📅 Day 1 – AI-Enhanced Climate Modeling & Earth System Intelligence
Theme: Augmenting Physical Climate Models with Data-Driven AI
- Overview of climate modeling frameworks:
- Global Climate Models (GCMs)
- Regional Climate Models (RCMs)
- Earth System Models (ESMs)
- Role of AI in climate science:
- Model emulation and acceleration
- Bias correction and statistical downscaling
- Machine learning methods for climate data:
- Regression, deep learning, and spatio-temporal models
- Handling large-scale, high-dimensional climate datasets
- Hybrid and physics-informed AI approaches for climate modeling
- Model evaluation, uncertainty quantification, and robustness in climate projections
👉 Outcome: AI-augmented climate modeling and Earth system intelligence workflows.
Case Perspectives:
- AI-assisted downscaling of temperature and precipitation
- Climate model emulators used in international climate assessments
📅 Day 2 – AI for Extreme Event Detection & Risk Attribution
Theme: Understanding, Predicting, and Attributing Climate Extremes
- Types of climate extremes:
- Heatwaves, floods, droughts, cyclones, wildfires
- AI techniques for extreme event analysis:
- Event detection and classification
- Anomaly detection and trend analysis
- Spatio-temporal risk modeling:
- Forecasting frequency, intensity, and duration of extremes
- Event attribution:
- Linking extreme events to climate change signals
- Integrating socio-economic exposure and vulnerability data
👉 Outcome: AI-based extreme event detection and climate risk attribution models.
Case Perspectives:
- AI-based heatwave early warning systems
- Flood and drought risk attribution studies
📅 Day 3 – AI-Driven Scenario Modeling for Climate Policy & Planning
Theme: From Climate Projections to Policy-Ready Scenarios
- Climate scenarios and pathways:
- SSPs, RCPs, mitigation and adaptation pathways
- AI for scenario simulation, comparison, and uncertainty exploration
- Decision-support systems for:
- Climate adaptation planning
- Risk-informed infrastructure design
- Communicating AI-driven climate insights to policymakers and stakeholders
- Ethical, transparency, and governance considerations in climate-focused AI
👉 Outcome: Policy-ready AI-driven climate scenarios and decision-support frameworks.
Case Perspectives:
- AI-assisted policy scenario analysis for national climate plans
- Decision frameworks used in IPCC-aligned assessments
