AI for Climate Modeling, Extreme Events & Policy Scenario Analysis

About This Course

This three-module advanced course is designed for senior academicians, climate scientists, and PhD-level researchers who want to build a strong, integrative understanding of how AI and machine learning enhance climate modeling, extreme event analysis, and policy scenario evaluation.

Rather than treating AI as a “black box,” the course focuses on scientifically robust and policy-relevant workflows—including hybrid AI-physics approaches, uncertainty evaluation, interpretability, and decision intelligence that can be used by governments and international agencies. By the end, participants will be able to connect climate-AI research outputs to assessment and planning frameworks (e.g., scenario pathways and IPCC-aligned thinking) in a practical, usable way.

Aim

This course aims to provide senior academicians, climate scientists, and PhD scholars with a deep understanding of how AI/ML augment climate science—from accelerating and improving climate model outputs to building interpretable, uncertainty-aware systems that translate projections into policy-ready scenarios and decision-support.

Course Objectives

By the end of this course, participants will be able to:

• Understand how AI complements and enhances traditional climate models

• Apply AI methods to analyze, detect, and predict extreme climate events

• Design hybrid AI + physics models for climate risk assessment

• Evaluate uncertainty, robustness, and confidence in AI-driven projections

• Translate model outputs into policy-relevant climate scenarios and planning insights

• Align climate-AI research with international assessment frameworks and decision processes

Course Structure

Module 1: AI-Enhanced Climate Modeling and Earth System Intelligence

Theme: Augmenting physical climate models with data-driven AI

• Climate modeling foundations: GCMs, RCMs, ESMs

• How AI supports climate science: model emulation/acceleration, bias correction, statistical downscaling

• ML methods for climate data: regression, deep learning, spatio-temporal models

• Working with high-dimensional climate datasets

• Hybrid and physics-informed AI approaches

• Model evaluation, uncertainty quantification, and robustness

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

Module 2: AI for Extreme Event Detection and Risk Attribution

Theme: Understanding, predicting, and attributing climate extremes

• Extreme event types: heatwaves, floods, droughts, cyclones, wildfires

• AI techniques: event detection/classification, anomaly detection, trend analysis

• Spatio-temporal risk modeling: forecasting frequency, intensity, duration

• Event attribution: linking extremes to climate change signals

• Integrating socio-economic exposure and vulnerability datasets

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

Module 3: AI-Driven Scenario Modeling for Climate Policy and Planning

Theme: From climate projections to policy-ready scenarios

• Scenarios and pathways: SSPs, RCPs, mitigation and adaptation pathways

• AI for scenario simulation, comparison, and uncertainty exploration

• Decision-support systems for adaptation planning and risk-informed design

• Communicating climate-AI insights to policymakers and stakeholders

• Ethics, transparency, governance, and responsible use of AI in climate policy

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

Who Should Enrol?

This course is ideal for:

• Senior academicians in climate science and environmental modeling

• PhD scholars and postdoctoral researchers

• Climate policy analysts and government research teams

• Professionals working in climate risk assessment, resilience, and adaptation planning