Climate Science & AI
Academic overviews of key topics at the intersection of climate science and artificial intelligence — from physics-informed neural networks to operational AI weather forecasting, with inline citations sourced from peer-reviewed literature and arXiv preprints.
Watch — a short tour of this page, narrated in my own AI-cloned voice.
What’s in this branch
Introduction
Overview of the climate science and AI intersection: predictive modelling, data analysis, and sustainable energy solutions.
Historical Context
Evolution of climate modelling from early statistical methods to process-based models and the integration of AI.
Methodologies
Data pipelines, hybrid physics–AI architectures, deep learning approaches, and uncertainty quantification.
Applications
AI weather forecasting, extreme-event detection, renewable energy optimisation, emissions monitoring, and adaptation.
Challenges & Limitations
Data quality, generalisation to novel climate states, interpretability, compute cost, extremes, and governance.
Future Directions
Next-generation climate modelling, sustainable building design, plant health, and emerging research avenues.
References
Full bibliography for the Climate Science & AI branch, with DOIs and stable URLs where available.
Conventions
Each page is a standalone academic note with inline bracketed citations (e.g., [1]) that resolve to the master References page within this branch. Sources are drawn primarily from peer-reviewed journals (Nature, Science, ACM Computing Surveys, Geophysical Research Letters) and arXiv preprints.