Senator Barbara Boxer has said that the e-mails supposedly stolen from a computer at the Climatic Research Unit in the UK should lead to prosecution of the hacker who did it. This rather obvious attempt to divert attention from the content of the emails, to the manner in which the e-mails were obtained, led my wife to make an interesting observation.

What if the intercepted emails uncovered medical researchers discussing the fudging and hiding of cancer research data, and trying to interfere with the peer review process to prevent other medical researchers from getting published? There would be outrage from all across the political spectrum. Scientists behaving badly while the health of people was at stake would not be defended by anyone.

So why should it be any different with Climategate? Unnecessary restrictions on (or price increases for) energy use could needlessly kill millions of people who are already poverty stricken. Cancer research affects many of us, but energy costs affect ALL of us.

In a little over a week I will be giving an invited paper at the Fall meeting of the American Geophysical Union (AGU) in San Francisco, in a special session devoted to feedbacks in the climate system. If you don’t already know, feedbacks are what will determine whether anthropogenic global warming is strong or weak, with cloud feedbacks being the most uncertain of all.

In the 12 minutes I have for my presentation, I hope to convince as many scientists as possible the futility of previous attempts to estimate cloud feedbacks in the climate system. And unless we can measure cloud feedbacks in nature, we can not test the feedbacks operating in computerized climate models.

WHAT ARE FEEDBACKS?

To review, the main feedback issue is this: In response to the small direct warming effect of more CO2 in the atmosphere, will clouds change in ways that amplify the warming (e.g. a cloud reduction letting more sunlight in, which would be a positive feedback), or decrease the warming (e.g. a cloud increase causing less sunlight to be absorbed by the Earth, which would be a negative feedback)?

In the former case, we could be heading for a global warming catastrophe. In the latter case, manmade global warming might be barely measurable (and previous warming would be mostly the result of some natural cause). All climate models tracked by the IPCC now have positive cloud feedbacks, by varying amounts, which partly explains why the IPCC expects anthropogenic global warming to be so strong.

Obviously, we need to know what feedbacks operate in the climate system.

ESTIMATING FEEDBACKS: AN UNSOLVED PROBLEM

I am now quite convinced that most, if not all, previous estimates of feedback from our satellite observations of natural climate variability are in error. Furthermore, this error is usually in the direction of positive feedback, which will then give the illusion of a ‘sensitive’ climate system. More on that later.

The goal seems simple enough: to measure cloud feedbacks, we need to determine how much clouds change in response to a temperature change. But most researchers do not realize that this is not possible without accounting for causation in the opposite direction, i.e., the extent to which temperature changes are a response to cloud changes.

As I will demonstrate in my AGU talk on December 16, for all practical purposes it is not possible (at least not yet) to measure cloud feedbacks because the two directions of causation are intermingled in nature. As a result, it is not possible with current methods to measure feedbacks in response to a radiative forcing event such as a change in cloud cover, or even a major volcanic eruption, such as that from the 1991 eruption of Mt. Pinatubo.

The reason is that the size of the radiative forcing of a temperature change overwhelms the size of the radiative feedback upon that temperature change, and our satellite measurements can not tell the difference. There are only two special situations where it can be done: (1) the theoretical case of an instantaneously imposed, and then constant amount of radiative forcing…which never happens in the real world; and (2) the real world case where temperature changes are caused non-radiatively. While I will not go into the evidence here, satellite observations suggest that cloud feedbacks in the latter case are strongly negative.

Now, if you have an accurate estimate of the radiative forcing of temperature change, accurate estimates of radiative feedback can be made. But we do not have good estimates of this forcing during natural climate variations. Only in climate model simulations where a known amount of radiative forcing is imposed upon the model can this be done. (In another method, if you try to estimate feedback by measuring how fast the ocean responds, you also run into problems because your answer depends upon how fast and how deep in the ocean you assume the temperature change will extend.)

EXAMPLE 1: FEEDBACKS FROM THE CHANGE IN SEASONS

Once one realizes that clouds causing a temperature change (forcing) corrupts our estimates of temperature causing a cloud change (feedback), it becomes apparent that many of the previous attempts to estimate feedback will not work.

For instance, many researchers think that you can estimate feedbacks from the seasonal cycle in average solar illumination of the Earth and the resulting temperature response. There is about a 7% peak-to-peak variation in the amount of solar energy reaching the Earth during the year, with a maximum occurring in March and September, and the minimum in June. So, one would think we could measure by how much this change in solar heating causes a change in temperature.

The trouble is that global circulation patterns also change dramatically with the seasons, mostly due to the large difference in land masses between the Northern and Southern Hemispheres. Since cloud formation is affected by a variety of circulation induced effects (fronts, temperature inversions, etc.), the cloud cover and thus the natural shading of the Earth by clouds also changes with the seasons, through these seasonal circulation changes.

These non-temperature effects on cloud cover will confound the estimation of feedbacks, because their magnitude is considerably larger than the magnitude of the feedbacks. If the Earth was 100% covered by ocean that had a constant depth everywhere, then it might be possible to estimate feedbacks in this way…but not in the real world.

EXAMPLE 2: FEEDBACKS FROM EL NINO & LA NINA

Researchers have also made feedback estimates from the anomalously warm conditions that exist during El Nino, and the cool conditions during La Nina. But this runs into a similar problem as estimating feedbacks from the change in seasons: there are substantial variations in global circulation patterns between El Nino and La Nina, especially in the tropics. These circulation changes can induce cloud changes – wholly apart from temperature-induced changes – and there is no known way to separate the circulation-induced cloud changes (forcing) from the feedback-induced changes.

THE ERRORS WHICH RESULT FROM PREVIOUS FEEDBACK ESTIMATES

So, how do these problems impact our estimates of feedback? Except under certain circumstances, they will always cause a bias toward positive feedback. The reason is that radiative forcing and radiative feedback always work in opposition to each other. (Here I am speaking of the net feedback parameter, which also contains the increase in loss of infrared radiation by the Earth in direct response to warming).

Since our satellites measure the two effects combined, if you assume only feedback is being measured when both feedback and forcing are occurring, then you will underestimate the feedback parameter, which is a bias in the direction of positive feedback.

THE IMPACT ON CLIMATE MODEL VALIDATION

I can predict that the climate modelers will claim that we really do not need to know the direction of causation…we can just measure the temperature/cloud relationships in nature, and then adjust the models until they produce the same temperature/cloud relationships.

While this might sound reasonable, it turns out that the radiative signature of forcing is much larger than that of feedback. As a result, one can get pretty good agreement between models and observations even when the model feedbacks are greatly in error. Another way of saying this is that you can get good agreement between the model behavior and observations whether the cloud feedbacks are positive OR negative. This is another fact I will be demonstrating on December 16.

WHERE DO WE GO FROM HERE?

My first task is to convince both observationalists and modelers that much of what they previously believed about atmospheric feedbacks operating in the real world can be tossed out the window. Obviously, this will be no small task when so many climate experts assume that nothing important could have been overlooked after 20 years and billions of dollars of climate research.

But even if I can get a number of mainstream climate scientists to understand that we still do not know whether cloud feedbacks are positive or negative, it is not obvious how to fix the problem. As I suggested a couple of blog postings ago, maybe we should quit trying to test whether a climate model that produces 3 deg. C of warming in response to a doubling of carbon dioxide is “true”, and instead test to see if we can falsify a climate model which only produces 0.5 deg. C of warming. As someone recently pointed out in an email to me, a climate model IS a hypothesis, and in science a hypothesis can only be falsified — not proved true.

From what I have seen from my analysis of output from 18 of the IPCC’s climate models, I’ll bet that we can not falsify such a model with our current observations of the climate system. I suspect that the climate modeling groups have only publicized models that produce the amount of warming they believe “looks about right”, or “looks reasonable”. Through group-think (or maybe the political leanings of, and pressure from, the IPCC leadership?), they might well have tossed out any model experiments which produced very little warming.

In any event, I believe that the scientific community’s confidence that climate change is now mostly human-caused is seriously misplaced. It is time for an independent review of climate modeling, with experts from other physical (and even engineering) disciplines where computer models are widely used. The importance of the issue demands nothing less.

Furthermore, the computer codes for the climate models now being used by the IPCC should be made available to other researchers for independent testing and experimentation. The Data Quality Act for U.S.-supported models already requires this, but this law is being largely ignored.

As a (simple) modeler and computer programmer myself, I know that the modeling groups will protest that the models are far too complex and finely tuned to let amateurs play with them. But that’s part of the problem. If the models are that complex and fragile, should we be basing multi-trillion dollar policy decisions on them?

The lack of coverage of Climategate by the mainstream news media over the last 2 weeks has been breathtaking. Richard North at the EUReferendum blog advanced a way to measure media bias of this kind with what he calls his “Tiger Woods Index”, where he compares the number of Google search web matches to Google search news matches. The idea is that if the media are avoiding an issue, then the number of news matches relative to web matches will decrease. If the media are overly obsessed with an issue compared to what the public is discussing on web pages, then the number of Google search news matches will increase relative to the number of web matches.

I decided to play around with this idea with searches on several names and phrases of my own. Since “media bias” is such an ugly phrase, I will characterize the resulting statistics as a Media Interest Index. I found that for issues where the news media seems to have about the same level of interest as the public, the ratio of web page matches to news page matches was somewhere in the range of 500 to 1,000.

By using a totally scientific process (since I’m a scientist) to compare the media’s interest to the public’s interest, I decided that a ratio of 1,000 would represent equal media and public interest. Please do not ask for the data I used, since I will either hide it or delete it before I give it up.

The Media Interest Index is then 1,000 times the number of news matches divided by the number of web matches. The result is 1 for equal interest between the media and the public. A value of 2 would mean the news media are “twice” as interested as the public. A value of 0.5 would mean that the media are only half as interested as the public.

See? Totally scientific.

The following chart shows the results for Google searches on several words, phrases, and names. The phrase “health care reform” is of great interest to both the media and the public, and we see a Media Interest Index value of 1.27, indicating roughly equal interest between the media and the public.

On this scale, “Climategate” has a MII of only 0.14, suggesting much less interest (about one-seventh) from the media than from the public in general. Of course, some news outlets, especially those in the UK and FoxNews, have been providing heavy coverage, so the low value is being dragged down by some other news organizations.

Similarly, the phrase “media bias” has an MII value of 0.2, indicating the rather obvious fact that the media are reluctant to do news stories on “media bias”, an issue that is of considerable interest at many non-news websites.

But there are even bigger extremes to be seen in the above chart. For instance, there seems to be a disproportionate interest of the media in “Obama”, with an MII value of 6.5. Even that, however, is not as interesting to the media as “Bush’s gaffe”, with an index value over 27!

And while “Obama” has a disproportionately large interest from the media, the interest in “Obama’s gaffe” is disproportionately small, with an MII value of only 0.01! Thus, the media seems to be about 2,000 times more interested in gaffes uttered by Bush than those uttered by Obama. Similarly, comparing the media’s interest in “global warming” to their interest in “Climategate”, the ratio of those two MII values approaches 30.

For any social scientists, pollsters, or political pundits who might object to the above analysis and would like to vent, you can contact me at idontreallycare@hotmail.com.
This research was not supported by any coal or oil company…but I wish it was.

It has been two weeks since Climategate revealed that some of the IPCC’s leading researchers have conspired to manipulate temperature data, hide data from other researchers, and bully those scientists who do not agree with them by interfering with the peer review process.

(If you haven’t heard about Climategate, it might be because you are still watching ABC, CBS, or NBC. Google ‘Climategate’, though, and you will get 20,000,000 to 30,000,000 web page matches.)

Supporters have claimed that there is nothing to see there…that the Climategate e-mails released to the world by a whistleblower just show how scientists normally work. This is a particularly bad strategy, and the public knows it. Scientists do NOT behave this way…at least not in my world.

Others have claimed that a few bad apples do not spoil the whole IPCC barrel. Well, if it wasn’t for the fact that these are the core people who gave us the primary thermometer evidence of 20th Century warming (Phil Jones), and the Hockey Stick temperature reconstruction which conveniently did away with the previous 10 or more centuries of natural climate change (Michael Mann), I might be inclined to agree with them.

I will admit that it seems unlikely (but not impossible) that a reanalysis of the thermometer data will lead to a much reduced rate of warming in recent decades. But my bigger concern is that the “it’s-OK-to-fudge” attitude pervades the entire IPCC apparatus.

These e-mails are from the observational side of the IPCC, that is, the research into temperature observations of the past. What I am more concerned about, though, is the manipulation of climate models, which are used to predict the future state of the climate system. Computer models are much easier to manipulate than real data, and one can get just about any answer one wants out of them.

Now that we have seen that the temperature observation guys ‘wanted’ to get a certain result, it is reasonable to wonder whether the modelers are also incentivized to produce particular results. I’m sure the hundreds of millions of dollars being poured into global warming research – money that would dry up if the threat evaporated — has not influenced their objectivity.

Now, trillions of dollars in global warming legislation are riding upon these model ‘black boxes’ that relatively few people understand the inner workings of. The models are so complex, with many adjustable parameters which have no known true values, that it is unlikely that they can ever be replicated by other researchers. In case you hadn’t heard, reproducibility is a basic requirement of scientific research.

The IPCC has gotten around this problem by relying upon many modeling groups running different climate models. The presumption is that the full range of warming estimates produced by 20 different climate models would surely bound the ‘truth’, that is, the true amount of warming that will occur for a given amount of additional greenhouse gas emissions. But do these models really bound the problem?

I’ve been reminded recently that in science you really can not prove any hypothesis to be true; you can only prove a hypothesis false. How does this help us when it comes to model predictions of future warming? Well, if we can not prove whether a model that produces 2, or 4, or 6 deg. C of warming is correct…can we prove whether a model that produces only 0.5 deg. C of warming is false?

If we build a model that produces very little warming – less than that produced by any of the IPCC models – is that model any less realistic in its behavior than the models that produce a lot of warming? In other words, how do we know that the IPCC models really do bound the problem? I suspect that one or more modeling groups have already done this, but the IPCC leadership probably nixed the idea of letting the public find out about it.

Maybe there is a disgruntled modeler out there who is now willing to spill the beans, just as happened with the Climategate emails. We can call it Climategate II.

Of course, the IPCC turns the argument around, and shows us a few models which produce huge amounts of warming. But as I’ve said before, extraordinary claims require extraordinary evidence. If a couple of their models suggest it is theoretically possible to have catastrophic warming, should I be any more concerned than, say, the possibility that a new particle accelerator used by nuclear physicists will suddenly cause the Earth to explode?

While it would be easy to simply not build or use the particle accelerator, it is much more difficult to reduce global fossil fuel use by, say, 50% or more.

In the wake of Climategate, I fully expect a renewed IPCC assault on our common sense using the climate models as their ultimate climate ‘truth’. It will be claimed that the observations involved in Climategate aren’t important anyway…it’s the computer models that are telling us what the future will be.

Yeah, right.

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 +0.286 +0.274 +0.297 +0.326
2009 11 +0.496 +0.418 +0.575 +0.493

The global-average lower tropospheric temperature anomaly rebounded from +0.29 deg. C in October to +0.50 deg. C in November. Both hemispheres, as well as the tropics, contributed to this warmth. The global anomaly for November of +0.50 deg. C is a period record for November (since 1979); the previous November high was +0.40 deg C. in 2004 2005. [NOTE: These satellite measurements are not calibrated to surface thermometer data, but use on-board redundant precision platinum resistance thermometers.]

Following is the global-average sea surface temperature anomalies through November 2009 from the AMSR-E instrument on NASA’s Aqua satellite:

As usual, the trend line in the previous figure should not be construed as having any predictive power whatsoever — it is for entertainment purposes only.

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.