Over at Lucia’s, she’s having her monthly betting game to determine the November anomaly for UAH. I still haven’t won a single Quatloo over there, so I’m developing a can’t-fail system (gamblers out there know how this goes) to win some.
In one of the comments over there, somebody mentioned they would like to see SST and Aqua Ch. 5 daily anomalies from the AMSU website on the same graph, in order to determine the time lag between when SST begins to change and when the atmospheric temps go with it. This sounded interesting and so I thought I’d try using the SST temps to guess the Nov UAH anomaly.
Also one with SSTx3 to help show the lag:
So, we can see that generally SST falls and then the Aqua temps follow, but for a prediction we’ll need a better estimate of the actual lag time. I thus tried (or rather, a Macro tried) lag times between 0-200 days for Aqua ch.5 data and attempted to see which resulted in the best correlation. First, for all days since 2003:
In this case, our best R is 0.683, and it occurs at 49 days.
However, we can see that the lag time appears to be longer in the big peaks and troughs more recently, so I went ahead and tried everything from 2006-Present:
Here, we get our best correlation of 0.766 at 112 days, which is a substantial change from before. Finally, I went ahead and tried with only the most recent years (2008 – present):
Here, we get our best R=0.811 at 87 days.
From these graphs, clearly we have a wide range of lag times to choose, and so this ”system” will be far from exact. My general impression from the graphs is that during large dips the lag time is longer, and so I’m going to go forward with the 112 day lag time for this La Nina. Furthermore, I’m going to take the slope of 1.57 that we find between the SST and AquaCh5 data when using the 112 day lag.
Our new graphs (the first is 2006-Present only) show a better match-up:
The actual prediction: 0.118 C
The 112 day lag puts us back at days 2755 through 2784 for SST, which yields an average anomaly of 0.021. Multiplying this by our slope of 1.57 yields 0.033 C. But this is relative to our 2003-2009 November baseline. The UAH anomaly is reported relative to the 1979-2009 baseline. The 1979-2009 November baseline is actually 0.085 lower. Thus, we have 0.033 + 0.085 = 0.118.
-This estimate was actually to determine what the average daily anomaly for November in the Aqua ch5 data would be…I’m not sure exactly how well this corresponds to the reported UAH anomaly.
-According to the AMSU website, the temps having actually been climbing pretty quickly for the first few days of Nov, once again calling into question the guarantee of this can’t-fail system…