Did I miss the thread about the CRU Fraud?

[Wezil]

So far HL has presented the best arguments in this thread.

He trumps the name calling.

[BlackCat]

Oops! I guess some people owe some others an apology or some very amusing backpedaling.

Here is something from the other trench (bolded an interesting comment on the 'trick')

http://www.dailymail.co.uk/news/article-1235395/SPECIAL-INVESTIGATION-Climate-change-emails-row-deepens--Russians-admit-DID-send-them.html

SPECIAL INVESTIGATION: Climate change emails row deepens as Russians admit they DID come from their Siberian server

The claim was both simple and terrifying: that temperatures on planet Earth are now ‘likely the highest in at least the past 1,300 years’.

As its authors from the United Nations Intergovernmental Panel on Climate Change (IPCC) must have expected, it made headlines around the world.

Yet some of the scientists who helped to draft it, The Mail on Sunday can reveal, harboured uncomfortable doubts.

In the words of one, David Rind from the US space agency Nasa, it ‘looks like there were years around 1000AD that could have been just as warm’.

Keith Briffa from the University of East Anglia’s Climatic Research Unit (CRU), which plays a key role in forming IPCC assessments, urged caution, warning that when it came to historical climate records, there was no new data, only the ‘same old evidence’ that had been around for years.

‘Let us not try to over-egg the pudding,’ he wrote in an email to an IPCC colleague in September 2006.

‘True, there have been many different techniques used to aggregate and scale data - but the efficacy of these is still far from established.’

But when the ‘warmest for 1,300 years’ claim was published in 2007 in the IPCC’s fourth report, the doubters kept silent.

It is only now that their concerns have started to emerge from the thousands of pages of ‘Warmergate’ emails leaked last month from the CRU’s computers, along with references to performing a ‘trick’ to ‘hide’ temperature decline and instructions to resist all efforts by the CRU’s critics to use the Freedom of Information Act to check the unit’s data and conclusions.

Last week, as an official inquiry by the former civil servant Sir Muir Russell began, I tried to assess Warmergate’s wider significance.

The CRU’s supporters insisted it was limited. ‘In the long term, it will make very little difference to the scientific consensus, and to the way politicians respond to it,’ Professor Trevor Davies, the university’s Pro-Vice Chancellor and a former CRU director, told me. ‘I am certain that the science is rock solid.’

He admitted that his CRU colleagues had sometimes used ‘injudicious phrases’, but that was because they kept on being ‘diverted’ from their work by those who wished to scrutinise it. ‘It’s understandable that sometimes people get frustrated,’ he said.

The only lesson the affair had for him was that ‘we have got to get better in terms of explanation. Some scientists still find it quite it difficult to communicate with the public.’

Others, however, were less optimistic. Roger Pielke, Professor of Environmental Studies at the University of Colorado, could in no sense be described as a climate change sceptic, let alone a ‘denier’.

‘Human-caused climate change is real, and I’m a strong advocate for action,’ he said. ‘But I’m also a strong advocate for integrity in science.’

Pielke’s verdict on the scandal is damning.

‘These emails open up the possibility that big scientific questions we’ve regarded as settled may need another look.

'They reveal that some of these scientists saw themselves not as neutral investigators but as warriors engaged in battle with the so-called sceptics.

‘They have lost a lot of credibility and as far as their being leading spokespeople on this issue of huge public importance, there is no going back.’

Climate science is complicated, and often the only way to make sense of raw data is through sophisticated statistical computer programs.

The consequence is that most lay individuals - politicians and members of the public alike - have little choice but to take the assurances of scientists such as Davies on trust.

He and other ‘global warmists’ often insist that when it comes to the IPCC’s main conclusions - that the Earth is in a period of potentially catastrophic warming and that the main culprit is man-made greenhouse gas emission - no serious scientist dissents from the conventional view.

Hence, perhaps, Gordon Brown’s recent comment that those who disagree are ‘behind-the-times, antiscience, flat-Earth climate sceptics’.

In fact, there is a large body of highly-respected academic experts who fiercely contest this thesis: people such as Richard Lindzen, Professor of Meteorology at the Massachusetts Institute of Technology and a disillusioned former IPCC member, and Dr Tom Segalstad, head of geology at Oslo University, who has stated that ‘most leading geologists throughout the world know that the IPCC’s view of Earth processes are implausible if not impossible’.

These dissenters focus their criticisms on the IPCC’s analysis of the way the atmosphere works and the models it uses to predict the future.

However, Warmergate strikes at something more fundamental - the science that justifies the basic assumption that the present warming really is unprecedented, at least in the past few thousand years.

Take the now-notorious email that the CRU’s currently suspended director, Dr Phil Jones, sent to his IPCC colleagues on November 16, 1999, when he wrote he had ‘just completed Mike’s Nature trick’ and had so managed to ‘hide the decline’.
For example, some suggest that the ‘medieval warm period’ was considerably warmer than even 1998. Of course, this is inconvenient to climate change believers because there were no cars or factories pumping out greenhouse gases in 1000AD - yet the Earth still warmed.


The CRU’s supporters have protested bitterly about the attention paid to this message. In the course of an extraordinary BBC interview in which he called an American critic an ‘****hole’ live on air, Jones’s colleague Professor Andrew Watson insisted that the fuss was completely unjustified, because all Jones had been talking about was ‘tweaking a diagram’.

Davies told me that the email had been ‘taken out of context’ adding: ‘One definition of the word “trick” is “the best way of doing something”. What Phil did was standard practice and the facts are out there in the peer-reviewed literature.’

However, the full context of that ‘trick’ email, as shown by a new and until now unreported analysis by the Canadian climate statistician Steve McIntyre, is extremely troubling.

Derived from close examination of some of the thousands of other leaked emails, he says it suggests the ‘trick’ undermines not only the CRU but the IPCC.

There is a widespread misconception that the ‘decline’ Jones was referring to is the fall in global temperatures from their peak in 1998, which probably was the hottest year for a long time. In fact, its subject was more technical - and much more significant.

It is true that, in Watson’s phrase, in the autumn of 1999 Jones and his colleagues were trying to ‘tweak’ a diagram. But it wasn’t just any old diagram.

It was the chart displayed on the first page of the ‘Summary for Policymakers’ of the 2001 IPCC report - the famous ‘hockey stick’ graph that has been endlessly reproduced in everything from newspapers to primary-school textbooks ever since, showing centuries of level or declining temperatures until a dizzying, almost vertical rise in the late 20th Century.

There could be no simpler or more dramatic representation of global warming, and if the origin of worldwide concern over climate change could be traced to a single image, it would be the hockey stick.

Drawing a diagram such as this is far from straightforward.

Gabriel Fahrenheit did not invent the mercury thermometer until 1724, so scientists who want to reconstruct earlier climate history have to use ‘proxy data’ - measurements derived from records such as ice cores, tree-rings and growing season dates.

However, different proxies give very different results.

For example, some suggest that the ‘medieval warm period’, the 350-year era that started around 1000, when red wine grapes flourished in southern England and the Vikings tilled now-frozen farms in Greenland, was considerably warmer than even 1998.

Of course, this is inconvenient to climate change believers because there were no cars or factories pumping out greenhouse gases in 1000AD - yet the Earth still warmed.

Some tree-ring data eliminates the medieval warmth altogether, while others reflect it. In September 1999, Jones’s IPCC colleague Michael Mann of Penn State University in America - who is now also the subject of an official investigation --was working with Jones on the hockey stick. As they debated which data to use, they discussed a long tree-ring analysis carried out by Keith Briffa.

Briffa knew exactly why they wanted it, writing in an email on September 22: ‘I know there is pressure to present a nice tidy story as regards “apparent unprecedented warming in a thousand years or more”.’ But his conscience was troubled. ‘In reality the situation is not quite so simple - I believe that the recent warmth was probably matched about 1,000 years ago.’

Another British scientist - Chris Folland of the Met Office’s Hadley Centre - wrote the same day that using Briffa’s data might be awkward, because it suggested the past was too warm. This, he lamented, ‘dilutes the message rather significantly’.

Over the next few days, Briffa, Jones, Folland and Mann emailed each other furiously. Mann was fearful that if Briffa’s trees made the IPCC diagram, ‘the sceptics [would] have a field day casting doubt on our ability to understand the factors that influence these estimates and, thus, can undermine faith [in them] - I don’t think that doubt is scientifically justified, and I’d hate to be the one to have to give it fodder!’

Finally, Briffa changed the way he computed his data and submitted a revised version. This brought his work into line for earlier centuries, and ‘cooled’ them significantly. But alas, it created another, potentially even more serious, problem.

According to his tree rings, the period since 1960 had not seen a steep rise in temperature, as actual temperature readings showed - but a large and steady decline, so calling into question the accuracy of the earlier data derived from tree rings.

This is the context in which, seven

weeks later, Jones presented his ‘trick’ - as simple as it was deceptive.

All he had to do was cut off Briffa’s inconvenient data at the point where the decline started, in 1961, and replace it with actual temperature readings, which showed an increase.

On the hockey stick graph, his line is abruptly terminated - but the end of the line is obscured by the other lines.

‘Any scientist ought to know that you just can’t mix and match proxy and actual data,’ said Philip Stott, emeritus professor of biogeography at London’s School of Oriental and African Studies.

‘They’re apples and oranges. Yet that’s exactly what he did.’

Since Warmergate-broke, some of the CRU’s supporters have claimed that Jones and his colleagues made a ‘full disclosure’ of what they did to Briffa’s data in order to produce the hockey stick.

But as McIntyre points out, ‘contrary to claims by various climate scientists, the IPCC Third Assessment Report did not disclose the deletion of the post-1960 values’.

On the final diagram, the cut off was simply concealed by the other lines.

By 2007, when the IPCC produced its fourth report, McIntyre had become aware of the manipulation of the Briffa data and Briffa himself, as shown at the start of this article, continued to have serious qualms.

McIntyre by now was an IPCC ‘reviewer’ and he urged the IPCC not to delete the post-1961 data in its 2007 graph. ‘They refused,’ he said, ‘stating this would be “inappropriate”.’
‘Any scientist ought to know that you just can’t mix and match proxy and actual data’

Yet even this, Pielke told me, may not ultimately be the biggest consequence of Warmergate.

Some of the most controversial leaked emails concern attempts by Jones and his colleagues to avoid disclosure of the CRU’s temperature database - its vast library of readings from more than 1,000 weather stations around the world, the ultimate resource that records how temperatures have changed.

In one email from 2005, Jones warned Mann not to leave such data lying around on searchable websites, because ‘you never know who is trawling them’.

Critics such as McIntyre had been ‘after the CRU station data for years. If they ever hear there is a Freedom of Information Act now in the UK, I think I’ll delete the file rather than send to anyone’.

Yesterday Davies said that, contrary to some reports, none of this data has in fact been deleted. But in the wake of the scandal, its reliability too is up for grabs.

The problem is that, just like tree rings or ice cores, readings from thermometers or electronic ‘thermistors’ are open to interpretation.

The sites of weather stations that were once open countryside become built up areas, so trapping heat, and the type of equipment used changes over time.

The result is what climate scientists call ‘inhomogeneities’ - anomalies between readings that need to be ‘adjusted’.

But can we trust the way such ‘adjustments’ are made?

Last week, an article posted on a popular climate sceptic website analysed the data from the past 130 years in Darwin, Australia.

This suggested that average temperatures had risen there by about two degrees Celsius. However, the raw data had been ‘adjusted’ in a series of abrupt upward steps by exactly the same amount: without the adjustment, the Darwin temperature record would have stayed level.

In 2007, McIntyre examined records across America. He found that between 1999 and 2007, the US equivalent of the Met Office had changed the way it adjusted old data.

The result was to make the Thirties seem cooler, and the years since 1990 much warmer. Previously, the warmest year since records began in America had been 1934.

Now, in line with CRU and IPCC orthodoxy, it was 1998.

At the CRU, said Davies, some stations’ readings were adjusted by unit and in such cases, raw and adjusted data could be compared.

But in about 90 per cent of cases, the adjustment was carried out in the countries that collected the data, and the CRU would not know exactly how this had been done.

Davies said: ‘All I can say is that the process is careful and considered. To get the details, the best way would be to go the various national meteorological services.’

The consequences of that, Stott said, may be explosive. ‘If you take Darwin, the gap between the two just looks too big.

‘If that applies elsewhere, it’s going to get really interesting. It’s no longer going to be good enough for the Met Office and CRU to put the data out there.

‘To know we can trust it, we’ve got to know what adjustments have been made, and why.’

Last week, at the Copenhagen climate summit, the Met Office said that the Noughties have been the warmest decade in history. Depending on how the data has been adjusted, Stott said, that statement may not be true.

Pielke agreed. ‘After Climategate, the surface temperature record is being called into question.’ To experts such as McIntyre and Pielke, perhaps the most baffling thing has been the near-unanimity over global warming in the world’s mainstream media - a unanimity much greater than that found among scientists.

In part, this is the result of strongarm tactics.

For example, last year the BBC environment reporter Roger Harrabin made substantial changes to an article on the corporation website that asked why global warming seemed to have stalled since 1998 - caving in to direct pressure from a climate change activist, Jo Abbess.

‘Personally, I think it is highly irresponsible to play into the hands of the sceptics who continually promote the idea that “global warming finished in 1998” when that is so patently not true,’ she told him in an email.

After a brief exchange, he complied and sent a final note: ‘Have a look in ten minutes and tell me you are happier. We have changed headline and more.’

Afterwards, Abbess boasted on her website: ‘Climate Changers, Remember to challenge any piece of media that seems like it’s been subject to spin or scepticism. Here’s my go for today. The BBC actually changed an article I requested a correction for.’

Last week, Michael Schlesinger, Professor of Atmospheric Studies at the University of Illinois, sent a still cruder threat to Andrew Revkin of the New York Times, accusing him of ‘gutter reportage’, and warning: ‘The vibe that I am getting from here, there and everywhere is that your reportage is very worrisome to most climate scientists ... I sense that you are about to experience the “Big Cutoff” from those of us who believe we can no longer trust you, me included.’

But in the wake of Warmergate, such threats - and the readiness to bow to them - may become rarer.

‘A year ago, if a reporter called me, all I got was questions about why I’m trying to deny climate change and am threatening the future of the planet,’ said Professor Ross McKitrick of Guelph University near Toronto, a long-time collaborator with McIntyre.

‘Now, I’m getting questions about how they did the hockey stick and the problems with the data.

‘Maybe the emails have started to open people’s eyes.’
Yes, emails came from here - but we didn't do it, say Russians

Russian secret service agents admitted yesterday that the hacked ‘Warmergate’ emails were uploaded on a Siberian internet server, but strenuously denied a clandestine state-sponsored operation to wreck the Copenhagen summit.

The FSB - formerly the KGB - confirmed that thousands of messages to and from scientists at the University of East Anglia’s Climatic Research Unit were distributed to the world from the city of Tomsk, as revealed by The Mail on Sunday last week.

Now, it has emerged that IT experts specialising in hacking techniques were brought in by the Russian authorities following this newspaper’s exposure of the Tomsk link.

They have gathered evidence about how and where the operation was carried out, although they are not prepared to say at this stage who they think was responsible.

A Russian intelligence source claimed the FSB had new information which could cast light on who was behind the elaborate operation.

‘We are not prepared to release details, but we might if the false claims about the FSB’s involvement do not stop,’ he said. ‘The emails were uploaded to the Tomsk server but we are sure this was done from outside Russia.’

The Kremlin’s top climate change official, Alexander Bedritsky, denied the Russian government was involved in breaking into the CRU’s computer system.

[BlackCat]

The short of it is this: You make baby Occam cry.

Well, here is a handkerchief for the baby - though, beware, the nutcase is a former meteorologist at NASA

http://www.drroyspencer.com/research-articles/satellite-and-climate-model-evidence/

Satellite and Climate Model Evidence Against Substantial Manmade Climate Change (supercedes “Has the Climate Sensitivity Holy Grail Been Found?”)
by Roy W. Spencer, Ph.D.

December 27, 2008 (last modified December 29, 2008)
ABSTRACT

Three IPCC climate models, recent NASA Aqua satellite data, and a simple 3-layer climate model are used together to demonstrate that the IPCC climate models are far too sensitive, resulting in their prediction of too much global warming in response to anthropogenic greenhouse gas emissions. The models’ high sensitivity is probably the result of a confusion between forcing and feedback (cause and effect) when researchers have interpreted cloud and temperature variations in the real climate system. (What follows is a brief summary of research we will be submitting to Journal of Climate in January 2009 for publication. I challenge any climate researcher to come up with an alternative explanation for the evidence presented below…I would love to hear it…my e-mail address is at the bottom of the page.)
1. INTRODUCTION & BACKGROUND

Since computerized climate models are the main source of concern over manmade global warming, it is imperative that they be tested against real measurements of the climate system. The amount of warming these models predict for the future in response to rising concentrations of carbon dioxide in the atmosphere is anywhere from moderate to catastrophic. Why is this?

It is well known that most of that warming is NOT due to the direct warming effect of the CO2 by itself, which is relatively weak. It is instead due to indirect effects (positive feedbacks) that amplify the small amount of direct warming from the CO2. The most important warmth-amplifying feedbacks in climate models are clouds and water vapor.

Cloud feedbacks are generally considered to be the most uncertain of feedbacks, although all twenty climate models tracked by the Intergovernmental Panel on Climate Change (IPCC) now suggest cloud feedbacks are positive (warmth-amplifying) rather than negative (warmth-reducing). The only question in the minds of most modelers is just how strong those positive feedbacks really are in nature. This article deals with how feedbacks are estimated from satellite observations of natural climate variability…and describes a critical error in interpretation which has been made in the process.

Since at this point you are probably dazed and confused about what a ‘feedback’ is, let’s do a simple thought experiment. Imagine you are out in space, observing the Earth, and feeling the radiant energy it gives off from sunlight reflected off of clouds and from the infrared (heat) radiation it emits in proportion to its temperature.

Now imagine that the Earth’s surface and atmosphere suddenly warm by 1 deg. C, everywhere. In this case the Earth would immediately give off an extra 3.3 Watts per square meter of infrared energy (just as a hot stovetop element gives off more infrared energy than a warm one).

This example represents the “no feedback” case…only the temperature has changed in the system, resulting in extra infrared energy being given off, at a rate of 3.3 Watts per square meter for every degree C of temperature increase. But in the real world, any source of warming (or cooling) causes other changes in clouds, water vapor, etc., to occur. These can cause extra warming if they either increase the amount of absorbed sunlight (e.g. fewer low clouds), or reduce the rate of infrared radiation to outer space (e.g. more water vapor, our main greenhouse gas). These warmth-amplifying changes are called positive feedbacks.

Alternatively, cloud and water vapor changes could decrease the amount of absorbed sunlight or increase the amount of emitted infrared energy, thus reducing the warming. This is called negative feedback.

That number (3.3) thus represents the magic boundary between positive and negative feedback. If satellites measure more than 3.3 Watts per square meter given off by the Earth per degree of global warming, that is evidence of negative feedback. If the number is less than 3.3, that is positive feedback. If the number reached zero, that would correspond to a borderline unstable climate system. The 20 climate models tracked by the IPCC have feedbacks ranging from about 0.9 to 1.9 (all corresponding to positive feedback since they are less than 3.3).

The central importance of feedbacks to the global warming issue can not be overstated. How the radiant flows of energy in and out of the Earth system change with temperature is THE most critical piece of knowledge we need in order to predict whether manmade global warming will be benign — or catastrophic. It is obvious that good estimates of feedbacks are needed from our observations of natural climate variability. This would provide the most important test of climate models: do the models exhibit feedbacks consistent with those we observe in nature?

Unfortunately, such testing of the models has been surprisingly difficult. I am now convinced that the main difficulty in diagnosing feedbacks from observations of natural climate variability has been related to the issue of causation when observing cloud behavior. The best way to introduce what I mean by this is with the following question:

When low cloud cover is observed to decrease with warming, is the cloud change the result of the warming, or is the warming the result of the cloud change?

Some will claim that the direction of causation does not really matter, and that all we really need to know is the average relationship between temperature and clouds — but I will show that this is incorrect.

Decreasing low cloud cover caused by warming would be a positive feedback, since it would let more sunlight in. But what if, say, a change in atmospheric circulation patterns caused the decrease in low cloud cover? In that case, warming would be the ‘effect’, rather than the ’cause’. And as we shall see, this can give the illusion of positive feedback — even when negative feedback really exists.

For instance, if the Earth warms by 1 deg C and our satellites measure only 1 watt per square meter of extra radiant energy being given off, since that is less than the magic 3.3 value we might be tempted to say that strong positive feedback is the cause. But this assumes the change in radiant energy is the RESULT of the warming. As we will see, the same result can be obtained if the true feedback number is, say, 6 (strong negative feedback), but most of the cloud change was actually the CAUSE of the warming.

The issue I am raising is not a new one, as there have been general concerns previously published about the diagnosis of feedbacks (e.g. Stephens, 2005; Aires and Rossow,2003). I am merely getting very specific about what has previously been a more general concern.

In the theoretical analysis by Forster and Gregory (2006) of the factors impacting feedback estimation, it was claimed that internal natural variability in clouds would not contaminate the estimation of feedbacks…or that it would, at most, only make feedback estimates from satellite measurements “noisy”, with a variety of diagnosed feedbacks clustering around the “true” feedback value. But as we showed in Spencer and Braswell (2008), something as simple as daily random variations in cloud cover will cause diagnosed feedbacks to not only be ‘noisy’, but also to be biased in the direction of positive feedback.

Here I will show further evidence, from both climate models and satellite data, that this issue is so serious that it might well have caused climate modelers to mistakenly conclude that cloud feedbacks in the climate system are positive when in fact the evidence, when more critically examined, suggests they are negative.

There has also been a persistent concern in the climate research community that feedbacks diagnosed from relatively short satellite datasets, even if they were accurate, might not have anything to do with feedbacks involved in long-term global warming.

Here I will briefly address both of these issues. Specifically, I will show that:

1) the IPCC climate models indicate that short-term and long-term feedbacks in those models are substantially the same, and

2) a simple climate model tuned to mimic recent NASA Aqua satellite observations of global radiative imbalance, sea surface temperatures (SSTs), and tropospheric temperature (Tair) variations, suggests that short term feedbacks in the real climate system are strongly negative.

Taking (1) and (2) together, one must then consider the possibility that current climate models are too sensitive — possibly by a wide margin — and they are therefore forecasting too much global warming and associated climate change in response to anthropogenic greenhouse gas emissions.
2. FEEDBACKS IN THE IPCC CLIMATE MODELS

Feedbacks are not explicitly input into climate models. They are instead the net result of all the different physical processes contained in the models…especially those related to clouds and water vapor. Feedbacks are diagnosed from model output in much the same way as they are diagnosed from satellite measurements of the Earth: by comparing (1) global average temperature variations to (2) global average variations in the radiative balance of the Earth (variations in the approximate balance between absorbed sunlight and emitted infrared radiation averaged over the whole Earth).

The following figure shows yearly- and globally-averaged near-surface (2 meter) air temperature (T2m) variations versus top-of-atmosphere longwave (LW) infrared radiative variations from three IPCC climate models. The yearly averages are plotted every month from the first 60 years of ‘transient’ greenhouse gas experiments in which the radiative forcing from extra carbon dioxide is slowly increased over time.

Plotting yearly averages every month in Fig. 1 allows the time-evolution of the model runs to be visualized, which turns out to be a critical step in the physical interpretation of the models’ behavior.

Fig. 1. Sixty years of global average variations in near-surface air temperature versus top-of-atmosphere infrared radiative flux in three of the climate models tracked by the IPCC. See text for details.

We are particularly interested in the linear striations which appear in Fig. 1. I have previously documented these striations in satellite measurements, and argued that their slope corresponds to the strength of feedback in the climate system. Unfortunately, two research papers containing evidence of this were both rejected for publication based upon poor reviews from a single reviewer – a rather unusual basis for total rejection by any science journal.

Well, once again the linear striations appear – but this time in the climate models themselves! And their slopes do indeed correspond to the long term feedbacks (dashed lines in Fig. 1) diagnosed by Forster and Taylor (2006) from these models’ response to anthropogenic greenhouse gas forcing.

In addition to the linear striations, we also see evidence of spiral patterns in Fig. 1. These spirals were seen, to a lesser or greater extent, in all 18 IPCC models we analyzed. More examples of these spiral patterns are seen in Fig. 2 for the CNRM-CM3.0 model.

Fig. 2. As in Fig. 1, but for the emitted infrared longwave (LW), reflected solar shortwave (SW) and total (LW+SW) radiative fluxes in the CNRM-CM3.0 model, for various data averaging time intervals.

Note how the raw monthly averages (the panels on the left side of Fig. 2) produce just a ‘cloud’ of points on the graph. Independent yearly averages also produce a cloud of points. It is only when we plot overlapping averages — e.g., yearly averages computed every month — that we see these linear and spiral patterns appear.

Next I will show that the linear striations and spiral patterns can only be explained by feedback and radiative forcing, respectively. In the current ‘forcing-feedback’ paradigm of climate variability, those are the only two kinds of radiative variations possible – forcing and feedback (or, loosely speaking, ’cause and effect’) — and they have distinctly different signatures in the data. There are no other physical explanations for such patterns.

As shown by Spencer & Braswell (2008) radiative feedback can only be accurately diagnosed from satellite data when it is in response to non-radiative forcing of temperature change: primarily variations in evaporation and precipitation. As shown in Fig. 3 (on the right), a simple model whose temperature is forced with only non-radiative forcing (panel a), produces a perfect feedback signal, with the temperature and radiative flux changes falling neatly along a line, the slope of which is the feedback I specified in the model run (2 Watts per sq. meter per degree).

But as increasing amounts of randomly varying radiative forcing is mixed in with the non-radiative forcing (panels b and c), spiral patterns begin to appear, decorrelating the data and reducing the slope of a line fit to the data. Finally, if the forcing is 100% radiative (panel d), then no ‘feedback stripes’ are evident — even though the same feedback (2 Watts per sq. meter per degree) is occurring in all four panels. In this case, the slope of a line fit to the data is zero. And since a total feedback parameter of zero corresponds to a borderline unstable (highly sensitive) climate system, this represents a potentially serious problem when it comes to diagnosing feedbacks from satellite data.

The importance of this can not be overstated. To the extent that natural cloud fluctuations are occurring — as evidenced by spiral patterns in the data — the diagnosis of feedbacks by fitting a line to the satellite data will result in a feedback value biased toward zero. This can be mistakenly interpreted as positive feedback — even if strong negative feedback is operating. I suppose you can call this a ‘false positive’, as sometimes occurs in the medical diagnosis of disease.

[BlackCat]

continued

3. FEEDBACKS IN SATELLITE MEASUREMENTS OF NATURAL CLIMATE VARIABILITY

Now let’s examine what kinds of temperature-radiative flux relationships are seen in the NASA Aqua satellite data. Plots of the total (reflected solar plus emitted infrared) radiative imbalance versus temperature variability are shown in Fig. 4 at various averaging intervals from five years of satellite measurements of global average SST variations (left panels) and tropospheric temperature variations (right panels). The radiative fluxes come from the CERES instrument, the SSTs from the AMSR-E instrument, and the tropospheric temperatures come from the AMSU-A instrument flying on the NOAA-15 satellite.

Fig. 4. Aqua satellite-measured total [LW+SW] radiative flux versus SST (left panels) and versus tropospheric temperature (right panels) at averaging times of: (a, b) 1 day; ( c, d) 31 days; (e, f) 91 days; and (g, h) 365 days. (Note that the averaging times now vary vertically, while in Fig. 2 they varied horizontally).

The slopes of the striations seen in the right panels of Fig. 4 (relative to atmospheric temperature) correspond to strongly negative feedback: around 6 Watts per square meter per degree K of temperature change (6 W m-2 K-1). In fact, even though we expect feedbacks diagnosed from the data to be biased toward zero, here the lines fitted to all the data have slopes actually approaching that value: 6 W m-2 K-1. Translated into a global warming estimate, a feedback of 6 W m-2 K-1 would correspond to a rather trivial 0.6 deg. C of warming in response to a doubling of atmospheric CO2.

A couple of the SST plots on the upper left in Fig. 4, however, have very different slopes…but as averaging times get longer, the line slopes also end up corresponding to negative feedback (3.7 W m-2 K-1 in Fig. 4g translates to about 1 deg. C of warming for a doubling of atmospheric CO2).

This very different response for SSTs versus tropospheric temperatures at short time scales is due to the episodic, non-radiative transfers of heat between the surface and atmosphere mentioned earlier (see Spencer et al., 2007, for more evidence of these oscillations).

To demonstrate this physical interpretation, I modified the simple climate model of Spencer and Braswell (2008) to include an atmosphere, an ocean mixed layer, and a deeper ocean layer which slowly exchanges heat with the mixed layer. (These modifications were added one by one, as necessary, to explain characteristics of the satellite data which could not be expalined without those modifications.) The model was driven by radiative and non-radiative forcings that varied randomly in time, having a time scale of days to weeks. The radiative forcing only directly affects the ocean mixed layer, as might be expected with variations in low cloud cover causing varying amounts of absorbed sunlight by the ocean.

In contrast, the non-radiative forcing affects both the mixed layer and the atmosphere, since enhanced evaporative cooling of the ocean surface must be matched by enhanced latent heating of the atmosphere by precipitation systems. As a result, on short time scales, the ocean surface temperature and atmospheric temperaures will be negatively correlated.

The adjustable parameters in the model were then tuned to mimic the lag correlation and autocorrelation structures computed from the five years of daily satellite satellite measurements of tropospheric temperature, sea surface temperature, and radiative flux (see Fig. 5, below, if you are interested in this level of detail).

Fig. 5. Lag-correlation and autocorrelations in the five year time series of daily satellite measurments in global oceanic SST, tropospheric temperature, and total (solar +IR) radiative flux.

After this tuning to mimic the satellite measurements, I obtained model behavior (Fig. 6) that looks quite like the satellite data shown in Fig. 4. (If you have a fairly large computer display, you can bring this same web page up in another browser, and compare Figs. 4 and 6 side-by-side.)

Fig. 6. Five years of variability output from a simple 3-layer climate model tuned to mimic several statistical characteristics of the satellite observations (see text for details)

The important lesson to take from this model simulation is that a strongly negative feedback (6 W m-2 K-1) had to be specified in the model in order to mimic the satellite data in Fig. 4. Note that even though the specified feedback was 6 W m-2 K-1, NONE of the lines in Fig. 6 have a slope that large.

What this means is that the line slopes diagnosed from the satellite data in Fig. 4 might actually be an UNDERESTIMATE of the true feedback occurring, which could be 7 W m-2 K-1 or more.
4. AND FINALLY, FROM NASA’S TERRA SATELLITE…

The data presented above from NASA’s Aqua satellite covered the time period from August of 2002 through August of 2007. But NASA’s Terra satellite also carried a CERES instrument for monitoring radiative fluxes, and those data extend back to March of 2000. This is of particular interest since global temperatures were just beginning a two year long warming trend at that time.

In Fig. 7 (below) I have plotted Terra CERES total radiative flux variations versus NOAA-15 AMSU channel 5 tropospheric temperatures. The data points are 91-day averages plotted every day — just like the Aqua data plotted in Fig. 4g, but now extending back 2.5 years earlier. What is noteworthy in this figure are the clearly displayed feedback stripes, which have an average slope of about 8 W m-2 K-1.

Fig. 7. Terra CERES total radiative flux versus NOAA-15 AMSU tropospheric temperature variations over the global oceans from March 2000 through August 2007.

Together, the CERES data from two separate satellites thus display evidence of what I have used a simple model to explain theoretically: strong negative feedback is observed to occur on shorter time scales in response to non-radiative forcing events (evaporation/precipitation), which are superimposed upon a more slowly varying background of radiative imbalance, probably due to natural fluctuations in cloud cover changing the rate of solar heating of the ocean mixed layer.
5. CONCLUSIONS & DISCUSSION

What I have presented here is, as far as I know, the most detailed attempt to reconcile satellite observations of the climate system with the behavior of climate models in the context of feedbacks. Instead of the currently popular practice of building immensely complex and expensive climate models and then making only simple comparisons to satellite data, I have done just the opposite: Examine the satellite data in great detail, and then build the simplest model that can explain the observed behavior of the climate system.

The resulting picture that emerges is of an IN-sensitive climate system, dominated by negative feedback. And it appears that the reason why most climate models are instead VERY sensitive is due to the illusion of a sensitive climate system that can arise when one is not careful about the physical interpretation of how clouds operate in terms of cause and effect (forcing and feedback).

Indeed, climate researchers seldom (if ever) dig into the archives of satellite data and ask the question, “What are the satellite data telling us about the real climate system?” Instead, most climate research money now is funneled into building expensive climate models which are then expected to provide a basis for formulating public policy. Given the immense effort that has been invested, one would think that those models would be more rigorously tested.

There is nothing inherently wrong with a model-centric approach to climate research…as long as the modeler continues to use the observations to guide the model development over time. Unfortunately, as Richard Lindzen at MIT has pointed out, the fact that modelers use the term “model validation” rather than “model testing” belies their inherent preference of theory over observations. The allure of models is strong: they are clean, with well-defined equations and mathematical precision. Observations of the real climate system are dirty, incomplete, and prone to measurement error.

The comparisons modelers make between their models and satellite data are typically rather crude and cursory. They are not sufficiently detailed to really say anything of substance about feedbacks — in either the models or the satellite data – and yet it is the feedbacks that will determine how serious the manmade global warming problem will be.

And as I have tried to demonstrate here, the main reason for the current inadequacy of such methods of comparison between models and observations is the contaminating effect of clouds causing temperatures to change (forcing) when trying to estimate how temperatures cause clouds to change (feedback). This not a new issue, as it has been addressed by Forster and Gregory (2006, applied to satellite measurements) and Forster and Taylor (2006, applied to climate model output). I have merely demonstrated that the same contamination occurs from internal fluctuations in clouds in the climate system.

The bottom line from the model and observational evidence presented here is that:

Net feedbacks in the real climate system — on both short and long time scales — are probably negative. A misinterpretation of cloud behavior has led climate modelers to build models in which cloud feedbacks are instead positive, which has led the models to predict too much global warming in response to anthropogenic greenhouse gas emissions.

What climate researcher Bob Cess said in a 1997 interview with Science magazine’s Richard Kerr seems to be still true today:

“…the [models] may be agreeing now simply because they’re all tending to do the same thing wrong. It’s not clear to me that we have clouds right by any stretch of the imagination.”

I challenge climate modelers to “validate” their models to the level of detail I have in my comparisons here between satellite observations (Fig. 4 & 5) and a simple climate model (Fig. 5 & 6). Once their climate models can behave in the same way as the satellite observations suggest the real climate system behaves on a year to year basis, then we can revisit how much global warming those models predict for the future. Until that happens, I consider the IPCC climate model forecasts of strong global warming in the coming decades to be completely unreliable for basing policy decisions on.

[Kuciwalker]

riding the tldr train here

[Straybow]

[Q=Kuciwalker;5721731]riding the tldr train here[/Q] Then you can't really claim you understand the objections to AGW, can you?

[KrazyHorse]

He doesn't need to eat the whole egg to know it's bad, ****.

[HalfLotus]

Mike Hulme, founding Director of the Tyndall Centre for Climate Change Research at the University of East Anglia:

“The idea of climate change should be seen as an intellectual resource around which our collective and personal identities and projects can form and take shape. We need to ask not what we can do for climate change, but what climate change can do for us.”

“Because the idea of climate change is so plastic, it can be deployed across many of our human projects and can serve many of our psychological, ethical and spiritual needs.”

“We will continue to create and tell new stories about climate change and mobilise them in support of our projects.”

“These myths transcend the scientific categories of “true” and “false.”

http://www.youtube.com/watch?v=BmGii...eature=related

[SpencerH]

some of the salient points from the abstract

What I have presented here is, as far as I know, the most detailed attempt to reconcile satellite observations of the climate system with the behavior of climate models in the context of feedbacks. Instead of the currently popular practice of building immensely complex and expensive climate models and then making only simple comparisons to satellite data, I have done just the opposite: Examine the satellite data in great detail, and then build the simplest model that can explain the observed behavior of the climate system.

The resulting picture that emerges is of an IN-sensitive climate system, dominated by negative feedback. And it appears that the reason why most climate models are instead VERY sensitive is due to the illusion of a sensitive climate system that can arise when one is not careful about the physical interpretation of how clouds operate in terms of cause and effect (forcing and feedback).

Indeed, climate researchers seldom (if ever) dig into the archives of satellite data and ask the question, “What are the satellite data telling us about the real climate system?” Instead, most climate research money now is funneled into building expensive climate models which are then expected to provide a basis for formulating public policy. Given the immense effort that has been invested, one would think that those models would be more rigorously tested.

There is nothing inherently wrong with a model-centric approach to climate research…as long as the modeler continues to use the observations to guide the model development over time. Unfortunately, as Richard Lindzen at MIT has pointed out, the fact that modelers use the term “model validation” rather than “model testing” belies their inherent preference of theory over observations. The allure of models is strong: they are clean, with well-defined equations and mathematical precision. Observations of the real climate system are dirty, incomplete, and prone to measurement error.

The very first critique I ever saw of the global warming models back in the late 80's (I think) was that they didnt include clouds - with the comment by the "cloud expert" that they tend to cool the earth.

[Lorizael]

So far HL has presented the best arguments in this thread.

He trumps the name calling.

This assessment is untrue for a couple reasons. The most important reason is the fact that he isn't actually arguing against climate change; he's arguing in favor of some sort of NWO conspiracy. So far, he's presented no reasonable arguments to support this position.

Second, whenever he's called out on untrue assertions he's made, he refuses to actually debate the subject. I've challenged him on a number of issues that he has so far failed to defend.

And thirdly, I've yet to engage in any kind of name-calling with him. I've only asked that he support certain suspect claims that he's made. Again, he has so far failed to do so. That doesn't speak well of his ability to present arguments.

[Lorizael]

Well, here is a handkerchief for the baby - though, beware, the nutcase is a former meteorologist at NASA

Unless this nutcase/meteorologist is peddling grandiose conspiracy theories, your post has nothing at all to do with mine.

[Wezil]

This assessment is untrue for a couple reasons.

Sorry Lori. Judge Wezil has made his ruling.

Better luck next time.

[HalfLotus]

The feedback problem with the models is addressed pretty clearly by MIT's Richard Lindzen.

In this video he shows 11 IPCC climate models, no doubt part of the "consensus", which directly contradict observed satellite data with respect to feedback. There's actually more than 11 models shown, they go off the slide in view. edit: Nope, there are 11, I had counted from the slide-show .pdf which shows the full slide. Anywho...

The eye-opening part starts at 4:20, but I recommend the entire video series (edit: this is part 5 of 6, here's part 1):

[Lorizael]

Uh. Okay. I'd just really, really like HalfLotus to respond to at least one of the arguments I presented him with. It's really frustrating to have someone come into a thread, make bogus claims, and then never try to defend them at all. Of course, it's quite possible that I'm just being brilliantly trolled. *shrug*

[HalfLotus]

Lori the language you use belies your stated intent to have a rational discussion.

If I recall correctly, one of your first posts directed at me in this thread called me "bat**** crazy", and you also chose to speak for "everyone here" in that view. You may be right about those two things, but that kind of language doesn't indicate seriousness on your part, or a willingness to engage in a productive discourse. There are several other examples, but I don't think it's necessary to re-quote several posts from earlier in the thread - we can all read and follow along.

As far as I can tell, you're one of the many name-calling trolls and majority-rules circle-jerkers here at Poly, so I'll ignore your posts for the most part.

I do want to acknowledge a mistake that I made in a previous post which you pointed out, that Limits to Growth was published after First Global Revolution. That was not a lie as you stated, in a presumption of my intent to deceive, I was simply wrong.

Now to back to CRU and general climate science fraud.