The Blackboard

Lucia,

Saying you believe in AGW but think the IPCC is wrong is like saying you believe in Catholicism but don’t believe the Pope is infallible. It may be a perfectly reasonable stand to take but many Catholics will still reject you as a heretic. I also think that the majority of sceptics would agree that ~1 deg/century is a reasonable estimate of the effect of CO2 (e.g. Pat Micheals).

On another note:

Why are the uncertainties for UAH higher than the rest?

Raven– My parents baptized me Catholic, but I don’t believe the Pope is infallible. (Anyway, the Pope himself is fallible. There is some specific way in which he is infallible– in certain writings or something. )

The greater uncertainties, when computed statistially, come from greater scatter around the mean trend. This is usually due to lack of precision. Precision is different from accuracy. Oddly enough, statistical treatments I know of can’t give us an estimate of bias–which related to inaccuracy. (That’s actually one of the reasons I don’t like to pick one instrument. By picking all of them, I can get some sort of idea about likely range of bias, even if I can’t say much statistically about it.)

Very nice and illuminating post, thank you. There is as you say no reason why one should not accept your conclusions, still think the IPCC is right about the main outlines, still think AGW is real, still think we should act immediately to lower CO2 emissions. But this seems not to be how it works socially or psychologically with the proponents of AGW.

One important characteristic of the movement is that its followers feel compelled to defend a l’outrance even propositions which are both plainly wrong, and not central to the thesis. It is as if in earlier periods when defending the merits of Newtonian physics, people should have felt compelled to defend the great man’s views on alchemy. In my own field, we have seen it from Apple advocates, who defended the one button mouse for years after it became clear that this was both an idiotic user interface, and not a core element of the Apple UI. We have seen similar approaches in religion and politics. We see similar efforts to defend the indefensible in regard to MBH98’s statistics.

It is, as Pielke suggests, potentially an enormous intellectual tragedy in the making. AGW has become so entwined in the public mind with environmentalism, and so entwined by its proponents with these peripheral issues, that it risks crashing itself should it fail on some of them, and it also risks crashing environmentalism as a whole.

When analyses as reasonable as this one evoke a chorus of abuse, which it is doing and will, it is only human to stop listening to anything the chorus says. And yet, the central thesis may be correct, and is of fundamental importance. It is just that, sadly, the extreme advocates are, as so often, its own worst enemies, though they cannot see this.

On the question of data sets, I think there is a good non statistical reason to reject at least two of them for calculations over relatively short time frames: the potential for bias. On the one hand the GISS data set does not have a hands off relationship with Jim Hansen, one of the major alarmists, and for whatever reason, it shows the highest trends over the past decade or so. On the other hand, the UAH does not have an arms length relationship with Roy Spencer, one of the prime skeptics, and low and behold, the UAH data set shows the least warming since inception in 1979. These may be perfectly coincidental, but confidence in the integrity of the data sets would be enhanced if there were steps taken to ensure separation. Mind you I am not saying that there is biased intervention, rather there is the potential for small unconscious manipulations in the assembly and averaging of the data to conform to the prevailing view.

Fred’s point about risking credibility is spot on. The AGW hypothesis is subject to rejection if there is an extended cooling period, and with it would likely come a discredit of the movement. However I want to point out that advocates would scream louder than ever. The social psychologist Leon Festinger, who developed the concept of cognitive dissonance, did studies showing even stronger belief and louder prosletyzing after disconfirmations. In his book Failed Prophecy he reports on a California doomsday cult whose successive predictions of specific days for the end of the world passed with rescue by UFOs. The group became more shrill and more convinced that the next time it would happen. Look for advocates to become ever louder and more opaque in their explanations.

On the technical front, I took a shot at seeing if correlations could be substantially reduced by linking the residuals of OLS to the ENSO index. I did a cross correlation between the residuals for the OLS fit to Hadley data and the MEI(multivariate ENSO index). The best fit is for a three month lag between the temperature signal and the MEI, with ENSO leading as it should. However the r^2 is only 0.27, so only 27% of the residuals is explained by ENSO. This gives a little over 20% reduction in the 1- month and 2-month autocorrelations. So C-O or some other treatment is still warranted.

Fred

There is as you say no reason why one should not accept your conclusions, still think the IPCC is right about the main outlines, still think AGW is real, still think we should act immediately to lower CO2 emissions.

In fact, I think all these things! I’ve been for alternative energy sources since…. the oil embargo in the ’70s! At the time, my thoughts were not related to CO2 accumulation, but this is now an additional important factor.

In terms of getting action, I can’t help but believe that admitting uncertainty in our ability to predict average surface strengthens rather than weakens the case for action on developing alternative energy sources. After all, there are many reasons to diversify the source of energy for electricity generation (going Nuclear), to increase the amount of solar or wind power, or to have sewage treatment plants run co-generation and use less natural gas.

If the only reason ever advanced for these things is AGW, then how are we to convince those who simply don’t believe to make the monetatry investments to act?

Roger:
I agree that ideally, the GMST data should be supervised by people who don’t have strong POV’s with regard to the theories we test using the GMST data. Yet, it’s probably unrealistic to expect to achieve this ideal. After all, simply working on a project causes one to develop strong POV’s.

If more data were available, I might consider the POV of the supervisors and reject both GISSTemp, and UHA for those reasons. But, otherwise…. well, there are only 5. And, as far as I am aware, most practitioners seem to believe these data are sound.

Both groups correct data when error in algorithms are uncovered. (Though, my impression is Spencer found his own errors, announced and corrected. In contrast, Hansen’s were found by outsiders. Still, the fact that outsiders now have access to the NASA methods is useful from a statistics/bias perapective.)

Roger,
“On the one hand the GISS data set does not have a hands off relationship with Jim Hansen, one of the major alarmists, and for whatever reason, it shows the highest trends over the past decade or so.”

HadCRUT is controlled by Phil Jones – Hansen’s alarmist twin from the UK.
RSS is controlled by a warmer group as well.

I tend to trust the satellite measurements more because there are two groups with opposite biases using the exact same datasources and their algorithms are public knowledge. This competition keeps them both ‘honest’.

The point by Raven is well taken, and his/her observations suggest that an unbiased approach would be the average of the two satellite data sets.

I have personal experience in how subtle the effects of bias can work. More than 10 years ago my company was involved in a patent law suit with another company. The details aren’t important, but the issue resolved down to the molecular weight of a polymer backbone for a particular product. If the MW was about 1200, we would win; if it was around 1500, the opposite side would win. The judge ordered each side to pick three outside experts to do the measurements and determine the right answer. Polymer MW measurements can be tricky because of entangling and cross-linking effects, etc. The selected experts were top analytical chemists of unquestioned expertise and integrity. Our experts came in with an answer of 1200; their experts got 1500. How could this be? The difference was that the opposite side told their experts beforehand what they thought the right answer was. And sure enough you could see in their experts’ lab books where they had made decisions that took them to 1500. It was not conscious bias, just a mental “tilt” that influenced their work and ultimately the answer. Since then I have had a great respect for the influence of POVs in so-called “hard” science.

I would also like to point out that it matters a great deal how large AGW is, because it goes to the policy for dealing with it. And the differences are not nuances. Even taking the IPCC mid-range case as a given, integrated assessment models deveoped by the prize winning environmental economist William Nordhaus (The Challenge of Global Warming: Economic Models and Environmental Policy, April 2007)show that a 50-year wait before enacting restraints is very close to optimum policy in terms of benefit/cost ratio. He further finds that Kyoto-like cap-and-trade policies are “inefficient and ineffective.” If AGW effects are indeed smaller than IPCC projections, as is suggested by empirical results, optimum economic policy tilts yet more in favor of developing more economical emissionless technology rather than reducing economic growth, especially in the developing world, through restrictions and deploying current uneconomical technology.

Both groups correct data when error in algorithms are uncovered. (Though, my impression is Spencer found his own errors, announced and corrected. In contrast, Hansen’s were found by outsiders. Still, the fact that outsiders now have access to the NASA methods is useful from a statistics/bias perapective.)

Actually Lucia this isn’t quite true, the errors in the satellite method used by UAH were first pointed out by others, Fu et al., Mears et al., Prabakhara et al., Wentz et al. among others. Spencer & Christie somewhat reluctantly accepted them and implemented changes which had the effect of giving a warming trend rather than the original cooling.

Fred,

The whole idea of combating an environmental threath with less consumption is bit odd, as the underlying assumption is that we can stop and go back to something that was sustainable. However I find little support for the idea that our society ever was sustainable in the industrial age. Instead we have managed so far only because of rapid development (and sometimes not rapid enough, e.g. sulphur dioxide emissions were cut by a factor of 1000 in less than 40 years thanks to technological developments, but some forrests still died).

If we instead realize that our current state of affairs in not sustainable and that going back is not an option, the conclusion is instead that we need more development of technology, agriculture methods and produce, along with more specialization/trade etc. That takes wealth and freedom for a lot of players to pursue the optimal solution. Hence neither crashed economies nor heavy handed regulation is likely to lead to better environmental stewardship. Wealth, obviously, is needed for development to take place. Regulation are often counterproductive (as an example, I recently developed a technology that has huge potential to reduce energy consumption, but struggle to implement it as I cannot use the material I need because of the REACH directive in Europe; maybe I will find a way around it, but if not, a significant reduction in environmental load will not be realized because I cannot use a few kg of a not very harmful substance).

The environmental movement, which for a long time felt like home to me, has abandoned many of its likable features and has instead entered a mindset where enterprices are enemies, those who disagree are evil, and hope for deliverance has been replaced with a fervour of pessimism in spite of all the progress both the world and the movement has achieved historically. It is in this context that Kyoto and the scare stories of global warming were born.

Phil!
Thanks for clarifying! I was misinformed.

So, in both cases, the errors were found by outsiders. Well, one of the strengths of science is that outsiders can and do look at methods and data. Then, if the criticism are warranted, the community does eventually come aound.

I would trust Spencer and Christy 110% Would not be surprised if there original data ends up back up there

Vincent, Part of the problem we see in over politicized science is this:

1. a refusal to admit small errors.
2. banishment for small errors.

[...] in temperatures in the last 12 months here a decline in temperatures in the last 7 years reported here, decline in the last 10 years here, and now, indications of atmospheric stability back to 1995, or [...]

Phil,

It seems your recollection is a little different to mine – I’m mainly going from memory here, so I could be wrong.

Fu et al made a number of criticisms of the UAH MSU data set. As far as I am aware, all of these were found to be without merit and have not been included in the current data set. I’m unfamiliar with the Prabakhara work so can’t comment on that.

Mears and Wentz of Remote Sensing Systems generated their own analysis of the MSU data, and found some discrepencies. Spencer and Christy provided their code for the diurnal drift calculation, and Mears and Wentz identified an error in the software. This was duly recognised by Spencer and Christy and fixed, with due credit given to the RSS team. (Why do you refer to these as “Mears et al” and “Wentz et al”? Its like arguing MBH98 was authored by “Mann et al”, “Bradley et al” and “Hughes et al”)

Far from changing a “cooling” to a “warming”, it increased the warming trend from around 0.09 deg C/decade to around 0.12 deg C/decade – the change was actually within the stated error bars for the trend calculation (stated at 0.05 deg C / decade at the time). Of course since then the trend has further increased to 0.14 deg C / decade (due to new measurements, not more software errors).

It should be noted that Spencer and Christy returned the favour, helping identify a software error that was causing the RSS data to become too cold in recent months. Mears and Wentz also promptly updated their code and acknowledged Spencer and Christy in doing so.

The reasons for divergence of the UAH and RSS data has not been fully assessed as far as I am aware, although two obvious notes, the UAH includes more of the South Polar region (which has not warmed to the same extent as the rest of the world, so introduces a warming bias to the RSS data), plus there have been some questions raised regarding the diurnal correction (I believe RSS uses a climate model to estimate the correction required, whereas UAH uses surface station data)

I’m only aware of one significant correction (the diurnal drift) being identified by anyone other than Spencer and Christy themselves, so I’m not sure why you are pushing the idea that there were lots of problems spotted by lots of people.

So far, the UAH and RSS teams seem to be getting on with good science, helping each other to develop the best possible data sets. It would be nice to see this kind of approach elsewhere in climate science, rather than people digging their heels in and refusing to give any ground even when errors are blindingly obvious.

Spence_UK
Thanks for the elaboration. On the “et al.” terminology, Phil is an academic. It is quite likely he is referring to some specific papers, that were written by more than two people. Academics get in the habit of referring to papers the way they would type the words in a manuscript. (I used to do the same thing all the time.)

Quite honestly, I think all he measurement groups are doing their best to provide data products they thing best reflect the real earth temperature. UAH and RSS seem to collaborate well, and that is to be applauded.

Following Roger’s account of bias, I offer the story of the psychology professor delivering a lecture on ’suggestion’. His class demonstrated their mastery of the subject by prearranging that each time the eminent gentleman moved to the right side of his lectern, they would pay rapt attention, whereas any movement to the left would be greeted by coughs, yawns, nose-picking, and heads in hands. Needless to say, the poor man wound up almost in the corridor.
Now, some here (me too) may say psycology is about as “soft” as science can get, even suppsing it’s science at all, but I don’t thnik that’s quite the point. I would say most psychology professors would be at least as concerned about not appearing to be a douchebag , manipulated by their own class, as any “hard” scientist would be about leaning on his data to get a ‘favourable’ result. Some may also say, psychology professors are not ‘poor’ men, but deserve all they get except their salaries, but that be lacking in human compassion, so shame on you.

Anyhow, kudos to all here (from a math free sceptic) for open minds and good manners. Let’s get to the bottom of this thing. Go, Lucia! You’ll have to ecplain your results at doggy level for me to understand, but I believe you can do that.
Advanced doggy level, then…

Woof.

Lucia,

Thanks, I am familiar with the et al terminology But it is unusual to refer to a single paper, written by Mears and Wentz (link below), as both “Mears et al” and “Wentz et al” in the same sentence, especially without clarifying that you are referring to just one paper. Given that Phil was trying to highlight the number of different people who had found errors in the UAH code, that seems a peculiar way to make a point.

http://www.sciencemag.org/cgi/...../5740/1548

Mears and Wentz have published various other documents regarding satellite measurements (including one, quite recently, which was quite critical of model predictions, here), but the one above is the only one I’m aware of that resulted in a correction to the UAH code.

PS. Nice work on the data sets, by the way. The IPCC method of estimating confidence bars for model outputs is, IMHO, seriously flawed, and your analysis helps to illustrate that. Ironically, the bigger error bars that sceptics would like to see on model outputs would make the AGW hypothesis more difficult to falsify. Who would have expected that?

Spence_UK March 26th, 2008 at 4:22 pm says:
“Ironically, the bigger error bars that sceptics would like to see on model outputs would make the AGW hypothesis more difficult to falsify.”

Large error bars would have to be propagated over time and would ultimately undermine the credibility of the model – especially if the error bars make it look like cooling is a possible outcome in 100 years. Think about it. How much weight would you put on a prediction of 3 degC +/- 25 degC?

Spence UK, I was actually referring to different papers as Lucia supposed.
There were in fact several corrections dating from around ‘98, Fu et al. (U of W) identified stratospheric cooling as a source of error, correctly, whether they overcorrect isn’t the point.
Wentz & Schabel identified the decay of orbits as a major source of error, particularly for LT, Christy suggested that other diurnal corrections etc. would counteract this error. However Prabhakara et al. did a reanalysis limited to near nadir data which also gave a warming trend contrary to S & C which reinforced W & S. Mears et al. also performed a reanalysis which agreed with P et al. and identified differences in the treatment of variations in the temperature of the hot calibration target as a source of error.
As I recall the reason that RSS don’t go beyond 70S for the LT product is related to interference with the microwave signal by the ice (they also don’t take data for areas above 3000m for the same reason), Christy doesn’t accept that, I don’t recall the reference.
At the time of the W&S correction S&C was showing a cooling of 0.05ºC.

Spence_UK.
Oddly enough, large error bars both does and does not make falsification more difficult.

One can falsify a central tendency against weather data. That means the 2C/century is not consistent — within uncertainty of weather data. You can draw large or small error bars around the 2C/century, that specific number is still falsified.

What matters with regard to this test is the uncertainty in the trend that is consistent with the data.

However, if the IPCC had large error bars, then, those regions within their error bars that are consistent with data would not be falsified. So, if, for example, their error bars included 1.0C/Century. That would not be falsified. So, they wouldn’t be “wrong”.

But 2C/century would remain just as wrong as if they had provided no error bars.

Looking forward what one would hope is that a group making predictions will publish realistic uncertainty intervals. Policy makers and the public need these to make realistic decisions.

Agreed that your reconstruction of the process is a reasonable interpretation of the rather opaque prose. But its a quite extraordinary situation if it is correct. We’ve the error due to observation from the stations. Then there is the error due to the first level models failing to match the observations precisely. Then there’s the error from the second level models failing to match the first level ones precisely. Then we are invited to become seriously alarmed at what these second level models show. If anyone proposed developing an engineering package to be used in construction or naval architecture like this, they would be thought mad. But basically what we are talking about here is something which should be a sort of Prolines or Maxsurf for the planet. Extraordinary.

Martin Ringo read my dicussion of β error. That is, the likelyhood that if the IPCC is wrong we would fail to falsify. Though many unfamiliar with statistics assume the difficulty with small amounts of data is that one is more likely to reject the IPCC projections when they are correct, that is untrue. That likelihood is dictated by the “α” selected for the test. (I have chose α=55).

In my dicussion, I pointed out that the major disadvantage of small amounts of data is that we can’t falsify. This error is β error. The power of a test is defined as 1-β.

I said I hadn’t calculated this value, but I would assume it was high. One of my statistician readers was curious about this, calculated this value. In his test, he calculated the power of a hypothesis test applied to the low end of the IPCC range: 1.5C/century, and provides results of the power as a function of both α and an assumed “real value” for the trend. (Power tests need this to be assumed.

Here are the results:

This example may help those unfamiliar with these test understand the graph:

Supposed the IPCC predicts the underlying trend, stipped of weather noise, is 1.5C/century, and people will accept their projections as true until shown inconsistent with data..

Of course, we can’t know the real underlying trend, stripped of weather noise.

Suppose the real value for the trend is 0C/century. That is: the IPCC is wrong, and high by 1.5C/century.

Supposed to “falsify” 1.5C/century, we pick a confidence level of 95%– that is α=1-0.95 = 5%. That is: we say ” We won’t lose confidence in the IPCC unless you show the prediciton is inconsistent with actual weather, and the weather we get would have happened less than 1 time in 20 by pure random chance. That’s about the rate of flipping a coin head between 4 and 5 times in a row, starting with flip “1″– not cherry picking from a string of 100 flips.)

In this hypothetical case, run to do statistics, the IPCC is wrong. So, you would think that most of the time, you would find that, right? No.

To find the power of the test, find “0.00 C/century” on the horizontal axis.
Now, trace up to the 5% line. Now trace to the left, and read the power. It’s roughly 10%.

This means that, given the amount of data you have, if the IPCC were wrong and the “real” value of the trend is 0.0C/century, we would get the correct answer only 10% of the time. That correct answer is “The IPCC is falsified”.

What happens the other 90% of the time? We get the incorrect result: “We failed to falsify”.

Basically: in this hypothetical, the IPCC is wrong. Because we have very little data we find:

The likelyhood proving them wrong is only 10%. So, 90% of the time, we don’t get the right answer– but the error is on the side of assuming the IPCC is right. This means β error is 90%.

Meanwhile, on the flips side, we have the other hypothetical: What if the IPCC is right? Well, in that case, the way we set up the experiment so that we would mistakenly conclude they are wrong 5% of the time. (This is α error and it’s called a “false positive”.)

How do we decrease α error? We just pick a lower α The analyst picks this. Obviously, if we set α to 0.0000001% we will practically never falsify anything. Oddly enough, the amount of data we take doesn’t change the rate of false positives.

How do we decrease β error? There are two ways. The most common one is to hold α constant and take more data. The next most common way is to increase α.

Increasing α to as high as 50% is commonly done by “normal people”. If, for example, your boss had a hypothesis that you thought was falls. You suggest that he’s wrong, but don’t have any data. Your boss, having some confidence in you, might be willing to consider his hypothesis is wrong.

So, he might say: Ok. Take a few samples and compare to my hypothesis. Come back, and if you can show me there is an α=50% chance I am wrong, I will give you more funds to investigate further.

So, this sounds like a reasonable boss, right? So, what about science;

Considering the possibility that a new untested projection might be wrong when it fails at a confindence of α=50% is a rather common in science. After all, this means the projecition is more likely wrong than right. Stubbornly insisting that it cannot possibly be wrong because it falls inside wide uncertainty intervals is rather novel.

Phil,

I’ve referenced the only paper I’m aware of that resulted in a required change to the processing due to an error being found. The fact that you can dredge up a whole bunch of spitballs that were hurled at Spencer and Christy, and that most were found to be without merit, is rather unimpressive.

As I made clear, I am aware that Fu et al highlighted what they perceived as an error, but their analysis, as you note, actually makes the error worse rather than better; this was already discussed 8 years PRIOR to the Fu paper in the following paper:

“Precise monitoring of global temperature trends from satellites”, Spencer and Christy 1990, Science

So Fu basically raised a point that Spencer and Christy had already investigated in depth and had rejected as increasing the error term in the output data. Hardly someone else discovering Spencer and Christy’s errors, as you suggest, but someone else doing a reanalysis of something Spencer and Christy had already addressed and that person failing to understand the valid reasons as to why it was rejected – despite that reason already being a part of the peer reviewed record. So your categorisation of this as someone else finding Spencer and Christy’s error is a particularly peculiar form of revisionism.

I looked at the Wentz and Schabel paper. As you note, they take a guess at what might be the difference between their analysis and Spencer and Christy, and get it wrong. How is this “someone else finding Spencer and Christy’s errors”? Based on what, the notion that two wrongs make a right? Rather than finding and fixing the actual problem, the diurnal correction, lets put in an additional – lets call it – “adjustment” for orbital decay that counteracts the software error in the diurnal correction. I guess “hey, it’s climate science” applies here.

As for turning a cooling to a warming, again you don’t understand the consequence of adding new data. In their 1998 paper, W&S perform analysis on the set 1979 to 1995, giving a -0.05K / decade trend – barely different from zero, given the error bars, so questionably a “cooling” in the first place. Their 1998 paper failed to find the error in the diurnal correction. By the time the error was found, some seven years later, the trend (1979-2005) was +0.09K / decade. So your claim that the fixes have turned “cooling” to “warming” is another odd claim. Additional data turned the “cooling” into a “warming”, irrespective of the fixes. The fixes do change the data, admittedly, but since the magnitude of the error was slightly less than the stated trend error bars, the most change possible would be from “marginal, probably insignificant cooling” into “no significant trend”.

As noted, I’m not familiar with “P et al” although your perception of the events above do not make me particularly inclined to follow it up. The only paper from Prabhakara I could find on the topic claimed the MSU data were too contaminated by clouds and rain to be used to detect global warming trends – clearly not a generally held view today. Can you link to the original paper, and to the change in processing that Spencer and Christy made as a direct result of that paper?

Finally, yes I am well aware of the reasons why RSS stop at 70S, which have some merit. However, this does not change my stated position that Antarctica has not warmed as much as the rest of the globe over the satellite era, and the lack of these data introduces a warming bias with respect to global mean temperature, irrespective of the reasons for the lack of data.

I reiterate my position that the UAH and RSS teams have worked together well, fixing each others errors and generating high quality climatic data sets. Quite why the climate science community feels the need to continually denigrate and misrepresent the good work of Spencer and Christy is beyond me.

Raven: indeed, I think that may be an important driver!

Lucia: sorry, you’re probably not aware of my own view – that weather noise exhibits self-similarity on increasing scales – which has rather nasty consequences and means the method being used to test for 2C/century trends would be inappropriate. However this is an assumption I make, not the IPCC; as such, your test is completely valid in terms of a test of the IPCC claims. Such a test would not be valid under my assumptions, and in fact a 2C/century trend would be surprisingly difficult to falsify. But then, you’re not testing my assumptions I hope that all makes more sense to you than it did to me when I typed it.

Hey Spence if you want to be a propagandist for the S&C position fine, however the facts are that far from being ’spitballs’, orbital decay was a problem, particularly for the LT, stratospheric cooling can’t be ignored, calibration was an issue. Agreed that UAH and RSS seem to work together well, this wasn’t the case in 98 as S&C had clear ‘ownership’ issues.
In 98 Spencer said: “The temperatures we measure from space are actually on a very slight downward trend since 1979 … the trend is about 0.05 C per decade cooling.”
Wentz & Schabel and Prabhakara et al. produced results in 98 showing that this was in error. The P et al. reference and abstract are given below:
The work of S&C is not being denigrated in the scientific community quite the opposite, as Hansen said:
“In crediting Wentz and Schabel for discovering the satellite altitude effect, we should not forget the credit that Christy and Spencer deserve for pioneering MSU analysis and bringing it to the point that a correction of 0.1ºC has such a large effect on interpretations of climate change.”
That their initial calculations included errors which were identified by themselves and others and corrected is part of the normal progress of science.

GEOPHYSICAL RESEARCH LETTERS, VOL. 25, NO. 11, PAGES 1927–1930, 1998

Global Warming Deduced from MSU

C. Prabhakara, R. Iacovazzi Jr., J. -M. Yoo, G. Dalu

Abstract

Microwave Sounding Unit (MSU) radiometer observations in Channel 2 (53.74 GHz) made from sequential, sun-synchronous, polar-orbiting NOAA operational satellites have been used to derive global temperature trend for the period 1980 to 1996. Christy et al. (1998) emphasize that they find a tropospheric cooling trend (−0.046 K decade−1) from 1979 to 1997 with these MSU data, although their analysis of near nadir measurements yields a near zero trend (0.003 K decade−1). Using an independent method to analyze the MSU Ch 2 nadir data separately over global ocean and land, we infer that the temperature trends over both these regions are about 0.11 K decade−1, during the period 1980 to 1996. This result is in better agreement with trend analyses based on conventional surface data.

Phil, you’re just too funny – but “propagandist” is no insult when it comes from someone so far from a neutral point of view as you are.

Hansen, of course, does not a climate community make, and he is hardly being graceful even in the quote you give there. I still remember Raypierre on RealClimate denigrate Spencer and Christy’s “serial egregious errors” – note plural errors, repeated in your post above where you say “… errors were pointed out”. Yet I’ve asked you over and over again to show that more than one error has been identified, and so far the only actual error found is the diurnal correction fix – just one error, which I pointed out and linked to.

Thanks for the abstract of the Prabhakara paper. Alarms bell ring the minute Prabhakara decide to pick a different date range to analyse (probably makes no difference, but why would they do that?) UAH trend between 1980 and 1996 is +0.032K / decade AFTER the diurnal correction fix. So most of the discrepancy between the UAH and Prabhakara findings still exists. Hmm, lets check RSS. Oh, they find a trend of +0.104 K / decade for the same period, almost identical to Prabhakara. Hmm maybe this analysis is not so independent. So it seems this discrepancy still isn’t resolved, even after the diurnal correction fix. So, tell us all Phil, what error have Spencer and Christy made to cause this remaining discrepancy? If you don’t know what is presently causing this discrepancy, then isn’t it just possible that the error is down to an error on the part of Prabhakara, Mears or Wentz, just as much as it could be Spencer and Christy’s analysis? Or that someone has made an incorrect, or just different, assumption? Or someone is comparing apples and oranges?

So come on, Phil. You used “errors” plural as did Raypierre, but so far we have just one error, the diurnal correction fix, which had a net effect of less than the error bars on the data. Where, specifically, are all these other errors, plural, that you were talking about earlier?

Lucia, since most people have confirmation bias wired into their brains they may never get beta. NEVER EVER.

Steve_UK, Phil,
Am I going to have to pretend I’m your Mother and tell you to escalate to names like “propagandist” or argue about which of you, or which “side” is more biased?

You both have some points to support your views.

(BTW: Everyone is biased. Even me! We just all display our biases differently, and approach testing them differently. )

Lucia
I think you meant Spence_uk.
I’m staying out of this!!

You can still pretend to be my mother though…

@Steve, UK,

The funny thing on blogs is you never know who’s young enough to be your kid, and who’s old enough to be your Dad!

Although, I know Phil is just old enough to be my older brother.

Lucia, I am too, most probably!
But not quite knowing is fun, I think

Now, in maths, I’m way behind. But I know some other stuff, you know! Just not maths…

That’s beautiful stuff Steve! I’m afraid I’m hopeless at art. The closest thing I do to art is design the sweaters I knit (when I do knit. Which is less recently.)

Phil’s math is better than mine. He also knows a lot more about radiation in participating medial. I know because I read some of his papers when I was in grad school.

Oh. I clicked about me– yes. You are just old enough to be my older brother (if I had an older brother. )

Too busy number-crunching, hey? That’s how I came on here, you have a name for the least usual combination of blogging topics, knitting and statistics.
But I remember my granny knitting, and counting, always counting. But she only used to get to about 38, whereas you go way further down the line!

Anyway, nice looking sweaters are better than sculptures, especially with the earth cooling like …. no, ok, I’ll shut up about that already!!!

er, “participating medial” ???
Wooo. You’re showing off, now…

Too busy number-crunching, hey?

Actually, I post much more on weekdays when my husband is not at home. Weekends in general we host our in-laws. My mother in law has Alzheimers, and it gives my father in law a break. So, that eats into Friday evening, most of Saturday afternoon and Sunday.

But, this week, there are other things too. Hubby took Friday off, and we are painting/ installing flooring and planting bareroot plants. (I planted hops along the trellis today. Jim imagines that smelling hops will be good, and it will screen the yard a bit. I will be planting Clematis, and we are going to see which out-competes which.) Jim was washing walls in preparation for painting, but needed me to periodically help when two hands were required.

Monday, I need to write some reports for work. So, most likely, coherent responses won’t come until Tuesday.

[...] Tisdale: Roger and Lucia: The MEI by its… Comparing IPCC … [...]

planting hops? Are you going to homebrew?

I did that once. It was interesting enough to maybe try it again sometime.

I will have to dig for the paper I saw and perhaps someone here will remember it, but the paper suggested 1.5 to 2 degrees C warming/per doubling of CO2. It may have been this paper: Chylek, P., and U. Lohmann, 2008. Aerosol radiative forcing and climate sensitivity deduced from the Last Glacial Maximum to Holocene transition. Geophysical Research Letters, 35, L04804, doi:10.1029/2007GL032759.

which was featured at World Climate Report in mid-February.

I once thought AGW was true, then I thought it was false. Now I have no idea where I am. I’m leaning toward “true” with caveats (mainly, the catastrophism is probably over the top and overblown). I generally just tell people “we’ll see.” While I haven’t read the paper referenced and only read its feature at World Climate Report, it seems to line up with what you suggest here.

Terry,
Yes. Jim wants to make homebrew. He even wants to grow barley– but I have no idea where he can do this. This is a suburban lot and we have trees.

There are a variety of papers with values for steady state sensitivity to doubleing of CO2. However, those aren’t predictions we can compare directly to measured temperature data. We’d need to double CO2, stay there and wait for the world to equilibrate. That experiment hasn’t been done!

[...] evidence of climate stabilizations reported to date. Stratosphere temperature stabilizes Global surface temperature stabilizes Ocean temperatures show no warming Most climate commentators believe that temperatures will [...]

[...] recent falsification of the IPCC AR4 projection of 2C/century was criticized as flawed [...]

[...] Comparing IPCC Projections to Individual Measurement Systems [...]

[...] Comparing IPCC Projections to Individual Measurement Systems [...]

[...] Comparing IPCC Projections to Individual Measurement Systems [...]

Can I ask a couple of questions here:

Am I right in understanding that the orange ‘Average’ line is a fit to all data points from all series using C-O and OLS? Or are you averaging all five values at a given time and then fitting to that?

What baseline period are you using for each series? Is it the one used by the collator, or have you shifted them?

Have you considered that the IPCC 2C/century is only quoted to one figure and should be more properly considered as a range between 1.5C and 2.5C/century?

You have RSS and UAT, which particular series’ are you using?

By NOAA, do you mean NCDC? If so, which series?

Hi JM–

The Orange line.
The orange line corresponds to my ‘baseline’, which is Cochrane Orcutt and listed on the top line of the table. I average data from all five first, then fit T=m time + b.

I use CO because residuals to OLS are not white. I’ve posted lag plots, correlation residuals etc. after C-O in other blog posts. The remaining residuals after C-O currently appear white; so with regard to this data, the “weather noise” appears red. (The very first month, they were just a hair above the 95% confidence interval for being non-white, but that vanished the next month. The major reason, was, believe it or not, the various agencies updating their old monthly data. They have interesting algorithms that mean past data are not always static.)

I am looking into using ARMA and other methods. (And frequent commenters and I are looking at phenomenological explanations for the “flat trend”, as it truly in unusually flat if the underlying trend is 2C/century.)

Baseline
I use whatever baseline period each agency choses and average the data they provide. With respect to finding trends, the baseline doesn’t matter and it doesn’t even need to be consistent. The equation is:

T = m * time + b

We seek “m”. The baseline shifts “b”.
If you go through the math, if you average 5, this results in a different “b”. But as we don’t care what it is, that doesn’t matter.

Have I considered the IPCC uncertainty range?
Yep.

My first post discussed the full range and illustrated this on an IPCC graphic. However, with regard to the IPCC range, what this compuation shows is that based on data available as of this post, under the linear assumption, 2C/century was out. Had the ranges just hit 2.0 C/century, this would mean the upper range of the IPCC projections would be falsified. Period. They would be inconsistent with weather data regardless of the existence of the lower range.

However, it would mean the IPCC’s uncertainty bars might have been reasonable estimates of the uncertainty in their method of predicting the future trends.

I would note however, that if you examine the data in the table, you will see that Cochrane-Orcutt says the trend fall in the range -1.1±2.2 C/century. This would place the upper bound of the 95% confidence interval of trend consistent with the data at 1.1 C/century, which is less than 1.5 C/century. So, when this particular post was posted, the data were also falsifying 1.5C/century.

So, yes, I have considered the range. I could talk about 1.5 C/century or any range you prefer. Do you want me to?

Which data?
Links to the web pages for the data sources I am using are provided in the footnotes. Feel free to click them. Bear in mind: I download these shortly before processing and reposting. They are updated by the services.

Thanks Lucia

Please accept my apologies if you already know what I’m about to say.

“updating their old monthly data.”

They do that because they change the methodology occasionally to refine it as errors are discovered (in the analysis, not the data). Then they rerun the whole dataset to keep the published versions consistent.

“Links to the web pages for the data sources ”

Yes, but you link to pages with several dataseries on them, you don’t identify which ones you actually used and why.

Which brings me to my next point. You do realise that while GISStemp and HADcru are using the same input data from (largely) the same set of instruments, and are therefore measuring the same thing – ie. land/sea temperate – that the others aren’t (“NOAA” aside)?

RSS measures the troposphere ie, a column of air, at each series and does it at different heights. The series you choose will make a difference. Similarly UAH.

So when you average 5 values you are averaging 3 surface values and 2 mid air values at different heights. The “measurement” you’ve just created does not reduce error in any way, it greatly expands it. One month your averaged height may be x, but in the next month it will be y and so on (because your average is linear but the temperature gradient varies with the weather). I think you’ve accidently created a blended “measurement” series that resembles a rollercoaster through your data.

So your data now has a huge error range – you can see it from your plotted points on each vertical segment.

Next point. I’ll use just GISStemp and HADcru to make this one, but it gets worse if the others are included.

First thing you’ll need is the ascii data files, you can get GISStemp here:

http://data.giss.nasa.gov/gist.....GLB.Ts.txt

HADcru is also available but it’s a little more difficult to find, and I don’t have it bookmarked on this machine.

Both these data series are quoted against different baselines. GISS is the average temperate from 1951-80, HAD is average temperate from 1961-90

The point is this, let’s say you have two files with a data point in each:

GISStemp: 0.7
HADcru: 0.6

And you plot them. Are they the same?

Yes, because GISStemp means 14.0 (baseline 1950-80) + 0.7, while HADcru means 14.1 (baseline 1960-90) + 0.6 (sorry don’t have the real figure in front of me, this is just an example)

But having plotted them, HADcru looks cooler.

And if you average them, you’ll get an “average anomaly” of 0.65, *but* they are actually identical.

To combine them, you have to adjust them.

The other data series do the same thing, but with different baselines.

So, if you’re doing what I think you say you’re doing you’re introducing large sources of error in at least two ways.

You’re not dealing with instruments here, you’re dealing with consolidated aggregates. And they are measuring different things. Your procedure – if I’ve understood it correctly – would introduce very large errors.

JM – on your second point; absolute values don’t matter when you’re calculating the slope. Lucia doesn’t care about the absolute value average here, only the trends, so points from different baselines don’t matter in this.

On your other points – lucia just posted a spreadsheet that probably answers a lot of what you’re looking for (which of the actual data series are being used), which you can download from recent posts on another thread here…

They do that because they change the methodology occasionally to refine it as errors are discovered (in the analysis, not the data). Then they rerun the whole dataset to keep the published versions consistent.

Yes. This is why. I’m simply pointing out that it’s done. It’s important to know that when comparing results based on data downloaded in Feb as opposed to March.

Yes, but you link to pages with several dataseries on them, you don’t identify which ones you actually used and why.

Sorry– I generally link to the precise data page, and one instances: NOAA. Also, there is potential ambiguity for RSS: For that page, I use the maximum area, as it’s the only choice that makes sense for GMST.

HEre are specifics:

For HADcrut the link in the blog posts goes to: http://hadobs.metoffice.com/ha.....sh/monthly. This is Hadcrut nh+sh monthly, which, most would figure out by a) looking at the nh+sh/ monthly in the url or visiting the Hadcrut web page. I use this as their version of the “global mean” because they recommend it as the set to use for the GMST for “ordinary purposes”.

UAH which I linked.
http://vortex.nsstc.uah.edu/pu.....glhmam_5.2
The header says: MONTHLY MEANS OF LOWER TROPOSPHERE LT5.2 You can learn more at the UAH site.

For NOOA I linked this:
http://lwf.ncdc.noaa.gov/oa/cl.....alies.html This was slighly ambiguous.
Since I use monthly data, you might have guessed that I used a monthly set. Since I’m looking at global values, you might have guessed I used the global value. The data are here:
ftp://ftp.ncdc.noaa.gov/pub/da.....00mean.dat

I liked this for RSS:
http://www.remss.com/pub/msu/m....._v03_1.txt
I use -70.0/ 82.5 as that covers the maximum amount of the globe.

The url is self explanatory: RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_1.txt . If you want other information, you can hack back to remiss.com.

The Link in the post points to:
http://data.giss.nasa.gov/gist.....s+dSST.txt

The headers say :

GLOBAL Land-Ocean Temperature Index in .01 C base period: 1951-1980

sources: GHCN 1880-3/2008 + SST: 1880-11/1981 HadISST1
12/1981-3/2008 Reynolds v2
using elimination of outliers and homogeneity adjustment
Notes: 1950 DJF = Dec 1949 – Feb 1950 ; ***** = missing:

As I used monthly data, I think it should be clear that I used the monthly data in that file.

Which brings me to my next point. You do realise that while GISStemp and HADcru are using the same input data from (largely) the same set of instruments, and are therefore measuring the same thing – ie. land/sea temperate – that the others aren’t (“NOAA” aside)?

I am aware the instruments overlap and the data differ due to slighly different choices for methods of getting ocean data, and dealing with the poles.

However, they are not “the same thing”; if they were, their results would be identical within a constant value. They are not.

To the extent that the various groups make slightly different choices, their “effective instruments” are slightly different. Those components of ” instrumentnoise” that are orthoganal will tend to average out. Those that are correlated will not. This occurs naturally as a result of averaging, and is not a problem for my analysis. (If I took NOAA five times and averaged, I would simply reproduce the results for NOAA, with the original uncertainty intervals.)

RSS measures the troposphere ie, a column of air, at each series and does it at different heights. The series you choose will make a difference. Similarly UAH.

Of course.

So when you average 5 values you are averaging 3 surface values and 2 mid air values at different heights. The “measurement” you’ve just created does not reduce error in any way, it greatly expands it. One month your averaged height may be x, but in the next month it will be y and so on (because your average is linear but the temperature gradient varies with the weather). I think you’ve accidently created a blended “measurement” series that resembles a rollercoaster through your data.

I think you are incorrect about in creasing uncertainty. However, if this were true, then the residuals would increase, and the increased uncertainty would automatically be reflected in the uncertainty intervals in the linear regression. So, this would be of little practical significance to the fit except in two regards:

* The white noise component would be almost certainly be greater after averaging than before.
* My calculated uncertainties bands would be larger, making it more difficult to draw conclusions. However, the larger values would arise in the calculation naturally. (Try it– just take a set of data for a system with a trend, then add more white noise. You’ll see the calculated uncertainty bands are larger.

In reality, the white noise components drops when I average.

So your data now has a huge error range – you can see it from your plotted points on each vertical segment.

You are mistaken. The major reason the data have a huge spread around the trend is weather. That variability has little has nothing to do with the instrument noise or averaging over instrument.

As for the rest: It’s possible to average and get the trend. The intercept drops out. For plotting. I do rebaseline to the center of the interval for analysis, as indicated on the graph. Sorry if I mistated before.

Arthur

If I gave you two glasses of water, one at 32F, the other at 0C (pretend they’re still liquid) and asked you to mix them. Would you say the average temperature is 16F?

Alll these dataseries are using *different* scales. You can’t just “average” them

Sorry Lucia

I responded to Arthur before I saw your post, can I read for a while please?

Lucia

“It’s possible to average and get the trend.”

Please see my post to Arthur.

In regards to the differing heights issue, think averaging an apple and an orange. You are trying to create an average from measurements of different things.

If I’ve got any comments re. your dataset choice I’ll get back to you.

Thanks

Arthur:
When you are finished with all that, most my readers can tell you which questions are most important. Two questions we are all discussing are:
1) What if the noise isn’t red? (We need a better analysis method.)
2) What if there is large about of energy at some known time scale longer than the period of analysis? (Which time periods might be important? )

There is reason to believe that we hit “a perfect storm” of downterms due to natural variation all around the turn of the century. These sorts of things can cause a single realization of weather to be an outlier compared to some sort of “ensemble average” of all possible weather. However, had the IPCC better communicated the real uncertainty in these things when making projections, we might not get falsifications.

I think it’s important for those communicating to understand that no matter what anyone says about ensembles, realizations, averaging etc., in the end, all projections are going to be compared to the single most important realization: the weather that happens on earth.

People care about this realization.

Arthur (and Lucia)

Please read this: http://tamino.wordpress.com/20.....ment-18058

JM–

You are mistaken. This what happens if you both T_a and T_b have identical trends with time (m), but different baselines.

T_a=mt+b_a
T_b=mt+b_b

Define: T_average= (T_a+T_b)

Then:

T_average= mt+(b_a + b_b)/2

The baseline shifts but the trend is the same.

I would also like to mention that saying “apples to oranges” is not a magical incantation that makes your argument correct. You must show that two things being compared are different in some relevant way. The baseline doesn’t matter when determining the trend. It just doesn’t.

JM:
The comment you linked is one you posted and says this:

JM // May 1, 2008 at 4:18 pm
I think I just found another version of this on someone else’s blog.

I had already read that blog posts. How could I miss it? Tamino’s followers often like to debate by simply splattering links to irrelevant rather than posting an actual argument and supporting it.

As it happens, issue of comparing probability density functions for anomalies defined on different baselines is entirely irrelevant to that of determining a trend. If you think otherwise, you will need to explain why you think so in your own words.

However, the algebraic proof for the case of averaging two quantities is provided above. Do remember: all anomalies are given in degrees C, not F and C. Obviously, if GISS reported in C and Hadcrut reported in F, I would need to convert to maintain consistency of units.

“what happens if you both Ta and Tb have identical trends with time”

You’re assuming your conclusion.

“Ta = mt + ba
Tb = mt + bb”

But m is not the same. Your own analysis shows that for this short segment of the series, GISStemp differs from HADcru.

“You must show that two things being compared are different in some relevant way”

Ok. Now let’s try a thought experiment. It’s nightime here and about 10C, lets assume you measure a point 1000m over my head and get 0C

Average = 5C

Now let’s do it during the daytime 20C at ground level 5C above

Average 12.5C

Does (12.5 – 5) = 7.5 actually equal the real change in temp of 10C
at ground level or 5C at 1000m? No it represents a change in temperature at some intermediate point.

JM….
I guess I should have added the noise term and discussed ensemble averaging.

In principle, since both GISS and Hadcrut measure the temperature trend for the same planet (earth) over the same time frame, the underlying trend in GMST over time is supposed to be the same. If you can find a statistically significant difference in the trend, that would be news and you should report it widely.

BTW: Should you show the trend in GMST based on GISS and HadCrut are different and the difference is statistically significant, you will find the “temperature measurements are bad” contingent of skeptics and denialists applauding you wildly. They are the main group suggesting such a difference may exist due biases introduced by scientists who are finding reasons to adjust historic data up and down.

JM. Hadcru and GISS do not use the same stations, they do not use the same methods. Neither does NOAA.

I will simplify for you.

Imagine there are 5 stations in the world to measure temperature. 1,2,3,4,5.

GISS selects station 1,2,and 3. They compute their average, using their own unique technique.
Hadcru selects 2,3, and 5. They compute their average using their own unique technique.
NOAA Selects, 2,4 and 5. They compute their average, using their unique technique.

So Giss gives you one answer, Hadcru another, Noaa a third. They sample a global database
of stations. They select different statios. they use unique techniques to adjust and compute averages.

So. do you pick 1 of the three? Average all three? Or Do what Lucia did. Average all three
and then ALSO analyze each in isolation.

Ok Lucia, one more question, which I think is pretty fundamental to the statistical analysis if you are trying to “falsify” something: the independence of the investigation from the phenomenon being investigated. Perhaps exactly the study you have done here has been done at other times, but it did not lead to “falsification” because the trend was up, not down, and well within expectations, so nobody took any notice. Or perhaps it hasn’t been done. But the fact is, the reason there is an interest in this right now is because temperatures have been on a (warm) plateau for about 10 years. You started this investigation recently (January?), not back in 2001, the start of the data you are looking at. To have a truly unbiased study, you would have to start the study where both you and the subject being examined have no knowledge: you could do that now by starting from the April 2008 numbers going forward, and see how things go, of course.

Or you could look at adjusting the start and termination dates of your comparison period, to see how robust the “falsification” conclusion is to those effects.

Or you could try to use (as I said, I’m a stats novice, but I know something I think) Bayes’ theorem. Say you have, given the chosen data period, a 4% probability that the data matches the IPCC trend. Now, we also know that the present period of 3-4 years is the first time in about 3 decades that we’ve had this sort of plateau. I.e. the likelihood we’d be looking at it and see this sort of thing, all else being equal, over the past 3 decades is about 1 in 10. Then Bayes’ theorem tells us the actual likelihood that the trend matches IPCC is 40%, not 4%, because of that factor-of-10 observational bias.

Unless I’m missing something else in what you’ve done here?

Arthur–
I disagree that there is only interest in testing the IPCC projections only because they currently look incorrect. I am equally interested in data comparison whether the IPCC projections looks right or wrong. I happen to believe it is important to do data comparisons, in a systematic way, no matter how they look. I would assume everyone is interested in this sort of test.

Presumably, those who have been making and endorsing the projections are interested in comparisons of this sort, and in principle would always have been interested in these comparisons.

If anyone is uninterested verification of projections or predictions as a matter of routine, I would like read their explanations why they think data comparisons should not be done.

I picked the date of 2001 not because the temperatures turned flat around that time. As far as I can see, there are two rational dates for testing IPCC AR4 projections: Start when AR4 was published, which would be 2007 or start the year when they claim their projections start this century , and apply this century. Their projections show data comparison as hindcasts up through 2000, and project starting after wards. That means start data comparisons in 2001 makes sense (as far as I can determine.) Otherwise, a case could be made for 2007, but in which case, we end up in ridiculous situation for purposes of comparison. The next document is scheduled for publication in 2014. So, are we seriously going to make a rule that we can never test “current” projections using more than 7 years data?

I’m starting based on 2001. Not at the 1998 high, not at the 2000 low, not at the 2002 sort of high, but in 2001, which I picked based on the claims in the AR4. If you wish to start with data from 2007, feel free. I would be happy to watch the progress.

For my part, I plan to repeat this when the fifth report emerges, if I haven’t moved on to another interest, and will test regardless of current state of the weather.

As for your suggestion on Bayes methods. I’m not entire clear what you are suggesting, but I sort of think I know what you are saying. If you have a concrete idea, and want to do it, I’d love to see it.

I’m just applying the traditional methods taught undergraduates who need to test hypotheses. So, those are the results I’m getting.

I would also point out that when you are trying to detect the rate of “flat spots” by looking at historic data, to say what we are seeing is not rare, you need to find a collection of “flat spots” for which all of the following three are true:
a) 2C/century was expected to occur under the theory of AGW. (This does not apply to periods ealier than 2000.)
b) There were no volcano eruptions during the period and
c) protracted flat or negative trends persisted for at least 8 years.

Matching the circumstances is necessary if you want to fish out the probability of this flat trend from historic data while also testing the 2C/century hypothesis.

Re #2475 (Arthur Smith):

Yes, I think that you ARE missing a lot in what Lucia has done here and on other blogs in the past few months. Some of her most informative posts have been directed to answering the questions that you’re now raising – see, in particular, the ‘Raniers or Maraschino? Accusations of Cherry Picking and Climate Change’ thread on this blog and some contemporaneous posts at David Stockwell’s ‘Niche Modeling’.

On my count, Lucia has made 11 posts on 3 different threads on this blog within the past 12 hours – and during that time she has also made a valuable contribution at Climate Audit (Unthreaded #34, post #13). These posts alone total some 4000 words, and many of them were detailed and patient responses to your inquiries. I urge you to read the previous discussions before posing further questions in language that seems to me to be needlessly combative.

Arthur,

I hadn’t seen Lucia’s Comment 2479 when I made my Comment 2481. It fully confirms my point.

I’ll try again

Two glasses of water, one at 0C, one at 273K.

Mix them Actual temp = 0C or 273K

Avg them =(273+0)/2 = -136.5C / 136.5K

Which answer is correct?

You cannot mix numbers from different scales (baselines) even if they have the same ticksize

You *must* apply a correction factor or the “hotter” baseline will “ccool” the result

I think you should redo your analysis

Best regards

JM, your averaging of points is pointless (excuse the pun). Also you’re mixing temperature scales C & K, which is not the same as using a different base in the same temperature scale.

What’s important, as Lucia has pointed out, is the trend. In Lucia’s example above she used 2 linear functions with the same
trend m. & the same scale. However say they have different trends, m1 & m2. When you average the 2 functions the resultant trend will be (m1 +m2)/2.

Say the 2 functions have different base periods, as they do in these different time series (HADCRU, GISS etc). You decide to first bring them to the same base, before averaging, by adding a constant term to the constant term of the relevant function. The trend of the modified function doesn’t change, the linear function just moves up or down. When you average these modified functions, the trend is still (m1 + m2)/2.

So there is really no need to bring all series to a common base.

Lucia – if it’s a robust “falsification”, it shouldn’t matter whether the starting point is January 2001, July 2001, January 1999, July 2004, etc. except the different time spans will inherently give you different uncertainty numbers because of the different numbers of data points. If the 7-year span is too short to do this kind of robustness analysis because the uncertainties are inherently large, and if you feel the need to only compare data from after the prediction was (effectively) made, then we really should go back to the TAR and compare its numbers with trends from 1995 or so on. You could do the same with AR4 once we have enough years beyond 2001 to make a meaningful analysis.

The point is not that you were deliberately trying to bias things – I am pretty sure you wouldn’t do that. The point is that you did have to make a choice on starting point – you have “rational” arguments for it, but a proper statistical analysis really needs to show whether making that choice as opposed to all the other possible choices for starting (and ending) was in itself a low- or high-likelihood event, that’s where Bayes comes in.

Ian – I’ve visited here when Lucia started up this blog, but I’ve been away a while. I did read (some of?) Lucia’s posts on this before commenting, and many of the comments as well. I am aware there’s an issue with solar cycle forcing, but I hadn’t noticed any previous discussion of the two issues I have raised: the actual meaning of the +- in Lucia’s tables (she answered that question just fine) and this Bayesian a priori probability issue.

JM – if each glass of water has some internal heat source and one is increasing temperature at 1 degree C per hour, and the other is increasing at 3 K per hour, since the relative units are the same, when you mix them it doesn’t matter whether you normalize to the same baseline or not, you get the same average rate of increase. I think you’re being misled by tamino’s critique of something very different that Anthony Watts did comparing absolute anomaly numbers; it’s not an issue for what Lucia’s doing here.

Arthur,

Lucia – if it’s a robust “falsification”, it shouldn’t matter whether the starting point is January 2001, July 2001, January 1999, July 2004,

This is silly.

First, we can’t fairly “falsify” or “verify” a prediction using data from 1999 or earlier for two quite obvious reasons.

* The IPCC AR4 projections don’t apply to 1999. They don’t hindcast 2C/century into the past; the 2C/century values is higher than values in the past and applies only to this century. So, using data from 1999 or earlier to test the 2C/century prediction is like determing the average height of Swedes by measuring Norwegians. (However, if are really serious that the start data doesn’t matter, and you wish me to do the illogical exercise, I could perfectly well test to see if 2C/century is consistent with the past trend, starting in… on 1950? Yes, that’s reducto ad absurdum. But the fact is, the IPCC hindcast doesn’t “post-dict” 2C/century for the 90s.)

Second, the IPCC AR4 projections, and the method for creating them are tested and developed by hindcasting over past data. You cannot “verify” a prediction using data used to develop the prediction. Moreover, one would expect these things to hindcast relatively well— had hindcasting been pitiful, those predicting would surely adjust their method. (Everyone in science and engineering would do this. To do otherwise, actually violates the scientific method! But, even outside science, we don’t test the accuracy of prediction by including hindcast data; we don’t test psychic’s predictive skill by letting them predict the outcome of the 1960-2004 presidential election in 2006, reading their prediction in 2008, and then testing using data from 1960-2008 to falsify or verify. Of course the test won’t falsify. The stuff from 1960-2004 was not a prediction!)

Minde you, is true that we can show methods are wrong by showing they don’t even hindcast, but in that case we aren’t falsifying a prediction or projection. In the case of the psychic we might be showing he has access to a poor library, or has a poor memory.

So, having dispensed with why we can’t start with data before the projections are made, let us discuss why we can’t expect falsification to be “robust” as we decrease the amount of data. We know, the beta error (or type II) errors in these sorts of tests are infinite when we have little data. This means, if we were to begin falsifying using data from Feb 2008– simply because Arthur Smith decrees I can’t use data before it entered my mind to test a prediction– then the type II error for the test is 100%. We can’t falsify because we will have zero degrees of freedom for the test. So, the uncertainty intervals on a slope determied from two data points are infinite. We will never falsify even horrifically poor projections. The beta error decreases as we get more and more data.

So, it is reasonable to pick the data that gives the maximum data permissible to test a falsification. In fact, this is the only reasonable choice.

I’m not sure where people are getting the idea that falsification needs to be “robust” to be meaningful. Falsification in at early times is rarely “robust”, unless the prediction is insanely incorrect. For example, had the IPCC projected 10C/century, we would be getting falsifications which will never reverse to “fail to falsify”.

What we are likely to see– and I have blogged about this– is the measured trend will oscillate about the true underlying trend. Supposing this is, say 1.5C/century, the central tendency in the TAR, and the lower bound from the AR. If that value is correct, we are going to see the best estimate of the trend oscillate around 1.5C/century, sometimes exceeding 1.5, sometimes falling below. As we accumulate more and more data, the uncertainty intervals will narrow, and we will eventually falsify 2.0C/century in what you might call “robust”.

In contrast, if 2C/century is correct, we would expect to get “falsification” at a rate of roughly 5%, because we’ve set alpha (type 1) error to 5%. The fact that we got one, the first time I happened to test it, does cast serious doubt on 2C/century.

The fact of the matter is, if the IPCC projection of 2C/century is correct, getting a value as low as we got over a period as long as we got using the only reasonable start date, is an unusual event that requires explanation. The possibilities are:

a) this is totally random in the way you can flip 6 heads in a row,
b) the IPCC decision to not includes effects like solar/ land use etc in their projections is leading to biased results
c) we hit a “perfect storm” of unusual weather events in 2001 (Example: simultaneously hitting the solar peak, a turn in the PDO, yada, yada, yada) in
d) the scenarios fed to the models are unrealistic or
e) the models themselves are biased even if the correct scenarios are fed to them.

Conditions (a) and (c) would still mean the statistical result is “falsified” to the 95% confidence interval. It is simply a fact that statistical tests alway have some level of uncertainty, and so there is a finite rate of false positives. For a 95% confidence interval, that rate is 5%. It could happen now.

Conditions (b), (d) and (e) would indicate deficiencies in the IPCC process.

Ian– I’m fine with Arthur’s questions. I know questions often sound a bit more confrontational in blog comments than intended. (So do questions at professional society meetings. )

As for Arthur’s response; It’s true that I have not directly addressed the Bayesian issue in those terms. However, I’m not entirely sure what specifically Arthur meant. However, I think I may have indirectly addressed Arthur’s issue when responding to Stoat’s comment about the frequencies of flat spots in past data. I replied to that here:
http://rankexploits.com/musing.....2ccentury/

I included this figure which shows that all negative and flat spots embedded in swiftly rising periods of time occur as a result of volcanic eruptions:

I don’t expect everyone to read every one of my posts. (Plus, creating better archives is on my “to do” list.) But it is true that I’ve commented on many of the recurring issues here:
http://rankexploits.com/musing.....snt-apply/

Lucia

Please stop distracting people

Please address my point at 2486.

You said at 2449:

“Baseline:


I use whatever baseline period each agency choses and average the data they provide. With respect to finding trends, the baseline doesn’t matter and it doesn’t even need to be consistent.

T = m * time + b

We seek “m”. The baseline shifts “b”.
If you go through the math, if you average 5, this results in a different “b”. But as we don’t care what it is, that doesn’t matter.”

I understand this to mean that you do no correction to bring anomalies into line before averaging.

“… the baseline doesn’t matter and it doesn’t even need to be consistent …”

“… the baseline doesn’t matter …”

Both statements seem pretty definitive to me. You’ve gone to some effort to confirm them in subsequent comments.

Are they true or not?

And please don’t talk about slopes again, I’m talking about data acquisition, not analysis.

JM wrote:

Lucia

Please stop distracting people

JM, I snorted coffee out my nose when I read this.

For what it’s worth, I don’t think answering Arthur or Ian’s comments is “distracting” people. Also, it may surprise you to learn this is my blog. If I want to “distract” visitors who elected to come here and have conversations with me, I will do so.

If you believe that differences in the baseline affect a trend, I suggest you find a copy of EXCEL, down load some data from any of the agencies. Afterwards, find the trends. Then rebaseline and find the trends over time.

If you do it right you will find the baseline doesn’t affect the trend.

Or, better yet, if you think it does, and you don’t want to do the work yourself, why don’t you go back to Tamino’s thead, and ask him to do it for you?

Geoff Larsen: “s not the same as using a different base in the same temperature scale.”

Thanks Geoff, that is my point exactly. Each of these data series use a different base. Kelvin and Celcius – same ticksize, different base.

GISStemp, HadCru, RSS, UAH, NCDC – same ticksize, different base.

[blather about interception points and slopes] – irrelevant.

Arthur Smith: “JM – if each glass of water has some internal heat source and one is increasing temperature at 1 degree C per hour [etc]”

OMG: Arthur, please try and understand the example. You have two glasses of ice water and mix them together.

What is the temperature of the mix?

Lucia

Please address my point at 2486.

What is the correct temperature of the mix? 0C or the calculated average of -136.5C?

JM.

What point? That you intentionally made both a sign error and a unit conversion error in a problem involving thermodynamics? And which has nothing to do with the determination of a slope? And is, in short irrelevant?

If you wish to discuss baseline, the correct answer are infinite. They include the average temperature is

a) T= OC,
b) The anomaly is T’=+1C relative to a baseline of To=-1C where T’= T-To.
c) The anomaly is T’=-1C relative to a baseline of To=+1C where T’= T-To.
d) T= 273 C relative to a baseline of -273C.

In any case, when graphed, my data are set to a common baseline. So, even if you were correct about this making a difference in principle, it does not in practice. The data are on a common baseline.

Lucia

Sorry I missed this bit of your post:

“down load some data from any of the agencies. Afterwards, find the trends. Then rebaseline and find the trends over time.”

So you recognize “rebaselining” is important?

Good.

Progress.

But it completely contradicts your earlier description of how you’ve done this,, where you’ve said many times “… the baseline doesn’t matter …”

How do you rebaseline then? And when? Before or after find the trends?

Because you should do it before.

Lucia: “In any case, when graphed, my data are set to a common baseline”

I’m talking about data acquisition.

How do you get it on a common baseline? And when? Before or after averaging? Before or after trending?

You’ve previously said categorically that baselines don’t matter and that the *don’t* do it.

What’s the story?

I can’t figure out what you’re doing here unless you tell me.

Lucia: “when graphed, my data are set to a common baseline.”

When graphed?

That is simply not good enough

I want you to put them on a common baseline before you even start to analyse them

Do you do that?

Spence_UK. Well put

JM – my goodness, you’ve been answered a dozen times here already. Try actually reading some of the responses to your comments and think about them carefully. Several us have experience as practicing scientists, PhD’s, etc, and we don’t agree on many things. But on this one you are just wrong. Go do what Lucia said, get a spreadsheet yourself and try it with real data. Let me repeat, the baseline does not matter when what you are interested in is the rate of change with time!!!

Do you know any calculus? What Lucia has been studying here is dT/dt – the rate of change of temperature with time. If you modify the temperature T by a constant value C, then it is a constant adjustment. It is pretty fundamental in calculus that the derivative of a constant is exactly zero. I.e. d(T+C)/dt = dT/dt.

The baseline does not matter!

Arthur: “JM – my goodness [etc]”

Arthur, don’t teach your grandmother to suck eggs.

Yes I do understand calculus and a lot more besides.

I doubt however, that Lucia understands her input data.

Get out of the way.

Lucia

Do you put your datasets on a common baseline before averaging or not?

You first said that you didn’t, and went to quite some effort to tell me it wasn’t necessary.

Now you say you do.

Which is it? And when do you do it?

JM–

Sometimes I rebaseline; sometimes I don’t. I do it at different times as a matter of convenience. It doesn’t matter whether or not one does it or when. As many have patiently explained to you, it doesn’t matter.

Also, I do not permit visitors to say things like “don’t teach your grandmother to suck eggs”.

I am adding your name to the “slow down boris” plugin. and possibly modifying to deal with your special habits. Maybe Arthur can suggest a special feature just for you.

“Sometimes I rebaseline; sometimes I don’t.”

Then you are condemned out of your own mouth.

Read the footnote at the bottom of the GISS file that you download each month,

Try to understand it.

Your results mean nothing.

Me: “Read the footnote at the bottom of the GISS file that you download each month,”

Oh, and do read the usage notes on the web pages for every other data file as well. They say exactly the same thing.

Geoff (2487)

Just before I go, I want to address the point you are trying to make. You’re saying that baseline doesn’t count when looking at trend right?

But it does in Lucia’s procedure. Averaging two values assumes that both values make equal contributions to the result, but if you look at my example with K/C you’ll find that the C value makes *no* contribution to the result.

The result is skewed towards the higher valued scale (K), in my example Kelvin. This is how the “hotter zero” -> “cooling” effect works. The measure against the hotter zero point contributes less to the result and therefore acts to “cool” the result.

I thought I was making that clear in my example (which I’ve restated in a couple of different ways in this thread) but it appears that Lucia doesn’t understand it.

This is why you *cannot* mix values from different scales. The differing zero points completely screw up the assumption of the “divide by 2″ assumption,

If you don’t agree, please let me know and we can discuss.

“In fact, I think all these things! I’ve been for alternative energy sources since…. the oil embargo in the ’70s! At the time, my thoughts were not related to CO2 accumulation, but this is now an additional important factor.”

No it isn’t. Look Lucia. There is just no need to be compromising with these lunatics. The starting point of this debate always must be that we are in a brutal and pulverising ice age. You cannot let yourself be beaten down by the sheer weight of mindless leftist idiocy.

In the middle of this current food and energy crisis we cannot let these environmentalists repeat the mass-killing they pulled off with the DDT bans by spuriously agreeing with their conclusions, even after showing they have the science wrong.

Now is not the time. If we have ubiquitous, saturation nuclear power, then we can think of these things 50 years down the track when a reduction in CO2 output isn’t going to lead to the starvation of millions. These are not honest mistakes that people like JM make.

JM – you say your 0 C/273 K average proves “the C value makes *no* contribution to the result.”

But your proof is wrong – the zero does contribute just as much as the 273. It doesn’t matter if the number is 0 or -10000 or 10^26, when you average it with another number, half of the resulting value comes from one, and half from the other.

When you average degrees C and K, the averaged unit has a zero point half-way between the zero points of the two. The “baseline” of the average is at exactly 273/2 K, and -273/2 degrees C, i.e. +136.5 K or -136.5 degrees C. This is a well-defined and perfectly valid, though unusual, temperature scale. In this temperature scale, the freezing point of water is at a value of +136.5. Not coincidentally, that’s exactly what you get when you average 0 C and 273 K – you get 136.5 in these new units.

If you take measurements on different baselines and average them all together you get a new measurement with a modified baseline. That is proved in your 0 C/273 K example, and it’s perfectly valid with the averaging done by Lucia here.

Now, there is and issue with the troposphere (UAH, RSS) numbers fundamentally measuring something different from the surface (GISS, HADCRU) numbers – sometimes people divide the troposphere numbers by a factor of 1.2 to make them comparable, so maybe that should have been done, and that would have changed the effect of averaging. But the differing baselines have no effect on the averaging.

Arthur, Lucia, et al., take note. Do not think, when engaging with JM, that you are engaging with an honest interlocutor, he is in fact the opposite. How else can one regard the following statement made by JM at the following site, where I myself was engaging with him, about Lucia:

“Sorry DB, I tried, but I cannot take Lucia seriously. Anyone who thinks mixing two iced drinks creates liquid helium is beyond my capacities to argue with.”

http://catallaxyfiles.com/?p=3.....ment-93438

You might also ask him to consider the situation if he were right. What are the implications for each of the major datasets themselves seeing as they are collections of temps. at different instrument stations each with its own adjustments, etc.? Further, what are we to make of the ensemble means of the IPCC projections that represent the results of seperate models, each with different parameterisations, etc.?

Apologies, Lucia, for ever pointing such a dissembler as JM to your site.

Arthur (2516)

You are simply wrong. Water does not freeze at -136.5C, it freezes at 0C. No mathematics can change that

Refer to Lucia’s comment at 2416:

“Obviously, if GISS reported in C and Hadcrut reported in F, I would need to convert to maintain consistency of units”

That is a correct statement.

But while GISS and Hadcrut report in the same *units* (C), they report on different *scales* (zeropoints)

You have to adjust to get them on the same *scale* as well as units.

Lucia does not do that. I can have no doubt about it – she has spent a lot of effort telling me – so her results are meaningless.

She should make the adjustments, redo her analysis and present her new results.

[...] changes in ocean currents. This is why temperatures have not been increasing as noted here and here. So it is natural to ask, if natural cycles are masking global warming, why haven’t them [...]

JM, 2513

Whats to discuss? You are simply wrong as many have pointed out to you with the reasons why. This is simple high school stuff.

If you take say GISSTEMP & HADCRU anomaly time series which are drawn on bases a & b (I think the base lines differ by .1C) and average them, you come up with a new “composite” anomaly time series with a new base line(a + b)/2.

It doesn’t matter what the base of any anomaly time series is, they are all valid. You convert one time series to the other by adding or subtracting the difference between the base lines. It doesn’t matter whether you average first then adjust, or adjust first then average, as long as you make the correct adjustments you get exactly the same “composite” anomaly time series. If you are doing trend analysis, as Lucia is doing, there’s not even a need to adjust, as trends don’t change when you change the base line.

JM, 2522

“Water does not freeze at -136.5C, it freezes at 0C”

You have created a new temperature scale with the freezing point midway between 0 & 273, lets call it, in your honour, the JM scale.

So water freezes at: -

0 C
136.5 JM
273 K
32 F

2519, dover_beach

Point taken

Geoff: “as long as you make the correct adjustments ”

Well I can agree with that, but Lucia has been very insistent that she makes *no* adjustments.

Geoff: “as trends don’t change when you change the base line.”

They do if you’re trending an average of 5 figures off *different*, unadjusted baselines

“I also think that the majority of sceptics would agree that ~1 deg/century is a reasonable estimate of the effect of CO2 (e.g. Pat Micheals).”

I think its too high. I think Pats letting the nutballs push him around too much. He doesn’t have the data to back that up. It may seem like a reasonable compromise to take Pats point of view. But if you don’t have the evidence then its just pure speculation and way up on the high side.

I shouldn’t be surprised if it made that difference over many thousands of years however. Because I’d expect our planets temperature to be highly serendipitous, and so if the extra CO2 could, for example, slow the advance of glaciers, then perhaps it could make a bit of a difference over the longer term. What we have to get away from is this light-and-air model of whats going on. And we ought to get away from air temperature as our metric. Its clearly accumulating and decumulating joules in the oceans that are going to tell us which direction we are headed in. And we don’t want to think of warming as some instantaneous light and air show, but rather a strata and heat budgets deal. Strata and heat budgets. Not gasses and light.

JM – while Graeme is totally wrong in his assessment of the impact of CO2, he is absolutely right in his critique of your argument. JM, you say “[trends change] if you’re trending an average of 5 figures off *different*, unadjusted baselines” – no they don’t. Do the math yourself. Here’s an example with 3 figures instead of 5:

x(t) = m1 t + b1
y(t) = m2 t + b2
z(t) = m3 t + b3

If we adjust to a common baseline, say b1, we add (b1-b2) to y, and (b1 – b3) to z. So “adjusted” would be:

x’(t) = x(t) = m1 t + b1
y’(t) = m2 t + b1
z’(t) = m3 t + b1

Ok, let’s average.

Unadjusted: (x+y+z)/3 = (m1 + m2 + m3)/3 t + (b1 + b2 + b3)/3

Adjusted: (x’+y’+z’)/3 = (m1 + m2 + m3)/3 t + b1

The only difference is a constant – b1 vs. (b1 + b2 + b3)/3, which has no effect on the slope which is the value we’re trying to get: (m1+m2+m3)/3 is the same for both.

If you believe something different, show your math, because you’re making absolutely no sense with your arguments so far.

Arthur–
Let me know what you conclude about the Troposphere. This is the lower troposphere, but yes, it is true it’s not literally the surface. So, that can be an issue. One of the reasons I show results individually and collectively is so readers can get a real idea of the type of spread we are getting depending on ‘instruments.’

Obviously, I could easily average the troposphere measurements and the surface measurements. Then we could examine each of the two averages separately. I don’t know if the version of the spreadsheets you have shows any of the “peaking” I’ve done at cross-correlations in the deviations between a particular ‘instrument’ and the mean. If it does, you may be as puzzled as I am about which instrument correlate with which. (Or, you may know something I don’t know and understand why things correlate as they do.)

Arthur

I agree with everything you say (including not worrying about Graeme)

Except it doesn’t apply. Arithmatic only works if there is only one zero. There can be only one.

Lucia’s input dataset – unadjusted – has five.

Her subsequent processing has no validity unless she reduces those five to one by applying corrections *before* any subsequent calculations occur (which is a point that Geoff made)

Lucia. You have insisted throughout this thread that adjustments are not necessary. If you still believe that, could you please run your spreadsheet on both an adjusted and unadjusted set of input data and post the results here.

I’d appreciate it if you could also post the actual Excel files themselves rather than just GIFs.

That would be very nice.

Thank you

” I could easily average the troposphere measurements and the surface measurements.”

Lucia, my understanding is that this is an issue of some controversy.

I’m not sure, and I can’t give you a reference, but I also understand that the standard practice is to apply a factor of 1.2 to troposphere measurements.

You’ll have to conduct your own research there.

“JM – while Graeme is totally wrong in his assessment of the impact of CO2…”

Lets see some backup for that Arthur. We could all use some good news. Given that our starting point is the brutal ice age we are in. One which appears to be getting worse over time.

Graeme–

As much as I hate cold weather, and complain through out Chicago lands wretched winters, we are not currently in an ice age. During the Wisconsin glaciation, my house was buried under glaciers. I’m only 30 miles south of Wisconsin. It’s a cool spring, but my tulips are blooming nicely.

Lets get the terminology right. We have been in an ice age for 39 million years. We have glaciations and interglacial periods within this ice age. Not only are we in an ice age but we are in a particularly nasty phase of this ice age. And have been so since North and South America fused. The interglacials don’t last very long. The glacial periods last a very long time and are very nasty for terrestrial life. Not only this but the longer term trend has been for cooling for the last 55 million years and there is no indication that matters have turned around.

After splitting the protagonists up into climate rationalists and climate alarmists still we find that most people are not starting the argument from the fact that we are in a truly brutal ice age. And in not doing so they are seriously at fault. This must be the starting point to our understanding of what industrial-CO2 release is all about. The sort of silliness that JM gets up to distracts people from the only rational starting point to this issue.

Ahhh– I see what you mean. Yes. I’m used to people calling the glaciations “ice ages”, and the interglacial otherwise.

Right. It may seem like a language-nazi point to be making. But from that starting point industrial-CO2 becomes the best dumb luck the human race ever stumbled upon if it “works” at all. And this warming panic becomes the biggest case of wrong-way-Corrigan behaviour yet seen in all allegedly scientific discourse. JM and others will see us all splitting hairs in some argument cul de sac when the real issue is whether or not industrial-CO2 is so marvellous that it can warm things up a little bit, even as it makes nature more robust and agriculture more productive.

Some arguments have a middle ground to them. But not this one. This one the alarmists are idiotic in every constituent part to their argument and we must try hard not to compromise with them.

Graeme – read Jim Hansen’s latest. If we exceed 450 ppm CO2, we’re likely to be out of the whole “ice age” thing altogether before long. No more Greenland ice, no Antarctic ice. Oh, and sea levels hundreds of feet higher. If you think the planet would be better that way, go ahead and make your case… In any case, there’s no doubt in the science on the fact that additional CO2 brings warming.

JM – as I mentioned in my first post on this thread, Lucia *has* posted her spreadsheet on another thread – here’s the link directly:

http://rankexploits.com/musing.....solar2.xls

Arthur

Thank you for the link, I’ll have a look

And ignore Graeme, if you don’t you’ll out why soon enough

Lucia

I’ve just had a look at your Excel spreadsheet, and I’d like to give you some advice. Take it as you will.

You’re averaging the raw input anomalies. You should not do that, and you should ignore anyone who makes an argument from calculus.

You are not doing calculus, you are doing numerical methods.

Numerical methods are arithmetic approximations to calculus, but they are not calculus – they rely on arithmetic operations (+-*/) which do *not* work if you have five zeros in your data.

Please try rerunning your spreadsheet on *corrected* anomalies. I think you’ll find you get quite different results.

Another thing. Compare your March and April trendlines. They seem to have moved quite a bit for the addition of one months data, which looks unusually sensitive to me. Particularly as that data has 2 positive anomalies, 2 negative and one at zero. Something’s wrong there.

I also think it would be a good idea to plot the ordinary least squares fit in addition to your other method. Least squares is something we are all familiar with.

“Graeme – read Jim Hansen’s latest. If we exceed 450 ppm CO2, we’re likely to be out of the whole “ice age” thing altogether before long.”

Hansen doesn’t have any evidence for this. What is your evidence? 450ppm won’t have an effect even close to that. This is all make-believe. One of you is going to have to make a case. Don’t throw it back onto me when none of you has shown that CO2 can do anything like this globally. If Hansen was right then we would be fine since we would have a convenient thermostat. If Hansen was right we ought to just throw one big street party. But Hansen isn’t right and so lets have some evidence. Hansen thinks a doubling of CO2 will give you a 6 degree increase in temperature. I’m not popping any champagne bottles over that since he doesn’t have any evidence for this. Annan thinks a doubling of CO2 will give you a 3 degrees increase. He has no evidence for this either. But notice between the two of them they cannot get their story straight.

Start from the fact that we are in a brutal and pulverising ice age. Never lose that context. But make your case.

Hansen works on the basis that this is just a light and air show. That any energy generation or capture by the earth as a whole is just neither here nor there. And that the oceans are a fudge factor or a delay factor rather than the real deal.

We cannot be thinking in this way. For one thing when models that make these assumptions are used to focus on the preindustrial past they never work. Goddard tried to train its computer model on the snowball earth and the computer could not account for it. So Goddard being a refuge for bad scientists, rejected the empirical evidence and reaffirmed their computer model. But a model which was based around strata and heat budgets and not just air and light would easily have been able to mimic the earth freezing over, accumulating heat, melting and freezing over again. I call Goddards problem the curse of the lone paradigm. And whats bad about this controversy is that both climate rationalists and climate alarmists are suffering from it. So we have to keep pushing things back to strata and heat budgets and away from air and light.

“In any case, there’s no doubt in the science on the fact that additional CO2 brings warming.”

This is not true at all. But its worthwhile to see how such an assumption comes about. It comes about because the climate rationalists are better scientists than the climate alarmists. So what happens is this. Immediately the climate rationalists admit that their best estimate is that CO2 is likely to have some warming of some sort. “Thats our best guess…” say the climate rationalists. The CO2 might have a cooling effect. It might have no effect. Or it might have a warming effect. But since it appears most likely that the effect would be a warming effect the climate rationalists immediately admit this. HENCE THE ASSUMPTION IS IMMEDIATELY bipartisan. But the fact is the assumption has not been proven.

So we cannot just lock this assumption in and move on. Yes for sure. My assumption also is that C02 would have some sort of minor warming effect. Thats why the Pat Michaels assumption is so seductive. But the fact is none of this has been proven. And a slight cooling effect cannot be ruled out.

Nature laughs at science-sociology and takes no account of the structure of our debates. A like situation happened back in the 60’s and 70’s. There was an argument between the Steady State and Big Bang folks. The Steady State people admitted that their version of their theory wasn’t as good as the theory that the Big Bang people were spruiking. They did so not because the Big Bang theory was any good. But simply because their own theory was being shown to be bogus and because they were very good scientists. Fred Hoyle and that crowd. From there the Big Bang theory was held to be bipartisan and we are stuck with it though it looks sillier every year.

So neither side of this debate can yet lock in the idea that CO2 has a global warming effect, as reasonable as this assumption seems. Because it has not been proven and its not time to put it in cement. The only thing we know for sure is that this CO2-warming is either so slow-acting or weak that its not yet shown up clearly in the data. And that therefore if its effect is warming it cannot-not be a good thing. It must be a good thing. Its a positive externality that must never be subject to taxation.

If this “brutal and pulverizing ice age” produced our current civilization, we should try not to end it.

In fact, I think this means I’m for more brutal and pulverizing things in general.

Well it probably helped produce our species via subjecting us to pulsing holocausts and cutting us off from eachother, so allowing evolution to work by subjecting all the struggling clans to a like stress in a context where the gene pool could be altered by partial isolation. It probably helped produce our species also by destroying virtually all of our food supply and forcing us to the coast where we had to spend a lot of time in the water. Freezing our butts off and trading shell-fish for animal skins.

So you could say it produced our SPECIES. But you wouldn’t say it produced our civilisation. Its the interglacial that did that. And we don’t need another holocaust in my view.

Can we vote on that? I’m saying we don’t need another holocaust. The environmentalists want a holocaust and I’m against it. Do we get to vote?

Can we vote on that? I’m saying we don’t need another holocaust. The environmentalists want a holocaust and I’m against it. Do we get to vote?

Shouldn’t you be fighting the jihadists somewhere? Please?

Graeme–
I don’t think Holocaust is precisely the correct word for species going extinct or adapting to environmental pressures.

I think there are risks for people both if it gets too hot or too cold. I sincerely doubt even the most alarmist of warmers want an ice age or even to return to the little ice age. I also doubt even the most stone-cold denialist wants the temperatures to rise 2C in a decade.

For the most part, the disagreements are over how much or little warming GHG’s may cause, how fast it would occur, can it be predicted etc. Ramping this up to “holocaust” is unwise and unhelpful.

Now lucia. Seperate the SCIENCE SENTIMENT from the SCIENCE EVIDENCE.

Do that and you will know that there is absolutely no chance at all that its going to get too warm. There is no evidence for the likelihood of catastrophic warming. You know when you are pounded with this manufactured consensus day after day, world without end, it can make you lose the context. But this planet in its current form displays a one-way bias towards catastrophic cooling, no likelihood of damaging warming, and no seriously bad warming problem for many tens of millions of years. There hasn’t been a warming catastrophe on this planet (if ever) for at least 55 million years.

If you doubt it try producing the evidence. Find me some sort of evidence for the likelihood of catastrophic warming. And for the idea that a little bit of human-induced warming is a BAD thing during a brutal and pulverising ice age. And also for the idea that if human action weren’t in the equation that the temperature would be in some sort of static equilibrium.

You ought never lose sight of the fact that this argument begins with us in a brutal, nasty, ice age which is entirely inimical to terrestrial life, but as it turns out is pretty kind to life in the sea. Where is the evidence even that this industrial-CO2 release will stave off the oscillation down to a cold, dry world where hominid numbers are thinned right down?

We have to keep circling back to the relevant context. Which is not a leftist context but a scientific context.

Lucia, sorry for coming back to this after a break, but I’ve had to be concerned with a more important personal matter for the last week or so.

I’ve got a couple of questions about the spreadsheet implementation of your model (the one you posted here and Arthur pointed me to)

Sheet: ‘Raw_Temperature’
——————————

Raw_Temperature:B9 contains 0.05 which is described as amplitude, and subsequently used in column J ‘Solar’.

Q1. Is this a representation of the sunspot cycle and variation?

Q2. If so, does it represent variation in the Solar Constant or TSI? Because if it does, it’s too large. TSI variation over the sunspot cycle is 0.001 not 0.05 (ie. 0.1% not 5%). (The solar constant at the earth varies by more (7%) but that’s an annual cycle not 11 years.)

Q3. Or is it a temperature variation with an amplitude of 0.05C? I have some vague memory of a comment on your blog that the solar cycle causes temperature variation at the earth of 0.1C, but I couldn’t find it today. If so, there are two problems:

- your model assumes no thermal inertia, even though it is considerable. A simple COS curve seems to be far too simple a model for solar forcing, no matter what it’s magnitude

- it’s still too large IMHO, because it would imply that the much larger annual variation of 7% in the solar constant should induce an annual cycle between January and July of 7C which would swamp all other monthly anomalies, something that is just not present in the data.

Q4. Given all the above can be corrected (or you can convince me that it’s valid), shouldn’t you use the solar corrected model as the basis for an analysis of underlying trends?

I’d also comment that the lack of this correction for reduction in solar forcing is to pull down the trend towards the end of the your 7 year period – almost monotonically. Your inclusion of a reduction in solar forcing in your main graph is almost entirely responsible for your cooling effect.

Implementation of Cochrane-Orcutt
—————————————-

Sheet: ‘Regress_All’

I have some comments and questions about the implementation of Cochrane-Orcutt here.

Firstly, in column I, you do a simple average of the input anomalies across all series. I’ve commented on this before – it’s not valid as they have different bases.

Q5. Have you noted that a regression on your average has an r2 of only 0.0038? Isn’t that rather small to be drawing conclusions from?

Next you have a circular reference here (which Excel detects as an ‘Inconsistent Formula”)

The remaining residuals in column L are calculated as

=m_co*G8+b_co-H8

But m_co and b_co are calculated as LINEST() of columns G and H in row 113 and therefore feedback into the calculation above.

You may not have noticed this because your sheet appears to stop Excel complaining about this by what appears to be a manual cut and paste of the rho value from J5 to H5. Excel can no longer detect the circularity.

Q6. Have you checked that your calculations are unaffected by this circularity?

Q7. What is your convergence criteria? Why do you iterate only once?

You comment here (http://rankexploits.com/musing.....ne-orcutt/) that OC is iterative and should be carried out until convergence occurs in the error term. Specifically Pold and Pnew.

So you’re looking at the correlation of residuals at each iterative step, but there’s no evidence of convergence there. In J5 and L5 you correlate the new and old residuals and get two values 0.568 and -0.054. Markedly different. Was that your convergence criteria? Shouldn’t you have done it one more time to make sure?

(I tried to check this by cutting and pasting and doing another iteration, but I may have done it wrong – it appeared to me to be diverging rather than converging)

Q8. Have you backtested this model against other 7 year periods?

JM–
Many of your current questions reflect that you a) have not read the previous blog posts and b) don’t know how CO is done. It is an iterative process, as has been discussed both here, in the links I posted when I first posted the CO etc.

The discussion of the comparison to past 7 year periods will be discussed this week. The comparison will be made to periods known to be unaffected by major stratospheric volcanic eruptions.

Lucia

“Many of your current questions reflect that you a) have not read the previous blog posts”

I have read them. I am either making specific criticisms of some of those choices, or more pertitently, questioning your implementation rather than your methods.

“… CO … is an iterative process”

I agree. Why doesn’t your spreadsheet implement it iteratively then?

(And what is your justification for 0.1C of solar forcing over the cycle?)

JM–

“… CO … is an iterative process”
I agree. Why doesn’t your spreadsheet implement it iteratively then?

I iterate manually. It converges instantly.

(And what is your justification for 0.1C of solar forcing over the cycle?)

Had you read the post on the solar cycle, you would know that this is a computation done at JohnV’s request based on a precise quote from the IPCC AR4. He requested that we do a back of the envelop, speculative calculation to see how much difference the solar cycle could possibly make. I did this to show him the answer if we used is value.

You will also find that various people, including me, think that magnitude of effect is dubious. So, even though it has an 11 year periodicity — outside the 84 month band– it does not explain the falsification.

Had you read the posts, you would be aware of this.

Lucia: “I iterate manually.”

Do you? How? Your spreadsheet doesn’t, it calculates one error term and then does a plot. If you iterate outside the spreadsheet until convergence then you’re not plotting or publishing those results.

“It converges instantly.”

Hmmmm. Do you mean without iteration, with one iteration, with two, or what?

“you would know that this is a computation done at JohnV’s request based on a precise quote from the IPCC AR4.”

Sorry, I don’t recall seeing it. Could you give me a specific pointer to the comment with the calculation (and also the AR4 quote), perhaps it will satisfy my curiosity.