"In other words, it has become more difficult to predict end-of-melt-season outcomes" This is not a conclusion that can be drawn from the data shown here. To see why, consider the this data set: (10, 11), (11, 10), (10, 10) and (11,11). There is no correlation. And this dataset has no correlation either: (0, 1), (1, 0), (0, 0) and (1,1). Now, if you add these datasets together, suddenly there is a good correlation: a high number on the first datapoint always goes together with a high number on the second, and a low number with another low number. This is exactly what happened in the arctic. In the 80s, there was high spring extent and high summer extent. In the '07-'12 period, there was much lower spring extent and summer extent. Apart, there is no correlation (natural variability overcomes the trend). Taken together, there is a correlation (the trend overcomes natural variability). This phenomenon occurs in every dataset with a trend and random peturbations, so in itself this tells nothing about the predictability of the end-of-melt-season outcomes. A better way to test that hypothesis is to try to 'predict' each year by using the data of the other years, and see if there's a trend in the quality of the predictions. If people are interested, I can do that kind of analysis, but I suspect that any trend found is not significant. There's not enough data.

In the category weather wiplash: http://www.youtube.com/watch?v=0m_3ggLbyLQ From frost to 100 Degrees in 58 Hours...