Adversarial Threats in Climate AI: Navigating Challenges and Crafting Resilience

Abstract

The convergence of Artificial Intelligence (AI) with climate science is a double-edged sword. AI-enhanced modeling has transformative potential for the field, but it comes with new vulnerabilities, especially from adversarial machine learning. Such adversarial tactics can distort AI-driven climate models, producing misleading projections on phenomena like sea-level changes and temperature predictions. Beyond just mod-eling, AI-enhanced systems in resource management, conserva-tion, and agriculture are at risk. Tampering with climate da-tasets can undermine decades of global research and erode trust, while adversarial misinformation campaigns can skew public discourse. Ethically, distorted data risks magnifying socio-environmental disparities. Addressing these challenges necessitates robust modeling using advanced techniques, data defense with cryptographic solutions, AI-driven infrastructure safeguards, and AI algorithms to detect and counter misinfor-mation. Simply put, securing AI in climate science is not just a technical challenge, but a global imperative.