The Rise of AI in Weather and Climate Information and its Impact on Global Inequality
arXiv:2603.05710v1 Announce Type: cross Abstract: 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 unrepresentative validation
arXiv:2603.05710v1 Announce Type: cross Abstract: 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 unrepresentative 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 Summary
This article critically examines the impact of AI adoption in weather and climate information on global inequality. The authors highlight the asymmetry in High-Performance Computing and data infrastructure, demonstrating that AI development is concentrated in the Global North. This infrastructure inequality perpetuates itself through models' inputs, processes, and outputs, leading to systematic performance gaps and biased climate information. The authors propose a data-centric approach, Climate Digital Public Infrastructure, and knowledge co-production to address these disparities. They emphasize the need for a shift from model-centric to data-centric development, human-centric evaluation metrics, and a move from producer-consumer dynamics to knowledge co-production to democratize compute sovereignty and ensure systemic resilience.
Key Points
- ▸ AI in weather and climate information exacerbates the North-South divide
- ▸ Infrastructure inequality perpetuates itself through models' inputs, processes, and outputs
- ▸ Data-centric approach, Climate Digital Public Infrastructure, and knowledge co-production are proposed to address disparities
Merits
Strength
The article provides a comprehensive analysis of the impact of AI adoption on global inequality, highlighting the need for a data-centric approach and knowledge co-production.
Strength
The authors provide concrete examples from three different domains to illustrate the infrastructure inequality and its consequences.
Strength
The article offers a clear and actionable set of recommendations for addressing the disparities in AI development and deployment.
Demerits
Limitation
The article primarily focuses on the Global North and Global South divide, without exploring other potential sources of inequality.
Limitation
The proposed solutions may require significant investment and coordination across different stakeholders and institutions.
Limitation
The article does not provide a detailed roadmap for implementing the proposed data-centric approach and Climate Digital Public Infrastructure.
Expert Commentary
This article provides a timely and critical examination of the impact of AI adoption in weather and climate information on global inequality. The authors' proposal for a data-centric approach, Climate Digital Public Infrastructure, and knowledge co-production offers a promising framework for addressing the disparities in AI development and deployment. However, the article's limitations, including its focus on the Global North and Global South divide, suggest the need for further research and exploration of other potential sources of inequality. Nevertheless, the article's contributions to the broader discussion on AI and inequality are significant, and its implications for policymakers and practitioners are clear and actionable.
Recommendations
- ✓ Develop and implement data-centric approach and Climate Digital Public Infrastructure to address disparities in AI development and deployment.
- ✓ Prioritize knowledge co-production and human-centric evaluation metrics to ensure the equity and fairness of AI development and deployment.