Guest essay by Leland Park
Before Climate Science, basic physics differentiated between the terms heat and temperature because they are related – but do not have the same meaning. The classic heat equation, from physics provides the principal relationship. Accordingly, the relationship between the heat content of a substance and changes in its temperature is given by:
Q = m * c * ΔT
where m is the mass and c is the heat capacity of the substance being measured
From the USHCN we have a record of near-surface temperature readings from the 1800s to the present day. The “monthly” versions of USHCN data are composed of yearly station records containing both monthly and annual averages. These records can be used to construct year to year incremental temperature changes for each USHCN station. The function displayed in Figure 1 is a composite network averages of the incremental changes in Tmax (annual average high temperature). The number of actively reporting stations varies, but reaches about 1100 in the 1930 to 1940 period.
Several observations can be made based on Figure 1
- No unambiguous warming trend is evident in the unadjusted Tmax records
- Heat change is cyclic between warming and cooling phases.
- Tmax change is well-behaved throughout the USHCN history, despite significant local differences among the many USHCN stations.
- Complex dynamics are evident in the pattern of heat changes..
Figure 2 is a histogram of the same data that contributes to the function in Figure 1. The fact that the histogram is symmetrical about the 0 axis is confirmation that there is no long-term warming (or cooling) trend. We know this because of the special case that makes it unnecessary to have values for the mass and heat capacity.
Q = m * c * ΔTmax = m * c * [0] = 0
Energy Balance
Interestingly, the same analysis on Tmin data tells us a lot about the energy balance over time. That is because Tmin, (the daily low temperatures) represents the point at which the nightly cooling ceases and daily warming resumes. The temperature change analysis for Tmin is presented in Figure 3:
As with Figure 1, there is no unambiguous trend of warming or cooling in the Tmin change function. This is further confirmed by the histogram of the data in Figure 4:
Absence of a warming or cooling trend for annual Tmin is, again, given by the heat equation:
Q = m * c * ΔTmin = m * c * [0] = 0
Whatever heating takes place during the daily and seasonal warming cycles is being fully dissipated by the corresponding daily and seasonal cooling cycles. Otherwise there would be a heat change trend in Tmin data. Bear in mind that all of the figures are based on annual average values so seasonal effects are subsumed in the analysis..
Analysis Using Adjusted Tmax Data
The same analysis on adjusted versions of the Tmin data yields identical results. As with the unadjusted data there is no unambiguous heat change trend in evidence. In fact, there are only marginal differences in the amplitudes of the warming and cooling cycles.
The Real “Global Warming”.
Climate Science looks for Global Warming in computer models that are designed to produce it – in small amounts. Meanwhile the observational data reveals massive warming. Everyone (in the Northern Hemisphere) knows that winter is colder than summer. The winter to summer warmup is a natural seasonal pattern that is offset by the summer to winter cool-down. This annual cycles caused by normal patterns of solar levels.
Figure 6 displays the pattern of seasonal warming over time based on the difference in temperature between January and July, the peak temperature points. For the US, the seasonal warming is around 45°F while the range is from about 40 to 55°F. That is a massive amount of warming and a large amount of variation. It is much larger than the presumed trigger level for global warming, yet is unremarkable to climate scientists.
The mystery of Climate Science is that massive, regular seasonal warming cycles are unremarkable, but small changes in annual temperatures signal catastrophic climate change. Go figure.
Analysis Considerations
Since proof through data is not a specialty of those who support the consensus, there are a few issues that might be raised concerning evaluation of the analysis.
- Greenhouse effects, if any, cannot be distinguished by these methods.
- Urban heat island effect, though real, is not significant in this analysis method.
- Using annual averages minimizes the significance of short-term weather effects.
- Tavg is not a measurement parameter, conflates Tmax and Tmin behaviors, so it is not used in the analysis.
- Using incremental changes to Tmax and Tmin effectively normalizes otherwise disparate station data and permits aggregation.
Reference: “monthly” versions of USHCN data here http://cdiac.ornl.gov/epubs/ndp/ushcn/monthly_doc.html
you might find my robust test for temperature trends of some interest
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2631298
With a Fort Collins population of 2000 in 1890 to a current population over 150k, I’m going out on a limb and suggesting that it’s one of the poster cities for demonstrating urban heat island effect. In fact most of the Colorado east slope is in that same category.
Hawaii is a good test for that. Go to CAG and look at the temp history for the cities offered – HIlo, Honolulu, Kahului and Lihue.
Hilo (pop 43K), excepting 2015 – there’s no warming since the mid-1960’s
Kahului (pop 26K) – strong cooling since 1980.
Lihue (pop 6.5K) – cooling since about 1980.
Honolulu (pop. of 836K) strong warming up to about 1995 – then some cooling to present.
Your paper is interesting and the observation that the robust test indicates cooling trends at Barrow, Fairbanks and Anchorage is very intriguing. In fact, it raises questions about the loss of permafrost.
USHCN = United States Historical Climatology Network
TY PWDDADMN
It is a good idia.
However, the author observed that “Urban heat island effect, though real, is not significant in this analysis method”. This is mostly could be due to the counteraction by rural cold island effect.
Dr. S. Jeevananda Reddy
@ Dr S, 2 58 pm, “rural cold island effect”, that to me is the truer temperature. and by giving the warmists that name they’ll now want more funding to study it.
There is a large potential bias built into nearly all climatic data records. That is the simple fact that regardless of whether the data is from a rural or an urban context, data is almost always being collected from sites that are subject to effects from human activity. In fact, “rural” effects are likely to be stronger initially than urban effects, and those earliest rural effects have a strong potential to be warming effects as well. The UHI effect is the best understood, but agricultural regions are typically subject to clearing for crop production and and may also be irrigated. The former increase insolation of the ground surface and formation of local inversion layers. Irrigation may depending upon circumstances either cool or simply dampen diurnal and seasonal temperature swings. This bias is due quite simply to the fact that temperature and weather forecasting were always conducted to support commerce from transportation and crop production to warning urban citizens about whether they would need an umbrella tomorrow. “Climatological” data has rarely been collected for purely scientific purposes; that NOAA and NASA are operated under the auspices of the Department of Commerce is an historical relict of this fact. The CRN system was established to correct that, but the available data record is too short to truly act as a reference yet.
Firstly, it would be good to specify what you are plotting as “delta T ( yr to yr)”, the difference of what exactly?
Is it the average of all the daily station Tmax readings for every day of the year, then plotted as annual differences?
This relates to the heat content of what? Please be more specific.
BTW your equation should be:
ΔQ = m * c * ΔT
I was wondering the same thing, and thought delta Q would be more precise in this case. Although using absolute zero as a base would get you to Q.
The equation is correct as given by Mr. Park: Q[Btu] = m[lb]*c[Btu/(lb-°F)]*ΔT[°F]
Or, for our metrically-minded friends: Q[cal or J] = m[g]*c[(cal or J)/(g-°C)]*ΔT[°C].
Heat Q is energy. It cannot be directly measured. It is either added to or removed from a system, so this equation is a transfer equation. This is appropriate to demonstrate the subject process where Q is the energy transferred into or out of the system that manifests as a temperature change. Yes, there is a “change” in the “heat (energy),” but it is not appropriate to use ΔQ because Q is defined thermodynamically as a process quantity and not a state variable, which can be directly measured and thus substituted into an equation to calculate the process quantity.
If you were a real climate scientist, you would use the homogenised/manipulated/tortured data for your study, not that dodgy unadjusted stuff.
I feel confident that would produce the ‘settled science’ result, as required by the Klimate Establishment.
LOL
While eyeballing the data is a good start to get an overview , it hardly ‘proves’ anything without some numbers.
Are we supposed to be able to tell by eye if either graph has a mean of exactly zero ? Since this is essentially a rate of change plot a small offset from zero could be masked by the fairly large swings. Differentiation does exaggerate the high frequency content.
Also you Tmax histogram clearly has a deficit in -1 values, How much of a warming does that represent? Is it significant?
You are already plotting a rate of change ( change in Tmax per year ) though you incorrectly label you y-axes.
If you are expecting to see a “trend” in these graphs it would represent an *acceleration” of temperature, not a “trend”. Any trend ( steady rate of change ) will be seen as an offset in y axis, which is why you need to do some calculations.
“Figure 1 is a composite network averages of the incremental changes in Tmax (annual average high temperature). ”
…. so this is an annual difference (1y) of an annual average (12mo) of a monthly mean across all stations ( variable number ) of each stations’ monthly mean (30d) of daily Tmax . Is that it ?
I have done much the same thing with daily change, and have similar results.
https://micro6500blog.wordpress.com/2015/11/18/evidence-against-warming-from-carbon-dioxide/
I do find regional steps in Tmin, and very flat Tmax.
Overall there is a slight cooling.
UHI would show up as a higher summer temp, but by winter, any excess warmer is completely lost to space.
I’ve gone futher and looked at the rate of seasonal change, it too shows no sign of a Co2 warming bias/loss of night time cooling.
It’s not Co2.
Actually UHI is most noticeable in increased winter Tmin.
Since 1940 there’s no global trend in Tmin, there are multiple regional steps up and down, but there isn’t a warming trend (78 some million surface station records from NCDC GSoD data set, which comprise the set of stations with +360 samples per year).
All my data and code are available for you to try and find it, I can’t.
Now this is also not to say that in the summer a city isn’t 5 or 10 degrees warmer on a sunny day, just that by the time winter rolls through any excess heat has long since blown away (you ever walk around on a windy 0F day in a city? It’s fracking cold!
I would agree that that should be the case. Heat dissipated by heated buildings and retained by asphalt and concrete must affect nightime temps. So why do I read constantly that higher nightime temps are the primary example of global warming? I have, in fact thought that this was probably caused by UHI. Living in a Canadian city, it is hard to believe that all the heating equipment going on a -30C night has no effect on outside temps. It is not unusual on these nights to see every chimney on every building pumping exhaust ( water vapour and nasty, nasty CO2) into the night. Something like 50-100,000 btu per 1000 sq. ft of building per hour. 100% of this heat is going into the environment. It has to effect local temps.
This is silly. By taking year to year differences you are removing the trend. Not surprising you get the results you do.
@fredb: “By taking year to year differences you are removing the trend. Not surprising you get the results you do.”
Hmmm… is that so? I would think that to get a long-time trend, POSITIVE year-to-year differences should be either more numerous, or larger, or both, than NEGATIVE differences (think of a slow steady increase as a simplified example that would have a POSITIVE difference between every two adjacent years, and never a negative one). What we have here is more like a pendulum swinging between warmer and cooler than average with no obvious preference for the warm side. Note that the variation of Tmax and Tmin _from one year to the next_ can easily be (and has been) double or triple the “dangerous” 2 deg. limit, with nobody suffering much, apart from the age-old whining about a cold summer or (less frequently) a mild snowless winter.
I figure that plants and animals that can easily stomach 5 or 6 degrees more or less _from one year to another_ won’t be much impressed by an average rising a degree or two within their whole lifetime, or (for many species) across multiple generations. Lifeforms that depend on an extremely narrow range (bordering on constant) temperature to thrive must have disappeared long ago, simply because Planet Earth does not work this way and never has…….
Fredb,
I agree with Chris. I think there cannot be a long-term trend without there being more observations that show year-to-year increases, and there is no obvious trend in figure 1. However, these data do not tell us if the US has overall warmed or cooled. There could be no long-term trend in the annual max and min temperatures (one datapoint per year) but, if there are more hours per day that are warmer or cooler, there could be a warming or cooling trend. Still, it’s an interesting analysis.
No, year to year differences do not remove or show no trend. The mean of the year to year change is the trend. If there is a trend in the year to year differences, that means the annual rate of change is changing. If you look below at the chart I post using HadCRUT4 data, the LOESS lines show the change in the rate of change.
A most important statement, worth repearting, to wit:
It always amuses me that the daily warm-up lags high noon by a couple of hours, and the seasonal warm-up lags the solstice by a few weeks, but the lag for the CO2 induced warm-up, we are told, takes 40 years.
Steve , that was one of the first red flags that got me involved . The idea that changes in the atmosphere would have any lag at all was obviously nonscience . http://cosy.com/views/cg84invt.htm
Bob Armstrong said: … that was one of the first red flags that got me involved ….
Thanks for the post and link. Always nice to know when your opinion is shared.
I’ve studied first differences in temperature series extensively. It can be revealing. But it can be hard to see the trees (what is happening over time) because of the forest (the noise in the data).
Before I would accept the author’s contention that there is no trend in the data, I’d want to see a LOESS trend line plotted through it. Readers should be aware that a FLAT “trend line” through first differences does not mean “no trend.” It means no change in trend over time. The value of the “trend line” read off the y-axis is the trend at that point in time (unless separately scaled on the right axis, as below).
If Park does not software to do loess regression, I’d be happy to do it, but I’d need the data plotted in Figure 1. Meanwhile, I do have HADCRUT4 monthly data handy, to illustrate what I’m referring to:
http://i.imgur.com/BcKj45m.jpg
The “raw” data are seasonal differences of the monthly HadCRUT4 series. Shown are two “loess” trend lines. The one scaled to the left, with the monthly HadCRUT4 data, doesn’t show much, unless you squint real hard. So it is rescaled on the right. View this as a “trend of the trend” line. In the latter 19th Century the trend was declining. Then it began to rise until the 1930’s, after which it declined again. At its lowest point in the 1940’s, the trend was about 0.04. After that it began to rise dramatically. Whether that represents a real rise (as opposed to an artifact of data manipulation), and whether any real rise is natural, or anthropogenic, I’m not saying. All I am saying is that there can be trends in first differences that are hard to see unless you look more closely.
I’d like to see a version of Figure 1 that plots LOESS regression lines similar to what I’ve done for HadCRUT4.
Basil
Basil,
Could I get you to look at my page here https://micro6500blog.wordpress.com/2015/11/18/evidence-against-warming-from-carbon-dioxide/
I do a station by station day to day Tmin, Tmax, plus today’s increase in temp and tonight’s falling temp.
I have csv files of all of the data, and would be very interested in your opinion of the results.
Wow, lots of analysis there. I may take a look at your data, but I have to think about what you’ve done, first. Correct me if you are wrong, but when you take “diffs” you are taking daily diffs (today, versus yesterday). Thinking “out loud” it would tend to obscure seasonal trends. Over any period of a few days, weather is essentially random within seasonal parameters. What we’re looking for is not a signal that shows that today is cooler/warmer than yesterday, but whether today is cooler/warmer than it was this day a year ago. And even that might still get buried in the noise, which is why I like monthly data, with the seasonal difference being a 12 month difference. That 12 month difference then works out to be an annual rate of change, and multiplying that by 10 gives a decadal rate of change. When I get some time, I will download your data and take a look at it.
Basil
When I average a year, it basically removes the seasonal change. But I also average day to day, you get weather with a single station (or small area), to which I think when you average multiple stations from a(n) (large) area, that averages out weather.
I think what people don’t take advantage of, we have a natural solar shutter, we can measure the effect on temperature of a few minutes less Sun tomorrow, and in 6 months, we can measure the effect of a few more minutes.
“Correct me if I am wrong.”
I’m looking for a change in the rate of cooling(warming) as the length of day changes, and if this changes from year to year.
Micro …. You have one really interesting graph I’d like to ask you to tease out an answer from. The sign wave graph plotting temp at station 100 over a year for years 1950-2010. What would happen if you calculated the averages of groups, 1950-1975, 1975-2000, 2001-present. …. Plot those averages as a sign wave and compare the averages. It would seem to me that if there was warming, then the later average sign wave should show a difference from the earlier sign wave. I’m guessing this is just for one station …. Yes? … Or is this for the entire network?
How about the plot of each year’s rising and falling slopes on every station in the northern hemisphere*?
These still need more refinement, I do these on a 4 point lat lon pair, and lookup any stations in that box out of the *NCDC’s Global Summary of Days, and generate a report. Since I’m looking for the effect of the changing day, they should really be done on the extratropics, I think this is done from the equator.
All this data should be in source forge.
I am slightly confused by your opening sentence “Before Climate Science, basic physics differentiated between the terms heat and temperature because they are related – but do not have the same meaning.” I am pretty sure basic physics maintains this distinction after climate science too.
“Figure 1 is a composite network averages of the incremental changes in Tmax (annual average high temperature). ”
Can you explain what the annual average high temperature is please? There are 1000 or so stations, with daily readings. Can you explain how the annual average maximum is calculated? This would be very helpful.
“Can you explain how the annual average maximum is calculated?”
==================
The mean is the average of the numbers.
It is easy to calculate: add up all the numbers, then divide by how many numbers there are.
In other words it is the sum divided by the count.
Many of these debates about weather data seem increasingly similar to earlier debates about how many angels can dance on the head of a pin. We already know for certain that the planet has had not one but many ‘Ice Ages’ and the warm cycles are very much shorter and come with some regularity and that humans changed the flora and fauna of the earth massively due to our hunting and farming methods.
If the humanoid apes didn’t figure out how to attach chipped up rocks to pieces of trees, we would still have most of the Ice Age animals here today such as mastodons, for example.
We don’t have them anymore, alas. We definitely changed the planet’s life evolutionary cycles. Now the question is for us humans, what happens next? Some say we will burn in h*ll and others, freeze to death.
I think our planet will roll onwards relentlessly continuing the sudden warm/long cold cycles.
The warming rate during the rapid warming period after the early 1970s is a couple hundredths of a degree per year, which is obscured by the above graphs that show larger short term noise.
If I understand what Leland Park is arguing, temperature records in USHCN do not show any trend for the life of the record. It is usual to report standard deviations, and probabilities based on that measure, to quantify that lack of trend ( the graphs look like both figures would be small).
I would also infer that either the USHCN is peculiar, or the other historical records (GISS and HADCRUT as examples) are “corrected” beyond all recognition.
Standard deviations of first differences are often quite high, render normal hypothesis testing (is the mean significantly different than zero) almost impossible. For the HadCRUT4 data, the seasonal monthly difference is 0.007 and the standard deviation is 0.20. Frankly, this data is more akin to the stock market, and a Hurst exponent shows the data to be pretty much a random walk. That doesn’t mean there is no trend, but it does mean that it is hard to get a handle on it.
“Using incremental changes to Tmax and Tmin effectively normalizes otherwise disparate station data and permits aggregation”
This technique at first glance seems far superior to the GISS/NOAA homogenization techniques that smears bad data over good. The author’s method preserves more data and the original values. In the early part of the century, the temperatures that were observed WERE the temperatures. Adjusting them to be something else is no longer science and no longer an observation. This method preserves temperatures as they were observed.
One more time.
If you do a simple average of tmax your answer will be wrong.
Not even wrong.
Or even what is meant by wrong. Wrong in what sense?
“One more time.
If you do a simple average of tmax your answer will be wrong.
Not even wrong.”
For a question that onlya statist environmentalist would ask.
“Several observations can be made based on Figure 1
1. No unambiguous warming trend is evident in the unadjusted Tmax records”
What do you expect to see? These are differences. A trend is shown by average positive values, not increasing. If there was, say, a 2°F/Century trend, that would be an average annual difference of 0.02°F. Is it clear from Fig 1 that the average difference could not be 0.02?
Likewise with the histogram. IT is smoothed to a scale of 1°F. There is no chance of seeing whether the central point is 0 or 0.02. The problem is that temperatures are noisy anyway, and differencing makes it much worse.
I’m still waiting for somebody to do one of these “look at my latest origami algorithm.” exercises, and have the image of a baboon pop out of the data.
It would then become necessary to prove that it was the same baboon, that ‘hit on the tune’ by just playing around on the piano keys.
G
George:
You made my day and I am still laughing. It is a much more valuable commentary than Mosher’s drive-by.
I guess I’m confused by what the delta in deltaTmax is. 1yr average Tmax compared to what? What does the 365-day average Tmax look like over the same time period? That would make more sense to me.
Persistent trends are never apparent as such in first-differenced data, because they are converted into departures from an otherwise zero mean. Sadly, the author, who’s wholly unaware of this fundamental analytic fact, convinces himself that he’s come up with a highly insightful, original data analysis method. Such patent illusions should have no place in any serious scientific discussion.
I believe that 20th century warming is much more evident in Tmin than in Tmax, i.e. warmer nights, pretty scary eh?
Has anyone done a Fourier analysis on that dataset (for the first graph)? Looks rather like a sinusoidal function to me.
Paul: agreed. It reminded me of one of the solar activity charts.
Why do you assume mass is constant in your formula? Do you have evidence mass is constant? Changes in humidity changes the mass and therefore effects the formula. How can anyone reasonably claim we know if the earth is absorbing more energy or less when we don’t even have all the variables. Especially for historical records.
Your correct in that temperature does not equal heat. Sometimes the relationship is not even linear. How come when “weather” i.e. el nino/nina moves air from one spot to another on the planet we get “warming” or “cooling”, the energy didn’t just magically go away because an air current changed direction.
S
It does not seem to be a “specialty” of Leland Park either. He has not even bothered to follow comments and address the multiple mistakes in his lightweight, number-free graph plotting “analysis”.
Global temps are reckoned to have warmed 0.7K in a century. That is an average year to year difference of 0.07K or perhaps 0.1 deg. F. Is he seriously suggesting that an offset from zero of that magnitude, if it exists, would be visible by eye in his graphs ?
What Mosh’ was hinting at is that this should be done in station anomalies anyway.
The whole article is a farce.
Anthony should up the QA on the articles he accepts. There’s been too much of this kind of think recently.
This is remarkable common sense. A similar scribe of mine talks about what is a temperature anyway and as the author above points out the idea that (Tmin+Tmax)/2 is a measure of anything is unclear. https://logiclogiclogic.wordpress.com/category/climate-change/ The article is “what is a temperature anyway?”