Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
22.5 h + 7.5 h
Q2
Teacher(s)
Massonnet François;
Language
English
Prerequisites
Having followed LPHYS2162 and LPHYS2163 is an asset
Main themes
Mechanisms of climate fluctuations from sub-seasonal to millennial time scales ; weather vs climate predictability ; sources of climate predictability ; modes of weather and climate variability, oscillations, extreme events ; approaches used to generate climate predictions ; data assimilation ; verification of predictions ; bias correction ; climate model ensembles ; processing and interpretation of climate predictions.
Aims
At the end of this learning unit, the student is able to : | |
| 1 | a. Contribution of the teaching unit to the learning outcomes of the programme (PHYS2MA and PHYS2M1) AA1: AA1.1, AA1.5 AA2: AA2.3, AA2.4 AA3: AA3.3, AA3.4 AA4: AA4.1 AA5: AA5.1, AA5.2 AA6: AA6.1, AA6.3 AA7: AA7.5, AA7.6 b. Specific learning outcomes of the teaching unit At the end of this teaching unit, the student will be able to: 1. describe the main sources of predictability in the climate system and detail the underlying physical mechanisms ; 2. assess the skill of sub-annual to decadal climate forecasts based on available information (forecasts and observational references) ; 3. develop simple bias-correction techniques to calibrate ensembles of climate forecasts ; 4. apply the concepts of data assimilation in a simple framework (toy climate model) to different problems, including state and parameter estimation ; 5. describe the hierarchy of models used to generate climate predictions and argue about their limitations. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Predictability of weather and climate : physical drivers
- Sources of uncertainty in climate predictions and projections
- Modes of weather and climate variability from day to decades
- Climatic extreme events
- Approaches to climate prediction and projection
- Verification of climate predictions and projections
- Data assimilation, state estimation, parameter estimation
- Interpretation of model ensembles, post-processing, constraints
Teaching methods
Lectures (slides available from MoodleUCL)
Integrative project.
Practical computer sessions.
List of articles to read.
Integrative project.
Practical computer sessions.
List of articles to read.
Evaluation methods
Written presentation (report) of a group project.
Individual oral examination based on a group project, aiming at verifying that the student masters the theoretical concepts addressed in the course.
Individual oral examination based on a group project, aiming at verifying that the student masters the theoretical concepts addressed in the course.
Bibliography
Jolliffe, I. T., and David B. Stephenson. Forecast verification : a practitioner's guide in atmospheric science. Chichester, West Sussex, Eng. Hoboken, N.J: J. Wiley, 2003.
Kalnay, Eugenia. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge: Cambridge University Press, 2002.
Palmer, Tim, and Renate Hagedorn. Predictability of weather and climate. Cambridge New York: Cambridge University Press, 2006.
Kalnay, Eugenia. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge: Cambridge University Press, 2002.
Palmer, Tim, and Renate Hagedorn. Predictability of weather and climate. Cambridge New York: Cambridge University Press, 2006.
Faculty or entity
PHYS
