701-1252-00L  Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation

SemesterSpring Semester 2020
LecturersD. N. Bresch, R. Knutti
Periodicityyearly recurring course
Language of instructionEnglish


701-1252-00 VClimate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation
Lecture starts 24 Feb 2020,
2 hrs
Mon08:15-10:00LFO C 13 »
D. N. Bresch, R. Knutti
701-1252-00 UClimate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation
Exercises start 24 Feb 2020,
1 hrs
Mon10:15-12:00LFO C 13 »
D. N. Bresch, R. Knutti

Catalogue data

AbstractThe course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation.
ObjectiveStudents 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.
ContentThe 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 notesPowerpoint slides will be made available.
LiteratureMany papers for in-depth study will be referred to during the lecture.
Prerequisites / NoticeHands-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)

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersD. N. Bresch, R. Knutti
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

Documentsclimada - the open-source model
Only public learning materials are listed.


No information on groups available.


Places30 at the most
Waiting listuntil 28.02.2020

Offered in

Atmospheric and Climate Science MasterClimate Processes and FeedbacksWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
MAS in Sustainable Water ResourcesElective CoursesWInformation
Science, Technology, and Policy MasterRessources and EnvironmentWInformation
Environmental Sciences MasterClimate Processes and FeedbacksWInformation
Environmental Sciences MasterModeling and Statistical AnalysisWInformation