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Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change.
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This content will become publicly available on March 1, 2027
Earth System Predictability across Time Scales for a Resilient Society: A Research Community Perspective
Abstract With extreme weather events becoming more frequent and severe, accelerating progress in Earth system predictability is urgently needed to deepen fundamental understanding, improve predictive tools, and provide reliable, actionable information for societal resilience. Building on prior and ongoing efforts by the broader community and informed by discussions at a National Science Foundation’s National Center for Atmospheric Research (NSF NCAR) workshop on Earth System Predictability Across Time Scales, this essay articulates a perspective on the scientific and structural priorities needed to advance Earth system predictability from short-range weather forecasts to century-scale projections, underscoring the urgency of a comprehensive, integrative approach capable of meeting emerging societal needs. Three scientific grand challenges are highlighted: understanding interactions across spatial and temporal scales, across interconnected Earth system components, and the influence of external forcing on predictability. To address these grand challenges, we identify potential implementation priorities across five key areas: 1) enhancing observations and data accessibility, 2) advancing data assimilation techniques, 3) improving modeling frameworks, 4) developing artificial intelligence (AI) and machine learning (ML) methods, and 5) applying convergence research. To support these areas, we outline four intersecting pillars of an integrated strategy: (i) a multiscale and multidisciplinary approach; (ii) closer coordination across modeling, observations, data assimilation, and AI/ML; (iii) intentional convergence research; and (iv) codevelopment of science with users. We also propose a collaborative path forward focused on strengthening scientific and technical connections, rewarding interdisciplinary and team-based science, expanding support for engagement with users, and investing in relationship building, shared language, and trust across scientific and societal domains. Significance StatementThis perspective highlights the need to advance Earth system predictability to enhance societal resilience amid increasingly frequent and severe extreme weather events. Grounded in broader community insights, including those from a National Science Foundation’s National Center for Atmospheric Research (NSF NCAR)–hosted workshop, it outlines a strategy to deepen understanding, improve predictive tools, and enable actionable outcomes across time scales from days to decades. This perspective identifies key scientific grand challenges: understanding scale interactions, Earth system component interactions, and the influence of external forcing. It also outlines corresponding implementation needs—including improved observations, data assimilation, modeling tools, and artificial intelligence and machine learning. Presented ideas emphasize an integrative strategy built on four intersecting pillars: a multiscale and multidisciplinary approach, coordination across tools, intentional convergence research, and codevelopment with users.
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- Award ID(s):
- 2414425
- PAR ID:
- 10685473
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Bulletin of the American Meteorological Society
- Volume:
- 107
- Issue:
- 3
- ISSN:
- 0003-0007
- Page Range / eLocation ID:
- E326 to E351
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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