This is a reply and extension to Pat Frank’s “Earth Abides” post (sorry, couldn’t resist) which appeared here recently. The post features an intriguing interpretation of the temperature record to deduce climate sensitivity to CO2. I thought I would try to recreate it and see where it took me. First I got the HadSST records from http://www.cru.uea.ac.uk/cru/data/temperature/.
I generally prefer to use sea-surface temperatures when looking at global trends for several reasons:
- they have a lower variance, indicating a better stability to short-term perturbations
- the surface water temperature is measured directly, eliminating some of the definitional issues of surface air temperature
- SST’s are free of siting issues, UHI, land use, and other local human climate effects
- The seas are 70% of Earth’s surface and its major heat reservoir. Temperatures can go up and down on land like a wagging tail, but the oceans are the dog.
So let’s take the SST record and fit a sinusoid to it. However, a linear fit to the secular rise simply can’t be right. It would retrodict an ice age right at the peak of the Roman Empire. Since things in nature are much more often cyclical, I tried fitting another sinusoid to the secular rise, using all the HadSST data back to 1850:
Here we have the data as dots, decadal smoothing in blue, and the fitted sum of cosines in red. (Decadal smoothing means that I convolved the data with a Gaussian with a 5-year standard deviation.) The red curve is simply the sum of two cosines, one of period 62.7 years, the other 259.9. Just how good a match is it for the data? Ignoring intra-decadal variability (weather, not climate!), let’s plot the decadally-smoothed residual:
Something very unusual happened around 1950 — that’s nearly an 8-sigma excursion. And I haven’t the slightest clue what it was. (There was a major mode shift in the PDO about that time; it was also the era of atmospheric nuclear weapon testing … and there was probably a drop in the number of pirates.) If you look at the actual data you’ll see that 1945 marks the only really drastic discontinuity in the entire record — so I feel reasonably comfortable saying that something unusual happened then. Given that the fit was so good outside the “1950 notch”, I did the fit treating the notch as an outlier (yellow line) for an even better fit (especially to recent temperatures). (That means, of course, that the model isn’t just the fit but the fit with an exception for the notch.) The red lines are one standard deviation, the magenta two. But outside of the notch, this model — a tiny one, 6 parameters — fits the decadal average SST to within 0.05 degrees for 160 years.
Here are the variances, again with the notch taken out (We take the notch out because it makes all the series correlated, so the variances wouldn’t sum. Since we explicitly say the model can’t explain the notch, we’ll concentrate on where it does match the data.):
| Raw SST data | 0.0687 |
| Model fit curves | 0.0534 |
| Residual to fit | 0.0153 |
| Data – smoothed (decadal variability) | 0.0145 |
| Smoothed – fit (model error) | 0.000513 |
In other words, decadal variability (weather!) accounts for 21% of the variance of the raw temperature series, the model accounts for 78%, leaving about 1% unaccounted for. (There’s still a tiny amount of correlation.)
But this kind of messes up the notion that there was a V-shaped piecewise linear structure to the residual across the twentieth century: the data much more clearly show a straight line with a dip than falling and rising linear trends. Yet Frank’s graph looked a lot more like the trends — what happened?
The key to the puzzle is that his data were (or included?) land temperatures, the CRUtemp data. Let’s plot that too, also as a residual to our fit curve:
Lo and behold, there really is a linear rise above the sinusoid in the land data — which isn’t there in the SST data. In other words, the divergence since 1950 is more a land-water difference than a CO2-no CO2 one. Sorry to rain on the parade, but I can’t really buy the climate sensitivity deduction. As mentioned, there are several possible explanations for the difference. We can add another one, even assuming the land temperature measurements are perfectly accurate: cloud feedbacks may operate differently over land and ocean.
EFS_junior is arguing with everything just for the sake of argument; I’ve smoked out a crank. He says:
“I thought you people abhored paleo reconstructions.”
First, we are not ‘you people,’ we are mostly scientific skeptics – the only honest kind of scientist. And junior’s statement is, not surprisingly, wrong. Ice core data is real world data. It is the paleo treemometer reconstructions that are on shaky ground. As for the misinformation claiming that ice cores do not show a global correlation… wrong again. Both hemispheres correlate closely in temperature fluctuations.
In fact, AGW remains an evidence-free assumption. Junior doesn’t like it, and neither does he try to refute my basic argument: that the trend line from the LIA remains intact, and there has been no accelerated warming attributable to increased CO2 over natural variability. CO2 may cause minuscule warming, but it is so insignificant that it is unmeasurable. That’s why folks are still arguing over sensitivity.
Ask yourself this simple question. Why does “Climate Science” add “science” to its name? Does Physics, Astronomy, Meteorology, Chemistry, Mathematics, Biology, Medicine? Why is climate science unique? Because Climate Science is no more a science than the People’s Democratic Republic is a democracy.
Start with the phrases “the science is settled” or “the evidence is incontrovertible”. Climate science is unique in this regard among all the sciences. In no other branch of science is the science “settled” or the evidence “incontrovertible”. This is strong evidence that Climate Science is not science at all. If it was, then why is it unique as compared to all other branches of science?
The stones. Bring me the stones.
“No assesment is made of the fact that not all wavelengths change equally during the solar cycle.”
Which completely ignores the photoelectric effect, for which Einstein won the Nobel prize in Physics in 1921. The energy of a photon is not related to its intensity. It is related to is wavelength.
Think of it this way. We are going to shoot you. With a 22 caliber bullet. One we will shoot from a straw using human breath, the other from a gun. Both will be shot from a distance of 1 foot at a rate of 1 bullet per second.
It makes no difference if we fire the bullets from a gun or a straw as to the number of bullets that hit you. That is the intensity of the bullets. The TSI. However, if makes a huge difference whether the bullets are from a gun or a straw as to their energy level.
Now, consider your body temperature. Even if we double or tripple the intensity of the bullets fired from the straw, your body temperature is not likely to change very much. However, if we vary the energy level of the bullets, and fire them from a gun rather than the straw, your body temperature is likely to vary a great deal.
The same goes even if you are firing the bullets into say water, though the direction of temperature change is likely to be reversed. The same intensity of bullets – 1 per second – will have a much greater warming effect on the water temperature if the bullets are fired from a gun (higher energy) than from a straw (lower energy).
Climate Science totally ignores this when they talk about TSI. Thus, Climate Science is non Science. Climate Nonsense.
@ferd berple
“Climate Science is non Science. Climate Nonsense.”
We could call it Climate Séance from now 🙂
ferd berple says:
June 6, 2011 at 11:50 am
Ask yourself this simple question. Why does “Climate Science” add “science” to its name? Does Physics, Astronomy, Meteorology, Chemistry, Mathematics, Biology, Medicine? Why is climate science unique?
I’ve heard of the big bang Theory but I never heard the term AGW Theory. I don’t think they use the word Theory either.
Does UHI explain most, if not all, of the linear rise in land temps since? Or do you need Hansen’s post 2000 “adjustments”? We already know GISS plots from 1998 & 2008 so significant changes: cooling of the 1920-1950 period and warming of the 1990-2000 period.
Bill
ferd berple says:
June 6, 2011 at 12:19 pm
Pretty sure leif knows enough about this..
Where do the two time periods come from (i.e., 62.7 and 259.9 years)? If they come from a physical theory then I think you might have something with this. If from the data then I’m not sure what this says but I don’t think it has predictive capability.
Just like ‘Social Science’, and ‘Political Science’, neither sciences in any strict sense. Once upon a time there was Climatology. What happened to it?
/Mr Lynn
CLIMATE OSCILLATION
(Mathematics is the science of patterns)
Study of the annual global mean temperature anomaly (GMTA) shows the following pattern
1) A 30-years long alternate cooling and warming oscillation of 0.6 deg C and
2) A 30-years warming of 0.18 deg C
This pattern can be validated using the following observed data
Year=>GMTA (deg C)
1880=>-0.22
1910=>-0.6
1940=>0.1
1970=>-0.3
2000=>0.5
http://bit.ly/fizsCE
We start with the GMTA of –0.22 deg C for 1880.
GMTA for 1910 (1880 to 1910 cooling) => -0.22 – 0.6 + 0.18 => -0.64 deg C
GMTA for 1940 (1910 to 1940 warming) => -0.64 + 0.6 + 0.18 => 0.14 deg C
GMTA for 1970 (1940 to 1970 cooling) => 0.14 – 0.6 + 0.18 => -0.28 deg C
GMTA for 2000 (1970 to 2000 warming) => -0.28 + 0.6 + 0.18 => 0.50 deg C
GMTA for 2030 (2000 to 2030 cooling) => 0.50 – 0.6 + 0.18 => 0.08 deg C
Comparison of simple model and observed data
Year=>GMTA (Observed) => GMAT (Model)
1880=>-0.22=>-0.22
1910=>-0.6=>-0.64
1940=>0.1=>0.14
1970=>-0.3=>-0.28
2000=>0.5=>0.5
The above comparison shows excellent agreement between model and observed data.
Conclusion: Based on 130 years of observed data, the annual global mean temperature anomaly is cyclic with alternate 30-years warming and cooling of 0.6 deg C, in addition to an overall warming of 0.6 deg C per century.
Pay no attention to 1950. It was not a glitch in the Matrix. Move along now. Nothing to see here.
Imagine yourself to be an extradimensional physicist. You’ve managed to access a locus of electrical energy in our dimension (wall outlet) but only by very indirect means. You manage to establish that there is an amazingly stable periodicity to the signal (60Hz). After oberving it for a while, you are reasonably confident in predicting the magnitude of the signal at any time for the indefinite future. You’ll probably add a lot of caveats, but simple observation leads to reasonable prediction, even without understanding all the nuts and bolts of the process.
The only thing we lack is, perhaps, a sufficiently long record to bolster our confidence. But I’ll take “uncertain reality” over “fantasy model” any day.
From 194-45 a lot of cities burnt and a lot of oil went into the Atlantic, the North Atlantic especially, and also the Pacific.
The half life of the remains of Hamburg, Dresden, Tokyo, e.t.c. would have been a year to two years.
The oil on water would have stopped evaporation and would have slowed heat transfer.
Just add another frequency or two into the mix, and you can cover the “anomalous” period, too. How about plotting a PSD, so you can see the frequencies where the significant peaks occur directly?
I have often wondered if the SST anomaly in the 1940’s couldn’t simply been due to the fact that most ships were traveling in convoys 1939-45. Ships in convoys typically were organized into columns, which means that most SST measurements would have been of water that had been churned up by several ships, mixing cooler deep water with surface water.
Has anybody looked at the geographical spread of the anomaly? Convoys were not used in the South Atlantic and most of the Indian Ocean.
Numerological cherry-picking.
Curve-fitting of this nature unconstrained by any physical theory to define the frequency of the curves is arbitary and extremely dependent on the vageries of the data.
For instance, if the data is taken from the 1950s, starting at the 6 sigma ‘anomaly’ it would probably be possible to accurately follow the curve with two different frequencies and amplitudes of cosine curve.
In fact small changes in the data would lead to a very large number of possible cosine ‘fits’. But it is unclear this mathematical manipulation would have any meaning or implications for the CAUSE of the warming. You may be matrching inhomogenities in the data caused by changes in measurement method; bucket/inlet/satellite.
Using satellite data on sea level rise is probably the best source of measurement for the actual physical changes that have ocurred globally. The sea level rise is an unambigous reflection of the extra energy that has been absorbed by the climate system over the past decades.
@- Smokey
“Ice core data is real world data. It is the paleo treemometer reconstructions that are on shaky ground. As for the misinformation claiming that ice cores do not show a global correlation… wrong again. Both hemispheres correlate closely in temperature fluctuations.”
Well there are quite a lot of ‘skeptics’ who doubt the ice-core data citing Jaworski(?) and possible errors in CO2 diffusion, pressure leaching etc.
But the other thing is that the North and South polar data do NOT “correlate closely”, except in as much as the Holocene record shows an antiphase relationship, with warming in the Antarctic accompanied by cooling in the Arctic and vica-versa.
depts.washington.edu/isolab/papers/SteigAlley.pdf
“Comparison of climate records from Antarctic and Greenland ice cores shows that the two regions respond asynchronously during millennial-scale climate changes. The apparent out-of-phase relationship between the records has been described as a climate “seesaw” in which cooling in the Northern Hemisphere is balanced by warm-ing in the Southern Hemisphere.”
Izen,
You believe your eyes are lying to you? Obviously there is strong correlation between the hemispheres. Here’s another view of Vostok, EPICA and GISP-2. Obviously they’re in phase with each other.
The alarmist contingent will go to any lengths to try and prove that white is black, evil is good, down is up, and in-phase is out of phase. *sheesh* Cognitive dissonance in action. When is the flying saucer due?
Reliance on UEA. CRU SSIs is a No-No!
I spent some 17 years recording and reporting weather on Merchant Ships, as used by the UEA.CRU.
Their Premisses for obtaining Global Temperatures ( particularly SSTs) and their 1850 -present graph are at best laughable.
Only the World’s major sea lanes have any reliable record of SSTs.There is no record covering almost 50 % of the Oceans for most of the period.
We always recorded the temperatures to the nearest whole degree F. Working out corrections of half a degree is therefore useless.
@-Smokey says:
June 7, 2011 at 7:57 am
“You believe your eyes are lying to you? Obviously there is strong correlation between the hemispheres. Here’s another view of Vostok, EPICA and GISP-2. Obviously they’re in phase with each other. ”
I believe my eyes…
I especially believe that my eyes are incapable of seeing a ~500 year anti-phase relationship between the N and S polar ice cores when the graph is such low resolution that 5000 years is the minimum division.
Try looking at the raw data, or a graph which resolves the ice-cores to less than thousand year data-points, then the ‘antiphase’ or contrary warming/cooling can be seen by everyone but the dogmatically purblind.
Izen,
That’s funny, coming from someone every bit as coginitive dissonance-afflicted as one of Harold Camping’s true believer followers.
If you can post a graph contradicting the graphs I posted, showing a temperature peak in one hemisphere overlaid by a temperature trough in the opposite hemisphere, I’ll start to pay attention. Until then, not so much.
izen says:
June 7, 2011 at 6:57 am
“Curve-fitting of this nature unconstrained by any physical theory to define the frequency of the curves is arbitary and extremely dependent on the vageries of the data.”
Which is why I stated above a PSD should be used to nail down the frequencies to be used in the fit. You don’t have to have a full blown physical theory. But, one does need some reason for picking the frequencies one uses.
If someone will tell me where to get the data, I will give you the frequencies.
Smokey says:
June 7, 2011 at 9:01 am
“If you can post a graph contradicting the graphs I posted, showing a temperature peak in one hemisphere overlaid by a temperature trough in the opposite hemisphere, I’ll start to pay attention. Until then, not so much.”
web.mit.edu/~phuybers/www/Doc/Synchronize.pdf
The graph you want is on page 7.