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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Human–Earth system interactions under climate change

This special issue, titled ‘Focus on human–Earth system interactions under climate change,’ compiles cutting-edge research that deepens our understanding of the complex feedback mechanisms between anthropogenic activities and Earth system processes in the context of a changing climate (figure 1). The featured articles employ a diverse array of methodologies—including field studies, remote sensing, modeling, and socio-ecological analyses—to explore these interconnections. A unifying theme across these contributions is the imperative to bridge disciplinary divides, integrating physical Earth sciences with social sciences to fully capture the spectrum of human-Earth system interactions. Key topics addressed the impacts of human activities on natural systems, encompassing land-use change and water resource management on biogeochemistry, climate dynamics, and feedback on the climate system and socioeconomics from local to global scales under a changing climate.

Li, Huidong [Vanderbilt Univ., Nashville, TN (Unit↗
DOE OSTI2022

Computational discovery of two-dimensional rare-earth iodides: promising ferrovalley materials for valleytronics

Two-dimensional Ferrovalley materials with intrinsic valley polarization are rare but highly promising for valley-based nonvolatile random access memory and valley filter devices. These ferromagnetic materials exhibit valleys at or near the Fermi level with intrinsic magnetism. The strong coupling between magnetism and spin–orbit coupling induces intrinsic valley polarization. Using Kinetically Limited Minimization, an unconstrained crystal structure prediction algorithm, and prototype sampling based on first-principles calculations, we have discovered new Ferrovalley materials, rare-earth iodides RI 2 , where R is a rare-earth element belonging to Sc, Y, or La-Lu, and I is Iodine. The rare-earth iodides are layered and demonstrate either 2H, 1T, or 1T d phase as the ground state in bulk, analogous to transition metal dichalcogenides (TMDCs). The calculated exfoliation energy of monolayers (MLs) is comparable to that of graphene and TMDCs, suggesting possible experimental synthesis. The MLs in the 2H phase exhibit ferromagnetism due to unpaired electrons in d and f orbitals. Throughout the rare-earth series, d bands have valley polarization at K and $\overline{K}$ points in the Brillouin zone in the vicinity of the Fermi level. Large intrinsic valley polarization in the range of 15–143 meV without external stimuli is observed in these Ferrovalley materials, which can be enhanced further by applying an in-plane bi-axial strain. These valleys can selectively be probed and manipulated for information storage and processing, potentially offering superior performance beyond conventional electronics and spintronics. Here we further show that the 2H ferromagnetic phase of RI 2 MLs possesses non-zero Berry curvature and exhibits anomalous valley Hall effect with considerable anomalous Hall conductivity. Our work will incite exploratory synthesis of the predicted Ferrovalley materials and their application in valleytronics and beyond.

2D materials↗
DOE OSTI2022

Global centroid moment tensor solutions in a heterogeneous earth: the CMT3D catalogue

SUMMARY For over 40 yr, the global centroid-moment tensor (GCMT) project has determined location and source parameters for globally recorded earthquakes larger than magnitude 5.0. The GCMT database remains a trusted staple for the geophysical community. Its point-source moment-tensor solutions are the result of inversions that model long-period observed seismic waveforms via normal-mode summation for a 1-D reference earth model, augmented by path corrections to capture 3-D variations in surface wave phase speeds, and to account for crustal structure. While this methodology remains essentially unchanged for the ongoing GCMT catalogue, source inversions based on waveform modelling in low-resolution 3-D earth models have revealed small but persistent biases in the standard modelling approach. Keeping pace with the increased capacity and demands of global tomography requires a revised catalogue of centroid-moment tensors (CMT), automatically and reproducibly computed using Green's functions from a state-of-the-art 3-D earth model. In this paper, we modify the current procedure for the full-waveform inversion of seismic traces for the six moment-tensor parameters, centroid latitude, longitude, depth and centroid time of global earthquakes. We take the GCMT solutions as a point of departure but update them to account for the effects of a heterogeneous earth, using the global 3-D wave speed model GLAD-M25. We generate synthetic seismograms from Green's functions computed by the spectral-element method in the 3-D model, select observed seismic data and remove their instrument response, process synthetic and observed data, select segments of observed and synthetic data based on similarity, and invert for new model parameters of the earthquake’s centroid location, time and moment tensor. The events in our new, preliminary database containing 9382 global event solutions, called CMT3D for ‘3-D centroid-moment tensors’, are on average 4 km shallower, about 1 s earlier, about 5 per cent larger in scalar moment, and more double-couple in nature than in the GCMT catalogue. We discuss in detail the geographical and statistical distributions of the updated solutions, and place them in the context of earlier work. We plan to disseminate our CMT3D solutions via the online ShakeMovie platform.

58 GEOSCIENCES↗
DOE OSTI2024

Stellar wind impact on early atmospheres around unmagnetized Earth-like planets

ABSTRACT Stellar rotation at early ages plays a crucial role in the survival of primordial atmospheres around Earth-mass exoplanets. Earth-like planets orbiting fast-rotating stars may undergo complete photoevaporation within the first few hundred Myr driven by the enhanced stellar XUV [X-rays and extreme ultraviolet (EUV)] radiation, while planets orbiting slow-rotating stars are expected to experience difficulty in losing their primordial envelopes. Besides the action of stellar radiation, stellar winds induce additional erosion on these primordial atmospheres, altering their morphology, extent, and causing supplementary atmospheric losses. In this paper, we study the impact of activity-dependent stellar winds on primordial atmospheres to evaluate the extent to which the action of these winds can be significant in the whole planetary evolution at early evolutionary stages. We performed 3D magnetohydrodynamical (MHD) simulations of the interaction of photoevaporating atmospheres around unmagnetized Earth-mass planets in the time span between 50 and 500 Myr, analysing the joint evolution of stellar winds and atmospheres for both fast- and slow-rotating stars. Our results reveal substantial changes in the evolution of primordial atmospheres when influenced by fast-rotating stars, with a significant reduction in extent at early ages. In contrast, atmospheres embedded in the stellar winds from slow-rotating stars remain largely unaltered. The interaction of the magnetized stellar winds with the ionized upper atmospheres of these planets allows us to evaluate the formation and evolution of different MHD structures, such as double bow shocks and induced magnetospheres. This work will shed light on the first evolutionary stages of Earth-like exoplanets, which are of crucial relevance in terms of planet habitability.

Astronomy & Astrophysics↗
DOE OSTI2020

Gravimeter Search for Compact Dark Matter Objects Moving in the Earth

Dark matter could be composed of compact dark objects (CDOs). These objects may interact very weakly with normal matter and could move freely inside the Earth. A CDO moving in the inner core of the Earth will have an orbital period near 55 min and produce a time-dependent signal in a gravimeter. Data from superconducting gravimeters rule out such objects moving inside the Earth unless their mass m D and or orbital radius a are very small so that m D a < 1.2 × 10 - 13 M ⊕ R ⊕ . Here, M ⊕ and R ⊕ are the mass and radius of the Earth, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Ultrafast measurements of the interfacial spin Seebeck effect in Au and rare-earth iron-garnet bilayers

We investigate picosecond spin-currents across Au/iron-garnet interfaces in response to ultrafast laser heating of the electrons in the Au film. In the picoseconds after optical heating, interfacial spin currents occur due to an interfacial temperature difference between electrons in the metal and magnons in the insulator. We report measurements of this interfacial longitudinal spin Seebeck effect between Au and rare-earth iron-garnet insulators, i.e. RE 3 Fe 5 O 12 , where RE is Y, Eu, Tb, Tm. By systematically varying the rare-earth element, we modify the total magnetic moment of the iron-garnet. We use time domain thermoreflectance (TDTR) measurements to characterize the thermal response of the bilayer to ultrafast optical heating. We use time-resolved magneto-optic Kerr effect (TR-MOKE) measurements of the Au layer to measure the time-evolution of spin accumulation in the Au film. Replacing Y with other rare-earths enhances the electron-magnon conductance G e-m at the Au iron-garnet interface by as much as a factor of three. The electron-magnon conductance does not follow the trend of either the total magnetization of the iron garnet or the magnetic moment of the rare-earth.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effective point-charge analysis of crystal fields: Application to rare-earth pyrochlores and tripod kagome magnets 𝑅 ⁢3⁢ Mg⁢ 2 ⁢Sb⁢ 3⁢ O⁢ 14

An indispensable step to understand collective magnetic phenomena in rare-earth compounds is the determination of spatially anisotropic single-ion properties resulting from spin-orbit coupling and crystal field (CF). The CF Hamiltonian has a discrete energy spectrum—accessible to spectroscopic probes such as neutron scattering—controlled by a number of independent parameters reflecting the point symmetry of the magnetic sites. Determining these parameters in low-symmetry systems is often challenging. Here, we describe a general method to analyze CF excitation spectra using adjustable effective point-charges. We benchmark our method to existing neutron-scattering measurements on pyrochlore rare-earth oxides and obtain a universal point-charge model that describes a large family of related materials. We adapt this model to the newly discovered tripod kagome magnets (𝑅 3 ⁢Mg 2 ⁢Sb 3 ⁢O 14 , 𝑅 = Tb, Ho, Er, Yb) for which we report broadband inelastic neutron-scattering spectra. Analysis of these data using adjustable point-charges yields the CF wave functions for each compound. From this, we calculate thermomagnetic properties that accurately reflect our measurements on powder samples and predict the effective gyromagnetic tensor for pseudospin degrees of freedom—a crucial step to understand the exotic collective properties of these kagome magnets at low temperature. We present further applications of our method to other tripod kagome materials and triangular rare-earth compounds 𝑅⁢MgGaO 4 (𝑅 =Yb, Tm). Overall, this study establishes a widely applicable methodology to predict CF and single-ion properties of rare-earth compounds based on interpretable and adjustable models of effective point charges.

Crystal-field theory↗
DOE OSTI2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.

Wang, Xiao↗
DOE OSTI2020

Redox state of Earth’s magma ocean and its Venus-like early atmosphere

Exchange between a magma ocean and vapor produced Earth’s earliest atmosphere. Its speciation depends on the oxygen fugacity (fO 2 ) set by the Fe 3+ /Fe 2+ ratio of the magma ocean at its surface. Here, we establish the relationship between fO 2 and Fe 3+ /Fe 2+ in quenched liquids of silicate Earth-like composition at 2173 K and 1 bar. Mantle-derived rocks have Fe 3+ /(Fe 3+ +Fe 2+ ) = 0.037 ± 0.005, at which the magma ocean defines an fO 2 0.5 log units above the iron-wüstite buffer. At this fO 2 , the solubilities of H-C-N-O species in the magma ocean produce a CO-rich atmosphere. Cooling and condensation of H 2 O would have led to a prebiotic terrestrial atmosphere composed of CO 2 -N 2 , in proportions and at pressures akin to those observed on Venus. Present-day differences between Earth’s atmosphere and those of her planetary neighbors result from Earth’s heliocentric location and mass, which allowed geologically long-lived oceans, in-turn facilitating CO 2 drawdown and, eventually, the development of life.

58 GEOSCIENCES↗
DOE OSTI2023

Chalcogen isotopes reveal limited volatile contribution from late veneer to Earth

The origin of Earth’s volatile elements is highly debated. Comparing the chalcogen isotope ratios in the bulk silicate Earth (BSE) to those of its possible building blocks, chondritic meteorites, allows constraints on the origin of Earth’s volatiles; however, these comparisons are complicated by potential isotopic fractionation during protoplanetary differentiation, which largely remains poorly understood. Using first-principles calculations, we find that core-mantle differentiation does not notably fractionate selenium and tellurium isotopes, while equilibrium evaporation from early planetesimals would enrich selenium and tellurium in heavy isotopes in the BSE. The sulfur, selenium, and tellurium isotopic signatures of the BSE reveal that protoplanetary differentiation plays a key role in establishing most of Earth’s volatile elements, and a late veneer does not substantially contribute to the BSE’s volatile inventory.

58 GEOSCIENCES↗
DOE OSTI2026

Defining Electrode-Level Metrics for Enabling Earth-Abundant, Mn-Rich Cathodes: A Technoeconomic Analysis of Experimental Materials

Manganese-rich oxides are attractive options as next-generation, earth-abundant cathodes and significant efforts are being directed toward commercial implementation. We report here an updated techno-economic analysis of the lithium- and manganese-rich (LMR) class of earth-abundant cathodes for electric vehicle applications. BatPaC modeling was used to define the cell-level metrics that must be met for these materials to be cost and energy competitive with the current commercial earth-abundant benchmark, LiFePO 4 , as well as anticipated variations such as LiMn 0.8 Fe 0.2 PO 4 . The model was used to evaluate a high-performance material from the literature and subsequently define R&D targets as the likely limits of practical performance for similar LMR systems. Experimental validation and BatPaC evaluation of an advanced, cobalt-free LMR cell system was also conducted. Results show that the advanced LMR cells come within ∼5% of the defined limits and exceed the energy of LiFe(Mn)PO 4 cells at a similar cost. Excellent cycle-life, low impedance, and low impedance rise were also achieved under the conditions tested and reveal that cobalt is not necessary to achieve high-performance LMR oxides. Although the analysis conducted herein reports on LMR cell systems, the methodology and target values defined for performance metrics easily extend to the evaluation of other systems under consideration as earth-abundant options.

Chen, Jiajun [Argonne National Laboratory (ANL), A↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2020

Benchmarking Simulated Precipitation in Earth System Models

Earth system models (ESMs) bridge observationally based and theoretical understanding of the Earth system. They are among the most often used tools to study a variety of questions related to variability and changes in Earth’s climate. For many applications, ESMs must realistically simulate observed large-scale precipitation patterns and seasonal cycles that have a multitude of societal and national security implications. Despite steady improvement in the simulation of precipitation, model errors in many aspects of precipitation characteristics limit the use of ESMs both in understanding Earth system variability and change and for decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

gdess: A framework for evaluating simulated atmospheric CO 2 in Earth System Models

Atmospheric carbon dioxide (CO 2 ) plays a key role in the global carbon cycle and global warming. Climate-carbon feedbacks are often studied and estimated using Earth System Models (ESMs), which couple together multiple model components—including the atmosphere, ocean, terrestrial biosphere, and cryosphere—to jointly simulate mass and energy exchanges within and between these components. Despite tremendous advances, model intercomparisons and benchmarking are aspects of ESMs that warrant further improvement (Fer et al., 2021; Smith et al., 2014). Such benchmarking is critical because comparing the value of state variables in these simulations against observed values provides evidence for appropriately refining model components; moreover, researchers can learn much about Earth system dynamics in the process (Randall et al., 2019). We introduce `gdess` (a.k.a., Greenhouse gas Diagnostics for Earth System Simulations), which parses observational datasets and ESM simulation output, combines them to be in a consistent structure, computes statistical metrics, and generates diagnostic visualizations. In its current incarnation, `gdess` facilitates evaluating a model's ability to reproduce observed temporal and spatial variations of atmospheric CO 2 . The diagnostics implemented modularly in `gdess` support more rapid assessment and improvement of model-simulated global CO 2 sources and sinks associated with land and ocean ecosystem processes. We intend for this set of automated diagnostics to form an extensible, open source framework for future comparisons of simulated and observed concentrations of various greenhouse gases across Earth system models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Interpretable Deep Learning for the Earth System with Fractal Nets

Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations

Some of the key challenges in Earth system prediction arise from an uncertain representation of unpredictable natural variability, across time and space scales. This variability, hereafter referred to as noise, represents the quantity against which the strength of a signal of interest is measured to assess predictability. Underestimating or overestimating this noise in Earth system models can lead to issues ranging between overconfidence in an erroneous prediction and a lack thereof in an otherwise accurate one. Noise is part of the physical processes in the Earth system and can amplify or damp a signal of interest in a complex manner that is not systematically characterized. Specifically, while there has been significant progress in our understanding of multiscale interactions among known modes of variability (potentially predictable signals), how these are impacted by the noise is not clear. Recent effort toward stochastic parametrization schemes that provide a representation of noise due to uncertain sub-grid processes in climate models (Berner et al., 2017 and references therein) has shown promise not only in reducing biases and improving probabilistic prediction but also in improving the representation of natural variability in the model toward what is observed. However, today’s standard schemes for stochastic parametrization are still simplistic as they do not take the state of the atmosphere into account (other than by the use of multiplicative noise for model tendencies). Furthermore they require hand-tuning and trial-and-error testing for the magnitude of the added noise, which is difficult in a chaotic system that is as complex as the Earth system.

58 GEOSCIENCES↗
DOE OSTI2021

ERDE: The Earth-Field Resonance Detection and Evaluation Devices

Researchers at Los Alamos have developed the Earth-field Resonance Detection and Evaluation devices, or ERDE (German for “Earth”) which brings portable chemical analysis and identification into the field. ERDE uses the Earth’s magnetic field to perform nuclear magnetic resonance measurements without the need for sample preparation, costly cryogens, or calibration procedures, meaning it is extremely user-friendly. In fact, using the Earth’s magnetic field allows detection and identification of chemical species through non-magnetic pipes without sampling, reducing the likelihood of costly biofouling. This innovative new device brings together these outstanding benefits to improve processes in the detection of noxious chemicals in water supplies as well as the identification of toxic materials anywhere. There are two fully-functional ERDE prototypes and efforts are underway to miniaturize the system further for improved portability, increased sensitivity, and expanded capabilities. The Laboratory is seeking qualified licensing or CRADA partners to finalize development of this system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗