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The Rise of AI in Weather and Climate Information and its Impact on Global Inequality

Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup, Stefano Materia, Gina E. C. Charnley, Lluis Palma, Eulalia Baulenas Serra, Dragana Bojovic, Paula Checchia, Aude Carreric, Francisco Doblas-Reyes

TL;DR

The global asymmetry in High-Performance Computing and data infrastructure is outlined, demonstrating that the development of foundation models is almost exclusively concentrated in the Global North and how this infrastructure inequality continues through models' inputs, processes and outputs.

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.

The Rise of AI in Weather and Climate Information and its Impact on Global Inequality

TL;DR

The global asymmetry in High-Performance Computing and data infrastructure is outlined, demonstrating that the development of foundation models is almost exclusively concentrated in the Global North and how this infrastructure inequality continues through models' inputs, processes and outputs.

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.
Paper Structure (7 sections, 1 figure, 3 tables)

This paper contains 7 sections, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Manifestations of Inequality
  3. Input Level
  4. Process Level
  5. Output Level
  6. A Call to Action: Forging an Equal and Equitable Path Forward
  7. Supplementary Tables

Figures (1)

  • Figure 1: The lifecycle of AI model development in weather and climate sciences, tracing the workflow from initial data acquisition to downstream application. The bottom panels qualitatively illustrate the varying intensity of four key dimensions across these stages: the level of human agency and oversight, the degree of transparency and trust, the magnitude of infrastructure requirements, and the existence of market demand.