Zakaria: Skeptics say there’s still conflicting evidence on global warming.
Chu: I urge everyone to do this: Google the 2007 IPCC report. The 100-year trend is unmistakable. The first thing to emphasize is don’t get excited about one or two years. It’s just like you should not get excited that one very bad hurricane is evidence there’s global warming.

So I followed his advice. Having read most of the (4) IPCC assessment reports and numerous CA threads such as this of course it was very familiar territory. The first and most prevalent 100 year trend I could find in the Summary for Policy Makers was the subject graphic for this thread. So the graphic in this thread, with all its weaknesses, appears to be the favorite substantiation for AGW for the new DOE Secretary. I doubt he is aware of the shortcomings, perhaps he needs a brief.

It seems to me that this question has been examined previously. I don’t know which thread but it may have been in late 2005 or early 2006. It seems that the “smoothing” for the end points was actually only an extension with the same slope as the last two normally calculated points. I believe that this was in context with some paper by Mann.

This says absolutely nothing about the actual method used in dealing with the endpoint smoothing.

Seems to be a topic that is skipped very often in climate publications. These smoothed present-day ‘comparisons’ to ’30s and past millennium are quite disturbing.

What is the Finnish for “Where’s the data?!!”.

The data might be somewhere in here http://www.smhi.se/hfa_coord/nordklim/index.php?page=dataset . Too much work to find out.. But I’m sure the author knows English well, if someone wants to ask directly.

Reliable estimation of climatic variations in Finland
( https://oa.doria.fi/bitstream/handle/10024/2634/reliable.pdf?sequence=1 … I can’t seem to get the link tags working properly here today.)

on p. 23, the author explains in great detail about the Gaussian smoothing, referred to as G3 and G10, used in the thesis. When it comes to the endpoints, however, the author says:

The first (last) few values in the filtered series are mainly determined by the original data following (preceding) the year in question. The filtered values near the both ends of the time series must therefore be interpreted with some caution. The shape of the curves can change when new values are added.

This says absolutely nothing about the actual method used in dealing with the endpoint smoothing. What is the Finnish for “Where’s the data?!!”.

Some detective work

Finnish Meteorological Institute Contributions

Reliable estimation of climatic variations in Finland

Tuomenvirta, Heikki; Doctoral dissertation

Maybe this is somewhat related to the data. See Figure 5.4 along with this one:

Quite close. Table 5.1 tells that T4(adj) is based on 4 station data (Helsinki, Kuopio, Kajaani and Oulu). Can’t answer to end-point handling problem, though.. Interesting one is the Fig. 5.2.

Where this data is compared to Jones and Moberg (2003) Fennoscandia grid-average. Waldo is in Jones data, but not in the other (not implying anything, but there’s always something about ’30s ) ..

What does that uncertainty interval mean

What it really means and how the AR4 and SPM authors are trying to use it are probably two different things. They are trying to use it to suggest that the increasing trend is robust, is unlikely to occur by random chance alone. First, they are trying to suggest that the smoothed trend fits the actual annual data, which is why the dots and lines are plotted on the same graph. Second, they are trying to suggest that the trend in the line underlies an equal trend in the annual data (points), that instrumental error is not so large to preclude this inference.

What it really means – I don’t know. Putting the measuement error in with the sampling error makes no sense to me. You want to know how accurate your instruments are, sure. But as far as deducing a trend and attributing it to something like CO2, you would want a robust estimate of the stochastic (internal climatic) variability around that trend. Maybe ocean-caused long-term persistence would be such a large source of internal variability that this trend could occur by random chance alone? I don’t know. Myself, it is not clear that the inference they are trying to make is justified by the data. My advice is to ask Wegman.

Apples to oranges comparison, that’s why I said this figure is confusing.

And if you want to see oranges to apples comparison, see Global surface temperatures over the past two millennia (2003) by Mann and Jones, Fig. 2c:

http://www.geocities.com/uc_edit/divergence.html

This seems to be decadal reconstruction, smoothed by 40 year (!) lowpass filter, but using uncertainties from ‘standard reconstructions’. In practice, Mann guesses annual temperature for 20 future years, and then makes a statement (my bold):

This warmth is, however, dwarfed by late 20th century warmth which is observed to be unprecedented at least as far back as AD 200.

Unbelievable, and then the story continues with response to Soon et al 04, On smoothing potentially non-stationary climate time series where he finally reveals how this smoothing was done.

Great work, Dr. Mann. I’ve collected a set of pictures for you, check them here

Without seeing the data itself, it is hard to be particularly certain, but the behaviour of the smoothing at the endpoints looks to be consistent with a moving average method that I have preferred to use in the past. For each specific calculation, the series is padded with the value of the smoothed result itself. Thus, the padding value changes as you get nearer to the endpoint. In actual practice this is equivalent to truncating the weights used in the smoothing. For example, when using relative weights of 1-4-6-4-1, the second-last point uses 1-4-6-4 and the end point would use 1-4-6. This tends to favour the endpopints slightly, but does not usually give extreme results as seen in the MBH papers.