NOAA is first of the three main indices to be off the mark with June 2009 at 0.617 deg C, bouncing off the relatively low values of 2008. Given that the data is essentially common to HadISST, this is unsurprising.
The difference between RSS and NOAA/HadCRU values is interesting in terms of the Big Red Spot (enhanced tropical troposphere temperatures: RSS should be going up faster than NOAA/HadCRU, not the opposite.) Here is raw RSS and NOAA data: note that the reference periods are different! (I left the original reference periods to separate the lines a bit better.) You can see how NOAA surface is gaining on RSS T2LT rather than the opposite.
I’ve done quite a bit of experimenting recently with an interesting program called strucchange (Achim Zeilis). I originally experimented with this program in connection with hurricane data: to test the supposed regimes of Holland and Webster. I looked at it again in connection with the new USHCN algorithm (in case the presently secret USHCN adjustment program was ever disclosed – it uses breakpoints methods as well.)
More recently, I applied it to various crosscuts of the TRP satellite and surface data: RSS vs UAH, Land vs Ocean and various cross-profiles. Many interesting results that I’m assimilating.
Today, I’ll show one such crosscut: Tropical RSS vs NOAA, crosscutting Land, Ocean and All.
As you can see, this particular algorithm finds “significant” breakpoints in these crosscuts. Aside from what the algorithm finds, visually there’s a big difference between the Land and Ocean patterns that seems like it would be hard to justify in climate terms. (The same thing happens with UAH vs NOAA, it just looks different.) There are also significant breakpoints between UAH and RSS.
In most cases, the breakpoint location can be plausibly associated with either the start of one satellite or the end of another. The tricky thing about this association is that there are lot of stitches in the satellite record and the mind is prone to finding associations. More on this below. For now, take a look at the graphic.
But in this case, when one examines the literature on satellite adjustments, I think that there’s pretty good reason to anticipate that breakpoints could occur at satellite switches. Complicating matters is that the literature also reports issues with “drift”.
Also the most cursory examination of the satellite literature shows that it is highly statistical in concept. In some case, there is limited ground truthing between satellites, so they end up having to estimate the adjustment – a statistical operation.
My take on this is that there are going to be at least 6 adjustments that need to be estimated. In some cases, there is both a step adjustment and a drift adjustment. If one admits the possibility of statistical error into the procedure, then you no longer have an AR1 error model in trend estimations (something that I’ll show in another post.) It looks to me like there are 5 or 6 or more step adjustments, which generate highly significant AR1 coefficients, but the underlying process is different and more complicated. This would be a big and interesting project.
In the bottom panel of the above graphic, the increase of NOAA relative to RSS T2LT over land in the past 10 years is particularly consistent. Again, Big Red Spot Theory predicts the opposite. At this point, I’m not inclined to view any of this as “falsifying” Big Red Spot theory, but, more likely, as evidence of “drift” or “bias” in both surface and satellite records. There is certainly food here for people who think that the surface land record is affected by measurement bias.
However, I’m far from convinced that the satellite records are revealed truth. It seems quite possible to me that quite different satellite trends could emerge if there were a couple of inter-satellite adjustment errors. I don’t know right now how one would estimate the potential magnitude of the adjustment errors, but, as soon as one introduces potential step adjustment errors, it becomes pretty hard to estimate trends. More on this on another occasion.