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Earth system models can predict the planet’s future, but their own fate hinges on funding.

In recent years, Earth system models have evolved from simple representations of climate components into highly integrated simulations that weave together atmosphere, oceans, ice, land, chemistry, and biology. These sophisticated tools help scientists anticipate not just tomorrow’s weather but the trajectory of the planet’s climate, with implications for energy grids, agriculture, coastal planning, and public safety. Now, a leading model in this field faces significant funding pressures that could slow progress, shift expertise overseas, and threaten the United States’ role as a global leader in climate science. The potential loss of senior scientists and the tightening of federal support loom over a research community that depends on long-term investment, stable careers, and access to the world’s most powerful computing resources.

The Evolution and Promise of Earth System Models

The story of Earth system models begins with the recognition that climate is a complex, interlinked system where the atmosphere, oceans, ice sheets, land surfaces, and biological processes interact in dynamic, sometimes nonlinear ways. In the 1960s, pioneering meteorologists began running early weather simulations on basic computing platforms and observed that tiny differences in initial conditions could generate dramatically different outcomes. This realization, often associated with the butterfly effect, highlighted the inherent sensitivity of weather and climate systems to starting conditions. It was a moment that underscored both the limits of prediction at fine scales and the promise of deeper understanding through refined modeling.

Since those early days, computational power has grown by orders of magnitude, enabling scientists to construct simulations that not only track physical processes but also incorporate chemical reactions, biological activity, and the cycles of water and energy that connect different parts of the planet. The modern class of simulations, known as Earth system models, or ESMs, treats the Earth as an integrated network of components that constantly influence one another. The development of traditional physical climate models in the 1960s and 1970s laid the groundwork for subsequent integration of ocean dynamics and atmospheric processes, followed by increasingly sophisticated representations of land surfaces, sea ice, and biogeochemical cycles.

The central idea behind ESMs is to couple distinct models—an atmosphere model, an ocean model, a sea ice model, and a land model—into a cohesive framework that yields a more complete picture of how the physical system operates. As the field matured, researchers began enriching these coupled systems with chemistry and biology to reflect processes like carbon uptake by forests, nutrient cycling in the oceans, and the formation of clouds through complex microphysical interactions. This evolution expanded the scope of questions ESMs could address, from the mechanics of storms and heat waves to the long-term responses of ecosystems and energy networks to a changing climate.

A striking example of what a modern ESM can reveal emerged from a project that used a highly refined model to explore the far-reaching consequences of seemingly local features. In a landmark 2023 result, the Energy Exascale Earth System Model demonstrated that the shapes of cavities within Antarctic ice could influence tidal patterns thousands of miles away on the North American coast. This finding—an unexpected connection between a microstructural detail of ice and coastal ocean dynamics—illustrates how including additional variables in an ESM can alter real-world outcomes in surprising ways. It also underscores the value of examining the planet as an interconnected system where small-scale features can cascade into large-scale effects across regions.

E3SM, short for the Energy Exascale Earth System Model, stands as one of the world’s premier representatives of this modeling approach. Built and refined over more than a decade, it reflects a concerted effort by U.S. researchers to push the boundaries of what is knowable about Earth’s climate system. The model’s development has involved close collaboration among national laboratories and research institutions, with leadership and contributions from the Department of Energy (DOE) and its affiliated laboratories. E3SM’s design emphasizes not only physical realism but also the integration of energy system considerations, recognizing that climate impacts are inseparable from how power grids, water resources, and infrastructure respond to environmental change. This dual orientation—examining both climate processes and their intersections with human systems—sets E3SM apart from many other climate models that focus primarily on physical dynamics.

The goal of ESMs extends beyond understanding climate in the abstract. Researchers aim to create highly reliable tools that can inform practical decisions at multiple scales, from regional planning to national policy. By simulating a wide array of processes and their interactions, ESMs help scientists anticipate the likelihood of extreme events, quantify potential shifts in land use and agriculture, and examine how evolving climate conditions might stress electricity networks, water supplies, and other critical systems. In short, ESMs are intended to translate climate science into actionable knowledge that communities can use to prepare for and adapt to a changing world.

The ongoing push to improve ESMs has been tied to advances in high-performance computing. The exascale era—where supercomputers perform approximately a quintillion calculations per second—has provided the computational backbone that makes ever more detailed and expansive simulations feasible. This computational leap enables researchers to run higher-resolution models, incorporate finer representations of physical and biogeochemical processes, and test a broader array of scenarios. For communities and sectors dependent on climate information, the result is better, more nuanced guidance about risks and opportunities, such as where to fortify infrastructure, how to design resilient power systems, and how agricultural practices might need to adapt to evolving moisture and temperature regimes.

In practice, the value of ESMs is measured not only by their theoretical sophistication but also by their track record in delivering insights that align with observed realities. Stakeholders in energy, agriculture, water management, and public safety rely on ESMs to simulate potential futures and to understand how different policy choices or technological developments could alter outcomes. For example, ESMs that capture the interplay between climate and energy systems can reveal how extreme heat or heavy rainfall might stress the grid, alter demand patterns, or necessitate changes in cooling strategies for power plants. Such insights help planners evaluate risk and invest in more resilient infrastructure, better weather forecasting, and targeted adaptation measures.

Yet, the full promise of ESMs depends on sustained investment, long-term talent, and continuing access to the world’s most capable computing resources. The complexity of these models is immense: codebases stretch to many millions of lines of software, requiring teams of scientists and engineers to maintain, validate, and extend them. The work is collaborative across disciplines—meteorology, oceanography, chemistry, biology, computer science, and data science—and it hinges on stable funding streams that support not only new development but the routine maintenance that keeps models reliable and reproducible over time. As the field advances, it becomes clear that the success of ESMs depends as much on people and institutions as on algorithms and hardware.

The interplay between model development and practical application is a recurring theme in the ESM narrative. Scientists work to refine the physics and biology represented in the models, calibrate the outputs against observational data, and ensure that the simulations remain relevant for decision makers. This requires a balance between pursuing fundamental scientific questions—the nature of feedbacks between climate components, the sensitivity of regional climates to greenhouse gas forcing, and the representation of complex biogeochemical cycles—and delivering decision-relevant results that can inform policy and planning. The dual emphasis on discovery and applicability has driven sustained investment in ESMs and related research, even as funding landscapes shift and political priorities evolve.

In this context, the story of E3SM is emblematic of broader ambitions in climate science: to push the envelope of what can be simulated with fidelity, to extend the reach of climate information into everyday life, and to preserve a lead in global scientific capacity that informs not only national interests but planetary well-being. The model embodies a long-term view of science as a public good—a resource whose value may be realized only through consistent support, collaborative networks, and the cultivation of expertise across generations of researchers. The hopes vested in E3SM reflect a conviction that understanding and managing climate risk require a continuous and forward-looking investment in both technology and talent.

E3SM: A Leading Earth System Model

Among the suite of advanced Earth system models, E3SM stands out for its high level of detail and its strategic alignment with energy and environmental research priorities. Developed and refined under the aegis of the Department of Energy, the model has benefited from leadership at Lawrence Livermore National Laboratory in California, in collaboration with the National Center for Atmospheric Research and other major institutions. The model’s architecture emphasizes coupling diverse components—a comprehensive atmospheric model, an ocean model, a sea ice module, and a land-surface representation—so that the simulated climate system can respond to perturbations in a coherent, physically consistent manner. By integrating these components, E3SM seeks to reveal how the planet’s systems interact in ways that are not apparent when models consider each component in isolation.

A defining feature of E3SM is its emphasis on exascale computing capabilities. The project has been positioned to exploit some of the world’s most powerful supercomputers, enabling unprecedented resolution and complexity. The exascale transition means simulations can capture finer spatial scales and more detailed processes, potentially improving the reliability of forecasts and scenario projections. The model’s development has progressed through distinct iterations, with significant milestones achieved over time. In 2023, researchers announced a terrestrial simulation built for an exascale system, marking a major achievement in the quest to push the envelope of what a climate model can represent. This milestone set the stage for the fourth iteration of the project, which is anticipated to begin in 2026, assuming stable funding and continued collaboration among participating laboratories.

Beyond its scientific ambitions, E3SM is valued for its practical contributions to energy and infrastructure planning. The model’s detailed simulations help researchers analyze how weather extremes—such as floods and heat waves—could affect power generation, transmission reliability, and grid resilience. By exploring the interactions between climate dynamics and energy systems, E3SM provides decision-makers with insights into how to design more robust energy networks capable of withstanding climate-related stress. This capability is particularly important as extreme weather events become more frequent and intense, with direct implications for electricity prices, infrastructure investment, and public safety.

The origins of E3SM lie in a broader history of community modeling that began decades ago at NCAR. The Community Earth System Model (CESM) served as an early, foundational platform from which E3SM evolved. The collaboration between NCAR and DOE researchers—encompassing shared objectives and complementary strengths—shaped how the model progressed. Over time, the two groups pursued slightly different priorities: one stream focusing more on fundamental climate science and the other leaning toward applications in energy and policy-relevant questions. This divergence reflects a pragmatic approach to leveraging the best available resources and talent to advance both climate science and its practical uses.

The management and governance of E3SM have involved discussions about resource allocation, strategic priorities, and the balance between climate science and energy research aims. The DOE’s leadership has steered the project through a period of rapid growth, progress in computational capabilities, and expanding interdisciplinary collaboration. The model’s trajectory has been characterized by a combination of ambitious scientific goals and careful consideration of where investment would yield the greatest return in knowledge, capability, and societal benefit. The result is a platform that not only advances climate science but also serves as a tool for evaluating policy options, infrastructure planning, and risk mitigation strategies in the face of climate uncertainty.

As with any large, collaborative scientific initiative, E3SM has depended on the steady support of its funders, the sustained engagement of researchers across institutions, and the availability of essential resources. The model’s developers have emphasized that the project’s progress is tightly linked to the stability of funding, the continuity of laboratory leadership, and the ongoing recruitment and retention of top-tier talent. The workforce behind E3SM—comprising senior scientists, software engineers, data analysts, and interdisciplinary researchers—forms the backbone of the project’s capacity to innovate, test hypotheses, and translate results into practical guidance for policymakers and engineers.

In addition to the technical and scientific considerations, E3SM’s story highlights the importance of building and maintaining a research ecosystem that can absorb and integrate new ideas over time. The model’s development has benefited from cross-institutional collaboration, knowledge transfer, and the cultivation of a community that shares best practices in model development, verification, and interpretation. The ecosystem around E3SM reflects a broader philosophy in climate science: progress arises not from any single tool or laboratory but from a distributed network of expertise, infrastructure, and open science practices that enable researchers to build upon one another’s work.

A crucial question for the future is how to sustain the momentum of E3SM in a shifting fiscal and political environment. The project’s potential future hinges on continued investments that recognize the model’s value for scientific discovery and for practical decision-making in energy, water, and climate resilience. As the field moves forward, the ability to maintain and expand E3SM will depend on retaining senior scientists who carry institutional memory, mentoring the next generation of researchers, and ensuring that laboratories can attract and retain the leadership necessary to guide complex software systems through ongoing updates and innovations. The human capital behind E3SM is, in many ways, as important as the hardware and algorithms that power the model.

The collaborative culture surrounding E3SM reflects a broader commitment to open science and shared infrastructure. The model has served as a platform for not only DOE researchers but also independent scientists who seek to answer their own pressing questions about climate and its interplay with human systems. For example, a researcher pursuing the societal implications of irrigation practices used E3SM to examine how human activity influences not only environmental conditions but also human health and livelihoods. The study found that irrigation can contribute to increased atmospheric moisture and a kind of “moist heat” that can pose risks to workers in agricultural settings. These results underscore the need for careful interpretation of model outputs and the importance of evaluating a range of scenarios to capture the full spectrum of possible outcomes.

In practice, researchers often compare results across multiple ESMs to build confidence in findings. Although each model embodies a distinct representation of physics, chemistry, and biology, there is only one underlying reality—Earth’s processes. The diversity of models helps scientists identify robust patterns and understand where uncertainties remain. This multi-model approach is a cornerstone of climate science, enabling researchers to interpolate between different representations and gain a more holistic understanding of how the system might respond to various stimuli, such as greenhouse gas forcing, land-use changes, or climate feedbacks. The ongoing habit of cross-model comparison strengthens the evidence base used to guide policy and adaptation efforts.

Beyond theoretical insights, E3SM and similar models have produced tangible results with implications for environmental management and public policy. Recent work using E3SM has shown that rising average temperatures could alter the balance of carbon sources and sinks in agricultural landscapes, potentially turning some croplands into carbon sources rather than sinks. Other studies have linked heavy rainfall events to nutrient transport into coastal ecosystems, contributing to concerns about eutrophication and water quality. In the Pacific region, researchers have observed that certain hurricane pathways—those that initially move west and then curve northward—may be associated with changes in regional fire regimes in the American Southwest. While these findings illustrate the broad utility of ESMs in addressing a variety of climate-related questions, they also highlight the complexity of Earth’s systems and the importance of interpreting model outputs within the context of their assumptions and uncertainties.

The practical value of ESMs extends to everyday decision-making, not just high-level policy analysis. By improving the understanding of how climate variables interact with regional characteristics, these models can inform a wide range of decisions—ranging from planning summer activities to assessing adaptation measures for sea-level rise along coastal communities. The aspiration is to produce results that are not only scientifically credible but also accessible and actionable for local governments, planners, and the public. In the future, as ESMs become more reliable and higher in resolution, their role in shaping local and regional strategies could become even more pronounced, helping communities tailor adaptation measures to their specific climate risks and resource constraints.

Of course, the story of E3SM is inseparable from the broader policy and funding environment. The DOE and its scientific ecosystem have faced fluctuating levels of support for climate research, with political debates shaping the pace and scope of funded activities. The tension between advancing fundamental science and aligning with immediate policy priorities has influenced decisions about priorities, staffing, and investments in computing infrastructure. As the model’s capabilities expand, so too does the demand for skilled professionals who can manage, validate, and extend the codebase, interpret complex outputs, and translate findings into practical guidance for decision-makers. The stability of these human resources—senior scientists who mentor others and maintain continuity across generations—is a critical determinant of whether E3SM will continue to thrive in the years ahead.

The E3SM project embodies a broader global theme: the need to maintain leadership in climate science through sustained investment in advanced modeling, high-performance computing, and interdisciplinary training. As researchers push the frontiers of what is possible with ESMs, the importance of maintaining a healthy pipeline of talent cannot be overstated. The loss of senior scientists can create a gap that is difficult to fill, potentially slowing progress for many years. The long-term consequences of such workforce erosion would extend beyond a single model or laboratory, affecting the overall capacity of the nation to produce reliable climate intelligence and to respond to rapidly evolving scientific questions.

In sum, E3SM represents a convergence of scientific ambition, computational prowess, and practical relevance. Its development, use, and future trajectory illustrate how modern climate science seeks to connect deep theoretical understanding with real-world decision-making. The model’s ability to simulate complex interactions among atmospheric physics, ocean dynamics, land processes, and biogeochemical cycles—alongside its integration with energy system considerations—marks it as a pivotal tool in the global effort to comprehend and respond to climate change. The challenge now is to ensure that the momentum, talent, and infrastructure that sustain E3SM remain intact, so that this leading-edge tool can continue to illuminate the pathways by which humanity can adapt to and mitigate a warming world.

Funding Pressures and Policy Context

The health and momentum of Earth system modeling are closely tied to the stability and direction of public funding. In recent years, high-profile shifts in budgetary priorities at the federal level have posed challenges to climate research programs, including those focused on E3SM and related Earth system modeling efforts. The policy environment surrounding climate science has been characterized by debates over how government laboratories, universities, and research organizations allocate scarce funds among competing scientific priorities, national security concerns, and the imperative to develop knowledge that informs climate resilience and energy policy.

Within this context, the E3SM initiative has faced explicit funding scrutiny. Budget documents and policy discussions have signaled a reallocation of resources away from some climate-related activities, with proposals to reduce overall funding for climate research and to reframe the scope of supported work. In some versions of the budget, language has emerged that suggests curtailing or terminating certain climate-related activities within energy and environmental modeling programs. Although the exact mechanisms and timelines remain subject to revision and interpretation, the underlying message from policy discussions is clear: the government is considering a contraction of the core capabilities that support climate modeling and the broader ecosystem of research that relies on these tools.

The budgetary landscape includes specific numbers and proposals that highlight how funding might shift. For example, projections have indicated substantial reductions in subprogram funding dedicated to Earth and environmental system modeling, with a move to consolidate resources under tighter priority areas. In such scenarios, the allocation of funds for high-resolution modeling, model development, and the maintenance of computational infrastructure could be reduced. This would have direct implications for researchers who rely on stable access to computing resources, long-term project timelines, and the capacity to extend existing models or to develop new iterations that push beyond current limits.

Moreover, funding decisions are not made in isolation. The broader science funding ecosystem—comprising the National Science Foundation (NSF), the National Oceanic and Atmospheric Administration (NOAA), and other federal agencies—also shapes the environment in which ESMs operate. The NSF funds research through facilities and centers that support climate modeling, while NOAA plays a crucial role in observational data collection and in providing applications that depend on modeling outputs. Budget proposals that reduce support for these agencies or their climate-related programs can ripple through the modeling community, affecting everything from data availability and validation opportunities to the training of future scientists and the maintenance of long-running observational records that models rely on for calibration and evaluation.

The implications of potential funding reductions extend beyond the laboratory walls. If resources for ESMs decline, the pace of scientific discovery could slow, and the consistency of results across different research groups might be affected. Independent researchers who rely on model outputs to test hypotheses, evaluate adaptation strategies, or explore new research questions could encounter greater difficulty in obtaining funding, data, or computational time. The result could be a slowing of cross-institution collaboration, fewer opportunities for early-career researchers to gain essential experience, and a narrower pathway for innovative ideas to mature into publishable findings.

There is also concern about the geographic dimension of research capacity. If domestic investment in ESMs wanes, there is a risk that other countries—particularly those with strong commitments to climate research and abundant computing resources—could expand their own modeling capabilities to fill the gap. The loss of U.S. leadership in climate modeling would not only affect national priorities but could reshape the global landscape of climate science collaboration, data sharing, and joint problem-solving. Scientists worry that a reduction in domestic expertise would slow the provision of regionally relevant projections and undermine the ability to analyze how climate change intersects with national energy systems, public health, and critical infrastructure.

The policy and funding environment also has psychosocial and professional dimensions. Senior scientists who have spent decades building research programs face uncertainty about the trajectory of their labs, the availability of graduate students and postdocs, and the opportunities to train the next generation of researchers. The fear of losing irreplaceable human capital is a recurring theme in conversations about climate modeling programs under stress. The ability to sustain a living community of experienced mentors and leaders—people who can guide young researchers through complex technical challenges and navigate the politics and funding cycles that shape scientific work—becomes a central question as budget proposals circulate and decision-makers weigh competing priorities.

Despite the uncertainties, there are strategies that scientists advocate to safeguard essential capabilities. One approach is to create redundancies and international collaborations that maintain access to high-performance computing, model development expertise, and shared data resources even if funding for a particular program in one country becomes constrained. Another strategy involves documenting and preserving the code, data, and workflows that constitute the model ecosystem, ensuring that knowledge is not lost and that future researchers can reconstitute, extend, or rebuild critical tools if political or budgetary changes require it. Historical precedents demonstrate that, when researchers and institutions commit to collective stewardship—backed by archives, open data policies, and robust documentation—scientific capabilities can endure even in difficult funding climates.

The policy landscape continues to evolve, and the scientific community monitors signals from lawmakers and funding agencies to anticipate shifts that could impact Earth system modeling. Open and constructive dialogue between researchers, funding agencies, industry stakeholders, and policymakers is essential to articulating the value of ESMs in terms of societal benefits, risk assessment, and resilience planning. In this environment, scientists emphasize that robust, sustained investment in climate research is not merely a scientific luxury but a practical necessity for a society that must navigate an uncertain climate future and safeguard critical infrastructure, food and energy security, and public health.

Impacts on Research, Talent, and Global Leadership

Beyond the immediate technical capabilities of the model, funding dynamics have profound implications for the people who drive climate science forward. A key concern voiced by researchers is the potential migration of expertise to countries with stronger or more stable funding frameworks. When budgets tighten or priorities shift away from climate modeling, it can become harder to attract and retain seasoned scientists who have built careers mentoring generations of students, developing complex software systems, and guiding long-term research programs. The loss of senior scientists is described in stark terms by observers: their experience, leadership, and institutional memory are not easily replaced, and the cessation of their work can create gaps that take many years to fill.

The risk is not just about losing individuals; it is about eroding an ecosystem that supports knowledge creation and transfer. The emergence of a critical mass of talent around a flagship model like E3SM hinges on stable, long-range funding, opportunities for professional advancement, and the capacity to maintain large, coordinated software projects. When these elements are disrupted, there is a danger that the entire workflow—from model development and validation to data analysis and interpretation—could become fragmented. The consequence could be slower scientific progress, fewer interdisciplinary collaborations, and reduced capacity to address urgent climate questions that demand integrated modeling approaches.

The international dimension adds another layer of complexity. If the United States pulls back from maintaining leading-edge modeling capabilities, other nations—particularly those with robust scientific infrastructures—may assume greater responsibility for developing and deploying next-generation Earth system tools. This shift could reshape the global distribution of climate modeling leadership, potentially altering how different regions influence the direction of climate science, the standards of model evaluation, and the availability of climate projections that are used by policymakers, businesses, and civil society worldwide. The sense of loss in the scientific community is not only about scientific prestige; it is about ensuring that the country remains at the forefront of a field with broad implications for public safety and national security.

The human costs of funding reductions extend to the pipeline of upcoming scientists. Early-career researchers, postdoctoral fellows, and graduate students rely on opportunities to participate in large, collaborative projects, contribute to code development, and publish impactful results. If these pathways contract, a generation of potential leaders in climate science could be stymied or redirected toward other disciplines. The long arc of scientific progress depends on nurturing talent, encouraging risk-taking, and providing stable environments where researchers can pursue ambitious questions over many years. The absence of a strong, experienced community can have lasting consequences for the discipline’s vitality and its capacity to deliver timely, policy-relevant insights.

At stake is more than a single model or a single laboratory. The broader ecosystem of climate science—including universities, national laboratories, federal agencies, and allied research centers—relies on a shared commitment to rigorous software development practices, transparent evaluation, and reproducible results. When funding supports this ecosystem, it creates a virtuous circle: senior scientists mentor junior colleagues, who in turn contribute fresh ideas and new technical approaches, sustaining a culture of excellence. If funding erodes this ecosystem, the resulting drift could undermine the reliability of climate projections, the speed of scientific discovery, and the ability to respond quickly to emerging environmental threats.

The conversation around talent and leadership also intersects with concerns about scientific communication and language. There have been comments within the policy domain about the careful wording of scientific discussions, particularly in contexts where messaging could be interpreted as political or controversial. For some researchers, this climate of sensitivity underscores the importance of maintaining a clear, accurate, and accessible narrative about what climate models do, what they can tell us, and where uncertainties remain. In times of funding tension, clear communication becomes essential for sustaining public understanding and support for continued investment in climate science and the people who sustain it.

In short, the potential erosion of senior scientific leadership tied to E3SM and related modeling initiatives carries implications that extend well beyond laboratory walls. It risks weakening national scientific leadership, constraining the ability to train the next generation of researchers, and reducing the capacity to generate climate information necessary for informed decision-making across sectors. The community emphasizes that preserving the human capital behind these advanced tools is as critical as maintaining the computational platforms and software infrastructures themselves.

The Digital Twin Ambition and Practical Implications

One of the most compelling horizons for Earth system modeling is the pursuit of a “digital twin” of the planet—a faithful, dynamic representation that mirrors Earth’s evolving state with high fidelity. The vision is audacious: to integrate biology, chemistry, physics, and human influences into a single, coherent virtual Earth whose responses can be examined in great detail. This digital twin would serve not only as a scientific instrument but also as a decision-support tool for engineers, policymakers, and communities planning adaptation and resilience strategies. Realizing such a twin would require unprecedented levels of model fidelity, comprehensive data assimilation, and the ability to translate complex model outputs into clear, actionable guidance.

Achieving this lofty goal has been a long, iterative process. Even the current generation of Earth system models, while immensely capable, represents a substantial portion of a larger roadmap. Building, validating, and operating a digital twin of Earth demands substantial software development, careful calibration against observational data, and ongoing collaboration among scientists, engineers, and data specialists. The existing E3SM initiative provides a strong foundation for this vision, combining detailed process representations with a focus on energy system interactions. However, moving toward a fully realized digital twin would demand broader and deeper integration across disciplines, as well as more robust governance of a sprawling codebase that must remain stable while accommodating continual advances in knowledge and technology.

This ambition underscores the importance of long-term planning and sustained funding. The path to a digital twin requires not only investment in computation but also in data infrastructure, model interoperability, and standardized testing procedures. It calls for a culture of openness and collaboration among institutions, with shared best practices for model development, evaluation, and documentation. The value proposition of such a digital twin extends beyond science: it would empower planners to stress-test adaptation measures, simulate the consequences of different energy strategies, forecast coastal impacts under various sea-level rise scenarios, and explore the interplay between environmental changes and human systems in a holistic framework.

Historically, the road to an integrated, highly accurate digital twin has required decades of software development and institutional cooperation. E3SM’s progress illustrates both the potential and the challenges of this journey. The model has already demonstrated the utility of coupled components and the benefits of delivering high-resolution results over regional domains. It is precisely this level of sophistication that positions E3SM as a stepping stone toward the broader digital twin ideal: a living, adaptive representation of the Earth that can be used to test hypotheses, explore policy responses, and guide pragmatic decision-making in a complex, changing world.

From a practical standpoint, the utility of ESMs like E3SM lies in their capacity to inform decisions that affect daily life. For communities facing rising sea levels, intense rainfall, or heat stress, model outputs can shape planning, zoning, and funding for protective measures. For energy systems operators, the insights from ESMs can help anticipate demand fluctuations, optimize grid operations, and evaluate resilience strategies under future climate scenarios. In agriculture, water resources, and public health, climate-informed projections support risk assessments and proactive adaptation. The overarching aim is to translate model science into tangible benefits that reduce harm, enhance preparedness, and promote sustainable development in the face of climate change.

The story of E3SM also highlights the delicate balance between ambition and feasibility. The pursuit of higher resolution, more comprehensive representations of chemical and biological processes requires ongoing investment, not only in software development but in the people who design, code, test, and validate these complex systems. The model’s success depends on a community of scientists, software engineers, and data specialists who work across institutions to build, maintain, and improve the platform. Their collective expertise ensures that E3SM remains a credible, usable tool for researchers and decision-makers alike, capable of producing results that withstand scrutiny and provide reliable guidance in uncertain times.

As researchers continue to push the boundaries of what Earth system models can achieve, they remain mindful of the trade-offs inherent in model design and deployment. Higher complexity can yield more realistic simulations but also increases the risk of introducing errors or complicating interpretation. Ensuring that models remain transparent, well-documented, and reproducible is essential for maintaining trust in projections and for enabling independent verification by other scientists. The ESM community emphasizes the importance of validation against observational data, rigorous sensitivity analyses, and clear communication about the assumptions and limitations that underlie model outputs. In this way, the model’s value is maximized, even as its capabilities expand.

The broader climate modeling enterprise continues to evolve as new data streams, improved algorithms, and innovative computing approaches become available. The combination of climate science, computational science, and policy relevance makes Earth system modeling a dynamic field with far-reaching implications. E3SM’s ongoing development embodies this dynamism and serves as a touchstone for the kinds of integrated tools that researchers will rely on to understand and respond to climate change in the decades ahead. The ambition to create a planetary digital twin remains aspirational, but every step toward that goal—every improvement in resolution, every enhancement of physics and chemistry, every step toward better coupling—brings the scientific community closer to a tool that can illuminate the pathways through which humanity might navigate a warming world.

International Responses and Alternatives

In a global landscape where climate research is both highly collaborative and strategically contested, the fate of U.S. Earth system modeling efforts is closely watched by scientists around the world. The potential retrenchment of domestic investment in ESMs has prompted discussions about how other countries might respond to fill gaps, preserve critical capabilities, or pursue parallel paths of development. There is a widely shared understanding among researchers that climate science benefits from international collaboration, data sharing, and the cross-pollination of ideas. Yet when funding and institutional priorities shift, different nations may take different routes—some strengthening in-house capacity, others expanding regional partnerships, and still others pursuing shared platforms through international consortia.

One plausible response is the proliferation of international collaborations that resemble the collaborative spirit of the ESM ecosystem. If a country recognizes the strategic value of exascale-level Earth system modeling, it may pursue substantial investments in its own modeling programs, recruit top scientists from partner nations, and invest in high-performance computing infrastructures. Such moves could accelerate global competition in climate modeling, potentially increasing the pace of scientific advances but also introducing new challenges in terms of data interoperability, model standardization, and the governance of shared tools and datasets.

At the same time, the scientific community has demonstrated a practical inclination toward archiving and safeguarding critical datasets, software, and knowledge bases. Past experiences with climate data preservation—where researchers and citizens took proactive steps to ensure data remained accessible—underscore the importance of long-term stewardship in climate science. Archives and public repositories that document model configurations, version histories, and key results help sustain continuity even when political and funding environments shift. The creation of robust preservation strategies is not merely a practical precaution but a strategic investment in scientific resilience, enabling future researchers to pick up where earlier work left off, reconstruct models as needed, and continue progress across generations.

Another potential pathway for maintaining modeling capabilities is the reconstitution of models or model-like systems in alternative settings. It is conceivable that a country or consortium could replicate essential components of a sophisticated model platform, building on open methodologies and shared software practices. The concept of reestablishing a model in a different national context is not new; researchers have previously collaborated to ensure the continuity of data and tools during periods of political change. However, for a model as complex as E3SM—enmeshed with specialized software, large-scale data assimilation workflows, and tightly coupled physical and chemical processes—the practicalities of translation, adaptation, and long-term operation across institutional boundaries would be formidable. Success would require a coordinated, well-funded, multi-institution effort that addresses intellectual property considerations, software maintenance, and the training of personnel capable of running, updating, and validating the model in a new environment.

Experts also recognize that even if the United States reduces its domestic emphasis on climate modeling, the field is unlikely to vanish. Other national and international laboratories, universities, and research centers will continue to pursue Earth system modeling, and perhaps with renewed urgency as they seek to fill perceived gaps. The global community has shown a willingness to collaborate on shared objectives, share data, and adopt common validation standards that facilitate cross-border scientific work. In this sense, the ESM enterprise is inherently international, and the knowledge produced by U.S.-led efforts can contribute to a broader, worldwide understanding of climate dynamics, even if some domestic capacity wanes.

Within the U.S., researchers and institutions have proposed several practical safeguards to mitigate disruption. One strategy involves preserving a core capability through partnerships with universities and national laboratories, ensuring continued access to high-performance computing and data resources. Another involves maintaining a flexible, modular software architecture that allows components to be updated or swapped as needed, preserving interoperability even if funding streams change. The overarching aim is to build resilience into the modeling ecosystem so that the loss of any single program does not terminate essential capabilities. This approach emphasizes modularity, clear documentation, and robust governance structures that can outlast political cycles.

The scientific community also notes the potential value of public understanding and support for climate modeling. Communicating the practical benefits of ESMs—how they inform infrastructure planning, emergency preparedness, and environmental protection—can help articulate why sustained investment matters. In an environment where policy priorities shift, concrete demonstrations of the societal returns from climate modeling can help build a broader constituency for continued funding. Clear, accessible communication about model limitations, uncertainties, and decision-relevant outputs is essential for maintaining trust and ensuring that climate science remains a trusted resource for communities and policymakers alike.

In sum, the international landscape for Earth system modeling is likely to continue evolving as nations pursue strategic responses to climate science needs. The ESM community’s emphasis on collaboration, data stewardship, and robust software practices provides a foundation for navigating this complex environment. Even in the face of domestic funding uncertainty, the global network of researchers can help ensure that essential climate knowledge continues to grow, diversified by multiple perspectives, datasets, and modeling approaches. The resilience of the field will depend on the ability of scientists, institutions, and policymakers to maintain the core principles of open science, shared infrastructure, and a continued commitment to training the next generation of experts who will carry forward the work of understanding Earth’s intricate climate system.

The Road Ahead: Risks, Opportunities, and a Call to Action

Looking forward, the trajectory of Earth system modeling is a case study in how science advances through the interplay of curiosity, computation, and funding. The promise of tools like E3SM—capable of simulating nuanced couplings among atmosphere, ocean, land, ice, chemistry, and biology, and capable of informing energy and infrastructure decisions—remains compelling. At the same time, the risks associated with abrupt reductions in support are real: lost personnel, diminished modeling capabilities, and a potential shift in global leadership for climate science. The health of this enterprise will hinge on deliberate, evidence-based policy choices that recognize the broad value of climate modeling to society.

A core concern is the preservation of human capital—the senior scientists, software engineers, and project leaders whose decades of experience keep complex systems coherent and credible. These individuals are the custodians of institutional memory, the mentors who train new generations, and the navigators who translate intricate model developments into practical insights. The loss of such talent would ripple across institutions, affecting not only ongoing projects like E3SM but also the broader climate science landscape. As the field grows more interdisciplinary, attracting and retaining top researchers will depend on a combination of stable funding, career-development opportunities, and a work environment that values scientific integrity, collaboration, and innovation.

The potential for international collaboration to compensate for domestic shortfalls represents both an opportunity and a challenge. On one hand, a more interconnected global climate science community can share knowledge, validate results across different platforms, and pool computing resources to keep essential work moving forward. On the other hand, disparities in funding levels and strategic objectives between countries can complicate coordination, data sharing, and the alignment of standards. The climate science community will need to navigate these dynamics carefully to maintain the reliability and credibility of model results while ensuring that collaboration remains constructive and transparent.

From the policy perspective, a balanced approach would recognize the critical value of climate research for public safety, infrastructure planning, and economic resilience. Investments in high-performance computing, advanced software development, data assimilation, and model validation are not luxuries but necessities for producing timely, credible projections that withstand scrutiny. A well-justified, long-term funding strategy would incorporate the realities of software lifecycles, the need for regular updating to reflect new science, and the importance of training a diverse and capable workforce that can adapt to evolving computational paradigms.

Within the research community, stakeholders advocate for several practical steps to safeguard progress:

  • Maintain dedicated funding lines for core climate modeling activities, with predictable multi-year support to enable long-term planning and major software upgrades.
  • Invest in human capital through stable career pathways for senior scientists, postdocs, and engineers, ensuring mentorship and continuity across generations.
  • Support international collaboration and data-sharing frameworks that advance modeling capabilities while preserving scientific independence and quality control.
  • Prioritize funding for high-performance computing infrastructure and efficient, modern software practices that reduce technical debt and improve model reliability.
  • Promote open science principles, reproducible workflows, and comprehensive documentation to ensure that modeling results can be independently verified and built upon.

These measures would help sustain the momentum of ESMs and related modeling efforts, ensuring that the scientific community remains equipped to address pressing climate questions and to respond to evolving policy needs. The benefits of continuing to invest in Earth system modeling extend beyond academia: robust climate projections inform infrastructure investments, crop planning, disaster preparedness, and public health strategies. They also support national security by enabling a deeper understanding of environmental factors that influence resource availability and resilience. The broader society stands to gain from more accurate, timely, and regionally relevant climate information that helps communities prepare for, adapt to, and mitigate climate risks.

Ultimately, the future of ESMs like E3SM will be determined by a combination of scientific merit, strategic funding, and political will. The field has demonstrated its capacity to uncover surprising connections within the Earth system, to improve the accuracy of extreme-event forecasts, and to illuminate how climate change interacts with human activities and infrastructure. Preserving this capability requires recognizing that climate modeling is a public good—an investment in the knowledge and tools that enable safer, more resilient societies. As scientists, funders, and policymakers engage in this ongoing dialogue, the shared objective should be clear: sustain the human and technical infrastructure that makes Earth system modeling possible, so that it can continue to inform decisions that protect people, economies, and ecosystems around the world.

Conclusion

Earth system models, exemplified by E3SM, stand at the intersection of scientific curiosity, computational possibility, and real-world impact. They bring together physics, chemistry, biology, and energy system dynamics to illuminate how a warming planet will unfold across regions and sectors. The potential loss of funding and talent for these models poses a serious risk to scientific leadership, national security, and the capacity to provide decision-relevant climate information. The future of Earth system modeling depends on a sustained commitment to invest in people, software, data, and computing infrastructure, and on maintaining strong international collaboration and knowledge-sharing networks. By preserving senior leadership and talent, supporting stable funding streams, and fostering an ecosystem that rewards rigorous science and practical applications, the climate modeling enterprise can continue to deliver insights that help societies anticipate, prepare for, and adapt to a changing climate. The stakes are high, but so are the opportunities for knowledge, resilience, and informed action in the face of climate change.