Unit 0: Preschool
Python for Climate Science (Full-day tutorial for beginners to moderate users)
Unit 1: Introduction to Climate Science and Cryosphere
Global energy balance
Earth-System and its components: Geosphere, Cryosphere, Hydrosphere, Atmosphere, Biosphere interactions.
Global and local energy balance: Black body, shortwave and long wave radiation, Greenhouse effect.
A 0-d climate model.
Cryosphere-climate interactions, Albedo feedback, Snowball earth events.
Global circulation
Natural Forcing over different time scales: Orbital cycles, solar variability, volcanic aerosols.
Anthropogenic Forcing: GHG, black carbon/aerosols.
1-d climate model: Meridional heat imbalance, global circulation (winds and ocean currents), heat and moisture flux.
Climate variability and change
Global scale processes and variability; ITCZ and its seasonality, ENSO, teleconnections …
Regional circulation Systems: Monsoon and Western Disturbances (WD). Role of the Himalayan orography.
Anthropogenic climate change
Unit 2: Fundamentals of Climate and Glacier Modelling
Fundamentals of Climate Modelling
Introduction to Climate Modelling: Definition and purpose of climate models, Historical evolution: From simple energy balance models (EBMs) to modern Earth System Models (ESMs), Importance of modelling glaciers in climate science)
Basic Components of Climate Models: Atmospheric dynamics (Navier-Stokes equations, energy balance, Radiation and feedback mechanisms, Role of greenhouse gases and aerosols, Cryosphere representation (glaciers, ice sheets, permafrost)
Glacier Modelling Fundamentals: Energy and mass balance approach in glacier modelling, Ice flow and thermodynamics, Feedback processes (e.g., ice-albedo feedback), Challenges in modelling glacier responses to climate change
Types of Climate Models and Cryosphere Representation
Overview of Climate Model Types: ESMs, AGCMs, Glacier-specific Models
Representation of the Cryosphere in ESMs: Snow cover, ice sheets, glaciers, permafrost, Ocean-cryosphere interactions (e.g., ice-ocean feedback), Limitations: Coarse resolution, lack of fine-scale ice dynamics
Role of Models in Climate Science: Simulating past, present, and future climate-glacier interactions, Policy and decision-making: How model outputs inform climate action, briefly discuss CMIP
Evaluating Models and Applying Results
Model Evaluation: Observational datasets for model validation (e.g., satellite, reanalysis data, Performance metrics: Bias, RMSE, skill scores, Case studies: Evaluating glacier mass balance in global models
Multi-Model Ensembles: Why use multiple models? Addressing uncertainties in climate projections, CMIP and GlacierMIP initiatives, Projecting future glacier mass balance using ensemble techniques
Sensitivity and Feedback Analysis in Glacier Models: Assessing climate sensitivity in glacier models (e.g., temperature vs. precipitation impacts). Quantifying feedbacks: Ice-albedo, meltwater lubrication, and debris-cover effects on glaciers.
Unit 3: Global and Regional Cryosphere Projections
Glacier and ice-sheet response to climate change
- Predicting glacier response to climate forcing at global to regional scale - context and questions
- Overview of modelling tools/results to address them
- Downscaling to glacier scale …
Himalayan Cryosphere Under Climate Change
- Change and variability of Himalayan climate (RCM, renalysis, ..)
- computing the response of Himalayan snow and glaciers.
- reconciling observed vs. modelled glacier loss
- open questions
Future of Himalayan cryosphere (Lecture 9)
- Future climate scenarios and glacier response
- Uncertainties in Climate forcing, and that in glacier models
- Open questions
Unit 4: AI/ML for Climate-glacier interaction:
Prerequisites for deep learning
- Basics of probability and statistics
- Fundamentals of linear algebra
- Optimisation
- Intuitions of High dimensional spaces
Basics of deep learning
- Introduction to artificial neural networks
- Basics of Deep Learning
- Training a neural network
- Popular Networks architectures
Applications to climate science
- Deep learning for climate modeling
- Deep learning for weather modeling
- Deep learning for glacier modeling