Thursday, March 5, 2015

Klotzbach revisited

Not a perfect title; it's actually my first comment on the 2009 GRL paper by Klotzbach, Pielke's, Christy et al. It was controversial at the time, but that was pre-Moyhu, or at least in very early days. And I hadn't paid it much attention. But it surfaced again today at Climate Etc, so I thought I should read it.

The paper is very lightweight (as contrarian papers can be). It argues that observed surface trends since 1979 actually exceed troposphere trends, as measured by the UAH and RSS indices, which CMIP etc modelling suggests that the troposphere should warm faster.

Now for global you can simply get those trends, and many more, with CIs from the Moyhu trend viewer. You might say, well, figuring out what the models said should be rated substantial. But they way oversimplified, were corrected at Real Climate (Gavin) and had to publish a corrigendum. There has been more discussion then and over the years. Here, for example, is a post at Climate Audit, with Gavin participating. But the audit didn't seem to pick up the CI issue, though other methods were discussed. Later a Klotzbach revisited WUWT post (my title echoes) two years ago; more on that from SKS here. And now another update.

But what no-one, AFAICS, has noticed is that the claims of statistical significance are just nuts. And significance is essential, because they have only one observation period. The claim originally, from the abstract, was:
"The differences between trends observed in the surface and lower-tropospheric satellite data sets are statistically significant in most comparisons, with much greater differences over land areas than over ocean areas."
I've noticed that the authors are quieter on this recently, and it may be that someone has noticed. But without statistical significance, the claims are meaningless.

Update: I think that the CI's they are quoting may relate to a different calculation. They computed the trends in Table 1, with CI's, and in Table 2 the differences. They say in the abstract that these are differences of trends, but the heading of Table 2, which is not very clear, could mean that they are computing the trends of the differences (a new regression) and giving CI's for that. That is actually a reasonable thing to do, but they should make it clear. I have got reasonably close to their numbers for comparisons with UAH, but not with RSS; it may be that the RSS data has changed significantly since 2009.

I'll describe this in more detail below the jump.

Tuesday, March 3, 2015

Early comment on global February

According to my NCEP/NCAR based index, February was globally pretty warm. Very warm indeed around the 12th, but a cool start and finish. The hotspot was Central Asia/Mongolia (daily eyeballing estimate). It ended up just a little cooler than October, which after May was warmest in 2014.

 I'll have a TempLS surface report in a few days.

Monday, March 2, 2015

Derivatives, filters, odds and ends

I've been writing about how a "sliding" trend may function as a estimate of derivative (and what might be better) here, here and here. There has been discussion, particularly commenter Greg. This post just catches up on some things that arose.

Saturday, February 21, 2015

Regression as derivative

In two recent posts here and here, I looked at a moving OLS trend calculation as a numerical derivative for a time series. I was mainly interested in improving the noise performance, leading to an acceleration operator.

Along the way I claimed that you could get essentially the same results by either smoothing and differentiating the smooth, or differencing and smoothing the differences. In this post, I'd like to develop that, because I think it is a good way of seeing the derivative functionality.

This has some relevance in the light of a recent paper of Marotske et al, discussed here. M used "sliding" regressions in this way, and Carrick linked to my earlier posts.

Wednesday, February 18, 2015

Google Maps and GHCN adjustments

Google Maps and GHCN adjustments

A fortnight ago I posted a Google Maps gadget for viewing GHCN stations colored according to the effect on them of GHCN adjustments. I've been doing some improvements, and rewriting the code in the process. This simplifies the logic, and I'm hoping to produce a generic application to operate on any supplied data.

For the moment, the main improvement is that it displays a count of whatever is colored on the screen. So you can quickly show how many have been adjusted up, or down, with selection criteria specified. The other improvement is that the popup data includes a link to the GHCN display page, giving extensive history and graphs of observations and adjustments.

I have also updated the data to Jan 2015.

The plot is below. And below that, some details about the usage logic. The field Trend_Adj is the trend difference over whole of life made by adjustment, in °C/cen. It is set to NaN for stations with less than 360 months of adjusted data in total (maybe with gaps).

Monday, February 16, 2015

January GISS up from 0.72 to 0.75°C

GISS showed a small rise. TempLS mesh dropped slightly from 0.66C to 0.64C. TempLS grid also dropped, from 0.65C to 0.63C. Based on this, I would expect a small drop in NOAA and HADCRUT. But maybe not. Both satellite indices rose - RSS significantly, from 0.28C to 0.37C. Since the recent warming seems to be SST driven, this lag makes sense. Maps are below. TempLS is continually reported here.

Slightly O/T, but there has been a recent spike in February, according to the NCEP/NCAR index. It has now pulled back a bit.

Thursday, February 12, 2015

Adjusting in the finance world

There has been much talk recently about homogeneity adjustments. Some in the mainstream madia, and none that made much sense. It's one of the most extraordinary scandals of our time. Maybe even criminal:

"Is history malleable? Can temperature data of the past be molded to fit a purpose? It certainly seems to be the case here, where the temperature for July 1936 reported ... changes with the moment," Watts told

"In the business and trading world, people go to jail for such manipulations of data."

So I thought I'd see what does go on in the trading world. I originally commented on this at Paul Homewood's site. I looked up the chart for BHP's share price on our national exchange, ASX. Scrolling down, I read:

"Adjustments - The charts are adjusted to smooth out the effect of bonus issues, rights issues, special dividends, share splits, consolidations, capital reductions, or to link historical values that represent the company's primary equity security. The chart also assumes that all company issued options and convertible securities are converted into ordinary shares."

In other words, not the historic prices at all. And ASX won't show you a chart of the "raw data". One complaint about GHCN adjustments is that they are constantly changing the past. But see what happens here. As with climate, present adjusted values are held equal to present market price. So what happens when BHP issues such a dividend? Its price drops by about the amount of the dividend. ASX adjusts all past prices down, to "smooth" the drop.