Aeroporti Milano - 1930 Was Different
This airport replaced a large artificial lake from when long distance air travel was largely by sea plane. I think it is fairly clear that shifting from a large puddle of water to acres of tarmac will have some effect…
Aeroporti Idroscalo - 1930 Was Different
Exactly the same airport? No. But given how GIStemp glues together temperature series that doesn’t really matter, now does it? The temperature at “the airport thermometer” in Milan from 1930 will have changed rather a lot as it migrates to the Jetport tarmac from lakeside.
From: http://milan-lin.airports-guides.com/
Also close to Milan’s Linate Airport is the man-made Idroscalo Lake (Lake Idroscalo), which was actually previously used as an airport for flying boat-planes. Today Lake Idroscalo boasts a state-of-the-art floating stage, complete with electric roof and regularly hosts a series of open-air concerts.
and from: http://en.wikipedia.org/wiki/Linate_Airport
It was built next to Idroscalo of Milan in the 1930s when Taliedo Airport (located 1 km (0.62 mi) from the southern border of Milan), and one of the world’s first aerodromes and airports, became too small for commercial traffic. Linate was completely rebuilt in the 1950s and again in the 1980s.
So which of them to do you think will be warmer?
AGW and GIStemp Measure The Growth of Jet Age Airports
Earlier, I found that GIStemp used lots of airports to ‘correct’ for Urban Heat Island effect.
https://chiefio.wordpress.com/2009/08/23/gistemp-fixes-uhi-using-airports-as-rural/
It treats at least 500 airports as pristine rural areas even though airports are typically hotter than the world around them. They are cleared of vegetation in most cases, and whenever possible have either tarmac or concrete runways and taxi ways, along with lots of tarmac skirts, airplane parking areas, car parking areas, etc. They also typically have a lot of auto and truck traffic, major buildings, etc.
Oh, and often many tons of kerosene and / or Avgas being burned…
So it was perplexing that they would use so many airports to correct for UHI effect.
We also saw that as time passed, thermometers marched from the frozen north to ever warmer latitudes.
https://chiefio.wordpress.com/2009/08/17/thermometer-years-by-latitude-warm-globe/
Gee, I wondered, how many of those southern marching, warming, thermometer records were at airports? Could there be a “Double Dip” with the thermometers moving South and to Airports? And having done the code to match airport flags to temperature records, it wasn’t a big leap.
And the leap is directly off a cliff of “Global Warming” by putting thermometers in Tropical Airports. Then adding The Jet Age. I think it is time to redefine the A in AGW to be Airports. We have “AGW”, it’s just “Airport Global Warming”…
I don’t know how much a Tropical Airport exceeds the temperatures of nearby forests nor how much they exceed a Scandinavian Woods, but I do know they will be much warmer… Certainly far warmer as a local phenomenon than the tenths and hundredths of a degree of “AGW” that we are supposed to be panicked about. Far more than anything attributed to CO2.
It’s pretty simple, really: Black asphalt and burning tons of Jet-A Kerosene with constant Diesel and Gasoline ground vehicles and huge areas of buildings with industrial scale air conditioning makes for a very hot place. “Dig Here” if you want to explain “Global Warming”. So I’m going to coin a (hopefully) new term: AHI – Airport Heat Island effect.
In my opinion, we need to take a representative sample of airports. Put a Stevenson screen a few miles upwind, downwind, and cross wind to compare with the readings at the airport. Then document exactly how much AHI there is at several representative airports. Only after that is subtracted from the present temperatures will we have any clue what the “Global Average Temperature” might be.
UPDATE 26 August 2009 per AHI Prior Use
Update: [A new day, a new Google search]. I’ve found two prior uses of AHI, so I’ve not coined a new term, but almost! One is in a comment at Climate Audit:
http://www.climateaudit.org/?p=2852
Where they talk about “PC”s and the need for proper statistical analysis. See comment 84 by hswiseman.
hswiseman:
March 29th, 2008 at 5:57 pm
Re: #84
JFK or ORD and the like have ASOS stations arguably located in the middle of 24/7/365 blast furnaces. But as you correctly point out the instrumental record is probably as accurate as you are going to get. That would make these sites suitable longinatudally as long as you acknowledged that airport heat island effects are baked into the nominal temperature record. ASOS also would allow you to compare ASOS TAN (Taunton MA), a prop job daylight onesy-twosey airstrip to nearby KBOX (NWS Taunton) to nearby BOS and PVD. With a little work, you might tease out a valid AHI factor. It is the absence of conventions in measurements and phony baloney adjustment factors that have transformed the entire ground level temperature exercise into a huge waste of time money oxygen and bandwidth. There are alot worse starting points than ASOS. Maybe that’s why they are the stations making into the grids.
Another is at:
http://scienceblogs.com/deltoid/2009/03/tim_curtin_thread.php
Where in a comment a poster says:
Gaz: try GISS for Honolulu Airport for starters, then Google Honolulu Observatory, and then Mauna Loa temperatures.
Nathan’s integrity – or rather lack of any smidgeon of that – can be judged from his failure to apologise for fasely claiming that I had said Mauna Loa does not measure CO2. Then he hurls abuse at me which had that come from me to him would have led to Lambert’s disemvowelling. Well, when Gaz puts up the GISS graph for Hon. Airport, Nathan will see not UHI but AHI – airport heat island – effect with spendid upward TREND since 1960 that is not matched either at Mauna Loa or Hon. Observatory.
Any correlation between Mauna Lo CO2 and Hon. Airport’s temps is spurious, as there is none between the CO2 and Hon. Observatory temps.
Nathan, instead of abuse just check out the data. If I am wrong, be my guest. But I bet you will not because you can not.
stephenk: I should apologise for loose wording, whatever BOM may send to Hansen at GISS (and it certainly does have more non-airporst than airport stations) he only uses a handful of stations that so far as I can see are mostly airports. I think the 75% claim came from Climate Audit but cannot be sure. I am about to travel overseas so do not have time to do a full check, but you could easily compare the GISS station list with BOM’s list of stations.
Posted by: Tim Curtin | August 4, 2009 4:36 AM
I don’t know anything about the site, or about Tim Curtin, but the blog operator ends the thread with a notice that Tim Curtin has been banned from the site…
It looks like two folks got there before me, but not by much! By my reading of the dates, “first use” credit ought to go to hswiseman from March 29th, 2008. About 17 months ago. OK, maybe not important, but one does need to give proper attribution.
With that out of the way, back to what I found in the analysis.
OK, the data.
First, a word about the limitations of the data. It’s astoundingly poor for this kind of analysis. You would really like to know the history of a site over time. WHEN did it become an airport? WHEN did it change from a grass field to gravel, or tarmac? WHEN did it change to 100,000 jet flights a year? What you get is a single “A” character to tell you that, Right NOW it is an airport. The past? Who knows!
[Basically, the “airstn” flag ought to be attached to the annual temperature record in the v2.mean file; not to the Station Information record in the v2.inv file. That would let us know what happens over time. Further, if it were up to me, I’d make it a flag that encodes for “grass field” vs “gravel” vs. “private prop planes” vs. “asphalt / regional” vs “large” vs. “Military” vs. “international jet port” vs. … Basically, a size and type of aircraft flag.]
A startling example jumped out at me. In the “by all years” data, the first thermometer is in 1701. This chart shows that in the decade ending in 1709 ALL the thermometers were in the N. Cold latitude. (The data run from South Pole, through the EQuator, to the North Pole). The bottom record with DecLApPct: is the Decade by Latitude Airport Percent. It is this computed record that will be used in the following tables.
Thermometer Location by Latitude Band
Year S.P S.C S.T S.W EQ. N.W N.T N.C N.P Total
LAT pct: 1701 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1702 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1703 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1704 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1705 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1706 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1707 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1708 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
LAT pct: 1709 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
DecLatPct:1709 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100.0
DecLApPct:1709 0.0 0.0 0.0 0.0 0.0 0.0 0.0 69.2 0.0 69.2
So here we have 69% “airports” in 1709. Looking at the “raw” data I saw that the record from 1701 is classed as an “Airport”. “I don’t think so Tim…”
A bit of digging turned up that it started life as a Knights Templar site. I don’t think they were flying airplanes then…
From:
http://en.wikipedia.org/wiki/Berlin_Tempelhof_Airport
The site of the airport was originally Knights Templar land in medieval Berlin, and from this beginning came the name Tempelhof. Later, the site was used as a parade field by Prussian forces, and by unified German forces from 1720 to the start of World War I. In 1909, Frenchman Armand Zipfel made the first flight demonstration in Tempelhof, followed by Orville Wright later that same year.[6] Tempelhof was first officially designated as an airport on 8 October 1923. Lufthansa was founded in Tempelhof on 6 January 1926.
Later that decade, a second thermometer joined the history of temperatures, but it never grew into an Airport, so we only have a 69% “airports” statistic for the decade on average.
For our purposes, we need to be aware of this, but it is actually a bit conservative. We will see The Arrival Of The Jet Age starting ‘way early’ in the 1700s, but that also means that any tendency to “More Airports In Recent Years” that we do see will, in fact, be significantly understated. So if we see anything it is a big something.
One other cautionary note: This airport has been decommissioned recently. Eventually, the GHCN “type” will change to no longer be an airport. At that time, the entire history of aviation at that site will evaporate from the temperature record. It will be “just another open space”… From Orville Wright, to WWII, to the Berlin Air Lift, and Lufthansa will all be “disappeared” into a shopping mall, or soccer field, or museum, or whatever it becomes. This has significant implications for our temperature history.
We can also “draw a line in time” at about 1909 as the start of the age of aviation. We will then know that all the “airports” in the record prior to that time were not airports yet, but grew into being airports in later years. We can still see the airport growth, just not as precisely in time as we might like.
So in this first group, up to 1899, we can see The March Of The Thermometers South; and, since we know that aviation did not exist in those years, we can also see that they changed, in later years, to become airports over time. Exactly when is not available to us in the GHCN data set. For that, we would need to go digging elsewhere. There is significant opportunity here, IMHO, to do a decent paper on “The Change In Land Use at Global Temperature Sites Over Time” (or some such.) If you are looking for a paper thesis “Dig Here!”.
Percentage of sites that are AIRPORTS NOW, by decade of record Year S.P S.C S.T S.W EQ. N.W N.T N.C N.P Total 1709 0.0 0.0 0.0 0.0 0.0 0.0 0.0 69.2 0.0 69.2 1719 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.1 0.0 9.1 1729 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.7 0.0 16.7 1739 0.0 0.0 0.0 0.0 0.0 0.0 0.0 52.4 0.0 52.4 1749 0.0 0.0 0.0 0.0 0.0 0.0 100.0 60.6 0.0 67.5 1759 0.0 0.0 0.0 0.0 0.0 0.0 50.0 50.0 0.0 50.0 1769 0.0 0.0 0.0 0.0 0.0 0.0 42.9 32.3 0.0 36.4 1779 0.0 0.0 0.0 0.0 0.0 0.0 32.6 29.5 0.0 30.7 1789 0.0 0.0 0.0 0.0 0.0 0.0 27.5 34.0 0.0 30.7 1799 0.0 0.0 0.0 0.0 0.0 100.0 30.9 29.3 0.0 31.0 1809 0.0 0.0 0.0 0.0 0.0 100.0 33.2 26.5 0.0 31.5 1819 0.0 0.0 0.0 0.0 0.0 63.6 35.0 27.0 0.0 31.8 1829 0.0 0.0 0.0 0.0 100.0 78.9 36.6 18.0 0.0 28.9 1839 0.0 0.0 0.0 0.0 25.0 67.6 31.8 18.9 0.0 26.4 1849 0.0 0.0 100.0 0.0 38.9 36.2 30.9 17.2 0.0 25.2 1859 0.0 0.0 44.0 0.0 14.3 45.0 34.1 14.6 0.0 27.0 1869 0.0 0.0 36.4 0.0 3.2 47.0 31.5 15.9 0.0 26.3 1879 0.0 0.0 27.1 25.0 15.2 40.4 36.8 22.7 36.8 32.7 1889 0.0 100.0 21.0 44.9 28.0 37.8 34.3 28.1 33.3 32.6 1899 0.0 66.7 19.3 38.8 31.8 36.5 27.0 30.9 32.4 28.3
So here we can see that about 1/3 of all the early sites became (and stayed to now!) airports. We can also see that the first thermometers to arrive in the south tend to become Airports in later years. I would speculate that further investigation will show that thermometers tended to follow the spread of empire and military bases, to eventually become airports when aviation joined the arsenal.
OK, on to the next batch. In 1909 we have aviation, but it is from grass fields with small planes and dinky engines. It rapidly grows to large planes by WWII, so the 1949 decade ought to represent the zenith of the Piston Age. Then comes the Jet Age starting in the 1950’s.
Pay particular attention to the equatorial zone. See how it rises from 29% airports in 1909 (so airports were ADDED to 29% of those sites over time AFTER 1909) and it rises to 68% by 1999. Is it any wonder we had a “hot year” in 1999? 66% of the thermometers at the Equator were located at airports, and many of those were burning tons of kerosene per day over black tarmac. Almost identical is the Southern Warm latitude band (28% to 65% today). That Tropical Zone is dominated by Airports today.
And we can also see that a lot of the “new” thermometer records must be at airports (given that the records counts rise in those year, as shown in prior postings, and with the airport percent continuing to rise, the only possible conclusion is the the newer records are disproportionately at airports.)
So we have found far more than “anything” and it is very clearly a big “something”.
Notice also the “Northern Polar” band. It rises from 44% (32% in 1899 – so many sites from 1909 BECAME airports later) rising to 65% in 1989, then dropping a little to 57% now. (One wonders if the “base realignment” in those years had an impact, shutting down some airports… Another “Dig Here”?) Looks like all those military bases in Alaska we saw in the “airports as rural correction” posting. Bet the Russians did the same thing during “The Cold War” Can you say “DEW Line?”… I would speculate that we now know why “the north” is “warming” so much. More of the polar thermometers moved onto nice warm airports with runway deicing and jet exhaust. Are we simply seeing the rise and fall of the Cold War in the temperature record? The irony in that accidental pun is just too delicious to pass up.
But truly fascinating is the Southern Temperate zone. From 15% to 74% (which certainly means really from 0% in 1909 to 74% today, those 1909 sites being converted to aviation later in their life).
The Southern Cold band does the odd thing of dropping from 42% to 18%, but since it is all of 20 thermometers today (and never exceeded 1% of the world thermometers) the impact of the S.C. band is minimal in the temperature history. That it was 2 thermometers in 1899 as it entered the 1909 decade rising to 9 by the end (see prior posting) simply says that a few of the thermometers we had there, during part of that decade, at some future date became an airport. It does not say that 42% of the records in 1909 were measured at an airport in 1909. We see this pattern in the earlier years chart above (prior to 1909) where we occasionally have 100% as the first value. Just showing that the early first non-airport facility eventually became an airport. While the rest of the record had “gotten over” this effect (as the thermometer count rose to larger sizes) the S. Cold band is still such a small count in these later years that such effects can have sway.
Percentage of sites that are AIRPORTS NOW, by decade of record Year S.P S.C S.T S.W EQ. N.W N.T N.C N.P Total 1909 0.0 42.0 15.1 28.2 29.2 36.7 22.8 33.3 44.4 25.4 1919 0.0 36.4 12.8 23.5 25.1 37.7 20.9 35.0 39.8 24.1 1929 0.0 37.0 11.9 27.4 27.7 32.7 20.4 35.9 56.4 24.1 1939 0.0 43.9 17.6 32.0 33.8 29.1 20.2 36.2 51.0 25.1 1949 0.0 32.3 24.4 37.6 44.4 31.8 23.3 39.3 60.9 29.1 1959 0.0 24.0 35.0 50.0 59.4 39.4 30.9 41.0 62.9 37.3 1969 0.0 18.1 39.3 53.2 63.2 40.2 31.4 41.1 61.5 39.0 1979 0.0 17.9 39.1 52.0 64.2 40.7 28.8 41.1 62.3 37.7 1989 0.0 20.7 41.5 52.5 67.8 41.9 29.1 40.8 64.9 37.7 1999 0.0 21.0 53.5 57.4 68.0 53.0 32.6 49.0 59.0 41.6 2009 0.0 17.9 74.0 64.7 66.5 51.5 30.2 45.4 57.3 41.0
The conclusion is pretty damn simple:
Move Polar thermometers onto airports, often military bases, and they warm up. Add a bunch of thermometers at tropical airports and you measure an increasing average of temperatures.
“AGW” is a measurement of military activity and the Jet Age vacation traffic to the tropics. “Airport Global Warming”.
Musings from the Chiefio 
Curiouser and curiouser. You suggested ‘Dig here’ on Berlin Tempelof, so I did.
GIStemp lists the site as having a population exceeding 3 million, so they might just have done a UHI correction on it. As I’m not techie enough to have worked out how to set up a blink comparitor, I just opened the three pages on different tabs and switch between them.
1. Plot of “raw GHCN Data + USHCN corrections” (note I am unsure what corrections have been done by this stage)
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617103840000&data_set=0&num_neighbors=1
This is the first (longest) of five plots for Berlin-Tempelhof. THe others are shown as dotted lines.
2. Plot of “after combining sources at the same location”
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617103840005&data_set=1&num_neighbors=1
This one is not plotted on the same Y scale
3. Plot of “after homogeneity ajustment”
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617103840005&data_set=2&num_neighbors=1
Comparing 1. and 3. show that the 1880s are make cooler and the 1920s-1949s are made warmer. No doubt that gives a better anomaly.
Oh Berlin-Dahlem is even better
1. and 2. are identical but 3. is a lovely neat, warming plot.
1. or 2. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617103810000&data_set=1&num_neighbors=1
3. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617103810000&data_set=2&num_neighbors=1
Lindenberg and Broken, rural areas, don’t change.
Hamberg-Fuls (pop 1.7M)
1. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617101470000&data_set=0&num_neighbors=1
2. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617101470001&data_set=1&num_neighbors=1
OOOH. just the same – the older data gets cooler.
3. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=617101470001&data_set=2&num_neighbors=1
Hard to see change as the scales change
OoooH here is a really juicy one.
Milano-Linate (Airport, City of 1.7M)
1. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=623160800000&data_set=0&num_neighbors=1
2. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=623160800000&data_set=1&num_neighbors=1
Both warming to 1950s then step change to flat or cooling.
BUT
3. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=623160800000&data_set=2&num_neighbors=1
What a difference. When you flick back and forth it is like a seesaw (teeter-totter).
Now nearby Milano-Malpensa (also airport, but rurally located and defined as ‘rural area’) shows cooling in all three data types and is unadjusted.
3. (homogenised data) http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=623160660000&data_set=1&num_neighbors=1
The data set stops in the early 1980s, which would have been before significant growth of the airport (so my comments the other day here:
it is not a UHI)
An interesting paper (from 1989) discussing heat island issues at airports: (see pg 18 of 45, re the Akron-Canton airport)
Click to access V089N2_001.pdf
It also uses the phrase “airport heat island.”
And, here is one from National Weather Service, a part of NOAA, using the term “airport heat island” with reference to the Memphis, TN airport, from July 2004.
We can conclude that NOAA was familiar with the term and thus knew exactly what they were doing by using airports to fill in missing data — it provides some (maybe most) of the “warming.”
http://www.arh.noaa.gov/arhdata/archive/FXUS64KMEG/FXUS64KMEG_04203201519
Here is the abstract of a paper where the UHI and AHI are quantified in Alaska
The urban heat island effect at Fairbanks, Alaska
MAGEE N. (1) ; CURTIS J. (1) ; WENDLER G. (1) ;
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska,
Abstract
Using climatic data from Fairbanks and rurally situated Eielson Air Force Base in Interior Alaska, the growth of the Fairbanks heat island was studied for the time period 1949-1997. The climate records were examined to distinguish between a general warming trend and the changes due to an increasing heat island effect. Over the 49-year period, the population of Fairbanks grew by more than 500%, while the population of Eielson remained relatively constant. The mean annual heat island observed at the Fairbanks International Airport grew by 0.4°C, with the winter months experiencing a more significant increase of 1.0°C. Primary focus was directed toward long-term heat island characterization based on season, wind speed, cloud cover, and time of day. In all cases, the minima temperatures were affected more than maxima and periods of calm or low wind speeds, clear winter sky conditions, and nighttime exhibited the largest heat island effects.
Journal Title
Theoretical and applied climatology ISSN 0177-798X
Source
1999, vol. 64, no1-2, pp. 39-47 (11 ref.)
These are the relevant traces for Fairbanks Airport
2. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425702610000&data_set=1&num_neighbors=1
3. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425702610000&data_set=2&num_neighbors=1
In this case the UHI correction method applied by GISS has warmed the past to account for the UHI in the present and soften the trend.
@Ellie
Interesting “tool” you’ve cooked up there! Opening 3 panels and doing an “eyeball compare” works nicely for me!
@Roger
So, what’s it called when someone KNOWS they are doing something wrong and does it anyway, with serious financial consequences for a 3 rd party?…
To me, there are only 2 choices. GIStemp / NASA never bothered to see what stations they were using and how they changed over time (and did a backwards “UHI” adjustment), so it’s negligence. OR, they knew exactly what they were doing. Not pretty.
Over the 49-year period, the population of Fairbanks grew by more than 500%, while the population of Eielson remained relatively constant.
The “population” at the Air Base may have stayed relatively constant, but we entered the Jet age, runways got longer, fuel and cargo loads greater, etc. The pertinent issue at an Air Base is not population, but “operations” and infrastructure build out. (One guy can fuel a Piper Cub or a cargo jet…)
Roger, I’ve pasted the appropriate section of that collection of papers pdf here:
THE HEAT ISLAND OF AKRON-CANTON
9:30 AIRPORT. Nirmala Kochar and Thomas
W. Schmidlin, Department of
Geography, Kent State University, Kent, OH
44242.
Air temperatures measured at airports may not
represent the temperatures of surrounding rural
areas because of extensive paved surfaces,
buildings, lack of tall vegetation and flow of
traffic. These factors may cause the presence
of a heat island which is generally an urban
phenomenon. So, the temperature of the airports
may not represent that of the surrounding rural
land. This was verified by studying the
temperature and wind of the Akron-Canton Airport
and eight nearby rural sites between December
1987 and October 1988. The results showed that
an airport heat island did not exist under
cloudy conditions or when the wind was not calm
over 4 or more rural sites. However, the
airport was a heat island for 75% of the calm
and clear nights. Hence, the airport
temperature is not representative of the
surrounding rural land under clear, calm
conditions and it would be appropriate to
establish instruments at a truly rural site.
I bolded the bit where they say that there is an AHI effect…
Now generalize that to 60% of the SW thermometers, 74% of the S.Temperate or 2/3 of the Equatorial thermometers…
So we have a study confirming AHI.
We have a history of airport growth over time.
We have a history of increasing airports in GHCN over time.
We have a history of Airports increasing in the S.H. dramatically.
Clearly, it must be CO2… \sarcoff>
Gee, from that Memphis report we have:
AREA FORECAST DISCUSSION
NATIONAL WEATHER SERVICE MEMPHIS TN
305 PM CDT WED JUL 21 2004
.DISCUSSION…
PERSISTENCE SEEMS TO BE THE WEATHER WORD RIGHT NIGHT. UPPER RIDGE
STILL SQUARELY PLACED OVER THE MIDSOUTH WHILE HIGH PRESSURE IS
OVER THE SOUTHEASTERN UNITED STATES. EXPECT DAYTIME CUMULUS TO
DISSIPATE AFTER SUNSET AND CIRRUS FROM THE STORMS IN THE MIDWEST
SHOULD THIN OUT AS THEY WORK INTO THE RIDGE. THUS…EXPECT A
MOSTLY CLEAR NIGHT. WINDS WILL BECOME LIGHT AND TEMPERATURES DROP
INTO THE LOWER 70S…EXCEPT FOR THE MEMPHIS AIRPORT HEAT ISLAND
WHERE IT WILL ONLY GET DOWN TO THE MID 70S.
That implies a couple of degrees of AHI (low 70s moved to mid 70s).
Ed,
A couple of points. First, trying to think like the “other side,” which is always useful when lawyers try to assess the merits of their client’s position. Not that anyone here is my client, but just thinking out loud, as it were. GISS has a lot of data missing but wrote a computer program that requires a full data set. The question then became how to fill in the missing data. The available candidate sites included airports, which (it seems to me) had/have few missing data points. The alternative was to infill the data with non-airport sites. Perhaps the airport infill choice was one of convenience, easier to code, fewer human decisions to make, more consistent, somewhat easier to compensate for with the various corrections, who knows. We may never know.
Second, there are at least five categories of legal cause of action when information between parties is not fully disclosed. (disclaimer: this is not intended to be legal advice, but merely summarizing some law from one jurisdiction, California. If anyone needs legal advice for their particular situation, they should consult an attorney).
A) if a fiduciary relationship exists, very high disclosure is required; the cause of action is concealment of a material fact ( it may have other names in some states; I am using California law as the basis for these comments);
B) no fiduciary relationship exists, there may be intentional misrepresentation. IM requires a knowing false statement by defendant, intended to induce another to change position to their detriment, and the other does so change position in reliance on that false information and thereby incurs detriment;
C) no fiduciary relationship exists, there may be negligent misrepresentation. NM requires a false statement that caused another to change position to their detriment, but no intent by defendant. Instead, defendant failed to use due care, as a reasonable person in that situation would have done, to ascertain whether the statement was true or false, or had no reasonable grounds for believing the statement was true;
D) no fiduciary relationship exists, there may be concealment of a material fact, and
E) no fiduciary relationship exists, there may be a false promise, if defendant made a promise he did not intend to keep to another and which promise was the basis for the other to change position to his detriment.
The entire matter is complicated by the fact that scientists are involved, and they will be held to a standard of what reasonable scientists would have done under similar circumstances (the reasonable person standard applied to professionals). To the extent there are contracts involved, and particularly state or federal money, there may be additional causes of action.
What GISS/NASA did very likely does not rise to intentional misrepresentation (we need a smoking gun memo/letter disclosing an intent to deceive); no fiduciary relationship exists; no false promise appears likely; no intentional misrepresentation appears likely; but concealment of a material fact (the march of the thermometers, and airport heat island usage for infilling) seems more likely; and negligent misrepresentation could be a good argument.
A defendant (NASA and AGWers) could argue that no detriment has occurred, indeed, they would argue the opposite – they are in fact saving the planet.
I would like to see the comments in the NASA code you are examining, those could contain the smoking gun. One never knows!
After 1990 or so, in the ROW, CRU and GISS rely only MCDW data (entirely as far as I can tell), which is heavily biased towards airports – a point discussed on a number of occasions at CA – I’ve referred to the CRUtem index as the CRU_ tar index to honor its measurements of tarmac.
According to recent revelations, CRU seems to have spliced pre- and post- airport locations without even recording when the splice was made or maintaining the integrity of the unspliced data.
Further reflection shows that NASA could use the Kochar and Schmidlin paper to support their use of airports as infill stations, because the heat island effect seems to only occur during calm and clear conditions. To the extent that a day’s weather was neither calm nor clear, they would argue that it was appropriate to use the airport’s data for infilling. Further, since airports are/were generally sited at a location with some wind, and preferably a stable wind, they could argue that it was almost never calm at the airport.
This is less of an issue for modern jets due to the high speeds a jet requires for takeoff and landing. Choosing a windy site was more important in the days of slow-speed propeller aircraft. For example, if takeoff speed was 80 miles per hour, it was important to have an airport with average wind speed of 20 miles per hour. But for a modern large jet airliner, takeoff speeds approach 300 miles per hour. Other factors become important in choosing an airport site, such as long and clear approaches without obstacles, proximity to urban centers and mass transit, and noise tolerance by surrounding areas.
Roger Sowell
I would like to see the comments in the NASA code you are examining, those could contain the smoking gun. One never knows!
The code is “up” at:
One of the first things I did was to put the source code for most steps up so that I could look at it when in a browser (without a telnet / ssh to my Linux development box…)
The comments start with a “C” in the first position of the line or, sporadically, following a “!” as end text in a line.
FWIW, I didn’t really think anything ‘actionable’ was going on… I was just venting a bit of frustration… IMH(non-lawyer)O they just totally ignored the “A” airstn flag. There is little or no evidence that at any point the their program they even bother to break it out as a distinct data item. They pick out the Latitude and Longitude from the v2.inv file (and some other bits) but the airstn flag and things like distance to ocean seem to be simply ignored. IMHO, negligent behaviour, but then I’m more “picky” than a lot of folks about data quality and algorithm neatness; and it is doubtful that it rises to any legal standard… but it’s still wrong…
And Hanlon’s Razor (Never attribute to malice that which is adequately explained by stupidity) would say that the interpretation “they just missed it” is the one to accept. Given when this code was first designed, I doubt if there was much AHI effect literature to expect them to have read…
But it’s still wrong…
Steve McIntyre
After 1990 or so, in the ROW, CRU and GISS rely only MCDW data (entirely as far as I can tell), which is heavily biased towards airports – a point discussed on a number of occasions at CA – I’ve referred to the CRUtem index as the CRU_ tar index to honor its measurements of tarmac.
Steve, if you have links to those threads, please post them here. There is so much on CA that it can take a while to find a particular item.
According to recent revelations, CRU seems to have spliced pre- and post- airport locations without even recording when the splice was made or maintaining the integrity of the unspliced data.
I’ve noticed (in the article above) that the very structure of the GHCN data do not preserve the history of airport change. MANY airports are commissioned, then decommissioned, over time, yet we have a single “A” flag for the present state. There is no way to know (as the Berlin-Temple site demonstrates) WHEN a site became, or ceased to be, an airport in the GHCN data structure. So you can make the case that by its structure GHCN “spliced pre- and post- airport locations without even recording when the splice was made”.
IMHO, this lack of attention to “airport-ness” is all it takes to explain all of the “AGW” we see…
For anyone who finds it hard to believe that interpolation involves sites as far away as 1000km, here it is in the Comments in FORTRAN program PApars.f :
C**** This program combines for each urban station the rural stations
C**** within R=1000km and writes out parameters for broken line
C**** approximations to the difference of urban and combined rural
C**** annual anomaly time series
In regards to your post on airports, I noticed the following comment on WUWT in the Tips & Notes tab which might be of interest to you:
Espen (04:37:17) :
I noticed that GISS showed a rather high anomaly for eastern Norway for the last 5 years (the red spot approximately around Oslo here: http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=07&sat=4&sst=0&type=anoms&mean_gen=0112&year1=2003&year2=2008&base1=1951&base2=1980&radius=250&pol=reg)
So I tried to find out which station data they’re using, and it seems they’re using the data from Gardermoen Airport:
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=634013840003&data_set=2&num_neighbors=1
The station is marked as “rural”, but is in fact located at what became Norway’s main international airport in 1998. I think the graph shows quite clear that the temperature has been noticably higher since 1998.
And, btw., here’s the chart for Oslo Blindern, which is in the city itself (not downtown, but at the university campus, where one of the real climate guys works (the norwegian meteorological institute)):
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=634014890010&data_set=2&num_neighbors=1
(not much trend there, except that the late 30s seem to have been the longest warm stretch for the whole record period… this seems to have been the case for a lot of northern latitude stations)
I am apprecative of your efforts to peer into the blackbox of GISS. I would like to help but my Fortran talents were minimal at their best many year ago.
@Mike:
Thanks for the info. The graph for Oslo Blindern looks to me (via an imprecise “visual integration”) like there is a 60 year cyclicality with a low in about 1900, high in 1930’s, low near 1960, high near 1990’s (with possible Pintubo? volcanic dip), heading lower now?
I’d guess that all the real world is doing is a PDO and related ocean cycles flip flop. Then GIStemp layers a load of airports on top and, voila, “Global Warming”.
Going forward, they can still use the Airport thing a little bit, but most of the infrastructure is built out already – especially the Jet Age build out in the tropics. With the PDO flipped to the cold side, the AGW thesis is in for a couple of rough decades. All we need now is one medium sized volcano to pop off and make for a very cold winter in Russia, Canada, USA, China … say, right about December in Copenhagen ;-)
HI EM Smith
Hope this is in the right place please relocate as appropriate.
I have been analysing longer temperatyure data sets and am writing an article on my findings. Thought you would be intersted in this snippet as it relates to the Uppsala temperature record you carried recently.
“I want to take you on a brief journey through time to the Little Ice age thermometers that predate the CRU dataset.
Here are CRU global temperatures commencing 1850
Amongst the longer lived records are two that I wish to highlight, as they complement each other. Stockholm commenced recording in 1756
It provides interesting information as the graph shows clear peaks and troughs, but particularly intriguing is that a couple of years ago Stockkholm recorded its mildest winter since ‘records began’ (in 1756) which heralded the start of much publicity about global warming.
However, by a delicious irony, we find that the home city of Arrhenius himself-Uppsala-had an even longer temp data set than Stockholm, from which we can see the upturn in temperatures to a period warmer than today during the 1720-1740’s which includes a series of very mild winters.
Click to access upps_www.pdf
Intriguingly, both cities have had substantial studies made on them to identify the Urban heat island effect. Uppsala for example expanded three fold from 1850 to 1890 and continues to develop. Both data sets are due to be amended to reflect this. (Note there are lots of caveats with siting, uhi, instrument reliability etc)
Click to access 0189.pdf
If we look before 1850 we can see considerable temperature variations belying the notion that today is unprecedented and that variability in the past was limited, as co2 at 280ppm was not sufficiently high to be a primary driver, in contrast to today.
If we then go to the granddaddy of them all- Central England Temperatures (CET) not only can we see the huge temperature fluctuations each year (the data is not smoothed) but a confirmation of the peaks around 1720 (when Uppsala commences) and in this case ending at a l trough in 1660.
We know that temperature goes through other peaks and troughs-for examples the 1530’s and 40’s are known to have been very warm, as were the 1420’s and 30’s, the 1300’s generally were very cold and a peak of warmth was reached in the Medieval warm period from around 850 to 1250AD (although there were cold spells within that.)
To the surprise of no one we have got warmer since the end of the LIA, but we are able to see the modern era in a much better context as just part of a constant variation.
Natural variability has enabled our present civilisation to enjoy what appears to be a period of ‘comfortable normality’ with our age comfortably placed as instrumental temperatures meander gently somewhere between the LIA and the MWP values-despite liberal enhancement of UHI in some cases. Our equitable situation doesn’t require legislation or expensive remedies. Enjoy it while you can- until nature throws the next extreme at us
tonyb
EM.
Here is another grep also – no hurry though. Oslo/Gardermoen Airport (ID 634013840003) shows substantial warming since late 1980s.
Is Gardemoen used as a reference station, and if so how many times and for what station IDs? Hope that’s not a big ask.
@Ellie:
I assume you mean in STEP2 where the UHI “adjustment” is done. It is not a USHCN station so will not get the “fill in and widget” that they get in STEP0. STEP1 combines bits of different records and does a splice job, but does not look to do much “reaching” for fill in data (though I’ve not gone through it in a while and might have forgotten some strange bit…). In STEP3, the “stuff” created in STEP2 gets smeared around into grids and boxes, so depending on what Oslo is “near” it might well “adjust” a near by box.
But concentrating on STEP2:
[chiefio@tubularbells STEP2]$ cd work_files/
[chiefio@tubularbells work_files]$ grep 013840003 PApars.statn.use.GHCN.CL.1000.20
[chiefio@tubularbells work_files]$
Note that the country code is deleted from the search. For some obscure reason, they drop the country code in some of the log files. Error risk? Yes. So you must check each positive match for a false positive…
But we got no result, so it was not used. Inspection of v2.mean shows that the first data is from 1986 and that makes this a “young” station. This step likes to use old stations first, so there were most likely many older stations for use in the UHI “adjustment” step.
[chiefio@tubularbells work_files]$ grep 013840003 PApars.noadj.stations.list
[chiefio@tubularbells work_files]$
Not in the “no adjust” list either.
[chiefio@tubularbells work_files]$ grep 013840003 PApars.statn.log.GHCN.CL.1000.20
[chiefio@tubularbells work_files]$
Hmm. Doesn’t seem to be in any logs:
[chiefio@tubularbells work_files]$ grep 013840003 PApars*
[chiefio@tubularbells work_files]$ grep 013840003 station.log
[chiefio@tubularbells work_files]$ grep 013840003 *
Just to show that the “grep” is valid, I repeated it with a station ID from the USA. This is what you get when a station is used:
[chiefio@tubularbells work_files]$ grep 722130010 *
PApars.GHCN.CL.1000.20.log:*** urb stnID: 722130010 # rur: 17 ranges: 1894 2006 Radius: 500.
PApars.list:425722130010 0.173 -0.032 1926 2.475 0.005 0.722 12.111 13.428 1894-2006 1893-2007 0
PApars.statn.log.GHCN.CL.1000.20:year dTs-urban dTs-rural StnID=722130010
padjust.log: station 722130010 WAYCROSS 4NE 3SC425 adjusted
padjust.log: station 722130010 WAYCROSS 4NE 3SC425 saved 2
station.log: 0 722130010 124 20 30 1529
[chiefio@tubularbells work_files]$
Thanks. That is reassuring. There are 4 individual records for Gardemoen, which get spliced to the final record. The most warming one is the one that begins in 1986.
Thought it would be interesting to compare the global airport biased temperature record with jet fuel consumption (US data)
@B Louis79
Absolutely Marvelous! I’d thought of doing something similar (plotting fuel vs overall AGW) but did not know where to get the fuel data. Your choice ( jetA vs airport temps specifically) is an even better idea.
May I use it in an article?
Actually just US JetA vs Global temp anomaly. Perhaps tightening up the inputs might tighten the correlation. The data is freely available. So free for anyone to use. I have done no manipulation. 5yr rolling averages might be more useful?
I’d love to see someone smarter do a fancy computer model on the JetA proportion used at airports and it’s predicted temperature effect on the land-based thermometers accounting for heat diffusion.
REPLY: [ So I take that as a “Yes, you can use it.” BTW, while it is admirably humble to say “I just plotted public data” credit still must go to the person who thought to do it and discovered a tight fit. Sure, it could use more variations; but this is EXACTLY what Citizen Science is all about. We each do “a bit” that moves understanding forward. We each build with our own strengths on what was done by another.
And remember the programmers law of mutual superiority: “Anything you program, I can improve; and anything I program, you can improve”. Just substitute “investigate” or “research” for “program” and you get one of the guiding lights of Citizen Science: “Anything I can investigate, you can improve; and anything you can investigate, I can improve.” So I looked at airports and found a broad point. You looked closer and found a marvelous correlation. Now you, me, or others can look even closer and find all sorts of interesting things… That is the reason I put “Dig Here!” flags in some of my postings. “We together” can do far more that I ever can.
BTW, I’d noticed a “dip” in the Hawaii temperature during economic recessions. I suspected it was from reduced vacation flights to Hawaii, though it ‘eyeballed’ with a lag (prepaid vacations?). It might be very interesting to plot Hawaii temps vs Hawaii JetA especially since the surviving thermometers are are at the airports and JetA will be more contemporaneous with actual travel. -E.M.Smith ]
BTW, the fuel data includes monthly statistics from 1986. The temp data has monthly statistics. So there is lots of data to play with!
@B Louis79, E.M.,
That jet fuel use is a nice one. I had started on an energy correlation a while ago. I was initially thinking about air conditioner use (beside thermometers), but ended up broadening it.
I used data from here: http://www.eia.doe.gov/aer/txt/ptb1105.html
Here: http://www.eia.doe.gov/emeu/aer/coal.html
And eventually here (the best): http://www.iiasa.ac.at/~gruebler/Data/EoE_Data.html
Others have had a go at the topic e.g.: http://www.warwickhughes.com/hoyt/ffburning.htm
And extensively and controvercially by Bo Nordell here: http://www.ltu.se/shb/2.1492/1.5035?l=en and thesis here: http://www.ltu.se/polopoly_fs/1.5035!summary.pdf
Thinking about the ‘missing heat’ I looked at ‘food as fuel’ since we all produce heat and food is our fuel. I used human population growth data and growth in domestic animals from here: http://faostat.fao.org/site/573/default.aspx#ancor, along with published measures/estimates of animal heat production, since this is also ‘above backgound’. I was surprised – it is considerable (if my unit conversions are right, and i think they are)
My final graph is here: http://1.bp.blogspot.com/_vYBt7hixAMU/SydsRiuZP1I/AAAAAAAAAK4/dTUVNQjMCL0/s1600-h/Oil+use.png
If you are interested I’ll document my sources/calculations more exactly. Please feel free to use. I had kind of moved on.
Nordell’s work appears to explain 75% of earth warming from heat generation by civilization. The 25% left could still be fallacious, since alternative non-ground based data suggests warming at half the rate. If CO2 is a red herring and all evidence suggests it is, then the best route to going green is to use renewable energy sources and not nuclear. Without fictitious forcing, we have time to work on the problem without Chicken Little.
Nordell’s energy graphs seem a good fit for the raw temperature records and are similar to fossil fuel consumption. The temperature trends in Nordell’s paper are probably smoothed and not raw and fit less well than the raw data.
warming at half the rate is more likely to be actual (and natural IMO).
Exactly. One of the reasons I parked the analysis and intended to come back to it. It was fun to do the graphs and calcs anyway.
The other obvious thing to study is the change in square footage of tarmac over time.
GHCN V2 mean is still showing 136 stations of record for 2009. Most are airports. Two of those stations are missing 20 values out of 22.
Sometimes I think I’m talking to a brick wall when I talk about airports. It’s such a basic point and there is no getting around proper site selection. People are still arm-waving about adjustments when there are more fundamental issues in play.
I also wonder if Anthony has the CRN ratings for the current 136 stations in the U.S. Oh, wait. They’re all protected by HomeSec and nobody can inspect them.
If I were a judge, I’d strike airport temperature data in any case involving an attempt to prove global warming. If I were on a jury, I would discount any trends shown in it by about 90%.
At this point, Nordell’s work looks like an alternative method of “fit the numbers in the computer model”. Somehow, he converts observed temperatures and masses into heat quantities. I found it interesting that geothermal energy of 175×10^18KJ dwarfs man’s useage of 20×10^18KJ from all energy sources, yet he says geothermal energy has nothing to do with global warming!
Until I recognized the magician’s sleight of hand, he had me bought.
There are too many missing assumptions. Not all consumed energy is turned into heat. Chemistry and physics tell us that.
I’m warming up to Nordell and Gervet’s analysis.
If we assume they are right,, then we need to explain the missing 25% warming.
According to sciencbits, the magic number for “climate sensitivity parameter” (lambda) is 0.35. Rather lower than the IPCCs 1.5 minimum, 23.3%.
http://www.sciencebits.com/OnClimateSensitivity
So it is feasible that thermal pollution is responsible for 75% of warming and CO2 forcing is responsible for 25%.
If the quoted warming is lower, I’m inclined to downgrade the thermal pollution figure, since some manufacturing processes use energy to transform materials that store energy rather than wasting it all.
REPLY [ The biggest issue I see with all of this is that we have no idea what the actual “warming” has been, what with CRU cooking their code, GIStemp being a worm sandwich, and NCDC having thoroughly buggered GHCN via thermometer deletions from cold places in recent years. Feel free to discuss alternatives about the ‘25%’, just remember it may be a ‘fudge fantasy’…-E.M.Smith ]
@B Louis79
We are probably thinking similar things. I did not like Nordell and Gervet’s inclusion of natural sources of warming either, since, although probably variable to some extent, we have to assume that they should be reasonably constant in a gross sense over the period in question. I started to look for the missing 25% in the biosphere (although this was an intellectual excercise, since I agree with your last statement).
My analysis is: If we assume that the populations of animals in the world have been reasonably constant over the last 1000 years or so, the main difference is going to be the growth of the human population and, to feed man, the corresponding growth of farmed animals. (OK we have to leave out man’s effect on natural populations, such as the demise of the bison herds etc.)
All food is converted in some part into heat energy as a byproduct of metabolism and I assumed, after reading various sources, a conservative value of 2000 kcalories/person/day or 8368kJ/day. I subsequently discovered an interesting discussion here (although my estimates are slightly lower):http://mb-soft.com/public2/humaneff.html
If it seems a bit spurious to include this, consider that design of ‘sustainable’ buildings takes into account occupancy rates and human heat production as well as heat produced by computers etc. I am familiar with one such building and I have to say it is impressive; an external heat source (wood fuel) is required on only a few days per year.
For animal heat production calculations I looked at various sources (finding a good one for pigs: doi:10.1016/j.jtbi.2009.07.039) and making estimates for larger and smaller animals, then using stats from the FAO. In medieval times co-habitation with animals was common to make use of the heat with byres frequently below or beside peasent living quarters.
The question of course is how much of this heat might be retained in the local environment long enough to affect measurements. And both energy use heat and biosphere heat will be patchy.
To think this started out with me thinking about factors that might cause a rural station to show warming if it was sited near even a small settlement….
I have to qualify my last post. “Climate sensitivity” is a moving target. The IPCC quotes a scientific equation or change in surface temperature caused by radiative forcing(RF). deltaTs=lambda*RF. Lamdba is “climate sensitivity parameter” K/W/m2. IPCC also quotes CO2 doubling temperature deltaTx2 (degC). deltaT2=lambda*3.7.
Both measures of “climate sensitivity” are bandied about.
IPCCs preferred values for CO2 doubling temperature rise are 1.5-4.5(most likely 3 to IPCC), which represents a lambda of 0.81. Shaviv in sciencebits estimate is 43% of IPCCs best estimate.
It may be that Nordell and Gervet exclude human/animal energy because it is essentially solar radiation stored in plants and animals and released by consumption. As such it may have little effect on the equilibrium.,
Re solar radiation stored in plants
Plants are very inefficient ~1% conversion of light energy to chemical energy and thence to biomass IIRC, but with little ‘thermal waste’ (I think); I’ll have to look that up.
From an equilibrium perspective, it matters not how efficient plants are at capturing solar energy, since that energy was going to be there anyway. What is stored in plants is liberated or stored by organisms that consume them. It is a short-term parallel to what happens with fossil fuels over a much larger timeframe. So on the scale of the history of the earth, does fossil fuel burning have an energy impact? Earth has been there before with much higher temps and CO2 levels. So probably not.
I agree and disagree.
1. The photobiosphere captures energy that would ‘be there anyway’, but without photosynthesis would make no thermal contribution. Plants capture the light energy and convert it to build biomass, which is a form of energy storage; the use of biomass results in the release of some of the embodied energy as heat.
2. You’re right that said stored energy would be released over the longer term, either seasonally or when the plant biomass decays, however human intervention results in many changes in the equilibrium:
– grazing, harvesting and cultivation stimulate growth rates and primary production;
– fertilization (whether intentional or though release of nutrients into waterways/oceans) stimulates primary production.
3. Humans have also altered the spatial distribution of the equilibrium; where we might have had slow growth, long storage and decay over a period of seasons to years, we now have rapid consumption (of food) and release of the heat in localised areas, even possibly rural ones.
This brings me back to the original intention of my examination of the issue, namely to investigate ‘other’ issues that might affect rural thermometers. So if we consider agricultural production in these terms, it might make a measurable contribution to, say land use changes that are now emerging as more important than previously considered.
I like being proved wrong – one less thing to worry about, but I think in this instance there could be a role for Thermal Theory in some form or other.
REPLY: [ IMHO it’s all about ‘storage equilibrium’. A mature forest is in equilibrium. Solar in and decay out balance. For a brief time it is out of equilibrium when we chop it down and start doing annual farming. They we are back to a balance of solar vs decay. The major difference is that much of the decay now lands in a (hot) sewage plant… Only a few biospheres are out of balance (for example, swamps accumulate muck and the ocean floor accumulates muck) and they tend to stay that way. -E.M.Smith ]
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This is a very interesting article. It’s funny this same topic crossed my mind the other day. Now actually seeing indepth numbers with the percentage chart. The airport in St Thomas has a landing strip in the water itself. All that jet fuel runs off the tarmac. It’s usually 105 degrees any given day.