Citation: Kioumarsi, H., Fatiqin, A., & Sagar, A. (2025). AI for climate system modeling and policy. Springer Nature Communities. https://communities.springernature.com/posts/ai-for-climate-system-modeling-and-policy

Artificial Intelligence (AI) is progressively transforming climate science by ensuring enhanced accuracy, velocity, and scope of climate data analysis and climate prediction resulting in accurate decision-making. Traditional climate models are constrained by high computational costs, poor spatial resolutions, and underlying uncertainties in explaining complex earth systems. AI, and particularly machine learning techniques, address these challenges by enabling more accurate representation of climatic processes, faster simulations, and accurate localized forecasts essential for regional planning and disaster preparedness. AI-driven climate simulation applies large and diverse data sets like satellite imagery, ground sensors, and IoT devices to analyze intricate patterns readily undetectable for conventional models. This capability improves understanding of climate processes. Improved predictive accuracy enables more reliable forecasts of extreme events such as floods, droughts, and wildfires, and this is critical for climate resilience and adaptive policy-making. Besides modeling, AI facilitates real-time environmental monitoring and early warning systems. It tracks ecosystem health indicators such as loss of forest cover, water quality, and biodiversity to ensure rapid response to environmental shocks and enhanced management of natural resources. AI systems support refining climate adaptation strategies by simulating multiple climate futures and evaluating the impact of mitigation interventions, thus supporting policymakers to craft tailor-made evidence-based interventions that balance ecological sustainability and socioeconomic development. Moreover, the value addition of AI extends to facilitating communication and integration of the rising amount of climate science literature. Scientific assessment, as by the Intergovernmental Panel on Climate Change (IPCC), is supported by AI-enabled evidence synthesis in addressing the research output explosion. Integration provides more open, whole, and up-to-date knowledge communication, with enhanced quality and timeliness of scientific advice for global climate policy. Yet the use of AI in climate research and policy is confronted with issues of data quality issues, ethics, and technical constraints. Effective governance frameworks are necessary to ensure transparency, accountability, and equitable utilization of AI technologies, protecting scientific integrity and the confidence of the public. The future for AI in this field will likely consist of increasing integration with other solutions and additional areas of application, such as energy efficiency from renewable sources, intelligent control of smart grids, and disaster response, further driving the transition to climate systems that are resilient and sustainable. Overall, AI significantly enhances climate system modeling and policy through improved environmental observation and decision-making based on evidence. These contributions are fundamental in addressing the challenges posed by climate change, emphasizing AI’s critical role in fostering sustainable and adaptive climate governance.

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