and rainfall. The role of tropical SST is to determine the locations of these regions of persistent precipitation, which, in general, lie over warmest waters. When SST in the eastern Pacific increases (during warm phases of ENSO), the regions of persistent precipitation expand eastward into the central and eastern Pacific and may affect the west coast of South America while moving away from the western Pacific, causing droughts in the normally wet regions around the Indonesian archipelago. This motion of the regions of persistent precipitation affects higher latitudes similarly, but less robustly.
In the vicinity of the tropical Pacific, where the variability of temperature and precipitation is low, knowing tropical Pacific SST translates directly into knowing average temperature and precipitation over land. Peru, Ecuador, Chile, Australia, and the Pacific Islands use these forecasts directly to plan their agriculture and water management. In the midlatitudes (for instance, the Pacific Northwest of the United States), where weather variability is high, knowing tropical Pacific SST allows prediction of shifts in the probable averages of temperature and precipitation, but the information must be used with care since there is so much variation around the averages. In such regions, it requires a certain sophistication to use the information effectively. For example, it can be useful to have estimates of the likelihoods of particular outcomes at some variance from the predicted average.
An objective measure of the skill of a series of forecasts is defined by comparison of a quantity forecast with the quantity observed at the forecast time. For example, if the quantity forecast is the NINO3 index (the SST spatially averaged over the eastern tropical region 90°W to 150°W, 5°S to 5°N), then records of forecast and observed NINO3 would be correlated and a single number, the correlation coefficient of the two time series, would represent the measure of how accurate, on the average, the phasing of the forecasts has been. Similarly, the root mean square (rms) difference of the values in the observed time series and the values in the forecast time series would indicate how accurate, on the average, the amplitude of the forecasts has been. These two numbers, the correlation coefficient and the rms error, then give objective measures of how good the long series of forecasts has been.
We emphasize that these measures of skill apply only to long series of forecasts, not to an individual forecast. In order to think about the accuracy of an individual forecast, we must think of the individual forecast as a probability of occurrence. To oversimplify, if the forecast system exhibits an averaged correlation coefficient of .8 over a long series of forecasts