Lecturer
Schedule
21 April 2021 – 28 July 2021
Wednesdays, 10:00 – 12:00
14 lectures of 2 hours each
No lecture on 26 May 2021 (Pentecost break).
Location
Online via Zoom (link after registration via email)
Credits and Workload
ECTS: 3
Workload: 90 h
Contact hours: 30 h
Self study: 60 h
Duration: 1 semester
Medium of instruction: English
Evaluation
Paper presentation, project presentation, and written project report
About the course
The course aims to discuss the state-of-the-art in machine learning approaches that are used to address problems in climate research. It will first introduce the fundamentals of climate dynamics and the principles of atmospheric and oceanic circulation. The basics of climate data analytical approaches for both current and paleoclimatic time scales will also be covered. Following this, classical statistical learning approaches such as unsupervised clustering, component analysis, and trend tests will be covered. Next, the course will delve into artificial neural network based approaches that have been used to address predictive problems in climate. Additional topics include the use of complex network approaches in studying climate systems will also be introduced and the use of artificial neural networks in general circulation models.
Teaching methods
The lectures are designed to be interactive and dialogic, and will involve lectures as well as the reading of current journal articles. The final evaluation will be on the basis of of an oral presentation and a written report.
Learning target
At the end of the course, students will posses a basic understanding of how the climate works, and of how we measure our current and past climates. They will be able to review the current literature on relevant problems in climate science and be able to relate those problems to relevant machine learning approaches that can be used to tackle them.
Keywords
machine learning, climate science, statistics, climate models, paleoclimatology, meteorology
Enrollment information
Simply send an email to bedartha.goswami@uni-tuebingen.de expressing your interest to register in the course. You will be contacted later with more details (Zoom link, etc.).
Update 19 April 2021:
Registration for the course is now closed. A maximum of 30
participants is already reached.
Tentative Lecture plan
L1. 21 April 2021: Introduction and Preview
Highlights of course contents; evaluation criteria; general information
slides
L2. 28 April 2021: An introduction to the earth’s climate
What is climate; what are the components of the climate system;
a detailed look at the atmosphere; additional topics: climate forcings,
climate sensitivity
slides
L3. 5 May 2021: Measurement of weather and climate
how do we measure weather; station based measurements; satellite based
measurements; paleoclimate proxy records
slides
L4. 12 May 2021: Models of the earth’s climate
climate model basics; energy balance models; models of intermediate
complexity; general circulation models
slides
L5. 19 May 2021: Important climatic systems
El Niño Southern Oscillation; North Atlantic Oscillation; Arctic
Oscillation; Indian Ocean Dipole
slides
26 May 2021: Pfingstpause (Pentecost break)
No lecture
31 May 2021: Last date for project topic finalisation
Students have to hand in their project topics by this date.
L6. 2 June 2021: Non-neural network approaches
empirical orthogonal functions; clustering analysis
slides
L7. 9 June 2021: Neural network approaches
weather forecast post-processing; climate model parametrisations;
data-driven modelling; ENSO forecasting
slides
L8. 16 June 2021: Climate networks
what are climate networks & what do they tell us; potential pitfalls in
climate networks; climate networks from graphical models; climate
networks with machine learning
slides
L9. 23 June 2021: Machine learning for paleoclimate
climate field reconstructions; paleoclimate proxy climate networks
slides
L10. 30 June 2021: Tutorial lecture
EOF analysis of SST anomalies in the entire Pacific ocean region
ERA5 monthly SST data from 1979-01 to 2020-12 on a 1 deg x 1 deg
grid
Monthly Oceanic Niño index from 1979-01 to
2020-12
Monthly global mean surface temperature from 1979-01 to 2020-12
Monthly Pacific Decadal Oscillation index from 1979-01 to
2020-12
Jupyter notebook
Python script
L11. 7 July 2021: Introduction to scientific communication
structure and components of a scietific paper; language and style in
scientific writing; the process of writing; miscellaneous issues
slides
L12. 14 July 2021: Guest lecture by Peter Dueben, ECMWF, UK
Peter Dueben conducts research on high-resolute weather and climate
simulations at the European Center for Medium-range Weather Forecasting
(ECMWF) at Reading in the UK. He will talk to us about his views on
machine learning for weather and climate predictions.
L13. 21 July 2021: Presentations I
Project presentations by the students.
L14. 28 July 2021: Presentations II
Project presentations by the students.