Climate Science & AI
The Rise of AI in Weather and Climate Information and its Impact on Global Inequality
The rapid adoption of AI in Earth system science promises unprecedented speed and fidelity in the generation of climate information. However, this technological prowess rests on a fragile and unequal foundation: the current trajectory of AI development risks further automating and amplifying the North-South divide in the global climate information system. We outline the global asymmetry in High-Performance Computing and data infrastructure, demonstrating that the development of foundation models is almost exclusively concentrated in the Global North. Using three different domains, we show how this infrastructure inequality continues through models' inputs, processes and outputs. As an example, in weather and climate modelling, the reliance on historically biased data leads to systematic performance gaps that disproportionately affect the most vulnerable regions. In climate impact modelling, data sparsity and unrepresenta- tive validation risk driving misleading interventions and maladaptation. Finally, in large language models, dependence on dominant textualised forms of climate knowledge risks reinforcing existing biases. We conclude that addressing these disparities demands revisiting the three phases, i.e. models "Input", "Process" and "Output". This involves (i) a perspective shift from model-centric to data-centric development, (ii) the establishment of a Climate Digital Public Infrastructure and human-centric evaluation metrics, and (iii) a move from producer-consumer dynamics toward knowledge co-production. This integration of diverse knowledge systems would truly democratise compute sovereignty and ensure that the AI revolution fosters genuine systemic resilience rather than exacerbating inequity.
Executive Impact Snapshot
Key performance indicators highlighting the current state and potential future of AI in climate science, revealing both opportunities and challenges related to global equity.
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Improvement in forecast speed (simulated)
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Concentration of AI development in Global North
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Input Level Bias
Process Level Inequality
Output Level Disparities
Input Level Bias
The first and main entry point for biases is at the input stage. Research agendas are shaped largely by institutions and funders based in the Global North, which tend to prioritise a technocratic framing of climate challenges, emphasising efficiency, prediction accuracy, and high-technology solutions over equity-oriented or locally embedded forms of climate knowledge. This already impacts the goal and problem definition, upon which AI models will be based.
In terms of data acquisition, reanalysis datasets, which blend observations with past short-range weather forecasts, rerun with modern forecasting models serve as the cornerstone of AI-based weather and climate prediction due to their spatial and temporal consistency. Most frontier AI models are pre-trained or fine-tuned on ERA5, widely considered the gold standard for historical atmospheric data. However, this reliance on "maps without gaps" creates an illusion of uniform global quality. While variables such as temperature and pressure are directly assimilated from observations, the density of these inputs is highly unequal; in observation-sparse regions, the reanalysis is significantly less constrained by real-world data.
Process Level Inequality
Applying AI to climate and other climate-impacted domains comes with structural challenges that arise before model training. Different domains generate, curate, and store data in fundamentally different ways, meaning that harmonising them often requires substantial aggregation or other forms of coarse pre-processing. These early integration choices shape the information that ultimately becomes available for model training and evaluation.
For model training and evaluation, High-Performance Computing (HPC) infrastructure, the engine of modern weather and climate science, is key. These facilities not only run operational forecast systems but also serve as the primary repositories for the petabytes of training data required by modern AI. However, access to this "compute" is profoundly unequal. The major publicly funded HPC systems – those that form the backbone of global operational forecast- ing – are heavily concentrated in the Global North and East Asia. Consequently, frontier AI-weather models are almost exclusively developed through collaborations between these national public centres and a handful of multinational technology firms (e.g., Google DeepMind, NVIDIA, Microsoft, Huawei).
Output Level Disparities
Climate information and services play a central role in supporting decision-making across climate-sensitive sectors, informing policy design, risk management, and long- term adaptation planning. Advances in Earth-system science and data-driven methods have expanded the range of climate products available to governments, public agencies, and practitioners, from forecasts and early-warning systems to impact assessments and decision-support tools. These services are increasingly expected not only to improve predictive accuracy but also to translate scientific knowledge into actionable insights that can guide policy interventions and resource allocation. However, the potential of climate information systems to become supportive of decision-making depends criti- cally on how underlying data, models, and AI-driven tools are developed, evaluated, and deployed, raising important questions about whose risks are prioritised and whose decisions these systems ultimately serve.
Such limitations intersect with a deeper equality problem in forecasting capa- bilities. Despite continuous improvements in forecast skill over recent decades, a meaningful gap persists across income levels. Linsenmeier and Shrader found that temperature forecasts are substantially more accurate in high-income countries, to the extent that a seven-day-ahead forecast in wealthy nations outperforms a one- day-ahead forecast in low-income countries.
95%
of frontier AI models concentrated in the Global North
Enterprise Process Flow
Problem Definition
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Data Acquisition
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Data Assimilation & Cleaning
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Model Training
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Validation & Testing
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Application & Deployment
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Climate Information & Services
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Decision Support & Policy
| Region | Observed Data Quality | ERA5 Bias Examples | AI Model Impact |
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| Global North |
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| Tropical Regions |
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Case Study: AI in Climate Health
Problem: Climate-health studies combine weather/climate data with health data, which often suffers from underreporting, over-reporting, and pervasive biases (e.g., healthcare access, inconsistent testing, changing case definitions). This makes it extremely difficult to estimate the true global burden of many diseases, particularly infectious diseases which largely impact populations in extreme poverty, introducing substantial uncertainty into AI models trained on such data, leading to out-of-sample failures.
Solution: Addressing these issues demands rigorous evidence that AI improves population outcomes. Instead of sophisticated warning systems alone, investments in basic public health infrastructure may yield far greater impact. Hybrid approaches combining machine learning with mechanistic models and transdisciplinary collaborations can improve interpretability and guidance for interventions.
Outcome: Improved AI systems for climate-health require better data equity and infrastructure, focusing on local contexts and reducing bias amplification to avoid perpetuating environmental injustice.
Calculate Your Potential ROI
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Your AI Transformation Roadmap
A phased approach to integrate AI ethically and effectively into your climate intelligence strategy.
Phase 1: Data Infrastructure & Governance Review
Assess existing data pipelines, identify biases in observational and reanalysis datasets, and establish a framework for data-centric AI development, focusing on equitable data acquisition strategies in data-sparse regions. This includes auditing current HPC access and identifying sovereign compute needs.
Phase 2: Model Adaptation & Bias Mitigation
Develop or adapt AI models using fairness-aware loss functions and population-weighted metrics. Prioritize hybrid approaches that combine machine learning with mechanistic models to improve interpretability and address systematic performance gaps in vulnerable regions.
Phase 3: Co-production & Deployment for Impact
Implement AI-driven tools within a knowledge co-production framework, integrating diverse knowledge systems, including Indigenous and local expertise. Ensure transparent deployment, equitable access, and continuous evaluation based on human-centric outcomes rather than solely statistical aggregates, focusing on tangible societal benefits.
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