More Anthromes !
First off let me thank folks for all the comments and suggestions. I’m just starting to explore this data so perhaps I should explain how I go about doing this. First off, I am looking for a global bias in the record from UHI. It is well known that you can look through the data and stations and find “cases” where an urban station looks to have UHI. Doing this is easy. Pick the biggest cities you can. But, that doesnt get you an answer to the question: “what is the bias in the total record?” If you like remove those few bad apples and the answer you are left with is indistinguishable from the answer you get with those bad apples left in the record. Analytically, I prefer to take them out. However, the effect of removing them or including them is minimal. The concern is that the UHI we see in large cities ( more than 1M pop) is also present in smaller cities and perhaps even in villages.
After looking at the global averages of “urban” and non urban, the next step I like to take is to look at long series. I selected urban stations, but I did not require that the station have data during the entire period. In this test I test that decision. For this test I do the following
1. Select all stations that are “urban” in the year 2000
2. Window the data to 1900 to 2011
3. Select only those stations that have 1000 months of data. In this 112 year period they have to have 1000 of 1344 months present or about 75%
4. create 2 inventories:
A: start as urban in 1900 and end as urban in 2011: 474 stations
B: start as non urban and end as urban: 950 stations
Here are the maps for these stations: Urban designates “starts as urban”; Rural designates “starts as Rural” And dont bug me about graphics. I’m just exploring stuff here.
Next I show you a histogram of the “starting Anthrome” of the “rural” stations. Sorry no legend, I’ll explain in the text
The Anthrome key is as follows
21: Rice villages
22: Irrigated villages
23: Rainfed villages
24: Pastoral villages
31: Residential irrigated croplands
32: Residential rainfed croplands
33: Populated croplands
34: Remote croplands
41: Residential rangelands
42: Populated rangelands
43: Remote rangelands
51: Residential woodlands
52: Populated woodlands
53: Remote woodlands
54: Inhabited treeless and barren lands
61: Wild woodlands
62: Wild treeless and barren lands
What we see is that the sample is dominated by residential rainfed croplands being transformed into urban areas. There are a fair number of rice lands transformed and wooded areas transformed. This is interesting because we know from Imhoff that transforming wooded areas to urban areas yeilds the highest UHI. Why is this important? When you read a UHI study that shows a large UHI understand that the UHI is a function of the type of surrounding biome. Clearing woodland to make a city creates a high UHI– a HIGH differential from the surrounding area. creating cities in other biomes doesnt create a differential that is as high. A city in arid areas has no UHI according to Imhoff. In any case, there is more work to do here.
So, what’s the answer? Looking at long series ONLY, what happens to the warming signal when we shift land use and population from non urban( mostly croplands) to Urban?
This should look familiar. black curve is urban, red curve is “non urban” . Blue curve is the difference and I draw the slope of the difference. That slope is indistinguishable form the global answer. 0007C/year
More work I suppose
Steven Mosher's Blog 
Lets say the world is warming at .05C per decade since 1900 (from your graph).
Wouldn’t the obvious conclusion be that anything significantly over .05C per decade must be UHI?
https://sunshinehours.wordpress.com/2012/03/14/warming-and-cooling-tokyo-and-frostburg/
the graph is urban only. so ur comment makes no sense. its also an average. so as a matter of random sampling you must find cases higher than .05. spend more time thinking and ur questions will improve
Of the 1300+ stations warming since 1900, I show 900+ warming more than .05C / decade.
600+ are cooling.
There are a myriad of possibilities. 3 come to mind.
1) The earth is cooling and UHI makes it appear to be warming.
2) The earth isn”t warming and UHI makes it appear to be warming.
3) The earth is warming and UHI makes it appear to be warming more than it is.
The problem with your “analysis” is that it is not based on data but on speculation.
1. The 1350 stations have an “average” warming trend. The warming signal is not
deterministic. That means some will, OF NECESSITY, warm more while others
warm less.
2. These are urban stations. The rural stations show the same average.
3. There are other reasons why some more more and others warm less.
A. latitude ( polar amplification)
B. distance from coast
If you knew how to do a regression you would know that.
C. Being in the Eastern US.
http://sunshinehours.wordpress.com/2012/03/15/cooling-since-1900/
Which part of the CO2 theory of global warming accounts for so many cooling stations in one region, and such a wide variety of warming trends?
That’s easy. You should know that the theory does not predict a monotonic increase
or a spatially uniform increase. If you look at the evolution of temperature fields over time, say in the MWP or LIA, you would see that the fields are not uniform. There is random variation. There has to be. And you would see that some places get warmer while others get colder. The
average of course is going to be either slightly warmer or slightly cooler ( 0 is super rare )
Take that naturally varying field and add extra radiative forcing. The effect will not show up
UNIFORMLY, in fact, excess heat is transported poleward. On AVERAGE the entire globe will warm, but certain areas will warm more, other areas will warm less, and some areas must cool. But the average number goes up.
Your mistake is that you believe in a cartoon version of theory. When you try to disprove the cartoon, you really end up making no sense. Warming will not happen year in and year out. over time the average will go up. Warming will not happen uniformly across the surface. It can’t. It cant because the TOA is not uniform and it cant because heat moves to the poles.
“You should know that the theory does not predict a monotonic increase
or a spatially uniform increase.”
Does it predict cooling?
“There is random variation. There has to be. And you would see that some places get warmer while others get colder.”
Ahhh. Anything that happens fits the non-specific theory. The magical CO2 theory that cools stations that are cooling and warms stations that are warming, but makes no specific predictions that can be checked.
I believe the theory that you propose is cartoonish.
Does it predict cooling? It depends on the time frame. If you look at GCM runs, you will get realizations that cool over short periods of time.
If you think about how heat moves poleward in the system you should be able to understand that the warming effect is not uniform in time or space.
The specific predictions that can be checked are at higher order measures of merit. Same situation as many engineering models of complex processes.
More coming on long series and stations that supposedly cool.
Steven, historic and current data is good for one thing only, analysing natural cyclic trends and extrapolating such. I say this because …
Climatologists love to talk about energy being trapped by carbon dioxide and thus not exiting at the top of the atmosphere (TOA.)
It is nowhere near as simple as that. All the radiation gets to space sooner or later. Carbon dioxide just scatters it on its way so you don’t see radiation in those bandwidths at TOA. The energy still gets out, and you have no proof that it doesn’t, because you don’t have the necessary simultaneous measurements made all over the world.
In the hemisphere that is cooling at night there is far more getting out, whereas in the hemisphere in the sunlight there is far more coming in. This is obvious.
When I placed a wide necked vacuum flask filled with water in the sun yesterday (with the lid off) the temperature of the water rose from 19.5 deg.C at 5:08am to 29.1 deg.C at 1:53pm while the air around it rose from 19.0 to 31.9 deg.C.
What did the backradiation do at night? Well from 9:15pm till 12:05am the water cooled from 24.2 deg.C to 23.4 deg.C while the air cooled from 24.2 deg.C to 22.7 deg.C.
According to those energy diagrams the backradiation, even at night, is about half the solar radiation during the day. Well, maybe it is, but it does not have anything like half the effect on the temperature as you can confirm in your own backyard.
This is because, when radiation from a cooler atmosphere strikes a warmer surface it undergoes “resonant scattering” (sometimes called pseudo-scattering) and this means its energy is not converted to thermal energy. This is the reason that heat does not transfer from cold to hot. If it did the universe would go crazy.
When opposing radiation is scattered, its own energy replaces energy which the warmer body would have radiated from its own thermal energy supply.
You can imagine it as if you are just about to pay for fuel at a gas station when a friend travelling with you offers you cash for the right amount. It’s quicker and easier for you to just pay with the cash, rather than going through the longer process of using a credit card to pay from your own account. So it is with radiation. The warmer body cools more slowly as a result because a ready source of energy from incident radiation is quicker to just “reflect” back into the atmosphere, rather than have to convert its own thermal energy to radiated energy.
The ramifications are this:
Not all radiation from the atmosphere is the same. That from cooler regions has less effect. Also, that with fewer frequencies under its Planck curve has less effect again.
Each carbon dioxide molecule thus has far less effect than each water vapour molecule because the latter can radiate with more frequencies which “oppose” the frequencies being emitted by the surface, especially the oceans.
Furthermore, it is only the radiative cooling process of the surface which is slowed down. There are other processes like evaporative cooling and diffusion followed by convection which cannot be affected by backradiation, and which will tend to compensate for any slowing of the radiation.
This is why, at night, the water in the flask cools nearly as fast as the air around it. The net effect on the rate of cooling is totally negligible.
The backradiation does not affect temperatures anywhere near as much as solar radiation, even though its “W/m^2” is probably about half as much.
And there are other reasons also why it all balances out and climate follows natural cycles without any anthropogenic effect. This is explained in detail in my peer-reviewed publication now being further reviewed by dozens of scientists.
Click to access psi_radiated_energy.pdf
wrong.