0 How large a conspiracy?

Did you know that the observed decline in Arctic sea ice cover is all just a fake?  I didn't, but encountered folks who thought so.  Since I'm one of the people who would have to be involved in the conspiracy or at least being duped by the masterminds, I'll take a minute to ponder the matter and how someone who does not personally know many of the people involved could go about deciding whether there was indeed such a conspiracy.

One of the tools I find useful in considering issues is to follow a common mathematician's approach -- called the reductio ad absurdum.  That means reduction to absurdity.  What you do is assume the thing at hand to be true, and then pursue it logically to see where you go -- whether it leads you to absurdity.  This is also the starting point for a proof by contradiction.  Again, assume the thing to be true and see if it leads logically to a conclusion that contradicts what you know to be true.

So let's assume that there is indeed a fake involved in the decline in Arctic sea ice.  The people involved were a little particular -- the passive microwave sea ice record.

Some questions to pursue, then:

• How long would the conspiracy have to have lasted?
• How much data would have to be faked?
• Who all would have to be involved in the fakery?
• Are there other sources of data to confirm or refute the passive microwave observations?

* The period of the passive microwave data is 1979 (actually late October 1978) to the present.  Much of the time, only a single instrument was flying (easier to fake?).  SMMR from 1978 to 1987, DMSP SSMI F-8 for a few years, F-11 for some more, then F-13 1995 to 2009.  As we get more recent, though, there are multiple satellites, F-14 until late 2008, F-15 until 2008/present (the different dates depending on what algorithm you're using.  And a different passive microwave instrument, AMSRE from 2002 to present

So when does the fakery have to be?  The trend from start passed significance in the mid-1990s, and has only gotten moreso since then.  If the trend were faked, it had to start from some time around then.  Since the declining trend has only gotten larger since then, the conspiracy must have been getting bolder.

Probably at least 15 years -- perhaps nature produced that marginally significant trend back then, but then the conspiracy took the chance to intervene?

Anyone familiar with scientists has already rejected the conspiracy as reductio ad absurdum.  Scientists are very fond of talking about what they're up to.  You may have noticed some of that here.  The lifetime of a conspiracy of scientists is probably measured in seconds for that reason alone.

Let's continue, though.  Maybe (chortle) there are some wily scientists who are great conspirators.

* The scale of data involved is, well, every passive microwave instrument flying since 1979.  They're not terribly high-density instruments by modern standards.  About 0.08 Gb/day for SMMR and SSMI.  AMSRE is more demanding, but still only about 1 Gb/day.  Now you wouldn't have to fake every byte.  The Arctic ice pack at its maximum only covers (well, covered, wintertime maximum has also declined) about 15 million km^2 -- about 3% of the globe.    But only the decrease in area itself would need to be faked, something like 2.5 million km^2.  0.5% of the data, so a mere 50 Mb/day even for AMSRE.

On the other hand, it would have to be skillful fakery.  You can't just throw in random numbers.  The algorithms for finding sea ice concentration and ocean wind speed, among others would go nuts -- and be detected thereby.  The fake has to make sense as sea ice observations (of no ice) and as wind observations, and for each other the others as well.

But who has to be doing the faking?

* Who?  The SSMI are on US Department of Defense satellites.  So either the DoD are the original fakers, or somebody or some group has managed to hack the DoD satellite program.  And, for some reason known only to them, they mess only with the sea ice concentration observations.

With the AMSRE, the instrument is from Japan and is flying on a NASA platform.  So either the Japanese instrument makers decided to fake the observations carefully (only that 0.5% or so in the Arctic periphery), or NASA decided to fake what it relayed to ground -- and the Japanese team never noticed.

I confess that the numbers don't have to be very large here -- a handful of people in the DoD (or hacking DoD) and another handful in Japan and NASA (or hacking NASA).  A single group hacking both DoD and NASA would make the smaller number.  It might also make it easier to figure why the conspiracy would be faking a decline in sea ice cover.  Why Japan, NASA, and the DoD would want to fake a decline in sea ice is a bit of a mystery to me even assuming that there were a conspiracy.  NASA has rather different goals than DoD, and why Japan would want the same as either ...?  Numbers get bigger if you consider the next point.  Quite a lot larger.

* Are there other data sources?  Yes.  Quite a few, and this is where and why things really blow up for the conspiracy-minded folks.

• Instruments that use visible wavelengths -- MODIS, AVHRR, GOES, OLS, and so on.  These instruments are on platforms from the US -- NOAA, NASA, DoD, Europe (EUMETSAT), India, China, Japan, and probably several more.
• Flight observations from the US, Canada, Russia, Norway, and others.  Anybody flying a plane can see that there's no ice for hundreds of km that the conspirators are trying to lie about.  Anybody
• Ship observations from the US, Canada, Russia, Norway, and, again, others.  China, for instance, has started taking ships to the Arctic recently.
• Fishermen -- See The Deadliest Catch for some visuals and an appreciation of what they're doing.  In any case, fishermen pursue the ice edge because the fishing, at least for some things, is best near the ice edge.  If the ice edge were a lie, the fishermen would either not be catching what they need, or they'd be dead.  The location of the ice edge is a matter of life and death.
• Traditional cultures in the Arctic -- many different tribes in different part of the Arctic hunt from the ice.  They do use satellite information (something I know first hand).  If the satellite information were a lie, the tribes would be going to the wrong place, or I'd assume that they'd notice that they hadn't fallen in to the ocean when the satellite types said they should.

* Summing up -- The idea of conspiracy is absurd.  Too many different groups would have to be involved, and be involved for a long time.

Ok, you knew I was going to get to this conclusion.  After all, I'm one of the people who would have to be involved in the conspiracy, or be too embarrassed to admit that I'd been duped.  So the thing to do instead or in addition is to examine a point or three yourself.  Whether the visible satellites observations match up with the passive microwave is something you can check out yourself fairly easily.  Neven, for instance, routinely shows both types of observations.

Back in 2007, when the ice cover was behaving so strangely compared to prior years, that was one of the things I did myself.  Not that I was thinking conspiracy, but that maybe the sensor was going bad.  So I looked for some visible observations as a check against the passive microwave.  They agreed, something amazing really was going on.

Detail: How large a conspiracy?

0 Says who?

I think citations are a greatly underappreciated part of scientific works.  They also, for some of the same reasons, provide a way of assessing the strength of a source even if you don't know the topic that's involved.

My first real introduction to citations as being important was when a history teacher of mine in college was concerned that I'd committed academic dishonesty -- failed to cite a source for something she felt was obscure.  After a nervous couple of minutes for me, we had a nice chat.  What I'd done was to mention, without citation, Newton's prism experiment.  I hadn't cited it because it was something I'd been seeing mentioned for years without citation, so figured counted as 'common knowledge' and not in need of a citation.  My history teacher, on the other hand, had never heard of it before, so was looking for the citation to the person who had discovered the experiment (perhaps a citation to Newton himself; I now have the right book -- Newton's Opticks).

So that's one use of citations -- avoid annoying your teacher.  Somewhat more generally, credit people for the work they do.  That's an important thing in being a scientist, as the people you're giving credit to are your colleagues.  Conversely, your colleagues will be peeved, to put it mildly, if you fail to credit them for their work.

The use at hand, as the title suggests, is to provide the backup for your claims.  You could avoid some of that by providing full descriptions yourself, but then your article becomes impossibly long.  Instead you can write something like "The earth is round[1] and rotates[2].", where you then give the full address to 1 and 2 somewhere later in the document (in print media days) or hyperlink the words directly.  An alternate that I prefer is to provide the direct 'who' and 'when', such as "The earth is round [c.f. e.g. Aristotle, ca. 322 BC*] and rotates [Foucault, 1851]."  In this way the reader immediately sees something about who your source is, and how old it is, and retains some merit even in a hyperlinking medium.

If you could read infinitely fast, it might be doable to simply read everything from everywhere.  But for us humans, some means of trimming the candidates to manageable volumes is needed.  So, for myself at least, if I'm trying to learn about a scientific topic, I head for scientific sources, or as close to the original as I can understand.

The bibliography/citation list is a quick way to figure this out.  Places that are citing wikipedia articles, newspaper editorials, and so forth, for most of what they have to say are not strong sources.  If the topic has scientific merit, there will be scientific papers on it.  If I couldn't read, or would have a hard time finding and reading, the original scientific papers (which is true in most fields), then I want to be learning from someone who could and did.  The strong source is one which is providing me the ability to go in to the literature and start learning about the particular part of the article which caught my attention.

This last is another important purpose of citation: It helps readers learn more.  I would rather be learning the science from an author who is trying to help me learn it.

Now for the mirror test: How do my own postings hold up to that standard?  In this post, it does ok, in the sense that this isn't about the content of science; it's my opinion of some things to consider in looking for sources from which to learn the science.  In the science posts, not always as well as I'd like.  So I'll take this post as a reminder to myself to include more references and links.

In my blogroll, two that are particularly good with their citations are Skeptical Science and RealClimate, though I think almost all are pretty good -- at least better than I.

*
c.f., I translate to myself as meaning 'See, for example'.  It means that there's more than one source, and this is either the one that I used (though I know there are more), or that for some reason I prefer it.
Update: my self-translation is incorrect, see Nick and Peter's comments.  What I really want is 'e.g.', for exempli gratia  (free example is my translation here, unfortunately, it's my son who is the latinist.)

ca means 'about' (circa).

Detail: Says who?

0 Verifying forecasts 2

As I said last week, verifying predictions is difficult, and was prompted in to looking again at the matter by someone doing it wrong.  Of course the standard of 'wrongness' involved is mine.  Forecast verification is something of an art as well as mathematics and science.  But some points I think I'll get little argument from Allan Murphy* and his intellectual colleagues and descendants for are:

• You have to be clear what you're forecasting
• what variable
• at what time (or time span)
• for what place or area
• You have to be clear how the forecast is going to be evaluated
• You should evaluate all forecasts
• Forecast must be public
• Forecasts must be verifiable

That last might seem a little strange.  I hope not.  Suppose I said next July 20th at 3:34 PM at Washington National Airport the official temperature would be hot.  Very specific about what I'm forecasting and what it will be evaluated against.  But what is 'hot'?  To me, anything over 80 F (27 C).  As such, it's a near certainty that my forecast will be correct.  It's also awfully easy for me, on July 21st, to say, regardless of the temperature, that it was 'hot'.  This is one reason that we prefer numbers in science.  You can, and we do, work with qualitative predictions.  But it takes more work, as you have to find some way of making 'hot' objective, so that we can all agree that such a forecast was correct or not.

In general, if not as universal, we add a couple more items, at least desirable if not mandatory:

• Forecasts should specify their degree/nature of confidence
• It's a good idea to compare the quality of your prediction against an null forecast method (not a personal comment, means any method that doesn't know any of the science -- like straight line regression, or persistence; also goes by the principle of 'check how wrong you could be', which I'll illustrate later this week).
• A trivial matter (except that it comes up in Watts' Nov 23 2010 response to greenman3610) is that all predictions depend on what really happens.  Of course if it's colder, there'll be more ice, and if it's warmer there'll be less.  That's what you're supposed to be predicting!

Now, what prompted this was a video by greenman3610 video and the response from Watts up With That.  greenman observed a very bad forecast coming from WUWT, Watts said it was really rather good.  Figuring out good vs. bad isn't really a scientific question, and those aren't really the words used by either, so be fair to both.  Those are my words, but I think capture fairly the sense of their respective comments.

This is all regarding sea ice.  You can check my original comments from June on my May estimates -- that they were for September average, Arctic, sea ice extent, as measured by NSIDC.  Further, at least in what we submitted to the sea ice outlook, we mentioned what the standard errors in the predictions were.  Don't want it said that I have higher standards for others than I live by myself.

So what was Goddard's prediction?  That turns out to be hard to track down. Tamino and Neven have also looked in to the matter, Neven getting back to February (from his June check).  My selection:

1) On June 6th it is that "Conclusion : Based on current ice thickness, we should expect September extent/area to come in near the top of the JAXA rankings (near 2003 and 2006.) However, unusual weather conditions like those from the summer of 2007 could dramatically change this. There is no guarantee, because weather is very variable."
-- this does tell us what the verification data source is supposed to be, but not whether it is monthly average or daily minimum.  Fairly clearly it is September.  September's minimum and average for 2003, from JAXA, were (6.03, and 6.13) million square km.  September 2006 showed (5.78, 5.91).  The nearest to both would be their average, giving his June 6, 2010 forecast(s) as 5.905, 6.02 million square km for minimum day and monthly average, respectively.

It is not until comments at his personal, separate from WUWT, blog in September that it becomes clear to me that Goddard means the minimum day, not the monthly average.  JAXA's minimum September 2010 day is 4.81 million square km.  So Goddard's June 6 forecast is off by over 1 million km^2.  He gave no sense of variability at this point, but I'll observe my own prior estimate of 0.5 million km^2 for natural variability.  So 2 standard deviations errors.  (Aside: that others were off by as much or more does not affect our evaluation of Goddard's predictions.  n.b., I was not one of those others.)

2) On June 14th, the forecast has changed to "Conclusion : 2010 minimum extent is on track to come in just below 2006. With the cold temperatures the Arctic is experiencing, the likelihood of a big melt is diminishing."
Ok, what does 'just below' mean?  About the same as my 'hot', perhaps.  2006's minimum day at JAXA was 5.78 million km^2, September average of 5.91.  Observed 2010 was (4.81, 5.10).  I'm hard-pressed to call errors of (+0.97, +0.81) million square km 'just below', but the Goddard never defined the term.  (Hence that guide on verifying forecasts!)

3) On June 23rd the forecast becomes:
"I’m forecasting a summer minimum of 5.5 million km², based on JAXA. i.e. higher than 2009, lower than 2006."
The first time he directly names a specific number for the ice (well, one assumes extent, but he doesn't say here whether it's extent or area he means; nor whether it's minimum day or monthly average).  2009's JAXA numbers are (5.25, 5.38) for minimum day and monthly average extent, respectively.  2006 are (5.78, 5.91).  5.5 is between either the minimum day or the monthly average, so this didn't help clarify which he meant.  His September comments did (minimum day), and this comment is also more clearly consistent with minimum day. (0.25 above 2009, 0.28 below 2006, versus being much closer to the 2009 monthly average than 2006 monthly average).  This also gives us a sense of his level of uncertainty -- 0.25 million km^2.  If he were more uncertain than that, he would give a wider range of extents.  Whether that's one or two 'sigma' is also not clear, and, again, points to why we like these things specified.


I'll note that in following this up, I read every one of the WUWT 'sea ice news' posts from #2 to #30, as well as an August midweek update, and all 'verification' posts at Goddard's.  Plus some, but not all, comments in August's posts.  This matters some for what follows.

From July 4th through a comment of his on his own WUWT post August 24th, Goddard continues with 5.5 million square km being his prediction.   Quoting his comment (with date and time so you can find it; I've never figured how to link straight to comments):
"
stevengoddard says:
August 24, 2010 at 9:46 am
Scott,
Remember that NSIDC took a mulligan, changing their forecast in July. They started at 5.5 million.
I haven’t taken my mulligan yet ;^)
"

So at least as late as that the 24th, 5.5 is his prediction and he's taking pride in having not changed his forecast, when talking to WUWT readers.  That's odd, because in the August Sea Ice Outlook, whose due date for submission was mid-month (I did submit to it myself, on time), his prediction was 5.1 million km^2 for September monthly average at NSIDC.  As I mentioned before JAXA runs about 0.2 million above NSIDC, so a 0.4 million square km drop doesn't make sense.  On top of which is monthly average (which, at JAXA, runs about 0.15 million km^2 above minimum day, and more between NSIDC's monthly average and minimum day -- about 0.30 million km^2 this year).

To back that out:  If the prediction for minimum day was 5.5 according to JAXA for minimum day, subtract 0.2 to get NSIDC's minimum day, and then add 0.3 to get the September (NSIDC) average extent.  If his prediction hadn't fundamentally changed, the SEARCH submission should have been 5.6 million km^2.  Since it was 5.1 instead, there's a rather large change.  Surely worthy of a post of its own at either WUWT or his own blog.  In any case, given his August 24th comment, it had to be between then and the 31st.  (At least if it's going to be called an August prediction, which he does.)  That, or he was telling WUWT readers different things on the 24th than he was telling the Sea Ice Outlook.  Or Outlook let him submit late, or ... -- the point being, this shows why it is we want our forecasts to be clearly public.

August 29th Goddard is still referring only to his 'June' forecast of 5.5 million km^2.  No mention of an August prediction.

August 30th, Steven Goddard started blogging regularly at his own blog rather than WUWT.

On August 31st he seems to still like his 'June' forecast (actually, the at least 3rd forecast from June, the one on June 23rd) as he says:
"The video below shows current ice (thin red line) my June forecast (dashed line) and NSIDC’s forecast summer minimum (red horizontal line.)  Who do you think is going to be closest?"
-- and there is no mention of an August prediction.  Note, too, he doesn't mention that NSIDC is predicting a different thing than he is.

The first I see Goddard directly referring to his 'August forecast' is September 7th.  It is mentioned at WUWT the the day before by Watts.  Can you really call something that doesn't deserve a main-post mention until the end of the first week of September a prediction of September?  Ok, maybe I missed the post in which it showed up.  But clearly, given his August 24th and 29th comments, his prediction of 5.1 million square km doesn't surface publicly until after the morning of the 29th.

JAXA's observed ice cover on August 23rd was 5.60 million km^2 (last observation he'd have been able to look at in commenting on the 24th).  24th was 5.55.  August 31st was 5.33 (already 'busting' all of his 'June' forecasts).  The Sea Ice Outlook was released September 1st, so in the last week of August, apparently, after the June forecasts were busted, Goddard made a revised forecast.  (See point of 'how wrong could you be' above; I'll make it its own note later this week.  The answer is, for JAXA, not very if you get to predict the seasonal minimum day from August 23rd.)

It is also with the post of September 7th that I (finally) can be positive that Goddard means to verify minimum day's ice extent as computed by JAXA:
"My June forecast of 5.5 million km² (JAXA) is currently off by 7%."
-- you can't make that statement if you mean monthly average. Who knows what's going to happen the rest of the month?



So at last, I'll return to greenman3610 and Watts' comments on Goddard's prediction(s) made at WUWT.  One part of it being that fundamentally, greenman3610 is not focused on predictions as such.  It it, instead the months of Goddard talking of sea ice being in recovery.  That belief in recovery driving his predictions of ice extent.  But, fundamentally we're looking at at least 6 months of 'sea ice is recovering' posts from Goddard, with numbers or references that compute to numbers from 5.5 million km^2 to over 6 for the extent based on that belief.  Then, in the last 2 days of August, entering a forecast of 5.1 million km^2, which is less than 2009's 5.25.  It's a recovery, there's just less ice?  Don't follow that reasoning.

As to predictions as such, only the June predictions (between 5.78 and 6.03 June 6th, 'just below' 5.78 June 14th, 5.5 June 23rd; in the first two he was referencing years, I filled in the values for minimum day from JAXA for those years) seem to have been made in notes of their own at WUWT.  It's correct to refer to those as his WUWT forecasts lacking any sighting of a post with the 5.1 there before September, and his clear comment on the 24th of August of 5.5 (still) being his prediction with none others (no 'mulligan' as he called it) existing.  Further, after going through all the posts I could find on the topic, it's clear (see above) that he meant to forecast the minimum day's ice cover as computed by JAXA.   That figure, this year, was 4.81 million km^2. So his last (and, turned out to be, best, and the one he consistently referred to as his forecast from June 23rd to at least August 24th) June forecast was off by almost 0.7 million km^2.

On the other hand, Watts points, in November, to only the final prediction from Goddard, which had to have been made in the last two days of August, that was 5.1 million km^2 for September monthly average computed by NSIDC.  (You can tell by noting the horizontal line in the verification figure from SEARCH that Watts shows is at 4.9 million km^2, vs. JAXA's minimum day of 4.81, or NSIDC's minimum day of 4.60, or JAXA's monthly average of 5.10.)  The 5.1 is not so far off from 4.9.  At least SEARCH is more or less clear (not clear enough, I think, that'll be a different email) that this is what they're looking for.  Not clear at all to me that either Watts or Goddard realize the different quantities being forecast or verified with.  Pointing to only one of multiple forecasts violates one of the forecast verification principles I mentioned above. 

Was Goddard's forecast pretty good?  Pretty bad?  Off by 0.2, 0.3, 0.7, 1.1 million km^2?  Who knows.  We can get all those results and more by varying what we take to be his forecast and how what we choose to verify against.  That's why it's such a central point that you say just what you're predicting (guessing, estimating, ...) and how it's to be validated.


Ok, so all good fun in seeing why we want to do forecast verification in the direction that I like rather than waiting until afterwards to figure out what number will be compared against what other number.  There was one other point of contention between Watts and greenman3610 -- the business of Goddard talking of recovery or not.  From August 9th, for example, we see Goddard (he, or Watts, italicized it, so I'll follow suit) saying:
Can we find another year with similar ice distribution as 2010? I can see Russian ice in my Windows. Note in the graph below that 2010 is very similar to 2006. 2006 had the highest minimum (and smallest maximum) in the DMI record. Like 2010, the ice was compressed and thick in 2006. Conclusion : Should we expect a nice recovery this summer due to the thicker ice? You bet ya..

-- The DMI record is even shorter than JAXA, starting in 2005, vs. 2002.  Given that we want 30 years for climate purposes, either is too short for much use, except to cherry pick for 'highest in the record'.  Kind of like being the tallest person in my house.  Sure, I am.  But there aren't many of us here.  The satellite period as a whole only begins in October 1978, so even taking the whole period is pretty short.

Anyhow, there's kerfuffle between greenman3610 and Watts regarded whether there was a 'guarantee' of recovery in Goddard's comment.  You decide (go read the whole note, of course, if you're going to).

I'm hard pressed, to return to science, to see 2010 extent being below 2009 and all years 2006 and before that we have data for, as 'recovery'.  And that takes us back to what constitutes a forecast and how you'd verify it.


* ok, going way back for those who remembered that asterisk.  Allan Murphy was one of the major figures in meteorological forecast verification.  One of the people I discussed verification with fairly often had learned a fair part of what he knew from Murphy.  For the major 'small world' effect, I have a couple of textbooks that Murphy used himself.  (One is 'Strength of Materials', so I guess that he started out in engineering, as I had.)  Anyhow, if you were to read all his papers on verification, you'd be quite knowledgeable indeed.

Note:
I try to tell people when I write about their work.  But I could not find any email contact for Goddard at his blog.  I sent a note (available on request) via Watts' contact page 9:45PM Eastern time 24 Nov asking a couple questions and notifying him of this post's scheduled Monday appearance, and to greenman3610 about the same time.  Watts couldn't answer my questions, but did forward (he said evening of the 24th) my note to Goddard.  (No surprise that he couldn't -- they were about Goddard's actions and knowledge.)  Watts, in his response to me mentioned a prediction by Bastardi at WUWT.  It illustrates another bunch of violations of the principles I mention above, so gives another chance to discuss how to do forecast verification.  That'll appear later this week, as well as a consideration of how wrong could you be if you wait until the last week of August to make your prediction of the seasonal minimum.

Detail: Verifying forecasts 2

0 I am John Abraham

Those of you of a certain age, or a certain other age, will remember the scene from the movie Spartacus where everyone steps forward and declares himself or herself to be Spartacus.  So it goes now.  Fortunately, it is only threats against jobs and not, yet, lives which are at hand. 

Still, someone's job is indeed being threatened, and the 'transgression' involved is to address the scientific content, or lack thereof, in the whiner's threatener's presentations.  That would be no matter for concern if the threatener were some nonentity.  But it is a person who has testified to the US Senate regarding the science of climate change.  That makes it rather a serious issue -- this is not a marginal person whining from the distant reaches of the auditorium.  This is a person with the ear of US Senators.

The person being threatened is John Abraham.  He's a scientist at a small university in Minnesota who took the time to address the scientific claims of the person who styles himself as Lord Monckton, and, among other things, who recently was invited to address the US Senate on climate change.  Abraham's response is at his University of St. Thomas web page.  I encourage you to view/listen to the presentation Abraham made.  And, of course, to examine yourself the original comments of Monckton's.  And then to hit the scientific literature yourself to see who represented the science most accurately.

I'll include a raft more links below the fold, as many comments are already out there.

The thing which has me writing is the fact that this is such an absurd response from Monckton -- if he were at all interested in the science.  That places this in to the 'weeding sources' category.  If you're interested in the science, you, first, try to get it right yourself.  Then, if someone else points to places where you might have gotten your science wrong (and, in fact, spectacularly wrong), your response is to correct your errors.  You don't have to like it.  Scientists are human, after all, and nobody likes to have it shown that they're wrong.  Still, you do it.  What you don't do is try to get fired the person who showed that you were wrong.  But Monckton indeed responds to correction by trying to get his corrector fired. 

So I encourage you to send your support to Abraham, by facebook group, to email his university, or the like (see, for instance, Hot Topic's petition to sign).  We need more people who are willing to address the scientific content of public statements about climate.  And they need to be reasonably confident that they're not going to lose their jobs for trying to speak honestly about the science.

Facebook Group -- Prawngate*


Stoat

Deltoid

Hot Topic

Desmog blog (has full text of Monckton's latest)

Eli Rabett

Hot Topic - 2

Only In it For the Gold

Deltoid 2

Scruffy Dan (Dan also started the facebook group)  (see this post for the originals about 'prawn' and 'prawngate')

Tenny Naumer at Climate Change Psychology reproduces George Monbiot's Guardian article

Skeptical Science

Skeptical Science 2

* I should explain the 'prawngate' if you haven't seen it before.  The thing is, one of Monckton's early comments was to say that Abraham looked rather like a prawn.  I don't see the resemblance myself.  More to the point, Monckton is sensitive about his own appearance, which results from a disease he has.  You'd think that he'd be particularly sensible about not making fun of somebody's appearance. 

Detail: I am John Abraham

0 Which sources to trust?

Weeding sources is my tag for articles about deciding which sources to trust.  I'm far from the only person who considers this an important topic, of course.  The articles I prefer are, like mine, ones where there's some detailed effort to look at what a source doing and what is dishonest.

Lately Tim Lambert at Deltoid has been taking up the question of a journalist -- Jonathan Leake -- and paper (The Times, in the UK) and their reporting on climate.  The Rabett has written up a nice summary of Tim's posts on this theme.  The upshot being, Leake and The Times are unreliable sources, both for making up things and for not correcting their errors.  But see Tim's researches that establish those points.  Much more work there than Leake is putting in to his artifices.

Detail: Which sources to trust?

0 Successive approximations

The other title for this would be 'the relativity of wrong', if I were to steal the title that Isaac Asimov used for an essay on a related topic.  It's a common issue in science, and successive approximations is both a common and a powerful tool.  In the comment section for 800,000 years of CO2 we had a local introduction to the issue.  But I'll start with the example that Asimov used, and that I have a number of times myself.  (It's entirely possible that I used it because he did -- I've read most of his science and science fiction writing.)

Let's start with the shape of the earth.  At some distant time in the past (probably more distant than you're thinking) we start with the thought that the earth is flat.  A different version of thinking about this is that the radius of a spheroidal earth would be infinite.  Clearly there are local variations, as any runner, hiker, or biker can tell you.  But basically a flat surface.  Get out to the Great Plains, some of the major deserts, or, especially, the coasts, and it's obvious that the earth is flat.  Just look!



Of course it isn't actually flat.  And this was known by several hundred years BC.  Aristotle (died 322 BC) dismisses the shape of the earth being a sphere as common knowledge in his Meteorologica.  He offers a couple of illustrations of how we know this, but it's not a challenging matter.  Something like how today we might mention that the Beatles were an influential rock band and then point to how many records they sold, or number of number 1 songs.  The earth was known to be round no less than several hundred years earlier than that in India (though I can't lay my hands on the reference right now).  The approximate size of the spherical earth was figure out by Eratosthenes in the 200s BC.  How accurately he did so depends on which of several versions of 'stadia' (unit of distance) you choose.  But certainly far closer to correct than to take an infinite radius (flat earth).  That's one part of the relativity of wrong.  The very ancient people who took the earth to be basically flat were as wrong as it is to wrong to say that infinity equals 6400 (roughly the value, converted to km, of the most favorable estimate from Eratosthenes). 

Two things here.  One is, the measurement method that Eratosthenes used is one that middle and jr. high students can carry out today.  I'll be happy to help schools do this (you do need a pair of students at different latitudes, but same longitude).  It's an experiment well worth doing.  The second is, notice that we've started the successive approximations -- first estimate was infinity.  Second estimate is 6400 km.  That's a huge change.  But almost all that change was due to the fact that the second estimate was based on observation, versus saying 'it's obvious'.

If you continue your observations, and make them increasingly accurately by using methods that Eratosthenes didn't have available, you get towards the earth being a perfect sphere with a radius of 6371.2 km (give or take -- history intervened).

 But the earth is not a perfect sphere either.  Now, the error in flat earth vs. spherical earth is infinite (infinity - 6400 is infinity).  The error here is distinctly smaller.  Concern about it goes back to about Newton's Principia, and an argument that ensued between Newton and Cassini.  Both very sharp scientists, both very knowledgeable.  Newton argued that the earth should be bigger around the equator than from the center to the pole, because as the earth rotated, the equator should be slung out farther from the center.  Cassini argued essentially the converse.  (See Chandresekhar's Ellipsoidal Figures of Equilibrium for a more detailed discussion of the arguments and the history.)  It wasn't until observations made in 1738 by Maupertuis and Clairaut that the argument was resolved.  As mathematics, neither Cassini nor Newton had made an error.  Newton, however, was more correct as to what physical processes to be doing the math on.

The equator being slung out more means that the earth is not a perfect sphere, it is an oblate (bigger around the middle) spheroid.  We have 2 different radii now, the equatorial (6378 km) and a polar radius (6357 km).  The two differ by 21 km.  So, while it's incorrect to say that the earth is a perfect sphere, the difference between that and the oblate spheroid is no greater than 14 km (the 6371 km best sphere radius versus the polar radius) and generally smaller, depending on where you try to get the radius.

Once you finish building your mathematical model for an oblate spheroid, and get very specific about exactly what you mean by "the earth's surface", you can then find that the geoid (which is what is used -- the surface that water would all wind up at if there were no currents -- the 'bottom of the hill') deviates by upwards of 100 meters from the best oblate spheroid (which are now specified to about 1 meter, not the 1 km that I rounded to up there).  So, while oblate spheroid isn't exactly correct, it's within about 0.1 km out of the 6357 km polar radius, or 6378 km equatorial radius.  And once you get to paying attention to ocean currents, you need to make corrections of upwards of 1 meter for variations in the currents.  Plus, no doubt, there are other processes. 

As we learn more, our range of error decreases.  Each shape is an approximation to the real shape, so our successive approximations get increasingly better.  But, particularly once we discard a flat earth as candidate, we also need the prior estimate of the shape to make our improved estimate.  It's something of a bootstrap process as well.

Application 1 -- doing science and reading blog posts
The case that just played out in the comments on 800,000 years of temperature and CO2 is a simpler illustration.  I found the best fit line between temperatures 1000 years earlier and CO2 concentrations.  Afterwards, I computed the standard deviation of the difference between the CO2 you would expect, given the temperature, and the CO2 that was observed.  That was 11 ppm.  A reader did the step you would do before what I did and found 26 ppm.  What he did was take all CO2 observations, and the principle that we know nothing about what's going on -- a good starting point -- and ask what the standard deviation was. 

When we start from honest ignorance, we see 26 ppm standard deviation.  After we've looked farther at the data, we see that CO2 correlates well with temperature.  So much so that if we use our knowledge of temperature, we reduce the standard deviation -- of the difference between what we predict from that relation and what is observed -- to 11 ppm.  That's a measure of how much better our knowledge is now -- our initial uncertainty was 26 ppm, and we've cut that to 11 ppm by understanding something about the correlation between CO2 and temperature.  To do better (which we will) we'll have to bring in additional knowledge -- that both temperature and CO2 are affected by orbital variations.  The residual standard deviation then (residual meaning difference between our informed expectation and observation) will be something smaller.  How much smaller will tell us how much better our understanding has gotten.

One last bit here: From our initial honest ignorance, the modern 365 ppm (it's now about 387 ppm, but 365 was what was used in the data set we were analyzing in that previous post) was extremely surprising.  The average of all CO2 observations was 224 ppm, and had a 26 ppm standard deviation.  The modern figure was somewhat over 5 standard deviations from the norm, and was the only one more than about 2 away.  We would already suspect it was an outlier just from our initial honestly ignorant look at the CO2 data that something was extremely different about that last figure.  When we learn a bit more, that temperature and CO2 are correlated and temperature leads by about 1000 years, our surprise at the modern value increases enormously.  The modern figure is now 9 standard deviations away from that very good correlation between temperature and CO2.  The true modern figure, the 387 ppm, is 11 standard deviations away from the relationship that had held for the previous 799,000 years!  As we learn more about the system, the modern values will get even more astonishing.

Application 2 -- selecting sources and engaging in discussions
Honest ignorance is a very good thing.  And ignorance, in its own right, is nothing to be particularly concerned about.  We're all ignorant of enormous amounts.  If you think you're an exception, do as I once did: walk in to a library and ask yourself how many shelves there are where you've read every  book (if literature or the like), or know the full contents of the shelf (if it's, say, history of the Roman Empire shelf, or math, etc.).  There were a few shelves where I had the full content.  But it was very few.  So I'm not concerned about the fact of my honest ignorance of very many of those shelves, nor concerned if it should turn out that somebody else's honest ignorance includes a shelf that I do know.  Plenty of room for good discussion with someone honestly ignorant, and I'm sometimes the one asking those ignorant questions.  That's how I become less ignorant.

This is something of an issue with the relativity of wrong, and carrying on discussions in blog world or even the 3d world.  If someone says that they haven't looked at much data, but think the current CO2 is not really very much higher than it has been, you can have a discussion.  They're wrong, but it's an honest ignorance sort of wrong.  They know that they don't know much, and further information will be a plus.  Provide that information if you can; if you can't, bring in a friend who can.  The scale of error here can be very large (even as large as the earth being flat), but the person is not very invested in that conclusion.

Something of an intermediate situation is something I did here myself.  Namely, the 9 (or 11) standard deviations I computed for how different present CO2 is from what we'd expect, given the temperatures, has an (at least one) error.  The CO2 observations, and temperature observations, aren't all independent from each other, but my computation assumed that they were.  In truth, if it was hot and high CO2 for one thousand year period, then the next thousand years probably was too -- the figures aren't independent.  That means the correct standard deviation is different (larger) than I computed.  You have three situations here: 1) the authors knows what they're doing and trying to misrepresent reality 2) the authors know what they're doing and are trying to keep the illustration simple 3) the author doesn't know what he's doing and is just accepting an agreeable conclusion.  Maybe some more.  I'm one of those category 2 people (at least in these posts).

Category 2 folks will be wrong, but you'll have little difficulty explaining their errors to them and getting them to accept the correction.  Category 3 folks may or may not be wrong in their conclusion, but either way they can be very difficult to have a discussion with.  They think they know a lot, but are actually just committed to the answer that they like.  The reasoning is merely window dressing for the answer they like.  Category 1 folks, those who are intentionally misrepresenting the situation, are obviously (I hope it's obvious to you!) impossible to have a discussion with.  They might debate you, but debate is not discussion.  The scale of wrongness here is, say, to be treating the earth as a sphere rather than the more correct oblate spheroid.  It's wrong, but it's only wrong to a modest degree and it's one that (at least for categories 2 and honest 3s) can be fairly readily corrected.

Then out on the extreme, you have the people who say things like "Temperature leads CO2 in the ice age record therefore the current CO2 rise has nothing to do with humans."  They might be in the previous class, maybe categories 1 or 3.  But they're enormously extreme -- the magnitude of the error involved is no longer something where they can take moderate steps, or learn just a few things, to get to something reasonably close to what our scientific understanding is.  Here, it's more the person having decided that the earth is a cube, or an enormously flattened sphere (say 2000 km difference between polar and equatorial radii), or ..., well, quite a number of bizarre shapes that have been suggested.  They're so wrong that your first order of business can't even be to talk about the real observations of the shape of the earth.  If you engage this at all, I'll suggest that your first steps should be towards understanding how the person got to such a wildly incorrect view, and why they are so attached to it. 

Detail: Successive approximations

0 Sound and Fury at WUWT

From the question place, where a reader noted a high traffic item at Watt's Up With That  and asked for a science response.  Where to begin?  First, I guess I'll note that most of the post is bluster and personal attack.  Once you cross out those parts, it's a much shorter article.

Second, as always, go back to the original source.  In this case, it is a Mann et al. 2008 paper Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, with supplementary material.

Then, consider exactly what the claims (in this case, at WUWT) are, and just what evidence is produced for it. 

The fundamental claim at WUWT is that the entire reconstruction is upside down.  (We're treated to pictures of other things that are upside down.)  Right off, we know WUWT is wrong. 

There are three major features of temperature over the past 1000 or so years -- the 'Medieval Warm Period', the 'Little Ice Age', and the warming of the past century.  We've known about the first two since at least the 1970s -- Hubert H. Lamb's Climate: Past, Present, and Future.  The Mann and others reconstruction shows a Medieval Warm Period and a Little Ice age.

If WUWT were correct about Mann et al. having the curve upside down, then they (WUWT) must be insisting that it was a Medieval Cold Period and Little Warm Period -- which we've known for decades it wasn't, irrespective of anything that Mann or coworkers have done.  WUWT is simply wrong from the get go.

There is then a lot of sound and fury regarding 'Tiljander'.  This turns out to mean a paper by Tiljander and others in 2003, cited in the supplementary material of Mann and others.  WUWT cites CA citing personal communication claiming that Mann et al. used this data set upside down.  This looks more like a game of 'whispers' or 'telephone' than a serious scientific claim.  If Tiljander (then CA, then WUWT) had serious evidence of error by Mann et al., the scientific literature is the place for it.  Or, at the very least, Tiljander et al. could place a short note on their own blog/web site/university press release/....  Neither CA nor WUWT seem to cite any such thing, so I will draw the inference that this is because it doesn't exist.

Still, there might be a question as to whether the data were used correctly.  A more significant question is whether the data and its usage materially affect the reconstruction.  If, for instance, the only reason for showing a warming in the 20th century is Mann and others' use of this data set, we might be more concerned about whether there's been such a warming.  Or at least it becomes a much more important question whether they did use the data set properly.  So let's go back to the original source and see what usage was made, how, and what effects it has.

Page 2 of the supplementary material, under Sensitivity Analysis (NH Temperatures)Potential data quality problems.  First, we'll note that the authors do indeed consider the possibility of data quality problems. I'll quote that paragraph here (all typos mine, see the original):
In addition to checking whether or not potential problems specific to tree-ring data have any significant impact on our reconstructions in earlier centuries (see Fig. S7), we also examined whether or not potential problems noted for several records (see Dataset S1 for details) might compromise the reconstructions.  These records include the four Tiljander et al. (12) series used (see Fig S() for which the original authors note that human effects over the past few centuries unrelated to climate might impact records (the original paper states "Natural variability in the sediment record was disrupted by increased human impact in the catchment area at A.D. 1720." and later, "In the case of Lake Korttajarvi it is a demanding task to calibrate the physical varve data we have collected against meteorological data, because human impacts have distorted the natural signal to varying extents.").  These issues are particularly significant because there are few proxy records, particularly in the temperature-screned dataset (see Fig. S9) available back through the 9th century.  The Tiljander et al. series constitute 4 of the 15 available Northern Hemisphere records before that point.
They also note 3 other data sets with problems.

So, do the authors proceed blindly, pretending that all data are good (and equally good, at that)?  No, there's the reconstructed figure in S7, using all data, and another using all data except for the tree rings.  And then in figure S8, they show what happens after removing the 7 problematic data sets (The Tiljander 4 plus 3 from elsewhere).  Same kinds of curves either way -- still a Medieval Warm Period, a Little Ice Age, and a warm recent century.  As Mann and others note that before the 9th century there are few data if one withdraws Tiljander, I'm ignoring that part of the reconstructions.

From just reading the paper, we don't know whether the Tiljander data were used correctly.  We do, however, know that the answers are quite similar whether they're used or not (S7 vs. S8).  If there's an error, in other words, it's an error with little effect.

Contrast that with WUWT's initial claim of the whole reconstruction being upside down -- thereby turning the Medieval Warm Period into an ice age, and the Little Ice Age into a Warm Period.  They give no evidence at all that this is the case.  Irrespective of whether the Tiljander data were used wrongly, WUWT is wrong in their main claim.

Detail: Sound and Fury at WUWT

0 What fields are relevant?

I've never met someone who knew everything. Certainly I've met some very bright people, and people who knew quite a lot. But nobody has known everything. Conversely, I'm a bright guy, and know a lot of stuff, but I've never met anybody who didn't know things that I didn't. That including an 8 year old who was pointing out to me how to identify some animal tracks (they'd talked about this in her science class recently).

People know best what they've studied the most is my rule of thumb. That's why I go to a medical doctor when I'm sick, but take the dogs to a veterinarian when they're sick. I call up a plumber when the water heater needs replacing, and take my car to an auto mechanic when it needs work. And not vice versa on any of them. It might be true that the auto mechanic is also a good plumber. But, odds are, the person who focused on learning plumbing is the better plumber.

None of this should be a surprise to anybody, yet it seems in practice that it is once we come to climate. Let's be a little more specific in that -- make it the question of whether and how much human activity is affecting climate. There are many other climate questions, but it's this one that attracts the attention, and lists of people on declarations and petitions. If you look only at the people who have professionally studied the matter and contributed to our knowledge of the matter, then the answer to the question is an overwhelming 'yes', and a less overwhelming but substantial 'about half the warming of the last 50 years'.

I've tried to set up a graphic (you folks who have actual skills in graphics are invited to submit improved versions!) of 'the way to bet'. The idea is to provide a loose relative guide as to which fields most commonly have people who you can have the greatest expectations that they have studied material relevant to the question of global warming and human contributions to it from a standpoint of the natural science of the climate system.

Climatology, naturally, is on the top tier -- many people in that field will have relevant background. Not all, remember. Some climatologists look no further than their own forest (microclimatology of forests -- how the conditions in the forest differ locally from the larger scale averages) or other small area, or small time scale. Still, many will be relevant.

Second tier, fewer of the people will be climate-relevant, but still many. Oceanography, meteorology, glaciology.

Third tier, most people will not be climate-relevant. But some have made their way, at least, from those fields over to studying climate. That includes areas like Geomorphology (study of the shape of the surface of the earth) and quantum physics (the ones who come to climate were studying absorption of radiation).

Fourth tier, almost nobody is studying things relevant to the question I posed. The extremely rare exception does exist -- Judith Lean has come from astrophysics and done some good work (with David Rind, a more classically obvious climate scientist) regarding solar influence on climate. Milankovitch was an astronomer/mathematical analyst who developed an important theory of the ice ages.

Fifth tier, I don't think anybody has studied the question I posed directly. I do know a couple of nuclear physicists who have moved to climate-relevant studies. But they essentially started their careers over with some years of study to make the migration. In this, it's more a matter that they once were nuclear physicists. After some years of retraining, they finally were able to make contributions to weather and climate. At which point, really, they were meteorologists who happened to know surprisingly large amounts about nuclear physics.

Sixth tier, I wouldn't include at all except that they show up sometimes on the lists. My doctor is a good guy, bright, interested, and so on. But it takes a lot of work studying things other than climate to become a doctor, and more work after the degree is awarded to stay knowledgeable in that field. That doesn't leave a lot of time to become expert in some other highly unrelated field.

[Figure removed 14 September 2009 -- See Intro to Peer Review for details]


Suggestions of areas to add, or to move up or down, are welcome. I'm sure I have missed many fields and others are probably too high or low.

For now, though, if you're not an expert on climate yourself, I'll suggest that if the source is in the first two tiers, there's a fair chance that they've got some relevant background. If they're in the bottom 3, almost certainly not -- skip these. And the third level, is probably to skip but maybe pencil them in for later study, after you've developed more knowledge yourself from studying sources on the first two levels.

This ranking, of course, applies to the particular question asked. If the question is different, say "What are the medical effects of a warmer climate?", the pyramid would be quite different and MD's would be the top tier. Meteorology would move down one or two levels. Expertise exists only within some area. As I said, nobody knows everything.

Update:
frequent commenter jg has contributed the following graphic:


A general good change he's made is to split between general skills, that can transfer to studying climate, as well as what particular sorts of detailed skills or knowledge one might have. Almost everyone, for instance, involved in studying climate knows some statistics and mathematical analysis. Many fields also require such knowledge, so those would find it easier to move over to climate.

Different good change he made was to put the question directly into the graphic. This is important. As I said, but didn't illustrate, the priority list depends on exactly what question is at hand.

Detail: What fields are relevant?

0 How not to analyze climate data

Preface
The paper that prompts this post (and the preceding Introduction to time series analysis is McLean, de Freitas, and Carter, 2009. A reader suggested, in email, that I take a look. I'll recommend that to others as well. I won't carry out all suggestions, not least because I don't know all areas well enough to comment, but they are indeed welcome. And do at times result in a post here. There'll be some following notes as this paper opens several issues. For now, I'll stay with just the paper.

Comments have already appeared at OpenMind, Initforthegold, and Realclimate. In a fundamental sense, I won't be adding anything new. But the approach will differ and might show some features in ways that you might have missed in the comments over there. For instance, I mentioned the crucial bit that I'll be exploring here in a comment at Initforthegold, and Michael missed its significance on first reading. The fundamental was staring him in the face, but fundamentals aren't always easy to notice. When he did, it was 'forehead slap' time.

I've tagged this 'doing science' and 'weeding sources', as well as 'climate change'. Some issues of peer review will show up, as will a flag or two of mine which I find useful in weeding sources. The nominal topic of the paper "Influence of the Southern Oscillation on tropospheric temperature" is climate change. Recently I posted about scientific specificity. While it's entirely true that it doesn't work well to take that line in daily life, it's exactly what one should do with a scientific paper. One thing it means is that we keep an eye on whether the data used, or are used, support the argument that is made.

Begin
We start by reading the abstract. As a matter of doing science, the abstract usually makes the most eye-catching statements in the paper. It is the advertising section of the paper, so to speak. You want to say something here that will interest other scientists and get them to read your brilliant work. In this case, "That mean global tropospheric temperature has for the last 50 years fallen and risen in close accord with the SOI of 5–7 months earlier shows the potential of natural forcing mechanisms to account for most of the temperature variation."

SOI is the Southern Oscillation Index. It provides a number that is connected to the El Nino-Southern Oscillation (ENSO), which can then be used for further research, such as this paper. There are different ways of defining an SOI, which might be an issue if the effects the authors were working with were fairly subtle. But, as they are referring to explaining 68-81% of the variance (figure depends on which records are matched, and how large the domain examined is), we've left the realm of subtle. As the authors duly cite, there's nothing new in seeing a correlation between SOI and global mean temperatures. This is well-known. What is new is the extraordinarily high correlations they find, and that eye-catching conclusion that most of temperature variation for the last 50 years is driven by SOI.

For atmospheric temperatures, they use the UAH lower tropospheric sounding temperatures (paragraph 5) and for SOI, they use the Australian Bureau of Meteorology's index (para 7). If the abstract were an accurate guide, we'd expect that with those two time series in hand, they computed the correlations and found those very high percentages of variance explained. Or at least that they were that high with the noted 5-7 month lag. And here's where we get to the time series analysis issue that I was introducing Friday.

Three different things are done to the data sets before computing the correlations. One is to exclude certain time spans for being contaminated by volcanic effects on the temperatures. No particular time series analysis issue here. But the other two both have marked effects on time series. First (para 10) is to perform a 12 month running average. This, as I discussed Friday, mostly suppresses effects that are 1 year and shorter in period. Second is to take the difference between those means, 12 months apart (paragraph 14). As I described on Friday, this suppresses long term variation, and enhances short term variation. They assert that this removes noise, while, in fact, it amplifies noise (high frequency/short period components of the record). Alternately, they are defining 'noise' to be the long period part of the records -- the climate portion of the record.

The combined effect of the two filters is that both the high frequency and the low frequency parts of the records are suppressed. What is left is whatever portion of the two records lie in the mid-range frequencies. To return to my music analogies, what has been done is to set your equalizer in a V shape, with the highest amplitudes in mid-range. While the result has a connection to the original data, it is certainly no longer fair to say, as the authors do in the abstract, that their correlations are between SOI and temperatures.

Demonstration of filter effects -- sample series
The next 4 figures show k) the original time series, which I constructed by adding up some simple periodic functions l) the 12 month running average version m) the 12 month differencing of the original data and n) applying both filters as the authors did (minus volcanoes).

Original


12 Month Smoothing


12 Month Differencing


Both Filters


As expected, the running average smoothed out the series. In music terms, it suppressed the treble. That's the job of an averaging filter. The differencing made for a much choppier series than the original. That, too, can be desirable. But certainly the authors' comment about 'removing noise' is ill-founded. If we look at the variance in the time series, the original has a variance of 4.25. The running average decreased that to 2.69 (eliminating 37% of the variance). The differencing increased the variance 50%, to 6.47 (again, increased variance means more noise). Applying both filters produces the final figure, which has little resemblance to the original series. Not least, while the original looks to have a substantial amplitude at a period of 30 years (that appearance is entirely correct, I put in a 30 year period), the final product shows no sign whatever of the 30 year period. That is one of the jobs of a differencing filter -- remove the long period contributions. The filters have also suppressed the 15 year period that I put in, and, in general, turned my original series, which had equal contributions at 5 months, 1, 2, 3, 5, 7, 10, 15, 30 years into something that looks mostly like a 3 year period (count the peaks and divide that in to the time span for them) with a bit of noise.

Filter effects on SOI series
That was a warm up with a test series, where we know that there are no data problems of any sort, and we know exactly what went in. The real data of course have problems (this is always true, and one of the aspects of doing science), but they may not have problems that affect our conclusions. The next figure shows the smoothed (12 month running averages again) and then differenced (as in the paper) Australian SOI (labelled 'both' -- both the averaging and the differencing applied to the original data) (Note that I'm not showing the full curve, only 1950 to present, instead of 1879 to present -- the paper's analyses only covered, at most, 1958-2008).



You see that with both filters applied there are new peaks, missing peaks, and even the sign of the index can change (positive for negative, or vice versa). These are all signs that the filters have fundamentally altered the data set, so that whatever conclusion is drawn can only be drawn about 'data as processed by this filter', not the original data -- in contradiction to the statements in the paper and elsewhere by the authors that it is SOI that explains an extremely high portion of the variation in global mean temperature. Further, since the correlation is largely driven by the peaks, the high correlations can by largely a matter of how the filter creates or destroys peaks rather than the underlying data.

Response function
I mentioned Friday the amplitude spectrum -- show the amplitudes of the contributions from each period. Filters change the amplitude spectrum. That's their job. One thing, then, that you do to describe the filter is divide the amplitude at a period after processing with the amplitude before hand (this is known as the response function). An ideal filter will show a 1 for all periods except the ones you're trying to get rid of, where it will be 0. Real filters don't accomplish this, but that's the goal. So, to see the performance of the author's filter, I took their original SOI series, processed it through their filter, and then found the response function in this way. Those are the next figures. First is looking at cycles per year (frequency), letting us see well what happens at high frequencies. Second looks at the period (from 1-15 years).

Frequency Response Function


Period Response Function




There are some spikes in the curves, which have nothing to do with the filter. All that is happening there is the these are periods/frequencies which have little signal in the original series, so numerical processing issues can have large effects there (dividing by small numbers is hazardous). But the smooth curve is a fair description. The averaging filter suppresses the signal (response is close to 0) for frequencies of 1, 2, 3, 4, 5, 6 cycles per year. (With monthly data, 6 cycles per year is the highest that can be analyzed -- 2 months period.). The differencing filter also suppresses the very low frequencies (long periods), as we expected even with just the basic introduction from Friday. But take a look between 1.5 and 7 years. The response is greater than the input! Look, too, at the periods which are being amplified. A usual description of ENSO is 'an oscillation with a period of 3-7 years'.

Summary
So what do we really have? It isn't a correlation between SOI and global mean temperatures. Both were heavily filtered. What the authors actually compute is the correlation between the SOI time series and global mean temperature -- if you over-weight (response function is greater than 1, so it's an over-weighting) both series towards what is happening in the ENSO periods. The conclusion should really be "If you look only in the ENSO window, you see that ENSO accounts for a lot of variation in global mean temperature." One problem is, that isn't a new result. We already knew that ENSO was important in the ENSO periods. More important to the paper, in so doing, the authors cannot make any conclusion about explaining "most of the temperature variation". They've filtered out much of it, and never examined either the response function nor the effects of their filter on the inputs.

If what was desired was an analysis of global mean temperature response to SOI at ENSO periods, then both the authors should have been clear that this was their window, and they should have used a more suitable filtering process. When one goes back to the paper, it's also clear that no justification was ever made for using either filter, much less both. The filters were arbitrary, and as I've mentioned, we prefer to avoid arbitrary decisions in our papers. If no objective basis for setting up the filters could be found, the authors should have demonstrated that alternate choices did not affect their conclusions.

So, some 'weeding sources', or 'scientific specificity' signs:
* When a paper makes a conclusion about the correlation between A and B, verify that it is A and B that they are correlating.
* If a filter is applied, look for the authors to discuss a) why a filter is being applied at all, and b) why the particular filter they chose was used.




As is my custom, I've sent an email to one of the authors (de Freitas, the only one whose email was given in the paper) about this comment.

Some of the following blog posts will talk about the peer-review aspects that let this paper through. For now, see my old article peer review. One of the other notes (no idea when) will be about how the process continues after a bad paper gets through the peer review process. That is the comment and reply process, and I'll be writing Tamino about that (he's said in his comments that he's preparing a comment for the journal).

Detail: How not to analyze climate data

0 What cooling trend?

Nonsense about the 'current cooling trend' is rife across the blogosphere, and the science minded folks usually point to the fact that you need 20-30 years to define a climate trend. The lies as such don't interest me, or make for a good topic for this blog.

What's useful or interesting is that the statement itself, often linked to 'last 10 years', is not true even after allowing for substantial cherry picking. This brings us back to the interesting matter of trying to define climate. And a further reminder that if you're reading bad sources, you can't trust even the simplest of statements.

To find current temperature trends, I used the NCDC monthly temperature anomalies. The most recent month is May, 2009. To look in to current trends, then, I computed the trends from every month of the last 30 years, through to May 2009. The trend shown for January 1979 is the trend from then to May 2009. The trend for April 2009 is to May 2009. Figure 1 gives the results (actually back to 1977).



Wow, current warming trend of 120 C per century! Surely we're all going to be boiling soon? Of course not. That trend was computed from a 1 month span -- April to May of 2009. It is yet another reminder that short term variations, namely weather, can be large. It isn't climate. Climate shouldn't depend sensitively on when exactly you start your trend computation. Unfortunately that figure shows us nothing new, beyond confirming yet again (not a bad process itself, and part of the scientific approach) that weather happens, and weather variability is much larger than climate variability. So in figure 2, I zoom in a little and ignore positive trends greater than 20 C/century.



So now we can see that if someone chooses very carefully (namely, cherry picks) the starting date, they can find a cooling trend between then and May 2009 ('current'). But notice how carefully they have to choose that starting date. If it's 10 years (or any number greater than that, back to the record's start date), the trend is a warming. In fact, you can only get cooling trends occur if you choose a start date between January 2001 and January 2007 (including those months), or October, 2008. Anything farther back, or more recent, shows warming.

Both for deciding climate, and for doing science, we want our conclusions not to depend sensitively on arbitrary choices. Ending with the most recent data is not arbitrary, so we're ok there. But choosing a starting date? Science-minded folks take a figure in the range of 20-30 years, in particular 30, because over a century of experience says that 30 years is a good time period to be able to look at climate trends as opposed to weather fluctuations. i.e., not arbitrary. Choosing 2.4-8.4 years (and not 9.4, or 12, ...)? Why would we do that? Well, if we wanted to support some particular conclusion, we might do so. But that is not science.

Let's zoom our attention to the period in late 2006 through early 2007. The largest 'cooling trend' you can contrive is to start with September or October 2006, giving 3.3 C per century cooling. Of course you're flagrantly violating sensible climate practice by using 30 months instead of 30 years. But now look to April 2007, where the trend is already a warming of 3.3 C/century, and remains higher than 3.3 to the present, except for that 1 month, October 2008. If 30 months are ok for cherry pickers, why are 24 months not? They're not very different time periods; if either one is acceptable, both must be.

On the science side, as my results post illustrated, if you take 20-30 years to determine your trends, then changing the length doesn't change your answer much. We see this again in figure 2, where any trend computed with from 15-30 years of data gives nearly the same answer as to the current trend -- about 1.8 C per century (1.49 for 15 years, 1.79 for 20, 1.92 for 25, and 1.62 for 30 years). The figures do fluctuate some, which is to be expected. But changing from 30 to 24 years doesn't take us from a large cooling to an equally large warming, the way it can for months.

I'll probably take this up in a separate note, as it illustrates a different way of misleading yourself with graphs. For now, I'll just observe that if you compute the 10 year trends, rather than telling people to 'just look', then the most recent time there was a 10 year cooling trend was the 10 years ending with January, 1987 (with 0.03 C/century). The last time you had several months in a row where the 10 year trend to that month was a cooling was in the late 1970s -- 30 years and more back. At no time that the '10 year cooling trend' claim has been getting made, has it been true.

Detail: What cooling trend?

0 20 who deny CO2 is correlated with temperature

Over in my Does CO2 correlate with temperature? post, some of the commentators are claiming that nobody says CO2 is not correlated with temperature. This is odd, since I name a source there which does so. But, in the spirit of my 20 links game, here are 20 (more) who say so.

First, a word about doing science, and weeding sources. One thing about doing science is that you're supposed to read the source you're commenting on. As some commentators demonstrated, they didn't read my article before making their comment. That would have been the easiest way to discover a source which did as claimed. More work, but still easy, is to do a search and see if the comment is out there. Now, as you know from reading my 20 links game earlier, I think pretty much any statement you'd care to name is being asserted somewhere on the web, and probably in at least 20 different locations. If you've been looking at the web for a while, you've seen some pretty strange statements being made seriously.

Where the commentators agree with me is that it's absurd to claim that there's no correlation between temperature and CO2. Consequently, any source which does claim so is unreliable and you'd be better off moving on. (Oddly, they don't seem to agree with this part.)

So, here are 20 sources (well, 21) which assert no correlation between temperature and CO2, and are referring to recent (last 150 years) climate:

1. Joseph D'Aleo on Jennifer Marohasy's blog
2. Powerpoint presentation, see slide 47
3. Ken Gregory
4. Christopher Horner, Lawyer
5. Lee C. Gerhard, Center for Science and Public Policy
6. Warwick Hughes, repeat of preceding
7. Article by Dennis Avery, quoting Timothy Patterson
8. Article by Timothy Patterson, published in Financial Post, copied to this site
9. An englishman's castle, blog
10. Noel Sheppard, at Newsbusters
11. Glen Meakam, Pittsburgh Tribune Review 25 January 2009 (cached version)
12. Joseph D'Aleo, on his own site
13. Jim Manzi, taking Steve Milloy to task for claiming no correlation
14. Capitalism Magazine, quoting Timothy Ball
15. Martin Durkin, the producer of the documentary The Great Global Warming Swindle
16. Geoaffair
17. Jules has been engaging in discussion and documenting Hans Labohm's denial of any correlation between CO2 and Temperature (Labohm goes farther than any others I've seen, and denies it for all time scales from tenths of years to millions of years)
18. Christopher Monckton at Science and Public Policy
19. Paul Drallos, PhD (Physics)
20. user 'bravo22c' at the Telegraph, UK
21. Alan Caruba in the Canada Free Press

Took 90 minutes since I didn't accept blog comments -- all these are original articles, sources that get quoted elsewhere. I also didn't take many copies of the same sources, which, again, would have shortened the search. Also no videos. But I threw in a bonus cite. Some cherry pick their periods ('last 10 years', '1945-1970' or the like), some leave it at 'recent', without saying what 'recent' means.

As I discussed before, you need 20-30 years to be talking about a climate trend. So the sources which use only 10 or so are being doubly misleading.

Detail: 20 who deny CO2 is correlated with temperature

0 Unreliability at co2sceptics/climaterealists

Last Tuesday I wrote up Misleading yourself with graphs, prompted by plots on the main page at co2sceptics. As I mentioned then, it could have been an honest mistake, so I sent a note to them on their site (message form).

They've neither responded nor changed their site. So I'm afraid they're simply an unreliable source. Maybe the mistake is honest, maybe not. But a reliable site would respond on being shown that they'd made a mistake and were misleading themselves and their readers.

This is less of a surprise to me now since I searched on their name and found one of the references to them is an earlier post of mine about a different unreliable source icecap.us. In that case, they were being extremely un-skeptical.

In Does CO2 Correlate with Temperature, to appear shortly, I'll be taking a more quantitative look at the correlation they want readers to believe does not exist.

Detail: Unreliability at co2sceptics/climaterealists

0 Read original sources

I've mention it here and there before, but was reminded recently of the importance of reading the original sources. The recent round was a document which said the IPCC had not discussed some points about sea surface temperature. I thought that was odd, so I looked up the IPCC section that talks about sea surface temperature (SST) and in truth, they do talk about those issues. If somebody is lying about what someone else says in such an easily identified way, time to pitch that original source and go looking for someone honest to learn from. So that's one reason to read the original sources -- to see who's lying and weed them out.

There are also more pleasant reasons to follow up to original sources. One is, often the source is being cited for something that's a minor part of what they were talking about -- and the major part is even more interesting. In oceanography, one of the things often mentioned in classes is the Ekman Layer. Don't worry about what it is exactly. Usually, a reference says that Ekman (1905) studied the formation of what we now call the Ekman layer for conditions of an infinitely deep ocean, infinitely steady winds, with absolutely constant friction through the depth of the ocean. Well, it turns out that, although he did examine that highly idealized case, this was a small part of the paper. In fact he also examined varying winds and varying friction. And the results of doing so were much more interesting than what he's normally cited for.

A second reason can be particularly important in science. In the original papers on an idea, the author is explaining something new to the audience. He can't assume nearly as much as later papers on the idea will. You'll also get to see the first matchups of the idea against observations. So I usually learn more by reading the original than by reading the textbook description. (For the geophysical fluid dynamicists out there -- Charney's paper on baroclinic instability is the one exception I've found. There also turn out to be reasons why it came out that way.)

Part three is that you'll get to see much more discussion of why the approximation can be made, and where it can be expected to fail. So, for example, a modern paper talking about the color of the sky -- perhaps for a planet orbiting some other star -- will merely mention 'We apply Rayleigh scattering law and arrive at ...'. It's only when you read Rayleigh's papers that you find out that the 'law' is an approximation, and it will fail under certain, predictable, conditions.

It can be difficult to get hold of the original sources when you start going after Reynolds 1895, Ekman 1905, etc. So the ideal can't always be met. Still, the whole IPCC working group 1 (physical science) 4th report is readily available online or you can order a print copy (also groups 2 and 3, but I'm a physical science guy, so look more to that one). So, when you encounter someone making claims about what the IPCC says, go take a look yourself. And if the point isn't clear, or it is interesting to you, start following up the citations they give. (Unfortunately, I think that after the first report, the writing declined in terms of readability by nonprofessionals. Any more, once you're past deciding if someone lied about the IPCC, I think you'll have an easier time reading by hitting the cited sources rather than the IPCC reports.)

Detail: Read original sources