on long multi-decadal timescales. Climate predictions are typically communicated as deviations from “climatology,” or the background mean-state. If the mean-state is changing over time, the magnitude of the seasonal deviation will depend on the period used to define the climatology. Equivalently, predictions of deviations in the same direction as the “trend” can be credited with relatively high quality that is derived more from the slowly evolving trend than the interannual variability. For example, under anthropogenic climate change, the temperatures over most land areas are increasing relative to the mean state, say 1971–2000 (Trenberth et al., 2007). That does not necessarily mean that each year will be warmer than the year preceding it. However, predicting temperatures to be “above-normal,” will appear skillful by many measures because temperatures in this decade are very likely to be warmer than those of 30 years prior.

A relevant question is then: can the forecast system discriminate between conditions in a pair of forecasts more often than not? For example, if year X is observed to be warmer than year Y, was that predicted to be so? Discrimination tests of forecast-observation pairs of cases addressing this type of question can be applied to deterministic or probabilistic forecasts. A generalization of such discrimination tests is outlined in Mason and Weigel (2009), and in many cases the metric becomes equivalent to those described above, such as generalized ROC areas for tercile probabilistic forecasts.

CHALLENGES TO IMPROVING PREDICTION SKILL

This chapter has provided the historical perspective on climate prediction, pointed to where there are opportunities to improve prediction quality by improving our understanding and representation in models of sources of predictability, and reviewed the methods available to quantify skill. From the 1980s to the 1990s, seasonal prediction quality improved dramatically, but then did not improve further (Kirtman and Pirani, 2008, 2009). The challenges in going forward are not only to determine where to gain further improvements but also to assess and understand the reasons for any incremental gains in prediction quality that have occurred.

In the following section we examine the building blocks of intraseasonal to interannual forecasting. Improvements may stem from better observations, better models, and improved assimilation. Recent analyses demonstrate how improvements in these components of forecast systems are the source for improvements in forecast quality (Stockdale et al., 2010; Balmaseda et al, 2009; Saha et al., 2006; Fig. 2.13), and thus predictability.

At the same time improvements may result from changes in the way in which the community works. Kirtman and Pirani’s (2008, 2009) summary of the first World Climate Research Program Workshop on Seasonal Prediction indicates that that workshop recommended adoption of best practices in seasonal forecasting, including the adoption of common approaches to the production, use, and assessment of seasonal forecasts.

Thus, the challenges to improving intraseasonal to interannual prediction skill lie not only in improvements of the building blocks but also in how the community works together. Experimental modeling and examination of the incremental skill to be gained from new sources of predictability are needed. The three case studies provide examples of physical processes being examined as sources of predictability. A further challenge is to develop the community framework to nurture ongoing improvements to dynamical models.



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