Geophysical Research Articles

Characterizing and communicating uncertainty: lessons from NASA’s Carbon Monitoring System

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: addressing its origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

Supporting inclusion in teaching and learning STEM subjects within the DISCO+ project

Aim. The objectives of DISCO+ (Dropout and Inclusion at SChOol) project are two- fold: to implement an inclusive approach and prevent early school leaving in STEM sub- jects. The participating partner institutions apply cooperative engineering to develop units in mathematics and science subjects that can be implemented in different European educational contexts to meet the project objectives. Within the project (WP2) 16 transna- tional groups of pre- and in- service teachers take part in an eTwinning digital training sessions on the notion of school inclusion in Europe, the implementation of the principle of equality between girls and boys, the development of the critical thinking in learning scientific disciplines. Small group discussions refine each participant’s understanding of the issues involved, and the work carried out makes it possible to go beyond the local character of the definitions and to reach a common and shared understanding of the intercultural nuances present in each country. These exchanges will help build links be- tween participants and create a network of European colleagues. The groups’ experience books will serve as a basis for posters, which will be presented in June 2024. These pres- entations can be filmed and contribute to a 5-hour MOOC, which will be developed at a later phase in the project. Methods. By adopting a methodology which includes collectively designing teach- ing-learning sequences in transnational context, the project applies transnational engi- neering. Each partner involved in the creation of one or more teaching-learning sequenc- es must question the conditions of transferability of its point of view, its analyses and its testimonies in different educational contexts. In other words, the intercultural component of sequences designed jointly by partners from different countries should be ensured by addressing socially relevant issues and enriching their treatment with a variety of cul- tural viewpoints. The design of sequences on a transnational scale from this intercultural perspective will also make it possible to deal with the language issue in subjects such as mathematics and science. This methodology intends to develop a common culture on key issues such as sustainable development, inclusion and gender equality and to contribute to the creation of a European citizenship. Results. The activities will result in several deliverables, which include a round ta- ble, dedicated to the notion of school inclusion on a European scale, a four-month long eTwinning digital training in 16 transnational groups (teacher trainees, practioners) dis- cussing and exchanging ideas on inclusion in European education systems, teaching Sci- ence subjects, equality of girls and boys and developing critical thinking. Group experi- ence notebooks will be published as the result of exchanges between students, and will be dissemminated through the realisation of a poster session allowing the mutualisation of the work of the different working (16 transnational) groups. During the implementa- tion of „cooperative engineering” around inclusion and STEM teaching approaches by deconstructing representations of these objects to identify European variables and in- variants, an „online serious game”, and an accompanying guide is to be developed. The production of at least 4 teaching units (one per transnational team), two of which will be in mathematics and two in earth sciences (at least one unit is taught in the mother tongue and another in a CLIL approach) will be filmed and analysed by each “cooperative en- gineering” group on the eTwinning platform. Several activities will be used to produce resources, which in turn will be scripted into am5-hour MOOC training module to be made available on the platform already developed as part of the DICO+ project. Conclusions. In conclusion, the structure of the DISCO+ project will enable it to ad- dress the entire educational community in each country, including pupils, teachers, fu- ture teachers, trainers of trainers and inspectors.

Challenges and opportunities for open access book publishing: A perspective from a society publisher in the geosciences

AbstractThe American Geophysical Union (AGU), a scholarly society serving the Earth and space sciences, has a conventional books portfolio with their publisher, Wiley, and a new partnership with the Geological Society of London for an open access (OA) book series. The latter serves authors who need (because of funding mandates) or want to publish OA and comes with added benefits, including a production workflow to facilitate faster times to online publication. Flexibility in how the costs of individual volumes can be covered aims to encourage uptake, but challenges lie ahead in ensuring that all authors can equitably choose OA for their book project. This commentary contributes to the collection in Area about the evolving OA books landscape, addressing many of the challenges described by Gandy, and describing the start of AGU's journey towards OA books. It brings a perspective from the sciences, where books are mostly large, edited volumes with many international contributors, many of them physical geographers. Their experiences of funding, motivation and recognition for contributing to books may be quite different to human geographers more familiar with the sole‐authored monograph format.

Toward Utilizing Similarity in Hydrologic Data Assimilation

Similarity to reality is a necessary property of models in earth sciences. Similarity information can thus possess a large potential in advancing geophysical modeling and data assimilation. We present a formalism for utilizing similarity within the existing theoretical data assimilation framework. Two examples illustrate the usefulness of utilizing similarity in data assimilation. The first, theoretical example shows changes in the accuracy of the amplitude estimate in the presence of a phase error in a sine function, where correcting the phase error prior to the assimilation reduces the degree of ill-posedness of the assimilation problem. This signifies the importance of accounting for the phase error in order to reduce the error in the amplitude estimate of the sine function. The second, real-world example illustrates that timing errors in simulated flow degrade the data assimilation performance, and that the flow gradient-informed shifting of rainfall time series improved the assimilation results with less adjusting model states. This demonstrates the benefit of utilizing streamflow gradients in shifting rainfall time series in a way to improve streamflow timing—vital information for flood early warning and preparedness planning. Finally, we discuss the implications, potential issues, and future challenges associated with utilizing similarity in hydrologic data assimilation.

EarthCODE – ESA’s Earth Science Collaborative Open Development Environment

Abstract. Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

The coverage of basic and applied research in press releases on EurekAlert!

Research organisations routinely reach out to the media via press releases to announce research news and promote advances in science. This paper explores the presence of basic and applied research in press releases issued through EurekAlert!. Using a scientometric approach to classify research papers featured in press releases into basic and applied research, we found that more than half of the press releases in our dataset were related to basic research. This trend was particularly notable in life and earth sciences, physical sciences, and engineering fields. In contrast, press releases in the biomedical and health sciences, as well as in social sciences and humanities, were more frequently associated with applied research. Additionally, we present findings on the similarity and readability of press releases compared to their corresponding research papers, which confirm the role of institutional press officers in making research papers more accessible to the public and media. This adaptation appears to be more pronounced for basic research.

Bayesian Ensemble Kalman Filter for Gaussian Mixture Models

AbstractInverse theory and data assimilation methods are commonly used in earth and environmental science studies to predict unknown variables, such as the physical properties of underground rocks, from a set of measured geophysical data, like geophysical seismic or electromagnetic data. A new Bayesian approach based on the ensemble Kalman filter using Gaussian mixture models is presented to overcome the assumption of Gaussian distribution of the unknown variables commonly used in the data assimilation literature and to generalize the algorithm to inverse problems with multimodal probability distributions. In applications of subsurface characterization, the multimodality of the unknown variables is generally due to the presence of different rock types, also known as geological facies. In the proposed method, the weights of the Gaussian mixture model represent the facies proportions, and they follow a Markov chain model. The proposed Bayesian model generates the unknown model parameters conditioned on measured data using a Markov chain Monte Carlo sampler. The validity of the method is demonstrated on a data assimilation problem where the goal is to estimate the posterior distribution of the unknown rock density from a set of repeated measurements of acoustic wave velocity measured at different times. The proposed method provides accurate estimates with efficient computational times.

Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)

Generative artificial intelligence systems based on the GPT model have shown groundbreaking capabilities in producing scientific texts, revolutionising how geoscientists research and teach. However, there is a noticeable absence of generative AI systems tailored specifically for geoscience, and the efficacy of GPT technology has not been examined within the Earth Science realm. To fill these gaps, we propose a new artificial intelligence system (GeologyOracle) built upon the GPT-4o model and trained on Earth Science data. It is designed to simulate a conversation with a geoscientist, having the capabilities to analyse geologic datasets, suggest new geoscience hypotheses, explain Earth-Science concepts, and interpret geosites. To evaluate the performance of GeologyOracle, the system was tested with 152 geoscience questions provided and evaluated by a panel of geoscience academics on a scale of 0–10. Performance analysis revealed that 79.6% of the answers scored equal to or above the passing mark of 5, with a tendency to deliver high-quality responses (mean: 6.5; median = 7; interquartile range: 5–8). The results demonstrate that GeologyOracle is effective in performing complex geoscience tasks, such as identifying rocks, fossils, and minerals, and interpreting outcrop, core data, and quantitative datasets. The new AI system has exhibited noteworthy ability in the interpretation of internationally-acknowledged geosites situated within geoparks and nature reserves. The performance of the AI system is comparable to that of trained geoscience specialists, suggesting its potential as an assistant in various geoscience fields, including structural geology, palaeontology, geomorphology, sedimentology, and economic geology. While AI does not render geoscientists obsolete, it offers significant potential for accelerating scientific discovery, automating geoscience research, and assisting educators, students, and geotourists.

A Closer Examination of Earth and Life Science Teachers’ Science and Engineering Integration

A Closer Examination of Earth and Life Science Teachers’ Science and Engineering Integration

Electromagnetic and Radon Earthquake Precursors

Earthquake forecasting is arguably one of the most challenging tasks in Earth sciences owing to the high complexity of the earthquake process. Over the past 40 years, there has been a plethora of work on finding credible, consistent and accurate earthquake precursors. This paper is a cumulative survey on earthquake precursor research, arranged into two broad categories: electromagnetic precursors and radon precursors. In the first category, methods related to measuring electromagnetic radiation in a wide frequency range, i.e., from a few Hz to several MHz, are presented. Precursors based on optical and radar imaging acquired by spaceborne sensors are also considered, in the broad sense, as electromagnetic. In the second category, concentration measurements of radon gas found in soil and air, or even in ground water after being dissolved, form the basis of radon activity precursors. Well-established mathematical techniques for analysing data derived from electromagnetic radiation and radon concentration measurements are also described with an emphasis on fractal methods. Finally, physical models of earthquake generation and propagation aiming at interpreting the foundation of the aforementioned seismic precursors, are investigated.

A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Earth science for all? The economic barrier to European geoscience conferences

Abstract. Scientific meetings are vital for research development and networking. However, these events often reflect unconscious biases and barriers to diversity, particularly affecting marginalized groups. The future success of the geosciences depends on diversity, which enhances problem-solving and innovation through varied perspectives. This study examines the attendance diversity at the European Geosciences Union (EGU) General Assembly from 2005 to 2024, focusing on the impact of economic factors, distance, and population size on participation. Using publicly available data from the World Bank and the EGU, this study finds that gross national income (GNI) is the primary determinant of attendance, especially post-COVID. Distance also influences attendance but to a lesser extent, while population size shows a weak correlation. To improve diversity in academic conferences, we suggest facilitating donations, offering affordable accommodations, establishing additional travel funds, and rotating the conference location. Our actions must go beyond the EGU General Assembly and other geoscience conferences, as these actions can also help dismantle barriers to inclusivity in other areas of our community. By addressing these financial and systemic barriers, geoscience conferences can become more inclusive, benefiting the entire scientific community.

SYRMEP beamline: state of the art, upgrades and future prospects

SYRMEP is the hard X-ray imaging beamline of Elettra synchrotron offering X-ray full-field techniques, micro-computed tomography (microCT) and phase-contrast modality in the energy range 10–40 keV. The beamline operates in a multidisciplinary research context spanning from biomedical applications to botany, from zoology to food technology and cultural heritage, from materials engineering to geology and earth science. Thanks to the flexibility of SYRMEP setup, in situ experiments can be performed as well, novel imaging methods can be developed and implemented in a synergical manner with interested users and collaborators. SYRMEP peculiar wide beam together with the long sample-to-detector distance enables multiscale phase-contrast studies with optimized contrast and spatial resolution on rather large specimens, such as human lung phantoms. This is particularly relevant in view of future clinical lung imaging foreseen in the framework of Elettra 2.0 program. Here, the current beamline features and recent upgrades are illustrated, an overview of the imaging methods routinely offered to SYRMEP users’ community is presented, and the outlook for the new beamline SYRMEP-Life Science (SYRMEP-LS) is reported.

THE RESEARCH PRODUCTIVITY OF BATIK GREEN MATERIALS

Objective: This study aims to analyze the utilization of green materials in the batik production through the research productivity. Methods: It will determine research trends, institutional partnerships, and significant contributors in this subject by using bibliometric methodologies. It will also examine frequently used keywords, article citation networks, and popular study topics. Results: The results is the batik green materials research is still relatively new to be done. The initiation of this research itself began to be indexed by Scopus since 2012 and until 2022 there are 9 related documents. The dominant type of document is conference paper with 8 documents or 88.9%. The productive year for sustainable technology for batik research is 2020 with 4 documents. While the dominant publication source is the op Conference Series Earth And Environmental Science with 5 documents. The only country with publication related to the batik green materials research topic is Indonesia with 9 documents. Conclusion: Related to the development map in batik green materials research based on the terms co-occurrence obtained, two dominant terms were found, namely green manufacturing and methane. So that in the future batik green materials research can be carried out on different topics such as the theme of sustainable batik industry of dye, sustainable and environmentally friendly dyeing techniques, and other themes that are still related to the topics.

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, and heart palpitations, and is therefore of particular interest. The large size and complexity of patterns in time series data have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection. It provides a taxonomy based on anomaly detection strategies and deep learning models. Aside from describing the basic anomaly detection techniques in each category, their advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. Finally, it summarises open issues in research and challenges faced while adopting deep anomaly detection models to time series data.

BaHaMAs: a method for uncertainty quantification in geodetic time series and its application in short-term prediction of length of day

Some of the important geodetic time series used in various Earth science disciplines are provided without uncertainty estimates. This can affect the validity of conclusions based on such data. However, an efficient uncertainty quantification algorithm to tackle this problem is currently not available. Here we present a methodology to approximate the aleatoric uncertainty in time series, called Bayesian Hamiltonian Monte Carlo Autoencoders (BaHaMAs). BaHaMAs is based on three elements: (1) self-supervised autoencoders that learn the underlying structure of the time series, (2) Bayesian machine learning that accurately quantifies the data uncertainty, and (3) Monte Carlo sampling that follows the Hamiltonian dynamics. The method can be applied in various fields in the Earth sciences. As an example, we focus on Atmospheric and Oceanic Angular Momentum time series (AAM and OAM, respectively), which are typically provided without uncertainty information. We apply our methodology to 3-hourly AAM and OAM time series and quantify the uncertainty in the data from 1976 up to the end of 2022. Furthermore, since Length of Day (LOD) is a geodetic time series that is closely connected to AAM and OAM and its short-term prediction is important for various space-geodetic applications, we show that the use of the derived uncertainties alongside the time series of AAM and OAM improves the prediction performance of LOD on average by 17% for different time spans. Finally, a comparison with alternative uncertainty quantification baseline methods, i.e., variational autoencoders and deep ensembles, reveals that BaHaMAs is more accurate in quantifying uncertainty.Graphical

High-precision cyclic correlation as a basis for detailed paleoclimatic reconstructions for the Pliocene-Quartenary sections of Eurasia

An astrochronological (cyclostratigraphic) binding of the lithological and geochemical characteristics of Pliocene-Quaternary sediments of the Eurasian sections to the Milankovich cycles (eccentricity, obliquity of the ecliptic, precession) was carried out, an analysis of the relationship between the established variations in climate and paleotemperature with the moments of coincidence of different-order cycles of eccentricity and their paleoclimatic characteristics was given. The results of the implementation of the results obtained during the practice in geology at the Moscow State University in Chashnikovo are shown. During the research, materials from the exposition space of the Department of Regional Geology and Earth History and the Museum of Earth Science were used. The results of the study were used in conducting an educational practice in geology in Chashnikovo, and can also be used in the dismemberment and regional and global correlation of sections, in conducting geological surveys, creating cyclostratigraphic scales of the plains and mountainous regions of Eurasia, as well as in the creation and further development of the exposition space of museums — Moscow State University Gymnasium for example.

Dissolved silicon as a beneficial factor for biomineralization of disordered dolomite by a halophilic cyanobacterium

The genesis of sedimentary dolomite has long been a mystery in Earth science. It has been shown that the formation of low-temperature disordered dolomite, a crucial precursor of sedimentary dolomite, can be catalyzed by various heterotrophic microorganisms through organic mineralization. However, the potential role of cyanobacteria, which are common and cosmopolitan photoautotrophs in sedimentary environments, in the precipitation process of disordered dolomite has been largely underestimated. Moreover, recent emphasis on the beneficial influence of dissolved silicon (Si) in directly enhancing the Mg content in CaMg carbonates under oversaturated conditions has emerged. Nevertheless, the effect of dissolved Si on the formation of microbially-induced disordered dolomite remains poorly understood. To examine the influence and mechanisms of dissolved Si on the biomineralization of CaMg carbonates by cyanobacteria, biomineralization experiments were conducted using a halophilic cyanobacterium (Synechococcus elongatus FACHB-410) in solutions with varying concentrations of dissolved Si (ranging from 0 to 2 mM). Our findings revealed that the presence of dissolved Si resulted in higher Mg levels in the bio-precipitated CaMg carbonates compared to Si-free systems, with disordered dolomite formation observed at 2 mM Si. Importantly, an amorphous Mg-silicate phase formed prior to the crystallization of CaMg carbonates during the biomineralization process, intimately associated with the grains of CaMg carbonates. Subsequent abiotic carbonation experiments demonstrated its catalytic role in the nucleation of CaMg carbonates. These observations suggest that, in addition to the direct catalytic effect of dissolved Si, the positive role of Si in the biomineralization of disordered dolomite by cyanobacteria may also be attributed to the templating role of the neoformed amorphous Mg-silicate. These results have important implications for understanding the co-occurrence of Mg-silicate and dolomite observed in geological records.

Preface

Pursuing the successful previous seminar, it is our great honour to hold the 13th International Physics Seminar 2024 (IPS 2024). IPS is an annual meeting hosted by the Physics and Physics Education Department, Faculty of Mathematics and Natural Sciences, Universitas Negeri Jakarta (UNJ), supported by the Physical Society of Indonesia (PSI) Jakarta and Banten. The IPS 2024 was conducted on June 1, 2024, in Latief Hendradiningrat Hall at campus site of UNJ, Jakarta, Indonesia. The theme of the seminar is “Physics for Sustainable Future”. This seminar aims to bring together academics, scientists, students, and practitioners to share and discuss theoretical and practical knowledge of problems, new physics trends, and applications. We invited scientists, scholars, engineers, and students from universities worldwide and industry to present ongoing research activities and to support fostering research relations between the university and industry. This seminar provides an opportunity for delegates to discuss and share their experience of new applications directly, to build business or research relationships, and to find global partners for future collaboration. The scope of the conference covers a wide array of topics within the field of physics, including: 1. Material Physics 2. Instrumentation and Computational Physics 3. Medical Physics and Biophysics 4. Earth Science and Astronomy 5. Theory, Nuclear, and Particle Physics 6. Energy and Environmental Physics 7. Physics Education All submitted papers have been initially checked to meet the criteria before submitting to the system. All papers were carefully reviewed to ensure the quality for publication before being accepted. We acknowledge all authors who submitted their papers to IPS 2024. Finally, we express our gratitude to the organizing committee for ensuring the quality of the seminar and to the reviewers for investing the time to ensure the quality of the paper. We acknowledged the academic and financial support from Dean of Faculty of Mathematics and Natural Sciences and Head of Institute for Research and Community Services of Universitas Negeri Jakarta. This seminar has also been supported by the Naraya Hotel, Nutrifood, Walcoo, Mandiri Bank, Attin, and 101 Urban Hotel. We are looking forward to welcoming you in our future International Physics Seminar (IPS). Riser Fahdiran Universitas Negeri Jakarta, Indonesia Chairman of International Physics Seminar 2024 (IPS 2024) List of Editors and Organizing Committees are available in this Pdf.

A rapid compression large-volume press with a high pressure jump above 10GPa within milliseconds.

A high pressure jump through a rapid compression within milliseconds (ms) provides an important approach for searching novel matters and investigating their physical and chemical properties. Herein, we report a unique rapid-compression large volume press with a high pressure jump above 10GPa within 20ms by introducing a Walker-type module and hydraulic accumulators. The sample pressure can be rapidly increased from 1-5GPa to 12-16GPa by different modest compression rates varying from 10-2 to 633.5 GPa/s. The rapid pressure jump on the specimen has been successfully verified through the observed rapid phase transition of ZnTe from semiconductor to metal transitions, as evidenced by the variation in its electrical conductivity. The highest pressure jump of 10.2GPa within a rise time of 16.1ms is achieved, which is significantly higher than previous results (5.8GPa within 20ms) using a Bridgman-type anvils in such an apparatus. We found that rapid compression significantly enhances the phase transformation of C70 fullerene from a disordered graphite to an amorphous phase compared to static high-pressure experiments. The high pressure jump technique developed in this study will thus have great applications in materials science, physics, chemistry, and earth science.