# The First 18 Months of JGR: MLC

Enrico Camporeale, Raffaele Marino, John Rundle, Doris Folini, Yangkang Chen, Donald Lucas, Xiaofeng Li, Thomas Berger, Geoffrey Fox, Yixin Wen, et al.

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Enrico Camporeale, Raffaele Marino, John Rundle, Doris Folini, Yangkang Chen, et al. The First 18 Months of JGR: MLC. Journal of Geophysical Research. Machine Learning and Computation, 2025, 2 (2), pp.e2025JH000797. 10.1029/2025JH000797. hal-05412176

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Submitted on 11 Dec 2025

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# The First 18 Months of JGR: MLC

Enrico Camporeale¹,² ID, Raffaele Marino³ ID, John Rundle⁴ ID, Doris Folini⁵ ID, Yangkang Chen⁶ ID,
Donald D. Lucas⁷ ID, Xiaofeng Li⁸, Thomas E. Berger² ID, Geoffrey C. Fox⁹, Yixin Wen¹⁰ ID,
Chaopeng Shen¹¹ ID, and Renata Wentzcovitch¹² ID

¹School of Physical and Chemical Sciences, Queen Mary University of London, London, UK, ²Space Weather Technology, Research, and Education Center, University of Colorado, Boulder, CO, USA, ³Laboratoire de Mécanique des Fluides et d'Acoustique, CNRS - École Centrale de Lyon, Écully, France, ⁴Department of Physics and Astronomy, Department of Earth and Planetary Sciences, University of California, Davis, CA, USA, ⁵Department of Environmental System Science, Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland, ⁶Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA, ⁷Atmospheric, Earth, and Energy Division, Lawrence Livermore National Laboratory, Livermore, CA, USA, ⁸Institute of Oceanology, Chinese Academy of Sciences, Qingdao, P. R. China, ⁹Biocomplexity Institute and Computer Science Department, University of Virginia, Charlottesville, VA, USA, ¹⁰Department of Geography, University of Florida, Gainesville, FL, USA, ¹¹Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA, ¹²Department of Applied Physics and Applied Mathematics, Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA

**Abstract** As editors of *Journal of Geophysical Research: Machine Learning and Computation*, we are delighted to celebrate the successes of our first 18 months.

As editors of *Journal of Geophysical Research: Machine Learning and Computation*, we are delighted to celebrate the successes of our first 18 months. When we launched this new venture, we did so with cautious optimism. In a scientific publishing landscape that many consider saturated, we were unsure how the community would respond. Yet, the global geoscience community has embraced the journal enthusiastically. In 2024 alone, we received 352 submissions, and 81 articles were published, with an acceptance rate of 32%. At the time of writing, the number of published papers has surpassed 130. Our Editorial Board continues to grow and now includes 14 editors and 16 associate editors.

The journal was founded on the recognition that data science and machine learning have emerged as the fourth pillar of scientific discovery—standing alongside theory, experimentation, and simulation with equal efficacy. Unlike previous scientific revolutions, which were often marked by single landmark papers or associated with a few key individuals, the machine learning revolution we are witnessing is more gradually, yet unmistakably, reshaping how we approach scientific questions and understand the world. Educational institutions worldwide are rapidly embedding data-driven methods into their curricula.

We are proud that *JGR: Machine Learning and Computation* is playing an important role in disseminating cutting-edge discoveries to the broader AGU community. In fact, no topic within the realm of the American Geophysical Union has remained untouched by the recent advances in artificial intelligence, and this journal is a testament to that.

Our published articles span atmospheric and climate science, space, solar and planetary physics, seismology, tectonophysics, cryosphere, hydrology, oceanography, solid Earth and near-surface physics, geodesy, geomagnetism, and biogeochemistry.

"AI is the new electricity" is now a well-known phrase coined by machine learning pioneer Andrew Ng. However, just as electricity requires infrastructure to realize its full potential, progress in AI depends fundamentally on access to data and advanced computing facilities. In line with the AGU Software & Data policy (https://www.agu.org/publications/authors/journals/Data-software-for-authors), we enforce a strict requirement that all data and software necessary to reproduce (and ideally extend) published results be made publicly available. Developing and testing new data science methods is resource-intensive, but thanks to the open-source ethos of the computer science community and support from industry, AI tools have become widely accessible, resulting in a democratization of AI. Geoscience must follow this example: openness and transparency are essential for progress. To that end, we plan to launch a pilot code review initiative aimed at fostering a community of practice. This effort will focus on ensuring that software shared in FAIR repositories is not only available, but also functional and
citable. While we will not require active maintenance of the code, we do aim to establish shared standards of usability and recognition for community-contributed tools.

A prime example of how openly accessible data can benefit both science and society is the ERA5 reanalysis data set from the European Center for Medium-Range Weather Forecasts (ECMWF), which has underpinned a wide array of AI-driven weather and climate models. We acknowledge that hosting and maintaining large data sets often requires the resources of major institutions and public funding.

Yet, those efforts bear benefits that transcend geographical boundaries. Agencies such as NASA, NOAA, USGS, CDC in the US, the EU Copernicus program and ESA portal, the UK Met Office and British Geological Survey, the Japan Aerospace Exploration Agency (JAXA) are just a few examples of agencies that have provided freely accessible data, vital for many studies published in our journal, and elsewhere.

We are deeply concerned by proposals to restrict or delete such publicly funded data resources. History teaches us that the destruction of knowledge—whatever the form—has never served the interests of society.

Equally concerning is the prospect that data, and the AI tools built upon them, could become proprietary and exclusive. A world in which only a handful of private entities control access to cutting-edge AI technology not only raises serious ethical concerns but also feels increasingly out of touch with a globalized world. Over the past few decades, globalization has reshaped society in ways that no longer support monopolistic control. Nowhere is this more evident than in science, where international collaboration, open exchange, and inclusivity have become the standard.

As we look to the future, we remain committed to fostering an open, inclusive, and collaborative scientific ecosystem. JGR: Machine Learning and Computation will continue to champion transparency, reproducibility, and the responsible use of artificial intelligence in the geosciences. We thank our authors, reviewers, and readers for their trust and engagement over the past 18 months, and we look forward to building on this strong foundation together. The challenges ahead are significant, but so too is the potential for discovery. We are excited to be part of this journey with the global scientific community.