- Docente: Antonio Navarra
- Credits: 9
- SSD: GEO/12
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Physics (cod. 9245)
Also valid for Second cycle degree programme (LM) in Science of Climate (cod. 5895)
Second cycle degree programme (LM) in Sciences and Management of Nature (cod. 9257)
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from Sep 16, 2024 to Dec 20, 2024
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
- Introduction and Historical Developments
- Physical Description of the climate system
- Basic Numerical methods for constructing the Earth System Model
- Numerical Grids
- Finite Differences
- Spectral Methods
- Lagrangian Methods
- Spectral Elements
- Finite Volume
- Components of the Climate system
- Atmosphere General Circulation Model: adiabatic component
- Atmosphere General Circulation Model: diabatic processes
- Atmosphere General Circulation Model: convection
- Ocean General Circulation Model
- Sea-ice models
- Land
- Terrestrial ecosystems models (II
- Marine biogeochemistry
- Atmosphere chemistry
- The concept of climate system simulations
- Hierarchy of global coupled models
- The General Circulation Model as a Numerical Laboratory
- Strategies for the design of numerical experiments
- Verification, diagnostics and fidelity of global models.
- Sensitivity to small perturbations
- Probability Distributions
- The rise of AI in weather and climate sciences.
- Dynamical modeling and Statistical Modeling
- Modeling with Deep Learning methods
- 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
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.