| Abstract | The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation. |
| Learning objective | Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course. |
| Content | The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society.
The course provides an introduction to the following themes:
1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events
2) methods to optimize and constrain model parameters using observations
3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer)
4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources |
| Lecture notes | Powerpoint slides will be made available. |
| Literature | Many papers for in-depth study will be referred to during the lecture. For the exercises the CLIMADA platform- https://wcr.ethz.ch/research/climada.html - will be (extensively) used. |
| Prerequisites / Notice | Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence good understanding of scientific programming forms a prerequisite of the course, in Python (teaching language, object oriented) or similar. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required.
Examination: graded tutorials during the semester (benotete Semesterleistung) |
Competencies | | Subject-specific Competencies | Concepts and Theories | assessed | | Techniques and Technologies | assessed | | Method-specific Competencies | Analytical Competencies | assessed | | Decision-making | fostered | | Media and Digital Technologies | fostered | | Problem-solving | assessed | | Project Management | fostered | | Social Competencies | Communication | assessed | | Cooperation and Teamwork | fostered | | Leadership and Responsibility | fostered | | Self-presentation and Social Influence | fostered | | Sensitivity to Diversity | fostered | | Personal Competencies | Adaptability and Flexibility | assessed | | Creative Thinking | assessed | | Critical Thinking | assessed | | Integrity and Work Ethics | fostered | | Self-direction and Self-management | fostered |
|
Performance assessment as a semester course