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Environmental Systems Science
 
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Atmosphere and Climate

Expert advisor

» Prof. Reto Knutti

The study of the environmental system Atmosphere and Climate involves understanding atmospheric processes and their interactions, from the molecular to the global level. The time scale ranges from milliseconds (turbulence research) over minutes (cloud formation), hours (local phenomena such as thunderstorms) days (weather in general), weeks (seasonal weather), years (ozone trends) to centuries (climatic fluctuations) and even millions of years (palaeoclimatology).
  

 

This in depth course provides knowledge of the atmospheric processes, which are the basis for understanding and predicting the weather, physical-chemical exchange processes and atmospheric cycles and the climate. Apart from a solid grounding in mathematics, students need knowledge and methodology in physics and chemistry in order to understand the processes occurring within the earth‘s gaseous envelope. Until recently, atmospheric science concentrated on meteorology, that is, research on atmospheric streams (e.g. storms, hot or cold fronts) and small-scale physical processes (e.g. convection currents, thunderstorms). Today, chemistry plays an important role in the analysis of the manifold and complex processes in the overlapping area of physics and chemistry, and atmospheric chemistry has become a special field in its own right. Familiarity with biology and biomedicine is necessary for describing the interactions of the atmosphere with oceans and land and its effects on humans. Since water plays an important role in the atmosphere, there are many interconnections with hydrology and pedology.

The basis for understanding atmospheric and climatic processes is field measurements and laboratory experiments using state-of-the-art instruments. For the analysis, simulation and forecasting of atmospheric and climatic processes, large amounts of data must be evaluated. This requires the development and application of the newest methods in information technology and complex numerical models (development of more efficient algorithms and parametrics, i.e. under consideration of secondary processes, that can not be simulated in detail, methods of visualising large amounts of data, etc.).

 

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© 2012 ETH Zurich | Imprint | Disclaimer | 30 January 2012
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