Reasoning Models in Climate Science

Purpose
This webinar explains how deductive, inductive, and causal reasoning combine with AI to advance climate science.

Deductive Reasoning
Physical climate models rely on conservation laws to simulate Earth systems but face resolution and uncertainty limits.

Inductive Reasoning
Data-driven methods and machine learning extract patterns from observations and simulations.

Causal Reasoning
Causal inference separates correlation from mechanism, essential for attribution and policy.

Physics-Informed AI
Hybrid approaches embed physical laws into machine learning for robustness and generalisation.

Explainable AI
Transparency and interpret-ability are required for trust, risk assessment, and decision-making.

Key Message
The future of climate science lies in integrating physics, data, and causal reasoning with explainable AI.