Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation

Main content

Accordion. Press Tab to navigate to entries, then Enter to open or collapse content.

 

Course information

Course number
701-1252-00L
Time & Location

Monday 08.15-10.00 (CHN F46) starts on 29.02.16!

Exercise: Monday 10.15-12.00 (CHN F46)

Lecturer
Prof. R. Knutti (IAC ETH)
Dr. David Bresch (SwissRe)
Tutor
Maria Rugenstein, Martin Stolpe, Anina Gilgen

Further Information

If you have a Laptop bring it along!

Course objectives

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 (MATLAB), hence basic understanding of scientific programming forms a prerequisite of the course.

Course contents

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.

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 (MATLAB), hence basic understanding of scientific programming forms a prerequisite of the course.

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.

Course schedule & slides, lecture notes

First course starts on February 29, 2016, second week of the semester! Exercises every two weeks approximately. Powerpoint slides will be provided here.

Date Programme Lecturer
29.02.16 (1) Logistics, Introduction to probability, uncertainty and risk management (PDF, 2.3 MB) (RK, DB)
07.03.16

(2) Predictability of weather and climate, seasonal prediction, seamless prediction (PDF, 6 MB)

Exercise 1 (toy model) Exercise description (PDF, 237 KB) Observations mat file (MAT, 1 KB)

(RK)
14.03.16

(3) Detection/attribution, forced changes, natural variability, signal/noise, ensembles (PDF, 4.7 MB)

Exercise 2 (toy model) Observations long mat file (MAT, 3 KB)

(RK)
21.03.16 (4) Probabilistic risk assessment model: from concept to concrete application - and some insurance basics (PDF, 3.4 MB) (DB)
28.03.16 Ostermontag (no course)
 
04.04.16 (5) Model evaluation, multi model ensembles and structural error (PDF, 6.2 MB) (RK)
11.04.16

(6) Climate change and impacts, scenarios, use of scenarios, scenario uncertainty vs response/impact uncertainty (PDF, 2.5 MB)

Exercise 3 (toy model), preparation of presentation

(RK, DB)
18.04.16

(7) Model calibration, Bayesian methods for probabilistic projections (PDF, 2.4 MB)

(RK)
25.04.16

(8) Presentations of toy model work, discussion

(DB, RK)
02.05.16

(9) Basics of economic evaluation and economic decision making in the presence of climate risk (PDF, 598 KB)

Exercise 4 (introduction to climada) (PDF, 564 KB)

(DB)
09.05.16

(10) The cost of adaptation - application of economic decision making to climate adaptation in developing and developed region (PDF, 2.2 MB)

Exercise 5 (impacts)

(DB)
16.05.16 Pfingstmontag (no course)  
23.05.16

(11) Shaping climate-resilient development – valuation of a basket of adaptation options (PDF, 3.7 MB)

Exercises 6 (adaptation measures, preparation of presentation)

(DB)
30.05.16 Presentations of climada exercise, discussion (DB, RK)

Credit points

Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence basic understanding of scientific programming forms a prerequisite of the course. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required.

Examination: graded tutorials during the semester (benotete Semesterleistung) and one short presentation.

Presentations

Remarks

  • Groups of 2-3 people
  • 10 minutes (strict timing), ideally presented by the whole group
  • Exciting presentation with a clear take home message (assume everyone is familiar with the toy model and with was discussed so far in the course)
  • Show us one interesting/new/unexpected/important/exciting aspect relevant to the toy model or climada and the course.
  • Criteria: Timing/contributions from each person (25%), delivery/presentation (25%), scientific accuracy (25%) and relevance to the course (25%)

Presentation 1

Possible topics are

  • Choice of a model or model structure
    (linear or not? Trend in variance? Statistical or dynamical)
  • Parameter estimation/calibration (methods, uncertainty)
  • Monte Carlo simulation, probabilistic models
    (sample size, consideration of parameter uncertainty)
  • Damage functions (Nordhaus, Stern)
  • Mitigation (cost, strategy, cost dependence on scale)
  • Cost benefit analysis (appropriate or not, difficulties)
  • Precautionary principle
  • Learning with more data, robust decisions
  • Overall uncertainty analysis
    (what are the most relevant choices and uncertainties in the toy models) 

Presentations 2

 
 
Page URL: http://www.iac.ethz.ch/edu/courses/master/modules/climate-risk.html
28.07.2016
© 2016 Eidgenössische Technische Hochschule Zürich