Catálogo de publicaciones - libros

What are the results provided by state-of-the-art climate models? This chapter provides some perspective on current results and modeling efforts, taking into account the description of the climate system, climate models, and uncertainty. Selected results of recent climate model simulations are used to characterize and frame model uncertainties. The goal is to understand the uncertainty in climate model predictions of the future. A prediction without uncertainty, or with the wrong uncertainty, may be worse than no prediction at all. First, we briefly review some of the history and organization of modeling efforts. Second, we discuss what we want to know (predict) and how to use uncertainty. Third, we review the confidence in current predictions. Some climate model predictions have high confidence, for example, global average temperature. Other predictions are less certain, such as regional precipitation, sea ice and the carbon cycle. Highly uncertain predictions are most likely to be ‘wrong’ in that the actual result is out of the range of uncertainty. Sea level rise predictions dependent on ice sheet melt are an example of this. Predicting changes in extreme events such as tropical cyclones or floods presents unique issues. An example of prediction of regional climate and extremes in Colorado is used as an example.

How does a non-expert actually use climate models? Climate models aim to provide useful projections of future climate for practitioners who need to make policy, planning and management decisions. The challenges of communication and use of model projections in planning and management is not trivial. This chapter explores the use of model information, both conceptually and with case studies. The goal is to examine the processes involved in the use of model information to help the reader overcome barriers to use of climate model output. Conceptually models are useful because they compare well to observations (credibility), are produced by a known or reputable process (legitimacy) and produce relevant outputs for a particular problem (salience). Dealing with uncertainty in ways that policy makers can understand is critical. A key component of communication is a need for interpreters who can evaluate model output for particular disciplines and frame uncertainty.

This chapter sets out to synthesize the key points from the preceding chapters. The synthesis includes a summary of what is understood about predicting climate and what is uncertain. We summarize the basic principles behind climate models. We describe in a qualitative fashion the mechanics of how the different components of a climate model are constructed. In the process, we focus on critical aspects of the climate system that make the different pieces complex, uncertain, and interesting. For most parts of the earth system, important mechanisms for how climate works are not necessarily intuitive. Finally, we lay out some of the methods for evaluating models, and examined what climate models are good for, and what they are not good for. This includes a detailed look at uncertainty, and a look at the applications of models for decision making. To this we also add a summary of the future directions and challenges for climate modeling.