Never before published paper on UHI and siting – Goodridge 1987

Plus answers to yesterday’s Fun puzzle: Name these official stations.

Given that California Governor Jerry Brown has recently setup a website at the governor’s office basically telling skeptics to “shut up” I thought this would be a good time to publish this.

This is a paper that was presented at a climate conference by Jim Goodridge, former State Climatologist of California, titled Population and Temperature Trends in California at the Pacific Climate Workshop, in Pacific Grove, CA March 22-26, 1987.

In this paper, Jim presented what I believe to be the very first photos bringing attention to the issue of station siting. Yesterday, I published both of those photos on WUWT here: Fun puzzle: Name these official stations.

The answer to the first photo was correctly made by commenter “Hoser”:

Hoser says:
August 15, 2012 at 10:32 pm
It’s been a long time, but the top one might be Mt. Hamilton, Lick Observatory. That might be the astronomer’s dormatory behind the car. Yikes, 25 years since I’ve been there.

Yes, the official temperature at the Lick Observatory is measured on a concrete slab rooftop where cars can park and there’s a chimney nearby:

Surprisingly, that station is still in operation today. It has been converted to MMTS electronic thermometer, but from what I can tell, still appears to be at the same location as before. Note the walkway bridge and chimney shadow:

Lick Observatory – aerial photo from Bing Maps, annotated by Anthony

Interactive source map: http://binged.it/PscDx2

NOTE: Perhaps one of our WUWT readers in the Bay Area can make a trip up to the Lick Observatory this weekend to advise with a photograph if the station still exists on the same spot or not. You’d think that on such a hallowed grounds of science, they’d know enough to put the thermometer away from the chimney and concrete. Let’s see if they’ve figured it out in 25 years since then.

As for the other station near the incinerator, that is a Taylor max-min thermometer used by the Quincy, CA  Highway Department, now since closed. Nobody got that one, but there were some good guesses. 

 

Siting issues aside, Jim made some important discoveries in this paper where he looked at rural -vs- urban temperature trends. He only has a paper copy left, as the Mac disks this was done on have long ago been lost. I took the paper copy to Staples and had it scanned into a PDF file, which is presented in full below.

This page 9 of graphs below, figures 4 and 5 tell the story for California Surface Temperature data:

Mind you, this is data that Jim used prior to the big range of adjustments that have been applied by NCDC. Jim provides all that data in the paper. It might be interesting to compare the data then and now to see what has been done to it. Another important distinction of note is that this paper was presented over a year before NASA’s Dr. James Hansen went before the Senate in June 1988, and touted his science and model predictions, deeming it so solid that they had to turn off the air conditioning in the hearing room for “theatrical effect”.

Figure 6 and 7 on page 10 are also instructive:

But this set of graphs from page 12 is really interesting:

The trend for rural stations is interesting, because Jim found a correlation for it:

Here are figures 15 to 18:

And here is Figure 19. Indeed the similarity is remarkable.

The other conclusion to Jim’s paper is that there is a correlation between population trend and temperature trend for inland urban stations, as seen in this graph:

Jim eventually went on to publish a letter in the Bulletin of the American Meteorological Society in 1996 on this issue. This one graph from that letter was a “light bulb moment” for me:

The reply from Kwang-Y Kim, published next to Goodridge’s letter is an interesting admission:

Kim had co-authored a CO2 regional modeling paper with Gerald North in 1995, suggesting that temperatures were on the rise to CO2, but Goodridge in his letter had suggested their base temperature data had been polluted:

I have to wonder, if somebody had put Goodridge’s 1987 paper in front of Jim Hansen in 1987 or early 1988, would it have made any difference in his claims made in June 1988 before the Senate?

Probably not, because as we’ve seen, there seems to be an unwavering belief system that climatic scale temperature is controlled only by Carbon Dioxide concentration, and anyone who presents a contrary view is immediately denigrated and labeled. For example, Hansen’s CRU compatriot Dr. Phil Jones already had formed a strong opinion of Goodridge’s work, which we see thanks to Climategate 2 (bold mine):

file 4789.txt

date: Tue, 25 Apr 2000 09:25:14 +0100

from: Phil Jones <p.jones@xxxx>

subject: Re: CA climate

to: Tom Wigley <wigley@xxxx>,Mike Hulme <m.hulme@xxxxx>

Tom,

Bryan Weare is at US Davis. He would know about some of the things you

mention. The jerk you mention was called Good(e)rich who found urban

 warming at all Californian sites.

I’m away until today until May 5 in Nice and Geneva. I hope you can do

the temperature plots yourself and that Mike can do the precip ones.

Mike has the data as 5 degree grid boxes, so the it would be good if

you could define these for him. I think he’s back tomorrow.

It would be possible to use the 0.5 degree grid boxes but we’d have to

get Mark New to do that for us.

Cheers

Phil

At 12:13 PM 4/24/00 -0600, Tom Wigley wrote:

>Phil and Mike,

>

>I have to attend a meeting organized by EPRI and the California Energy

>Commission on June 12, 13.  The focus is future climate scenarios and the

>implied impacts.  It will include discussions of GCM results and

>statistical and LAM downscaling.  They want someone to address observed

>climate (homogeneity problems; E-W and N-S contrasts; ENSO effects;

>changes in circulation — such as increased offshore cyclogenesis, changes

>in storm tracks; etc.), but they don’t have anyone invited yet.  Chuck

>Hakkarinen (EPRI) says there is someone at UC-Davis who is an “expert” on

>CA climate.  Who is this?  Do you know any other Californians who are in

>the observed climate game and who you respect?  (From memory, there are

>some nitpicky jerks who have criticized the Jones et al. data sets — we

>don’t want one of those.  Wasn’t one of these guys called Goodrich?)

>

>For myself, I would like to have some monthly time series for the CA area

>average.  I can possibly do this for temperature, but certainly not for

>precipitation.  Is there any way you two could send me time series within

>the next day or so (before I leave for Australia)?  For the regions, I’d

>like results for the following separate areas (as near as you can do it):

>(1) 32-36degN, 115-121degW

>(2) 36-42degN, 118-124degW

>(3) 32-42degN, 114-124degW

>(4) 36-42degN, 106-114degW

>The last one represents the headwaters of the Colorado River.

>

>Finally, if you had some PDSI time series for the region, I’d very much

>like these too.

>

>Many thanks,

>

>

>Tom

>

>

>

>**********************************************************

>Tom M.L. Wigley

>Senior Scientist

>ACACIA Program Director

>National Center for Atmospheric Research

>P.O. Box 3000

>Boulder, CO 80307-3000

>USA

>Phone: 303-xxxx

>Fax: 303-497-xxxx

>E-mail: wigley@xxxx

>Web: http://www.acacia.ucar.edu

>**********************************************************

>

>

Prof. Phil Jones

Climatic Research Unit        Telephone +44 (0) 1603 xxxx

School of Environmental Sciences    Fax +44 (0) 1603 xxxx

University of East Anglia

Norwich                          Email    p.jones@xxxxx

NR4 7TJ

UK

—————————————————————————-

Tom Wigley and Phil Jones are some piece of work, aren’t they?

The entire 1987 paper by Jim Goodridge is available here as a PDF: Goodridge_1987_paper (16mb)

We owe Jim Goodridge some thanks, not only for the work he has done, but also for the abuse he’s suffered alongside us all from “The team”.

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.9 R!
Can’t possibly be climate science, way too accurate.

Excellent post. Bookmarked and flagged. Thank you.

faustusnotes

From page 2 of the paper:

Those records classified here as rural representing rural environments are arbitrarily selected as those with a slope (B) of the regression analysis less than 0.0125.

So what this paper is saying is that if you divide the weather stations into those with a low slope and those with a high slope, and call the high slope set “urban,” you will find that urban stations have a high slope.
That might explain why in his map of “urban” and “rural” sites many of the rural sites are sandwiched between or right next to urban sites. It also explains why

There is an unusual occurrences[sic] of an urban station, specifically; San Francisco in the list of rural stations.

His method shares something in common with your surface station audit, Anthony: he is not blinded to the trend when he categorizes whether a station is rural or urban. But at least his method is objective, which can’t be said for yours
REPLY: You are welcome to demonstrate why the method we use, Leroy 1999 and Leroy 2010 in station categorization is biased. Just saying it is, doesn’t make it so.
Read up on it here:
Leroy, M., 2010: Siting Classification for Surface Observing Stations on Land, Climate, and Upper-air Observations JMA/WMO Workshop on Quality Management in Surface, Tokyo, Japan 27-30 July 2010 http://www.jma.go.jp/jma/en/Activities/qmws_2010/CountryReport/CS202_Leroy.pdf
-Anthony

pat

meanwhile, down under, CSIRO have a new big scare:
17 Aug: ABC: Conor Duffy: Climate change sees tropical fish arrive in Tasmania
The CSIRO is warning climate change is having a big impact on the country’s oceans, with tropical fish turning up as far south as Tasmania.
A major report on oceans and climate change, to be released today, says the damage under the sea is much clearer than when it released its last report on the subject three years ago.
As well as causing a southern migration, climate change is causing a decline in some temperate fish stocks and ocean acidification is beginning to affect shellfish…
http://www.abc.net.au/news/2012-08-17/climate-change-sees-tropical-fish-head-south/4203830?section=tas

David Larsen

Good research and comparative analysis, Anthony. Three atta boys and one excellent job. Hopefully this will shut the monkeys up (haha).

Phil M.

Or how about this little gem from page 3:

In general the classification of records as urban or rural is fairly close to reality as the writer knows it from viewing most of the sites

Wow! That statement really inspires confidence. No wonder this ‘paper’ never got published.
REPLY: It was never submitted for publication, it was presented at a climate conference. Please do try to pay attention. And observation of the station situation is in fact a way to quality control the metadata. For example, explain why the San Francisco station (on a rooftop downtown at that time) was classified as rural. – Anthony

Phil M.

From page 2 of the paper:
Those records classified here as rural representing rural environments are arbitrarily selected as those with a slope (B) of the regression analysis less than 0.0125.
So what this paper is saying is that if you divide the weather stations into those with a low slope and those with a high slope, and call the high slope set “urban,” you will find that urban stations have a high slope.
That might explain why in his map of “urban” and “rural” sites many of the rural sites are sandwiched between or right next to urban sites. It also explains why
There is an unusual occurrences[sic] of an urban station, specifically; San Francisco in the list of rural stations.
His method shares something in common with your surface station audit, Anthony: he is not blinded to the trend when he categorizes whether a station is rural or urban. But at least his method is objective, which can’t be said for yours

Oh, the stupid…it burns, doesn’t it Anthony? You could try reading the articles, first. Just a suggestion. I mean, this is the second blog post today that you’ve completely whiffed on (the first being the helium article).
Feel the burn, bro.
REPLY: I only feel the haters, such as yourself and faustus. Hate on dude. You are welcome to answer the question I posed to him. – Anthony

Caleb

RE: faustusnotes says:
August 16, 2012 at 4:26 pm
The comparison between counties with populations over a million and those under a hundred thousand are not so “arbitrarily selected.”
You are just looking for a reason to deny the facts glaring you in the eye. Sad.
These facts have been obvious from the beginning, as has been your ( and Hansen’s) dedicated will-to-ignore-the-facts.
Twenty-five years! A quarter century!
That’s a long time to stay stupid. Sad.

Pity he didn’t separate rural agricultural from rural non-agricultural, as Christy found California agricultural areas were warming, while nearby non-agricultural areas showed a slight cooling trend. Christy ascribed the agricultural area warming to albedo changes from increased irrigation.
http://journals.ametsoc.org/doi/abs/10.1175/JCLI3627.1

Richdo

The Team Says:
“His method shares something in common with your surface station audit, Anthony: he is not blinded to the trend when he categorizes whether a station is rural or urban. But at least his method is objective, which can’t be said for yours”
My,my, my!
You seem to have hit a raw nerve Anthony. Just typical National Socialist Warmista personal attacks. They are a [snip] that can’t really debate the issues. But then we don’t really expect much from them in the way of informed debate, sadly.
Thanks for sharing Jim’s interesting work.
Rich

BarryW

One possible hypothesis for the warmer SST stations is their relationship to sewage water outflows, storm drain outflows, or power plants outflows.

Richdo

[snip]
lol – yeah, probably right, but gezzz.

Pat Frank

faustusnotes, you should have read past page 2. On page 3, Jim Goodridge wrote that, “In general the classification of the temperature records as urban or rural is fairly close to reality as the writer knows it from viewing most of the sites. (my bold)”
JG also mentions that both San Francisco and Vancouver BC are “well ventilated” coastal cities, in which local SSTs and air temperatures are well-correlated.
Even a cursory inspection of the station map in Figure 3 shows that your comment, “many of the rural sites are sandwiched between or right next to urban sites.” is wrong. Also, anyone familiar with the folded topography of California would not worry much about nearby spots on a map. The top of San Bruno Mountain, for example, is about 4 miles from the center of San Francisco, is almost completely rural (apart from broadcast repeaters), 1000 feet high, is constantly breezy and would overlap the spot for SF on that map.

Gil Dewart

Having spent some time in Cloverdale, this commenter can attest that it is indeed “rural”. (But don’t forget those orchard heaters!). It does suggest that the old-timers were right — it really was hotter in the ‘thirties.

Doug Huffman

I have always been suspicious – skeptical – of data acquired for a purpose, acquired with an objective in mind, for the potential of confounding corruption. This convenient data, predating our controversy, is doubly valuable.
I’m disappointed that I doubted my impulse to Lick Observatory for the architecture, but I honestly do not remember that spot, born and raised in Santa Clara Valley.

Pat Frank

Phil M, visually inspecting the station is central to making the urban/rural distinction and to validating the temperature record. Your dismissal betrays ignorance. The other critical validation, of course, is regular field calibration of the instrument, but that’s another issue.

Theo Goodwin

Pat Frank says:
August 16, 2012 at 5:34 pm
‘faustusnotes, you should have read past page 2. On page 3, Jim Goodridge wrote that, “In general the classification of the temperature records as urban or rural is fairly close to reality as the writer knows it from viewing most of the sites. (my bold)”’
Sorry, Pat, but the Warmists will not be able to understand what you have written. For example, you quote: “the writer knows it from viewing most of the sites.” The vast majority of Warmists will assume that the reference is to viewing photos on the internet. It will never occur to them that Goodridge traveled to the sites or that he could classify them by visiting them. It is all too practical for the Warmist. /sarc off

jorgekafkazar

Figures 10 (urban) and 11 (rural) have the exact same abscissa values. The latter appears to show rural stations with 22 and 26.5 million populations.
[I’ve lost this comment twice, already, when I clicked on a chart while trying to verify something, and was sent off to yaya land. I hope it didn’t end up posted.]

Reblogged this on Is it 2012 in Nevada County Yet? and commented:
This is an important paper for all California readers. It clearly shows that rural areas are cooler than urban areas.

Sean

You’ve just convincef me that AB 32 will indeed fix global warming in California. As the cost of doing business rises, many businesses and their employees will leave and the state population will decline. The de-urbanization should lead to lower temperatures. Those folks at CARB are brilliant!

AndyG55

“from viewing most of the sites”
yep.. like actually removing your butt from in front of the imagination model on the computer, and viewing reality.
Maybe a warmist… just one, somewhere, will try this…. eventually……maybe, perhaps
nah.. not a chance..

Marcos José

The PDF of the “Comments on Regional Simulations of Greenhouse Warming Including Natural Variability” – Goodridge (1996) is available here:
http://pt.scribd.com/doc/67524224/PDF-1996-Goodridge-Comments-on-Regional-Simulations-of-Greenhouse-Warming-Including-Natural-Variability

AndyG55

and Fartsus.. if you want to prove the categorisation is wrong, then go and do a history and site check on each and every one of the stations used.
Until you do that.. you got NOTHING !!!

The small-population counties in the “light bulb moment” graph shows a distinct cooling trend if one removes the first 15 years of data.
This, alone, completely falsifies the warmists’ cherished belief that increased atmospheric CO2 causes warming.
If the warming-due-to-CO2 is valid, it must be consistent. It cannot act arbitrarily nor capriciously.
I always ask the warmists, “How does it know which cities to ignore (or counties, or states)?”
Thus far, there has been no reply.

faustusnotes

So Anthony, no comment on the methods of the paper? Do you agree that the classification method makes the argument tautological, and if so are you going to update your post to include at least an acknowledgement of the tautology?
Pat Frank, “viewing most of the sites” is not objective. No doubt the US Census Bureau or the Post Office (or the CDC) holds a database of urban/rural classifications for every settlement and location in the USA, and that data could be used to classify the stations as rural/urban. Note also he says “most” so he doesn’t make clear how many of the sites don’t match his (regression-based) classification. Even his chart of population-based classifications isn’t objective, since it doesn’t use established definitions of urban/rural and it appears to rely on pooling data from multiple sites, some of which could be rural and some urban.
Richdo, there is no personal attack in my comment, which is purely a criticism of methods. It’s ironic that your own comment containing this complaint had to be snipped because of … a personal attack.
Goodridge has clearly divided the data set into two at the median regression slope (this is why there are 39 sites in each group). The median regression slope is >0, so that means that at least half of the stations had a warming trend in 1987. This means some of those classified as “rural” by his (regression-based) method probably have a warming trend. Given that Goodridge’s arbitrary classification system places some rural stations into the “urban” category, this means that warming trends were being observed in both urban and rural stations in 1987.
I would be interested to hear why anyone thinks that the station shown in the picture would be contributing to a misleading understanding of global warming. If AGW is not happening, by what mechanism is that station showing a warming trend that would mislead naive scientists into thinking warming is happening? And how could that mechanism be replicated across a majority of such sites in the contiguous USA?
REPLY: “Goodridge has clearly divided the data set into two at the median regression slope (this is why there are 39 sites in each group).”
Umm nooo, you can’t read or do simple math apparently. Figure 4, 35 Urban stations, Figure 5, 39 rural stations. Note that on page 2, he identifies 74 stations in his dataset.
39×2= 78, not 74.
35+39 = 74
So much for your argument. – Anthony

M Seward

Excellent Post, Anthony. What a revelation! And so early on that he cannot possibly be called a “denier”, I mean it resonates with pre-Hansenite scientific innocence.
Can I suggest that a Goodridge Study be carried out on all sets of stations world wide, I guess along the lines of your study in the US, a Wiki-Goodridge evaluation to coin a term. We could then aggregate the results to get a truly GOOD estimate of global temperature because clearly the BEST one is not what it seems. It would of course be termed the Goodridge Temperature in the man’s honour.

AndyG55

The very fact that he CAN select 39 stations that have, on average, a negative temperature trend, makes a TOTAL MESS of the term Global Warming..
gees, it isn’t even State Warming,
just URBAN warming.

faustusnotes

Still no comment on the fundamental tautology or on the subjective nature of the classification process?
So he hasn’t divided the stations on the median slope. So can you explain why he chose to divide them at that particular slope value? Why not 0? Is it the mean slope? There’s no objectivity in his method at all.
M Seward, if you used the same method on all stations world wide you would get the same result as Goodridge because it’s tautological. The method assumes the result.
Imagine if a climate scientist did the same thing: “stations were defined as ‘accurate’ if the regression slope of temperature was greater than 0.01. Accurate stations showed a significant warming trend, while inaccurate ones showed cooling. This is clear evidence that the world is becoming warmer.”
You guys would have a field day with that, wouldn’t you?

REPLY:
It’s a simple prescreening testing for an early result, to present at a climate conference of some peers. This was his first ever test of the issue IIRC, so yes the sample size is small. Remember this is one man working alone in spare time, not a funded project. His later work used a much larger set of stations, and it still holds up.
For example, from a post I made in 2007:

One plot which he shared with me today is a 104 year plot map of California showing station trends after painstakingly hand entering data into an Excel spreadsheet and plotting slopes of the data to produce trend dots.


LaDochy, S., R. Medina, and W. Patzert. 2007. Recent California climate variability: spatial and temporal patterns in temperature trends. Climate Research, 33, 159-169 You can download the paper in PDF format in its entirety here: ca_climate_variability_ladochy.pdf references Goodridge’s work. So it must not be as terrible as you try to make it out to be.
Here’s an updated chart from 2009, using hundreds of California COOP stations.

You are so busy trying to shoot it down you can’t even bother to look around. – Anthony

Gunga Din

faustusnotes says:
August 16, 2012 at 6:26 pm
….I would be interested to hear why anyone thinks that the station shown in the picture would be contributing to a misleading understanding of global warming. If AGW is not happening, by what mechanism is that station showing a warming trend that would mislead naive scientists into thinking warming is happening? And how could that mechanism be replicated across a majority of such sites in the contiguous USA?
==============================================================
Where I work we have data from 2 remote air temperature sensors. A few weeks one gave a reading of 106*F. The other read 89*F. I checked with the airport and it reported 88*F. So what was the temperature here at that time? 88? 89? 106? 94.33?

Well done Anthony. You are probably aware of the Torok et al paper “Urban heat island features
of southeast Australian towns” in Aust.Met.Mag 50 (2001) They found the relationship
ΔTu-r(max) = 1.42 log(population)-2.09
and say the UHI effect is likely to be smaller in Australian towns and cities than in North America and Europe. The article says Tapper, N.J. 1982. Atmospheric infrared radiation over Christchurch,
New Zealand. Ph.D. thesis, University of Canterbury, 384pp. found similar result in NZ. Oke (Oke, T.R. 1973. City size and the Urban Heat Island. Atmos. Environ.7, 769-79.) did a study including Hamiliton Canada and found higher UHI.
It is amazing that so-called climate scientists make upward adjustments of temperature of badly located weather stations rather than downward.
I have an outside temperature gauge on my SUV and just about every day I notice the higher temperatures 1 to 3C (day and night) at the town centre compared to the semi-rural outskirts where I live.. In rural & forested areas further out the temperature is about 1C lower.

faustusnotes

Anthony, I didn’t comment on the sample size. He could have a trillion weather stations and it doesn’t change the fundamental problem in his classification method – a problem that persists in your chart. How is aggregated temperature at the county level indicative of rural vs. urban heat effects? You have to analyze that using station-specific rural/urban classifications, which was not done in the original paper and is not done in the post you link to. And you need to do it with an objective classification of rural/urban that is independent of the data series – something that neither Goodridge nor you have done. Classify the stations according to the US Census Bureau’s definition of their local area and try again. Repeating Goodridge’s analysis with another 10 years of data doesn’t make it right – just wrong for longer.
I note your charts show a warming trend across all the stations, just stronger in the most heavily populated counties. Though of course you don’t give measures of uncertainty, as ever, so we can’t tell if the trend is significant. It’s possible of course that the largest urban areas are situated in areas most likely to warm fastest (flat lowlands, coastal areas, and low altitude locations?) so even then the difference in trends isn’t necessarily indicative of a UHI effect. You guys are always going on about correlation not equaling causation. This is a classic example of that problem…
REPLY: You’ll have to be more specific when you say “I note your charts show a warming trend across all the stations…” not a mind reader, please reference it. Several have been provided. If you want precise answers, ask precise questions.
BTW what do you do at the University of Tokyo? Are you James Annan who works from there and blogs here? or maybe this guy http://faustusnotes.wordpress.com/ who is caught up in the gaming scene? I just like to know who I’m talking to when somebody gets so wound up.- Anthony

Pat Frank

faustusnotes, viewing the station sites is as objective as viewing atomic emission spectra, or any other data. You’re implying a categorical dismissal of objectivity. There’s no reason to suppose that Jim Goodridge did not make a professionally valid examination of the sites.
Theo Goodwin is correct — actually traveling to the sites is the only way to validate their records in terms of sensor placement. That’s what Jim Goodridge did. Your criticism of him on that account fails.

Bob K.

*** MODERATORS ***
Would you please revisit:
“The entire 1987 paper by Jim Goodridge is available here as a PDF: Goodridge_1987_paper (16mb)”
I don’t want to be nitpicking, but as it is written, the “16 mb” would read “16 mili bits” (ie. 16/1000 of a bit), which is very different from intended “16 MB” reading “16 mega bytes”, ie. 16,000,000 bytes (and yes, I know that these are not parts of SI, but still…).
Please, don’t take me wrong. Mistakes such as this are commonly tolerated in ordinary daily newspaper articles, but this is, after all, a science blog – and the best one we have.
Thanks,
Bob

Gene Frodsham

I have a rather strong memory that in January of 2006 a WUWT post was about the rural temperatures in inland California and showed that there was no warming. I have not been able to find this again and the archives do not cover this time. Also, memory could be fuzzy.
Would someone know of this?

jorgekafkazar

Well, whether intended for publication or not, it’s flawed and needs to be corrected. Peer reviewer faustus has pointed out a genuine mistake in methodology. Peer reviewers are not obligated to rewrite a paper or to prove that the methodology results in incorrect results. It falls upon the authors to make the necessary corrections.
AndyG55 says: “The very fact that he CAN select 39 stations that have, on average, a negative temperature trend, makes a TOTAL MESS of the term Global Warming..”
Not quite. We know that the Earth has warmed from the Little Ice Age (LIA), so there must be a general upward trend, since, yes? Yet we clearly observe negative trends in many areas. Neither the observations nor the “climate models” have established that putative global warming or real, post-LIA global warming will result in similar trends in all locations. There will still be microclimate–chaotic, dynamic, inscrutable.
Such disparate results in a single State may (or may not) be statistically unlilkely, as Andy alleges. Goodridge’s anecdotal, hand-waving assessment of the validity of his rural/urban station assignments makes me suspect that his results are correct. But anecdotal assessments, based on “viewing most of the sites,” are not science, especially after the fact. Confirmation bias, you know?
The paper needs to be rewritten after applying a non-arbitrary, independent algorithm for rural/urban assignments. At a minimum, the author needs to document his validity test or, better, have the assignments done by others in a blind test. I hope no one is offended by this. Let’s all step back and take another look.
REPLY: The simpler thing to do is put up one of Jim’s later papers. As I mentioned, this was just a first effort for a conference presentation. I doubt he’ll re-write it because he’s got later versions that dealt with the problem using a larger sample size and different methods. – I ask for one of those to put up the next day or two. Bear in mind that Jim was the state climatologist, and it was his job to know about the stations that provided him data. He visited most every station in the state, something I wonder if other state climatologist bother to do today. So if he says the station is rural or urban, he likely has firsthand knowledge of the fact.
After he retired, I recall going on a road trip with him (spring 1992) to visit a station called Four Trees in the Feather River Canyon. A 4wheel drive was required to get there, and I gladly obliged since I had one. He was interested in this station because it showed the largest rainfall value in the state, and he wondered if the equipment had malfunctioned. He wanted to know firsthand.
You can look at data all you want, but it won’t tell you if a tree branch has dipped into the rain gauge. Only firsthand observation will tell you that. I find it comical that so many people miss this important part of the data quality process, and get so bent out of shape over his choices. I know the man, and he knows just about every weather station in the state. – Anthony

Following up on Pat’s comment: August 16, 2012 at 4:52 pm
“meanwhile, down under, CSIRO have a new big scare:
17 Aug: ABC: Conor Duffy: Climate change sees tropical fish arrive in Tasmania
The CSIRO is warning climate change is having a big impact on the country’s oceans, with tropical fish turning up as far south as Tasmania.”
Very interesting.
Up here at 19° South we have had a decidedly cool winter in some ways. Local BoM puts it down to lower SST. So – cooler up here, but so warm down there the fish are moving south?
How come the fish know the sea 3000km south of here is warmer? I suppose it’s possible …
Or maybe somebody got the idea from “Finding Nemo”?

Venter

Looks like a lot of people think that actually going into the field and making first hand observations to collect data is wrong and that sitting behind a desk and writing ” adjustment ” algorithms is the correct way. Welcome to the weird world of computer modelling and statistics without any science behind it.

Gunga Din

Gunga Din says:
August 16, 2012 at 7:07 pm
faustusnotes says:
August 16, 2012 at 6:26 pm
….I would be interested to hear why anyone thinks that the station shown in the picture would be contributing to a misleading understanding of global warming. If AGW is not happening, by what mechanism is that station showing a warming trend that would mislead naive scientists into thinking warming is happening? And how could that mechanism be replicated across a majority of such sites in the contiguous USA?
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Where I work we have data from 2 remote air temperature sensors. A few weeks one gave a reading of 106*F. The other read 89*F. I checked with the airport and it reported 88*F. So what was the temperature here at that time? 88? 89? 106? 94.33?
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Here’s what I first had typed before I decided to take out any info that might give an indication conditions of the sites. The point I was trying to make (the hard way, perhaps) is that without knowing the details of the sensors location, all you have is a number with no way of knowing if it’s really telling you what you think it is.
“An indirect answer. Where I work we have data from 2 remote air temperature sensors. One is a fairly new installation. It is on the south side of a brick building, in full sun over concrete. The other is about 4 miles away on the control house of a dam. A few weeks ago the newer gave a reading of 106*F. The dam read 89*F. I checked with the airport (about 4 miles in the opposite direction of the dam from the 106 sensor) and it reported 88*F. So what was the temperature here at that time? 88? 89? 106? 94.33?”
If the data from the sensor is only as good as the location and integrity of the sensor. Hansenites would have said the temperature was 94 or maybe even 95. Read Watts et al. Some “Climate Scientist” aren’t naive. They have an agenda or a dollar to make.
Is Al Gore a climate scientist? Do you consider him to be any kind of authority on the environment?
Mann. A scientist in any field may be personally disapointed if his hypothesis is shown to be wrong. But does he hide his data and his methods so that his hypothesis can’t be challenged?
Hansen. Does a scientist pretend his 100 year “projections” are right when they’re already way off just a decade or so out? Is “Coal Trains of Death” a scientific or a PR term?
Take a step back and think about it. The Earth can spare a few moments.

Maus

jorgejafkazar: “Peer reviewer faustus has pointed out a genuine mistake in methodology. ”
Ah yes, the Heckler’s Veto. But then I object to your peer review of peer reviewer faustus’ peer review so your post isn’t fit for publication. And if you have any decency you’ll remove it post haste. This is absurd of course. But the problem is that your post is capricious, non-responsive, and makes no attempt to establish the ‘genuine’ nature of any of alleged mistake.
Nor does Faustus do any better in kvetching about ‘arbitrary’ partitions. Zero degrees celsius is arbitrary as is zero degrees farenheit. As well 1 inch, 1 meter, or 1 second. So is the choice of any baseline period for ‘normal’ in climatic terms; be it 1945, 1954, when the Vikings were settling Greenland or during the last ice age.
The entire complaint raised in peer reviewer faustus’ peer review is a red herring to distract from the issue that temperature trends correlate with population. If one wants to object to drawing conclusions about UHI then the only appropriate objection is that correlation is not causation. And that it may simply be that biological organisms prefer warmer climes over colder ones. Thus it is the anomalistic temperatures themselves that caused the population increases; and thus explain the correlation.
But irrational and unthinking nonsense about the arbitrary notions chosen for clarity is itself arbitrary. And if arbitrariness is dispositive then peer reviewer faustus’ peer review is unfit for purpose. And your even more arbitrary sycophantism is even less fit still.

davidmhoffer

Venter;
Welcome to the weird world of computer modelling and statistics without any science behind it.
>>>>>>>>>>>>
I was about to make a rude and sarcastic remark about being able to build a computer simulation that would produce a global warming result no matter what the data. Then I thought…. Oh Mann, better not, someone might take me seriously and actually do it.

AndyG55

“There will still be microclimate–chaotic, dynamic, inscrutable.”
Yep.. Its called the Urban Heat Effect.. and it DOES affect urban thermometer readings….. A LOT,
but it is basically discounted by the likes of BEST and GISS, neither of whom has even BOTHERED to go out in the field to check the historical site changes.
Yet when someone does actually do some real research, gees, don’t the warmists get uppity !!!

Pat Frank

faustusnotes wrote, “You have to analyze that using station-specific rural/urban classifications, which was not done in the original paper and is not done in the post you link to. And you need to do it with an objective classification of rural/urban that is independent of the data series – something that neither Goodridge nor you have done.
The plain statements in the paper show that you’re wrong on both counts. Let’s note JG’s comment page 3 again: “In general the classification of the temperature records as urban or rural is fairly close to reality as the writer knows it from viewing most of the sites. (my bold)”
In the context of the paper, Jim Goodridge has an a priori knowledge of site quality, from having inspected the various stations as part of his job as CA State Climatologist. He then collected temperature records, and assigned to “rural” those he, “arbitrarily selected as those with a slope (B) of the regression analysis less than 0.0125.
We’re doing science here. The selection procedure amounts to a hypothesis that rural stations have a smaller slope. Choose the stations that have a smaller slope. Check whether those stations so-chosen are, in fact, rural. What does JG find? That, “the classification of the temperature records as urban or rural is fairly close to reality” based upon his direct knowledge of the stations.
What does “fairly close to reality” mean? Quantitatively, we don’t know. If we trust JG’s professional acuity and honesty, we can surmise the he means a correlation — between low slope and rural standing — strong enough to validate the subsequent analysis and conclusions.
JG then compared the urban/rural categories against SST, which are not subject to urban heating or artifact-producing heat sources, and which strongly influence land surface temperatures. He found a good correlation between SSTs and the rural category stations, but not between the SSTs and urban category stations. That amounts to an independent confirmation of his categorical distinction.
At this point, JG’s analysis passes the test for a valid study. Your objections are mooted.
You may wish to withhold granting JG the professional acuity and honesty to apply his a priori station knowledge validly, when he decided his slope-determined categorical choices. But if you do that, then you’re required to provide objective evidence demonstrating what would be an explicit charge of dishonesty and/or incompetence. You’ll have to give factual evidence of false science on JG’s part. Failing that, your dismissals have no standing.

Great thread that seems to have attracted some new eyes. Good to see them here. It really increases the value of WUWT.

BioBob

This paper is interesting and certainly should act as a cautionary. But it would seem not.
The fact that the nit-pickers are dropping discussion-nits all over this effort to quantify simple historical sampling error / bias tells you all we need to know about the current state of the data.
The nit-pickers are getting soaked by their tempests in teapots. But I can not blame them entirely, since the data sucks and nobody seems to be bothered by its lack. Good replicate random sampling data would have flattened these idiotic objections by the nit-pickers.
Until an adequate number of randomly selected sites with replicated instrumentation weather stations are created, we will NEVER be able to determine statistically accurate temperature and other environmental values with known error values, sources of error, and variance.
Adequate data should never need the “adjustments” that further taint the vast majority of current data sets with N = 1. Since there would be enough replicates to simply discard tainted samples.
Garbage In equals Garbage Out.

Maus

BioBob: “Adequate data should never need the “adjustments” that further taint the vast majority of current data sets with N = 1. ”
Amen. It’s a further interest that adjustments are provided, and considered, crucial in terms of fully working diffusion and heat equations for temperatures measured from wooden buckets on deck. (SST) But that minor considerations such as incinerators, asphalt, heat pumps and other unquestionable macroscopic features are considered happenstance nonsense that do not alter the record in any manner at all. (Leroy, Goodrich, Watts) It’s not simply GIGO, but self-refuting GIGO.

faustusnotes

Anthony, I’m not James Annan and my identity and job is unimportant to my contribution to this thread. I didn’t put my blog link in my header because I didn’t want a flurry of attention from this site, but you are right that I am the latter blogger. Also, incidentally, I’m not “worked up” nor am I a “hater.” I’m just raising specific and quite simple points about the basic research methodology.
I can’t refer to specific charts because you don’t label them with Figure numbers, but I think you can infer which chart I’m talking about – it’s the one you just put up in comments.
Pat Frank, Goodridge’s statement about his having checked the sites is full of qualifiers – “fairly closely” “most of the sites.” This is not an objective criterion. Classification requires valid and objective criteria, not just some guy’s opinion – even the state climatologist. I don’t know if there is a gold standard for a well-sited weather station, but I strongly suspect it is not “Jim Goodridge said so,” and I know that most Census bureaus have an objective standard for identifying whether a place is rural or urban – the British ONS has 7 categories, for example, and I’m sure that the US has categories too. Would you object to those categories being given more weight in the definition of what is “rural” than the opinion of a single state climatologist?
I don’t need to give factual evidence of false science on Goodridge’s part because I’m not accusing him of that. I’m accusing him of using a poor criterion for identifying urban vs. rural weather stations, which presupposes the conclusion of the paper and thus does not answer the question he is claiming to answer.
I’m still waiting for someone to explain the mechanism by which the pictured station would give a warming trend if there is no AGW…
REPLY: Ah, OK. There’s a station list and data at the end of the paper, it is pretty easy to check against which ones are rural/urban using a number of criteria. NOAA’s MMS database, NASA GISS database, etc. Even a Google Earth inspection can help. The question is: would you trust a state climatologist with firsthand knowledge of the stations to make the correct rural/urban distinctions a year before “global warming” was launched into the public consciousness by James Hansen? I would.
On that last note: temporal inhomogeneity. That will be the subject of an upcoming post. It is easy to understand when you’ve looked at as many weather stations as Jim and I have. Stay tuned. – Anthony

JJ

The urban/rural classification method employed in this paper does not support the paper’s claims. This criticism that the analysis is tautological is correct. Sites with low trend are compared to sites with high trend, and the sites with high trend are found to have higher trend than the sites with low trend. This is not a result, it is a recapitulation of the methods.
It is similar in nature to Hansen’s most recent paper, in which he cherry picks a base period with low mean and low variance, compares it to a different period with higher mean and higher variance, and “finds” that the distribution of the latter is higher and wider than the former, and further “finds” that more of the data from the higher, wider distribution fall above an “extreme” criterion than do the values from the lower, narrower distribution from which that “extreme” criterion was derived.
Pat is correct when he says “The selection procedure amounts to a hypothesis that rural stations have a smaller slope.” but that wasn’t how it was analsyed, wasn’t how it was presented, and it certainly isn’t how it is being interpreted here.
It would be interesting to revisit Goodridge’s data, and check to see what differences in siting exist between the low and high trend groups. I would drop the “urban/rural” bit though. It introduces confounding that can hide real effects. Sure, there are urban heat islands. There are also rural heat islands, and rural thermometers are often found in them. Apply comprehensive site quality criteria, and see if Goodridge’s subjective classification that works for “most” sites can be verified quantitatively.

Philip Mulholland

See also the reference to Goodridge 1985 Urban and rural differences in long range temperature trends in California recorded in this paper by G. Kukla, J. Gavin, & T. R. Karl
Urban Warming Journal of Climate and Applied Meteorology, Vol. 25, No. 9 (September 1986) pp. 1265-1270.

AndyG55

“it is pretty easy to check against which ones are rural/urban using a number of cirteria. NOAA’s MMS database, NASA GISS database, etc.”
I don’t know that i would trust either of those sources overly much. maybe they would help a bit, but let me see a written history and analysis of all changes close to, and in the surrounding area.