I am sorry that posting has been light, but I am currently working to migrate this site to WordPress from hosted Typepad.  This is a real hassle, as described at my other blog where I just completed a succesful migration.  I hope to have this blog moved over this weekend.

In the mean time, I thought my readers might need some help understanding James Hansen’s recent comments that flat world temperatures over the last 10 years and substantially cooler temperatures in 2008 were entirely consistent with the climate models that forecast  0.2-0.3C (or more) warming for this decade.  Most other natural sciences are stuck in the old and outdated practice of questioning forecasts when actual observational data diverges from the forecast by several standard deviations.  Not so modern, enlightened, consensus-based climate science.  Below is my graphical representation of how climate scientists evaluate their models in light of new data.

Will there ever be a time when sane people are not having to deconstruct yet another repackaging of Mann’s hockey stick, like some endless wack-a-mole game?  Mann is back with a new hockey stick and, blow me away with surprise, it looks a heck of a lot like the old hockey stick:

Willis Eschenbach, writing at Climate Audit, outlines a new statistical approach he claims can help determine the signal-to-noise ratio in such a multi-proxy average, and in turn determine which proxies are contributing the most to the final outcome.

His approach and findings seem interesting, but I need to withhold judgment and let the statistical geeks tear it apart.  I am always suspicious of algorithms that purport to sort or screen samples in or out of a sample set.

However, his climate-related finding can be accepted without necessarily agreeing with the methodology that got there.  He claims his methodology shows that two sets of proxies — the Tiljander sediments and the Southwestern US Pines (mainly the bristlecones) — drive the hockey stick shape.  This is reminiscent of Steve McIntyre’s finding years ago that just a few proxies in the original MBH 1999 drove most of the hockey stick form.  Interestingly, these two series are the very ones that have received the most independent criticism for their methodology and ability to act as a proxy.  In particular, the Tiljander Lake sediment data is out and out corrupted, and it is incredible that they could get past a peer review process (just reinforcing my feeling that peer review passes shoddy work that reinforces the professions prejudices and stands in the way of quality work by mavericks challenging the consensus).

Anyway, with these proxies removed, less than a quarter of the total, the hockey stick disappears.

Update: If you still have any confidence at all in climate scientists, I urge you to read this discussion of the Tiljander sediments.  Mann managed to make two enormous mistakes.  One, he used a series that the authors of the series very specifically caution has been disturbed and is not a valid proxy for the last 200-300 years.  And two, he inverts the whole series!  instead of showing it decreasing in the last 200 years  (again due to corruption the authors warned about) he shows it upside down, increasing in the last 200 years, which then helps him build his hockey stick on absolutely false data.

One might argue that this is just the indictment of one scientist, but everyone in the profession seems to rally around and defend this one scientist, and the flaws listed above have been public for a while and absolutely no one seems interested in demanding Mann correct his numbers.  In fact, most climate scientists spend their time shooting the messenger (Steve McIntyre).

This screen shot was sent by a reader, who titled the email “you have hit the big time.”  I suppose I have, or at least I have really ticked off James Hansen and Gavin Schmidt at NASA.  It appears that this site has been added to the list of sites blocked by the NASA servers as ostensiblybeing sexually explicit.  Well, I guess we have caught the GISS with their pants down a few times….

As usual, you may click on the image for the full-size version.  Thanks to a reader, who asked only that I hide his/her IP address.

Update: From the archives:

The top climate scientist at NASA says the Bush administration has tried to stop him from speaking out since he gave a lecture last month calling for prompt reductions in emissions of greenhouse gases linked to global warming.

The scientist, James E. Hansen, longtime director of the agency’s Goddard Institute for Space Studies, said in an interview that officials at NASA headquarters had ordered the public affairs staff to review his coming lectures, papers, postings on the Goddard Web site and requests for interviews from journalists.

Dr. Hansen said he would ignore the restrictions. “They feel their job is to be this censor of information going out to the public,” he said.

OK, I kindof mostly don’t think there is anything sinister here.  Coyote’s Law tells us that this is much more likely to be incompetence rather than evil intent.  But it would be interesting to see how Dr. Hansen would react if, say, the RealClimate site had been similarly filtered.  Anyone want to bet he would have thrown a conspiracy-laden hissy fit?

Update #2: Thanks for all those who pointed out that http://climate-skeptic.com was going to a park page with a bunch of ads.  That is fixed now.  Not sure if that was the cause or not.

OK, we continue to the final paragraph of Gavin Schmidt’s postadmitting a minor error in the October GISS numbers, and then proceeding to say that all the folks who pointed out the error are biased and unhelpful, in spite of the fact (or maybe because of the fact) that they found this error.

As I reviewed in part 1, most of the letter was just sort of petulant bad grace.  But this paragraph was worrisome, and I want to deal with it in more depth:

Which brings me to my last point, the role of models. It is clear that many of the temperature watchers are doing so in order to show that the IPCC-class models are wrong in their projections. However, the direct approach of downloading those models, running them and looking for flaws is clearly either too onerous or too boring. Even downloading the output (from here or here) is eschewed in favour of firing off Freedom of Information Act requests for data already publicly available – very odd. For another example, despite a few comments about the lack of sufficient comments in the GISS ModelE code (a complaint I also often make), I am unaware of anyone actually independently finding any errors in the publicly available Feb 2004 version (and I know there are a few). Instead, the anti-model crowd focuses on the minor issues that crop up every now and again in real-time data processing hoping that, by proxy, they’ll find a problem with the models.

I say good luck to them. They’ll need it.

Since when has direct comparison of forecast models against observation and measurement been the wrong way to validate or invalidate the forecast or model? I am sure there were lots of guys who went through the Principia Mathematica and tore apart the math and equations to make sure they balanced, but most of the validation consisted of making observations of celestial bodies to see if their motion fit the predicted results.  When Einstein said time would change pace in a gravity well, scientists took atomic clocks up in high-altitude airplanes to see if his predictions matched measured results.  And physicists can play with models and equations all day, but nothing they do with the math will be as powerful as finding a Higgs Boson at the LHC.

Look, unlike some of the commenters Schmidt quoted, there is no reason to distrust a guy because his staff made a data error.  But I think there is a big freaking reason to distrust someone who gets huffy that people are using actual data measurements to test his prediction models.

There is probably a reason for Schmidt to be sensitive here.  We know that Hansen’s 1988 forecasts don’t validate at all against actual data from the last 20 years (below uses the Hansen A case from his Congressional testimony, the case which most closely matches actual CO2 production since the speech).

More recent forecasts obviously have had less time to validate.  Many outsiders have found that current temperatures fall outside of the predicted range of the IPCC forecasts, and those that have found temperatures within the error bars of the forecasts have generally done so by combining large error bars, white noise, and various smoothing approaches to just eek actual temperatures into the outer molecular layers of the bottom edge of the forecast band.

As to the rest, I am not sure Schmidt knows who has and has not poked around in the innards of the models – has he studied all the referrer logs for their web sites?  But to some extent this is beside the point.  Those of us who have a lot of modeling experience in complex systems (my experience is in both econometrics and in mechanical control systems) distrust models and would not get any warm fuzzies from poking around in their innards.  Every modeler of chaotic systems knows that it is perfectly possible to string together all sorts of logically sound and reasonable assumptions and algorithms only to find that the whole mass of them combined spits out a meaningless mess.  Besides, there are, what, 60 of these things?  More?  I could spend 6 months ripping the guts out of one of them only to have Schmidt then say, well there are 59 others.  That one does not really affect anything.  I mean, can’t you just see it — it would be entirely equivalent to the reaction every time an error or problem measurement station is found in the GISS data set.  I am sure Schmidt would love us all to go off on some wild goose chase in the innards of a few climate models and relent on comparing the output of those models against actual temperatures.

No, I am perfectly happy to accept the IPCC’s summary of these models and test this unified prediction against history.  I am sure that no matter what temperature it is this month, some model somewhere in the world came close.  But how does that help, unless it turns out that it is the same model that is right month after month, and then I might get excited someone was on to something.  But just saying current temperatures fall into a range where some model predicts it just says that there is a lot of disagreement among the models, and in turn raises my doubts about the models.

The last sentence of Schmidt’s paragraph is just plain wrong.  I have never seen anyone who is out there really digging into this stuff (and not just tossing in comments) who has said that errors in the GISS temperature anomaly number imply the models are wrong, except of course to the extent that the models are calibrated to an incorrect number.  Most everyone who looks at this stuff skeptically understand that the issues with the GISS temperature metric are very different than issues with the models.

In a nutshell, skeptics are concerned with the GISS temperature numbers because of the signal to noise problem, and a skepticism that the GISS has really hit on algorithms that can, blind to station configuration, correct for biases and errors in the data.  I have always felt that rather than eliminate biases, the gridcell approach simply spreads them around like peanut butter.

My concern with the climate models is completely different.  I won’t go into them all, but they include:

• the inherent impossibility of modeling such a chaotic system
• scientists assume CO2 drives temperatures, so the models they build unsurprisingly result in CO2 driving temperature
• modelers assume WAY too much positive feedback.  No reasonable person, if they step back from it, should really be able to assume so much positive feedback in a long-term stable system
• When projected backwards, modeler’s assumptions imply far more warming than we have experienced, and it takes heroic assumptions and tweaks and plugs to make the models back-cast reasonably well.
• Its insane to ignore changes in solar output, and/or to assume that the sun over the last 40 years has been in a declining cycle
• Many models, by their own admission, omit critical natural cycles like ENSO/PDO.

By the way, my simple hypothesis to describe past and future warming is here.

As a final note, the last little dig on Steve McIntyre (the bit about FOIA requests) is really low.  First, it is amazing to me that, like Hogwarts students who can’t say the word Voldemort, the GISS folks just can’t bring themselves to mention his name.  Second, Steve has indeed filed a number of FOIA requests on Michael Mann, the GISS, and others.  Each time he has a pretty good paper trail of folks denying him data (Here is the most recent for the Santer data). Almost every time, the data he is denied is taxpayer funded research, often by public employees, or is data that the publication rules of a particular journal require to be made public.  And remember the source for this — this is coming from the GISS, which resisted McIntyre’s calls for years to release their code  (publicly funded code of a government organization programmed by government employees to produce an official US statistic) for the GISS grid cell rollup of the station data, releasing the code only last year after McIntyre demonstrated an error in the code based on inspection of the inputs and outputs.

At the end of the day, Hansen and Schmidt are public employees who like having access to government budgets and the instant credibility the NASA letterhead provides them, but don’t like the public scrutiny that goes with it.  Suck it up guys.  And as to your quest to rid yourself of these skeptic gadflies, I will quote your condescending words back to you:  Good Luck.  You’ll need it.

By accident, I have been drawn into a discussion surrounding a fairly innocent mistake made by NASA’s GISS in their October global temperature numbers.  It began for me when I compared the October GISS and UAH satellite numbers for October, and saw an incredible diversion.  For years these two data sets have shown a growing gap, but by tiny increments.  But in October they really went in opposite directions.  I used this occasion to call on the climate community to make a legitimate effort at validating and reconciling the GISS and satellite data sets.

Within a day of my making this post, several folks started noticing some oddities in the GISS October data, and eventually the hypothesis emerged that the high number was the result of reusing September numbers for certain locations in the October data set. Oh, OK.  A fairly innocent and probably understandable mistake, and far more minor than the more systematic error a similar group of skeptics, (particularly Steve McIntyre, the man whose name the GISS cannot speak) found in the GISS data set a while back.  The only amazing thing to me was not the mistake, but the fact that there were laymen out there on their own time who figured out the error so quickly after the data release.  I wish there were a team of folks following me around, fixing material errors in my analysis before I ran too far with it.

So Gavin Schmidt of NASA comes out a day or two later and says, yep, they screwed up.  End of story, right?  Except Dr. Schmidt chose his blog post about the error to lash out at skeptics.  This is so utterly human — in the light of day, most will admit it is a bad idea to lash out at your detractors in the same instant you admit they caught you in an error (however minor).  But it is such a human need to try to recover and sooth one’s own ego at exactly this same time.  And thus we get Gavin Schmidt’s post on RealClimate.com, which I would like to highlight a bit below.

He begins with a couple of paragraphs on the error itself.  I will skip these, but you are welcome to check them out at the original.  Nothing about the error seems in any way outside the category of “mistakes happen.”  Had the post ended with something like “Many thanks to the volunteers who so quickly helped us find this problem,” I would not even be posting.  But, as you can guess, this is not how it ends.

It’s clearly true that the more eyes there are looking, the faster errors get noticed and fixed. The cottage industry that has sprung up to examine the daily sea ice numbers or the monthly analyses of surface and satellite temperatures, has certainly increased the number of eyes and that is generally for the good. Whether it’s a discovery of an odd shiftin the annual cycle in the UAH MSU-LT data, or this flub in the GHCN data, or the USHCN/GHCN merge issue last year, the extra attention has led to improvements in many products. Nothing of any consequence has changed in terms of our understanding of climate change, but a few more i’s have been dotted and t’s crossed.

Uh, OK, but it is a bit unfair to characterize the “cottage industry” looking over Hansen’s and Schmidt’s shoulders as only working out at the third decimal place.  Skeptics have pointed out what they consider to be fundamental issues in some of their analytical approaches, including their methods for compensating statistically for biases and discontinuities in measurement data the GISS rolls up into a global temperature anomaly.  A fairly large body of amateur and professional work exists questioning the NOAA and GISS methodologies which often result in manual adjustments to the raw data larger in magnitude than the underlying warming signal tyring to be measured.  I personally think there is a good case to be made that the GISS approach is not sufficient to handle this low signal to noise data, and that the GISS has descended in to “see no evil, hear no evil” mode in ignoring the station survey approach being led by Anthony Watt.  Just because Schmidt does not agree doesn’t mean that the cause of climate science is not being advanced.

The bottom line, as I pointed out in my original post, is that the GISS anomaly and the satellite-measured anomaly are steadily diverging.  Given some of the inherent biases and problems of surface temperature measurement, and NASA’s commitment to space technology as well as its traditional GISS metric, its amazing to me that Schmidt and Hansen are effectively punting instead of doing any serious work to reconcile the two metrics.  So it is not surprising that into this vacuum left by Schmidt rush others, including us lowly amateurs.
By the way, this is the second time in about a year when the GISS has admitted an error in their data set, but petulently refused to mention the name of the person who helped them find it.

But unlike in other fields of citizen-science (astronomy or phenology spring to mind), the motivation for the temperature observers is heavily weighted towards wanting to find something wrong. As we discussed last year, there is a strong yearning among some to want to wake up tomorrow and find that the globe hasn’t been warming, that the sea ice hasn’t melted, that the glaciers have not receded and that indeed, CO₂is not a greenhouse gas. Thus when mistakes occur (and with science being a human endeavour, they always will) the exuberance of the response can be breathtaking – and quite telling.

I am going to make an admission here that Dr. Schmidt very clear thinks is evil:  Yes, I want to wake up tomorrow to proof that the climate is not changing catastrophically.  I desperately hope Schmidt is overestimating future anthropogenic global warming.  Here is something to consider.  Take two different positions:

1. I hope global warming theory is correct and the world faces stark tradeoffs between environmental devastation and continued economic growth and modern prosperity
2. I hope global warming theory is over-stated and that these tradeoffs are not as stark.

Which is more moral?  Why do I have to apologize for being in camp #2?  Why isn’t it equally “telling” that Dr. Schmidt apparently puts himself in camp #1.

Of course, we skeptics would say the same of Schmidt.  As much as we like to find a cooler number, we believe he wants to find a warmer number.  Right or wrong, most of us see a pattern in the fact that the GISS seems to constantly find ways to adjust the numbers to show a larger historic warming, but require a nudge from outsiders to recognize when their numbers are too high.  The fairest way to put it is that one group expects to see lower numbers and so tends to put more scrutiny on the high numbers, and the other does the opposite.

Really, I don’t think that Dr. Schmidt is a very good student of the history of science when he argues that this is somehow unique to or an aberration in modern climate science.  Science has often depended on rivalries to ensure that skepticism is applied to both positive and negative results of any experiment.  From phlogistan to plate techtonics, from evolution to string theory, there is really nothing new in the dynamic he describes.

A few examples from the comments at Watt’s blog will suffice to give you a flavour of the conspiratorial thinking: “I believe they had two sets of data: One would be released if Republicans won, and another if Democrats won.”, “could this be a sneaky way to set up the BO presidency with an urgent need to regulate CO2?”, “There are a great many of us who will under no circumstance allow the oppression of government rule to pervade over our freedom—-PERIOD!!!!!!” (exclamation marks reduced enormously), “these people are blinded by their own bias”, “this sort of scientific fraud”, “Climate science on the warmer side has degenerated to competitive lying”, etc… (To be fair, there were people who made sensible comments as well).

Dr. Schmidt, I am a pretty smart person.  I have lots of little diplomas on my wall with technical degrees from Ivy League universities.  And you know what – I am sometimes blinded by my own biases.  I consider myself a better thinker, a better scientist, and a better decision-maker because I recognize that fact.  The only person who I would worry about being biased is the one who swears that he is not.

By the way, I thought the little game of mining the comments section of Internet blogs to discredit the proprietor went out of vogue years ago, or at least has been relegated to the more extreme political  blogs like Kos or LGF.  Do you really think I could not spend about 12 seconds poking around environmentally-oriented web sites and find stuff just as unfair, extreme, or poorly thought out?

The amount of simply made up stuff is also impressive – the GISS press release declaring the October the ‘warmest ever’? Imaginary (GISS only puts out press releases on the temperature analysis at the end of the year). The headlines trumpeting this result? Non-existent. One clearly sees the relief that finally the grand conspiracy has been rumbled, that the mainstream media will get it’s comeuppance, and that surely now, the powers that be will listen to those voices that had been crying in the wilderness.

I am not quite sure what he is referring to here.  I will repeat what I wrote.  I said “The media generally uses the GISS data, so expect stories in the next day or so trumpeting ‘Hottest October Ever.'”  I leave it to readers to decide if they find my supposition unwarranted.  However, I encourage the reader to consider the 556,000 Google results, many media stories, that come up in a search for the words “hottest month ever.”  Also, while the GISS may not issue monthly press releases for this type of thing, the NOAA and British Met Office clearly do, and James Hansen has made many verbal statements of this sort in the past.

By the way, keep in mind that that Dr. Schmidt likes to play Clinton-like games with words.  I recall one episode last year when he said that climate models did not use the temperature station data, so they cannot be tainted with any biases found in the stations.  Literally true, I guess, because the the models use gridded cell data.  However, this gridded cell data is built up, using a series of correction and smoothing algorithms that many find suspect, from the station data.  Keep this in mind when parsing Dr. Schmidt.

Alas! none of this will come to pass. In this case, someone’s programming error will be fixed and nothing will change except for the reporting of a single month’s anomaly. No heads will roll, no congressional investigations will be launched, no politicians (with one possible exception) will take note. This will undoubtedly be disappointing to many, but they should comfort themselves with the thought that the chances of this error happening again has now been diminished. Which is good, right?

I’m narrowly fine with the outcome.  Certainly no heads should roll over a minor data error.  I’m not certain no one like Watt or McIntyre suggested such a thing.  However, the GISS should be embarrassed that they have not addressed and been more open about the issues in their grid cell correction/smoothing algorithms, and really owe us an explanation why no one there is even trying to reconcile the growing differences with satellite data.

In contrast to this molehill, there is an excellent story about how the scientific community really deals with serious mismatches between theory, models and data. That piece concerns the ‘ocean cooling’ story that was all the rage a year or two ago. An initial analysisof a new data source (the Argo float network) had revealed a dramatic short term cooling of the oceans over only 3 years. The problem was that this didn’t match the sea level data, nor theoretical expectations. Nonetheless, the paper was published (somewhat undermining claims that the peer-review system is irretrievably biased) to great acclaim in sections of the blogosphere, and to more muted puzzlement elsewhere. With the community’s attention focused on this issue, it wasn’t however long before problemsturned up in the Argo floats themselves, but also in some of the other measurement devices – particularly XBTs. It took a couple of years for these things to fully work themselves out, but the most recent analysesshow far fewer of the artifacts that had plagued the ocean heat content analyses in the past. A classic example in fact, of science moving forward on the back of apparent mismatches. Unfortunately, the resolution ended up favoring the models over the initial data reports, and so the whole story is horribly disappointing to some.

OK, fine, I have no problem with this.  However, and I am sure that Schmidt would deny this to his grave, but he is FAR more supportive of open inspection of measurement sources that disagree with his hypothesis (e.g. Argo, UAH) than he is willing to tolerate scrutiny of his methods.  Heck, until last year, he wouldn’t even release most of his algorithms and code for his grid cell analysis that goes into the GISS metric, despite the fact he is a government employee and the work is paid for with public funds.  If he is so confident, I would love to see him throw open the whole GISS measurement process to an outside audit.  We would ask the UAH and RSS guys to do the same.  Here is my prediction, and if I am wrong I will apologize to Dr. Schmidt, but I am almost positive that while the UAH folks would say yes, the GISS would say no.  The result, as he says, would likely be telling.

Which brings me to my last point, the role of models. It is clear that many of the temperature watchers are doing so in order to show that the IPCC-class models are wrong in their projections. However, the direct approach of downloading those models, running them and looking for flaws is clearly either too onerous or too boring. Even downloading the output (from here or here) is eschewed in favour of firing off Freedom of Information Act requests for data already publicly available – very odd. For another example, despite a few comments about the lack of sufficient comments in the GISS ModelE code (a complaint I also often make), I am unaware of anyone actually independently finding any errors in the publicly available Feb 2004 version (and I know there are a few). Instead, the anti-model crowd focuses on the minor issues that crop up every now and again in real-time data processing hoping that, by proxy, they’ll find a problem with the models.

I say good luck to them. They’ll need it.

Red Alert!  Red Alert!  Up to this point, the article was just petulant and bombastic.  But here, Schmidt becomes outright dangerous, suggesting a scientific process that is utterly without merit.  But I want to take some time on this, so I will pull this out into a second post I will label part 2.

via FailBlog:

On April 28, 2004, the SEC made a significant change in policy in the regulation of large investment banks.  On that day, they "decided to allow the five largest US investment banks to substitute advanced mathematical risk models for traditional capital requirements."  Al Gore has touted the "success" of such models as a reason to feel confident that computer models can accurately predict long-term climate trends.

But it turns out, as everyone is discovering this week, that computer models are not reality.  In fact, computer models are extraordinarily sensitive to their inputs, and small changes in their inputs, or the narrowing of models to ignore certain factors, can make them worse than useless.  Computer models are also very easy to force to a preferred conclusion.

The best way to get grant money nowadays is to try to draw from a torrent of global warming money.  I would say that the first rule of grant application writing today is "include climate change in your study."

Examples, sent by a reader:

• Global warming and pet allergies
• Global warming and fall foliage

I am still reading the new Douglas and Christy paper, so I won’t comment on it yet, but you can see Anthony Watts thoughts here.

However, in reading Anthony’s site this morning, I was struck by a quote in another one of his posts.  For a while, I have been telling folks that the main argument behind anthropogenic global warming is "we have looked at everything else, and we can’t think of what else it could be other than man."  Lacking positive correlation between CO2 and major shifts in temperature  (particularly when ice core evidence collapsed under the weight of the 800 year lag), scientists instead argue that they have gone through a long checklist (sun, clouds, volcanoes, etc) and have convinced themselves none of these others have caused late 20th century warming, so it must be man — that’s all that is left.

Here is an example, from Anthony’s site:

Bill Chameides, dean of Duke University’s Nicholas School of the Environment and Earth Sciences, said Spencer’s arguments are what magicians call “ignoratio elenchi” or logical fallacy.”We’ve looked at every possible form of heat, including clouds, and the only source of heat is greenhouse gases,” he said, adding it’s insulting that Spencer would suggest scientists are paid to come to this conclusion. “Scientists make their reputation on debunking theories.”

Well, a number of folks would beg to differ that scientists have truly eliminated every other possible cause, particularly Mr. Sun (more than really eliminating these effects, they seem to be seeking excuses to ignore them).  In fact, climate models of late have admitted that they don’t even include the Pacific Decadal Osculation in their models, or didn’t until recently.  So much for thinking of everything.

But if Mr. Chameides wants to talk in terms of logical fallacies, I will as well:  Just because scientists cannot image another cause does not mean that another cause does not exist.  Can you imagine the first astrophysicists to discover pulsars to say "well, we can’t think of anything else that would cause this phenomenon, so it must be space aliens."  Well, come to think of it, some people did say that.  But it turned out to be absurd, and after some decades of effort, we think we now understand pulsars.  But it is a bizarre form of arrogance to assume that it is not possible in our current degree of climate knowledge that there is some factor we don’t even know about.

Long Postscript:  I am working on a powerpoint presentation for next week on anthropogenic global warming, but here are two charts from that presentation that get at the "we can’t think of anything other than man that might be causing late 20th century warming."  The first is the correlation between 20th century temperature and the PDO cycle  (temperature numbers are Hadley CRUT3 and UAH combined as described here).   By the way, there seems to be some argument over exactly where and how often to call the turns in the PDO early in the 20th century — I have used one frequent estimate but others exist. 

The second interesting analysis is a sunspot number chart.  To highlight recent increases in activity, I have overlaid on the monthly International sunspot numbers (light blue) a 9.8 year moving average (in black) of sunspot numbers (9.8 selected as an average cycle length).  In the chart below, selection of the 50 average sunspot number as a reference value is arbitrary, but serves to visually demonstrate the increase in solar activity over the last 50 years.

The average monthly sunspot number from 1900-1949 was 48.  The average monthly number from 1950-1999 was 73.1, an increase of 52%.

Some of this increase is real, but some may be a measurement bias related to the ability to better detect smaller spots.  Anyone have any sources on how large this latter effect might be?  We are talking about an enormous percentage increase in the last half of the century, so my guess is that it is not all due to this bias.

I simply do not have the time to plow through the entire NOAA/NASA CCSP climate change report, so I focused on the 28-page section labeled Global Climate Change.

I will post my comments when they are done, but suffice it to say that this is yet another report written backwards, with the guts of the report written by politicians trying to push an agenda.  This is an incredibly shallow document, more shallow even than the IPCC report and possibly even than the IPCC summary for policy makers.  Call it the NASA summary for the mentally retarded. 

The report is a full-force sales piece for catastrophic global warming.  Not once in the entire chapter I read was there a hint of doubt or uncertainty.  Topics for which scientists have but the flimsiest of understandings, for example feedback effects, are treated with the certainty of Newtonian mechanics.  Any bit of conflicting evidence — whether it be the fact that oceans were rising before the industrial era, or that tropospheric temperatures are not higher than surface temperatures as predicted, or that large parts of Antarctica are gaining ice — are blissfully omitted. 

Many of the most important propositions in the report are stated without proof or citation.  Bill Kovacs wrote the other day that of the 21 papers that were cited, only 8 are available to the public prior to the August 14 deadline for public comment.  Just like with the IPCC, the summary is written months ahead of the science.  Much of the report seems to be cut-and-pasted from other sources  (you can tell, be graphs are reproduced exactly as they appear in other reports, such as the IPCC fourth assessment).  In many cases, the data between these various charts do not agree (for example, their charts have three or four different versions of 20th century global temperatures, none of which are either sourced or consistent). 

And, of course, the hockey stick, the Freddy Krueger of scientific analysis, is brought back yet again from the dead.

Let me give you just one taste of the quality science here.  Here is a precipitation chart they put in on page 28:

This is like those before-and-after photo games.  Can you see the sleight of hand?  Look at the legend for the green historic line.  It says that it is based on "Simulations."  This means that someone has hypothesized a relationship between temperature and precipitation (the precipitation line in this chart is tellingly nearly identical in pattern and slope to the "human + natural" temperature model output as shown at the top of page 26) and built that relationship into a model.  So the green line is a result of a) a model projecting temperature backward and b) the model taking that temperature and, based on a series of assumptions that temperature drives heavy precipitation events, generating this graph of heavy precipitation events.

Now, look at the caption.  It calls the green line "observed…changes in the heaviest 5 percent of precipitation events."  I am sorry, but model output and observations are not the same thing.  Further, note the circularity of the argument.  Models built on the assumption that temperature increases cause an increase in these events is used as proof that temperature increases these events. 

By the way, look at the error band on the green line.  For some reason, we have near perfect knowledge for worldwide precipitation events in the 1960’s, but are less certain about the 1990’s.

One of the weird aspects of climate science is the over-emphasis on peer review as the ne plus ultra guarantor of believable results.  This is absurd.  At best, peer review is a screen for whether a study is worthy of occupying limited publication space, not for whether it is correct.  Peer review, again at best, focuses on whether a study has some minimum level of rigor and coherence and whether it offers up findings that are new or somehow advance the ball on an important topic. 

In "big boy sciences" like physics, study findings are not considered vetted simply because they are peer-reviewed.  They are vetted only after numerous other scientists have been able to replicate the results, or have at least failed to tear the original results down.  Often, this vetting process is undertaken by people who may even be openly hostile to the original study group.  For some reason, climate scientists cry foul when this occurs in their profession, but mathematicians and physicists accept it, because they know that findings need to be able to survive the scrutiny of enemies, not just of friends.  To this end, an important part of peer review is to make sure the publication of the study includes all the detail on methodology and data that others might need to replicate the results  (which is something climate reviewers are particularly bad at).

In fact, there are good arguments to be made that strong peer review may even be counter-productive to scientific advancement.  The reason is that peer review, by the nature of human beings and the incentives they tend to have, is often inherently conservative.  Studies that produce results the community expects often receive only cursory scrutiny doled out by insiders chummy with the authors.  Studies that show wildly unexpected results sometimes have trouble getting published at all.

Poscscript:  As I read this, it strikes me that one way to describe climate is that it acts like a social science, like sociology or gender studies, rather than like a physical science.  I will ahve to think about this — it would be an interesting hypothesis to expand on in more depth.  Some quick parallels of why I think it is more like a social science:

• Bad statistical methodology  (a hallmark, unfortunately, of much of social science)
• Emphasis on peer review over replication
• Reliance on computer models rather than observation
• Belief there is a "right" answer for society with subsequent bias to study results towards that answer  (example, and another example)

For a while, I have written about the bizarre assumption made by climate scientists.  They cannot prove or show any good link historically between CO2 and warming.  What they instead do is show that they can’t explain some of the warming by understood processes, so they assume that any warming they cannot explain is from CO2.   Don’t believe me?

Researchers are trying to understand how much of the melting is due to the extreme natural variability in the northern polar climate system and how much is due to global warming caused by humans. The Arctic Oscillation climate pattern, which plays a big part in the weather patterns in the northern hemisphere, has been in "positive" mode in recent decades bringing higher temperatures to the Arctic.

Dr Igor Polyakov, an oceanographer from the International Arctic Research Centre in Fairbanks, Alaska, explained that natural variability as well as global warming is crucial to understanding the ice melt. "A combination of these two forces led to what we observe now and we should not ignore either forces" he said.

The consensus among scientists is that while the natural variability in the Arctic is an important contributor to climate change there, the climate models cannot explain the rapid loss of sea ice without including "human-induced" global warming. This means human activity such as burning fossil fuels and land clearing which are releasing greenhouse gases in the atmosphere.

"There have been numerous models run that have looked at that and basically they can’t reproduce the ice loss we’ve had with natural variability," said Dr Perovich. "You have to add a carbon dioxide warming component to it."

In other words, any warming scientists can’t explain is chalked up to, without proof mind you, CO2.  Why?  Well, perhaps because it is CO2 that gets the funding, so CO2 it is.  To show you how dangerous this assumption is, I note that this study apparently did not consider the effect of man-made soot from inefficient coal and oil combustion (e.g. from China).  Soot lands on the ice, lowers its albedo, and causes it to melt a lot faster.  Several recent studies have hypothesized that this alternate anthropogenic effect (with a very different solution set from Co2 abatement) may explain much of recent Arctic ice loss. 

Here is a big fat clue for climate scientists:  It is not part of the scientific method to confidently ascribe your pet theory (and source of funding) to every phenomenon you cannot explain.  Or, maybe climate scientists are on to something.  Why does gravity seem to work instantaneously at long distances? Co2!  What causes cancer cells to turn on and grow out of control?  CO2!  Hey, its easy.  All of our scientific dilemmas are instantly solved.

This is absolutely amazing.  I was checking out this article in the Ithaca Journal called "Climate Change 101: Positive Feedback Cycles" based on a pointer from Tom Nelson.

The Journal is right to focus on feedback.  As I have written on numerous occasions, the base effects of CO2 even in the IPCC projections is minimal.  Only by assuming unbelievably high positive feedback numbers does the IPCC and other climate modelers get catastrophic warming forecasts.  Such an assumption is hard to swallow – very few (like, zero) long-term stable natural processes (like climate) are dominated by high positive feedbacks (the IPCC forecasts assume 67-80% feedback factors, leading to forecasts 3x to 5x higher). 

So I guess I have to give kudos to an alarmist article that actually attempts to take on the feedback issue, the most critical, and shakiest, of the climate model assumptions. 

But all their credibility falls apart from the first paragraph.  They begin:

Our world is full of positive feedback cycles, and so is our society.
Popular children’s books like “If You Give a Mouse a Cookie” by Laura
Numeroff are excellent examples. In Numeroff’s tale, a mouse asks for a
cookie, leading it to ask for a glass of milk, and so on, till finally
it asks for another cookie.

Oh my God, they go to a children’s book to prove positive feedback?  If I had gone this route, I probably would have played the "sorcerer’s apprentice" card from Fantasia.  Anyway, they do soon get into real physics in the next paragraph.  Sort of.

Here’s an example everyone in Ithaca can relate to: the snowball. If
you make a small snowball and set it on the top of a hill, what
happens? 1) It begins rolling, and 2) it collects snow as it rolls.
When it collects snow, the snowball becomes heavier, which causes
gravity to pull on it with more force, making the snowball roll faster
down the hill. This causes more snow to collect on the snowball faster,
etc., etc. Get the picture? That is a positive feedback cycle.

OMG, my head is hurting.  Is there a single entry-level physics student who doesn’t know this is wrong?  The speed of a ball rolling downhill (wind resistance ignored) is absolutely unaffected by its weight.  A 10 pound ball would reach the bottom at the same moment as a 100 pound ball.  Do I really need to be lectured by someone who does not understand even the most basic of Newtonian physics.  (I would have to think about what increasing diameter would do to a ball rolling downhill and its speed — but the author’s argument is about weight, not size, so this is irrelevant."

Do you really need any more?  This guy has already disqualified himself from lecturing to us about physical processes.  But lets get a bit more:

And what happens to the snowball? Eventually the hill flattens and the
ball comes to a stop. But if the hill continued forever, the snowball
would reach some critical threshold. It would become too big to hold
itself together at the raging speed it was traveling down the hill and
it would fall apart. Before the snowball formed, it was at equilibrium
with its surroundings, and after it falls apart, it may again reach an
equilibrium, but the journey is fast-paced and unpredictable.

Two problems:  1) In nature, "hills" are never infinitely long.  And any hills that are infinitely long with minimal starting energy would find everything at the bottom of the hill long before we came into being 12 billion years or so into the history of the universe.  2)  Climate is a long-term quite stable process.  It oscillates some, but never runs away.  Temperatures in the past have already been many degrees higher and lower than they are today.  If a degree or so is all it takes to start the climate snowball running down the infinite hill, then the climate should have already run down this hill in the past, but it never has.  That is because long-term stable natural processes are generally dominated by negative, not positive, feedback. [ed: fixed this, had it backwards]

The author goes on to discuss a couple of well-known possible positive feedback factors – increases in water vapor and ice albedo.  But it completely fails to mention well-understood negative feedback factors, including cloud formation.  In fact, though most climate models assume positive feedback from the net of water processes (water vapor increase and cloud formation), in fact the IPCC admits we don’t even know the net sign of these factors.  And most recent published work on feedback factors have demonstrated that climate does not seem to be dominated by positive feedback factors.

It hardly goes without saying that an author who begins with a children’s book and a flawed physics example can’t take credit for being very scientific.  But perhaps his worst failing of all is discussing a process that has counter-veiling forces butfails to even mention half of these forces that don’t support his case.  It’s not science, it’s propaganda.

When I was in college, we mechanical engineers had little but disdain for practitioners of the various social sciences, who seemed more focused on advancing political ideologies than conducting quality science.  Apparently, denizens of these softer sciences have become convinced that the lack of objectivity or objective research that plagues their fields is par for the course in the hard sciences as well.  MaxedOutMamma describes this post-modernist view of science:

If some reader is not familiar with the full-bodied modern explications of post-modernism, the story of the Dartmouth professor who decided to sue her students will serve as an introduction. Here is her version of the problem with her students. Here is an article
she wrote about working as a post-doc researcher at Dartmouth Medical
School, which may give a hint as to why her students were so, ah,
unwilling to assent to her view of the world:

In
graduate school, I was inculcated in the tenets of a field known as
science studies, which teaches that scientific knowledge has suspect
access to truth and that science is motivated by politics and human
interest. This is known as social constructivism and is the
reigning mantra in science studies, which considers historical and
sociological understandings of science. From the vantage point of
social constructivism, scientific facts are not discovered but rather
created within a social framework. In other words, scientific facts do not correspond to a natural reality but conform to a social construct.

Lab:
As a practicing scientist, I feel these views need to be qualified in
the context of literary inquiry. My mentor, Chris Lowrey, is an
extraordinary physician- scientist whose vision of science is pragmatic
and positivist. My experience in his
lab has shown me that the practice of science is at least partly
motivated by the scientific method, though with some qualifications.
…
Through my experience in the laboratory, I have found that postmodernism
offers a constructive critique of science in ways that social
constructivism cannot, due to postmodernism’s emphasis on openly
addressing the presupposed moral aims of science. In other
words, I find that while an individual ethic of motivation exists, and
indeed guides the conduct of laboratory routine, I have also observed
that a moral framework—one in which
the social implications of science and technology are addressed—is
clearly absent in scientific settings. Yet I believe such a framework is necessary. Postmodernism
maintains that it is within the rhetorical apparatus of science—how
scientists talk about their work—that these moral aims of science may
be accomplished.

For
those of you who cling to scientific method, this is pretty bizarre
stuff. But she, and many others, are dead serious about it. If a
research finding could harm a class of persons, the theory is that
scientists should change the way they talk about that finding. Since scientific method is a way of building a body of knowledge based on skeptical testing, replication, and publication, this is a problem.

The tight framework of scientific method mandates figuring out what would disprove the theory being tested and then looking for the disproof.
The thought process that spawned the scientific revolution was
inherently skeptical, which is why disciples of scientific method say
that no theory can be definitively and absolutely proved, but only
disproved (falsified). Hypotheses are elevated to the status of
theories largely as a result of continued failures to disprove the
theory and continued conformity of experimentation and observation with
the theory, and such efforts should be conducted by diverse parties.

Needless to say postmodernist schools of thought and scientific method are almost polar opposites.

Reading this, I start to come to the conclusion that climate scientists are attempting to make Climate the first post-modernist physical science.  It certainly would explain why climate is so far short of being a "big-boy science" like physics, where replicating results is more important than casual review of publications by a cherry-picked group of peers.  It also explains  this quote from National Center for Atmospheric Research (NOAA) climate researcher and global warming action promoter, Steven Schneider:

We
have to offer up scary scenarios, make simplified, dramatic statements,
and make little mention of any doubts we have. Each of us has to decide
what the right balance is between being effective and being honest.

Additionally, it goes a long way to explaining why Steve McIntyre gets this response when he requests the data he needs to try to replicate certain climate studies (and here):

    We have 25 or so years invested in the work. Why should I make the data available to you, when your aim is to try and find something wrong with it. There is IPR to consider.

In big-boy science, people who run an experiment and arrive at meaningful findings will publish not only those findings but the data and methodology they used to reach those findings.  They do that because in most sciences, a conclusion is not really considered robust until multiple independent parties have replicated the finding, and they can’t replicate the finding until they know exactly how it was reached.  Physics scientists don’t run around talking about peer review as the be-all-end-all of scientific validation.  Instead of relying on peers to read over an article to look for mistakes, they go out and see if they can replicate the results.  It is expected that others in the profession will try to replicate, or even tear down, a controversial new finding.  Such a process is why we aren’t all running around talking about the cold fusion "consensus" based on "peer-reviewed science."  It would simply be bizarre for someone in physics, say, to argue that their findings were beyond question simply because it had been peer reviewed by a cherry-picked review group and to refuse to publish their data or detailed methodology. 

Some day climate science may be all grown up, but right now its far from it.

In 1497, Savonarola tried to end the Italian Renaissance in a massive pyre of books and artwork (the Bonfire of the Vanities).  The Renaissance was about inquiry and optimism, neither of which had much appeal to  Savonarola, who thought he had all the answers he needed in his apocalyptic vision of man.  For him, how the world worked, and particularly the coming apocalypse, was "settled science" and any questioning of his world view was not only superfluous, it was evil.

Fortunately, while the enlightenment was perhaps delayed (as much by the French King and the Holy Roman Emperor as by Savonarola), it mans questing nature was not to be denied.

But now, the spirit of Savonarola has returned, in the guise of James Hansen, a man who incredibly calls himself a scientist.  Mr. Hansen has decided that he is the secular Savonarola, complete with apocalyptic predictions and a righteousness that allows no dissent:

“James Hansen, one of the world’s leading climate scientists, will today call for the chief executives of large fossil fuel companies to be put on trial for high crimes against humanity and nature, accusing them of actively spreading doubt about global warming in the same way that tobacco companies blurred the links between smoking and cancer.

Hansen will use the symbolically charged 20th anniversary of his groundbreaking speech to the US Congress – in which he was among the first to sound the alarm over the reality of global warming – to argue that radical steps need to be taken immediately if the “perfect storm” of irreversible climate change is not to become inevitable.

Speaking before Congress again, he will accuse the chief executive officers of companies such as ExxonMobil and Peabody Energy of being fully aware of the disinformation about climate change they are spreading.”

It will be interesting to see if any champions of free speech on the left can work up the energy to criticize Hansen here.  What we have is a government official threatening prosecution and jail time for Americans who exercise their free speech rights.  GWB, rightly, would never get a pass on this.  Why does Hansen?