Understanding the Role of AI in Climate Science
Last week, Marc Alessi, a Science Fellow, participated in the National Academies of Sciences Roundtable on Artificial Intelligence and Climate Change. The event, titled Accelerating Climate Progress with AI: From Science to Action, brought together experts from academia, government, industry, and nonprofits to discuss how AI can help combat climate change. While the panel focused on using AI for climate action, an essential starting point is understanding how and why climate scientists integrate AI into their work.
AI as a Powerful Tool in Climate Research
Climate scientists utilize a variety of tools to explore and understand Earth’s climate system. Among the most well-known are climate models, which have evolved since the 1960s to simulate physical processes across the atmosphere, oceans, land surfaces, and vegetation. Over time, these models have become more sophisticated, aiming to mirror the complexity of real-world climate behavior.
Artificial intelligence (AI), particularly machine learning, has become a significant addition to this toolkit. Unlike generative AI, which is designed to create content, machine learning identifies patterns in large datasets to make predictions. This makes it especially useful in climate science, where the system is inherently full of patterns—such as those influenced by phenomena like El Niño. For instance, AI can quickly recognize that warming in the East Pacific often correlates with warmer conditions in the U.S. Pacific Northwest.
It is important to distinguish between traditional climate models and AI-based approaches. While climate models are grounded in physical equations, AI relies on statistical correlations. This means AI predictions, although efficient, do not always adhere to the underlying physical laws of climate systems.
Practical Applications of AI in Climate Science
AI has been used in climate science for over two decades, well before the recent surge in generative AI applications. One notable innovation is the climate emulator, developed by the non-profit Ai2. These emulators replicate the output of traditional climate models but operate at a fraction of the computational cost. The Ai2 Climate Emulator (ACE), for example, is 100 times faster and nearly 1,000 times more energy-efficient than conventional models, while producing similarly robust predictions.
Another critical application is downscaling, which addresses the limitations of climate models’ resolution. Traditional models simulate the Earth on a large grid, making it difficult to draw local predictions. AI-based downscaling analyzes model output alongside local observational data to offer refined forecasts for specific areas—such as predicting precise temperature changes in cities like Boston or New York.
Scientists are also exploring hybrid models that combine the strengths of AI and traditional climate simulations. Importantly, the AI used in these studies is significantly less energy-intensive than large language models like ChatGPT, making it a sustainable research tool.
Using AI to Bridge Climate Data Gaps
In Alessi’s research at the Union of Concerned Scientists (UCS), machine learning is being employed to fill in gaps in historical climate records in the Global South. Many of these regions lack reliable data, which hampers accurate climate attribution studies—essential for climate litigation and the operationalization of the United Nations Loss and Damage Fund.
By training AI on comprehensive datasets from high-resolution climate models, researchers can identify patterns during extreme heat events and predict missing temperature data. These AI-generated estimates are then validated against existing observations, making the approach a promising tool for enhancing global climate datasets.
AI for Climate Action and Adaptation
At the recent workshop, experts shared numerous examples of how AI is being used for climate mitigation and adaptation. AI tools are assisting in:
- Predicting the final size of wildfires from the moment of ignition
- Early detection of wildfires
- Supporting sustainable agriculture
- Guiding farmers on crop adaptation strategies
- Recommending urban tree planting for heat mitigation
- Managing water resources more sustainably across major aquifers
These applications demonstrate AI’s potential to translate complex scientific data into actionable insights that support both local and global climate resilience efforts.
Challenges and Ethical Considerations
Despite its promise, AI in climate science faces significant challenges. One major limitation is its reliance on historical data, making it difficult to predict unprecedented future climate scenarios. Furthermore, AI can function as a black box, offering little transparency about how decisions are made.
To address this, scientists are turning to Explainable AI, a growing field that aims to demystify the decision-making process within AI models. By understanding how AI identifies patterns and makes predictions, researchers can better evaluate its reliability and build trust in its outputs.
Moreover, there is a critical need for rigorous standards in AI research. Open-access methodologies, transparent documentation of confidence levels, and reproducibility are essential to ensure the credibility and applicability of AI in climate science.
Conclusion: AI as a Complementary Tool
Artificial intelligence is undoubtedly reshaping the landscape of climate research. From enhancing data quality to enabling faster and more localized predictions, AI offers valuable support to scientists. However, it must be viewed as an additional tool—not a replacement—for traditional scientific methods grounded in physical laws. With continued innovation, careful validation, and ethical deployment, AI can play a pivotal role in both understanding and addressing the global climate crisis.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.