In this approximation, the predictable signal is associated with the deterministic linear dynamics of the LIM, and the forecast error with the unpredictable stochastic noise. For any forecast lead time, an average signal to noise ratio can be estimated directly at each grid point from the LIM's parameters, and converted into a potential average forecast anomaly correlation skill score at the point. Such a map of potential average skill can then be compared with a map of actual average skill to assess the potential for further skill improvements. We have generated and compared such pairs of maps for seasonal Tropical SST forecasts, weekly tropical diabatic heating forecasts, and weekly northern hemispheric 750 mb streamfunction forecasts for the past 50+ years. The single most revealing result from these comparisons is that the actual skill maps, though somewhat weaker in magnitude, are strikingly similar in pattern to the maps of the potential skill. It will be argued that this result places important constraints on further skill improvements on these time scales even using comprehensive NWP and climate models. The value of LIMs in identifying, through a singular vector analysis, special initial structures from which relatively high skill forecasts are possible will also be discussed.
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