Search result: Catalogue data in Spring Semester 2019
Environmental Sciences Master  | ||||||
 Major in Atmosphere and Climate | ||||||
| Climate Processes and Feedbacks | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 credits | 3G | C. Schär, N. Ban | |
| Abstract | The course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON. | |||||
| Learning objective | At the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes. | |||||
| Content | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||
| Lecture notes | Slides and lecture notes will be made available at Link | |||||
| Literature | List of literature will be provided. | |||||
| Prerequisites / Notice | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming. Previous experience with PYTHON is useful but not required. | |||||
| 701-1232-00L | Radiation and Climate Change | W | 3 credits | 2G | M. Wild | |
| Abstract | This lecture focuses on the prominent role of radiation in the energy balance of the Earth and in the context of past and future climate change. | |||||
| Learning objective | The aim of this course is to develop a thorough understanding of the fundamental role of radiation in the context of Earth's energy balance and climate change. | |||||
| Content | The course will cover the following topics: Basic radiation laws; sun-earth relations; the sun as driver of climate change (faint sun paradox, Milankovic ice age theory, solar cycles); radiative forcings in the atmosphere: aerosol, water vapour, clouds; radiation balance of the Earth (satellite and surface observations, modeling approaches); anthropogenic perturbation of the Earth radiation balance: greenhouse gases and enhanced greenhouse effect, air pollution and global dimming; radiation-induced feedbacks in the climate system (water vapour feedback, snow albedo feedback); climate model scenarios under various radiative forcings. | |||||
| Lecture notes | Slides will be made available, lecture notes for part of the course | |||||
| Literature | As announced in the course | |||||
| 701-1252-00L | Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation | W | 3 credits | 2V + 1U | D. N. Bresch, R. Knutti | |
| 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 | - | |||||
| Prerequisites / Notice | 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) | |||||
| 701-1235-00L | Cloud Microphysics  Number of participants limited to 8. Priority is given to PhD students of D-USYS majoring Atmospheric and Climate Science. Open spaces are availble to Master students in Atmospheric and Climate Science & Master in Environmental Sciences. All participants will be on the waiting list at first. Enrollment is possible until February 17th. The waiting list is active until February 19th. All students will be informed on February 20th at the latest if they can participate in the lecture. The lecture takes place if a minimum of 5 students register for it. | W | 4 credits | 2V + 1U | U. Lohmann, Z. A. Kanji | |
| Abstract | Clouds are a fascinating atmospheric phenomenon central to the hydrological cycle and the Earth`s climate. Interactions between cloud particles can result in precipitation, glaciation or evaporation of the cloud depending on its microstructure and microphysical processes. | |||||
| Learning objective | The learning objective of this course is that students understand the formation of clouds and precipitation and can apply learned principles to interpret atmospheric observations of clouds and precipitation. | |||||
| Content | see: http://www.iac.ethz.ch/edu/courses/master/modules/cloud-microphysics.html | |||||
| Lecture notes | This course will be designed as a reading course in a small group of 8 students maximum. It will be based on the textbook below. The students are expected to read chapters of this textbook prior to the class so that open issues, fascinating and/or difficult aspects can be discussed in depth. | |||||
| Literature | Pao K. Wang: Physics and dynamics of clouds and precipitation, Cambridge University Press, 2012 | |||||
| Prerequisites / Notice | Target group: PhD and Master students in Atmosphere and Climate | |||||
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