Extremes Of Temperature Decreasing In The US

See, your mistkae here is you used the real data. To get the right answer your supposed to use a model.

Someone should have presented this at Kodra’s dissertation defense.

Question: Are the thermostats that are located throughout the U.S. a good representation of the data collected? Let’s say you have 100 thermometers in areas where temperatures are usually hotter and only 50 thermometers in areas where temperatures are just hot or to the cooler side of things, how is something like this smoothed out for good average data? Or am I being silly asking this question?

Convergence. (sorry for the drive by, couldn’t resist.)

i would love to comment, but I legally can’t; because The Nature conservancy persuaded me into signing into a gag easement……… so I lost my right to voice my opinion.

Steven Mosher says:
July 31, 2014 at 2:15 pm
“If you live in the illusion that there is a historical truth that you can recover you will always be fustrated. The best you get is a prediction of what you think the past was based on the present evidence you have of the past.”
I don’t think you understand the real problem. Measuring temperature is not the same as measuring energy flux which is what you are really trying to do. Averaging to produce an anomaly will show a cooler past than the present.
Simply taking the highs or lows and comparing them produces a much more accurate ‘prediction’ of the past than creating anomalies. Try it, it isn’t that hard 🙂

Percent of CONUS Area….
Are the thermometers evenly distributed across the CONUS? No.
Let us not forget there is a UHI effect and a Zombie effect to deal with.
Both effects tend toward the increase in extremes of heat and reductions in the extremes of cold.
Let’s start with the Zombie effect. In recent years, up to 45% of the stations, which contribute to the CONUS area, have been discontinued and infilled, i.e. fabricated, by using surviving stations “nearby” according to some regional homogenization. What the radius of influence is for this influence, I don’t know, but I think we need to know.
Also over the century, almost every existing station has experienced UHI influence, as population rises, energy use increases, streets are paved and widened, buildings built, and irrigation use increases.
Not all stations suffer the same degree of UHI. I argue that the Zombie stations, the ones shut down are disproportionately from rural, low UHI effected stations. But even if Zombie stations were evenly distributed across the UHI influence, the are the surviving stations used to infill the Zombies coming from a higher average UHI effect than the station that was discontinued? Is the net result that the Zombie effect is magnifying the UHI problem? That is the way I’ll bet.

All I’ve read so far has led me to think the following about climate and radiative gases:
– The primary effect of radiative gases is to smear heat around the atmosphere and resist large changes in air temperature. This is why humid tropical areas have little temperature variation across the day, while deserts have massive diurnal variation. It also explains the minimal temperature variation in the atmosphere of Venus despite its remarkably slow axial rotation.
– There is also am impact of changes in radiative gas levels on global average temperatures, but it appears to be trivial compared to the smoothing and smearing effects. Small changes in cloud levels appear to have as much, if not more, influence than large changes in radiative gas levels to global average temperatures.
– Greater warming in the Arctic than elsewhere makes sense on this basis, but the different situation in the Antarctic suggests that changes in radiative gas levels aren’t the dominant driver of global temperature change. The reverse may be true. It appears reasonable, though, to state that man’s CO2 emissions have played a significant part in the increases of the last century.
– You would expect that, ceteris paribus, this would lead to higher minimum temps and lower maximum temps, with little change to ‘averages’. UHI, land use changes, data quality and confirmation bias impacting historic temperature adjustments may be causing a small exaggeration of this average temperature increase.
I’m not convinced by the analysis shown in this piece, but it certainly makes more sense than the gloom-and-doom political statements, sometimes masquerading as scientific studies, that seem designed to scare people into thinking a world with more greenhouse gases means more extremes of weather and much higher average temperatures. Less extremes appear to be more logical to expect, while empirical data suggests modest changes to global averages.

plowboy55 says:
July 31, 2014 at 3:51 pm
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The temperature is recorded at numerous points and then ‘gridded’, so that what you suggest regarding undue weighting to lots of stations in a small area is minimised (but there are other problems, aplenty). You bring up a good point about perception though.
If a greater and greater % of the population live in cities, with those cities growing larger, two things happen. First, larger cities create a larger heat-island effect, whereby all the concrete and tarmac infrastructure soak up solar heat during the day and release it during the night, making the nights and average temperatures hotter. Second, more people than before experience this, so more people are of the opinion that “it’s getting warmer” than would be the case if the rural/urban population balance was unchanged over the decades.
This urbanisation process makes it easier for the case of man-made global warming to be pushed, of which I’m sure the political advocates are well aware. If the end of this century sees almost everyone living in ever-larger conurbations, as per the UN Agenda 21, with rural temperature stations being increasingly phased out, the surface temperature record will have a large (artificial) component of warming and the general populace will find it congruent with their perception. Sneaky buggers.

Paul, it would be helpful to include the definitions of what you are plotting. What is “normal” and what does “much above” mean.
http://www.ncdc.noaa.gov/extremes/cei/definition
In each case, we define much above (below) normal or extreme conditions as those falling in the upper (lower) tenth percentile of the local, period of record. In any given year, each of the five indicators has an expected value of 20%, in that 10% of all observed values should fall, in the long-term average,
As per usual with this kind of propaganda, there is the stupid idea that the average over some arbitrary period is somehow “normal” which suggests to the reader that any deviation is “abnormal”.
Then the top and bottom 10% are defined as being “extreme”. While this is technically correct, in the sense that the two ends of a piece of wood can be called the extremes, it is not the way this will be read by the layman public this presentation is aimed at who will understand this as OMG extreme weather. Indeed this is clearly the spin they are trying to put on it.
As always you should include a definition of what you are plotting. I had to go and dig this out from NOAA to understand what you were showing. I suggest you include the paragraph or link above in the article.

re. Greg Goodman says: July 31, 2014 at 8:40 pm
Apologies to NOAA , the last graph with the crappy running mean that was not centred correctly was Paul’s handiwork, not NOAA. I was mislead by the link below it which I understood to be a link to the original.
It was also incorrectly described as a “ten year average” which would have one point every ten years. What is shown is a ten year running mean ( a crappy distorting filter ) with a 5 year lag.
Hopefully Paul will take a tip from NOAA and find himself a proper filter.