Tuesday, January 27, 2015

Echo Chamber at Climate Audit

I have been posting comments at Climate Audit for about eight years. They have generally been at odds with the prevailing thinking there, but have been, I think, fact-based, referenced, on topic and polite. And they have generally appeared, and attrracted a vogorous response.

Recently there have been difficulties. I mentioned here back in September a problem that was affecting me at all Wordpress blogs. That was basically due to Akismet and has now gone away. This one is new.

At some stage during this thread, all my comments started going into moderation. At CA that is a semi-ban; moderation can take a day, and it is impossible to engage in any sort of dialogue. But then they started not to emerge at all.

Steve edits firmly at times, in ways that I don't object to. But it's usually transparent. What I do find objectionable is that recent threads have often been quite erroneous, and correction strongly resisted, and now suppressed.

A recent example of error was this, accusing Sven Teske of being a leader of the Nazca vandalism, based on a post of Shub Niggurath. In fact Shub hadn't said that, and clarified his comment. There was no other evidence, but although this was pointed out early (not first by me) there was no response or correction.

I'm writing now about his latest post. It is headed "Important New North American East Coast Proxy Data", and introduces the results of Sicre et al on alkenone analyses off Newfoundland. It suggests that they undermine the results of Marcott et al: "Obviously the Sicre 2014 results provide further evidence against Marcott’s supposed early-20th century blade. At the time, I pointed out that the Marcott blade does not exist in the data and is entirely an artifact of incorrect data handling. To borrow a term from Mark Steyn, the Marcott blade was f……..flawed. It is reprehensible that Marcott and coauthors have failed to issue a corrigendum."

And darkly suggests that they are quietened by the consensus: " Unsurprisingly, the new data was not press released and has thus far attracted no attention."

Sunday, January 25, 2015

Trends, breakpoints and derivatives - part 2

In part 1, I discussed how trends worked as a derivative estimate for noisy data. They give the minimum variance estimator for prescribed number of data points, but leave quite a lot of high frequency noise, which can cause confusion. I also gave some of the Savitsky-style theory for calculating derivative operators, and introduced the Welch taper, which I'll use for better smoothing. I've chosen Welch (a parabola) because it is simple, about as good as any, and arises naturally when integrating (summing) the trend coefficient by parts.

I gave theory for the operators previously. The basic plan here is to apply them, particularly second derivative (acceleration) to see if it helps clarify break points, and the general pattern of temperatures. The better smoothing might seem contrary to detecting breakpoints, since it smooths them. But that actually helps to avoid spurious cases. I'll show here just the analysis of GISS Land/Ocean.

I'll start with the spectrum of acceleration below. As I said in Part 1, you can actually get much the same results by differencing the smooth (twice for accel), or smoothing the difference. But the combined operator shows best what is happening in the frequency domain.

Wednesday, January 21, 2015

Trends, breakpoints and derivatives

This post is partly following a comment by Carrick on acceleration in time series. We talk a lot about trends, using them in effect as an estimate of derivative. They are a pretty crude estimate, and I have long thought we could do better. Acceleration is of course second derivative.

Carrick cited Savitzky-Golay filters. I hadn't paid these much attention, but I see the relevant feature here is something that I had been using for a long time. If you want a linear convolution filter to return a derivative, or second derivative etc, just include test equations applying to some basis of powers and solve for the coefficients.

I've been writing a post on this for a while, and it has grown long, so I'll split in two. The first will be mainly on the familiar linear trends - good and bad points. The second will be on more general derivatives, with application to global temperature series.

Historic progress of temperature records

2014 as a record warm year has been in the news lately. I made plots of the progress of the current "record year" in each of the usual datasets (as plotted here). Each rectangle shows on left, the height of the then record year, and the time it held the record. Datasets are listed below the graph.

There have been suggestions that records are a figment of adjustment processes. The TempLS plots shown are based on unadjusted GHCN and ERSST 4.

The plots are based on annual averages to date. For eg HADCRUT and Cowtan and Way, that means 2014 to November. Use the buttons to click through.



Tuesday, January 20, 2015

So 2014 may not have been warmest?

That has been the meme from people who don't like the thought. Bob Tisdale, at WUWT, gives a rundown. There is endless misinterpretation of a badly expressed section in the joint press release from NOAA and GISS announcing the record.

The naysayers drift seems to be that there is uncertainty, so we can't say there is a record. But this is no different from any year/month in the past, warmest or coldest. 2005 was uncertain, 2010 also. Here they are, for example, proving that July 1936 was the hottest month in the US. Same uncertainties apply, but no, it was the hottest.

So what was badly expressed by NOAA/GISS. They quoted uncertainties without giving the basis for them. What do they mean and how were they calculated? Just quoting the numbers without that explanation is asking for trouble.

The GISS numbers seem to be calculated as described by Hansen, 2010, paras 86, 87, and Table 1. It's based on the vagaries of spatial sampling. Temperature is a continuum - we measure it at points and try to infer the global integral. That is, we're sampling, and different samples will give different results. We're familiar with that; temperature indices do vary. UAH and RSS say no records, GISS says yes, just, and NOAA yes, verily. HADCRUT will be very close; Cowtan and Way say 2010 was top.

I think NOAA are using the same basis. GISS estimates the variability from GCMs, and I think NOAA mainly from subsetting.

Anyway, this lack of specificity about the meaning of CIs is a general problem that I want to write about. People seem to say there should be error bars, but when they see a number, enquire no further. CI's represent the variation of a population of which that number is a member, and you need to know what that population is.

In climate talk, there are at least three quite different types of CI:
  • Measurement uncertainty - variation if we could re-measure same times and places
  • Spatial sampling uncertainty - variation if we could re-measure same times, different places
  • Time sampling uncertainty - variation if we could re-measure at different times (see below), same places
I'll discuss each below the jump. (The plot that was here has been moved to new post)

Thursday, January 15, 2015

Temperatures 2014 summary

I headed the last post on 2014 "Prospects for surface temperatures 2014 final". In my town, the evening paper used to come in three editions, announced by many newsboys - Final, Late Final, and Late Final Extra. So this is Late Final - my excuse is that GISS is dragging its feet (and NOAA hasn't even posted its November MLOST file).

I ran the TempLS Grid version, and it showed a considerable rise for December - from 0.518°C to 0.638°C. That actually makes December the warmest month of 2014. TempLS Mesh is also showing a greater rise with extra data, now from 0.59°C to 0.655°C. So I think it is time to make predictions (while we wait):

2014 Jan-Dec2010 Jan-Dec
GISS Land/Ocean0.670.66
NOAA L/O0.680.65
HADCRUT 40.5630.556


This is on the basis that GISS agrees with TempLS mesh, and NOAA/HADCRUT with TempLS grid. As you see, HADCRUT and GISS narrowly reach a record, NOAA with more to spare. Actually, my GISS estimate came to 0.675, so 0,68 is equally likely.

Update: GISS and NOAA have now released their results with a  joint press release. GISS gave 0.68°C as their 2014 value; NOAA announced 0.69°C (re 20th Cen ave, it's worse than I thought ;)).

Update. There is an active plot of the historic record years of all major indices (and also both TempLS) in this later post.

Friday, January 9, 2015

December TempLS up 0.045°C - some 2014 records likely

After earlier (false) signs of a greater rise, with 3833 stations reporting, TempLS mesh has risen from 0.591 in Nov to 0.636 in Dec 2014. The Nov number rose a little with later data, so Dec is now back to October levels. The report is here.

The Ncep/Ncar index showed a similar fall/rise, but only came back to about August level. GISS should track the TempLS mesh level reasonably, so a record is likely there, as with NOAA. HADCRUT remains uncertain.