CLIMATE SCI FOR DEC MAK: MOD, ANL & AP

Both human and natural systems are growing more vulnerable to climate variability (e.g., the anomalous weather induced by the El Nino-Southern Oscillation, or the increase in hurricanes that occurs when ocean currents warm the Atlantic) and to human-induced climate change, which manifests itself primarily through increases in temperature, precipitation intensity, and sea level, but which can potentially affect all aspects of the global climate. This course will prepare you to estimate climate hazards in your field thereby accelerating the design and implementation of climate-smart, sustainable practices. Climate models are the primary tool for predicting global and regional climate variations, for assessing climate-related risks, and for guiding adaption to climate variability and change. Thus, a basic understanding of the strengths and limitations of such tools is necessary to decision makers and professionals in technical fields.

This course will provide a foundation in the dynamics of the physical climate system that underpin climate models and a full survey of what aspects of the climate system are well observed and understood and where quantitative uncertainties remain. Students will gain a fundamental understanding of the modeling design choices and approximations that distinguish Intergovernmental Panel on Climate Change (IPCC)-class climate models from weather forecasting models and that create a diversity of state-of-the-art climate models and climate projections.

This course will provide an overview of the ways in which climate model output and observations can be merged into statistical models to support applications such as seasonal and decadal projections of climate extremes, global and regional climate impacts, and decision-making. Students will develop the skills to visualize, analyze, validate, and interpret climate model output, calculate impact-relevant indices such as duration of heat waves, severity of droughts, or probability of inundation, and the strategies to characterize strengths and uncertainties in projections of future climate change using ensembles of climate models and different emission scenarios.