Climate Skeptic

For some months now, I have struggled with this site.

In the political and economics arena, there never seems to be any shortage of stuff to write about.  That is in large part because when I and others take a position, folks who disagree will respond, and interesting discussions rage back and forth between blogs.

For some years on this site, I have endeavored as a layman to help other laymen understand the key issues in the science.  When I first started, I had assumed my role would be pure journalism, simplifying complex arguments for a wider audience.  But I soon found that my background in modelling dynamic systems (both physical and social) allowed me to spot holes in the science on my own as well.

But of late I began to run down.  Unlike in the political / economic world, there is little cross-talk between blogs on different sides of issues.  I could flood the site with stupid media misinterpretations of the site, but it is not what I am trying to do here and besides Tom Nelson has that pretty well covered.

As in the political world, I try to read blogs on all sides of the debate, but in the climate world there was far less interaction.  There is only so long I can go on repeating the same arguments in different ways.  The problem is not that these arguments and holes in the science get quickly dispatched on other sites, it is that they get ignored.  Both sides are guilty of this, but alarmists in particular thrive on knocking down straw men and refusing to address head on the best skeptics’ arguments  (which is not to say that certain skeptic sites don’t have the same problem).

But the new Global Climate Change Impacts Report (pdf) released yesterday has re-energized me.  This document represents such an embarrassment that it simply begs to be critiqued in depth.  So over the coming weeks I will work through the report, in semi-random order, picking out particularly egregious omissions and inaccuracies.

From the Guardian:

Today’s release of the study, titled Global Climate Change Impacts in the United States, was overseen by a San Francisco-based media consulting company…

The nearly 200-page study was scrubbed of the usual scientific jargon, and was given a high-profile release by Obama’s science advisor, John Holdren, and the head of the National Oceanic and Atmospheric Administration (NOAA), Jane Lubchenco.

Wow, that’s sure how I learned to handle a scientific report back when I was studying physics – scrub it of the science and give it to an activist PR firm!   Do you need any more evidence that climate science has become substantially dominated by post-modernist scientists, where ideological purity and staying on message is more important than actually having the science right?

I saw a draft of this report last year, but I am still trying to download this new version.  I expect to be sickened.  Here is a taste of where they are coming down:

If today’s generation fails to act to reduce the carbon emissions that cause global warming, climate models suggest temperatures could rise as much as 11F by the end of the century.

11F is about 6.1C.  I don’t know if they get this by increasing the CO2 forecast or by increasing the sensitivity or both, but it is vastly higher than the forecasts even of the over-apocalyptic IPCC.  I think one can fairly expect two things, though — 1) More than 2/3 of this warming will be due to positive feedback effects rather than Co2 acting alone and 2) There will be little or no discussion of the evidence that such positive feedback effects actually dominate the climate.

Apparently the report will make up for having all the science stripped out by spending a lot of time on gaudy worst case scenarios:

That translates into catastrophic consequences for human health and the economy such as more ferocious hurricanes in coastal regions – in the Pacific as well as the Atlantic, punishing droughts to the south-west, and increasingly severe winter storms in the north-east and around the Great Lakes.

The majority of North Carolina’s beaches would be swallowed up by the sea. New England’s long and snowy winters might be cut short to as little as two weeks. Summers in Chicago could be a time of repeated deadly heat waves. Los Angelenos and residents of other big cities will be choking because of deteriorating air quality.

Future generations could face potential food shortages because of declining wheat and corn yields in the breadbasket of the mid-west, increased outbreaks of food poisoning and the spread of epidemic diseases.

This strikes me as roughly equivilent to turning in a copy of Lucifer’s Hammer in response to a request for a scientific study of the physics of comets.

I thought this post by Steve McIntyre at Climate Audit was pretty amazing, even by the standards of climate science.

We begin with a felt need by fear mongers to link CO2 and global warming to bad stuff, in this case a decline of growth or calcification rates on the Great Barrier Reef.  So, abracadabra, some scientist-paladins generate this, which is eaten up by the media:

Wow, that looks bad.  And if we stop there, we can write a really nice front-page article full of doom and gloom.  Or we can do some actual science.  First, lets pull back and look at a longer trend:

Hmmm.   That looks kind of different.  Like the recent decline is by no means unprecedented, and that in fact one might call the 1850-1950 levels, rather than the recent drop, the anomaly.  This latter is a tough question, of course, in all of climate science.  Just what is normal?

Anyway, we can go further.  McIntyre notices the plot looks awfully smooth.  What if we were to move out of USA Today mode and look at the raw data rather than a pleasantly smooth graph.  This is what we would see:

Wow, that looks really different.  That must be some amazing smoothing algorithm they used.  Because what I see is a generally increasing trend in reef growth, with a single low number in 2005.  Rather than being some change in slope in the whole trend, as portrayed in the smoothing, this is a single one-year low data point.  (It turns out there are several smoothing approaches one can take that put inordinate value on the end point — this was a trick first found in Mann’s hockey stick trying to make hay of the 1998 high temperature anomaly).

I think just looking at the raw data would cause any reasonable person to shake their head and determine that the author’s were grossly disingenuous in their smoothing and conclusions.  But, as they say on TV, “Wait, there’s more.”

Because it turns out the 2005 drop seems to be less a function of any real drop but in fact due to serious gaps in the data set.  The black line is a close-up of the raw growth data, while the pink area is the size of the measurement data set used, with its scale on the right.

Just by the strangest of coincidences, the large drop in 2005 occurs at the same time the number of data points in the data set drops down to 2!  While most of the data has been driven by measurement of 40 or more reefs, the key two years that drive the entire conclusion come from just 2 reefs?  This is the worst possible science.  Most real scientists would have dropped out the last several years and probably would have dropped all the data since about 1990.   Or else go out and get themselves some more freaking data.   It is easily possible, in fact quite likely, that the 2005 drop was due to mix, as high growth measurement site were dropped out of the data set, leaving only lower growth sites in the average.  These changes in mix say absolutely nothing about underlying growth rates.

I am just visually integrating the pink curve, but its reasonable to guess that there are about 600 measurements in the post 1980 period when the averaged trend in the first chart above turns down.  Somehow these guys have come up with a methodology that allows 4-5 measurements in 2004-5 to grossly outweigh the other 600 and pull the whole curve down.  Unless there is something I do not understand, this borders on outright fraud.  This can’t be accidental – the authors simply had to understand the game they were playing here.

Update: Here is another interesting one — An apparent increase spike in ocean heat content:

Just coincidentally turns out to exactly coincide with a change in the source for the data.  The jump occurs exactly at the splice between two data sets.  And everyone just blithely accepts this jump as a physical fact??

This is particularly hard to accept, as the ARGO data set (the newer data) has shown flat to declining ocean heat content since the day it was turned on.  So what is the justification for the spike at the splice?

I try really, really hard not to get pulled into the ad hominem attacks that fly around the climate debate.  So the following is just for fun on a Friday, and is not in any way meant to be a real climate argument.  However, since so many alarmists like to attack skeptics as being anti-science, I thought I would have a bit of fun.

This diagram was spurred by this post from Reason’s Radley Balko:

The Science Blogs are having fun with the “wellness editor” at the Huffington Post, a woman who claims to have a “doctorate in homeopathic medicine.” An odd choice for a lefty website that makes such hay of the right’s hostility to science. I like this comment: “…a doctorate in homeopathic medicine would be a blank piece of paper soaked in a 1:10,000,000 tincture made from the ink of an actual doctor’s diploma.”

Just to head off the obvious, I have no doubt a similar Venn diagram could be created for skeptics and people who believe the world is only 4000 years old.  Both arguments are equally meaningless when it comes down to whether the science is correct.

This could easily be a business case:  Two managers.  One sits in his office, looking at spreadsheets, trying to figure out if the factory is doing OK.  The other spends most of his time on the factory floor, trying to see what is going on.  Both approaches have value, and both have shortcomings.

Shift the scene now to the physical sciences:  Two geologists.  One sits at his computer looking at measurement data sets, trying to see trends through regression, interpolation, and sometimes via manual adjustments and corrections.  The other is out in the field, looking at physical evidence.   Both are trying to figure out sea level changes in the Maldives.    The local geologist can’t see global patterns, and may have a tendency to extrapolate too broadly from a local finding.  The computer guy doesn’t know how his measurements may be lying to him, and tends to trust his computer output over physical evidence.

It strikes me that there would be incredible power from merging these two perspectives, but I sure don’t see much movement in this direction in climate.  Anthony Watts has been doing something similar with temperature measurement stations, trying to bring real physical evidence to improve computer modellers correction algorithms, but there is very little demand among the computer guys for this help.  We’ve reached an incredible level of statistical hubris, that somehow we can manipulate tiny signals from noisy and biased data without any knowledge of the physical realities on the ground  (“bias” used here in its scientific, not its political/cultural meaning)

Any number of folks have achnowleged that, nowadays, the surest road to academic funding is to tie your pet subject in with climate change.  If, for example, you and your academic buddies want funding to study tourist resort destinations (good work if you can get it), you will have a better chance if you add climate change into the mix.

John Moore did a bit of work with the Google Scholar search engine to find out how many studies referencing, say, surfing, also referenced climate change.  It is a lot.  When you click through to the searches, you will find a number of the matches are spurious  (ie matches to random unrelated links on the same page) but the details of the studies and how climate change is sometimes force-fit is actually more illuminating than the summary numbers.

I have no idea what is driving this, whether it be a crass payback for campaign contributions (as implied in the full article) or a desire to stop those irritating amateur bloggers from trying to replicate “settled science,” but it is, as a reader said who sent it to me, “annoying:”

There are some things science needs to survive, and to thrive: eager, hardworking scientists; a grasp of reality and a desire to understand it; and an open and clear atmosphere to communicate and discuss results.

That last bit there seems to be having a problem. Communication is key to science; without it you are some nerd tinkering in your basement. With it, the world can learn about your work and build on it.

Recently, government-sponsored agencies like NIH have moved toward open access of scientific findings. That is, the results are published where anyone can see them, and in fact (for the NIH) after 12 months the papers must be publicly accessible. This is, in my opinion (and that of a lot of others, including a pile of Nobel laureates) a good thing. Astronomers, for example, almost always post their papers on Astro-ph, a place where journal-accepted papers can be accessed before they are published.

John Conyers (D-MI) apparently has a problem with this. He is pushing a bill through Congress that will literally ban the open access of these papers, forcing scientists to only publish in journals. This may not sound like a big deal, but journals are very expensive. They can cost a fortune: The Astrophysical Journal costs over $2000/year, and they charge scientists to publish in them! So this bill would force scientists to spend money to publish, and force you to spend money to read them.

I continue to be confused how research funded with public monies can be “proprietary,” but interestingly this seems to be a claim pioneered in the climate community, more as a way to escape criticism and scrutiny than to make money (the Real Climate guys have, from time to time, argued for example that certain NASA data and algorithms are proprietary and cannot be released for scrutiny – see comments here, for example.)

I get emails asking why I haven’t reported on this or than nose count survey of climate scientists, or such and such declaring himself a skeptic, or whatever.  Someone in Senator Inofe’s office constantly spams me with that stuff.

The answer is that I don’t think headcounts are a particularly relevent or interesting way to conduct science.  Interestingly, Russel Roberts answered a similar but different question in much the same way I would have:

A number of people have asked why my name was missing from the petition against the spending package [which appeared as an ad in the NY Times].  The simple answer is that I didn’t know about it. But I probably wouldn’t have signed it anyway. I decided a while back not to sign these kind of petitions. First, there’s usually something I don’t agree with in the text, and second, the whole thing is a little weird about the whole thing–the idea that people should care that there are a bunch of economists who feel this way, especially given that there are a bunch of economists on the other side of the political spectrum who feel the exact opposite. So is the idea that we have more Nobel Laureates than they do? But what if it’s fewer?

I am still trying to get a copy of the article in Science on which this is based, but the AZ Republic writes:

Western forests that withstood wildfire, insect attacks and drought are now withering under an even greater menace.

Heat.

Rising temperatures are wiping out trees faster than the forests can replace them, killing pines, firs, hemlocks and almost every other kind of tree at almost every elevation from northern Arizona to southwestern Canada.

Writing today in the journal Science, a team of 11 researchers says global warming is almost certainly the culprit behind a sharp spike in tree deaths over the past several decades. The higher death rates, which doubled in as few as 17 years in some areas, coincide with a regional increase in temperature and appear unrelated to other outside factors.

Perhaps this question is answered somewhere in the unreported details, but my first reaction was to want to ask “Dendroclimatologists like Michael Mann reconstruct history from tree rings based on the assumption that increasing temperatures correlates linearly and positively with tree growth and therefore tree ring width.  Your study seems to indicate the correlation between tree growth and temperature is negative and probably non-linear.  Can you reconcile these claims?’    Seriously, there may be an explanation (different kinds of trees?) but after plastering the hockey stick all over the media for 10 years, no one even thinks to ask?

Normally, I just ignore the flood of such academic work  (every study nowadays has global warming in it — if these guys had just wanted to study the forest, they would have struggled for grant money, but make it about forest and global warming and boom, here’s your money).  The reasons I picked it out was because I just love the quote below — I can’t tell you how often I see this in climate science-related work:

Scientists combed more than 50 years of data that included tree counts and conditions. The sharp rise in tree mortality was apparent quickly. Researchers then eliminated possible causes for the tree deaths, such as air pollution, fire suppression or overgrowth. They concluded the most likely culprit was heat.

Again, I need to see the actual study, but this would not be the first time a climate study said “well, we investigated every cause we could think of, and none of them seemed to fit, so it must be global warming.”  It’s a weird way to conduct science, assuming Co2 and warming are the default cause for every complex natural process.  No direct causal relationship is needed with warming, all that is required is to eliminate any other possible causes.  This means that the less well we understand any complex system, the more likely we are to determine changes in the system are somehow anthropogenic.

Speaking of anthropogenic, I am fairly certain that the authors have not even considered the most likely anthropogenic cause, if the source of the forest loss is even man-made at all.  From my reading of the literature, nearby land use changes (clearing forests for agriculture, urbanization, etc) have a much greater affect on local climates and particularly moisture patterns than does a general global warming trend.  If you clear all the surrounding forest, it is likely that the piece that is left is not as healthy as it would have been in the midst of other forested land.

The article, probably because it is making an Arizona connection, makes a big deal about the role of a study forest near Flagstaff in the study.  But if the globe is warming, the area around Norther Arizona has not really been participating.  The nearest station to the forest is the USHCN station at the Grand Canyon, a pretty decent analog because it is nearby, rural, and forested as well.  Here is the plot of temperatures from that station:

Its hard to tell from the article, but my guess is that there is actually a hidden logic leap embedded.  Likely, their finding is that drought has stressed trees and reduced growth.  They then rely on other studies to say that this drought is due to global warming, so then they can get to the grant-tastic finding that global warming is hurting the forest.   But the “western drought caused mainly by anthropogenic warming” is not a well proven connection.  Warming likely has some contribution to it, but the west has been through wet-dry cycles for tens of thousands of years, and has been through much worse and longer droughts long before the Clampetts started pumping black gold from the ground.

Over at the Blackboard, Lucia has a post with a growing set of comments about anthropogenic warming and the tropical, mid-tropospheric hotspot.  Unlike many who are commenting on the topic, I have actually read most of the IPCC AR4 (painful as that was), and came to the same conclusion as Lucia:  that the IPCC said the climate models predicted a hot spot in the mid-troposphere, and that this hot spot was a unique fingerprint of global warming (“fingerprint” being a particularly popular word among climate scientists).  Quoting Lucia:

I have circled the plates illustrating the results for well mixed GHG’s and those for all sources of warming combined. As you see, according to the AR4– a consensus document written for the UN’s IPCC and published in 2007 — models predict the effect of GHG’s as distinctly different from that of solar or volcanic forcings. In particular: The tropical tropospheric hotspots appears in the plate discussing heating by GHG’s and does not appear when the warming results from other causes.

OK, pretty straight-forward.   The problem is that this hot spot has not really appeared.  In fact, the pattern of warming by altitude and latitude over the last thirty years looks nothing like the circled prediction graphs.  Steve McIntyre does some processing of RSS satellite data and produces this chart of actual temperature anomalies for the last 30 years by attitude and altitude  (Altitude is measured in these graphs by atmospheric pressure, where 1000 millibars is the surface and 100 millibars is about 10 miles up.

The scientists at RealClimate (lead defenders of the climate orthodoxy) are not unaware that the hot spot is not appearing.  They responded about a year ago that 1)  The hot spot is not an anthropogentic-specific fingerprint at all, but will result from all new forcings

the pattern really has nothing to do with greenhouse gas changes, but is a more fundamental response to warming (however caused). Indeed, there is a clear physical reason why this is the case – the increase in water vapour as surface air temperature rises causes a change in the moist-adiabatic lapse rate (the decrease of temperature with height) such that the surface to mid-tropospheric gradient decreases with increasing temperature (i.e. it warms faster aloft). This is something seen in many observations and over many timescales, and is not something unique to climate models.

and they argued 2) that we have not had enough time for the hot spot to appear and they argued 3) all that satellite data really has a lot of error in it anyway.

Are the Real Climate guys right on this?  I don’t know.  That’s what they suck up all my tax money for, to figure this stuff out.

But here is what makes me crazy:  It is quite normal in science for scientists to have a theory, make a prediction based on this theory, and then go back and tweak the theory when data from real physical processes does not match the predictions.  There is certainly no shame in being wrong.  The whole history of science is about lurching from failed hypothesis to the next, hopefully improving understanding with each iteration.

But the weird thing about climate science is the sort of Soviet-era need to rewrite history.  Commenters on both Lucia’s site and at Climate Audit argue that the IPCC never said the hot spot was a unique fingerprint.  The fingerprint has become an un-person.

Why would folks want to do this?  After all, science is all about hypothesis – experimentation – new hypothesis.  Well, most science.  The problem is that climate science has been declared to be 1)  A Consensus and 2) Settled.    But settled consensus can’t, by definition, have disagreements and falsified forecasts.  So history has to be rewritten to protect the infallibility of the Pope the Presidium the climate consensus.  It’s a weird way to conduct science, but a logical outcome when phrases like “the science is settled” and  “consensus” are used as clubs to silence criticism.

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.