub\u3e

Distributed systems missions (DSM), also known as swarm or constellation missions, is an upcoming class of mission design that is changing the current landscape.Swarms enable multipoint observations and higher fidelity science data collection. Autonomy is a critical feature that DSM will require in order to run successfully, especially beyond earth-centric missions and in dynamic environments due to increased delays between ground and space

Advancing the Scientific Frontier with Increasingly Autonomous Systems

A close partnership between people and partially autonomous machines has enabled decades of space exploration. But to further expand our horizons, our systems must become more capable. Increasing the nature and degree of autonomy - allowing our systems to make and act on their own decisions as directed by mission teams - enables new science capabilities and enhances science return. The 2011 Planetary Science Decadal Survey (PSDS) and on-going pre-Decadal mission studies have identified increased autonomy as a core technology required for future missions. However, even as scientific discovery has necessitated the development of autonomous systems and past flight demonstrations have been successful, institutional barriers have limited its maturation and infusion on existing planetary missions. Consequently, the authors and endorsers of this paper recommend that new programmatic pathways be developed to infuse autonomy, infrastructure for support autonomous systems be invested in, new practices be adopted, and the cost-saving value of autonomy for operations be studied.Comment: 10 pages (compared to 8 submitted to PSADS), 2 figures, submitted to National Academy of Sciences Planetary Science and Astrobiology Decadal Survey 2023-203

An international intercomparison of stable carbon isotope composition measurements of dissolved inorganic carbon in seawater

We report results of an intercomparison of stable carbon isotope ratio measurements in seawater dissolved inorganic carbon (δ 13C‐DIC) which involved 16 participating laboratories from various parts of the world. The intercomparison involved distribution of samples of a Certified Reference Material for seawater DIC concentration and alkalinity and a preserved sample of deep seawater collected at 4000 m in the northeastern Atlantic Ocean. The between‐lab standard deviation of reported uncorrected values measured with diverse analytical, detection, and calibration methods was 0.11‰ (1σ ). The multi‐lab average δ 13C‐DIC value reported for the deep seawater sample was consistent within 0.1‰ with historical measured values for the same water mass. Application of a correction procedure based on a consensus value for the distributed reference material, improved the between‐lab standard deviation to 0.06‰. The magnitude of the corrections were similar to those used to correct independent data sets using crossover comparisons, where deep water analyses from different cruises are compared at nearby locations. Our results demonstrate that the accuracy/uncertainty target proposed by the Global Ocean Observing System (±0.05‰) is attainable, but only if an aqueous phase reference material for δ 13C‐DIC is made available and used by the measurement community. Our results imply that existing Certified Reference Materials used for seawater DIC and alkalinity quality control are suitable for this purpose, if a “Certified” or internally consistent “consensus” value for δ 13C‐DIC can be assigned to various batches.publishedVersio

Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry

Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions

Leveraging Open Science Machine Learning Challenges for Data Constrained Planetary Mission Instruments

International audienceWe set up two open-science machine learning (ML) challenges focusing on building models to automatically analyze massspectrometry (MS) data for Mars exploration. ML challenges provide an excellent way to engage a diverse set of experts withbenchmark training data, explore a wide range of ML and data science approaches, and identify promising models based onempirical results, as well as to get independent external analyses to compare to those of the internal team. These two challengeswere proof-of-concept projects to analyze the feasibility of combining data collected from different instruments in a singleML application. We selected mass spectrometry data from 1) commercial instruments and 2) the Sample Analysis at Mars(SAM, an instrument suite that includes a mass spectrometer subsystem onboard the Curiosity rover) testbed. These challenges,organized with DrivenData, gathered more than 1,150 unique participants from all over the world, and obtained more than 600solutions contributing powerful models to the analysis of rock and soil samples relevant to planetary science using various massspectrometry datasets. These two challenges demonstrated the suitability and value of multiple ML approaches to classifyingplanetary analog datasets from both commercial and flight-like instruments.We present the processes from the problem identification, challenge setups, and challenge results that gathered creative anddiverse solutions from worldwide participants, in some cases with no backgrounds in mass spectrometry. We also present thepotential and limitations of these solutions for ML application in future planetary missions. Our longer-term goal is to deploythese powerful methods onboard the spacecraft to autonomously guide space operations and reduce ground-in-the-loop reliance

Europan Molecular Indicators of Life Investigation (EMILI) (Invited)

International audienceThe Europan Molecular Indicators of Life Investigation (EMILI) is an instrument concept under development for a potential future landed mission to Europa. The goal of EMILI is to help search for potential molecular biosignatures in samples collected from the icy surface. The sample material may contain compounds that originated in the subsurface ocean on Europa, and have migrated to the surface. Key classes of organic molecules as well as patterns of molecular groups could be indicative of life. To detect the widest possible range of such compounds, EMILI includes both capillary electrophoresis and gas chromatography sample processing and separation systems, as well as detectors based on laser fluorescence, conductivity, and mass spectrometry, in an integrated instrument package

Europan Molecular Indicators of Life Investigation (EMILI) for a Future Europa Lander Mission

International audienceThe Europan Molecular Indicators of Life Investigation (EMILI) is an instrument concept being developed for the Europa Lander mission currently under study. EMILI will meet and exceed the scientific and technical/resource requirements of the organic composition analyzer identified as a core instrument on the Lander. EMILI tightly couples two complementary analytical techniques, based on 1) liquid extraction and processing with capillary electrophoresis and 2) thermal and chemical extraction with gas chromatography, to robustly detect, structurally characterize, and quantify the broadest range of organics and other Europan chemicals over widely-varying concentrations. Dual processing and analysis paths enable EMILI to perform a thorough characterization of potential molecular biosignatures and contextual compounds in collected surface samples. Here we present a summary of the requirements, design, and development status of EMILI with projected scientific opportunities on the Europa Lander as well as on other potential life detection missions seeking potential molecular biosignatures in situ