The main message here is well taken -- namely, that ENSO is not the only game in town when it comes to winter climate prediction in the U.S. Other well-known climate patterns such as AO, NAO, EP/NP, and others can play important roles also. In fact, we had two blog pieces about some of these other patterns: http://www.climate.gov/news-features/blogs/enso/other-climate-patterns-… and http://www.climate.gov/news-features/blogs/enso/how-much-do-climate-pat…. Although it was not clear whether the comment implied that these other sources of predictability are somewhat triggered or controlled by ENSO or not, it should be said that some of them may at least partially be so controlled, although they also likely have their own independent component. A point of differentiation between ENSO and these other phenomena is that ENSO tends to be better predicted than the other patterns, and MUCH better predicted once it has locked into one of its two phases (El Nino or La Nina), normally by September or October. (Note that this year is an exception, where even in November it is not entirely clear whether we will have a weak El Nino or not this winter, as the atmosphere has not completely been playing ball even if the SST has recently clearly exceeded the minimum threshold). Applying the idea of multi-faceted controls to analog forecasting, such a forecasting tool should be more effective if it were able to capture several phenomena instead of just ENSO alone. Such analog forecast systems have been developed. The main problem, however, is that the period of record from which to find analog matches is usually only several decades, and looking for more ways to define a good match (not just for ENSO) makes it even harder to find such a match. So the basic flaw in analog forecasting (lack of enough possible past cases to choose from) bites us even more severely when we look for a match in several dimensions instead of just one or two. As for localized, downscaled forecasts, I believe they are possible to the extent that the local data are built into the forecast system. Once the analog year(s) are picked, the resulting forecast can be applied to anything, whether the predictability is good or poor. To summarize my response, I say that analog forecasts may become slightly better (for both large-scale climate anomaly predictions or more downscaled, localized ones) when more dimensions are included in the analog search (e.g., more than just ENSO), but that the increment in value is not large because of the lack of predictability in the non-ENSO phenomena (with the possible exception of long-term climate change-related trends) and particularly because of the lack of a huge sample of past cases.