One comment I might have is that your model assumes the average temperature on a given day of the year is constant across years. I would have to spend some time thinking of how to control for it, but have you considered the impact of climactic oscillation? Your data seems to reflect these patterns to some degree, and removing/reducing them might make the overall shift clearer.
Yeah, that is an interesting point and highlights the difference between "weather" and "climate" - it also brings up a tension between two different objectives of the chart: to visualize weather patterns over time intuitively, and to draw general conclusions about climate trends. In the context of the first intention, climatic oscillations like El Niño are interesting signals - you can see how they affect weather throughout the country in unexpected ways. But in the context of the latter goal, they are noise which should be filtered out/corrected for.
That makes sense; my only thought was that if you are graphing "anomalies" you might want to filter out non-anomalous behavior. Higher highs or lower lows are actually to some degree expected in those years. I suppose it could be best not to control for oscillatory behavior though as the affect of any climate shift on those oscillations is possibly not insignificant.
One comment I might have is that your model assumes the average temperature on a given day of the year is constant across years. I would have to spend some time thinking of how to control for it, but have you considered the impact of climactic oscillation? Your data seems to reflect these patterns to some degree, and removing/reducing them might make the overall shift clearer.
http://en.wikipedia.org/wiki/Effects_of_the_El_Ni%C3%B1o%E2%...