GIStemp – Islands In The Sun

A skinny atol turns into a wide airport

Marshall Islands Airport - Where's The Thermometer?

Like A Man Without A Home

UPDATE (10 Sept 2009):

While chasing down the WSMO (Weather Service Meteorological Observatory) at Guam, I stumbled on this interesting confirmation of the Airport Bias in the weather station record. I bolded a bit From:

http://www.crh.noaa.gov/ilx/?n=nws-wb-history

Brief History of National Weather Service Offices Past and Present

The National Weather Service (known before 1970 as the Weather Bureau) has had many offices. Many of them evolved as the aviation industry expanded, supporting local airport observations. Many also had varying warning and forecast duties. However, in the 1990’s, the NWS was consolidated into 120+ offices, each with roughly the same duties.

The following is an attempt to list all past and present NWS/WB offices. If you have information on any of these, or ones that were missed, please contact Chris.Geelhart@noaa.gov . Several offices have more detailed histories available, which can be accessed by clicking on the city name.

Get a copy while you can, it may not stay “up” long if folks notice 8-)

While I still have not found the location of the WSMO, I did find this nifty infra red image of the place. You ought to be able to do a direct comparison of the land station and the surrounding “boxes” of water and decide for yourself how valid it is to say they are ‘the same’ as GIStemp does…

http://www.nws.noaa.gov/gu_sat_tab.php

The nearest I’ve been able to find is a celebration picture posed on a grass field that claims to be at the WFO (Forecast Office). But notice the airplane tail in the background:

http://celebrating200years.noaa.gov/activities/postcards/postcard208.jpg

Found on:

http://celebrating200years.noaa.gov/activities/postcards.html

And this is a wonderful link to a site that is also trying to find the thermometer… but has linked in the Google Satellite view of where it ought to be (though notes that the LAT LON do not have enough accuracy to validate that view…) A very interesting site worth a visit:


http://weather.gladstonefamily.net/site/PGUM

At 13.4783 N 144.7945 E there is a building with a light blue roof. This looks a lot like the building with a light blue roof in the pdf linked by Ellie down in the comments, so I think it is the right place. Behind it is a path to a white box that looks to me to be about 10 feet on a side. It has potential. There is also a white square in front of it to the right side a little near the road fork and the requisite ‘side yard’ on the left with a collection of whitish things in it and what looks like a fence. Of these, I think I’d go with the side yard just because I’ve seen that a lot and the thing at the end of the path looks a bit large. It sure would be nice to have “eyes on the ground” take a look there. Know anybody in Guam?

The link Ellie gave shows many sites with an odd white “observatory dome”. Perhaps to protect equipment from hurricanes? If so, then that “10 feet on a side white box” might just be one of these domes. That would place the equipment near the airplane parking lot.

http://www.faa.gov/news/conferences_events/pacific_aviation/agenda/media/NWS.pdf is a decent read (and mostly pictures) and shows close ups of several of the Pacific Island sites. As Ellie pointed out, it also states that ALL the sites are going to be at airports. Well, at least now we know where the future warming is going to come from…

Why all the interest in Guam? Ellie, in the comments, found that there were two sites with a very large increase in temperature. These two sites will warm the boxes for 20 degrees of lat and long around them. That is an area about the size of the continental USA, or slightly larger than Europe (excluding Russia, who have always been rather ambiguous about their European nature…). The other one is the Marshall Islands, but the airport there so completely dominates everything that it is pretty clear what it is doing. For Guam, there are more places to hide the Stevenson Screen ;-)

Basically, we have two stellar examples of growth of aviation making a site warm, then using those sites to “warm” a 20 x 20 degree chunk of the globe. It would be very interesting to compare these two sites to a couple of islands “nearby” with no airports one sunny day…

So, it looks to me, like we have a very simple explanation for the “warming of the planet”. It is simply measuring the growth of Aviation and the expansion of Airports, with the Airport Heat Island effect, around the world; to islands and “underdeveloped” nations post WWII, and with growth and development in the industrialized states.

Original Islands In The Sun

Well, I decided to do a bit of a “peek ahead” into the last land step – STEP3.

This was partly because I’d gotten some flack from Warmistas that “the anomaly would save them” or that “Grids and Boxes were immune to thermometer count changes!”. They have had to slowly retreat as each STEP was shown to “have issues” that lead to warming based on the code and process, not on the data. They were now standing on this last island of code and tossing rocks. OK, then that is where I go next.

It was also partly because in the Pisa thread Peter O’Neill had gotten ahead of me so I thought I ought to do a “look ahead” just to stay competitive, if nothing else.

Peter is doing a great job and will be producing important results (and more formally packaged than mine) so watch for his work to be published. But I’m not ready to abandon the field, so I decided to “plough the field ahead” a bit and see if I could turn up something new (even if Peter may run ahead of me next week ;-)

Finally, there was also the issue of “STEP2 begins the anomaly steps” (it uses anomalies as part of the UHI “correction” ) and I wanted to see how much commonality there was to the code (a fair amount was ‘re-hacked’ from earlier steps and shows it) and partly because it also does a swap of data format from fixed format CHARACTERS to a more free form FORTRAN specific binary. That makes some of my “quick” benchmarking not-so-quick in that I need to write programs to read the binary format files and, frankly, I’d become a bit saturated on that front and needed a diversion to “recharge”.

So I did a “peek ahead”.

Up until now, we’ve watched the data warm as The March Of The Thermometers heads south. We’ve seen airports used as pristine “rural stations” for UHI adjustment when they are not. We’ve seen GIStemp add about 1/2 C of warming to the data in the first two STEPs. We have also seen that the older and more stable records do not show warming, while new younger records carry all the signal. And we’ve seen that the 6 zones they use in the zonal step of STEP2 are just way too few to get any decent protection from The March Of The Thermometers. Then Peter turned up the heat by showing that the UHI “Adjustment” was sensitive to exactly which stations make it into the UHI “Adjustment” batch to the tune of 0.4 C for Pisa. (That is, take out mountain stations over 900 M and the bogus “cooling” of the past for Pisa changes from -1.4C to -1C based on that alone.)

And no, the “anomaly” processing in STEP2 did not save the AGW thesis nor did it save GIStemp from the observation that the 1/10C place is sensitive to changes in exactly what thermometer records are used for UHI “adjustment” to the tune of a bit shy of 1/2C. (And those records are basically randomly chosen. The selection criteria are not as rational as one would like and distance it not sufficiently controlled. Hot sub-urban airports from 1000 km away can be “nearby rural”.) So the 1/10 C position of the GIStemp data are basically randomly chosen since they are derived from this process.

But “Grids And Boxes And Anomalies – Oh My” will save it all! Was the chant from the Warmistas. So I took a look.

And the Warmistas are looking more and more like “A Man Without A Home”…

Floating On A Log

My first step is just to examine the code. Then I examine the log files. The “code review” was uneventful. More of the same. Semi-cryptic stuff built as a “hand tool” with lots of parameters for cherry picking and tuning. Clear evidence of making the program fit the data. (Things like using large zones so the program doesn’t dump everything into the “not enough data to fudge together some in-fill” bucket. They use an “80 Region for the whole globe” pass THEN fill in the smaller 100 “sub-boxes” in the Region. Dig Here!)

Then I look in the log files.

It didn’t take long. On the 3rd ASCII log file I starting hitting “issues”. That file is:

to.SBBXgrid.1880.GHCN.CL.PA.1200.log

To decode that name a bit, we can see that it takes the temperature data (smeared over 6 equal 30 degree latitude bands in STEP2, with a variety of infilling, stretching, and homogenizing already done) and tries to pin it into a SBBX “sub-box” on a grid; the data have a cut off in the cold period of 1880 and is based on the GHCN set (with amendments and modifications) and the STEP2 PA anomaly process was applied; while in this step the “Reference Station Method” look around was stretched out to 1200 km in the hopes of finding a station, any station, that could be used to fill in missing boxes.

That the file name encodes the “tweaking” done to parameters is an interesting testimony to the hand tuning that was done. They needed to keep straight which run produced what output. It tells you the parameters that were cherry picked, in a way. (Another “Dig Here”).

So how well did they do?

Well, pretty good, I guess… The code looks like it uses an 80 x 100 matrix for boxes. That’s about 8000 of them. (If I read the code right, it’s a set of 80 “regions” from N to S pole each with 100 “sub boxes” in in. A gross size to enable more “spread” if needed, then smaller boxes for detail, if available.)

It manages to fill in a lot of them, even though we know the ‘data’ filled in are a complete fiction since the early steps have less than 8000 thermometers for the whole planet; and the lions share of them are concentrated in Europe and North America (as we saw in looking at The March Of The Thermometers; the Southern Hemisphere has a lot of “Big Empty”…). So at the end of this, how many boxes are still “left blank”? “Only” 1/8 of them:

$ grep "NO STATIONS" to.SBBXgrid.1880.GHCN.CL.PA.1200.log | wc -l
   1026
$ 

Though there are a bit over 300 that have no data in the baseline period but do have some data now. (Dig Here! One could also do a general “quality metric” for grid boxes based on some sort of “stations used” vs grid box and “station-months” vs grid box. It would also be interesting to look into those boxes with more stations to see what impact comes from adding stations over time… )

Not too bad, I guess! So we probably have about 1/4 of the planet decently instrumented. About 1/4 “so-so”. That only leaves about 1/4 for “poor” and 1/4 for “OMG” bad (with only half of them still being completely hopeless) after all the torturing of the data done so far.

I took a look into the log file and noticing that the further down the list I got into Southern Hemisphere boxes, the more I ran into “NO STATION” flags. So it looks like there are plenty of boxes that can get a new thermometer in the Southern Hemisphere and continue to contribute to warming the “Northern Hemisphere winter” on a global average basis.

FWIW, the exact log entries look like this:

 REGION       56   134849      182 STATIONS USED
 LAT,LON,STN-MNTHS,STNS,IDS -4325-17250  2143       5   939870000 934360010 932920000 935460000 933730000
 LAT,LON,STN-MNTHS,STNS,IDS -4325-16950   779       2   939870000 932920000
 LAT,LON,STN-MNTHS,STNS,IDS -4325-16650   237       1   939870000
 LAT,LON,STN-MNTHS,STNS,IDS -4325-16350   237       1   939870000
 NO STATIONS FOR CENTER     57     7
 NO STATIONS FOR CENTER     57     8
 NO STATIONS FOR CENTER     57     9
 NO STATIONS FOR CENTER     57    10
 LAT,LON,STN-MNTHS,STNS,IDS -4093-17250  2824       6   939870000 933090000 934360010 932920000 935460000 933730000
 LAT,LON,STN-MNTHS,STNS,IDS -4093-16950  1135       3   939870000 932920000 933730000
 LAT,LON,STN-MNTHS,STNS,IDS -4093-16650   237       1   939870000
 LAT,LON,STN-MNTHS,STNS,IDS -4093-16350   237       1   939870000
 NO STATIONS FOR CENTER     57    17
 NO STATIONS FOR CENTER     57    18
 NO STATIONS FOR CENTER     57    19
 NO STATIONS FOR CENTER     57    20
 LAT,LON,STN-MNTHS,STNS,IDS -3869-17250  3290       7   939870000 933090000 934360010 939940001 932920000 933730000 931120000
 LAT,LON,STN-MNTHS,STNS,IDS -3869-16950  1135       3   939870000 932920000 933730000
 LAT,LON,STN-MNTHS,STNS,IDS -3869-16650   237       1   939870000
 NO STATIONS FOR CENTER     57    26
 NO STATIONS FOR CENTER     57    27
 NO STATIONS FOR CENTER     57    28
 NO STATIONS FOR CENTER     57    29
 NO STATIONS FOR CENTER     57    30
 LAT,LON,STN-MNTHS,STNS,IDS -3652-17250  1971       5   939870000 939940001 932920000 933730000 931120000
 LAT,LON,STN-MNTHS,STNS,IDS -3652-16950  1375       3   939870000 939940001 932920000
 LAT,LON,STN-MNTHS,STNS,IDS -3652-16650   237       1   939870000
 NO STATIONS FOR CENTER     57    36
 NO STATIONS FOR CENTER     57    37
 NO STATIONS FOR CENTER     57    38
 NO STATIONS FOR CENTER     57    39
 NO STATIONS FOR CENTER     57    40
 LAT,LON,STN-MNTHS,STNS,IDS -3441-17550  3811       8   939870000 933090000 934360010 939940001 932920000 930120000 933730000 931120000
 LAT,LON,STN-MNTHS,STNS,IDS -3441-17250  1971       5   939870000 939940001 932920000 933730000 931120000
 LAT,LON,STN-MNTHS,STNS,IDS -3441-16950   892       1   939940001
 NO STATIONS FOR CENTER     57    45
 NO STATIONS FOR CENTER     57    46
 NO STATIONS FOR CENTER     57    47
 NO STATIONS FOR CENTER     57    48
 NO STATIONS FOR CENTER     57    49

I’ve left this log file ‘truncated right’ rather than letting it wrap and strip out blanks. The exact station ID’s off the right edge are not so important just yet.

The first line is the summary for the prior “region” number 56 showing the totals used in it. The rest of the entry is for the next “region” of 57 as we fill in the sub-boxes.

For now, just notice that there are a LOT of boxes with no stations…

This is as you step through the LAT / LON for box locations.

Now if you look at one WITH data, you will see an embedded “side header” that says the record consists of the latitude and longitude (without decimal or degree marks), the “station months” of data that go into that box value, the number of thermometers that contribute to that box, then the list of thermometers (Station IDs) used to make that box value.

OK, so what?

Well, first I noticed that several of the boxes have a “1” for thermometer count. A SINGLE thermometer for the whole BOX. So much for multiple thermometers with gridding, averaging, and boxing smoothing out any anomalies from the particular locations! (“Dig Here!” It would be very interesting to rank the boxes by that thermometer count and look for patterns. How many in each rank. Geographic asymmetry. Percent airports. Etc.)

Then I noticed that a lot of these started with the same STATION ID.

939870000

My but that gets around a lot, I think. Wonder who it is?

A brief “grep” in v2.inv file pulls up the record:

50793987000 CHATHAM ISLAN -43.95 -176.57 49 0R -9HIxxCO 1A-9WATER A 0

and it is flagged as an airport (the “A’ at 1A-9WATER)

Hmmm. Add an airport on an island and your Airport Heat Island effect can warm 1200 x 1200 x PI square kilometers around. That is 4,523,893.344 square km or 1,746,684.268 square miles. Take just a moment to look at those numbers again. 1.7 Million square miles or 4.5 Million square km all controlled by one little box near the runway… and there are thousands of islands to choose from.

Do this where the box was not an airport in prior years, you can warm a great number of boxes. All those boxes warmed with both The March Of The Thermometers to warmer climates and The Airport UHI Correction “issues”. Now look at the ones with more than a single Station ID. The second station is often the same. Even when there are multiple records, they may simply be using 2 Airports to make a box rather than one… The “box and grid” does not get rid of the Airport bias.

After all of about 10 seconds pause, I pondered Diego Garcia. A tropical paradise that was part of The British Empire from time to time but has recently had a bunch of big airplanes added. Wonder what’s happening there?

Graph of Temperature History at Diego Garcia from GISS data

Temperature History at Diego Garcia from GISS

A “grep” Is A Terrible Thing To Waste – So I Didn’t

(The Unix / Linux command to find text in a file is the “grep” command, that stands for “Globally search with a Regular Expression and Print”)

16161967000 DIEGO GARCIA -7.30 72.40 3 0R -9FLxxCO 1A-9WATER C 17

So are there any records in the log file for STATION ID 61967000?

Oh Yeah…

 LAT,LON,STN-MNTHS,STNS,IDS -1507  7537   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1507  7762   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1507  7987   291       1   619670001
 NO STATIONS FOR CENTER     52    37
 NO STATIONS FOR CENTER     52    38
 LAT,LON,STN-MNTHS,STNS,IDS -1507  8662   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS -1507  8887   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS -1271  6862  1618       3   619860003 619880003 619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1271  7087   887       2   619880003 619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1271  7312   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1271  7537   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1271  7762   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1271  7987   291       1   619670001
 NO STATIONS FOR CENTER     52    47
 NO STATIONS FOR CENTER     52    48
 LAT,LON,STN-MNTHS,STNS,IDS -1271  8662   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS -1271  8887   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS -1037  6862  1618       3   619860003 619880003 619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1037  7087   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1037  7312   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1037  7537   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1037  7762   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1037  7987   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS -1037  8212   291       1   619670001
 NO STATIONS FOR CENTER     52    58
 LAT,LON,STN-MNTHS,STNS,IDS -1037  8662   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS -1037  8887   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS  -805  6862   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS  -805  7087   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS  -805  7312   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS  -805  7537   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS  -805  7762   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS  -805  7987   291       1   619670001
 LAT,LON,STN-MNTHS,STNS,IDS  -805  8212   291       1   619670001
 NO STATIONS FOR CENTER     52    68
 NO STATIONS FOR CENTER     52    69
 LAT,LON,STN-MNTHS,STNS,IDS  -805  8887   288       1   969960000
 LAT,LON,STN-MNTHS,STNS,IDS  -574  6862   291       1   619670001

As a SMALL sample. And notice how many of THEM have a 1 for station count…

So we search for that Station Id in the log file, then count how many lines that is with the unix “wc -l” command (“word count, but count whole lines only”).

$ grep  619670001 to.SBBXgrid.1880.GHCN.CL.PA.12 | wc -l
     64
$ 

So this says 64 distinct lines have that STATION ID number in them. That is a LOT of boxes… Now multiply THAT behaviour by all the Tropical Pacific Islands…

Nothing Wrong with A Pristine Tropical Island Paradise

Nope, not at all. If only these were such. Now Chatham Island is more a temperate Island Paradise near New Zealand. And it is not too bad; away from the airport it comes close, except it is a commercial airport.

http://en.wikipedia.org/wiki/Chatham_Islands_/_Tuuta_Airport

And it has grown over time. From:

http://en.wikipedia.org/wiki/Chatham_Islands

The grass landing-field at Hapupu, at the northern end of the Island, proved a limiting factor, as few aircraft apart from the Bristol Freighter had both the range to fly to the islands and the ruggedness to land on the grass airstrip. Although other aircraft did use the landing field occasionally, they would often require repairs to fix damage resulting from the rough landing. Hapupu is also the site of the JM Barker (Hapupu) National Historic Reserve (one of only two in New Zealand) where there are momori rakau (Moriori tree carvings).

In 1991, after many years of requests by locals and the imminent demise of the aging Bristol Freighter aircraft, the construction of a sealed runway at Karewa, Tuuta Airport, allowed more modern aircraft to land safely. The Chathams’ own airline, Air Chathams, now operates services to Auckland on Thursdays, Wellington on Mondays, Wednesdays and Fridays and Christchurch on Tuesdays. The timetable varies seasonally, but generally planes depart the Chathams around 10.30 am (Chathams Time) and arrive in the mainland around noon. Then they refuel and reload, depart again at around 1 pm back to the Chathams. Air Chathams operates twin turboprop Convair 580 aircraft in combi (freight and passenger) configurations and Fairchild Metroliners.

Graph of Temperature History at Chatham Island from GISS data

Temperature History at Chatham Island from GISS

It looks to me like the temperature series is more or less dead flat through about 1980, though it becomes a bit more volatile in the ’60s through ’80s with higher highs and more low lows, then the bottom end gets pulled up after the airport move. Maybe warm tarmac keeping nighttime lows up a 1/2 degree or so or warming the day by a degree so the minimum average is higher? It would not take much to move the low range of that “average of averages” up by that 1/2 C.

More dramatic is Diego Garcia that has had rapid and major expansion into a world wide crossroads for Military Heavy Lift aircraft.

Here is one fellow’s memory book of his time there:

http://carlvillanueva.tripod.com/id22.htm

And a bit more “formal” look at the place:

http://www.globalsecurity.org/military/facility/diego-garcia.htm

And I’m sure you have all noticed when on vacation to tropical “paradise” that the tarmac gets much hotter under the tropical sun than it does “up north”. Direct angle of illumination. 12 hours of it. Little seasonal relief. etc. Just as I’m sure you’ve also noticed how much cooler it is under the palm trees near the jungle or near the ocean.

From these two semi-randomly selected samples we have a temperate airport warming the cold southern band open water 1200 km south; and we have a tropical military base airport warming open ocean all around it. Kind of makes you wonder what Guam and Palau are doing… (“Dig Here!”)

ISLAND Heat Island Effect?

So, do we need IHI too? The ability to plant a jet airport on some tiny rock in the middle of the ocean and warm dozens of surrounding grid cell boxes of open water? Sure looks like it to me…

And I’m pretty sure that all those remote tropical island getaways spots that have sprouted airports since WWII did not have them in 1900 …

Now take a look at a globe. Notice that the Tropical Pacific has a lot more islands than anywhere else? Nature has a bias here. There are not very many islands in the “circumpolar cold band” near Antarctica. There are not very many islands north of Hawaii and south of Alaska. There are not very many islands in the Cold Atlantic. Etc. So nature has a bias toward lots more islands in tropical places; and STEP3 will have taken that bias and run with it. And a big “Dig Here” would be verifying that most of those island records come from Airports. (My sampling says so, but an exhaustive chart and graph would be authoritative.)

So my first “dip of a toe” into the last “land” step, STEP3, finds a continuation of prior themes. Data spread too thin and too far to where there are none. Missing data “filled in” by using lots of Airports; “rural” means near the tarmac and the jet exhaust. Southern Hemisphere still slowly being “filled in” as The March Of The Thermometers continues…

And no, Virginia, Grids, Zones, and Boxes have most decidedly NOT prevented a single hot thermometer record from warming lots of surrounding “turf” even if it is open water… Though I’m left to wonder how much “near the tarmac Diego Garcia Military / Airport” is representative of “over the water 1200 km away”…

The only step after this is STEP4_5, where “4” just lets you get an updated version of the Hadley SST anomaly map and “5” just merges it with the output of STEP3. So the buggering of the “land” data is complete at this point, even when “land” looks to be boxes of nearby sea…

I have not bothered to look into how Hadley “re-imagine” their SST data, but given the history of lost original data, obfuscation, resistance to FOIA requests, non-publishing of methods, etc. I suspect that there is not much of merit to find there.

In Conclusion

This “first look” shows “islands” are surprisingly important to STEP3 (just as cold mountains play a big role in Pisa in STEP2).

It sure looks to me like we need a “Station Survey” of islands, looking to check the integrity of those stations and just how many of them are at airports and too near the tarmac, hangers, and terminal buildings. (Of course, this also means one must do “due diligence” and find a proper alternate site to put the thermometer… One near the palm trees and not too far from the beach. Maybe near a thatched cabana… It would be very important to monitor the location for at least 24 hours; and to prevent “warming bias” from your physical presence, many cold beverages will need to be applied ;-) Any volunteers? 8-)

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About E.M.Smith

A technical managerial sort interested in things from Stonehenge to computer science. My present "hot buttons' are the mythology of Climate Change and ancient metrology; but things change...
This entry was posted in AGW GIStemp Specific, Favorites, GISStemp Technical and Source Code and tagged , . Bookmark the permalink.

30 Responses to GIStemp – Islands In The Sun

  1. Rob R says:

    I have only scanned through this so I may have missed a few things. You might like to check the latitude for Chatham Island. Its something like 43 deg South, i.e. middle latitude and not at all tropical. The climate there is cool-mild, moist and windy.

    Rob R

  2. JLKrueger says:

    RobR,
    I think the point being made is less about “tropicness” and more “Airport Heat Islandness”. A tarmac at 43 S is no less prone to enhanced heating than one at 43 N. And the heat island effect of the Catham Island airport is used to “infill” vast tracts of ocean with a higher temperature than would otherwise be the case.

  3. E.M.Smith says:

    @Rob R , JLKrueger

    I “confounded” two things. I’ll polish the text a bit to try and make it a bit more clear.

    One is the “Island Airport Heat Island Effect”. Yes, Chatham is near N.Z. and not tropical, but it has the thermometer at a commercial airport. Exactly how representative is that “tarmac patch on a rock” of a box of ocean 1200 km SOUTH ?

    The other is a statement that, to the extent we have more islands in the Tropical Pacific than anywhere else, there will be a secondary effect from the non-random distribution of islands with their (by definition in the ‘land’ phase) abnormally high impact on surrounding boxes of water with no ‘land’ thermometers. This does not apply to Chatham, but does apply to all those islands in the Indian Ocean and the Tropical Pacific.

    There are a lot more islands between N30 to S30 than between the poles and 30 degrees. This will bias the impact. A chart of “NO STATIONS” vs latitude compared with Stations Used vs latitude would be a great “Dig Here!”.

    I wanted to put both points into the posting, but looks like I need to clarify a bit.

  4. Ellie in Belfast says:

    @E.M. A chart of “NO STATIONS” vs latitude compared with Stations Used vs latitude would be a great “Dig Here!”.

    if you can come up with the data I’ll happily graph it for you.

  5. Ellie in Belfast says:

    E.M. You are right. The data for Diego Garcia shows a steep warming trend over the timescale of the expansion of Diego Garcia as a military base. Graph here:
    http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=161619670001&data_set=2&num_neighbors=1

    I quick tour of the Pacific flagged up these two as having steep warming trends in the last 2 decades.
    Majuro/Marsha 7.1 N 171.4 E 531913760000 rural area 1955 – 2009
    Wsmo Agana, G 13.6 N 144.8 E 529912170000 rural area 1956 – 2004

    Both will warm large boxes.

  6. e.m.smith says:

    Ellie, I’ll take a look at the two you flagged and see what’s in the log file.

    Per the “no stations” data, I’ll take a look.

  7. rob r says:

    E.M.S.

    Understood.

    Anthony Watts surfaces stations project has demonstrated that it doesnt take much to create a bias in an individual temperature record. If there is such an effect at the Chathams airport then it will likely be over-represented in the global anomaly by the GIStemp process.

  8. E.M.Smith says:

    Ellie in Belfast I quick tour of the Pacific flagged up these two as having steep warming trends in the last 2 decades.
    Majuro/Marsha 7.1 N 171.4 E 531913760000 rural area 1955 – 2009
    Wsmo Agana, G 13.6 N 144.8 E 529912170000 rural area 1956 – 2004

    Both will warm large boxes.

    Majuro / Masha is the Main Marshall Islands hub. It’s an International airport:

    Specs: http://www.airnav.com/airport/PKMJ
    7897 ft. of grooved asphalt and not much else… The picture in the side bar is stunning. A skinny little ribbon of atol suddenly widens into a broad asphalt solar collector… I’m sure THAT was not there in 1930 …

    General: http://en.wikipedia.org/wiki/Majuro

    Talk about an understatement!

    For Majuro / Marsha we have an interesting bit of data. Here are two of the records:

    LAT,LON,STN-MNTHS,STNS,IDS 1746 17437 1194 2 912450000 913760000
    LAT,LON,STN-MNTHS,STNS,IDS -344 16987 3626 8 916430003 913760000 915330000 916100002 915300010 916290000 915070000 915270000

    Now compare those two “latitude” numbers. A 20 degree range… (yes, these are the extremes).

    The longitudes do something similar. Picked out ‘by eye’ so may not be exactly the extremes:

    LAT,LON,STN-MNTHS,STNS,IDS 1037-17887 366 1 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 805 16087 3581 7 913660001 913480001 913340000 913760000 915300010 913560000 912500000

    I make that about 20 1/4 degrees (180 is the same as -180)

    So we have a roughly 20 degree by 20 degree “box” warmed by that single record.

    Though it shares the space with more other sites than does Diego Garcia. Full data, ragged format, spaces compressed:

    $ grep “913760000” to.SBBXgrid.1880.GHCN.CL.PA.1200.log

    LAT,LON,STN-MNTHS,STNS,IDS 344-17887 1218 3 913760000 916100002 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 574-17887 1218 3 913760000 916100002 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 805-17887 911 2 913760000 916100002
    LAT,LON,STN-MNTHS,STNS,IDS 1037-17887 366 1 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 114 16537 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 114 16762 3409 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 114 16987 3409 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 114 17212 3409 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 114 17437 3490 7 916430003 913660001 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 114 17662 2733 6 916430003 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 114 17887 2740 6 916430003 913760000 915330000 916100002 917010000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 344 16312 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 344 16537 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 344 16762 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 344 16987 3409 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 344 17212 3409 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 344 17437 3101 6 913660001 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 344 17662 3101 6 913660001 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 344 17887 2130 4 913760000 915330000 916100002 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 574 16087 3581 7 913660001 913480001 913340000 913760000 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 574 16312 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 574 16537 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 574 16762 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 574 16987 3297 7 913660001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 574 17212 3409 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 574 17437 3101 6 913660001 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 574 17662 2789 5 913660001 913760000 915330000 916100002 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 574 17887 1491 3 913760000 916100002 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 805 16087 3581 7 913660001 913480001 913340000 913760000 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 805 16312 2857 6 913660001 913480001 913760000 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 805 16537 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 805 16762 4041 8 913660001 913480001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 805 16987 3297 7 913660001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 805 17212 3297 7 913660001 913760000 915330000 916100002 915300010 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 805 17437 2482 4 913660001 913760000 915330000 916100002
    LAT,LON,STN-MNTHS,STNS,IDS 805 17662 2150 4 913660001 913760000 916100002 916290000
    LAT,LON,STN-MNTHS,STNS,IDS 805 17887 1184 2 913760000 916100002
    LAT,LON,STN-MNTHS,STNS,IDS 1037 16312 3264 6 913660001 913480001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 16537 3264 6 913660001 913480001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 16762 3809 7 913660001 913480001 912450000 913760000 916100002 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 16987 3065 6 913660001 912450000 913760000 916100002 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 17212 3065 6 913660001 912450000 913760000 916100002 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 17437 1843 3 913660001 913760000 916100002
    LAT,LON,STN-MNTHS,STNS,IDS 1037 17662 1843 3 913660001 913760000 916100002
    LAT,LON,STN-MNTHS,STNS,IDS 1037 17887 1184 2 913760000 916100002
    LAT,LON,STN-MNTHS,STNS,IDS 1271 16312 3264 6 913660001 913480001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 16537 3264 6 913660001 913480001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 16762 2520 5 913660001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 16987 2520 5 913660001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 17212 2212 4 913660001 912450000 913760000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 17437 2017 3 913660001 912450000 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 17662 1298 2 913660001 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 17887 639 1 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 16537 3264 6 913660001 913480001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 16762 2520 5 913660001 912450000 913760000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 16987 2212 4 913660001 912450000 913760000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 17212 2212 4 913660001 912450000 913760000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 17437 2017 3 913660001 912450000 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 17662 1194 2 912450000 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 16987 2212 4 913660001 912450000 913760000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 17212 2017 3 913660001 912450000 913760000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 17437 1194 2 912450000 913760000
    LAT,LON,STN-MNTHS,STNS,IDS -344 16987 3626 8 916430003 913760000 915330000 916100002 915300010 916290000 915070000 915270000
    LAT,LON,STN-MNTHS,STNS,IDS -344 17212 3898 8 916430003 913760000 915330000 916100002 916500000 915300010 916290000 916480000
    LAT,LON,STN-MNTHS,STNS,IDS -114 16762 4381 8 913660001 913760000 915330000 916100002 915300010 913560000 916290000 915070000
    LAT,LON,STN-MNTHS,STNS,IDS -114 16987 4091 7 913660001 913760000 915330000 916100002 915300010 913560000 916290000
    LAT,LON,STN-MNTHS,STNS,IDS -114 17212 3086 6 916430003 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS -114 17437 3086 6 916430003 913760000 915330000 916100002 915300010 916290000
    LAT,LON,STN-MNTHS,STNS,IDS -114 17662 3367 7 916430003 913760000 915330000 916100002 915300010 916290000 916480000

    There are a total of 69 records with that Station ID:

    $grep “91376000” to.SBBXgrid.1880.GHCN.CL.PA.1200.log | wc -l
    69

  9. E.M.Smith says:

    Ellie in Belfast I quick tour of the Pacific flagged up these two as having steep warming trends in the last 2 decades.
    Majuro/Marsha 7.1 N 171.4 E 531913760000 rural area 1955 – 2009
    Wsmo Agana, G 13.6 N 144.8 E 529912170000 rural area 1956 – 2004

    Agana is the GUAM site.

    Now this has got to have an impact: http://guamairport.com/

    History of growth from 1930’s:

    http://guamairport.com/index.php?pg=the-airport/history

    http://www.airnav.com/airport/GUM
    10,000 feet of asphalt and concrete. LAT LON a bit different, but only a bit. Would be interesting to “google earth” the actual temperature station.

    For Wsmo Agana it is fewer stations by a smidgeon:

    $grep 91217000 to.SBBXgrid.1880.GHCN.CL.PA.1200.log  | wc -l
         57
    $ 
    

    And more of the records are averaged over 6 or more sites (only one at 5, by eye.) And it looks like the box of influences shrinks to about 18.9 x 15.75 (by eyeball record scan and hand subtraction, so you might find a better set… but I doubt it ;-)

    Still, a significant influence over a large area…

    The Data:

    [chiefio@tubularbells work_files]$ grep 912170000 to.SBBXgrid.1880.GHCN.CL.PA.1200.log

    LAT,LON,STN-MNTHS,STNS,IDS 344 14737 3635 8 912120001 913340000 912170000 940440000 940140003 940270000 912180000 912030000
    LAT,LON,STN-MNTHS,STNS,IDS 574 13837 3770 7 914130000 914080000 912120001 912170000 975600000 912180000 912030000
    LAT,LON,STN-MNTHS,STNS,IDS 574 14737 3609 8 914130000 912120001 913340000 912170000 940440000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 574 14962 3507 8 912120001 913480001 913340000 912170000 940440000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 574 15187 3183 6 912120001 913480001 913340000 912170000 940440000 912180000
    LAT,LON,STN-MNTHS,STNS,IDS 805 13837 3830 8 914130000 914080000 912120001 912170000 975600000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 805 14062 3830 8 914130000 914080000 912120001 912170000 975600000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 805 14287 4091 8 914130000 914080000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 805 14962 3507 8 912120001 913480001 913340000 912170000 940440000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 805 15187 2976 7 912120001 913480001 913340000 912170000 912180000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 805 15412 3224 7 912120001 913480001 913340000 912170000 912180000 913560000 912500000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 13837 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 14062 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 14287 4091 8 914130000 914080000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 14512 3127 7 914130000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 14737 3127 7 914130000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 14962 3025 7 912120001 913480001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 15187 2976 7 912120001 913480001 913340000 912170000 912180000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1037 15412 3284 8 912120001 913480001 913340000 912170000 912180000 913560000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 13837 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 14062 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 14287 4091 8 914130000 914080000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 14512 3127 7 914130000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 14737 3127 7 914130000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 14962 3025 7 912120001 913480001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 15187 2976 7 912120001 913480001 913340000 912170000 912180000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1271 15412 2976 7 912120001 913480001 913340000 912170000 912180000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 13612 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 13837 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 14062 3367 7 914130000 914080000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 14287 2403 6 914130000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 14512 3127 7 914130000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 14737 3127 7 914130000 912120001 913340000 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 14962 2787 6 912120001 479910003 913340000 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 15187 3531 7 912120001 479910003 913480001 913340000 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1507 15412 3746 8 912120001 479910003 913480001 913340000 912170000 912180000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 13612 4129 8 914130000 914080000 912120001 479450002 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 13837 4344 8 479710000 914130000 912120001 479450002 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 14062 3582 7 479710000 914130000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 14287 3582 7 479710000 914130000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 14512 4352 8 479710000 914130000 912120001 479910003 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 14737 3364 7 479710000 912120001 479910003 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 14962 2787 6 912120001 479910003 913340000 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 15187 2787 6 912120001 479910003 913340000 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1746 15412 3002 7 912120001 479910003 913340000 912170000 912180000 912500000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 13837 4344 8 479710000 914130000 912120001 479450002 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 14062 4344 8 479710000 914130000 912120001 479450002 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 14287 2594 6 479710000 912120001 912170000 912180000 912030000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 14512 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 14737 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 14962 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 1988 15187 2063 5 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 2234 14062 3092 6 479710000 912120001 479450002 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 2234 14287 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 2234 14512 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 2234 14737 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    LAT,LON,STN-MNTHS,STNS,IDS 2234 14962 3100 6 479710000 912120001 479910003 912170000 912180000 912320000
    [chiefio@tubularbells work_files]$

  10. E.M.Smith says:

    Ellie,

    The “no stations” thing is a bit more complicated than can be handled quickly. This step does a couple of strange things where it makes 80 REGION boxes then makes 100 sub boxes inside of them. It does not just neatly increment LAT and LON. So you can get the REGION and SUBBOX numbers, but then you have to “undo” the numbering to find out the LAT and LON … and the mapping is not, er, direct. (Systematic, yes, simple, no. Just look at the lat / long cycling in the listed data sets and you will see that it cycles through a line, then returns to the prior edge of the box, then repeats… )

    So that bit will have to wait.

  11. Ellie in Belfast says:

    E.M.,

    Re Guam, yes I didn’t look at the identity last night as it was late, but I realised this morning and I found the airport history too. Unfortunately finding the weather site(s) is not easy. See –
    http://weather.gladstonefamily.net/site/PGUM
    http://weather.gladstonefamily.net/site/PGUM?tile=10;showall=1

    Does your grep data mean there are 50ish boxes that Guam is used to influence (along with each of the other stations listed)?

    Re the ‘no stations’ thing, you know I am happy to leave the timing to you.

  12. E.M.Smith says:

    The “grep” is Unix for “Global find using a Regular Expression and Print what you find”. Normal folks would call it “find”… but in Unix land, “find” is a different command that does something else …

    So yes, what it does is “find” all lines with the selected text (in this case the station ID) in the line somewhere and print out that line. We then do another Unix / Linux thing and “pipe” the printout into another command that counts the number of lines. That is “wc -l” for “word count, but really count each line”. The result is a composite command that does “Count how many lines in this file contain the text {Station ID}”. And since each line is a “Sub-Box” of 1/100 a “region”, the command actually returns the “number of sub-boxes modified by use of this station.”

    It is that kind of neat trick that makes Unix so useful. It is the cryptic way it does it that makes it so painful to learn ;-)

    I really do think you have run onto something with these two stations. They are warming the most, and growing the most. “Nearby” there are places that grew less, and warmed less. Very clear evidence for an Airport Heat Island…

    On the “someday” list: A plot of temperature “anomaly” for a site vs airport growth profile. I think it would be very telling…

  13. Ellie in Belfast says:

    E.M.,
    On the “someday” list: A plot of temperature “anomaly” for a site vs airport growth profile. I think it would be very telling…

    My thoughts exactly. It jumped straight to the top of my ‘to do in the spare time I spend on climate data and blogs’ list as soon as I saw the history of Guam airport. Very postable.

  14. Murray Duffin says:

    A UHI equivalent of the IHI effect will be seen also in South America, where a very few measurement stations that have experienced major urban growth in the last 40 to 50 years influence maybe 1/8th of the earth’s surface. If you have time and inclination, please look at South America next. Murray

    REPLY: Sounds good to me. Any suggestions? I was thinking maybe Brasilia, coastal Brazil stations, and a couple of sites in Argentina and Chile? I’ll poke around… -ems

  15. j ferguson says:

    If no-one has “qualified” the stations whose data are being extended over many grids, you could find yourself with something like this:

    Aunt Mildred takes a job in the building whose ac condenser is next to the thermometer station. She likes it cold so she turns down the AC a few notches. Result is that AC runs more, is off less. This effect would be driven by outside air temperature and humidity.

    Now there is a very positive feedback to whatever warming might be going on outdoors for the thermometer station to sense.

    If the outside air temp goes up a bit, Aunt Mildred’s condenser will really perk. And we’ll see a major heat bump all over the South Pacific.

    On the other hand, if they move the station away from the AC unit, maybe it will be too remote for Aunt Mildred to check it every day.

    Nuts.

  16. Adam Gallon says:

    Gods above! What a heap of foetid halibut giblets.
    Do the “climatologists” realise that their temperature measurements are built upon such quicksand foundations (& Thus knowingly defraud us?) or don’t know and thus reveal an assinine depth of stupidity in utilising such contaminated readings without even a casual check that there are other factors at play?

    REPLY: Willful deceit or astounding negligence. Not a very pretty choice, is it? Now you know why I’m sinking my time into this effort…

  17. Ian Beale says:

    Tongue firmly in cheek –

    I thought Mann’s original temperature hockey stick might have had potential as a predictor of the rise of turbo-jet engines

  18. E.M.Smith says:

    @Ian Beale

    If you thought that, you ought to take a look at:

    https://chiefio.wordpress.com/2009/08/26/agw-gistemp-measure-jet-age-airport-growth/

    Where we have pictures now of the Milan SEAPLANE port that mutates into a Jet Airport with tarmac…

    Yeah, the growth of turbo-jets… No tongue in cheek… (but beer in hand ! 8-)

  19. H.R. says:

    Hey! Hey!

    Three cheers for the images at the beginnings of posts.

    They are really helpful as a divider and as an easy way to find a particular post.

    Hip! Hip! Hooray!
    Hip! Hip! Hooray!
    Hip! Hip! Hooray!

  20. E.M.Smith says:

    @H.R.:

    Well, with a bit of Ellie’s help, I’m slowly catching on to this “pictures and graphs” stuff 8-) On the one hand, I can just look at a table of numbers and see the pattern so I don’t quite “get it” that other folks need the graph (I understand it at an intellectual level, but to me they ‘taste the same’…) On the other hand, it takes some fair amount of effort to get good at doing graphs and charts; and that is effort I’d not put into it before (see “one hand” for why ;-)

    But a little bit at a time, I can learn new tricks… Glad you like the results.

  21. Ellie in Belfast says:

    I too like the visuals, as I have siad before. I know what you mean about being able to eyeball data and see patterns. I can do that with my own data. You have the advantage of working with the code here, but I am only beginning to really understand how the data is presented and to know what to look for. For anyone who drops in here the visuals are a benefit – afterall you want to get the message out to more than just people who are looking at the code.

    I am about to send you three good (IMHO) graphics re Guam.

  22. Peter O'Neill says:

    @E.M. Smith

    Peter is doing a great job and will be producing important results (and more formally packaged than mine) so watch for his work to be published. But I’m not ready to abandon the field, so I decided to “plough the field ahead” a bit and see if I could turn up something new (even if Peter may run ahead of me next week ;-)

    Then Peter turned up the heat by showing that the UHI “Adjustment” was sensitive to exactly which stations make it into the UHI “Adjustment” batch to the tune of 0.4 C for Pisa. (That is, take out mountain stations over 900 M and the bogus “cooling” of the past for Pisa changes from -1.4C to -1C based on that alone.)

    I do need to advise some caution – my quick comparison of the influence on Pisa of two very obvious groups of rural stations, Italian low altitude and non-Italian high altitude, is certainly not sufficient to draw conclusions already at this stage. Considerable further investigation for other urban stations will be needed, and while the information I am finding as I add further instrumentation is indeed interesting, it is still possible that Pisa could just prove to be an “interesting exception”.

    I have just posted on the original thread for anyone interested the results for Pisa using those mountain stations instead for adjustment.

    Pisa incidentally appears to be a milder example of the “airport cool island” adjustment which first lead me to take an interest in Gistemp: my home airport, Dublin Airport, has changed from the rather minor airport I remember from the 1960s to become now the 8th (or perhaps 7th – I’m not sure which) busiest airport in Europe, and a likely candidate for a UHI adjustment. The raw data shows little obvious warming trend in the past 70 years, but the adjustment is impressive, if counter-intuitive. All I can say for it is that the knee does seem to correspond to the time of the last station move.

    REPLY: No problem, Peter. This is just a blog, not a peer reviewed publication. So we can have a bit of “energy” about the discovery of something interesting (knowing all the time it may fall on further investigation) without the need to soak everything in ice water from the starting gate. ;-) That was what I was hoping to imply with my “more formally packaged” comment. Put another way: We’re “in the field” here; a bit more dust on the rock hammer and the beer is warm, but no starch needed in the shorts and the talk around the campfire is more pleasant than when we get “back to town, suited up with tight shoes, at the podium.” That will be the time for tables and graphs with 20 sites and formal statistical distributions and all the rigamarole of publication… And frankly, I suspect that is more your domain than mine. I’m more the field guide type, winching the Land Rover across the river… and saying “Well, now, will you look at THAT one! Never seen a croc like that one before; think it’s a new type? Big teeth on him!”.

    BTW, what do you think of the “Island Heat Island” effect? Interesting, eh? -ems.

  23. waymad says:

    I can vouch for the Chatham Islands growth. Spent a month there in the early 70’s blasting rock out of a quarry to help build the road to the soon-to-be-upgraded airport. Flew out on one of the old Bristol freighters.

    The rise of the airport has coincided with the decline in coastal shipping. Looking at the temp graph above, the main feature from my time there onwards is the raised volatility, which would be consistent with time-of-day or week measurements, and relatively infrequent flights in and out.

    Airports near very small settlements also act as a social hub, with everyone congregating on scheduled flight days to gossip, meet and greet, and all travelling via their pickup trucks and people movers.

    Climatology, meet anthropology….

    REPLY: Very interesting point. I “grew up” in a small rural town, and you are right. Anything “special” happens, 1/2 the town shows up and it’s a pick-nick and / or tail gate party BBQ… And all the vendors meet the tourists at the arrival gate too… Hard to explain to “city folk” that it can be a “big event” when the mail truck arrives or one guy comes home from the Army. ;-) -ems.

  24. Ellie in Belfast says:

    You are going to LOVE this: All Pacific Weather Service Offices to be located at Airports
    http://www.faa.gov/news/conferences_events/pacific_aviation/agenda/media/NWS.pdf

    REPLY: OMG, how could they! Well, at least it will be easy to identify what stations have an Airport Heat Island bias 8-{ ems.

  25. Ellie in Belfast says:

    Guam ‘grid box’ with AHI = 0.72 deg C DeltaT/Century
    Guam ‘grid box’ without AHI = 0.38 deg C DeltaT/Century
    that is without post 1986 data.

    The AHI is at least 1 degC and is visually obvious in graphs.

    REPLY: Um, wouldn’t it be 0.72 – 0.38 = 0.34 C for AHI?

  26. Ellie in Belfast says:

    No. You need to look at the graphs to see what I mean. Sent to you.

  27. C says:

    It looks as if the island had a full met-station at a different location prior to the recent military interest: –see the caption under the fifth image from the top:
    http://www.zianet.com/tedmorris/dg/tara.html

  28. C says:

    Woops, I should have said that the above image was for old met station at Diego Garcia. The ninteeenth image from the bottom on the following page shows the air-traffic control hut in December 1972 – it sits on top of four oil barrels:
    http://www.members.tripod.com/carlvillanueva/id22.htm

    -Definitely not the sort of thing that you would want to stand in when controlling taxying B-52’s !!

    I wonder if I have misunderstood what you are saying: Does this single corruped station record end up being given the same weight as a whole gridcell in the USA/Europe, when it comes to calculating the global temperature anomaly ?

    REPLY: [ Single warm stations are “waved off” by the AGW crowd with the assertion that Grids and Boxes will average any station in with dozens of others in finding a cell anomaly; and via UHI correction based on nearby rural stations, the particular station will be just fine. In the end, the box and grid cell will be Just Fine too. But this fails when the thermometer in question has no “rural neighbor” so can not have a UHI correction via the “reference station method” and especially so when it has no box neighbors, so the nearby boxes must look to it alone for their created anomaly. The “reach” of the Grid and Box step is 1200 km. So a single station at a hot airport is used to “guess” the temperatures, and through that the anomalies, up to 1200 km in all directions. Not just in it’s own grid cell, but in any and all grid cells up to 1200 km away. 2400 km “edge to edge”. That space is roughly the size of 1/2 of the continental U.S.A. This is part of what makes the thermometer deletions in The Great Dying of Thermometers in the NCDC product GHCN so problematic. It is most easily seen on isolated islands, but the effect will exist in any isolated thermometers (and there are lots of isolated thermometers…) -E.M.Smith]

    Is there some dilution stage that occurs later on (perhaps from sea surface temperatures)?

    REPLY: [ Not in the GIStemp product proper. GIStemp through STEP3 is “all land data all the time” that then, via the grid and box process, can spread land data up to 1200 km out to sea in those boxes over water. There is a completely optional per the code and documentation STEP4_5 that does a blending of the HadCRUt Sea Surface Anomaly map via an interpolation process, but given the recent revelations about CRU and HadCRUt being ‘cooked books’ I’m not so sure I’d want to hang my hat on that particular hook as “saviour”. And, as mentioned, it is an entirely optional step. Also note that at best this can give you “half a loaf” of repair, as the broken GIStemp box value is interpolated against the HadCRUt box. So you could at best theoretically reduce the bias / error some, but not remove it; assuming of course that the HadCRUt SST map is right, which is doubtful… -E.M.Smith ]

  29. findor says:

    I know this is a bit late for this thread but I have been looking at the results produced by GISTemp for the anomaly map around Diego Garcia. I chose 1982 to 1989 for my test since this is before the land data at Diego Garcia gets fragmented and is after they switched from the Hadley ship/buoy data to the (Reynolds-Rayner-Smith 2001) satellite data for SST’s.

    I did 3 runs with 1200 km smoothing then looked at the grid data produced in the area around Diego Garcia – -5 to -9 Lat, 69 to 75 Long

    Run 1 was Land Data only – GISS Analysis
    Run 2 was Ocean Data only – Reynolds et al
    Run 3 was Land & Ocean data combined

    As you would expect with only land data, all the grid cells were the same except for to at the edge.

    The ocean data had a different value in each cell as you would expect from the satellite readings.

    Land & Oceans combined produced EXACTLY THE SAME DATA as Oceans alone.

    This says to me that GISTemp is not using small islands at all for its Land & Oceans results. Maybe they are excluded because of not enough stations or whatever, but if they were combining the data it should have shown up to at least the 3rd or 4th decimal point.

    Since the primary index that GISTemp produce is their Land & Oceans one and this is the ‘headline’ index the world looks at, small island effects aren’t going to matter. So it becomes a question of major land records & ocean records.

  30. Ruhroh says:

    @Findor

    It has become clear that the date-of-download is the closest that NASA comes to a revision_level_control. Revision history? Perhaps you enjoy a pleasant dream-life.

    Clearly the GISTemp analyzed by el Jefe is not the exact one now on-line for the maps; the 250km optional ‘outreach’ parameter does not exist in his dissected_and_reanimated version.
    (why do they only allow it for ‘adjusted data’ but not the GHCN ‘unadjusted’)

    After getting burned a few times upon making a conclusion from a single test case, I’d encourage you to explore this a bit with variation of parameters.

    You could very well be correct, in which case the consistency of result will be readily evident.

    RR

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