Well, maybe not my top 10…but the first ten that I thought of.

1. The term “climate change” itself. Thirty years ago, the term “climate change” would have meant natural climate change, which is what climate scientists mostly studied before that time. Today, it has come to mean human-caused climate change. The public, and especially the media, now think that “climate change” implies WE are responsible for it. Mother Nature, not Al Gore, invented real climate change.

2. “Climate change denier”. A first cousin to the first annoyance. Again, thirty years ago, “climate change denier” would have meant someone who denied that the Medieval Warm Period ever happened. Or that the Little Ice Age ever happened. What a kook fringe thing to believe that would have been! And now, those of us who still believe in natural climate change are called “climate change deniers”?? ARGHH.

3. The appeal to peer-reviewed and published research. I could go on about this for pages. Yes, it is important to have scientific research peer-reviewed and published. But as the Climategate e-mails have now exposed (and what many scientists already knew), we skeptics of human-caused climate change have “peers” out there who have taken it upon themselves to block our research from being published whenever possible. We know there are editors of scientific journals who assist in this by sending our papers to these gatekeepers for the purpose of killing the paper. We try not to complain too much when it happens because it is difficult to prove motivation. I believe the day is approaching when it will be time to make public the evidence of biased peer review.

4. Appeal to authority. This is the last refuge of IPCC scientists. Even when we skeptics get research published, it is claimed that our research is contradicted by other research the IPCC has encouraged, helped to get funded, and cherry-picked to support its case. This is dangerous for the progress of science. If the majority opinion of scientists was always assumed to be correct, then most major scientific advances would not have occurred. The appeal to authority is also a standard propaganda technique.

5. Unwillingness to debate. I have lectured to many groups where the organizers could not find anyone from the IPCC side who would present the IPCC’s side of the story. I would be happy to debate any of the IPCC experts on the central issues of human-caused versus natural climate change, and feedbacks in the climate system. They know where to find me. (For the most common tactic used by the IPCC in a debate, see annoyance #4.)

6. A lack of common sense. Common sense can be misleading, of course. But when there is considerable uncertainty, sometimes it is helpful to go ahead and use a little anyway. Example: It is well known that the net effect of clouds is to cool the Earth in response to radiant heating by the sun. But when it comes to global warming, all climate models do just the opposite…change clouds in ways that amplify radiative warming. While this is theoretically possible, it is critical to future projections of global warming that the reasons why models do this be thoroughly understood. Don’t believe it just because group think within the climate modeling community has decided it should be so.

7. Use of climate models as truth. Because there are not sufficient high-quality, globally-distributed, and long term observations of climate fluctuations to study and better understand the climate system with, computerized climate models are now regarded as truth. The modelers’ belief that climate models represent truth is evident from the language they use: climate models are not “tested” with real data, but instead “validated”. The implication is clear: if the data do not agree with the models, it must be the data’s fault.

8. Claims that climate models have been tested. A hallmark of a good theory is that it should predict something which, upon further investigation, turns out to be correct. To my knowledge, climate models have not yet forecasted anything of significance. And even if they did, models are ultimately being relied upon to forecast global warming (aka ‘climate change’). As far as I can tell, there is no good way to test them in this regard. And please don’t tell me they can now replicate the seasons quite well. Even the public could predict the seasons before there were climate models. Predicting future warming (or cooling) is slightly more difficult, but not by much: a flip a coin will be correct 50% of the time.

9. The claim that the IPCC is unbiased. The IPCC was formed for the explicit purpose of building the case for global warming being our fault, not for investigating the possibility that it is just part of a natural cycle in the climate system. Their accomplices in government have bought off the scientific community for the purpose of achieving specific policy goals.

10. The claim that reducing CO2 emissions is the right thing to do anyway. Oh, really? What if life on Earth (which requires CO2 for its existence) is actually benefiting from more CO2? Nature is always changing anyway…why must we always assume that every single change that humans cause is necessarily a bad thing? Even though virtually all Earth scientists believe this, too, it is not science, but religion. I’m all for religion…but not when it masquerades as science.

The hundreds of e-mails being made public after someone hacked into Phil Jones’ Climatic Research Unit (CRU) computer system offer a revealing peek inside the IPCC machine. It will take some time before we know whether any illegal activity has been uncovered (e.g. hiding or destruction of data to avoid Freedom of Information Act inquiries).

Some commentators even think this is the beginning of the end for the IPCC. I doubt it.

The scientists at the center of this row are defending themselves. Phil Jones has claimed that some of the more alarming statements in his e-mails have been taken out of context. The semi-official response from RealClimate.org, a website whose roots can be traced to George Soros (which I’m sure is irrelevant), claims the whole episode is much ado about nothing.

At a minimum, some of these e-mails reveal an undercurrent of elitism that many of us have always claimed existed in the IPCC. These scientists look upon us skeptics with scorn. It is well known that the IPCC machine is made up of bureaucrats and scientists who think they know how the world should be run. The language contained in a draft of the latest climate treaty (meant to replace the Kyoto treaty) involves global governance and the most authoritarian means by which people’s energy use will be restricted and monitored by the government.

Even if this language does not survive in the treaty’s final form, it illustrates the kind of people we are dealing with. The IPCC folks jet around the world to all kinds of exotic locations for their UN-organized meetings where they eat the finest food. Their gigantic carbon footprints stomp around the planet as they deride poor Brazilian farmers who convert jungle into farmland simply to survive.

Even mainstream journalists, who are usually on board with the latest environmental craze, have commented on this blatant display of hypocrisy. It seems like those participating – possibly the best example being Al Gore — are not even aware of how it looks to the rest of us.

The elitist attitudes exist elsewhere, too. While the skeptics’ blogs allow those who disagree to post opinions as long as they remain civil about it, RealClimate.org routinely ignores or deletes posts that might cast doubt on their tidy worldview. The same thing happens at Wikipedia, where a gatekeeper deletes newly posted content that departs from the IPCC party line.

A few of the CRU e-mails suggest that manipulation of climate data in order to reduce the signature of natural climate variations, and to exaggerate the supposed evidence for manmade climate change, is OK with these folks. Apparently, the ends justify the means.

The defense posted at RealClimate.org actually reinforces my point. Do the IPCC scientists assume that this is how all climate scientists behave? If it really was how the rest of us behave, why would our eyebrows be raised up to our hairlines as we read the e-mails?

If all of this sounds incompatible with the process of scientific investigation, it shouldn’t. One of the biggest misconceptions the public has about science is that research is a straightforward process of making measurements, and then seeing whether the data support hypothesis A or B. The truth is that the interpretation of data is seldom that simple.

There are all kinds of subjective decisions that must be made along the way, and the scientist must remain vigilant that he or she is not making those decisions based upon preconceived notions. Data are almost always dirty, with errors of various kinds. Which data will be ignored? Which data will be emphasized? How will the data be processed to tease out the signal we think we see?

Hopefully, the scientist is more interested in discovering how nature really works, rather than twisting the data to support some other agenda. It took me years to develop the discipline to question every research result I got. It is really easy to be wrong in this business, and very difficult to be right.

Skepticism really is at the core of scientific progress. I’m willing to admit that I could be wrong about all my views on manmade global warming. Can the IPCC scientists admit the same thing?

Year after year, the evidence keeps mounting that most climate research now being funded is for the purpose of supporting the IPCC’s politics, not to find out how nature works. The ‘data spin’ is increasingly difficult to ignore or to explain away as just sloppy science. If it walks like a duck, and quacks like a duck…

The recent hacking of the University of East Anglia’s Climatic Research Unit (CRU) computer system has led to the release of hundreds, if not thousands, of e-mails which — if real — reveal the tactics and motivations of some of the top Intergovernmental Panel on Climate Change (IPCC) scientists. I hesitate to name names, but there are several websites now buzzing with all of the details and sample e-mails. The e-mails I have seen appear genuine, with obscure scientific details and language that would take considerable effort to create as part of a hoax. A few of the sites covering this unfolding story are:

Climate Depot

Anthony Watts: Watts Up With That?

Herald Sun: Andrew Bolt

Lubos Motl: The Reference Frame

While it is too early to tell just yet, there seems to be considerable damning evidence that data have been hidden or destroyed to avoid Freedom of Information Act (FOIA) data requests; data have been manipulated in order to get results that best suit the pro-anthropogenic global warming agenda of the IPCC; e-mails that contain incriminating discussions are being deleted. And, on the bright side, we skeptics seem to be quite a thorn in the side of the IPCC.

In reading these e-mails from the ‘other side’ of the scientific debate I am particularly amazed at the mindset of a few of these scientists. I exchange e-mails with other like-minded (read ‘skeptical’) scientists, as do the IPCC scientists with their peers. But never do I hear of anyone manipulating climate data to achieve a certain end. I must say that I am pleased to see that NCAR scientist Kevin Trenberth admits that it is a “travesty” that no one can explain the lack of global warming in recent years.

I think there is a good chance that this was an inside job…either a disgruntled employee at CRU, or someone who is simply getting fed up with the politicization of the IPCC’s science and wanted to reveal some of the inner workings of the IPCC process. I’m sure that further revelations will arise in the coming days.

As of this writing, the BBC is the first mainstream news source to cover the story. But instead of discussing the content of any of the e-mails, the BBC is focusing on the illegal nature of the computer system breach. An expert was quoted who alluded to the contentious nature of the global warming debate, and how both sides would resort to tricks to help their side.

That’s pretty rich. If the hacked e-mails — with incriminating content — just happened to be Sarah Palin’s, does ANYONE believe that news reports would avoid disclosing the content of those e-mails?

UPDATES:
Telegraph: ClimateGate

Guardian: Climate sceptics claim collusion

Register: Hackers cause data breach

Nature.com

RealClimate.org response

YR MON GLOBE NH SH TROPICS
2009 1 +0.304 +0.443 +0.165 -0.036
2009 2 +0.347 +0.678 +0.016 +0.051
2009 3 +0.206 +0.310 +0.103 -0.149
2009 4 +0.090 +0.124 +0.056 -0.014
2009 5 +0.045 +0.046 +0.044 -0.166
2009 6 +0.003 +0.031 -0.025 -0.003
2009 7 +0.411 +0.212 +0.610 +0.427
2009 8 +0.229 +0.282 +0.177 +0.456
2009 9 +0.422 +0.549 +0.294 +0.511
2009 10 +0.284 +0.271 +0.298 +0.328

The global-average lower tropospheric temperature anomaly in October 2009 fell from +0.42 deg. C in September to +0.28 deg. C in October. The tropical and Northern Hemisphere were responsible for this cooling.

The global-average sea surface temperature anomalies in October continued their fall from the peak in July, despite the irregular onset of El Nino conditions:

The daily running 3-day average SSTs through early November shows no let-up in this cooling:

As usual, the linear trend lines in the previous two figures should not be construed as having any predictive power whatsoever — they are for entertainment purposes only.

I keep getting requests to comment on the recent GRL paper by Lindzen and Choi (2009), who computed how satellite-measured net (solar + infrared) radiation in the tropics varied with surface temperature changes over the 15 year period of record of the Earth Radiation Budget Satellite (ERBS, 1985-1999).

The ERBS satellite carried the Earth Radiation Budget Experiment (ERBE) which provided our first decadal-time scale record of quasi-global changes in absorbed solar and emitted infrared energy. Such measurements are critical to our understanding of feedbacks in the climate system, and thus to any estimates of how the climate system responds to anthropogenic greenhouse gas emissions.

The authors showed that satellite-observed radiation loss by the Earth increased dramatically with warming, often in excess of 6 Watts per sq. meter per degree (6 W m^-2 K^-1). In stark contrast, all of the computerized climate models they examined did just the opposite, with the atmosphere trapping more radiation with warming rather than releasing more.

The implication of their results was clear: most if not all climate models that predict global warming are far too sensitive, and thus produce far too much warming and associated climate change in response to humanity’s carbon dioxide emissions.

A GOOD METHODOLOGY: FOCUS ON THE LARGEST TEMPERATURE CHANGES

One thing I liked about the authors’ analysis is that they examined only those time periods with the largest temperature changes – whether warming or cooling. There is a good reason why one can expect a more accurate estimate of feedback by just focusing on those large temperature changes, rather than blindly treating all time periods equally. The reason is that feedback is the radiation change RESULTING FROM a temperature change. If there is a radiation change, but no temperature change, then the radiation change obviously cannot be due to feedback. Instead, it would be from some internal variation in cloudiness not caused by feedback.

But it also turns out that a non-feedback radiation change causes a time-lagged temperature change which completely obscures the resulting feedback. In other words, it is not possible to measure the feedback in response to a radiatively induced temperature change that can not be accurately quantified (e.g., from chaotic cloud variations in the system). This is the subject of several of my previous blog postings, and is addressed in detail in our new JGR paper — now in review — entitled, “On the Diagnosis of Radiative Feedbacks in the Presence of Unknown Radiative Forcing”, by Spencer and Braswell).

WHAT DO THE AMIP CLIMATE MODEL RESULTS MEAN?

Now for my main concern. Lindzen and Choi examined the AMIP (Atmospheric Model Intercomparison Project) climate model runs, where the sea surface temperatures (SSTs) were specified, and the model atmosphere was then allowed to respond to the specified surface temperature changes. Energy is not conserved in such model experiments since any atmospheric radiative feedback which develops (e.g. a change in vapor or clouds) is not allowed to then feed-back upon the surface temperature, which is what happens in the real world.

Now, this seems like it might actually be a GOOD thing for estimating feedbacks, since (as just mentioned) most feedbacks are the atmospheric response to surface forcing, not the surface response to atmospheric forcing. But the results I have been getting from the fully coupled ocean-atmosphere (CMIP) model runs that the IPCC depends upon for their global warming predictions do NOT show what Lindzen and Choi found in the AMIP model runs. While the authors found decreases in radiation loss with short-term temperature increases, I find that the CMIP models exhibit an INCREASE in radiative loss with short term warming.

In fact, a radiation increase MUST exist for the climate system to be stable, at least in the long term. Even though some of the CMIP models produce a lot of global warming, all of them are still stable in this regard, with net increases in lost radiation with warming (NOTE: If analyzing the transient CMIP runs where CO2 is increased over long periods of time, one must first remove that radiative forcing in order to see the increase in radiative loss).

So, while I tend to agree with the Lindzen and Choi position that the real climate system is much less sensitive than the IPCC climate models suggest, it is not clear to me that their results actually demonstrate this.

ANOTHER VIEW OF THE ERBE DATA

Since I have been doing similar computations with the CERES satellite data, I decided to do my own analysis of the re-calibrated ERBE data that Lindzen and Choi analyzed. Unfortunately, the ERBE data are rather dicey to analyze because the ERBE satellite orbit repeatedly drifted in and out of the day-night (diurnal) cycle. As a result, the ERBE Team advises that one should only analyze 36-day intervals (or some multiple of 36 days) for data over the deep tropics, while 72-day averages are necessary for the full latitudinal extent of the satellite data (60N to 60S latitude).

Lindzen and Choi instead did some multi-month averaging in an apparent effort to get around this ‘aliasing’ problem, but my analysis suggests that the only way around the problem it is to do just what the ERBE Team recommends: deal with 36 day averages (or even multiples of that) for the tropics; 72 day averages for the 60N to 60S latitude band. So it is not clear to me whether the multi-month averaging actually removed the aliased signal from the satellite data. I tried multi-month averaging, too, but got very noisy results.

Next, since they were dealing with multi-month averages, Lindzen and Choi could use available monthly sea surface temperature datasets. But I needed 36-day averages. So, since we have daily tropospheric temperatures from the MSU/AMSU data, I used our (UAH) lower tropospheric temperatures (LT) instead of surface temperatures. Unfortunately, this further complicates any direct comparisons that might be made between my computations (shown below) and those of Lindzen and Choi.

Finally, rather than picking specific periods where the temperature changes were particularly large, like Lindzen and Choi did, I computed results from ALL time periods, but then sorted the results from the largest temperature changes to the smallest. This allows me to compute and plot cumulative average regression slopes from the largest to the smallest temperature changes, so we can see how the diagnosed feedbacks vary as we add more time intervals with progressively weaker temperature changes.

RESULTS

For the 20N-20S latitude band (same as that analyzed by Lindzen and Choi), and at 36-day averaging time, the following figure shows the diagnosed feedback parameters (linear regression slopes) tend to be in the range of 2 to 4 W m^-2 K^-1, which is considerably smaller than what Lindzen and Choi found, which were often greater than 6 W m^-2 K^-1. As mentioned above, the corresponding climate model computations they made had the opposite sign, but as I have pointed out, the CMIP models do not, and the real climate system cannot have a net negative feedback parameter and still be stable.

But since the Lindzen and Choi results were for changes on time scales longer than 36 days, next I computed similar statistics for 108-day averages. Once again we see feedback diagnoses in the range of 2 to 4 W m^-2 K^-1:

Finally, I extended the time averaging to 180 days (five 36-day periods), which is probably closest to the time averaging that Lindzen and Choi employed. But rather than getting closer to the higher feedback parameter values they found, the result is instead somewhat lower, around 2 W m^-2 K^-1.

In all of these figures, running (not independent) averages were computed, always separated by the next average by 36 days.

By way of comparison, the IPCC CMIP (coupled ocean-atmosphere) models show long-term feedbacks generally in the range of 1 to 2 W m^-2 K^-1. So, my ERBE results are not that different from the models. BUT..it should be remembered that: (1) the satellite results here (and those of Lindzen and Choi) are for just the tropics, while the model feedbacks are for global averages; and (2) it has not yet been demonstrated that short-term feedbacks in the real climate system (or in the models) are substantially the same as the long-term feedbacks.

WHAT DOES ALL THIS MEAN?

It is not clear to me just what the Lindzen and Choi results mean in the context of long-term feedbacks (and thus climate sensitivity). I’ve been sitting on the above analysis for weeks since (1) I am not completely comfortable with their averaging of the satellite data, (2) I get such different results for feedback parameters than they got; and (3) it is not clear whether their analysis of AMIP model output really does relate to feedbacks in those models, especially since my analysis (as yet unpublished) of the more realistic CMIP models gives very different results.

Of course, since the above analysis is not peer-reviewed and published, it might be worth no more than what you paid for it. But I predict that Lindzen and Choi will eventually be challenged by other researchers who will do their own analysis of the ERBE data, possibly like that I have outlined above, and then publish conclusions that are quite divergent from the authors’ conclusions.

In any event, I don’t think the question of exactly what feedbacks are exhibited by the ERBE satellite is anywhere close to being settled.

Despite the fact that the magnitude of anthropogenic global warming depends mostly upon the strengths of feedbacks in the climate system, there is no known way to actually measure those feedbacks from observational data.

The IPCC has admitted as much on p. 640 of the IPCC AR4 report, at the end of section 8.6, which is entitled “Climate Sensitivity and Feedbacks”:

“A number of diagnostic tests have been proposed…but few of them have been applied to a majority of the models currently in use. Moreover, it is not yet clear which tests are critical for constraining future projections (of warming). Consequently, a set of model metrics that might be used to narrow the range of plausible climate change feedbacks and climate sensitivity has yet to be developed.”

This is a rather amazing admission. Of course, since these statements are lost in a sea of favorable (but likely superfluous) comparisons between the models and various aspects of today’s climate system, one gets the impression that the 99% of the IPCC’s statements that are supportive of the climate models far outweighs the 1% that might cast doubt.

But the central importance of feedbacks to projections of future climate makes them by far more important to policy debates than all of the ways in which model behavior might resemble the current climate system. So, why has it been so difficult to measure feedbacks in the climate system? This question is not answered in the IPCC reports because, as far as I can tell, no one has bothered to dig into the reasons.

Rather unexpectedly, I have been asked to present our research results on this subject at a special session on feedbacks at the Fall AGU meeting in San Francisco in mid-December. In that short 15 minute presentation, I hope to bring some clarity to an issue that has remained muddied for too long.

To review, the feedback measurement we are after can be defined as the amount of global average radiative change caused by a temperature change. The main reason for the difficulty in diagnosing the true feedbacks operating in the climate system is that the above definition of feedback is NOT the same as what we can actually measure from satellites, which is the amount of radiative change accompanied by a temperature change.

The distinction is that in the real world, causation in the opposite direction as feedback also exists in the measurements. Thus, a change in measured radiative flux results from some unknown combination of (1) temperature causing radiative changes (feedback), and (2) unforced natural radiative changes causing a temperature change (internal forcing).

The internal forcing does not merely add contaminating noise to the diagnosis of feedback – it causes a bias in the direction of positive feedback (high climate sensitivity). This bias exists primarily because forcing and net feedback (including the direct increase of IR radiation with temperature) always have opposite signs, so a misinterpretation of the sum of the two as feedback alone causes a bias.

For instance, for the global average climate system, a decrease in outgoing radiation causes an increase in global average temperature, whereas an increase in temperature must always do the opposite: cause an increase in outgoing radiation. As a result, the presence of forcing mutes the signature of net feedback. Similarly, the presence of feedback mutes the signature of forcing.

The effect of this partial cancellation is to result in diagnosed net feedbacks being smaller than what is actually occurring in nature, unless any forcing present is first removed from the data before estimating feedbacks. Unfortunately, we do not know which portion of radiative variability is forcing versus feedback, and so researchers have simply ignored the issue (if they were even aware of it) and assumed that what they have been measuring is feedback alone. As a result, the climate system creates the illusion of being more sensitive than it really is.

One implication of this is that it is not a sufficient test of the feedbacks in climate models to simply compare temperature changes to radiation changes. This is because the same relationship between temperature and radiation can be caused by either strong forcing accompanied by a large feedback parameter (which would be low climate sensitivity), or by weak forcing accompanied by a small feedback parameter (which would be high climate sensitivity).

Only in the case of radiative forcing being either zero or constant in time – situations that never happen in the real world – can feedback be accurately estimated with current methods.

Our continuing analysis of satellite and climate model data has yet to yield a good solution to this problem. Unforced cloud changes in the climate system not only give the illusion of positive feedback, they might also offer a potential explanation for past warming (and cooling). [I believe these to be mostly chaotic in origin, but it also opens the door to more obscure (and controversial) mechanisms such as the modulation of cloud cover by cosmic ray activity.]

But without accurate long-term measurements of global cloud cover changes, we might never know to what extent global warming is simply a manifestation of natural climate variability, or whether cloud feedbacks are positive or negative. And without direct evidence, the IPCC can conveniently point to carbon dioxide change as the culprit. But this explanation seems rather anthropocentric to me, since it is easier for humans to keep track of global carbon dioxide changes than cloud changes.

Also, the IPCC can conveniently (and truthfully) claim that the behavior of their models is broadly “consistent with” the observed behavior of the real climate system. Unfortunately, this is then misinterpreted by the public, politicians, and policymakers as a claim that the amount of warming those models produce (a direct result of feedback) has been tested, which is not true.

As the IPCC has admitted, no one has yet figured out how to perform such a test. And until such a test is devised, the warming estimates produced by the IPCC’s twenty-something climate models are little more than educated guesses. It verges on scientific malpractice that politicians and the media continue to portray the models as accurate in this regard, without any objections from the scientists who should know better.