Unequal Forecasts: How AI Could Widen the Climate Divide

Author: Denis Avetisyan

The growing use of artificial intelligence in weather and climate prediction presents both opportunities and risks, potentially exacerbating existing global inequalities.

The development of AI models for weather and climate science proceeds through stages characterized by shifting balances between human oversight, transparency, infrastructural demands, and market forces-a lifecycle where reliance on automation often inversely correlates with readily available resources and verifiable trust, demanding careful consideration of these interconnected dimensions to ensure responsible implementation.

This review examines the potential for bias in climate data and infrastructure to create unequal access to crucial climate services and advocates for a more equitable approach to AI development.

While artificial intelligence promises unprecedented advances in climate science, its development paradoxically risks widening existing global inequalities. This paper, ‘The Rise of AI in Weather and Climate Information and its Impact on Global Inequality’, examines how concentrated computational resources and biased data inputs threaten to exacerbate the North-South divide in access to vital climate information. Our analysis reveals that infrastructural disparities and data limitations systematically impact model performance across weather prediction, climate impact assessment, and knowledge representation-perpetuating skewed outcomes for vulnerable regions. Can a shift toward data-centric development, coupled with a Climate Digital Public Infrastructure and collaborative knowledge co-production, truly democratize compute sovereignty and ensure an equitable AI-driven climate future?

The Limits of Prediction: Confronting Uncertainty in Climate Modeling

Despite decades of refinement, traditional numerical weather prediction faces inherent limitations when modeling Earth’s climate. These models, which discretize the atmosphere and oceans into a three-dimensional grid, demand immense computational resources; simulating higher resolutions-necessary for capturing finer-scale phenomena-quickly becomes intractable even with the most powerful supercomputers. This computational burden forces scientists to make approximations, particularly in representing processes occurring at scales smaller than the grid resolution. While successful in forecasting short-term weather, these simplifications accumulate over longer climate timescales, hindering the models’ ability to accurately depict the intricate interplay of atmospheric, oceanic, and land surface processes that govern the climate system. The sheer complexity of these systems-involving feedback loops, non-linear interactions, and chaotic behavior-poses a fundamental challenge, meaning even incremental improvements in computing power may not fully resolve the uncertainties inherent in long-term climate projections.

Climate models divide the Earth’s atmosphere and oceans into a three-dimensional grid, but many crucial processes – like cloud formation or turbulent mixing – occur on scales smaller than these grid cells. To account for these sub-grid-scale phenomena, models employ parameterizations – simplified representations based on empirical relationships. However, these parameterizations introduce inherent uncertainty, as they rely on approximations and statistical assumptions about processes they cannot directly resolve. This constitutes a significant bottleneck in climate modeling, limiting the ability to accurately simulate regional climate features and potentially masking critical feedback loops. While parameterizations are continually refined, their reliance on limited observational data and incomplete understanding of complex interactions means they remain a primary source of error and a key challenge in projecting future climate states.

Climate models, fundamentally built upon past observations and established physical laws, face inherent limitations when projecting future climate states that deviate significantly from historical norms. These models excel at refining predictions within the range of experienced conditions, but struggle to accurately anticipate entirely novel phenomena – such as abrupt shifts in ocean currents or the collapse of major ice sheets – that fall outside the scope of their training data. This reliance on the past creates a challenge in identifying and modeling tipping points, thresholds beyond which small changes can trigger cascading and irreversible effects on the climate system. Consequently, projections of long-term climate change may underestimate the potential for rapid, unexpected, and potentially catastrophic shifts, highlighting the need for innovative modeling approaches that incorporate uncertainty and explore a wider range of possible futures beyond those suggested by historical trends.

Augmenting Forecasts: AI as a Hybrid Solution

Deep learning models offer a means to accelerate climate forecasting by circumventing the intensive computational demands of traditional process-based modeling. These models, a subset of artificial intelligence, can approximate complex physical processes with learned statistical relationships, reducing the need for explicit numerical solutions to partial differential equations. This approach is particularly valuable for parameterizations-simplified representations of sub-grid scale processes-which are computationally expensive in global climate models. By training on large datasets of climate simulations and observations, deep learning models can emulate these parameterizations, enabling faster simulations and potentially higher resolution forecasts without necessarily sacrificing physical realism. The efficiency gains allow for increased ensemble sizes and exploration of a wider range of scenarios, ultimately improving forecast skill and uncertainty quantification.

AI models leverage datasets such as CMIP6 (Coupled Model Intercomparison Project Phase 6) and ERA5 (Fifth-generation reanalysis from ECMWF) to discern complex, non-linear relationships within climate data that are computationally prohibitive for traditional physics-based models to resolve. CMIP6 provides simulations from numerous global climate models, while ERA5 is a comprehensive reanalysis dataset combining observations and model data, creating a detailed record of the Earth’s recent climate. By training on these extensive datasets-spanning decades of atmospheric, oceanic, and land surface variables-AI algorithms can identify subtle patterns and predictive indicators previously undetectable, potentially improving the skill of climate forecasts and extending their range. These models do not replace physical understanding but augment it by statistically relating observed variables to future climate states.

The integration of physically-based climate models with Artificial Intelligence offers the potential to extend forecast horizons and improve accuracy by leveraging AI’s ability to identify complex patterns within large datasets. However, a significant disparity currently exists in forecast skill globally; current one-day-ahead forecast accuracy in low-income countries is statistically equivalent to a seven-day-ahead forecast in high-income countries. This discrepancy is attributed to limitations in data availability, computational resources, and model initialization practices within lower-income regions, hindering the equitable distribution of improved forecasting capabilities despite advancements in hybrid modeling techniques.

The Shadow of Bias: Data Quality and Equity in Climate AI

The performance of artificial intelligence models in climate science is directly dependent on the quality and characteristics of the datasets used for training. Systemic biases present in observational networks – arising from uneven geographic distribution of sensors, limitations in monitoring capabilities, or historical data collection practices – are not simply reflected in model outputs but are often amplified through the learning process. This means that pre-existing disparities or inaccuracies in the data can be exaggerated, leading to predictions that disproportionately favor certain regions or fail to accurately represent climate phenomena in under-observed areas. Consequently, models trained on biased data risk perpetuating and reinforcing existing inequalities in climate risk assessment and adaptation planning.

Significant disparities in climate data availability contribute to inequitable outcomes in AI-driven climate predictions and mitigation strategies. The Integrated Carbon Observation System (ICOS), while providing extensive data for developed nations, contrasts sharply with data scarcity in resource-constrained regions, limiting the ability to accurately model and address climate impacts in those areas. This data imbalance is compounded by the highly concentrated distribution of AI infrastructure; as of 2023, only 32 countries globally host hyperscale AI data centers, effectively centralizing the processing and analysis capabilities and potentially exacerbating existing inequalities in access to and benefit from climate AI technologies. This concentration limits the capacity of many nations to independently develop, deploy, and validate AI solutions tailored to their specific vulnerabilities and needs.

Data-centric AI represents a paradigm shift in climate modeling, moving focus from algorithmic complexity to the quality and representativeness of training datasets. Traditional AI development often assumes data is fixed and prioritizes model refinement; however, this approach can exacerbate biases present in incomplete or skewed data. Data-centric methodologies emphasize systematic data cleaning, validation, and augmentation, alongside strategies to actively address underrepresentation of specific geographic regions, socioeconomic groups, or climate variables. Prioritizing data quality through techniques such as error detection, outlier removal, and consistent data labeling can significantly improve model accuracy, reduce unfair predictions, and enhance the reliability of climate projections, ultimately leading to more equitable and effective climate action strategies.

Toward Resilience: Infrastructure and Collaborative Knowledge

A robust digital public infrastructure is increasingly recognized as fundamental to addressing the escalating challenges of a changing climate. This infrastructure transcends simple data provision; it’s a dynamic ecosystem encompassing accessible climate data, analytical tools, and modeling capabilities, all designed to empower a diverse range of users. Crucially, equitable access is paramount, ensuring that vulnerable communities and developing nations aren’t left behind in the pursuit of climate resilience. By democratizing access to information – from hyperlocal weather patterns to long-term climate projections – this infrastructure fuels innovation across sectors, enabling more informed decision-making in areas like agriculture, urban planning, and disaster risk reduction. Furthermore, a well-maintained digital climate commons fosters collaboration, allowing researchers, policymakers, and citizens to collectively develop and implement effective adaptation and mitigation strategies, ultimately accelerating the pace of climate action.

Artificial intelligence offers transformative potential for climate services, yet realizing this requires a deliberate shift towards integrated knowledge and collaborative approaches. Current climate modeling and prediction often operate in silos, limiting the capacity to address complex, interconnected challenges – for instance, accurately forecasting heatwaves and their subsequent impact on public health. Effective early warning systems, crucial for minimizing climate-related disasters, depend on synthesizing data from diverse sources – meteorological observations, hydrological models, and even social vulnerability assessments – which necessitates interdisciplinary collaboration. Similarly, climate-health studies benefit immensely from combining AI-driven data analysis with local ecological knowledge and community-based health monitoring. This integration not only improves the precision of predictions but also ensures that climate information is relevant, accessible, and actionable for those most at risk, fostering resilience and equitable adaptation strategies.

Effective climate action increasingly relies on knowledge co-production, a process that integrates scientific data with local, Indigenous, and traditional ecological knowledge to create solutions tailored to specific contexts and needs. This collaborative approach moves beyond top-down information dissemination, fostering trust and ensuring that climate strategies are both relevant and readily adopted by affected communities. Simultaneously, the tools driving this integration-particularly artificial intelligence-are poised for substantial growth, creating a parallel demand for resources; projections indicate a significant increase in water withdrawal-reaching 4.2 to 6.6 billion cubic meters by 2027-and a 4.5% rise in global energy demand by 2030. Addressing these escalating resource needs alongside the pursuit of co-produced knowledge is critical, demanding innovative strategies for sustainable AI development and responsible data management to prevent exacerbating the very challenges climate adaptation seeks to solve.

A Future Forged in Data: Harnessing AI for a More Equitable Planet

Realizing the full potential of artificial intelligence to combat the climate crisis demands a fundamental shift towards data equity, collaborative partnerships, and resilient infrastructure. Currently, access to the data necessary for effective AI-driven climate solutions remains unevenly distributed, potentially widening existing inequalities; prioritizing inclusive data collection and curation is therefore essential. Furthermore, breakthroughs require sustained collaboration between climate scientists, AI researchers, policymakers, and affected communities, fostering a shared understanding and ensuring solutions are both effective and equitable. Crucially, this convergence necessitates significant investment in robust computational infrastructure – including high-performance computing and data storage – to support the development and deployment of complex AI models and ensure widespread access to advanced climate forecasting capabilities, ultimately empowering all communities to adapt and thrive in a changing world.

The synergistic integration of artificial intelligence with established climate science, and crucially, with collaborative knowledge systems, presents a powerful pathway for bolstering community resilience. This convergence moves beyond simply predicting climate shifts; it facilitates proactive adaptation strategies tailored to local contexts. By incorporating diverse datasets – from satellite imagery and weather patterns to Indigenous ecological knowledge and citizen science observations – AI algorithms can generate hyper-local risk assessments and resource allocation plans. These insights, when co-created with affected communities, empower them to anticipate challenges, implement targeted interventions, and build adaptive capacity. This collaborative approach ensures that solutions are not only scientifically sound but also culturally appropriate, socially equitable, and effectively implemented, fostering a future where communities are active agents in navigating a changing world.

The advancement of climate modeling and predictive accuracy is inextricably linked to sustained investment in High-Performance Computing (HPC). Complex artificial intelligence models, essential for discerning subtle climate patterns and forecasting future scenarios, demand immense computational resources for both training and execution. However, simply increasing processing power isn’t sufficient; equitable access to these advanced capabilities is paramount. Current data curation practices and infrastructural deployments often concentrate benefits in wealthier nations, potentially widening the gap in climate resilience. A deliberate restructuring – prioritizing open-source data initiatives, decentralized computing networks, and capacity-building programs in vulnerable regions – is therefore crucial. This shift will ensure that the power of AI-driven climate forecasting extends beyond developed countries, empowering communities worldwide to proactively adapt to the challenges of a changing climate and fostering a more just and sustainable future.

The pursuit of increasingly complex climate models, driven by artificial intelligence, reveals a troubling pattern. The article rightly points to the potential for these systems to amplify existing global inequalities, not through malice, but through the subtle biases embedded within the data itself. As Sergey Sobolev observed, “The most dangerous errors are those that seem logical.” This sentiment perfectly encapsulates the risk: a seemingly rational system, built upon flawed foundations, can produce results that, while internally consistent, actively disadvantage vulnerable populations. The concentration of data curation and infrastructural power, as detailed in the paper, creates a feedback loop where existing disparities are not only perpetuated but reinforced through the very tools intended to offer solutions. If everything fits perfectly, one suspects a critical dimension of equity has been overlooked.

What’s Next?

The enthusiasm for artificial intelligence as a panacea for climate woes appears, predictably, to have outstripped careful consideration of its limitations. The presented analyses suggest a future where the benefits of increasingly sophisticated climate modeling and forecasting accrue disproportionately to those already possessing resources and infrastructure. This isn’t a technological failing, strictly speaking, but a predictable consequence of applying complex systems to fundamentally unequal ones. The question isn’t whether AI can improve climate services, but for whom.

Future work must move beyond demonstrating predictive skill and address the provenance and representation within datasets. An overreliance on easily accessible, high-resolution data from wealthier nations will only amplify existing biases. The challenge isn’t merely to identify these biases, but to actively construct datasets that reflect the vulnerabilities and needs of marginalized communities – even when those communities lack the capacity to generate ‘clean’ data. If the resulting models are less elegant, less statistically ‘significant’, so be it. Elegance is rarely a virtue in the face of injustice.

The concept of a ‘digital public infrastructure’ for climate information offers a potential pathway, but demands rigorous scrutiny. Such infrastructure is not neutral; it embodies choices about data governance, access protocols, and the very definition of ‘relevant’ information. A truly equitable system will require not just open access, but active investment in local capacity building, ensuring that communities can not only receive climate information, but interpret, validate, and apply it to their specific needs.

Original article: https://arxiv.org/pdf/2603.05710.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/