Climate Audit

Anthony Watts

For this thread continuation, here is the link at NCDC that Kristen Byrnes found that gives access to PDF files for B91 observer forms with original COOP data as written by the observer.

http://www7.ncdc.noaa.gov/IPS/getcoopstates.html

Links that appear with the PDF’s at NCDC are transient, generated as “orders” so be sure to save and PDF file you download, and then upload it to the appropriate station at

http://gallery.surfacestations.org/main.php

by navigating to the station first then uploading the file(s).

Anthony Watts

And now let’s do a spot check of our two favorite station pairs, Orland and Marysville:

Orland July 2007 31 days of data
http://gallery.surfacestations.org/main.php?g2_view=core.DownloadItem&g2_itemId=28549

Marysville July 2007 14 days of data
http://gallery.surfacestations.org/main.php?g2_view=core.DownloadItem&g2_itemId=28542

The difference? The guy at Orland I talked to takes his job as observer seriously, as do many amateur observers.

We may need to create a new quality classification for stations based on how much missing data there is on a regular basis.

Chris Harrison

With my knowing the schedule there you can bet that every weekend is missing in every record, so best case for Chico is 8/31*100 = 25.8% missing data.

From my days working on control systems for Gas distribution I know that power consumption is much higher on work days than at the weekend. If weekends are being interpolated from week days we might have yet another slight warming bias were the stronger week day UHI effect is polluting the weekend data.

I wonder if a comparison of weekend/holiday vs. weekday energy usage against weekend/holiday vs. weekday temperatures might be an interesting way to quantify UHI.

JerryB

FWIW, with max/min equipment, a two day gap followed by an observation would
make that observation a 72 hour max/min observation, so it would be a three day
problem, not just a two day gap.

JerryB

Re #9,

IanH,

Wrong thread for that comment. You might post it in the TOBs thread.

Murray Duffin

I think looking only at the 1900 to 2006 trend in the various differences plots obscures something stranger going on. It seems to me that if the methods of adjustment were consistently different, then the the trend should be consistent, but that is not what we see.
CRN5-CRN1 TOBS Trend 1900 to 1940+ down, 1940+ to 1980 up, then flat.
CRN5-CRN1 TOBS E of 95W practically no trend 1900-1970+, then steeply upwards. Big hole in difference 1990+-
CRN5-CRN1,2 TOBS E of 95 W Same trends, but not as pronounced, and hole in 1990+-, as might be expected.
CRN5-CRN1 TOBS W of 95W 1900-late 1930s down, late 1930s-late 1960s up, then flat.
CRN5-CRN1,2 TOBS W of 95W 1900-1950+ down, 1950+ to 1970+ up, then flat.
US48 NASA-CRN1,2 1900-1950+ flat, 1950+ – 1979 down, then up sharply.
US48 MOAA – NASA 1900-late 1930s up, late 1930s to 1979 down, then up sharply.
There is some tendency for the difference trend to follow the temperature trend. Ca 1980 to present is flat in 3 cases, up sharply in 3 cases. Seems very strange to me.
1980 to present flats are in total USA or W of 95W. 1980 to present upwards are E of 95W or NASA-CRN1,2, or NOAA-NASA. What happened ca 1980 that tilted NASA up vs CRN1,2, and tilted NOAA up vs NASA??? Murray

steven mosher

RE 10.

Don’t know if you caught that Jerry but on my first try I found a B91 that showed the
temp at Observation time at 8AM as being lower than the Tmin. Kinda cool. Anyway, the observer
struck through the Tmin and put the Observed temp in the box. If you like I can go find it
again.

Phil

#4 Anthony Watts says on September 30th, 2007 at 10:18 am:

(snip)…We may need to create a new quality classification for stations based on how much missing data there is on a regular basis.

There is some data quality historical info at the following urls:

http://cdiac.esd.ornl.gov/ftp/ndp019r3/qa94mean.asc
http://cdiac.esd.ornl.gov/ftp/ndp019r3/qa94mxmn.asc

It may not have been updated since 1994.

steven mosher

A while back I mentioned some issues with the USHCN daily records. Files I had downloaded before
are Now are missing huge chucks of data and/or the data looks FUBAR. I have not been back since that
posting.

steven mosher

Didnt Hansen promise a simplified version in a couple weeks?

steven mosher

RE 21. So you are reporting out a difference in trend of About .01C/year over the
period of 1960 to 2005? Am I Understanding that correctly Kenneth?

I think I am starting to get the grasp of your approach.

M. Simon

Chris Harrison says:
September 30th, 2007 at 11:02 am,

Weekend decline in electrical power consumption is also a fact.

David Smith

I notice that Monticello, Mississippi also lost the 1905-1953 portion of its time series. This happened at some point in the last four weeks.

Also, some of its 1960s and 1970s data appears to be changed, in what appears to be slight cooling of that period.

It’s like we’re witnessing history in the making.

John V.

Kristen Byrnes said…

I started with your and Steve’s favorite and the first one on John V’s CRN 5 list: Univ. of Tucson #1. John V’s data goes up to March 2006 so that’s where I started (it was off by 3 tenths). Then I went back a year to March 2005, John’s spreadsheet was blank for that month but the report is there, no days missing (same with Wickenburg, AZ in Jan 1999). Then I went back another year to March 2004, it was off by 5 tenths.

This really got me interested, so I started looking at the data myself. The first thing I did was build a spreadsheet for calculating the average temperature. It might be useful to others so here’s a link:

http://www.opentemp.org/_tools/ushcn/USHCNObserverRecord.xls

Note:
The spreadsheet calculates the average with different levels of rounding, because I thought the differences found by Kristen may have been caused by rounding. (It turns out the rounding wasn’t necessary).

Next I started looking at the observer records. On my first attempt I used USHCN station 028817 and nothing was matching. I then went back to Anthony Watts’ spreadsheet of stations and realized that the Tucson UofA site is 028815. Working with 028815 I got much better results. The list below shows the GHCN Unadjusted, USHCN Raw (Areal), and Observer Record temperatures:

Mar2006: 15.6 / 15.6 / 15.6
Feb2006: 15.2 / 15.2 / 15.2
Mar2005: ???? / ???? / 15.2
Mar2004: 19.9 / 19.9 / 19.9

I stopped checking after this.
For every month with USHCN values, the GHCN value is exactly the same.
For some reason March 2005 does not have a value in either USHCN or GHCN.

From this limited set of data, it looks like GHCN Unadjusted is the same as USHCN Raw (Areal). I can not explain why March 2005 is missing from USHCN and GHCN.

Kristen, please double-check these results as they are different than yours.
The missing stations from USHCN are a mystery but I am still comfortable stating that GHCN Unadjusted and USHCN Raw (Areal) match.

The observer records and my working spreadsheets are available here:
http://www.opentemp.org/_data/USHCNObserverRecords.zip

John V.

steven mosher #25:

Whew! that’s more reasonable so, .0056C per year over 45 years.
JohnV hows that line up with your expectation model?

My simple expectation model (posts #380 and #540 in the original thread) does not give a slope . It gives a total expected temperature change if a CRN5 station started as a CRN1 station. It’s a worst-case bias *if* my model assumptions are correct.

The range I came up with was 0.32 to 0.49 degC. The observed difference in total warming between CRN5 and CRN12 (rural) in my analysis was ~0.35C. Kenneth’s value of 0.0056C/year for 45 years works out to 0.25C total (the remaining 0.10C is probably in the other 50+ years).

JerryB

Re #28,

“… I am still comfortable stating that GHCN Unadjusted and USHCN Raw (Areal) match.”

FWIW: on converting USHCN “raw” temps from F to C, and rounding to
tenths, and comparing the monthly numbers to GHCN v2.mean “raw” numbers,
omitting the partial year data for 2006, I found exact matches except for
one month at one station: January 2003, at Goldsboro. 3.6 C vs 4.3 C.

John V.

#31 Chris Harrison:
My plan is to nest the triangles into a hierarchical structure. At the largest level (level 0) there are the 20 triangles of an icosahedron. Each of those 20 triangles is sub-divided into 4 triangles to make level 1 with 80 triangles. And so on. I will probably go as deep as level 6 with 81920 triangles. Each will have an area of 6226 square kilometers.

To calculate an average temperature for a given month, I will:

1. Calculate the average temperature for all level 6 cells with reporting stations
2. Combine level 6 cells to get averages for level 5 cells (each contains four level 6 cells)
3. Combine level 5 cells to get averages for level 4 cells (each contains four level 5 cells)
…
7. Combine level 1 cells to get averages for level 0 cells (each contains four level 1 cells)
8. At this stage, every level 6 cell has a direct temp or an inherited temp (from higher levels)
9. Calculate the overall (or regional) average from the level 6 direct or inherited temperatures

It’s early — I hope that makes sense.

Kenneth Fritsch

Re: #34

John V you are entering land that needs to be explored but where I dare not go with my limited abilities with Excel. I hope you do a study whereby you determine what is gained by using the more complicated algorithms.

John V.

#36 Kenneth Fritsch:
Although the algorithm seems complex, it can be easily automated and that’s the key for me. I don’t want to spend days partitioning stations from around the world (that is the eventual goal) into grid cells. The simple rectangular grids are ok, but have two disadvantages:
– irregular size and/or shape
– all aligned in the same direction

A high-resolution triangular grid overcomes station-clustering problems since the cells are small. The hierarchical structure overcomes problems with sparse coverage by averaging (but only where necessary).

The method is similar to your state-by-state geographic weighting, but every “state” is a triangle.

Kenneth Fritsch

Re: #34

John V, if 20 triangles makes an icosahedron what do we call a figure made up with 81920 triangles. That graphic is very appropriate to the point you are making. A picture worth 81920 triangles is worth as many words — or something to that effect.

Stan Palmer

re 38

A high-resolution triangular grid overcomes station-clustering problems since the cells are small. The hierarchical structure overcomes problems with sparse coverage by averaging (but only where necessary).

However, in effect, it would still give more weigting to stations in sparse areas as opposed to those in more dense areas. So the relatively fewer stations in the US west would be given more weight than the stations in the more densely served US south-east.

Is this an issue that needs to be addressed.

steven mosher

If you had one station in the us where would you put it?

How well would it measure the “average US temp”?
How well would it measure the trend?

If you added a second station, where would you add it? 3rd? 4th, 5th?

That’s if you PLanned the network…

Consider this: you dont want stations close to each other. You want to MAXIMIZE the number stations,
while simulataneously Normalizing the distince beween stations and the closest neighbors.

Total area coverage only matters if you are concerned about the Absolute average. I’ll play with this
thought and suggest something

Anthony Watts

Re41 The GISTEMP results do not show such wild temperature swings in the early years:

Again I’d point out that standard thermometer exposures/shelters weren’t well implemented until around 1900. Yes GISS doesn’t show it, but remember it has been heavily filtered and homogenized.

A microsite bias such as a thermometer going from hanging under a tree to a shelter is not something that can be adequately adjusted for, IMO without some historical records and some application of empircal analysis to the measurement environment change. Much of those changes don’t even have a record or the occurance, but you can often see the effect when the data “settles down” and the swings are minimized.

Thus I think it is prudent to use data from 1900 forward since the hit/miss shelter issue of the 1800’s cannot be quantified.

Rick Ballard

“If you had one station in the us where would you put it?”

Midway between Omaha and Kansas City – say Auburn, NE, swing a 600KM radius and you would cover an area with minimal UHI “effect”. ‘Course, the ‘Blue Norther’ effect might make for cooler winters but irrigation and other land use change effects would be minimal.

I specialize in answers to hypotheticals.

John V.

Steve Mc:
Rather than use triangles for OpenTemp, would you suggest using hexagons? I considered that route but rejected it because:

a. To completely cover a sphere you need to use some pentagons
b. Smaller hexagons are not contained in parent large hexagons

Triangles also have the advantage of simple geometry for determining which stations lie inside each cell.

(I realize that you were suggesting hexagons for ideal station placement, and I agree that hexagons are the best option for that).

Rick Ballard:
“I specialize in answers to hypotheticals.” — LOL

steven mosher

RE 47. What SteveMc said.

The difference is this. When you are planning sampling to set up sites in a regular way.

Our problem is that we have an IRREGULAR network.

The question is how do you “regularize it” You dont regularize it by picking
two stations 10 km apart. You dont regularize it by having ten thermometers in
one grid and 1 in another.

Kristen Byrnes

John V. #28 and 35,

I retraced my steps and was able to find the problem.

42572274002 (NASA GISS #) is the number you have on the first line of your spreadsheet. I put that into the NASA GISS identifier and it gave me Univ. of Arizona. I then went to the original observers report for Univ. of Arizona #1. But that is not the correct station name. The correct name for the station on your CRN = 5 list is Tucson WFO, not Tucson Univ. of Arizona #1. So what have we learned here? NASA GISS can’t even get the station names right. (I did compare the correct station to the monthly value on your spreadsheet and they are definitely within the possibility of rounding inaccuracy)

Second issue is rounding inaccuracies vs. changed values due to QC corrections (or probably a combination of both). The only way we are going to be able to find this out for sure is to compare GHCN daily values to the reported values on the original station reports. I do not have the software to display the station dailies from GHCN, is there anyone who can put those on an excel spreadsheet and post them somewhere?

Third issue is why are GHCN months missing when there are observers reports filed with no missing days. The USHCN QC says that a station month will be thrown out if more than 9 days are missing and/or flagged as suspicious or erroneous.
So let’s look at Tucson WFO aka GISS # 42572274002 and NCDC # 028817 (this is confirmed at MMS as being the one in the parking lot of the physics building)
Missing Months are:
11-05
06-05
03-05
None of these months are missing any days in the original observers reports. Therefore there are two possibilities:
1) Someone at NCDC goofed up and forgot to add the station for those months, or;
2) 9 or more of the daily values were flagged as suspicious or erroneous and the months were thrown out.

Go back farther to 1997 where there are no values for the same station in GHCN:
12-97
11-97
04-97
In these months the observer’s reports are not even listed at NCDC so someone somewhere might have forgotten to turn them in or lost them.

Now let’s look at Chico Univ. Farm, CA (GISS # 42572231006 also NCDC # 41715) where the GHCN values are blank for 03-06 and 11-99. These months are missing at least 9 daily values so they obviously have been excluded in accordance with the QC rules.

Last issue and not really just for John V (something I noticed while looking at the Univ. of AZ sites) but also for Anthony and Steve. A while back there was a big issue about the University of Arizona Station (Tucson WFO) that is in the parking lot of the campus there, mainly between Anthony and Steve Mc on one side and Nate Johnson (who writes the ATMOZ blog and is also a student there) and a couple of people at the university (I think professors) on the other side.
The two stations at Univ. of AZ are about 2 miles apart. Let’s start with March 2006. The MAXIMUM temperatures between the two stations are usually different with the one in the parking lot usually being cooler BY AS MUCH AS 11 DEGREES! But the MINIMUM temperatures for the same month are usually warmer at the parking lot station.

Now lets go to July 2006 where the MAXIMUM temperatures also are different almost every single day except this time it’s the other way around. In July the MAXIMUM temperatures at the station in the parking lot are warmer, usually by a few degrees but on one day by 8 degrees. MINIMUM temperatures are also usually warmer in the parking lot, on one day by 12 degrees. However, there were 5 days that month where the MINIMUM temperatures were cooler in the parking lot by 7, 8, 4, 7 and 1 degree.

The idea for John V is that there are many possible outliers here.

steven mosher

RE 54. Kristen I think One cool thing you could do for the whole community
is to draw the Data Flow Diagram for Climate data.

A DATA FLOW DIAGRAM ( you can google this) will show you two things.

1. Data sources.
2. Data processes.

So, draw the data flow diagram. What are the sources? Then, what processes get appllied to
that data. and what is the OUTPUT of that process.

I think this task will help you going forward in your education since you have a nose
for details. Every team needs a person who digs this stuff out and documents it.

Here is a link on DFDs: http://en.wikipedia.org/wiki/Data_flow_diagram

A free resource is here: http://en.wikipedia.org/wiki/Dia_%28software%29

You should do this! you have a good grasp of all the sources. It’s time
to put that knowledge into a form that OTHERS can benifit from.

PS. Have you talked to Dr Curry about her offer? I thought it most kind.

Anthony Watts

RE56, Steve spot on, I’ve been thinking we need the same thing. A flowchart does not exist to follow the data provenance and methodology.

This would be a worthwhile project, and I volunteer to help.

steven mosher

RE 54. Kristen you are being a bit unfair to JohnV

The sheet that we are are working from is the Surfacestations Excell sheet.

On that sheet we have the following for the first class 5.

1. USHCN ID: 28815.
2: Lat: 32.23 Lon: -10.95.
3. Name U Az.
4. GHCN: 72274002.

To Access the GHCNv2 File ( which GISS also uses ) We appended the US country code ‘425’ to the
GHCN number.

This is not a GISS ISSUE or a JOHNV issue.

John V.

#55, #59:
I haven’t checked steven mosher’s results but they seem consistent with mine (see my post #376 in the first part of this thread). I found a difference of 0.35C between CRN5 and CRN12R and he found a difference of 0.14C between CRN12345 and CRN1234.

JerryB

Regarding stations 028815 and 028817 and their names: the name of 028815 changed
in 1998. That change was not reflected in the USHCN station inventory, or in
the GHCN V2 station inventory. GISS presumably got the names from the GHCN
station inventory.

028815 TUCSON UNIV OF ARIZONA 19480701 19550701 32 14 00 -110 57 00 2421
028815 TUCSON UNIV OF ARIZONA 19550701 19610501 32 14 00 -110 57 00 2411
028815 TUCSON UNIV OF ARIZONA 19610501 19680901 32 15 00 -110 57 00 2431
028815 TUCSON UNIV OF ARIZONA 19680901 19860223 32 15 00 -110 57 00 2444
028815 TUCSON UNIV OF ARIZONA 19860223 19921007 32 15 00 -110 57 00 2444
028815 TUCSON UNIV OF ARIZONA 19921007 19970528 32 14 00 -110 57 00 2435
028815 TUCSON UNIV OF ARIZONA 19970528 19980101 32 13 47 -110 57 17 2435
028815 TUCSON NWSO __________ 19980101 20010802 32 13 47 -110 57 14 2478
028815 TUCSON NWSO __________ 20010802 20021218 32 13 45 -110 57 13 2435
028815 TUCSON WFO ___________ 20021218 99991231 32 13 45 -110 57 13 2435
028817 TUCSON U OF ARIZ #1 __ 19820501 19860709 32 16 00 -111 00 00 2300
028817 TUCSON U OF ARIZ #1 __ 19860709 19960625 32 16 00 -111 00 00 2300
028817 TUCSON U OF ARIZ #1 __ 19960625 20010802 32 15 29 -111 00 19 2315
028817 TUSCON U OF A #1 _____ 20010802 99991231 32 15 29 -111 00 19 2315

Kristen Byrnes

Steven Mosher # 56,

A data flow diagram for this junk? LOL NO! It would end up looking like a bowl of spaghetti LOL! Do you have any idea how messed up this stuff is? It’s as bad as the stations. I’ll ask someone to try to help but no promises.

As for Dr. Curry (yes I thought that was very nice of her) I told her that I’ll start to consider colleges at the end of next year. But it is a possibility because my step-grandmother has her masters from there and if I get to live in her very nice (and big) house with her 5 FAT (I’m talking enormous) cats that is a possible maybe. Then there is N. Carolina, UC Santa Barbara, Bates, Bowdoin, MIT, and about 50 others that asked me to talk to them too (I’m still holding out for something in Greece LOL).

Steven Mosher # 60

That was nothing bad about John V. I used his data and the only way I could identify his data was the number he has on the top of each column. I c-&-p’d it from his spreadsheet into the GISS locator just to find the station name. Then I went to the NCDC page where the original reports are. GISS has the wrong name on that station.

Anthony Watts # 61,

Correct! The correct name for 28815 (the one in the parking lot at the college) is Tucson WFO (GISS has it as University of Arizona).
The correct name for 28817 is University of Arizona #1.
I’m pretty sure that is correct because I have the original station reports (for the months I talked about in 54 above) printed out and right in front of me.

Kristen Byrnes

Correction to 54, 3rd paragraph, it should be 28815 not 28817

Anthony Watts

RE64, 67 University of Arizona #1 USHCN #28817 is across town, at the Agricultural experiment farm.

From the MMS “location” tab:
[2001-08-02] 9999-12-31 32.258060 (32°15’29″N) -111.005280 (111°00’19″W) GROUND: 2315 FEET — PIMA 07 – SOUTHEAST MOUNTAIN (+7)
Location Description: U OF A EXP FARM WITHIN AND 5 MILES NW OF PO AT TUCSON AZ

#28815 is the one on campus in the parking lot. The reason it is called Tucson WFO is the fact that its just a block away. The NWS Tucson office is located on campus at the University of Arizona. They jointly operate the 28815 station.

There has been no renaming of 28815 to 28817 in 1998, nor relocation of either station.

JerryB

The renaming in 1998 was of 028815 from “Tucson Univ of Arizona” to “Tucson NWSO”.
It was later renamed to “Tucson WFO”.

steven mosher

re 67. Kristen

I will always remain a booster and promoter of the begineers. Primarily because smarter people than I
blessed me with the opportunity to chat with them, take their consul, and form my own sense of things.

Let me recap. I asked you to look at ANTHONYS spread sheet and look at the Class1 instruments.
11 of 17 were ASOS. YOU ASKED ME WHAT I MEANT? Then you asked me to do the work for you.

You took MORE TIME in framing a response to me, than the orginal task would take.

A data flow diagram OF THIS JUNK would be hugely valuable. If you dont do it, I’ll merely
question your dedication. I have fixed many junky systems ( YOU USE THEM TODAY!) by
merely focusing on the DFD.

Throwing stones is easy kid. I’m a david from years long gone. Slain many goliaths.
That is why I like your spirit!. Still I need to paint a picture of what is beyond
this. ( Sorry, its the dad/prof in me)

Pick up a stone: What comes to mind? build a house? or slay a giant?

Just a question.

DO the DFD.

Sam Urbinto

I want a set of steak knives.

JerryB

USHCN station data somehow get from the NWS to USHCN people at NCDC.
They do their thing and forward “raw”, and adjusted data to GHCN. They
also publish their results in the USHCN directory on the NCDC FTP server.
Sometimes they do both, sometimes one or the other.

Some of what USHCN data currently reside on the NCDC FTP server also reside
on the CDIAC FTP server (minus the 2006 data), and a portion of a
previous version of USHCN data which had not been posted on the NCDC FTP
server has been forwarded to GHCN.

GISS recently began using the CDIAC set of USHCN data through 2005, the
GHCN “raw” USHCN data for 2006, and the GISS “patch” modifies the
pre-2006 data making it incompatible with USHCN adjustments.

I trust that “flow” is sufficiently obscure to fit a “climate science”
paradigm.

Mike

#74 Steven Mosher
She slayed Al Gore, isn’t that giant enough?

Steve McIntyre

C’mon – please stop nattering at one another. I’m going to do some deleting.

steven mosher

RE 77.

Kenneth If you persist in screwing up my Tom Sawyer Whitewash the picket fence moments
I will cut your achilles tendon and you will never golf again

Kenneth Fritsch

Steven, your motives were obvious and well intended. My big mouth gets me trouble with the parents of my grandkids. That achilles tendon thing has been become a Greek myth: witness my tearing one apart as middle-ager playing basketball and it mending in a matter of months. Now they can mend them even faster. I think breaking fingers would be effective in the age of the internet.

steven mosher

RE 87.

Not to worry. Sometimes I’m Thinking… Hey Kristin, stop bugging me with that important stuff
Me, Kenneth and JohnV are discussin METHODOLOGY. ( she won’t read this right?)
Anyway, I sent a bunch of out put from OpenTemp to Anthony, clayton, JohnV and St.mac.

But I am still puzzling on your method. Not questioning it, just trying to place it and
understand it. I was talking to JohnV the other day and mentioned how refreshing I thought
your approach was.. started to rip the cobwebs away beneath my grey hair. So, If its not
too much of an imposition can you walk me through it kindergarten style?

I just figured out what the heck I’m doing and why, so I’ll go next. JohnV is off tesselating
globes.

SteveSadlov

RE: #89 – Earlier someone mentioned would be nice to have an intern (or army of them). I am thinking, similarly, what a great little project, possibly leading to a publication, by some young, up and coming masters or PhD candidate. There have got to be a few such folks who read and post here.

steven mosher

Thanks JohnV I’ll download in a couple days.

1. When comparing to GISS do you account for the different definition that GISSuse for the year?
2. GISS Moving average is a 5 year centered plot. Do you have the actual figure for the 1950-1981
Average? And I thought you did a Trailing MA?

John V.

#92 steven mosher:
1. I believe that GISS uses calendar years for the averages that I’m using. I seem to recall checking that last month but I can’t find the spreadsheet I used:
http://data.giss.nasa.gov/gistemp/graphs/
http://data.giss.nasa.gov/gistemp/graphs/Fig.D.txt

2. I calculate the 5yr plots using a 5yr centred average for both my results and GISTEMP (I wrote “5yr trend” in my post by mistake). The 20yr trends are trailing trends.

Kenneth Fritsch

Using the USCHN Urban data set, I will attempt to mirror the calculations being made using the derived John V data set. Using the 1960-2005 time period, I compared the trends in degrees F between all the stations contained in the CRN1 through CRN5 categories with the USCHN stations remaining to be categorized. The CRN1 through CRN5 stations in this calculation numbered 395, while those in the remainder group numbered 826. I list the trends below in unadjusted form and again after using my state by state geographical adjustment.

Unadjusted Trends:
CRN1 through CRN5 = 0.0431
Remainder = 0.0378

Geographically Adjusted Trends:
CRN1 through CRN5 = 0.0440
Remainder = 0.0466

The adjusted comparison indicates that what has been analyzed is not significantly different from that which remains to be analyzed at this point, at least, in terms of temperature trends for the period 1960-2005.

SteveSadlov

RE: #95 – It is quite interesting that both the 1910s and 1950s adjustments have the effect of “cooling” the preceding period.

John V.

#97 steven mosher:
I concur that GISTEMP is not a gold standard. It is however the current standard and should be given some credit. The algorithms in GISTEMP manage to take a whole pile of crappy stations and spit out a result that closely matches the best stations (CRN12R) — for the USA lower 48 of course.

The CRN12R results from OpenTemp have two outstanding issues: lack of stations and suboptimal geographic weighting. Once these are resolved I will be comfortable calling CRN12R the standard for the USA lower 48.

I agree that it’s time to leave GISTEMP behind, and that we should be looking at quantifying micro-site issues.

My approach for determining the effect of station biases is a little different than what’s been done so far:

1. We need to distinguish UHI from micro-site. The best way to do this is to run Rural, Airport, Small, and Urban sets for each of the categories (CRN1 and CRN2 must be grouped because of the very small numbers of CRN1 stations):
– CRN12R, CRN12A, CRN12S, CRN12U
– CRN3R, CRN3A, CRN3S, CRN3U
– CRN4R, CRN4A, CRN4S, CRN4U
– CRN5R, CRN5A, CRN5S, CRN5U
Compare the trends in each of the categories to the rural trend for the same category to get the UHI effect.

2. The results above could potentially have some major problems with geographic distribution. Do some additional runs with more stations to validate results:
– CRN123R, CRN123A, CRN123S, CRN123U (should be midway between CRN12 and CRN3 results)
– CRN45R, CRN45A, CRN45S, CRN45U (should be between CRN4 and CRN5 but closer to CRN4)

3. Compare each run to its counterpart CRN12 run (eg. CRN5R to CRN12R) to get the effect of station bias.

4. Try to determine the error bars due to geographic distribution. I suggest running many random sets of CRN123R with ~20 stations in each set (CRN12R is too small for this). A little Monte Carlo magic and we’ve got error bars.

For any set of results we need appropriate metrics for comparison. Kenneth has been using 1960 to 2005. SteveSadlov likes 1930 to 2005. I prefer splitting the trend into key periods (1915-1935, 1935-1975, 1975-2005).

Thoughts?

John V.

#99 Kristen Byrnes:
I’m not sure what I should do with CRN5 data from early in the century.
What did you have in mind?

Anthony Watts

RE 99,102, Kristen/JohnV what I hope to do is to get NCDC to release B44 forms, which contain site sketches and descriptions. I have all of California’s and they are extremely useful in determining past micro-site issues. If we can get those, perhaps redacted to remove observer names/addresses then we can look at creating a timeline of CRN1-5 ratings

Kristen Byrnes

John V # 102,

The station in Portland was on roofs or in windows from Dec. 1874 to Dec. 1940. That’s 66 years of data that Anthony usually makes CRN-5. If you program your opentemp to adjust the geographic distribution every month in order to account for missing data (there’s a lot of missing data in 2006 for example) then you will not have geographic distribution 100 years ago based on geographic distribution today. The same with CRN rating. Your CRN rating is flawed going back in time because the micro site biases changed. Portland is CRN 2 today but was CRN 5 back in 1940. The same with many other stations.

Anthony 104,

I can get the sketches from NCDC (65 cents ea) for the temp stations already surveyed and accepted for opentemp but if you can get them for free, that would be better.

John V.

#105 Kenneth Fritsch:

Evidently the goal there is to determine how well his adjustments compare with GISS.

That seems to be a common misconception. I have been comparing to GISTEMP for a couple of reasons:
1. It’s the only “accepted” temperature trend for the USA lower 48 that I could find;
2. There has been interest here on problems with the GISTEMP algorithm

As I said above, I am ready to move on from GISTEMP. We can evaluate the effect of micro-site and UHI independently using OpenTemp (or manual processing) alone.

steven mosher

Hi JohnV.

I’ve really enjoyed using OpenTemp. I wish had this tool before. So, Glad to see that USHCN stuff
has been added. When I first started this stuff I never looked at GISS since GISS gets_ushcn();

Anyhow, I proceed to your comments..

“I concur that GISTEMP is not a gold standard.
It is however the current standard and should be given some credit.”

NOPE. When Hansen and Gavin run ModelE ( there GCM) which global temperature dataset do they
use to “calibrate?” HADCRU. We went down this path with Gavin. We cricticized GISSTEMP and his
response was “we dont use it.” So, TIN, not gold. Credit is earned in my book. OpenTemp showed
that microsite bias is real. We already knew that. GISSTEMP could do the same thing. If somebody outside
NASA could get the heap to compile. Now, I’ve asked for the ABSOLUTE TEMPS fom gavin on several occasions
so that Smoothing operations ( trailing, centered, gaussian etc etc ) become more transparent. do you have this.
The reason I ask is that Hadcru is WRT 1960-91. So anytime one complains about comparing HADCRU
to GISS you get this rejoiner about difference reference periods… etc etc etc. EVEN WHEN they share data
the refuse to calculate anomalies to the same reference period.

“The algorithms in GISTEMP manage to take a whole pile of crappy stations
and spit out a result that closely matches the best stations (CRN12R) — for the USA lower 48 of course. ”

At first blush yes. But when we say “closely match” we must say so with authority. The issue is
comparing 53 sites to 1221 and concluding “they are they same” On one hand you have a big sample
on the other a small sample. One cant conclude they are the “same” with any confidence. On the other hand
one approach is to test whether Class5 sites suck. This is NOT a AGW issue. This is a NOAA data quality
issue. So, I compared ALL 1221 sites to the 1160 sites that have not been rated a class5. And
what did I find? The 1160 sites were .05C cooler for the past 120 years. I think USHCN1221 is not
carved in Stone. Did god ordain 1221 perfect sites in the US? Nope. You have a site survey. You have a
piece of temperature analysis code that is solid. You have an argument to drop those 60 sites from the
USHCN1221.

“The CRN12R results from OpenTemp have two outstanding issues:
lack of stations and suboptimal geographic weighting.
Once these are resolved I will be comfortable calling CRN12R the standard for the USA lower 48.”

That’s fine. But that’s not my central issue. My issue is the difference between non standard sites
and sites meeting guidelines. That’s a scientific issue that I think is neat. Its relationship
to AGW antagonizes a bunch of people ( this amuses me so I play with it)

“I agree that it’s time to leave GISTEMP behind, and that we should be looking at quantifying micro-site issues.

My approach for determining the effect of station biases is a little different than what’s been done so far:

1. We need to distinguish UHI from micro-site. The best way to do this is to run Rural, Airport, Small, and Urban sets for each of the categories (CRN1 and CRN2 must be grouped because of the very small numbers of CRN1 stations):
– CRN12R, CRN12A, CRN12S, CRN12U
– CRN3R, CRN3A, CRN3S, CRN3U
– CRN4R, CRN4A, CRN4S, CRN4U
– CRN5R, CRN5A, CRN5S, CRN5U
Compare the trends in each of the categories to the rural trend for the same category to get the UHI effect.”

You can go down this path. That is the COOLEST thing about OpenTemp.

Let me arm wave about UHI for a paragrah or 56. If you go ack through all the literature about UHI
you will see that it is attributed to several causes. I’ll mention a few.

1. CANYON Effects: The geometrical configuration of the physical structures and the material properties
of those structures Combine to focus or intentsify radiation through multipath reflections.
Think TWT, traveling wave tube. Think corner reflector. However, the Canyon effect also can
inhibit reradiation as the “skyview” is occluded or vinetted. Note, canyon effects can also
shade a site from insolation
2. Wind Shelter. When the wind blows chances are you get vertical mixing and cooling of the surface.
So, the issue in Cities is you have very high barrier to wind. The structures are ALL bluff structures
( have a look at UHI wind studies and basic areo) So the structure impact the flow at the ground in
dramatic ways.

3. Evapotranspiration. Wet ground is cooler than hot pavement. Especially at night. So, asphalt
concrete, stone, rock etc etc, all give up heat with a time constant that differs from soil.
( Hey dad why do the snakes come out on the road when the sun go down)

4. Artificial heat: building heat, car heat, human body heat, human activity.

those are the basic causes of UHI. It’s been measured.In situ. From satillite.
It’s a tenet of Enviromentalism. The number
of studies documenting it are astounding. I can tell you the tempterature of the soil 6 inches from
a parking lot in phoenix with no tree cover.

MICROSITE BIAS is a sub species of UHI. All the causes are the same. The question is, in my mind,
how it manefests itself at the small scale. Do we see it all the time, most of the time, some of the time, rarely?
And based on that supposition.. what’s the best method to find it. Maybe we do not find it and upset years
and years of climate science near the ground. Was that last clear? The standard accepted consensus
understanding ( gieger) is that these issues matter near the ground. Finding that they don’t would be a challenge to
one of the einsteins of climate science.

“2. The results above could potentially have some major problems with geographic distribution. Do some additional runs with more stations to validate results:”

I’m more an more convinced that chopping the data sets into smaller peices is a fundamental error.
1. With every cut you increase your probability of finding a spurious correlation.
2. The warming of microsite should have a Poisson distribution. Basically, as you pointed out the 5C
bias will only happen rarely. The temp signal is a continous signal, but the sites quantize that
signal into a max and min on a daily basis. As the wind blows, as the clouds come and go, as the rain
comes and goes, the impact of MICRO_UHI shows up like shot noise. Now, if you compare SITE A to SITE B
( see OKE studies and the CRN studies) it is possible to detect these excursions as bias rather than noise.

However, if you mash 30 days together and then 365 days together, the bias just looks like noise.
Simply. Class5s will tend to look noisier. We have 53 Class1&2 ( urban and rural) and 58 Class 5.
It’s a noise fest. And I’m not to sure slicing and dicing move the ball forward. But Hey, I’ll slice
and dice the data till the cows come home. The Urban/rural distiction is problematic. The Population
is referenced to 1980!. The night lights is reference to 1995. The extent of a night lights pixel is 2.7km
Anecdotally Hansen rates Orland Ca as Urban. It’s Not. Peterson, on the other hand rates mineral california as a
small city, not rural. Population: 140. and these are seasonl folks. Its a ghost town.

The point I guess Is that I’m not prepared to go down the Urban/rural slice. Now, the equipment slice
and the ASOS slice are more interesting.

– CRN123R,
CRN123A,
CRN123S,
CRN123U (should be midway between CRN12 and CRN3 results)
– CRN45R,
CRN45A, CRN45S, CRN45U (should be between CRN4 and CRN5 but closer to CRN4)

3. Compare each run to its counterpart CRN12 run (eg. CRN5R to CRN12R) to get the effect of station bias.”

Ok. I’ll look at this stuff. HEY, when I ran the study you suggested.

CRN12345 VERSUS CRN1234. I found a .15C difference . That is, CLASS5 are warming.

SO. engineering hats on. Hypothesis: class 5s are warmer than class1234. True or false using OpenTemp
as the tool?

“4. Try to determine the error bars due to geographic distribution. I suggest running many random sets of CRN123R with ~20 stations in each set (CRN12R is too small for this). A little Monte Carlo magic and we’ve got error bars.”

I’m gunna leave the monte carlo magic to Kenneth. However, I would lobby to lump the class3 in with the class1&2.

On the assumption that Microsite bias is bursty. At class3 neither one of our “models” would suggest that
the bias could be discriminated from noise. ( Purists will scream )

“For any set of results we need appropriate metrics for comparison. Kenneth has been using 1960 to 2005. SteveSadlov likes 1930 to 2005. I prefer splitting the trend into key periods (1915-1935, 1935-1975, 1975-2005).”

You know what? I was an proponent of this regime thing, but I’m getting nervous and jerky about it.
I saw tamino do it ” it’s only natural to see these periods” And I looked back at my own thought process.
And I had thought ” pick the linear period.” this decision, of course, destroys the statitics to follow.
Since you dont have a random sample you have a sample cherry picked for its apparent linearity. There are techiques for
identifying regimes and regime changes. I would go that route. The other route I have taken is the following:
Here is the data from 1880 to present! I take what I think is an intersting approach and throw the red meat in the octagon.

“Thoughts?”

At some point this becomes a zen moment for me. So I will look at your cuts at things and ponder.
THAT SAID, class 3 is the weird one. I have to check my data but when I ranked anomaly curves from low to high
I think I got 3,12,4,5. Understand? Had I got 12,3,4,5.. anyway I was thinking about weird way of looking
at the problem.. you have 4 series CRN12, CRN3, CRN4, CRN5. And the hypothesis is that CRN5>CRN4>CRN3>CRN12
….. and then the thought petered out.. But I thought It would odd if the order came out CRN12,CRN3,CRN4,CRN5
Anyhoo.

I’ll look at your slices…

PS. GISS Anomaly? Do you have te absolute for 1950-81? How did you adjust for the differnce in MA approaches

Anthony Watts

110 “On the assumption that Microsite bias is bursty. ” yes and no…it depends on the type of bias, cooling biases related to plant/tree growth are subtle and long period.

Heat sink type microbiases, such as concrete/brick masses near the observing tend to bias Tmin more than Tmax. Biases like nearby traffic indeed can be bursty.

A big case was made by two presenters at Pielke’s recent conference to do away with Tmin and only look at Tmax due to Tmin having so many possible microsite biases compared to Tmax. Perhaps running opentemp on Tmax only with CRN12345 – CRN1234 would be a good test.

Sam Urbinto

Just because it matches once, (or however many times) doesn’t mean it always will. Seems a bit pre-mature, especially if you’re only checking 53 vs 1000+

That said, it would be nice if we could get a GISSTEMP “All minus class1-4” and see what it says….

Patience is in order I would suppose….

David Smith

It’s an error to assume that vegetation necessarily cools a site.

Cooling effects of vegetation include transpiration and blocking of direct sunlight

Warming effects of vegetation include blocking of outbound IR (esp. nighttime), lower albedo and less air mixing.

Vegetation type, height and proximity all play a role in determining the relative importance of the cooling and warming effects. It’s a complicated situation.

Concerning another microsite topic, it is conceivable that different sites respond differently to changes in cloud cover. Arizona asphalt may respond differently than Arizona soil to decadal-scale changes in cloud cover. Regional cloud cover does change on decadal scales, just as temperature and precipitation do.

John V.

Lots to respond to this time. It’s late but I’ll do my best…

steven mosher:
I do not have the absolute temperatures. An absolute temperature has little meaning. It’s the trends that matter. It is trivial to shift from a 1951-1980 reference to a 1961-1990 reference.

But when we say “closely match” we must say so with authority. The issue is
comparing 53 sites to 1221 and concluding “they are they same”

Sample size is not the issue. A few weeks ago there was pretty good consensus here that the rural CRN12 stations were the best stations and that they would give the most accurate temperature trend. Now that GISTEMP is found to match the CRN12R trend there is less confidence in the CRN12R trend. Can you honestly say that you would not be promoting the differences between CRN12R and GISTEMP if they were large? (You were promoting Hans Erren’s plot showing the divergence between CRN12R and GISTEMP a little while ago).

We do need to figure out the confidence bounds on the CRN12R trend. It’s possible that it’s just a coincidence that it matches GISTEMP. But right now, with the best available data we have, it appears that GISTEMP gives an accurate temperature trend for the USA lower 48.

You have an argument to drop those 60 sites from the USHCN1221.

I agree that the bad stations should be dropped. That will improve the accuracy of the temperature trend. The problem is that the ultimate goal is not the USA lower 48 trend, it’s the worldwide trend. We do not have the data to selectively remove stations outside the USA.

I’m more an more convinced that chopping the data sets into smaller peices is a fundamental error.

We have multiple influences on station trends. To separate them (without statistical wizardry that few would trust) we must analyze them independently. I can see grouping CRN123 against CRN45 (instead of the single cRN level groupings).

Your preferred method is removing a small number of stations from the large sample. I see no statistical difference between removing 60 stations from a set of 400 and adding 60 stations to a set of 0. The advantage of analyzing the 60 in isolation is that you can compare them to another set of 60.

The point I guess Is that I’m not prepared to go down the Urban/rural slice.

You have also been arguing that UHI is very real. I don’t see how you can quantify microsite issues if they are mixed in with UHI.

SO. engineering hats on. Hypothesis: class 5s are warmer than class1234. True or false using OpenTemp as the tool?

Absolutely true, as I have been saying for a while. My results from earlier today show CRN5 warming ~0.4C more than CRN12R from 1900 to 2005. Some of this is due to UHI but nobody knows how much.

Since you dont have a random sample you have a sample cherry picked for its apparent linearity. There are techiques for identifying regimes and regime changes.

The problem is that to reach any meaningful conclusions the data needs to be summarized in some way. My 20 year trailing trends were an attempt at that. My key period trend were another attempt. Looking at the entire range of data is just another way of cherry picking.

PS. GISS Anomaly? Do you have te absolute for 1950-81? How did you adjust for the differnce in MA approaches

I don’t have the absolute for 1951-1980. You can move the reference period by taking the average for the period of interest and subtracting. It doesn’t matter if the trend is already referenced to a different period. What are MA approaches?

John V.

Clayton B:

Also, (for JohnV) are you referencing the same value for all series or are you referencing the average of each series for that series? confused?

I subtract the average temperature for the entire region (USA lower 48) for the reference period from each yearly average. It’s just a shift up or down the vertical axis. It doesn’t do anything to the trends or the shape of the curve.

Joe Bowles

I am thoroughly enjoying the discussion on this thread. The discussion started with focus on the difference of a CRN 1,2 to a CRN 5 site. As far as I can tell, we only know the current condition of the sites, not their status in the past. So it seems to me that looking at the long term records for those sites as classified may be introducing noise into the analysis. It might be helpful to focus on a shorter term during which it would be reasonable to conclude that the sites were in their present condition. That would probably shorter the time period to sometime in the late 80’s to maybe the early 90’s. It might be efficacious to use the year immediately after the automatic systems went into service in the 90s.

If a significant difference shows up, then, I can see taking it back further in the records to see if the same result occurs, but it seems to me that starting with the full time series is counter-productive. It should be easier to spot effects, to the extent they occur, in the most recent period when at least the measurements are being made in a consistent fashion, right, wrong or indifferent.

I am particularly impressed with Kristen. I love the way she stands her ground. The issue of missing days she spotted seems of paramount importance to me, since the filled data is adding an artificial bias to the database. I suspect that checking the database by site for multiple days of the same value might provide an indication of the level of impact this is causing.

I wish I shared the computer expertise to make a contribution, but I take my hat off to all of you. Until the basic data is sorted out, the science isn’t going anywhere. It is a bit hard to know where to attribute the warming if we don’t even understand the variability of our measurements, much less the variability of nature.

John V.

#107 Clayton B:

Have you thought about how to get opentemp program to select stations based on certain things in a database (airport, elevation, CRN rating, etc.) in the code?

I have thought about a front-end for OpenTemp that could query the database to select stations, setup analysis parameters, and view results. Realistically though, I have been spending way too much time on this and won’t get to the front-end for a while. If there is enough information in the database, I can help with SQL queries for extracting stations.

John V.

#123 Joe Bowles:
I like the idea of using recent data, but I will be accused of cherry picking (again) if I go that route.

A major problem with using a short time frame for comparing CRN12 to CRN5 is that we are looking at trends, and trends take time. The CRN5 bias has to change for a trend to be observable. If they consistently read warm there will be no difference in the trend.

steven mosher

Wow. Lots to respond to. I won’t get to them all

For me the big issue is quantifying the effect of Microsite. ( a form of UHI)

When I analyzed all 1221 sites and then took out the CRN, the anomaly went down by .05C
across the board. since 1880. So, OpenTemp had an anomaly of around .6C ending in 2005.
removing 5% of the sites dropped this by .05C.

When I analyzed 400 surveyed sites CRN12345 and took out the CRN, the anomaly dropped .15C
consistently.

On the other hand, Like JOHNV points out, the CRN12 track the anomaly preety well. So I took
out CRN12 from all 1221. Sure enough, you get a small difference and you get zero crossing.

Puzzling.

I would like to do some urban/rural cuts, but I want better info than Nightlights, and better
info than 1980 population. Orginallly USHCN was going to do the classification by decade,
but they dropped the approach.

I’ll do more later, but good questions all around. I don’t have answers for them all.

Kenneth Fritsch

Steven, I post my constructed graphics using imageshack, a free online service that you transfer images to and then use that link to post here.

http://imageshack.us/

Kenneth Fritsch

Re: # 119

First, I’m not making any “adjustments”. I’m using OpenTemp to calculate average temperatures and temperature trends using data from GHCN or USHCN.

John V, I was confused as I had you ticketed for a much more ambitious project. It appears that you are saying that OpenTemp is a convenient program and means of doing trends and the like using GISS data sets. I would have to ask then why you have concentrated on the “raw” data set for the CRN category analyses?

I suppose your reference to geographical averaging and adjusting may have confused me.
Was that recent discussion confined to geographical adjustment for calculating trends and not to compare with some of the adjustments that are fundamental to GISS?

John V.

#132 Kenneth Fritsch:

It appears that you are saying that OpenTemp is a convenient program and means of doing trends and the like using GISS data sets.

That’s essentially right. It is an automated method of ingesting USHCN or GHCN data, processing it, averaging it across regions, and spitting out results.

I would have to ask then why you have concentrated on the “raw” data set for the CRN category analyses?

Initially I used GHCN raw because I had it available and I wanted to get results quickly. Now that I have added the USHCN parser I think it is best to use TOBS adjusted data because TOBS bias is a large effect that is not (too) controversial.

The USHCN urban adjustments fundamentally change the trends in the raw data to correct for UHI. My preferred method of understanding UHI and microsite issues is to isolate them without any fancy processing.

I suppose your reference to geographical averaging and adjusting may have confused me.
Was that recent discussion confined to geographical adjustment for calculating trends and not to compare with some of the adjustments that are fundamental to GISS?

That’s right.

Kenneth Fritsch

I am having problems understanding the choice of temperature regime period selections. When I use the entire USCHN Urban data set from 1960-2005 I calculate: Trend = 0.039 and R^2 = 0.39. When I do the same calculation for the period 1975-2005 I calculate Trend = 0.057 and R^2 = 0.33.

Now I was led to believe that the regimes were selected based on visual linearity considerations, but the actual calculations seem to provide better linearity periods. This is only a minor point for me as I would prefer to be able to measure something closer to the snapshot time than extended time periods.

steven mosher

RE 131. JohnV I think I solved the puzzle and found the flaw.

All we have from GISS is an ANOMALY from the GISS 1950-1981 Mean.

When you compared CRN12 to GISS in anomaly form what MEAN did you use?

1. You cannot have used the GISS mean. They dont publish it. They publish
Temp-X. Nobodoy knows what X is.

2. I assume you created the CRN12 anomaly using the CRN12 average from the 1950-81 period.
Temp-Y.

IF, the average of CRN12 ( WRT 1950-1981) is equal to the average of GISS ( WRT to 1950-1981)
then X is equal to Y then the anomalies are comparable.

Simply: ANOMALY GISS is equal to (Yearly Giss) – (Giss average for 1950-1981)
ANOMALY CRN as you calculated it is equal to ( Yearly Crn12) – (CRN12 average for 1950-1981)

You subtracted different amounts.

Solving this is simple.

1. What is the AVERAGE TEMPERATURE you subtracted from the CRN12 series to create the anomaly chart?
A. The average of GISS temp 1950-1981
B. The average of CRN12.
C. something else.
2. What is the average temp GISS subtracted from its series?

HERE is what I did.

1. Calculate the temp using OpenTemp for all 1221 sites.
2. Calculate a MEAN for 1950-1981. This figure was 11.2021875C
3. Calculate the Temp for CRN12.
4. Create an anomaly for CRN12 using OpenTemps 1950-1981 Anomaly. ( CRN12-11.2021875)
5. Download GISS data. 1880-2005. This is in anomaly form WRT to 1950-1981.
6. Smooth the two using the same smoothing. Trailing MA.
7. Difference them.

To compare OpenTemp anomaly for CRN12 with Gisstemp anomaly, you have to reference the
anomaly to the same mean. You cannot construct a CRN12 anonomaly referenced to its
1950-1981 mean and compare it to a GISS anonmaly that is referenced to ITS 1950-1981
Unless you believe the means are the same.

steven mosher

RE 122 Joe Bowles. Thanks. JohnV , ClaytonB and Kristin have all been great additions.

John V.

steven mosher said:
“JohnV I think I solved the puzzle and found the flaw.”
What flaw? Is the close agreement between GISTEMP and CRN12R (for the USA lower 48) a flaw?

Shifting the temperature plots to a reference period is merely a convenience to make it easier to compare trends. It has no effect on the trends, and the trends are what matter. The absolute temperature has little meaning (as Larry has told me repeatedly) but the trends determine if there is warming or cooling and that’s what it’s all about in the end.

Steve Sadlov

RE: “SteveSadlov likes 1930 to 2005.”

Actually, what I would really like would be something like 1500 – 2005, or even better, 0 – 2005. But I am a pragmatist.

Kristen Byrnes

John V 135,
The CRN 1,2 stations are already done. I have a ton of homework tonight and will ask someone to do the CRN 5’s.

Kristen Byrnes

Rural CRN12 Stations:
42572383001,34.7,-118.43 Fairmont CA (Not Surveyed, aerial photographs only but looks good) / No changes in location or equipment / records back to 1931
42572694004,44.63,-123.2 Corvallis State University OR / no change in location or equipment / records back to 1948

42572376006,35.27,-111.73 Fort Valley AZ / Equipment: 2004 – current “other temperature equipment” / 1986 – 2004 MMTS (LIG backup) / before 1986 LIG / Location same throughout / records back to 1948

42572273004,31.7,-110.05 Tombstone AZ Equipment: MMTS 2002 – current / LIG 1933 – 2002 / Location: same throughout / records back to 1933

42574501001,38.33,-120.67 Electra ph CA / Station closed 1997 / Equipment LIG throughout / Location: same throughout / records back to 1931

42572489002,39.97,-120.95 Quincy CA / Equipment: MMTS 1999 – present / LIG before 1999 Location: last change 2004 / records back to 1931

42572786006,48.35,-116.83 Priest River Experimental Station ID / no location or equipment changes / records back to 1911

42572533004,41.63,-84.98 Angola IN / Location: Location move reflected in elevation change 1977 / Equipment 1985 – Present: MMTS / LIG before 1985 / records back to 1887

42572743002,46.33,-86.92 Chatham experimental farm MI / Location: last move (short distance) probably not a problem / Equipment: LIG throughout / 1987: STN ESTABLISHED TO COMPARE WEATHER READING WITH CHATHAM EXP FARM 20-1484-2, 1.1 MI TO THE NNW / This station is a problem, the correct name is Chatham Exp Farm 2. The actual station is Chatham Exp Farm, which was active from 1948 to 1988 with no recorded station moves and LIG equipment throughout station history.

42574341005,45.13,-95.93 Milan 1 nw MN / Location same throughout / Equipment Nimbus 2006 to current / LIG before that / records back to 1948

42574341007,45.58,-95.88 Morris wc Experiment Station MN / Location: same throughout / Equip: LIG throughout / records back to 1948

42572235004,31.97,-91 Port Gibson 1 ne MS / Possible problem with either MMS or Surfacestations.org / MMS exposure & obstruction field do not match photos, MMS says: MMTS 260/10 F&P 90/7 TREE 100/75/29 140/100/25 HOUSE 160-220/90-120/14-7 TREES LINES 165-200/175-125/24-24 200-280/120-200/30-15, photos do not show these obstructions / Location changed 2001 / Equipment: MMTS 1988 to current / LIG before 1988 / records back to 1948

42572677004,45.92,-108.25 Huntley Experimental Station MT / Location and Equipment (LIG) unchanged / records back to 1951

42572488003,39.45,-118.78 Fallon Experimental Station NV / Location changed in 1992 / equipment the same (LIG) throughout

42572662003,43.97,-101.87 Cottonwood 2e SD / Location unchanged / Equipment: MMTS 1986 – current / LIG before 1986 / records to 1948

42572572005,40.55,-111.5 Snake Creek PH UT / Location: no changes / Equipment: Thermograph 1997 to current / LIG 1997 and before / records to 1948

42572403002,38.9,-78.47 Woodstock 2 NE VA / Location change 1994 / Equipment MMTS 1993 to current / LIG before 1993

steven mosher

RE 149.

Well, Kenneth, I’ve Pondered this for some time. My sense is this. If microsite Bias came in like
a step function of 5C it would a simple matter to detect.

If it came in gradually since the 1950s, buildngs here and there, some ashphalt, more runways more planes

it wuld be harder to detect.

Then, realize that some of these warming and cooling processes are mitigated and modulated by oter processes.

The temperature is a continuous function. It gets quantized into a daily high and daily low. During that period
you might get a microsite shock. The microsite bias, while real, while grounded in accepted climate science,
might be very hard to find, especially after monthly, yearly, averaging..

That’s one way I see of reconciling JohnVs finding and yours and mine.

Make sense? John Sees no substantial difference between GISS and CRN12 and I’m see effects when 5s are dropped.

So, is the effect of microsite REAL, but hard to pull out from the noise? And if we do drop 60 bad sites
can we actually improve the S/N.

Sorry, long day

steven mosher

RE 126.

JohnV I just settled the matter of cherry picking for my self by only
sending out data from 1880 to 2005. Gave me peace of mind. I think I posted this to Anthony once who chided me about going
back to 1880. If somebody else wants to slice times my conscience
is clear. Here’s the data. here’s what I did. Here’s how to repeat. As soon as I can figure out how to
post charts I’ll just post charts, Instructions, and leave the commentary to others.

Now, I am starting to use your tool for individual site analysis.. I wont put that stuff out until
I run it by you… ok?

Geoff Sherrington

Re Steve # 47

In the mining analogy, one can choose the interval down the drill core to be as small as 0.1 of a m (practically). This allows estimations of range from very short inetervals up to large intervals (from one end of the ore deposit to the other). So, it is easier to work out how far away a point can be before it loses its ability to predict for another point, weighted which way or whatever. I have been looking, without success, for a climate paper which systematically takes a cluster of stations, some close together, some well apart, and does the maths we were using in the 1970s. The methods work in 3D so time can be a variable as well, or altitude, as well as lats/longs. I remain totally unconvinced from the papers I have read that stations 1200 km apart have predictive relevance to each other. If close stations were properly treated my gut feeling is that prediction ends in well under 200 km. And as you know, contouring methods using triangular grids were also studied intensively in the 1970s. References to David, Matheron et al at Fontainebleau, France, then follow the trail.

Joe Bowles

Re 153

Kenneth:

I think Kirsten’s work may provide an a priori basis for time selection. I keep thinking about the upward bias of the MMTS systems. Based on what Anthony posted on his web site, those systems seem to set a discrete upward bias in the temperature, and seem to get worse with time. We have a lot of those in even the best sites, so maybe we need to figure out the adjustment for them, adjust the data for the MMTS contamination, then, try again to see what the microsite effects are.

At this point, I wonder if we should do comparisons within class between MMTS and LIG to see if there is a discernable difference. I would gather that the bias of the instruments would be similar across sites and classification, but I would still look within class. If that bears fruit, then, one of the major contaminents can be removed.

If we set a rule…even if it is arbitrary…for selection of a time frame, I think it gives us more power. The cherry picking complaint is that the selection of the endpoints is based on the end result, whether to increase or decrease the temperature trend, rather than on some other basis.

Joe Bowles

I wonder if we could better understand the effects of rating bias by taking a cluster of stations within a fairly close geographic dispersion and do pair comparisons. We know that CRN5 yields some upward bias in temperatures and seems to affect the trend, but we haven’t sorted out the effects as you move upward or downward through the classes.

Hansen used 40 stations in his adjustment process, but it isn’t intuitively clear how the adjustments were made on this basis. My guess is that he was trying to use a large sample set under the assumption that it would cover a multitude of sins. On the other hand, using a large sample also hides a lot of information.

In essence, I think we might pick up a lot more insight based on direct pair comparision within a specific area and then see how the implied adjustments would affect the adjustments in other geographic areas.

I see no logical reason that the adjustments would be the same since weather patterns are local/regional. I am thinking that once we figure how how to make the adjustments on the micro-level, we could extend the methodology to the macro. The number of pairs done across all stations would me massive, but doing them in geographically defined areas would be practical.

I think we can only get limited information based on CRN class alone since we have the compounding problems of equipment, TOBS, and UHI effects at a minimum.

John V.

#155 steven mosher:

But it can distort perceptions of the “match” between the anomalies

I believe the whole definition of anomalies is based on changes from a reference period for the series being analyzed. A latitide/longitude/elevation-compensated comparison of absolute temperatures could also yield some interesting results, but I am focussing on the trends right now.

Since GISS does not publish the 1951-1980 average, any absolute temperature comparisons would need to exclude GISTEMP. I don’t see that as a big loss since the GISTEMP results are not broken down by CRN anyways.

The discussions over CRN12 versus GISS are getting to dang heated for me and Since I am a hot head I figure it best for me to steer clear of that rumble.

I wish I would have followed you over here earlier.

Kenneth Fritsch

I looked at (snooped) some time period trends using the USCHN Urban data set over the extended period from 1960-2005. I have a problem going back beyond 1960 because it is at that this point that significant numbers of data points are missing from this data set – and increasingly missing as one goes back in time.

The CRN combinations that I looked at were the CRN45, CRN12 and CRN3 combinations and selected for the purpose of comparing larger samples and comparing them in order of audited quality. I looked at the entire 1960-2005 time period to present an overall look at trend differences and then went back in time to determine whether one could find a period of constant or nearly constant trends by CRN category and periods with changing trends by category. The trends were calculated and then adjusted geographically by my state by state procedure. The results are presented in the table below.

The period 1975-2005 shows very little differences in the 3 CRN combination category trends while the period 1960-1975 shows a large difference between CR45 and CRN12 with CRN3 in the middle as would be expected from the quality ratings. The 1960-1985 shows similar orderings and differences as the period 1960-1975 with somewhat smaller differences even when considering the longer time span of this period.

A simple explanation for the result shown here could be attributed to the quality differences detected by the Watts team snapshots occurring mostly 20 to 30 years ago and then remaining at a constant quality level over the intervening time. Before one could put any stock into this supposition one would need some independent evidence that this indeed was close to the general case. Further work is required to put any statistical significance to these results by going back and looking at differences between randomly selected groups from this population and for the time periods analyzed.

Joe Bowles

Re 163

I don’t think we can make the pair comparisons based on annual data. The annual data obscures too much information, especially since some of the microsite differences are probably seasonal. For direct comparison, we may need to move down to monthly or seasonal data.

I see the logic of making an altitude adjustment, but it isn’t clear to me that there is one adjustment that works everywhere…especially in places with differences in humidity. My thought is that we should try to extract that directly along with the other components. We may want to get the observer reports and lay out differences between the sites, along with the CRN classification. We might get some interesting results trying multiple regression on the various site components.

steven mosher

JohnV I will mail you the CRN3. I thought I gave you my whole database.

TEST GRAPHIC

Chart of OpenTemp ALL, OpenTemp CRN5, and CRN12R. Using GHCN Rural. 5year moving average, Trailing

Clayton B.

mosher,

you doubled up on your “http:\\”. Right click the red x and select properties – you’ll see what I mean.

Kristen Byrnes

John V,

I think including CRN 3, even if it’s rural, is a big mistake because you are trying to eliminate the micro site biases all together. We are already working on getting you enough data, please lend us your patience.

steven mosher

RE 167. Rural CRN3?

No, I dont have those. I sent you all the cuts I did. My next cuts are going to be Instrument cuts
and location cuts. ( christ its a multiple linear regression in pieces )

I am very very leary ( but not adverse ) about the Urban/rural distinction. I leary about it because There are two indices
that slice this data: 1980 population. 1995 nighlights.
If you like I think I can cobble together datasets to let you do this. It’s handwork, and so will come
with caveats.

I have a couple cuts I didnt send you.. ALL_except_CRN12 and some stuff I was doing on on the Titusville
site. that I havent tested

It’s Relative to Anthony’s work. here is what I am doing. ( kinda like Kenneths work).

PICK a class5. Use that site as a center. Pick sites within (Xkm) I picked approx 2deg in lat/lon

That gives me a sample that is local to the class5. Now, compare the Class5 site to all its neighbors.

There are some decisions here so its a work in progress and I dont mind being wrong.

The best thing for me to do is an update of Anythonys excell. Might take a day or so.

My sense. The class3, given the phenomenology of microsite bias should not be thrown out.
We are talking a small bias that shows up in a bursty way ( most cases) and so it will just
look like noise.

Whatever bias CRN3 have IT is SUB NOISE. ( I say this stuff to see who comes back with a strong
argument)

So lemme know what you want

steven mosher

First, let me frame this. Based on decades of climate science, we have good reason to believe
that the characteristices of class5 stations will bias the temperature measurements over time.

1. Suggest your methodology to identify and quantify this bias.

2. We have a program. It is Open. you can ask me or John or Clayton to run any comparison
as long as you provide the list of stations. It takes me 5 MINUTES.

3. I’m gunna do the comparisons I think are neat. You dont like it? Send me the station list you
want to run. I will be your data monkey. Send the list. Station ID, lat,lon.
txt file. easy as pie.

steven mosher

re170. Which will be warmer?

1. CRN12 or CRN3
1.CRN12R or CRN3R.

If CRN12 is warmer what will you conclude?
If CRN12R is warmer what will you conclude?

What will the diffeence in trend be?

Hypothesize.

steven mosher

RE 177. Crap. I forgot about that Offset. Warmer have a meaning, if we find something odd, but
Warming ( trend ) Is more relavant to the AGW stuff.

basically, your offset calc, means We are obligated to look at trend. Correct?

Clayton B.

179,

I’m tying to make that CRN3R file. It’s a lot of editing

Would it be easier if the DB was setup for quick queries ;)? By the way, any luck with that file I sent?

steven mosher

A musical Interlude. Forgive me.

John V.

Key Trends Revisited

Kenneth Fritsch said:

I am having problems understanding the choice of temperature regime period selections. When I use the entire USCHN Urban data set from 1960-2005 I calculate: Trend = 0.039 and R^2 = 0.39. When I do the same calculation for the period 1975-2005 I calculate Trend = 0.057 and R^2 = 0.33.

When I first checked this I got similar results. This morning I realized that I was using Excel’s RSQ() function with anomalies as the y’s and years as the x’s. That implies that I was calculating the correlation between the year and the measured temperature, not the correlation between a linear fit of the temperature and the measured temperature.

This morning I redid the comparisons against a linear trend. I used the automatic trendline available in Excel charts. The results are a little different:

CRN12 TOBS, Single Year
1960 – 2005: R^2 = 0.274
1975 – 2005: R^2 = 0.277

The noise in the signal is drowning the trend. I did the same using a 5yr centred average:

CRN12 TOBS, Five Year Centred Average
1960 – 2005: R^2 = 0.635
1975 – 2005: R^2 = 0.676

This doesn’t mean a lot in the debate about key trends, but it does at least say that my quick visual inspection of trends was accurate in this case. That is, the correlation with a linear fit is stronger for 1975-2005 than for 1960-2005.

Note also that Kenneth’s results were generated from all stations with urban adjustments, while mine come from only the rural CRN12 stations without urban adjustments.

Joe Bowles

Re 184

Thanks for pointing that out John V. I missed that entirely when I was playing with some of the data. It is very helpful.

John V.

OpenTemp v1 RC1
The first release candidate for OpenTemp is now available:

http://www.opentemp.org/_release/v1rc1.zip

I have added a few new features since Tuesday:
– ability to filter station data by start and end dates (in stations file)
– comments in stations file are supported
– optional prefix on output files (easier to identify result sets)
– random selection of N stations from the complete list (for Monte Carlo analysis)

There is a readme.txt file in the zip archive. I have included it below. I will try to work on better documentation.

To confirm that the analysis code has not changed, I ran a few of my old analyses. I would appreciate it if other users (steven mosher) could check their old results as well.

Until I get the http://www.opentemp.org website properly setup, please report any bugs here. (Steve McIntyre, let me know if you’d prefer that conversation to be moved elsewhere).

=====

OPENTEMP v1.0 RC1
October 5, 2007

Usage:
OpenTemp
[/ghcn=GHCN | /ushcn{uso}=USHCN]
/rgn=[region]
/stn=[stations]
{/stnpick=NN}
{/prefix=PRE}
{/offset} {/os} {/oc} {/ocs} {/oo} {/om} {/oy}
where
GHCN is the name of a GHCN data file to read
(GHCNv2 monthly data files are supported)
USHCN is the name of a USHCN data file to read
(USHCNv1 monthly data files are supported)
{uso} is one or two characters defining USHCN options (default is avg raw)
(‘1′,’2′,’3’ for max, min, avg temp;
‘+’,’A’,’C’ for TOBS, Filnet, Confidence)
[rgn] NOT IMPLEMENTED: is the name of a file that defines the regions to be analyzed
(currently the program is hard-coded to analyze the USA lower 48)
[stations] is the name of a file with a list of stations to analyze
– one station per line with: stationID, latitude, longitude, {start}, {end}
– {start} and {end} are optional dates in the form YYYY.MM
– for MM, use 01 for January, 02 for February, etc
– comment lines can be included using the pound sign (#)
{/stnpick} (optional) pick NN random stations from the complete list
{/prefix} (optional) use PRE as a prefix for all output filenames
{/offset} include to calculate and use series offsets to the region average
(offsets will be written to offsets.csv)
{/os} include this flag to write station details for all requested stations
{/oc} include this flag to write cell data for the region (cell location and size)
{/ocs} include this flag to write cell-stations (stations that can affect each cell)
{/om} include this flag to write monthly averages for each region
{/oy} include this flag to write yearly averages for each region
{/oscr} include this flag to write scribal variation results

Results will be written to data files with the following names (with optional prefixes):
monthly.csv contains monthly overall averages for each region
yearly.csv contains yearly overall averages for each region
station.csv contains monthly readings for every station in a convenient format
cell.csv contains cell information for the entire region
cellstation.csv contains cell-station interaction results
offsets.csv contains the static offsets applied to each series
scribal.csv contains the fraction of matching values for all series at each station
opentemp.log contains a complete processing log (same as console output)

Notes:
– Each output file is written only if the appropriate /o option is used
– If the /offset option is used:
– Monthly and yearly data are calculated using a static offset applied to each series
(Offset is equal to the average difference between the series and the monthly averages)
– station.csv has series data before applying the series offset
– offsets.csv file is written
=====

Joe Bowles

Re 187

Thanks for the information. I will give it a try when I finish some work that seems to be piling up. For some reason, my clients seem to think they should get priority.

SteveSadlov

Ping to #91 – there is only one “vertical dip-slip fault scarp” in the entire record, and it it between 1915 and the early 1920s. All other sharp slopes are “natural topography.”

steven mosher

RE 193.

YES, and it happens across all sites.

Big jump.

SteveSadlov

RE: #195 – What you’ll see on Y-Y is that it is a two (or maybe three) step jump. Expressed w/ 5 year smoothing it ends up as a fault scarp. To be fair, there is a small downward fault scarp late 70s / 1980ish. But nothing like this. Yes, yes, I’ve heard all the stuff about “stochastic process” and “multistable / quantum” system. But even with those caveats, this early 20th century jump, among all others, is the most notable. My auditor’s luck says, drill down here.

SteveSadlov

While the Hansen 9/10 adjustment does pull down 1917 slightly, it’s not a spike and the magnitude is only hundreths of the deg C. Interestingly, the Hansen 9/10 definitely pulls down the 1880s and 1890s noticeably. As a result, the entry point in the 20th century has been thereby slightly depressed.

steven mosher

SteveS. This stuff is OpenTemp. So No Hansenism. Its GHCN ( USHCN for the US)..

I think their are a couple faults like you note.

How many should we expect to find. IN A SERIES with autocorrelation? THAT’s the question.

.6C IN ONE YEAR? That a huge forced change, especially following a downward trend. The OIL TANKER
dont turn like a jetski!

steven mosher

RE 201.. I’m wondering about a metric for yearly change with memory…

HA wait.. acceleration. F=MA. What kind of force does it take to turn the good ship
climate on a dime.

Late night last night

Sam Urbinto

Your comment, my homework.

Joe Bowles

I wonder if we should constrain the analysis framework based on sample size. We are clearly losing a sizeable portion of the sample by rating as we go back.

John V.

#207 steven mosher:
The ocean has a lot of thermal inertia and so responds slowly. The atmosphere not so much. My whole issue with yearly comparisons (1934 vs 1998 for example) is that the differences are much smaller than the high frequency component. If we’re talking about *climate* (not weather), then multi-year trends are what matter.

I am planning to look at the yearly differences between runs relative to the characteristic noise in the signal as defined by the year-year differences.

Willis Eschenbach

John V, you say:

Key Trends Revisited

Kenneth Fritsch said:

I am having problems understanding the choice of temperature regime period selections. When I use the entire USCHN Urban data set from 1960-2005 I calculate: Trend = 0.039 and R^2 = 0.39. When I do the same calculation for the period 1975-2005 I calculate Trend = 0.057 and R^2 = 0.33.

When I first checked this I got similar results. This morning I realized that I was using Excel’s RSQ() function with anomalies as the y’s and years as the x’s. That implies that I was calculating the correlation between the year and the measured temperature, not the correlation between a linear fit of the temperature and the measured temperature.

This morning I redid the comparisons against a linear trend. I used the automatic trendline available in Excel charts. The results are a little different:

CRN12 TOBS, Single Year
1960 – 2005: R^2 = 0.274
1975 – 2005: R^2 = 0.277

The noise in the signal is drowning the trend. I did the same using a 5yr centred average:

CRN12 TOBS, Five Year Centred Average
1960 – 2005: R^2 = 0.635
1975 – 2005: R^2 = 0.676

This doesn’t mean a lot in the debate about key trends, but it does at least say that my quick visual inspection of trends was accurate in this case. That is, the correlation with a linear fit is stronger for 1975-2005 than for 1960-2005.

Note also that Kenneth’s results were generated from all stations with urban adjustments, while mine come from only the rural CRN12 stations without urban adjustments.

ALERT! ALERT! AUTOCORRELATION DETECTED! AUTOCORRELATION PRESENT! PUT DOWN THE MOUSE, SIR, STEP BACK FROM THE KEYBOARD, AND NO ONE WILL BE HARMED!

In a more serious vein, you desperately need to determine the significance of the R^2 value of the trends. My guess is that not a single one of them is significant . In addition, you need to calculate the standard error (or the 95% confidence interval) for the trends, to see if they are significantly different either from each other or from zero. In both of these calculations, it is mandatory to take autocorrelation into account. See Equation 22 here for Nychka’s method for accounting for autocorrelation.

w.

Kenneth Fritsch

Re: #205

I found a further error in converting -99s from the raw data to NA in that I had 4 zeros in one CRN4 station for the early 1960s. An error such as this can and did significantly change my results reported in the previous post (#205).

The trend differences by CRN category are primarily confined, with the above revision, to the period 1960-1985 and to the added time period of 1945-1985. Using randomly generated groups from the population of station measurements that CRN45 and CRN123 were drawn from, indicated that the null hypothesis, that CRN45 and CRN123 have the same trends, could not be rejected at the customarily used 5% level (but could at a 10% level).

I have not checked the period of 1945-1985 for statistical significance of the trend difference. I show a graph of the entire 1920-2005 time period and the anomaly differences for CRN45-CRN123. I used a power 3 polynomial primarily to point out the changing trend differences over that time period.

Willis E, I know auto correlation will affect the significance of a correlation, but I am not sure what effects auto correlation will have on my looking at trend differences with randomly selected samples and the adjustments in statistical significance, if any, one should make.

I am here to learn and if one learns by one’s mistakes I should have learned loads of late here.

John V.

Kenneth Fritsch:
I think your results using random sets could be very useful, but I am still uncertain why you are using USHCN urban-adjusted data. The urban adjustment combines the data from different stations (across CRN ratings), and therefore pollutes any trends that may be present.

John V.

Kenneth:
I do not understand the USHCNv1 urban adjustment very well either. This is my understanding (correct me if I’m wrong):
– they correct the urban stations using surrounding rural stations
– the other corrections (TOBS, MMTS, Filnet, SHAP) are all local to the station being adjusted

I am not confident that the “adjustmment for UHI has to be very small”.

steven mosher

Re 216. Have a look at Karls paper, citing above. We havent reviewed it here primarily because
GISS were not using USHCN Urban Adjusts.

Might be nice to get the stationlist from Karl

steven mosher

Re219.

One problem you’ll see is that Karl did a paired study based on population to determine
Urban Bias. So, if you compare a RuralCRN5 with an urban CRN12 You have a mixed bag of sorts.
To be fair Karl didnt have site surveys. He notes this weakness early on in the paper. The
suggestion is to use large numbers to overcome the lack of understanding about the micro
climate.

So, It would be instructive to revisit KARL using Opentemp.

John Lang

Remember the USHCN adjustments are on top of each other.

TOBS adds 0.35F (from 1920) to the RAW

MMTS adds 0.03F to TOBS

SHAP adds 0.25F to MMTS

FILNET adds 0.1F to SHAP

and FINAL subtracts 0.1F from FILNET

All together, the adjustments add 0.63F to the RAW trend (from 1920).

Kenneth Fritsch

Re: Post #212

I am finally filtering out all my previous errors and I must now correct the trends for CRN45 and CRN123 for the time period 1945-1985 reported in the table (bottom) in Post #212 were actually for the period 1920-2005. The 1945-1985 trend results were calculated as listed below:

CRN123 = -0.0152; CRN45 = -0.0003

Using randomly selected groups in 60 simulations of the above comparison indicated that the null hypothesis (that CRN123 and CRN45 have the same trends) could be rejected well under the customarily used 5% level.

I will double check this work for errors and report adjustments for geographical effects — if they are significant.

Joe Bowles

John V. says:

October 7th, 2007 at 9:52 am
John Lang:
I chose to stop at TOBS because it is (relatively) uncontroversial. I wasn’t so sure about MMTS and SHAP. What is your opinion?

At this point I am skeptical about all adjustments. The magnitude of each adjustment may fall in a reasonable range, but thee is no way to tell. I have no clue what the range of such adjustments are…and have the same problem with all of the adjustments being made.

I think at some point we need to compile the range of adjustments, though how to do this escapes me. I still think that the adjustments will tend to be at the upper end of the reasonable range. If they would release this information, the actual adjustments would be a lot easier to accept.

steven mosher

Re 223.

I think The MMTS adjustment is rather small and can also be double checked. SHAP
is the one I want to see the details on.

steven mosher

I think the best approach is to Decouple the data issue. What do I mean?

JohnV initially used GHCNv2. result? he was attacked in a bogus fashion.
Ideally, Opentemp would take a variety of inputs and compute a result.
Our focus ( indulge me) should be a creation of a package that allows
anyone to load the data set they want, process it, and publish results.
Then THEY are the lighting rod of data selection or ‘regime’ selection.

I still use GHCNv2. I’ll make that clear in My presentations. In the end
I do not think the CRN issue will TURN on data source selection. I reserve
the right to be utterly wrong.

SteveSadlov

http://wattsupwiththat.wordpress.com/2007/10/07/california-climate-pdo-lod-and-sunspot-departure/#more-308

Look at accumulated departure from average, for temperature, for California. Look at 1900 – 1920.

Geoff Sherrington

Re # 231 Steve

Polite correction, if I may – it’s the PRECISION of data prior to 1950 that is more important than the ACCURACY if one is looking at trends. If a thermometer has a contant accuracy error of 1 degree, it will still show the trend provided it was read precisely (and we have to make that asumption in the absence of alternatives).

I can get RAW T max and T min and some metadata for 1,700 Australian stations going back at times to the 1860s, with lat/long/alt. It is daily data. It is too big for my PC or my brain. Some of the stations have very little data, others have a lot.

Australia is interesting because many sites started early (gold rushes of 1870s etc) have remained isolated and rural until today. Thus, no UHI adjustment is indicated. They are candidates for comparison with urban.

My theoretical approach would be to divide all data into 5-year calendar periods. I would calculate the slope of best fit for each site for each 5 year period for both max and min. Then, for each 5-year period I would link the slope figure to create a continuous graph up to mid 2007. Within that graph there would be occasional changes to instruments, housing, TOB, etc but my gut feeling is that single events of these would even out in the weight of many observations.

Then, having visited most of the 1700 sites in my career, I would rank them as rural or early urban or late urban and do a 3-way split and repeat the exercise. I base this proposal on a lot of reading of various approaches tried elsewhere and the cans of worms they have generated.

I need help. Anyone interested?

Geoff Sherrington

Re # 235 John V

Thank you for your reply. I have a completely open mind on the sharing of data and I can be contacted at sherro1@optusnet.com.au

If you wish to swap notes on method, I no longer have a good computer and computing dept to help me. The data of which I speak come from the Bureau of Meteorology in Australia. I shall ask if they are still on open record and how much a DVD would cost.

It has been painful to see the reverse engineering and skilful reconstruction of the USA data reported on these pages. I feel that there is a good chance that treatment of the Australian data will have learned from this USA pilot run. My preference for gradients arises from reading the USA efforts and from a career in geochemistry, where the data were more spatial than temporal, though replicate analysis for quality control was temporal.

I do not wish to denigrate the work of Australia’s BoM with the following comment, but they seem to be heading towards an ever smaller public data set while claiming that is it higher quality. This seems to have stressed recent data and displaced old data. It also places stress on adjustments between comparison sites when these sites are quite distant. Geoff.

Kenneth Fritsch

I have not found any errors with my previous 1945-1985 analysis indicating that the trend differences between CRN45 and CRN123 are statistically significant. I have included below two graphs showing the CRN45-CRN123 anomaly differences for 1920-2005 using a 5 year and 10 year moving average. What I have found is that the difference in trends between these CRN groups occurred most visually and in most sustainable form in this time period.

I would like to make the same comparison using the John V TOBS data set. I have not looked at your site lately John V, but are all of the CRN categories, 1 through 5, available in Excel spreadsheets from your site through the TOBS Adjustment stage? I would not expect them to yield different results but think we need to do the comparison.

John V.

Kenneth Fritsch:
Your results for urban adjusted CRN45 vs CRN123 are similar to my results for rural CRN12 vs all CRN5:
http://www.climateaudit.org/?p=2069#comment-140922

I have not yet done a difference plot (CRN5R minus CRN12R) but will do so soon. I’m waiting on Steve McIntyre to post his CRN12 gridded results as an independent check on my method.

John V.

Joe Bowles:
I chose 5-year smoothing because that seemed typical from HadCRU and GISTEMP. That’s not a justification — I just chose to go with what was common.

Out of curiousity I had a look at the other smoothing methods you suggested. In all I looked at 3, 5, 7, 9, and 11 year centred periods using mean and median. Since 1934 vs 1998 keeps coming up, I looked at the difference between the warmest year in the 1930s (c1934) and the warmest year between 1993 and 2003 (c1998):

c1998 minus c1934 (GISTEMP, pre-SHAP, Sept 12/07):
1yr: -0.02C
3yr Mean: +0.24C
5yr Mean: +0.16C
7yr Mean: +0.16C
9yr Mean: +0.16C
11yr Mean: +0.20C
3yr Median: +0.08C
5yr Median: +0.08C
7yr Median: -0.15C
9yr Median: -0.07C
11yr Median: 0.00C

Interestingly, c1998 is warmer than c1934 for all of the mean periods (particularly for 3yr mean and 11yr mean). For the medians, c1998 is warmer for two smoothing periods and c1934 is warmer for two smoothing periods.

For completeness I have plotted the 3yr, 7yr, and 11yr averages (mean and median):

Note: The 5yr and 9yr averages were removed to make the plots easier to read.

Joe Bowles

John V.

I was surprised to find the same thing when I was playing with the data. I was playing with a couple different smooths. I started out by smoothing the months in the CRN 12 data using a three month median smooth of average temperature. Then, I found the min and max for each year based.

I then tried using 11 yr.smooths separately on the average low by year and the average high.
I ran regressions on them. The R^2 were low, but the results were significant at the .01 level.
On the median low, 11 year smooth, the R^2 was about 16.8% p

Joe Bowles

John V

Somehow my last one (244) got cut off.

Lo Median, 11 year smoothing.
SUMMARY OUTPUT

Regression Statistics
Multiple R 0.410326803
R Square 0.168368085
Adjusted R Square 0.161073069
Standard Error 0.448379553
Observations 116

ANOVA
df SS MS F Significance F
Regression 1 4.640075805 4.640075805 23.07987632 4.77101E-06
Residual 114 22.91904144 0.201044223
Total 115 27.55911724

Coefficients Standard Error t Stat P-value
Intercept -11.25938569 2.421626879 -4.649513014 9.01295E-06
X Variable 1 0.0060 0.00124327 4.804151988 4.77101E-06

The one for high median average was:
Regression Statistics
Multiple R 0.378019304
R Square 14.3%
Adjusted R Square 0.135380161
Standard Error 0.29196729
Observations 116

ANOVA
df SS MS F Significance F
Regression 1 1.620201384 1.620201384 19.00643218 2.86892E-05
Residual 114 9.717918443 0.085244899
Total 115 11.33811983

Coefficients Standard Error t Stat P-value
Intercept 15.12325011 1.576869048 9.590682323 2.50311E-16
X Variable 1 0.0035 0.000809569 4.359636702 2.86892E-05

I did one graph subtracting the median for the entire series from the scores and was surprised that the low median jumped significantly in 1998 and seemed to be flat at about 1.4 degrees C; while the median high jumped some, but just kept edging up after 1998, ending about .55 degrees C.

I think the use of medians is probably giving an unbiased indication. It looks like the data supports the idea that we have seen something on the order of 1 degree C since 1890.

I would post the graph, but don’t seem to be able to get it to copy.

Kenneth Fritsch

Re: #240

Kenneth Fritsch:
Your results for urban adjusted CRN45 vs CRN123 are similar to my results for rural CRN12 vs all CRN5:
http://www.climateaudit.org/?p=2069#comment-140922

John V, I see a significant trend difference between CRN123 and CRN45 in the 1945-1985 time period, but I do not see this in your data analyses. What am I missing here?

John V.

I should’ve posted a link to my more recent analysis using TOBS instead of the older analysis using raw data:
http://www.climateaudit.org/?p=2124#comment-144287

The results are essentially the same: significant CRN5 warming relative to rural CRN12 from 1935 to 1975. The TOBS results show some CRN5 warming (relative to rural CRN12) from 1975 to 2005 and CRN5 cooling (relatie to rural CRN12) from 1915 to 1935.

I hope to quantify the error bars due to the low number of stations in the next couple of days.

John V.

#251 John Lang:
I can try to get to that. My plan is to be thorough and learn as much as possible from rural CRN12 (the best stations) before moving on to other station subsets.

Clayton B.

Here’s a chart of coastal stations (within 10km based on GHCN station data) vs. non-coastal stations run through my version of opentemp – absolute temperatures shown. I’m not quite certain which version of opentemp I’m using and I’m not quite sure which version of data I’m using.

I also understand that opentemp’s annual average temperature calculation does not really make sense when looking at coastal versus non-coastal – but I still think there may be something to it. JohnV, can a new region be easily defined in the code to outline the inner contiguous states and leave out stations near the coast?

I’m sure this has already been looked at…

John V.

Clayton B:
OpenTemp is currently hard-coded for the USA lower 48. For a comparison like this where geographic bias is the whole intent you would be better off using the station.csv file and averaging the stations externally (vs using the geographic weighting).

The OpenTemp analysis code has not changed from the first version I released. Newer versions have just added some file parsing options and fixed the problem of writing averages for incomplete years.

As SteveSadlov pointed out, it can be difficult to draw any conclusions from these types of subsets. I continue to believe that we should be cross-cutting with more precision. For example, if the goal is to compare coastal to interior, then extract the rural CRN12 stations from the coast and interior. If there are not enough stations then the study can not be done. Without controlling for UHI and micro-site issues, thereare too many unknowns that could contaminate the results.

Michael Jankowski

Re#254 and #255,
http://www.oceanservice.noaa.gov/programs/mb/supp_cstl_population.html has links to “Population Trends Along the Coastal United States: 1980-2008.”

This spreadsheet goes back to 1960 http://www.census.gov/compendia/statab/tables/07s0029.xls – maintains the same percentage of coastal vs non-coastal. Of course, it does consider the Great Lakes as coastal, and I’m not sure if that is apples-to-oranges with your chart. And the Great Lakes population as a percentage of the “coastal” has dropped and shifted to the Pacific.

John V.

Kenneth Fritsch:
The missing data points are estimated by USHCN as part of the Filnet processing stage (the stages are areal, TOBS, SHAP, Filnet, and urban). From the readme.txt at the USHCN FTP site (emphasis added):

“The Filnet data is the Time of Observation data that have been adjusted for the
Maximum/Minimum Temperature System (MMTS) bias, station moves/changes bias, and
contains estimated values for missing/outlier data.”

OpenTemp calculates the average temperature every month using only the data available for that month. The geographic weighting is calculated independently every month.

My original analyses were done using GHCNv2 raw, only because I was in a hurry and the file was easy to parse. Since the TOBS adjustment is well accepted I would suggest using TOBS data instead of raw data.

There is also clean MMTS data (areal + TOBS + MMTS, no Filnet) available in the “OtherIntermediates” directory at USHCN (I don’t have the full URL with me right now).

Kenneth Fritsch

Re: #260

Some of the differences that I have observed and calculated by comparing the USCHN Urban and MMTS data sets using the CRN12 stations for the period of 1920-2000 are the following:

The missing points for the Urban set are primarily in the earlier years, i.e. only 1% missing data in the period 1945-2005, while the time period 1920-1945 has approximately 10% missing data points. In the case of the MMTS data set the missing data points come more from the later years. It would appear to me that in performing the progressive adjustment from MMTS to Urban the process excluded some early data points without attempting to fill them in (with the averaging algorithm) and then with the algorithm filling nearly all the latter year missing points. This difference in handling by time period would indicate that there were some earlier missing data points that could not be “averaged in”.

The absolute differences between the Urban and MMTS data sets averaged approximately 0.6 degrees F per station per year. These differences were sufficiently consistent over the years for most stations that the URBAN-MMTS trend was not all that large (approximately 0.0013 degrees F per year over the 1920-2000 time period). A few stations in the CRN12 group did show large trend differences between using the Urban and MMTS data sets.

I have thought about the use of monthly data in attempts to overcome the missing data problem, but I have not been able to convince myself that it truly solves the entire problem. Could you convince me with some details on how you did it?

John V.

Thanks for the heads-up Jerry B. I downloaded the new data tonight and ran CRN12R and CRN123R analyses. For those who like the top 10 list, 2006 for the USA lower 48 is not as warm as 1934 or 1998.

I have a bunch of graphs for CRN12R and CRN123R vs GISTEMP, but I don’t have time to post them tonight. There is very good agreement between GISTEMP and both CRN12R and CRN123R. Standard deviation of the 1-year differences is 0.13C and 0.10C respectively with near zero trend (~0.03C/century).

Will try to post the graphs tomorrow.

JerryB

I would not render any particular interpretation of the reasons for USHCN V2 not
being published yet. Five years ago, an NCDC person who was working on it,
expressed the hope, if not the opinion, that it would be ready within a few
months from then.

steven mosher

RE 264. I don’t think you can compare the GiSS ananomaly to your anaomaly for absolute difference.

Only trend. The GISS anamaly is calculated from the mean of ITS data. Say 1950-1981 had a mean of
11C. So, the anomaly = Actual temp that year – 11C. say 2006 has a 11.6C temp. Then it has
a .6C anomaly.

IF CRN12R has a 1950-1981 mean of 10.C and a 2006 temp of 11.6C you have 1.6C anomaly from 10C
but a .6C anomaly from 11C

Subtracting the mean shifts in Y, doesnt change the trend, BUT to compare anomalies in an
absolute sense they must have the same figure for the base period

steven mosher

JohnV sorry, I thought you were making that comparison. My fault.

JerryB

The SHAP (and MMTS) files have usually not been updated there, as indicated
by the fact that the most recent set there was vintage 2000. That someone
deleted the old files, but not the directory, may bode well.

As for “recent” USHCN dailies, see ftp://cdiac.esd.ornl.gov/pub/ndp070/

And for relatively recent daily max/min data, see ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/
but if you try to browse the \all\ directory, your browser may fall asleep;
there are over 32000 links in that directory.

John V.

CRN123R vs GISTEMP
Above, I compared the rural, non-airport CRN123 stations (CRN123R) to the rural, non-airport CRN12 stations (CRN12R). They were shown to be very similar. Due to the larger number and better distribution of stations, I decided to start using CRN123R as the basis for future comparison results.

Here I will compare CRN123R to GISTEMP (Sept 12, 2007).

The list of CRN123R stations is available here:
http://www.opentemp.org/_results/20071011_CRN123R/stations_crn123r.txt

The distribution of stations is shown in the map below.

The first graph compares the CRN123R and GISTEMP yearly results. All graphs are normalized to the 1996-2005 reference period so that differences are easier to spot in the early years. The bars indicate the yearly differences between the results. A linear trend on the differences is also shown.

The summary statistics for the differences (CRN123R minus GISTEMP) are:
– Mean = -0.02C
– StDev = 0.10C
– Max = +0.23C
– Min = -0.32C
– Kurtosis = 0.11
– Skew = -0.14
– R^2 = 0.956 (CRN123R anomalies vs GISTEMP anomalies)

The second graph is the same as the first, but using 5-year centred moving averages so that trends are easier to identify:
– Mean = -0.01C
– StDev = 0.05C
– Max = +0.13C
– Min = -0.12C
– Kurtosis = 0.74
– Skew = -0.56
– R^2 = 0.972 (CRN123R anomalies vs GISTEMP anomalies)

The third graph shows the trailing 22-year trend in deg C per decade. I previously used an 20-year trend but chose to increase it to 22-years to make it a multiple of the sun cycle. (The trend is now over 2 complete sun cycles, or 1 cycle if you consider the polarity). The summary statistics on the differences are:
– Mean = -0.003 C/decade
– StDev = 0.03 C/decade
– Max = +0.05C
– Min = -0.07C
– Kurtosis = -0.06
– Skew = -0.42
– R^2 = 0.976 (CRN123R trends vs GISTEMP trends)

Conclusions:
For the USA lower 48, there is excellent agreement between GISTEMP and my results using only the best stations (rural stations with CRN=1, 2, or 3). The increased number and improved distribution of stations in CRN123R vs CRN12R increases my confidence in the agreement.

Joe Bowles

Re 274

It is interesting that the last PDO switch was in 1976 and that starts the biggest increase in temperatures. It is supposed to switch back to its cold phase in 2007-2008. If temperatures react as they seem to in the past, we may see a lot of cooling. From what I gather, it seems to have about a 30 year duration in each phase and that is about what the graph shows.

SteveSadlov

On various of the past couple day’s plots, the early 20th century fault scarp. Looking as unnatural as ever.

John V.

#279 Hu McCulloch:
I used the USHCNv1 TOBS-adjusted data. I have been avoiding Filnet and Urban precisely because of the cross-contamination issues. I have considered using MMTS or SHAP but they are not currently available.

I wish there was a way to edit my posts.

John V.

#281 steven mosher:
That’s right — I’m comparing the anomalies relative to a 1996-2005 reference period.

steven mosher

RE 282. What are the absolute temp of GISS during that period? Anomaly are used for Trend
then cannot be used for “difference” charts like you did, as we agreed.

John V.

steven mosher:
We agreed that we can not compare *absolute* temperatures. I am comparing temperature *anomalies*. It’s only misleading if the anomalies are represented as absolute temperatures.

Think of the anomaly plots as the integral of single-year trends.

The comparison I am makng is exactly the same as the comparison Steve McIntyre made when comparing his gridded CRN12 to GISTEMP. (There is one small difference in that I am now using a 1996-2005 reference period to make accumulated differences easier to see).

SteveSadlov

RE: #287 – Some other highly interesting means would be 1925 – 2005, 1900 – 2005, 1925 – 1980, 1900 – 1980. Then it would be interesting to plug each of these in.

steven mosher

Gisstemp – CRN12 1996-2005 reference period

Clayton B.

A look at OpenTemp averaging distances
I ran OpenTemp at various cell point averaging distances to see the effect (if any). My initial thought is that 1000km is too large of a radius (see pic below):

So I looked at 100km, 250km, 500km using all USHCN stations and hcn_doe_mean_data.

Note that 42% of the cells in the 100km case had less than three contributing stations. 250km and 500km had no cells with less than three contributing stations.

steven mosher

Here ya Go. Three lines. Opentemp ALL CRN12345. OpenTempCRN123 OpentempCRN45.

Goldilocks chart.

Clayton B.

293,

I understand why he uses the 1000km but there’s no reason to keep using that for just an analysis of the lower 48. I think using all USHCN stations the best is 250 km since it was the smallest distance that returned no cells with less than 3 stations.

We still gotta get those 50 or so stations inside of the mesh.

Clayton B.

here’s what we have right now:

I haven’t been able to find a better boundary list online; they’re too detailed.

steven mosher

Clayton if you run all 1221 then you get a list of stations not in the mesh.
I started to look each up, there were a bunch. keywest, fort lauderdale..

steven mosher

Well,

I have been pondering this CRN thing. One thing that occurred to me is we repeatedly found good
stations turned into bad stations by changes in equipment. Notably the MMTS which moved the
sensor closer to buildings. So, I started to cut the data a bit differently.

1. Here is a first Cut. CRN123 WITH the MinMax gauges. Old school. Here are the anomalies

For GISS and For CRN123mm. 5 year smooth trailing.

steven mosher

OK. here are all the parameters.

“Mosh let me make sure I understand the above graph.

1. This is plots of traditional stations in Stevenson Screens using mercury max-min thermometers only.”

A.I took your spreadsheet. I sorted on INSTRUMENTS. I Selected the MINMAX. I assume this LIG old style
minmax in Stevenson shelters.

B. I took that list of MinMax stations (200+). I sorted on CRN. I selected CRN1, CRN2, CRN3. THIS gave
me 39 stations.

C. I Fed this list of stations to Opentemp.

2. The GISS plot is the same stations, using GISS’s own data source.

A. No GISS is the GISS anomaly published on NASA. It represents the anomaly from 1950-1981. This
IS ALL THE STATIONS USED by GISS. So, it includes the 39 stations. Basically, if I
removed the 39 stations from GISS the picture would get worse.

3. The CRN123mm plot is using USHCN data V1

The CRN123mm plot uses the ghcnv2 data. I have not switched to johns new data set and it wont
make a differene here. Later I will download the lastest data, but right now I am using the
Orginal OpenTemp and orginal Ghcnv2 data. For the most part JohnV, Kenneth and I have
had very similiar results without regard to data sources ( quibbling about Tobs, shap, filnet etc)
Nothing dramatic. SteveMc and JohnV have the newer ushcn data. He can double check my work. The best thing
you can do is a BLIND duplication of my station list. MINMAX ONLY, CRN123. give that to JohnV to run
and steveMC. Clayton can run it too as can Kenneth.

I Use 11.2C to create my anomaly. This is the 1950-1981 Mean for all 1221 sites.
SteveMc and JohnV do something slightly different. But the difference in absolute
temp was so huge, you cannot ignore it.

4. The output is from OpenTemp, most recent build.

I believe so. There havent been anychanges except some minor issues relating to 2006 and incomplete
years. STILL, I would insist that JohnV, kenneth, Clayton, SteveMc, rerun.

Did I miss anything in getting the parameters of this run defined?

Not really. here are my concerns.

1. My selection of stations. Clayton has a database built. he should double check. Same with StMac.
2. Stations in the grid. I got 38 ( one dropped) thats still a low number.
3. We are talking anomaly in my chart HERE. TREND is a differrent issue. JohnV and Kenneth will righfully
pummel me If I dont point this out. Still, I believe the KEY to finding a trend difference is
finding a difference in absolute C. ( HUNCH )
4. ASOS is Warmer than Norm and Warming.
5. GEOGRAPHY can screw this up. Especially altitude differences. But I can imgine why
high alt sites would be minmax? Could be a correlation.

Bottom line I have been slicing through the data in a bunch of different ways. I did the instrument slice
today for the hell of it and bam.

Need to get dates when each station switched over. Obviously ASOS and H0 83 dont go back to 1880

Was that confusing enough?

Steve McIntyre

Continued at 2201.