99509 - CLIMATE SYSTEM MODELLING

Academic Year 2024/2025

  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Sciences and Management of Nature (cod. 9257)

    Also valid for Second cycle degree programme (LM) in Science of Climate (cod. 5895)
    Second cycle degree programme (LM) in Physics (cod. 9245)

Learning outcomes

The student will learn the conceptual basis of earth system modelling and its major components . The student will learn how the atmospheric, oceanic, cryogenic and ecosystems components can be modelled separately and how they can be coupled using examples from state-of-the-art models. The student will be exposed to strategies to design numerical experiments, verification and validation procedures using both ensemble techniques and probabilistic approaches. At the end of the course, the student will have a grasp of logic and rationale framing of earth system modelling, and will develop a capacity to design numerical experiments and a critical understanding of the verification and validation procedures.

Course contents

  1. Introduction and Historical Developments
  2. Physical Description of the climate system
  3. Basic Numerical methods for constructing the Earth System Model
  4. Numerical Grids
  5. Finite Differences
  6. Spectral Methods
  7. Lagrangian Methods
  8. Spectral Elements
  9. Finite Volume
  10. Components of the Climate system
  11. Atmosphere General Circulation Model: adiabatic component
  12. Atmosphere General Circulation Model: diabatic processes
  13. Atmosphere General Circulation Model: convection
  14. Ocean General Circulation Model
  15. Sea-ice models
  16. Land
  17. Terrestrial ecosystems models (II
  18. Marine biogeochemistry
  19. Atmosphere chemistry
  20. The concept of climate system simulations
  21. Hierarchy of global coupled models
  22. The General Circulation Model as a Numerical Laboratory
  23. Strategies for the design of numerical experiments
  24. Verification, diagnostics and fidelity of global models.
  25. Sensitivity to small perturbations
  26. Probability Distributions
  27. The rise of AI in weather and climate sciences.
  28. Dynamical modeling and Statistical Modeling
  29. Modeling with Deep Learning methods
  30. Prospects of global climate modelling




Readings/Bibliography

Warren,M.W. and C.L. Parkinson, Three-dimensional Climate Modeling, University Science Books, CA.

Trenberth, K, Climate System Modeling (selected Chapters)

Notes and other readings may be provided during the course

Teaching methods

Lectures

Assessment methods

Written test and oral exam

Office hours

See the website of Antonio Navarra

SDGs

Climate Action Oceans Life on land Peace, justice and strong institutions

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.