AI for Climate Modeling, Extreme Events & Policy Scenario Analysis

This workshop aims to provide senior academicians, climate scientists, and PhD scholars with a deep, integrative understanding of how AI and machine learning augment climate modeling, extreme event analysis, and policy scenario evaluation. The program focuses on bridging climate science with AI-driven decision intelligence, enabling participants to design models that are not only scientifically robust but also policy-relevant, interpretable, and usable by governments and international agencies.

• Understand how AI complements and enhances traditional climate models
  
• Apply AI methods to analyze and predict extreme climate events
  
• Design hybrid AI-physics models for climate risk assessment
  
• Evaluate uncertainty and confidence in AI-based climate projections
  
• Translate model outputs into policy-relevant climate scenarios
  
• Align climate-AI research with international assessment frameworks
  
  

📅 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

• Senior academicians in climate science and environmental modeling
• PhD scholars and postdoctoral researchers
• Climate policy analysts and government researchers
• Professionals involved in climate risk assessment and adaptation planning

• A clear conceptual framework for AI-driven climate modeling
• Insight into cutting-edge research directions in climate AI
• Enhanced capacity to publish in high-impact climate and environmental journals
• Ability to contribute to policy reports, advisory panels, and assessments
• Strategic understanding of climate-AI funding and collaboration opportunities