It is difficult to assess the accuracy and uncertainty of model projections of climate change because opportunities for directly testing the models are limited. Climate changes of the past can be simulated by models, but during times of relatively abundant observations (recent decades), the changes are small, and for earlier times when changes were large (paleoclimates), the observations are sparse. Consequently, confidence in model veracity stems not primarily from their ability to simulate climate change, but from their ability to simulate a multitude of observable phenomena comprising present-day climate. Metrics can serve to summarize various aspects of model performance, but the relevance of proposed metrics to predictive capability remains largely unknown. We describe how metrics for climate models differ from those used in evaluating weather prediction models, and suggest that at present it may be better to retain an extensive suite of metrics to characterize model skill. Different models excel in simulating different aspects of climate, but best agreement with observations almost invariably occurs when we form a multi-model mean of simulated fields. Collapsing a suite of metrics to a single "performance index" is possible, but hides information that likely could leave the index vulnerable to misinterpretation. As our understanding of the relationship between skill in simulating present climate and predictive skill improves, however, we expect to work toward defining a reasonably small set of performance indices, each one designed to indicate how suitable a model is for a particular application (e.g., future climate projection, ENSO forecast, drought prediction).
AGU Fall Meeting Abstracts
- Pub Date:
- December 2007
- 1610 Atmosphere (0315;
- 1626 Global climate models (3337;