Temperature Measurement

Global Warming Is Caused by Computers

In particular, a few computers at NASA's Goddard Institute seem to be having a disproportionate effect on global warming.  Anthony Watt takes a cut at an analysis I have tried myself several times, comparing raw USHCN temperature data to the final adjusted values delivered from that data by the NASA computers.  My attempt at this compared the USHCN adjusted to raw for the entire US:


Anthony Watt does this analysis from USHCN raw all the way through to the GISS adjusted number  (the USHCN adjusts the number, and then the GISS adds their own adjustments on top of these adjustments).  The result:  100%+ of the 20th century global warming signal comes from the adjustments.  There is actually a cooling signal in the raw data:


Now, I really, really don't want to be misinterpreted on this, so a few notes are necessary:

  1. Many of the adjustments are quite necessary, such as time of observation adjustments, adjustments for changing equipment, and adjustments for changing site locations and/or urbanization.  However, all of these adjustments are educated guesses.  Some, like the time of observation adjustment, probably are decent guesses.  Some, like site location adjustments, are terrible (as demonstrated at surfacestations.org).

    The point is that finding a temperature change signal over time with current technologies is a measurement subject to a lot of noise.  We are looking for a signal on the order of magnitude of 0.5C where adjustments to individual raw instrument values might be 2-3C.  It is a very low signal-noise environment, and one that is inherently subject to biases  (researches who expect to find a lot of warming will, not surprisingly, adjust a lot of measurements higher).
  2. Warming has occurred in the 20th century.  The exact number is unclear, but we have much better data via satellites now that have shown a warming trend since 1979, though that trend is lower than the one that results from surface temperature measurements with all these discretionary adjustments.

On Quality Control of Critical Data Sets

A few weeks ago, Gavin Schmidt of NASAcame out with a fairly petulant response to critics who found an error in NASA's GISS temperature database.  Most of us spent little time criticizing this particular error, but instead criticized Schmidts unhealthy distaste for criticism and the general sloppiness and lack of transparency in the NOAA and GISS temperature adjustment and averaging process.

I don't want to re-plow old ground, but I can't resist highlighting one irony.  Here is Gavin Schmidt in his recent post on RealClimate:

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.

He is criticizing skeptics for not digging into the code of the individual climate models, and focusing only on how their output forecasts hold out (a silly criticism I dealt with here).  But this is EXACTLY what folks like Steve McIntyre have been trying to do for years with the NOAA, GHCN, and GISS temperature metric code.  Finding nothing about the output that makes sense given the raw data, they have asked to examine the source code.  And they have met with resistance at every turn by, among others, Gavin Schmidt.  As an example, here is what Steve gets typically when he tries to do exactly as Schmidt asks:

I'd also like to report that over a year ago, I wrote to GHCN asking for a copy of their adjustment code:

I’m interested in experimenting with your Station History Adjustment algorithm and would like to ensure that I can replicate an actual case before thinking about the interesting statistical issues.  Methodological descriptions in academic articles are usually very time-consuming to try to replicate, if indeed they can be replicated at all. Usually it’s a lot faster to look at source code in order to clarify the many little decisions that need to be made in this sort of enterprise. In econometrics, it’s standard practice to archive code at the time of publication of an article – a practice that I’ve (by and large unsuccessfully) tried to encourage in climate science, but which may interest you. Would it be possible to send me the code for the existing and the forthcoming Station History adjustments. I’m interested in both USHCN and GHCN if possible.

To which I received the following reply from a GHCN employee:

You make an interesting point about archiving code, and you might be encouraged to hear that Configuration Management is an increasingly high priority here. Regarding your request — I'm not in a position to distribute any of the code because I have not personally written any homogeneity adjustment software. I also don't know if there are any "rules" about distributing code, simply because it's never come up with me before.

I never did receive any code from them.

Here, by the way, is a statement from the NOAA web site about the GHCN data:

Both historical and near-real-time GHCN data undergo rigorous quality assurance reviews. These reviews include preprocessing checks on source data, time series checks that identify spurious changes in the mean and variance, spatial comparisons that verify the accuracy of the climatological mean and the seasonal cycle, and neighbor checks that identify outliers from both a serial and a spatial perspective.

But we will never know, because they will not share the code developed at taxpayer expense by government employees to produce official data.

A year or so ago, after intense pressure and the revelation of another mistake (again by the McIntyre/Watt online communities) the GISS did finally release some of their code.  Here is what was found:

Here are some more notes and scripts in which I've made considerable progress on GISS Step 2. As noted on many occasions, the code is a demented mess - you'd never know that NASA actually has software policies (e.g. here or here. I guess that Hansen and associates regard themselves as being above the law. At this point, I haven't even begum to approach analysis of whether the code accomplishes its underlying objective. There are innumerable decoding issues - John Goetz, an experienced programmer, compared it to descending into the hell described in a Stephen King novel. I compared it to the meaningless toy in the PPM children's song - it goes zip when it moves, bop when it stops and whirr when it's standing still. The endless machinations with binary files may have been necessary with Commodore 64s, but are totally pointless in 2008.

Because of the hapless programming, it takes a long time and considerable patience to figure out what happens when you press any particular button. The frustrating thing is that none of the operations are particularly complicated.

So Schmidt's encouragement that skeptics should go dig into the code was a) obviously not meant to be applied to hiscode and b) roughly equivalent to a mom answering her kids complaint that they were bored and had nothing to do with "you can clean your rooms" -- something that looks good in the paper trail but is not really meant to be taken seriously.  As I said before:

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.

This is Getting Absurd

Update:  The gross divergence in October data reported below between the various metrics is explained by an error, as reported at the bottom.  The basic premise of the post, that real scientific work should go into challenging these measurement approaches and choosing the best data set, remains.

The October global temperature data highlights for me that it is time for scientists to quit wasting time screwing around with questions of whether global warming will cause more kidney stones, and address an absolutely fundamental question:  Just what is the freaking temperature?

Currently we are approaching the prospect of spending hundreds of billions of dollars, or more, to combat global warming, and we don't even know its magnitude or real trend, because the major temperature indices we possess are giving very different readings.  To oversimplify a bit, there are two competing methodologies that are giving two different answers.  NASA's GISS uses a melding of surface thermometer readings around the world to create a global temperature anomaly.  And the UAH uses satellites to measure temperatures of the lower or near-surface troposhere.  Each thinks it has the better methodology  (with, oddly, NASA fighting against the space technology).  But they are giving us different answers.

For October, the GISS metric is showing the hottest October on record, nearly 0.8C hotter than it was 40 years ago in 1978 (from here).


However, the satellites are showing no such thing, showing a much cooler October, and a far smaller warming trend over the last 40 years (from here)


So which is right?  Well, the situation is not helped by the fact that the GISS metric is run by James Hansen, considered by skeptics to be a leading alarmist, and the UAH is run by John Christy, considered by alarmists to be an arch-skeptic.  The media generally uses the GISS data, so expect stories in the next day or so trumpeting "Hottest October Ever," which the Obama administration will wave around as justification for massive economic interventions.  But by satellite it will only be the 10th or so hottest in the last 30, and probably cooler than most other readings this century.

It is really a very frustrating situation.  It is as if two groups in the 17th century had two very different sets of observations of planetary motions that resulted in two different theories of gravity,

Its amazing to me the scientific community doesn't try to take this on.  If the NOAA wanted to do something useful other than just creating disaster pr0n, it could actually have a conference on the topic and even some critical reviews of each approach.  Why not have Christy and Hansen take turns in front of the group and defend their approaches like a doctoral thesis?  Nothing can replace surface temperature measurement before 1978, because we do not have satellite data before then.  But even so, discussion of earlier periods is important given issues with NOAA and GISS manual adjustments to the data.

Though I favor the UAH satellite data (and prefer a UAH - Hadley CRUT3 splice for a longer time history), I'll try to present as neutrally as possible the pros and cons of each approach.

GISS Surface Temperature Record

+  Measures actual surface temperatures

+  Uses technologies that are time-tested and generally well-understood

+  Can provide a 100+ year history

- Subject to surface biases, including urban heat bias.  Arguments rage as to the size and correctability of these biases

- Coverage can range from dense to extremely spotty, with as little as 20KM and as much as 1000KM between measurement sites

- Changing technologies and techniques, both at sea and on land, have introduced step-change biases

- Diversity of locations, management, and technology makes it hard to correct for individual biases

- Manual adjustments to the data to correct errors and biases are often as large or larger than the magnitude of the signal (ie global warming) trying to be measured.  Further, this adjustment process has historically been shrouded in secrecy and not subject to much peer review

- Most daily averages based on average of high and low temperature, not actual integrated average

UAH Satellite Temperature Record

+  Not subject to surface biases or location biases

+  Good global coverage

+  Single technology and measurement point such that discovered biases or errors are easier to correct

-  Only 40 years of history

-  Still building confidence in the technology

-  Coverage of individual locations not continuous - dependent on satellite passes.

-  Not measuring the actual surface temperature, but the lower troposphere (debate continues as to whether these are effectively the same).

-  Single point of failure - system not robust to the failure of a single instrument.

-  I am not sure how much the UAH algorithms have been reviewed and tested by outsiders.

Update:  Well, this is interesting.  Apparently the reason October was so different between the two metrics was because one of the two sources made a mistake that substantially altered reported temperatures.  And the loser is ... the GISS, which apparently used the wrong Russian data for October, artificially inflating temperatures.  So long "hottest October ever," though don't hold your breath for the front-page media retraction.

Another Urban Heat Island Example

I do not claim that urban heat island effects are the only cause of measured surface warming -- after all, satellites are largely immune to UHI and have measured a (small) warming trend since they began measuring temperature in 1979. 

But I do think that the alarmist efforts to argue that UHI has no substantial, uncorrectable effect on surface temperature measurement is just crazy.  Even if one tries to correct for it, the magnitude can be so substantial (up to 10 degrees or more F) that even a small error in correcting for the effect yields big errors in trying to detect an underlying warming signal.

Just as a quick example, let's say the urban heat island effect in a city can be up to 10 degrees F.  And, let's say by some miracle you came up with a reliable approach to correct for 95% of this effect  (and believe me, no one has an approach this good).  This means that there would still be a 0.5F warming bias or error from the UHI effect, an amount roughly of the order of magnitude of the underlying warming signal we are trying to detect (or falsify).

When my son and I ran a couple of transects of the Phoenix area around 10PM one winter evening, we found the city center to be 7 to 10 degrees F warmer than the outlying rural areas.  Anthony Watts did a similar experiment this week in Reno  (the similarity is not surprising, since he suggested the experiment to me in the first place).  He too found about a 10 degree F variation.  This experiment was a follow-on to this very complete post showing the range of issues with surface temperature measurement, via one example in Reno.

By the way, in the latter article he had this interesting chart with the potential upward bias added by an instrumentation switch at many weather stations


This kind of thing happens in the instrumentation world, and is why numbers have to be adjusted from the raw data  (though these adjustments, even if done well, add error, as described above).  What has many skeptics scratching their heads is that despite this upward bias in the instrumentation switch, and the upward bias from many measurement points being near growing urban areas, the GISS and NOAA actually have an increasingly positive adjustment factor for the last couple of decades, not a negative one  (net of red, yellow, and purple lines here).   In other words, the GISS and NOAA adjustment factors imply that there is a net growing cooling bias in the surface temperature record in the last couple of decades that needs to be corrected.  This makes little sense to anyone whose main interest is not pumping up the official numbers to try to validate past catastrophic forecasts.

Update:  The NOAA's adjustment numbers imply a net cooling bias in station locations, but they do have a UHI correction component.  That number is about 0.05C, or 0.03F.  This implies the average urban heat island effect on measurement points over the last 50 years is less than 1/300th of the UHI effect we measured in Reno and Phoenix.  This seems really low, especially once one is familiar with the "body of work" of NOAA measurement stations as surveyed at Anthony's site.

Why Does NASA Oppose Satellites? A Modest Proposal For A Better Data Set

One of the ironies of climate science is that perhaps the most prominent opponent of satellite measurement of global temperature is James Hansen, head of ... wait for it ... the Goddard Institute for Space Studies at NASA!  As odd as it may seem, while we have updated our technology for measuring atmospheric components like CO2, and have switched from surface measurement to satellites to monitor sea ice, Hansen and his crew at the space agency are fighting a rearguard action to defend surface temperature measurement against the intrusion of space technology.

For those new to the topic, the ability to measure global temperatures by satellite has only existed since about 1979, and is admittedly still being refined and made more accurate.  However, it has a number of substantial advantages over surface temperature measurement:

  • It is immune to biases related to the positioning of surface temperature stations, particularly the temperature creep over time for stations in growing urban areas.
  • It is relatively immune to the problems of discontinuities as surface temperature locations are moved.
  • It is much better geographic coverage, lacking the immense holes that exist in the surface temperature network.

Anthony Watt has done a fabulous job of documenting the issues with the surface temperature measurement network in the US, which one must remember is the best in the world.  Here is an example of the problems in the network.  Another problem that Mr. Hansen and his crew are particularly guilty of is making a number of adjustments in the laboratory to historical temperature data that are poorly documented and have the result of increasing apparent warming.  These adjustments, that imply that surface temperature measurements are net biased on the low side, make zero sense given the surfacestations.org surveys and our intuition about urban heat biases.

What really got me thinking about this topic was this post by John Goetz the other day taking us step by step through the GISS methodology for "adjusting" historical temperature records  (By the way, this third party verification of Mr. Hansen's methodology is only possible because pressure from folks like Steve McIntyre forced NASA to finally release their methodology for others to critique).  There is no good way to excerpt the post, except to say that when its done, one is left with a strong sense that the net result is not really meaningful in any way.  Sure, each step in the process might have some sort of logic behind it, but the end result is such a mess that its impossible to believe the resulting data have any relevance to any physical reality.  I argued the same thing here with this Tucson example.

Satellites do have disadvantages, though I think these are minor compared to their advantages  (Most skeptics believe Mr. Hansen prefers the surface temperature record because of, not in spite of, its biases, as it is believed Mr. Hansen wants to use a data set that shows the maximum possible warming signal.  This is also consistent with the fact that Mr. Hansen's historical adjustments tend to be opposite what most would intuit, adding to rather than offsetting urban biases).  Satellite disadvantages include:

  • They take readings of individual locations fewer times in a day than a surface temperature station might, but since most surface temperature records only use two temperatures a day (the high and low, which are averaged), this is mitigated somewhat.
  • They are less robust -- a single failure in a satellite can prevent measuring the entire globe, where a single point failure in the surface temperature network is nearly meaningless.
  • We have less history in using these records, so there may be problems we don't know about yet
  • We only have history back to 1979, so its not useful for very long term trend analysis.

This last point I want to address.  As I mentioned above, almost every climate variable we measure has a technological discontinuity in it.  Even temperature measurement has one between thermometers and more modern electronic sensors.  As an example, below is a NOAA chart on CO2 that shows such a data source splice:


I have zero influence in the climate field, but I would never-the-less propose that we begin to make the same data source splice with temperature.  It is as pointless continue to rely on surface temperature measurements as our primary metric of global warming as it is to rely on ship observations for sea ice extent. 

Here is the data set I have begun to use (Download crut3_uah_splice.xls ).  It is a splice of the Hadley CRUT3 historic data base with the UAH satellite data base for historic temperature anomalies.  Because the two use different base periods to zero out their anomalies, I had to reset the UAH anomaly to match CRUT3.  I used the first 60 months of UAH data and set the UAH average anomaly for this period equal to the CRUT3 average for the same period.  This added exactly 0.1C to each UAH anomaly.  The result is shown below (click for larger view)


Below is the detail of the 60-month period where the two data sets were normalized and the splice occurs.  The normalization turned out to be a simple addition of 0.1C to the entire UAH anomaly data set.  By visual inspection, the splice looks pretty good.


One always needs to be careful when splicing two data sets together.  In fact, in the climate field I have warned of the problem of finding an inflection point in the data right at a data source splice.  But in this case, I think the splice is clean and reasonable, and consistent in philosophy to, say, the splice in historic CO2 data sources.

Creating Global Warming in the Laboratory

The topic of creating global warming at the computer workstation with poorly-justified "corrections" of past temperature records is one with which my readers should be familiar.  Some older posts on the topic are here and here and here.

The Register updates this topic use March, 2008 temperature measurements from various sources.  They show that in addition to the USHCN adjustments we discussed here, the GISS overlays another 0.15C warming through further adjustments. 


Nearly every measurement bias that you can imagine that changes over time tends to be an upward / warming bias, particularly the urban heat island effect my son and I measured here.  So what is all this cooling bias that these guys are correcting for?  Or are they just changing the numbers by fiat to match their faulty models and expensive policy goals?

Update:  Another great example is here, with faulty computer assumptions on ocean temperature recording substantially screwing up the temperature history record.

The Missing Heat

From Josh Willis, of the JPL, at Roger Pielke's Blog:

we assume that all of the radiative imbalance at the top of the atmosphere goes toward warming the ocean (this is not exactly true of course, but we think it is correct to first order at these time scales).

This is a follow-up to Pielke's discussion of ocean heat content as a better way to test for greenhouse warming, where he posited:

Heat, unlike temperature at a single level as used to construct a global average surface temperature trend, is a variable in physics that can be assessed at any time period (i.e. a snapshot) to diagnose the climate system heat content. Temperature  not only has a time lag, but a single level represents an insignificant amount of mass within the climate system.

It is greenhouse gas effects that might create a radiative imbalance at the top of the atmosphere.  Anyway, here is Willis's results for ocean heat content.

Fig11  click to enlarge

Where's the warming? 

Phoenix Sets Temperature Record. Kindof. Sortof.

Yesterday, Phoenix set a new temperature record of 110F for May 19, exceeding the old record of 105F but well short of the May record (set in 1910) of 114F.


The media of course wants to blame it on CO2, but, if one really wants to assign a cause other than just normal random variation, it would be more correct to blame "pavement."  My son and I ran a series of urban heat island tests in Phoenix, and found evening temperatures at the official temperature measurement point in the center of town (at the airport) to be 8-10F higher than the outlying areas.  The daytime UHI effect is probably less, but could easily be 5F or higher.  As further evidence, a small town just outside of the Phoenix urban heat island, called Sacaton, was well short of any temperature records yesterday (Sacaton was the end point of our second, southerly, UHI temperature run).


Here, by the way, is the site survey my son and I conducted on the Sacaton temperature measurement station.  Bruce Hall has a great analysis demonstrating that, contrary to what one might expect, we have actually been setting fewer new state temperature records than we have in the past.

Urban Heat Biases in Surface Temperature Measurement

One of my favorite bits of irony is that the primary defenders of using surface temperature measurement over space-based satellite measurements is ... the Goddard Institute of Space Studies at NASA, and James Hansen (its director and friend-of-Al) in particular.  If find it amazing that people still want to use the GISS surface temperature numbers in preference to satellite figures, despite their proven biases and lack of consistent coverage.  But the GISS numbers give a higher number for warming (since they are biased upwards both by measurement biases and GISS-added adjustment factors) and that is what is important to global warming alarmists.  Its the "fake but accurate" meme brought to the realm of science.

But, since we do have to keep reminding people of the problems in surface temperature measurement, here is a study by Ren et al in 2008:

What was done
Noting that "a major divergence of views exists in the international climatological community on whether the urbanization effect still remains in the current global and regional average surface air temperature series," the authors employed a dataset obtained from 282 meteorological stations, including all of the ordinary and national basic and reference weather stations of north China, in order to determine the urbanization effect on surface air temperature trends of that part of the country over the period 1961-2000, dividing the stations into the following categories based on city size expressed in millions of people: rural (<0.05), small city (0.01-0.10), medium city (0.10-0.50), large city (0.50-1.00) and metropolis (>1.00).

What was learned
Ren et al. report that mean annual surface air temperature trends for the various station groups of north China over the 1961-2000 period -- in degrees C per decade -- were 0.18 (rural), 0.25 (small city), 0.28 (medium city), 0.34 (large city), 0.26 (metropolis), and 0.29 (national), which makes the urban-induced component of the warming trend equal to 0.07 (small city), 0.10 (medium city), 0.16 (large city), 0.08 (metropolis), and 0.11 (national), all of which results are significant at the 0.01 level.

The Zen and the Art of Surface Temperature Measurement

Readers of this blog will be familiar with the many problems of surface temperature measurement -  the measurement points are geographically spotty, of uneven quality, and are subject to a number of biases, the greatest of which is probably the encroachment of man-made urban environments on the measurement locations.  I have discussed these issues many places, including at the 1:00 minute mark of this video, in my book, and in posts here, here, and here.

I have not posted much of late on this topic, becuase I am not sure there is a lot of new news.  Satellites still make more sense than surface measurement, and the GISS still is working to tweak its numbers to show more and more warming, and Anthony Watts still finds a lot of bad measurement points.

In the last week, though, the story seems to be getting out further than just the online skeptic's community.  Steven Goddard has a good article in the UK Register online.  I don't think any of the issues he covers will be new to our readers, but it is a decent summary.  He focuses in particular on the GISS restatements of history:

One clue we can see is that NASA has been reworking recent temperatures upwards and older temperatures downwards - which creates a greater slope and the appearance of warming. Canadian statistician Steve McIntyre has been tracking the changes closely on his Climate Audit site, and reports that NASA is Rewriting History, Time and Time Again. The recent changes can be seen by comparing the NASA 1999 and 2007 US temperature graphs. Below is the 1999 version, and below that is the reworked 2007 version.

US temperatures: NASA's 1999 version

NASA's original data: 1999

US temperatures: NASA's 2007 version

NASA's reworked data: 2007

This restatement is particularly hard to justify as direct inspection of the temperature measurement points reveals growing urban heat biases, which should imply, if anything, adjustments up in the past and/or down in the present, exactly opposite of the GISS work.  I have written a number of letters and inquiries asking the GISS what systematic bias they are finding/assuming that biased measurements upwards in rural times but downwards in urban times, but I have never gotten a response, nor seen one anywhere online.

HT:  Anthony Watts

Update:  Similar article here

"Particularly troubling are the years from 1986-1998. In the 2007 version of the graph, the 1986 data was adjusted upwards by 0.4 degrees relative to the 1999 graph. In fact, every year except one from 1986-1998 was adjusted upwards, by an average of 0.2 degrees. If someone wanted to present a case for a lot of recent warming, adjusting data upwards would be an excellent way to do it.

What is the Temperature?

It seems like a simple question:  What is the temperature.  Well, we know now that surface temperature measurement is really hard, since its hard to get good geographic coverage when oceans cover 3/4 of the world and biases are a huge problem when most of the measurement points we had in the year 1900 have been engulfed by cities and their urban heat islands.

But John Goetz brings us a new answer to the question, what is the temperature?  Answer:  Whatever the GISS wants it to be, and they seem to change their minds a lot.  He only has the last 2-1/2 years of GISS data but finds an astounding amount of variation in the data over these couple of years.  Excerpt:

On average 20% of the historical record was modified 16 times in the last 2 1/2 years. The largest single jump was 0.27 C. This occurred between the Oct 13, 2006 and Jan 15, 2007 records when Aug 2006 changed from an anomoly of +0.43C to +0.70C, a change of nearly 68%.

I was surprised at how much of the pre-Y2K temperature record changed! My personal favorite change was between the August 16, 2007 file and the March 29, 2008 file. Suddenly, in the later file, the J-D annual temperature for 1880 could now be calculated. In all previous versions the temperature could not be determined.

Spreading Peanut Butter

NASA's GISS claims to have a statistical methodology to identify and remove urban biases.  After dealving into the numbers, it looks more like they are not removing urban biases, but spreading their effect around multiple stations like peanut butter.  My kids have a theory that I will not notice the fact they have not eaten their [fill in the blank] food if they spread it around the plant in a thin layer rather than leaving it in a single pile.  This seems to be NASA's theory on urban measurement biases.  In addition, the GISS statistical methodology seems to be finding an unusual number of stations with a cooling bias, meaning that for some reason the instruments are actually less urbanized than say 50 years ago.

Steve McIntyre digs into some of these issues:

In my previous post, I calculated the total number of positive and negative NASA adjustments. Based on present information, I see no basis on which anything other than a very small proportion of negative urban adjustments can be assigned to anything other than “false local adjustments”. Perhaps there are a few incidents of vegetative cooling resulting in a true physically-based urban cooling event, but surely this would need to be proved by NASA, if that’s their position. Right now, as a first cut, let’s estimate that 95% of all negative urban adjustments in the ROW are not due to “true urban” effects i.e. about 1052 out of 1108 are due to “false local adjustments”....

If the purpose of NASA adjustments was to do station history homogenizations (a la USHCN), then this wouldn’t matter. But the purpose of the NASA adjustments was to adjust for the “true urban” effect”. On this basis, one can only conclude that the NASA adjustment method is likely to be completely ineffective in achieving its stated goal. As other readers have observed (and anticipated), it appears highly likely that, instead of accomplishing an adjustment for the “true urban effect”, in many, if not most cases, the NASA adjustment does little except coerce the results of one poorly documented station to results from other equally poorly documented stations, with negligible improvement to the quality of whatever “signal” may be in the data.

This does not imply that the NASA adjustment introduces trends into the data - it doesn’t. The criticism is more that any expectation of using this methodology to adjust for urban effect appears to be compromised by the overwhelming noise in station histories. Needless to say, the problems are exacerbated by what appears to be poor craftsmanship on NASA’s part - pervasive use of obsolete station versions, many of which have not been updated since 1989 or 1990(!), and use of population data that is obsolete (perhaps 1980 vintage) and known to be inaccurate.

This is the second part of this post, where Mcintyre first quantified the number of the "nreverse" urban bias adjustments:

negative urban adjustments are not an exotic situation. In the ROW, there are almost the same number of negative adjustments as positive adjustments. In the U.S., there are about 50% more positive adjustments as negative adjustments - again a noticeable difference to the ROW. Some commenters on my Peruvian post seemed to think that negative urban adjustments were an oddball and very anomalous situation. In fact, that’s not the case, negative adjustments are nearly as common as positive adjustments.

A Timely Post on Phoenix UHI

Steve McIntyre, in a timely post for this site given the recent project on Phoenix urban heat islands, has a post on the Phoenix adjustment in the GISS database and Hansen's dicussion of Phoenix UHI in his 1999 paper. 

One is left to wonder whether a station that has a 2.5C error-corection adjustment tacked on should even be included in a data set that is attempting to measure a warming signal on the order of magnitude of 0.5C, particularly since any reasonable person would argue that the 2.5C adjustment likely has an error bar of at least plus or minus 0.5C.  I stand by my point that the signal to noise ratio in surface temperature measurement is terrible.

However, many GISS adjustments for site location and urbanization are negative, meaning urbanization has been reduced at the location since 1900, certainly an odd proposition.  In fact, if memory serves, the total net adjustment of all stations in the GISS system is negative for site location and urbanization.  I know, from here, the net USHCN adjustment for combined site location and urbanization is negative, adding 0.15F to current temperatures as compared to those in 1900, implying that site location quality has improved over time.  Anyway, McIntyre promises to tackle this issue tomorrow, which I look forward to.

It's like a Whole New Post

If you have not visited my post lately on my son's experiment on urban heat islands, go check it out, its like a whole new post.  Sixty comments and at least five updates.

I appologize to all the climate alarmist posters who have found my son's project (to measure the Phoenix urban heat island) to be insufficiently rigorous.  I am sure all your baking-soda-and-vinegar volcanoes in 8th grade were much better done.

More on Temperature Adjustments

With what facts / justification / data is the GISS reducing measuremet temperatures prior to the 1970's?  Climate Audit has more.  Whatever the justification, the adjustments are increasing station temperatures by as much as 3C, a signal correction that is far in excess of the signal (0.6 degrees C or so of warming).

More on adjustments here and signal to noise ratio in temperature measurement here.


In response to some emails, I have posted updates to the Phoenix urban heat island post.

Measuring the Phoenix Urban Heat Island

Note Updates at the Bottom.  Could we please agree to actually read the whole post and the updates before commenting?  All commenters welcome, and I never delete comments except in the case of outright advertisement spam

This is a project my son did for Science Fair to measure the urban heat island effect in Phoenix.  The project could also be called "Disproving the IPCC is so easy, a child could do it."  The IPCC claims that the urban heat island effect has a negligible impact, even on surface temperature stations located within urban areas.  After seeing our data, this claim will be very hard to believe.

In doing the test, we tried to follow as closely as possible the process used in the Nyuk Hien Wong and Chen Yu study of Singapore as published in Habitat International, Volume 29, Issue 3 , September 2005, Pages 547-558.  We used a LogTag temperature data logger.  My son used a map and a watch to mark our times, after synchronizing clocks with the data logger, so he could match times to get temperature at each location.  I called out intersections as we passed them and he wrote down the times.  At the same time, I actually had a GPS data logger where I gathered GPS data for location vs. time, but I did not share this with him because he wanted to track locations himself on the map.  My data below uses the GPS data, which was matched with the temperature data in an Excel spreadsheet using simple Vlookup calls.

To protect the data logger from the 60mph wind  (we tried to drive at exactly 60 so my son could interpolate distances between intersections) we put the datalogger in a PVC Tee:


We added some insulation to reduce the effect of heat from the car's roof, and then strapped the assembly to the roof with the closed part of the Tee facing forward (the nose of the car is to the left in this picture).


We drove transects two nights in a row.  Both nights were cloudless with winds below 5 mph.  Ideally, we would have driven between midnight and 6 AM, but this was my kid's science project and he needs to get to bed so we did it from about 9PM to 11PM.  We were concerned that the air might still be cooling during the test, such that as we drove out from town, it might be easy to mix up cooling with time and cooling with location.  Our idea for correcting this was to drive and gather data on an entire loop, starting in the center of town, going about 30 miles out, and then returning to the starting point.  That way, with data taken in both directions, the results could be averaged and the cooling rate would cancel out.  As it turned out, we didn't even bother to do the averaging.  The two trips can be seen in the plots, but the urban heat island shows through pretty clearly in the data and the slope of the line between temperature and distance was about the same on the inbound and outbound legs.

I used the GPS lat/long points to calculate the distance (as the crow flies) from the center of town (My son did it the hard way, using a tool on Google maps).

The first night we went north (click to enlarge):


The second night we went south.  The urban profile going south is a little squirrellier, as the highway we were traveling tends to dip in and out of the urbanization.


Here is the total route over the two nights.  I'm still trying to figure out the best way to plot the temperatures on the map (again, click to enlarge)


You can see the results.  Even at the too-early time of 9-11PM, the temperature fell pretty linearly by about 0.2-0.3 degrees F per mile from the city center (as the crow flies).

I would really love to do is to go down to Tucson and run this same test starting at the USHCN weather station there and driving outwards.  That may have to wait a few weeks until my job calms down a bit.

Update:  Per some emails I have received, it is theoretically possible for the urban heat island effect to be real and to have integrity in the surface temperature record.  The first way this could happen is if the official measurement stations are well sited and outside of growing urban heat islands.  I know for a fact by direct observation that this is not the case.  A second way this might be the case is if one argues that urban heat islands exist but their effect is static over time, so that they may bias temperatures but not the warming signal.  I also don't think this is very credible, give growth of urban areas over the last 50 years.

A better argument might be that because most US temperature stations are arriving at daily temperature averages from just measuring daily min and max temperatures.  It might be arguable that while urban temperatures cool more slowly at night, they still reach the same Tmin in the early morning as the surrounding countryside.  Unfortunately, I do not think this is the case -- studies like this one taken at 5AM have seen the same results.  But this is something I may pursue later, redoing the results at whatever time of day Phoenix usually hits its minimum temperature.

A good argument for the integrity of the surface temperature measurement system is NOT that scientists blind to local station installation details can use statistical tools to correct for urban biases.  After looking at two stations in the Arizona area, one urban (Tucson) and one rural (Grand Canyon) it appears the GISS statistical method, whatever this double-secret process may be [insert rant about government-funded research by government employees being kept secret] it actually tends to average biased sites with non-biased sites, which does nothing to get the urban bias out of the measured surface warming signal - it just spreads it around a little.  It reminds me a lot of my kids spreading the food they don't like in a thin layer all over the plate, hoping that it will be less noticeable than when it sits in one place in a big pile. 

Again, I have not inspected their procedure, but looking at the results there seems to be a built-in assumption in the GISS algorithms that they expect an equal chance of a site being biased upwards vs. downwards.  In fact, I seem to see more GISS corrections fixing imagined downwards biases than upwards biases.  I just don't see how this is a valid assumption.  The reality is that biases in outdoor temperature measurement are much more likely to be upwards than downwards, particularly over the last 50 years of urbanization and even more particularly given the fact that the preferred measuremnt technology, the MMTS station, has a very very short cable length that nearly gaurantees an installation near buildings, pavement, etc.

Update #2:  To this last point, consider this situation:  Thermometer one in the city shows 2 degrees of warming.  Thermometer two a few hundred kilometers away shows no warming.  Someone aware of urban biases without a dog in the hunt would, without other data to guide them, likely put their money on the rural site being correct and the urban site exaggerated or biased.  The urban site should be thrown out, not averaged in.  However, the folks putting the GISS numbers together are strong global warming believers.  They EXPECT to find warming, so when looking at the same situation, absolutely sure in their hearts there should be warming, the site with the 2 degrees of warming looks correct to them and the no warming site looks anomalous.  It is for this reason that the GISS methodology should be as public as possible, subject to full criticism by everyone.

Update #3:  I know that many commenters see one line or even a title to a post and jump to the comment section to bang out their rebuttal without reading the post. I typcally do not respond to such folks, but there are just so many here I feel the need to say:  Yes, the IPCC knows urban heat islands exist.  What I said, and I think it is true, is that the IPCC does not believe urban heat islands substantially bias the surface temperature record, and, if they do, their effect can be statistically corrected by approaches like that used by the GISS and discussed above in Update #1.  I admit that this experiment alone, even if the quality was perfect, would not disprove that notion, but it has to make one suspicious (skeptical, even?)  By the way, if you want to yell "Peterson!" at this point, see here.  The volume of interest, pro and con, on this post I think is going to motivate me to go down to Tucson and run the same test with this USHCN station as the urban starting point, and then we'll see.

By the way, my point is clearly not, as some skeptical supporters might make out, that urban heat biases in surface temperature measurement account for all historical warming.  Clearly that is not true, as satellites, which do not have this urban bias problem, have measured real global warming, though at a lower rate than the surface temperature record.

Update #4:  To some of you commenters:  give me a break.  This is a junior high school science project funded with a $65 temperature logger and a half tank of gas.  I am sure the error bars are enormous and the R-squared probably has little meaning  (to tell the truth, Excel just put it there when I asked it to draw a trend line through the data).  Some of the data on the second run in particular looks weird to me and I would want to do a lot more work with it before I presented it to my PhD review board.  That being said, I would be happy to put it in front of said board next to the typical junior high baking soda and vinegar volcano project.

Given our constraints, I think we did a moderately thoughtful job of structuring the project-- better, in fact, than the published Singapore study we emulated.  In particular, the fact that we did the run both ways tends to help us weed out the evening cooling effect as well as any progressive heating effect from the car itself.  I honestly had zero idea what we would find when we downloaded the data to the computer.  I kind of thought it would be a mess -- remember, we were not really doing this at the right time of day.   It was not until my son did the charts using his position log he took by hand that I thoughy, "wow, there is a big effect here."   That is when I decanted the data from my GPS logger to check his results using a little more accurate position vs. time data and produced the charts here.  As I said, I really should have averaged position data for the forward and reverse runs, but I think the charts were fairly compelling.

Update #5:  The other half of my son's project was to participate in the SurfaceStations.org survey of USHCN temperature stations.  He did a photo survey of two sites.  Below is a picture from the USHCN station at Miami, AZ.  Left as an exercise to the commenters who are defending the virtue of the US surface temperature netork:  Explain how siting the temperature instrument within six feet of a reflective metal building that is perfectly positioned to reflect the afternoon sun from the SW onto the instrument does not introduce any measurement biases.  As extra credit, explain why the black gravel and asphalt road and the concrete building 6 feet away don't store heat in the day to then to warm up the air around the instrument at night as the heat re-radiates.


More Surface Temperature Measurement Goofiness

I am still stunned that mainstream climate scientists continue to defend the suface temperature measurement record over much more sensible satellite measurement (mainly because the surface temperature readings give them the answer they want, rather than the answer that is correct).  However, since they do, we have to keep criticising until they change coarse.

Via Anthony Watt is this temperature station in Lampassas, Texas, part of the USHCN and GISS data bases (meaning it is part of the official global warming record).


The temerpature instrument is in the white louvred cylindar in teh center.  This installation is wrong in so many ways:  in the middle of a urban heat island, near asphalt, next to a building, near car radiators, near airconditiong unit exhausts.  Could we possibly expect this unit to read correctly?  Well, here is the temperature plot:


The USHCN data base says that this station moved here in the year 2000.  Hmmm, do you think that the temperature spike after 2000 is due to this site, or global warming.  By the way, the GISS calls it global warming.

But James Hansen and others at the GISS defend this station and others to the death.  In fact, the GISS extrapolates temeprature trends not only for Lampassas but for hundreds of kilometers around this location from this one station.  Hansen has opposed Anthony Watt's efforts to do a photo-survey of these stations, saying that his sophisticated statistical models can correct for such station biases without even seeing the station.  OK, let's see how the adjust this station.  Their adjustment is in red:


According to the GISS, the temperatures since 2000 have been just fine and without any bias that needs correcting.  However, they seem to think that the temperature measurement in Lampassas in the 1920's and1930's (when Lampassas was a one horse town with no urbanization) was biased upwards somehow.  Why?  Well, we don't know, but based on this adjustment, the GISS thinks this site has LESS urbanization today in this picture than in 1900.   The GISS adjustments have INCREASED the warming seen at this site.  Uh, right.

I think there is some bias that needs correcting, and the place to start may be in the GISS management.

A Junior High Science Project That Actually Contributes A Small Bit to Science

Tired of build-a-volcano junior high science fair projects, my son and I tried to identify something he could easily do himself (well, mostly, you know how kids science projects are) but that would actually contribute a small bit to science.  This year, he is doing a project on urban heat islands and urban biases on temperature measurement.   The project has two parts:  1) drive across Phoenix taking temperature measurements at night, to see if there is a variation and 2) participate in the surfacestations.org survey of US Historical Climate Network temperature measurement sites, analyzing a couple of sites for urban heat biases. 

The results of #1 are really cool (warm?) but I will save posting them until my son has his data in order.  Here is a teaser:  While the IPCC claims that urban heat islands have a negligible effect on surface temperature measurement, we found a nearly linear 5 degree F temperature gradient in the early evening between downtown Phoenix and the countryside 25 miles away.  I can't wait to try this for myself near a USHCN site, say from the Tucson site out to the countryside.

For #2, he has posted two USHCN temperature measurement site surveys here and here.  The fun part for him is that his survey of the Miami, AZ site has already led to a post in response at Climate Audit.  It turns out his survey adds data to an ongoing discussion there about GISS temperature "corrections."


Out-of-the-mouth-of-babes moment:  My son says, "gee, dad, doesn't that metal building reflect a lot of heat on the thermometer-thing."  You can bet it does.  This is so obvious even a 14-year-old can see it, but don't tell the RealClimate folks who continue to argue that they can adjust the data for station quality without ever seeing the station.

This has been a very good science project, and I would encourage others to try it.  There are lots of US temperature stations left to survey, particularly in the middle of the country.  In a later post I will show you how we did the driving temperature transects of Phoenix.

Update:  Here is the temperature history from this station, which moved from a more remote location away from buildings about 10 years ago.  I am sure the recent uptick in temperatures has nothing to do with the nearby building and asphalt/black rock ground cover.  It must be global warming.


GISS Chart - Updates

I've added a lot of updated information to my analysis of the GISS world temperature chart, including a really damning view of what data the GISS actually has to demonstrate the amount of fanciful extrapolation that is going on.  See here.


A few days ago, I wrote about sattelite temperature measurement:

Satellite temperature measurement makes immensely more sense - it has full coverage (except for the poles) and is not subject to local biases.  Can anyone name one single reason why the scientific community does not use the satellite temps as the standard EXCEPT that the "answer" (ie lower temperature increases) is not the one they want?  Consider the parallel example of measurement of arctic ice area.  My sense is that before satellites, we got some measurements of arctic ice extent from fixed observation stations and ship reports, but these were spotty and unreliable.  Now satellites make this measurement consistent and complete.  Would anyone argue to ignore the satellite data for spotty surface observations?  No, but this is exactly what the entire climate community seems to do for temperature.

Today in the Washington Post, Gavin Schmidt of NASA is pushing his GISS numbers that 2007 was really hot -- a finding only his numbers support, since every other land and space-based temperature rollup for the earth shows lower numbers than his do.  As Tom Nelson points out, the Washington Post goes along with Schmidt in only using numbers from this one, flawed, surface temperature rollup and never mentions the much lower numbers coming from satellites.

But here is the real irony -- does anyone else find it hilarious that #1 person trying to defend flawed surface measurement against satellite measurement is the head of the Goddard Institute for Space Studies at NASA?

Thoughts on Satelite Measurement

From my comments to this post on comparing IPCC forecasts to reality, I had a couple of thoughts on satellite temperature measurement that I wanted to share:

  1. Any convergence of surface temperature measurements with satellite should be a source of skepticism, not confidence.  We know that the surface temperature measurement system is immensely flawed:  there are still many station quality issues in the US like urban biases that go uncorrected, and the rest of the world is even worse.  There are also huge coverage gaps (read:  oceans).  The fact this system correlates with satellite measurement feels like the situation where climate models, many of which take different approaches, some of them demonstrably wrong or contradictory, all correlate well with history.  It makes us suspicious the correlation is a managed artifact, not a real outcome.
  2. Satellite temperature measurement makes immensely more sense - it has full coverage (except for the poles) and is not subject to local biases.  Can anyone name one single reason why the scientific community does not use the satellite temps as the standard EXCEPT that the "answer" (ie lower temperature increases) is not the one they want?  Consider the parallel example of measurement of arctic ice area.  My sense is that before satellites, we got some measurements of arctic ice extent from fixed observation stations and ship reports, but these were spotty and unreliable.  Now satellites make this measurement consistent and complete.  Would anyone argue to ignore the satellite data for spotty surface observations?  No, but this is exactly what the entire climate community seems to do for temperature.

Possibly the Most Important Climate Study of 2007

I have referred to it before, but since I have been posting today on surface temperature measurement, I thought I would share a bit more on "Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data" by Patrick Michaels and Ross McKitrick that was published two weeks ago in Journal of Geophysical Research - Atmospheres (via the Reference Frame).

Michaels and McKitrick found what nearly every sane observer of surface temperature measurement has known for years:  That surface temperature readings are biased by urban growth.  The temperature measurement station I documented in Tucson has been reading for 100 years or so.  A century ago, it was out alone in the desert in a one horse town.  Today, it is in the middle of an asphalt parking lot dead center of a town of over 500,000 people.

Here is what they did and found:

They start with the following thesis. If the temperature data really measure the climate and its warming and if we assume that the warming has a global character, these data as a function of the station should be uncorrelated to various socioeconomic variables such as the GDP, its growth, literacy, population growth, and the trend of coal consumption. For example, the IPCC claims that less than 10% of the warming trend over land was due to urbanization.

However, Michaels and McKitrick do something with the null hypothesis that there is no correlation - something that should normally be done with all hypotheses: to test it. The probability that this hypothesis is correct turns out to be smaller than 10-13. Virtually every socioeconomic influence seems to be correlated with the temperature trend. Once these effects are subtracted, they argue that the surface warming over land in the last 25 years or so was about 50% of the value that can be uncritically extracted from the weather stations.

Moreover, as a consistency check, after they subtract the effects now attributed to socioeconomic factors, the data from the weather stations become much more compatible with the satellite data! The first author thinks that it is the most interesting aspect of their present paper and I understand where he is coming from.

What they are referring to in this last paragraph is the fact that satellites have been showing a temperature anomaly in the troposphere about half the size of the surface temperature readings, despite the fact that the theory of global warming says pretty clearly that the troposphere should warm from CO2 more than the surface.

I will repeat what I said before:  The ONLY reason I can think of that climate scientists still eschew satellite measurement in favor of surface temperature measurement is because the surface readings are higher.  Relying on the likely more accurate satellite data would only increase the already substantial divergence problem they have between their models and reality.

Temperature Measurement Fact of the Day

Climate scientists know this of course, but there is something I learned about surface temperature measurement that really surprised me when I first got into this climate thing.  Since this is a blog mainly aimed at educating the layman, I thought some of you might find this surprising as well.

Modern temperatures sensors, like the MMTS that is used at many official USHCN climate stations, can theoretically read temperatures every hour or minute or even continuously.  I originally presumed that these modern devices arrived at a daily temperature reading by continuously integrating the temperature over a 24-hour day, or at worst averaging 24 hourly readings.

WRONG!  While in fact many of the instruments could do this, in reality they do not.  The official daily temperature in the USHCN and most other databases is based on the average of that day's high and low temperatures.  "Hey, that's crazy!" You say.  "What if the temperature hovered at 50 degrees for 23 hours, and then a cold front comes in the last hour and drops the temperature 10 degrees.  Won't that show the average for the day around 45 when in fact the real average is 49.8 or so?"  Yes.  All true.  The method is course and it sucks. 

Surface temperature measurements are often corrected if the time of day that a "day" begins and ends changes.  Apparently, a shift form a midnight to say a 3PM day break can make a several tenths of degree difference in the daily averages.  This made no sense to me.  How could this possibly be true?  Why should an arbitrary begin or end of a day make a difference, assuming that one is looking at a sufficiently long number of days.  That is how I found out that the sensors were not integrating over the day but just averaging highs and lows.  The latter methodology CAN be biased by the time selected for a day to begin and end (though I had to play around with a spreadsheet for a while to prove it to myself).  Stupid. Stupid. Stupid.

It is just another reason why the surface temperature measurement system is crap, and we should be depending on satellites instead.  Can anyone come up with one single answer as to why climate scientists eschew satellite measurements for surface temperatures EXCEPT that the satellites don't give the dramatic answer they want to hear?  Does anyone for one second imagine that any climate scientist would spend 5 seconds defending the surface temperature measurement system over satellites if satellites gave higher temperature readings?

Postscript:  Roger Pielke has an interesting take on how this high-low average method introduces an upwards bias in surface temperatures.

Surface Temperature Measurement Bias

Frequent readers will know that I have argued for a while that substantial biases exist in surface temperature records.  For example, I participated in a number of measurement site photo surveys, and snapped this picture of the measurement station in Tucson that has gotten so much attention:


Global warming catastrophists do not want to admit this bias, because it would undermine their headlines-grabbing forecasts.  In particular, they have spent the last year or two bragging that their climate models must be right because they do such a good job of predicting history.  So what becomes of this argument if it is demonstrated that the "history" to which their models correlate so well is wrong?  (In fact, their models correlate with history only because they are fudged and plugged to do so, as described here).

Ross McKitrick, a Canadian economist, performs a fairly simple and compelling test on recent surface temperature records.  The chief suspected source of bias is from urbanization.  The weather station above has existed in Tucson in one form or another for 100 years.  When it was first in place, it sat in a rural setting near a small town characterized by horses and dirt roads.  Now it sits in an asphalt parking lot near cars and buildings, a block away from a power station, in the center of a town of a half million people.

McKitrick looked at the statistical correlation between economic growth and local temperature records.  What he found was that where there was growth, there was warming;  where there was less growth, there was less warming.  He has demonstrated that the surface temperature warming signal correlates strongly with urbanization and growth:

Our new paper presents a new, larger data set with a more complete set of socioeconomic indicators. We showed that the spatial pattern of warming trends is so tightly correlated with indicators of economic activity that the probability they are unrelated is less than one in 14 trillion. We applied a string of statistical tests to show that the correlation is not a fluke or the result of biased or inconsistent statistical modelling. We showed that the contamination patterns are largest in regions experiencing real economic growth. And we showed that the contamination patterns account for about half the surface warming measured over land since 1980.

The half figure is an interesting one.  For years, it has been known that satellite temperature records, which look at the whole surface of the earth, both land and sea, have been showing only about half the warming as the surface temerpature records.  McKitrick's work seems to show that the difference may well be in urban contamination of the surface data.

So how has the IPCC reacted to his work?  For years, the IPCC ignored his work and his comments on their reports.  Finally, in the last IPCC report they responded:

McKitrick and Michaels (2004) and [Dutch meteorologists] de Laat and Maurellis (2006) attempted to demonstrate that geographical patterns of warming trends over land are strongly correlated with geographical patterns of industrial and socioeconomic development, implying that urbanization and related land surface changes have caused much of the observed warming. However, the locations of greatest socioeconomic development are also those that have been most warmed by atmospheric circulation changes (Sections and 3.6.4), which exhibit large-scale coherence. Hence, the correlation of warming with industrial and socioeconomic development ceases to be statistically significant. In addition, observed warming has been, and transient greenhouse-induced warming is expected to be, greater over land than over the oceans (Chapter 10), owing to the smaller thermal capacity of the land.

So the IPCC argues that yes, areas of high industrial and socioeconomic development do show more warming, but that is not because of urban biases on measurement but because of "atmospheric circulation changes" that happen to warm these same urban areas.  Now, this is suspicious, since Occam's Razor would tell us to assume the most obvious result, that urbanization puts upwards bias on temperature readings, rather than on natural circulation patterns that happen to coincide with urban areas. 

But it is more than suspicious.  It is a complete fabrication.  The report, particularly at the cited sections, has nothing about these circulation patterns either showing that they coincide with areas of economic growth or that they tend to preferentially warm these areas.   And does this answer really make any sense anyway?  A recent study in California showed warming in the cities, but not in the rural areas.  Does the IPCC really want to argue that wind patterns are warming just LA and San Francisco but not areas just 100 miles away? 

Urban vs. Rural Warming

CO2 Science links to this study.  Climate catastrophists bend over backwards to try to argue that there are no such thing as urban heat islands.  But of course, whenever anyone gathers actual data rather than trying to use goofy computer model approaches, the answer is always the same:

To assess the validity of this assumption, LaDochy et al. "use temperature trends in California climate records over the last 50 years [1950-2000] to measure the extent of warming in the various sub-regions of the state." Then, "by looking at human-induced changes to the landscape, [they] attempt to evaluate the importance of these changes with regard to temperature trends, and determine their significance in comparison to those caused by changes in atmospheric composition," such as atmospheric CO2 concentration....

The three researchers found that "most regions showed a stronger increase in minimum temperatures than with mean and maximum temperatures," and that "areas of intensive urbanization showed the largest positive trends, while rural, non-agricultural regions showed the least warming." In fact, they report that the Northeast Interior Basins of the state actually experienced cooling. Large urban sites, on the other hand, exhibited rates of warming "over twice those for the state, for the mean maximum temperatures, and over five times the state's mean rate for the minimum temperature."

I would have thought the following conclusion would have been a blinding glimpse of the obvious, but I guess it still needs to be said over and over:

LaDochy et al. write that "if we assume that global warming affects all regions of the state, then the small increases seen in rural stations can be an estimate of this general warming pattern over land," which implies that "larger increases," such as those found in areas of intensive urbanization, "must then be due to local or regional surface changes."

Anthony Watts With Another Valuable Study

One of the oddities about climate science is just how hard it is to get research that actually goes out and gathers new empirical data.  Every climate scientist seems firmly rooted in the office tweaking their computer models, perhaps as an over-reaction to meteorology being historically mostly an observational science.  Whatever the reason, study after study masticates the same old 30 or 40 historical proxies, or tries to devine new information out of existing surface temperature records.  If you ever read Isaac Asimov's book Foundation, you might remember a similar episode where a character is amazed that scientists no longer seek out new empirical data, but just manipulate data from previous studies.

The issue of how much urban heat islands bias surface temperature records is a case in point.  The two most prominent studies cited by the IPCC and the RealClimate.org folks to "prove" that urban heat islands don't really exist are Peterson and Parker.  Parker in particular really bent over backwards to draw conclusions without actually gathering any new data:

One of the main IPCC creeds is that the urban heat island effect has a negligible impact on large-scale averages such as CRU or GISS. The obvious way of proving this would seem to be taking measurements on an urban transect and showing that there is no urban heat island. Of course, Jones and his associates can’t do that because such transects always show a substantial urban heat island. So they have to resort to indirect methods to provide evidence of “things unseen”, such as Jones et al 1990, which we’ve discussed in the past.

The newest entry in the theological literature is Parker (2004, 2006), who, once again, does not show the absence of an urban heat island by direct measurements, but purports to show the absence of an effect on large-scale averages by showing that the temperature trends on calm days is comparable to that on windy days. My first reaction to this, and I’m sure that others had the same reaction was: well, so what? Why would anyone interpret that as evidence one way or the other on UHI?

I have always wondered, why can't someone just go out and measure?  It can't be that expensive to send a bunch of grad students out with identical calibrated temperature instruments and simultaneously measure temperatures both inside and outside of a city.  At the same time, one could test temperatures on natural terrain vs. temperatures on asphalt.  A lot of really good data that would be critical to better correction of surface temperature records could be gathered fairly cheaply.

Well, no one is doing it, so it is left to an amateur, Anthony Watts, to do it on his own time.  Watts is the same person who, in frustration that the government bodies who maintained the surface historical temperature network kept no good information on instrument siting, set up a data base and a volunteer effort to fill the gap.  The project continues to go well and grow at SurfaceStations.org, but it could still use your help.

Anyway, here is what Anthony is doing:

My experiment plan is this; by simultaneously logging temperature data and GPS readings on my laptop, I’ll be able the create a transect line. The Gill shield has a custom window clip which allows me to mount on the passenger window. The shield will be “aspirated” by driving. Should I have to stop for a signal. the GPS data will indicate a pause, and any temp data from that spot due to heat from the vehicle or others nearby can be excluded.

The temperature sensor and A/D converter for it both have NIST calibration, making them far better than the accuracy of an MMTS, but with the same resolution, 0.1°F.

The reason for the setup now is that I’m heading to Indianapolis next week, which was one of the cities presented in a study at Pielke’s conference.  Plus that, Indianapolis is nearly perfectly flat and has transect roads that match the cardinal compass points.

According to Parker 2006, “The main impact of any urban warming is expected to be on Tmin on calm nights (Johnson et al. 1991)” so that’s what I’ll be testing. Hopefully the weather will cooperate.

Unfortunately, this need for amateurs to actually gather empirical data because the climate scientists are all huddled around the computer screen is not unique in this case.  One of the issues with proxy data like tree rings, which are used to infer past temperatures, is that past proxy studies are not getting updated over time.  Over the last several decades, proxy measures of temperatures have diverged from actual temperatures, raising the specter that these proxies may not actually do a very good job of reporting temperatures.  To confirm this, climate scientists really need to update these proxy studies, but they have so far resisted.  In part, they just don't want to expend the effort, and in part I think they are afraid the data they get will cause them to have to reevaluate their past findings.

So, Steve McIntyre, another amateur famous for his statistical criticisms of the Mann Hockey Stick, went and did it himself.

Signal to Noise Ratio in Measuring Temperature

Well, posting has a been a bit light for what I hope is now a fairly obvious reason:  I have been working overtime just to get my climate video published.  Now that the video is out, I can get back to my backlog of climate material I want to post.

For a while, I have been fascinated with the topic of signal to noise ratio in climate measurement.  For most purposes, the relevent "signal" we are trying to tease out is the amount of warming we have seen over the last decades or century.  The "noise" consists of measurement inacuracies and biases.

Here are the NASA GISS numbers for US temperature over the last century or so:


The warming trend is hard to read, with current temperatures relatively elevated vs. the last 120 years but still lower than the peaks of the 1930's.  But we can learn something by going below the surface of these numbers.

These numbers, and in fact all numbers you will ever see in the press, are not the raw instrument measurements - they include a number of manual adjustments made by climate scientists to correct for both time of observation as well as changing quality of the measurement site itself.  These numbers include adjustments both from the NOAA, which maintains the US Historical Climate Network on which the nubmers are based, and from NASA's GISS.  All of these numbers are guesstimates at best.

Though the GISS is notoriously secretive about revealing much about its temperature correction and aggregation methodologies, but the NOAA reveals theirs here.  The sum total of these adjustments are shown on the following chart in purple:


There are a couple observations we can make about these adjustments.  First, we can be relatively astonished that the sign on these adjustments is positive.  The positive sign implies that modern temerpature measurement points are experiencing some sort of cooling bias vs. history which must be corrected with a positive add-on.  It is quite hard to believe that creeping urbanization and poor site locations, as documented for example here, really net to a cooling bias rather than a warming bias (also see Steve McIntyre's recut of the Peterson urban data here).

The other observation we can make is that the magnitude of these adjustments are about the same size as the warming signal we are trying to measure.  Backing into the raw temperature measurements by subtracting out these adjustments, we get this raw signal:


When we back out these adjustments, we see there is basically no warming signal at all.  Another way of putting this is that the entirety of the warming signal in the US is coming not from actual temeprature measurements, but from adjustments of sometimes dubious quality being made by scientists back in their offices.  Even if these adjustments are justifiable, and some like the time of observation adjustment are important, the fact is that the noise in the measurement is at least as large as the signal we are trying to measure, which should substantially reduce our confidence that we really know what is going on.

Postscript:  Yes, I know the US is just one part of the world.  But the US is the one part of the world with the best, highest quality temperature measurement system.  If signal to noise ratios are low here, then how bad are they in the rest of the world?  After all, we in the US do have some rural sites with 100 year temperature measurement histories.  No one in 1900 was measuring temperatures in rural Africa or China or Brazil.


Temperature Measurement Integrity

If you aren't worried about the integrity of historical temperature data under the care of folks like James Hansen, then you will be after reading this at Climate Audit.

Since August 1, 2007, NASA has had 3 substantially different online versions of their 1221 USHCN stations (1221 in total.) The third and most recent version was slipped in without any announcement or notice in the last few days - subsequent to their code being placed online on Sept 7, 2007. (I can vouch for this as I completed a scrape of the dset=1 dataset in the early afternoon of Sept 7.)

We’ve been following the progress of the Detroit Lakes MN station and it’s instructive to follow the ups and downs of its history through these spasms. One is used to unpredictability in futures markets (I worked in the copper business in the 1970s and learned their vagaries first hand). But it’s quite unexpected to see similar volatility in the temperature “pasts”.

For example, the Oct 1931 value (GISS dset0 and dset1 - both are equal) for Detroit Lakes began August 2007 at 8.2 deg C; there was a short bull market in August with an increase to 9.1 deg C for a few weeks, but its value was hit by the September bear market and is now only 8.5 deg C. The Nov 1931 temperature went up by 0.8 deg (from -0.9 deg C to -0.1 deg C) in the August bull market, but went back down the full amount of 0.8 deg in the September bear market. December 1931 went up a full 1.0 deg C in the August bull market (from -7.6 deg C to -6.6 deg C) and has held onto its gains much better in the September bear market, falling back only 0.1 deg C -6.7 deg C.

Note the volatility of historic temperature numbers.  Always with a steady bias - recent temepratures are adjusted up, older temperatures are adjusted down, giving a net result of more warming.  By the way, think about what these adjustments mean -- adjusting recent temperatures down means that our growing urban society and hot cities are somehow introducing a recent cooling bias in measurement.  And adjusting older temepratures down means that in the more rural society of 50 years ago we had more warming biases than we have today.  Huh?

Grading US Temperature Measurement Sites

Anthony Watts has initiated a nationwide effort to photo-document the climate stations in the US Historical Climate Network (USHCN).  His database of documented sites continues to build at SurfaceStations.org.  Some of my experiences contributing to his effort are here and here.

Using criteria and a scoring system devised years ago based on the design specs of the USHCN and use in practice in France, he has scored the documented stations as follows, with 1 being a high-conforming site and 5 being a site with many local biaes and issues.  (Full criterea here)


Note that category 3-5 stations can be expected to exhibit errors from 1-5 degrees C, which is huge both because these stations make up 85% of the stations surveyed to date and because this error is so much greater than the "signal."  The signal we are trying to use the USHCN to detect is global warming, which over the last century is currently thought to be about 0.6C.  This means that the potential error may be 2-8 times larger than the signal.  And don't expect these errors to cancel out.  Because of the nature of these measurement problems and biases, almost all of these errors tend to be in the same direction - biasing temperatures higher - creating a systematic error that does not cancel out.  Also note that though this may look bad, this situation is probably far better than the temperature measurement in the rest of the world, so things will only get worse when Anthony inevitably turns his attention overseas.

Yes, scientists try to correct for these errors, but so far they have done so statistically without actually inspecting the individual installations.  And Steve McIntyre is doing a lot of work right now demonstrating just how haphazard these measurement correction currently are, though there is some recent hope that things may improve.

USA Only 2% of Earth's Surface, But...

Several weeks ago, NASA was forced to restate downwards recent US temperature numbers due to an error found by Steve McIntyre (and friends).  The restatement reinforced the finding that the US really has not had much warming over the last 100 years.  James Hansen, emporer of the NASA data for whom the rest of us are just "court jesters" dismissed both the restatement and the lack of warming trend in the US as irrelevent because the US only makes up about 2% of the world's surface. 

This is a fairly facile statement, and Hansen has to know it.  Three quarters of the earth's surface is water for which we have no real long term temperature record of any quality.  Large masses like Antarctica, South America, and Africa have very few places where temperature has been measured for any long period of time.  In fact, via Anthony Watts, here is the world map of temperature measurement points that have data for all of the 20th century (of whatever quality):


So the US is irrelevent, is it?  There is some danger in trying to eyeball such things, but I would say that the US is about one-half to one-third of the world's landmass that has continuous temperature coverage.  I won't get into this today, but for all the quality issues that have been identified in US measurements (particularly upwards urban biases) these problems are much greater in the rest of the world.

Further to Hansen's point that the US does not matter, here is a quote from Hansen last week (emphasis added)

Another favorite target of those who would raise doubt about the reality of global warming is the lack of quality data from South America and Africa, a legitimate concern. You will note in our maps of temperature change some blotches in South America and Africa, which are probably due to bad data. Our procedure does not throw out data because it looks unrealistic, as that would be subjective. But what is the global significance of these regions of exceptionally poor data? As shown by Figure 1, omission of South America and Africa has only a tiny effect on the global temperature change. Indeed, the difference that omitting these areas makes is to increase the global temperature change by (an entirely insignificant) 0.01C.

Look at the map!  He is now saying that the US, South America, and Africa are irrelevent to world temperatures.  And with little ocean coverage and almost no coverage in Antarctica before 1960, what are we left with?  What does matter?  How is he weighting his temperature aggregations if none of these matter?  Fortunately, the code is finally in the open, so we may find out.

A Good First Step: Hansen & GISS Release the Code

One of the bedrock principles of scientific inquiry is that when publishing results, one should also publish enough information about the experimental process so that others can attempt to replicate the results.  Bedrock principle everywhere, that is, except in climate science of course.  Climate researchers routinely refuse to release key aspects of their research that would allow others to replicate their findings -- Mann's refusal to release information about his famous "hockey stick" analysis even in the face of FOIA's is just the most famous example.

A few weeks ago, after Steven McIntyre and a group of more-or-less amateurs discovered apparent issues in the NASA GISS temperature data, James Hansen and the GISS were forced to admit a programming error and restate some recent US temperatures (downwards).  As I wrote at Coyote Blog, the key outcome of this incident was not the magnitude of the restatement, but the presure it might put on Hansen to release the software code NASA uses to aggregate and adjust historical temperature measurements:

For years, Hansen's group at GISS, as well as other leading climate scientists such as Mann and Briffa (creators of historical temperature reconstructions) have flaunted the rules of science by holding the details of their methodologies and algorithm's secret, making full scrutiny impossible.  The best possible outcome of this incident will be if new pressure is brought to bear on these scientists to stop saying "trust me" and open their work to their peers for review.  This is particularly important for activities such as Hansen's temperature data base at GISS.  While measurement of temperature would seem straight forward, in actual fact the signal to noise ration is really low.  Upward "adjustments" and fudge factors added by Hansen to the actual readings dwarf measured temperature increases, such that, for example, most reported warming in the US is actually from these adjustments, not measured increases.

I concluded:

NOAA and GISS both need to release their detailed algorithms and computer software code for adjusting and aggregating USHCN and global temperature data.  Period.  There can be no argument.  Folks at RealClimate.org who believe that all is well should be begging for this to happen to shut up the skeptics.  The only possible reason for not releasing this scientific information that was created by government employees with taxpayer money is if there is something to hide.

The good news is that Hansen has released what he claims to be the complete source code.  Hansen, with extraordinarily bad grace, has always claimed that he has told everyone all they need to know, and that it is other people's fault if they can't figure out what he is doing from his clear instructions.  But just read this post at Steve McIntyre's site, and see the detective work that folks were having to go through trying to replicate NASA's temperature adjustments without the code.

The great attention that Hansen has garnered for himself among the politically correct glitterati, far beyond what a government scientist might otherwise expect, seems to have blown his mind.  Of late, has has begun to act the little Caeser, calling his critic's "court jesters," with the obvioius implication that he is the king.  Even in releasing the code he can't resist a petulent swipe at his critics (emphasis added):

Reto Ruedy has organized into a single document, as well as practical on a short time scale, the programs that produce our global temperature analysis from publicly available data streams of temperature measurements. These are a combination of subroutines written over the past few decades by Sergej Lebedeff, Jay Glascoe, and Reto. Because the programs include a variety of
languages and computer unique functions, Reto would have preferred to have a week or two to combine these into a simpler more transparent structure, but because of a recent flood of demands for the programs, they are being made available as is. People interested in science may want to wait a week or two for a simplified version.

LOL.  The world is divided into two groups:  His critics, and those who are interested in science.

This should be a very interesting week.

Signal to Noise Ratio in Climate Measurement

There is a burgeoning grass roots movement (described here, in part) to better document key temperature measurement stations both to better correct past measurements as well as to better understand the quality of the measurements we are getting.

Steve McIntyre
has had some back and forth conversations with Eli Rabbett about temperature measurement points, each accusing the other of cherry-picking their examples of bad and good installations.  McIntyre therefore digs into one of the example temperature measurement points Rabbett offers as a cherry-picked example of a good measurement point.  For this cherry-picked good example of a historical temperature measurement point, here are the adjustments that are made to this site's measurements before it is crunched up into the official historic global warming numbers:

Corrections have been made for:
- relocation combined with a transition of large open hut to a wooden Stevenson screen (September 1950) [ed:  This correction was about 1°C]
- relocation of the Stevenson screen (August 1951).
- lowering of Stevenson screen from 2.2 m to 1.5 m (June 1961).
- transition of artificial ventilated Stevenson screen to the current KNMI round-plated screen (June 1993).
- warming trend of 0.11°C per century caused by urban warming.

Note that these corrections, which are by their nature guesstimates, add up to well over 1 degree C, and therefore are larger in magnitude than the global warming that scientists are trying to measure.  In other words, the noise is larger than the signal.

  0.11C per century is arguably way too low an estimate for urban warming.

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