CLIM 102: INTRODUCTION TO GLOBAL CLIMATE CHANGE SCIENCE LAB
The scientific basis of computer models that simulate past and present climate and predict future climate change; How complex models are built, tested, and interpreted to better understand physical, chemical, and biological processes; how uncertainty is managed. Students conduct laboratory experiments through an online interface.
CLIM 713: ATMOSPHERE-OCEAN INTERACTIONS
Provides comprehensive observational and mechanistic understanding of El Nino and Southern Oscillation (ENSO) phenomena. Topics include observations and theories of seasonal and interannual changes in ocean circulation and temperature and interactions with atmosphere; equations of motion and theories of wind-driven circulation; mixed layer observations and theories; midlatitude and equatorial ocean waves; interannual variability and atmosphere-ocean coupling; and tropical oceanography and meteorology.
CLIM 670: EARTH SYSTEM MODELING
An Earth system model is composed of models simulating the evolution of the atmosphere, ocean, cryosphere, biosphere, and other components. Course introduces the component models, their interactions, and how they are used to predict the behavior of weather and climate on time scales that range from hours to centuries. Students will learn technical and scientific skills necessary to run an Earth system model and evaluate its output.
CLIM 680: CLIMATE DATA
How to process, analyze, and interpret environmental data for climate and related disciplines. Familiarizes students with software commonly used in atmospheric research and with techniques for working with large quantities of data. Examines mathematical tools for characterizing global physical data sets which vary in time and space, and applies the tools to observations and numerical model output.
CLIM 761: ADVANCED PREDICTABILITY AND PREDICTION OF THE ATMOSPHERE
Covers the theory and practice of predicting atmospheric circulation from daily weather to subseasonal weather regimes to seasonal climate. Discusses atmospheric data assimilation, the dynamics of rapidly amplifying modes, the role of large- scale instability and weather regime dynamics, and the role of boundary conditions. Students will design and carry out ensemble forecasts using a range of numerical models.