What Slow Fourier Transforms can tell us.
Guest essay by Stan Robertson, Ph.D., P.E.
On May 3, 2014, an article on WUWT by Willis Eschenbach entitled, The Slow Fourier Transform (SFT) was posted. As he noted, the amplitude of the Slow Fourier Transform components are in the same units as the fitted data, intervals of arbitrary length and irregular data can be used and periodicities rather than frequencies are automatically extracted. In addition to rediscovering a very useful mathematical tool, Willis went on to show that there were apparently no variations of temperature associated with solar cycle variations for several long term temperature records. Now my normal inclination would be to say that if Willis didn’t find any there probably aren’t many to be found. But, on the other hand, as I showed in an October 10, 2013 WUWT article entitled The Sun Does It: Now Go Figure Out How!, it does not take much of a temperature variation to represent a very significant solar contribution to ocean surface temperatures and heat content.
Several researchers, including Nir Shaviv (2008), Roy Spencer (see http://www.drroyspencer.com/2010/06/low-climate-sensitivity-estimated-from-the-11-year-cycle-in-total-solar-irradiance/) and Zhou & Tung (2010) have found that ocean surface temperatures oscillate with an amplitude of about 0.04 – 0.05 oC during a solar cycle. Using 150 years of sea surface temperature data, Zhou & Tung found 0.085 oC warming for each watt/m2 of increase of TSI over a solar cycle.
In my previous article, I showed that the changes of Total Solar Irradiance (TSI) over a solar cyle were too small, by at least a factor of 3.6, to cause temperature oscillations with an amplitude of 0.04 C. Since the variations of temperature considered were clearly associated with solar cycles, it seemed to me that the sun does something more to change ocean surface temperatures than just vary its TSI. But the whole idea would fall apart if there really are no significant variations of ocean temperature correlated with solar cycles. That motivated me to look in places where Willis had not and, in particular, to look at shorter and more recent temperature records that might be both more accurate and with better distribution over the ocean surfaces.
I downloaded the HADSST3 global sea surface temperature raw data (http://woodfortrees.org/plot/hadsst3gl ) and took a look at the data since 1954. This covers 60 years of data and about five and one half solar cycles. To get an idea of what sort of noise would be in these data, I fitted the sea surface temperatures to a cubic polynomial just to get rid of most of the systematic variations. The figure below shows a plot of the residuals for the last 60 years.
Figure 1 HADSST3GL residuals for the last 60 years
If we are looking for variations of about 0.04 C amplitude over the 5.5 solar cycles in the time period shown, then with apparently random variations of about 0.3 C amplitude in the record, the signal to noise ratio would be about 0.04 / 0.3 = 0.13. This would be a signal a long way down in the noise. So the question is, can we extract such a signal with a Slow Fourier Transform? To answer this question, I adopted Willis’ lovely SFT technique. I generated some test monthly data for a 60 year interval consisting of sine waves with a 10 year period plus monthly random noise in the range of +/-0.5 C. The slow FT results for waves with amplitude of 0.15 C, 0.1 C and 0.05 C would have signal to noise ratios of 0.3, 0.2 and 0.1, respectively. The results are shown in Figure 2.
Figure 2. Slow FT for test sine waves with 10 year period for a sixty year interval; 6 cycles.
As one might expect, the random variations would have both short period and long period apparent periodicities as shown in Figure 2. At a signal to noise ratio of 0.2 (blue line), or larger, the signal buried in the noise can be nicely extracted by the Slow FT. At a signal to noise ratio of 0.1, and none of the other curves to aid the eye, you might just have to believe that there might be a signal with a 10 year period. It is hardly bigger than the spurious noise peaks. Of course, there are much more sophisticated signal extraction processes than the Slow Fourier Transform. From comments that I have seen here on WUWT, there are some sharp readers around who could surely teach us some lessons. It might be expecting too much to see such a small signal in the noisy sea surface temperature data with an SFT method. But it is worth noting that in each of the test cases, the Slow FT peaks at 10 yr are smaller than the amplitudes that generated the test data by about ten to twenty percent with worse results at lower signal to noise ratios.
Since it is pretty clear that we will be looking for a small signal in a lot of noise, we probably ought to see where to look. A slow FT of the SIDC sunspot numbers for the years since 1954 shows a peak at 10.8 years as shown in Figure 3.
Figure 3. Slow FT for SIDC sunspot numbers 1954 – 2014
Now let’s have a look at the Slow FT for the sea surface temperature data. The average was subtracted to help suppress spurious long periods, but no smoothing was applied.
Figure 4 Slow FT for HADSST3gl sea surface temperatures
I leave it to the readers to decide whether or not there is a solar cycle signal in the HADSST3gl sea surface temperature record. Considering that the slow FT tends to understate the actual signal amplitude at low signal to noise ratios, I think that this might be a credible detection of a solar cycle driven temperature variation at a 10.4 year period with a signal to noise ratio of at least 0.065 C/ 0.3 C = 0.22.
For the remainder of this essay, I would like to extend and recapitulate some of my previous findings. The prevailing view in climate science is that the sun has contributed very little, if anything, to the warming of the last century. Finding that ocean temperatures are affected during solar cycles to a much larger degree than can be explained by the small changes of solar irradiance that reach the sea surfaces is a huge challenge to the prevailing view, but it rests on some bedrock physics. A detailed accounting for energy exchanges, including thermal energies is as fundamental as it gets.
I was able to account for the long term secular trends of both the sea surface temperature changes AND the ocean heat content since 1965 with a linearly increasing rate of surface heating. This involved numerically solving some heat transfer equations, including the absorption of solar energy, but it provided a simple, two parameter simultaneous fit to the sea surface temperature record AND the ocean heat content record. The two parameters found were a rate of increase of surface heat input of 0.31 watt/m2 per decade and an average thermal diffusivity of the upper oceans of 1 cm2/s. A fairly good fit to both trends was obtained as shown in Figure 5.
Figure 5. Measured and Calculated Sea Surface Temperature and Ocean Heat Content
A good fit was obtainable only for very narrow ranges of parameters. If the thermal diffusivity is taken to be too large, too much heat would be calculated for the ocean depths and surface temperatures would rise too little as the heat moves on to greater depths. If too small, the reverse occurs. If the input heating rate is too large, both rise too rapidly and if too small, both rise too little. The point of this exercise was to obtain a thermal diffusivity that could then be used to tell us how much surface temperature change could be produced by the changes of solar irradiance that occur during solar cycles. The answer is that the small variations of solar irradiance that reach the sea surfaces are far too small to produce temperature oscillations of even 0.04 C amplitude, much less the 0.065 watt/m2 amplitude suggested by Figure 4.
By the same computer program that I had used for my previous WUWT article, I have found that the amplitude of oscillating heat flux entering the ocean that would be required to produce surface temperature oscillations with the Figure 4 amplitude of 0.065 C would be 0.47 watt/m2 for thermal diffusivity of 1 cm2/s. How does this compare to the oscillating flux of solar radiation that reaches the sea surface? Let’s have a look at the solar irradiance changes over solar cycles. Figure 6 shows that TSI varies approximately sinusoidally over recent solar cycles with an amplitude of about 0.5 watt/m2 . (Thanks to Leif Svalgaard for TSI data.)
Figure 6 TSI variations for a few recent solar cyles.
As explained in my previous WUWT post, about 70% of one fourth of this amplitude, or 0.0875 watt/m2 enters the troposphere averaged over the earth area and day-night cycles. About
(160 watt/m2 /1365 watt/m^2) X 0.5 watt/m^2 = 0.0586 Watt/m2 is absorbed at the surface at wavelengths below 2 micron. About half the difference between the 0.0875 and 0.0586 watt/m2 reaches the surface at longer wavelengths and after scattering in the atmosphere. This give a solar TSI amplitude of 0.073 watt/m2 that is absorbed at the sea surface. This is about 6.4 times smaller than the 0.47 watt/m2 amplitude needed to drive surface temperature oscillations of 0.065 C. This result is in better agreement with the larger factors of 5 – 7 found by Shaviv (2008) ( see http://www.sciencebits.com/files/articles/CalorimeterFinal.pdf)
It is of some interest that my results were obtained without assuming any particular depth of an ocean mixing layer. For a thermal diffusivity of 1 cm2/s, the contribution to thermal gradients that vary with the solar cycle below the first ten meters would be much less than 0.001 C/m anyway. I saw no need to introduce a mixing zone with zero gradients and an arbitrary depth boundary.
This leaves us with a clear result that the TSI variations during solar cycles are not the direct drivers of the associated ocean temperature oscillations. Something else that varies with the solar cycles affects the amount of heat flux that penetrates the ocean surfaces. In my opinion, the most likely candidate would be cyclical variations of global cloud cover, but the mechanism that would control it is presently a research topic. Whatever the mechanism of the larger heating variations, it seems quite possible that it might be capable of producing long term secular trends under the control of the sun in addition to variations over solar cycles.
To examine this point, go back to the result shown in Figure 5. The heat flux required to account for the trends of increasing sea surface temperature and ocean heat content had to increase by 0.31 watt/m2/decade. Could this be due to greenhouse gases? CO2 is supposed to produce heating at a rate of about 3.7 watt/m2 per doubling period of its concentration. With concentration increasing at a rate of about 5% per decade, the doubling time would be about 14 decades. Since the heating effect is a logarithmic function of concentration, this would produce a linear heating at a rate of 3.7/14 = 0.26 watt/m2 per decade. This is certainly in the right ballpark to be part of the explanation of the apparent surface heating of the last few decades, however, when we recall that sulfate aerosols with negating effects would partially counter the CO2, it seems to me unlikely that CO2 is the entire explanation. Considering the similar period of rapid warming in the first half of the last century and the presently expanding and embarrassing pause of temperature increases, it seems to me that there is ample room for a significant solar contribution to the longer term warming periods. So I still think that the sun does a lot of it and I would still like to know how. Climate scientists would be well advised to spend some time trying to find out.
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richard verney says:
July 26, 2014 at 10:13 am
bones says:
July 26, 2014 at 8:32 am
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Bones
The point that Pamela makes (and upon which I expanded) is that prior to ARGO, there is no reliable data on SST. . . .
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I have to agree, however, my purposes here were very limited. First,I merely wanted to show that the SFT method could detect small signals and in comments I have noted that that can be improved upon in ways that might pull them out of the centuries long records that Willis examined. Secondly, I did not want the implication that there was no excess heating to stand unremarked. Shaviv and others have published peer reviewed works that have been ignored for too long.
The arguments associated with Figure 5 (Ocean Heat Content 0-700 m) are weak because the data for OHC prior to 2003 are themselves weak, noisy, undersampled, and biased in their sampling.
There is an excellent post by Tisdale, ARGO-Era NODC Ocean Heat Content Data (0-700 Meters) Through December 2010(WUWT March 25, 2011), particularly the Animation 1, location of XBT temperature profiles for 250-500m 1979-2003. This animation makes clear that these temperature profiles are so heavily weighted to the submarine patrol areas of the north Pacific and North Atlantic that the southern hemisphere is woefully undersampled.
The entire anomaly of Figure 5 for the 0-700 m column is about 10*10^22 Joules = 100 ZJ. It takes 27.5 ZJ to raise the 0-2000m water column 0.01 deg C So it takes about 10 ZJ to raise the 0-700 m water column 0.01 deg C. Therefore, the 100 ZJ change in the 0-700 meter column in Figure 5 amount to about 0.10 deg C. With the temperature profiles of the southern hemisphere oceans so undersampled in the pre-ARGO era, I cannot believe we know the temperature anomaly of the worlds 0-700 m oceans an accuracy of less than 0.10 deg C. The error bars pre-ALACE, pre 1992 are larger than the signal.
So I think in Figure 5, you are searching for parameters to fit what someone else’s theory of what the OHC should be rather that what was accurately measured.
Stephen Rasey says:
July 26, 2014 at 10:32 am
The arguments associated with Figure 5 (Ocean Heat Content 0-700 m) are weak because the data for OHC prior to 2003 are themselves weak, noisy, undersampled, and biased in their sampling.
There is an excellent post by Tisdale, ARGO-Era NODC Ocean Heat Content Data (0-700 Meters) Through December 2010(WUWT March 25, 2011), particularly the Animation 1, location of XBT temperature profiles for 250-500m 1979-2003. This animation makes clear that these temperature profiles are so heavily weighted to the submarine patrol areas of the north Pacific and North Atlantic that the southern hemisphere is woefully undersampled. . . .
The entire anomaly of Figure 5 for the 0-700 m column is about 10*10^22 Joules = 100 ZJ. It takes 27.5 ZJ to raise the 0-2000m water column 0.01 deg C So it takes about 10 ZJ to raise the 0-700 m water column 0.01 deg C. Therefore, the 100 ZJ change in the 0-700 meter column in Figure 5 amount to about 0.10 deg C. With the temperature profiles of the southern hemisphere oceans so undersampled in the pre-ARGO era, I cannot believe we know the temperature anomaly of the worlds 0-700 m oceans an accuracy of less than 0.10 deg C. The error bars pre-ALACE, pre 1992 are larger than the signal.
So I think in Figure 5, you are searching for parameters to fit what someone else’s theory of what the OHC should be rather that what was accurately measured.
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That may be so, but if the temperature and heat data have been inappropriately biased upward, the price that has to be paid is that they have to find a plausible heat source to cause it. Inadequate TSI variation creates a conundrum for the warmistas that makes me smile.
The increase in Ocean heat has been calculated to be measurable but not significant. See the following post Judith Curry likes as well. How does this increase compare to the present thesis being considered here?
http://motls.blogspot.com/2013/09/ocean-heat-content-relentless-but.html
Bones
Further to my two comments above.
See http://en.wikipedia.org/wiki/Ocean_temperature#mediaviewer/File:MODIS_and_AIRS_SST_comp_fig2.i.jpg
This is a typical profile of ocean temperature over depth. Fig (a) is the day time profile, fig (b) the night time profile. In fig (b) one can see the dinurnal response to ocean overturning.
This profile is typical, but in practice the profile varies from ocean to ocean, no doubt due to a combination of factors such as salinity, cleanliness/pollution, the prevalence of acquatic organisms, the amount of solar received, prevailing currents etc.
You will note that during the day there is little change in ocean temperature between 1mm and about 4m. But at a depth below 4 metres, ocean temperatures begin to fall rapidly. Of course, ships are sampling ocean temps from water drawn below 4 metres, so they are drawing water from this problem zone.
But at night, the profile is very very different. You will note that temperatures fall off rapidly as from 1mm below SST.
So a different correction needs to be made between day and night, to take account of the diurnal ocean overtunring.
There is aso a further problem thrown into the mix, and that is that the distribution of tonnage does not remain unfirom from year to year. Shipping responds to market conditions. Markets fluctuate and this has an impact on the volume and distribution of ships plying international trade. In one period ships of Panamax design may be favoured, in another it will be Handymax, in another Afromax. You will no doublt be familiar with the trend towards containerisation and how container ships have developed significantly over the years.
This means that you cannot have an average for the draft of international tonnage since the type of tonnage and its distribution will vary over say 5 year periods. The average draft of ships plying trade 15 years ago, will be different to the average draft of ships plying trade 10 years ago etc. So any adjustment required to reflect that ships sample ocean temps at depths will need to constantly be adjusted as shipping evolves year to year.
The margins of error in this data set are really very substantial.
It is the achiles heel of claimate science, that all data sets have issues, and are not fit for purpose. The data sets are constantly being over extrapolated and no one is objective enough to set out the true error bands which are significantly wide on all data sets.
I applaud your efforts, but you really are handicapped by the data sets from which you are working.
Just a couple of further thoughts on the idea I proposed at 10:02 above.
I’ve been using SSN as a proxy for TSI because it goes back further and though detection capability will have improved it doesn’t have all the complications of TSI measurement. I suppose I could have used radio flux but that only goes back to 1947. Based on a very crude assessment, the duration of SSN values of 75 and above have been higher during the period 1950-2000 that at any other time since SSN measurements started in 1750.
The second thought is that the modulation of GCRs by the solar wind is a potential albedo control mechanism as proposed by Svensmark. This albedo effect must also be enhanced by its duration and is therefore the combined effect of several solar cycles will affect the outcome.
The bottom line of my proposal is that multiple solar cycles of one extreme or the other show up in our climate record. Individual and mixed activity cycles are not noticed.
If I were an alarmist running a ‘model’, trying to ‘associate’ a short term warming observation with a tiny increase of CO2 in the atmosphere, I would have to use positive feedback with a magnitude of about 80. No problem…who objects to this and with what data? However, if I were trying to associate observed variation in TSI with variation of temperature signals on Earth, I might only have to use a positive feedback of about 4…but oh, the objections.
If I were to use the dT/T = 0.0125% argument I could not show the Earth’s atmosphere expanding enough to knock down satellites. Darn the empirical data.
Does not the Earths atmosphere expand during a solar maximum? Is the atmosphere not somewhat decentralized during a solar maximum? What is the variation of the decentralization? What is the variation in clarity of the atmosphere as the solar wind varies?
Seems that the real answer lies in the spectral variation and linkage with atmospheric physics. If I were to focus only on just TSI, it seems like I might be eligible to earn the ‘hobgoblin of little minds’ award.
” Maybe the delay could be estimated first by cross correlation.”
Already did that in response to David Evan’s “notch” Peak correlation at a lag of just over 10y and again at 21y. ( NB. The 10y wiggle is not significant against red noise ).
http://climategrog.wordpress.com/?attachment_id=958
CORRECTION
Further to my post at 10:56 am
In the cited plot, Fig (a) is night time profile, Fig (b) is daytime profile.
Fig (b) shows the daytime response to the absorption of solar at depth. There is a rapid drop off in temperature as from about 60cm depth. Unfortunately, since this is a profile, there is no scale on the plots, and thus change in absolute values is accordingly not readily apparent.
Schoedinger’s cat: “I strongly suspect that the latter half of the last century consisted not just high SSN and high TSI, but also an increase in the length of time the earth was receiving increased radiation.”
The logical response to that is the cumulative sum. However, if there is a warming effect there will feedbacks that tend to cause SST to fall back once the deviation ceases. This may most simply be characterised as a relaxation to equilibrium response which leads to an exponential decay.
The result of such a response can be found by convolution with a suitable decay time constant.
The result is what I posted above. Apparently know one seemed to get the significance.
http://climategrog.wordpress.com/?attachment_id=981
Schrodinger’s Cat, you say in your proposal that subtle trends only show up after a long period of time, but one individual strong difference does not show up. How is that possible?
“””””…..bones says:
July 26, 2014 at 7:08 am
Leif Svalgaard says:
July 26, 2014 at 6:40 am
. . . The standard formula for this is dT/T = (dTSI/TSI)/4, which with an amplitude [=half from min to max] of 0.05% in dTSI/TSI gives dT/T = 0.0125% [of T=299K] = 0.036 C , so the variation of TSI is just what is needed.
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Sorry Leif, the TSI variations are at the top of the atmosphere and that is where T must be taken as well…….””””””
Seems to me that Leif’s calculation is simply the small delta version of the Stefan-Boltzmann 4th power calculation, that presumes black body circumstances. And BB radiation laws tell us that the BB limit is the worst case.
So I don’t know what your gripe with Leif’s number is.
SB gives the worst case expectation, in the absence of positive or negative feedback interferences.
The fact that the global Temperature seems to NOT reflect even that small a TSI signal, suggests that a substantial negative feedback is at work.
Your claim that it matters where you take the signal, might be quite true. That simply confirms that other processes besides S-B are in play.
Dr. S’s calculation and figure are correct.
I don’t know why this is a problem; that this or that disturbance (TSI variation, or CO2 abundance changes or whatever), are occurring; but are not being manifested in climate changes.
Negative feedback cares not a jot, what the source of a perturbational disturbance is. Feedback (negative) fixes anything and everything; from Aerosols to Zspots, including TSI.
“This could be after a number of active cycles, so there would be a time lag.”
There is , as I showed in the lagged-correlation plot.
Removing the circa 10y ripple the long term correlation would peak at around 15y.
richard verney says:
July 26, 2014 at 10:13 am
bones says:
July 26, 2014 at 8:32 am
I took a detailed look at Hadley SST “corrections” over at Judith Curry’s a year or two back.
http://judithcurry.com/2012/03/15/on-the-adjustments-to-the-hadsst3-data-set-2
That covers many of the points raised here. It also compared the FFT of earlier and later SST and how the “bias correction” messed with frequency spectrum. A point I have also linked to here concerning the 9y lunar peak.
richard verney says:
July 26, 2014 at 9:28 am
Now of course, I am not suggesting that every shipowner engages in such practice. Of course they do not, but commercial influence is a fact of life when quite substantial sums of money are involved.
I was on a Naval Nuke ‘Nam era. Gun decking the numbers if the watch stander was careless was not unknown. And it was covered up by all concerned.
Some info on the “SFT“.
The decrease of ozone in July 2014 in the north to 10%.
http://exp-studies.tor.ec.gc.ca/cgi-bin/dailyMaps?language=e&today=20140722&srcf=0&ago=0&source=all&mvdt=-1&analysis=de®ion=g
The volume of water passing through a ship’s engine is quite substantial due to the size of the machinery, and therefore is not significantly heated by the short period of time it spends in the inlet manifold.
The heating is strictly from the delta P (pressure) and flow and some pipe friction. But both of those numbers are small. Heat transfer from the ship’s interior is minimal given flow rates even at slow cruising speeds. Ship power required goes up as the cube of the speed through the water. And the flow goes up at the about same rate for the cooling water in order to keep the delta T across the condenser as low as possible. Condenser temperature makes a huge difference in steam powered efficiency.
I might also mention that some years back Climate Audit looked at SST measurements and covered a lot of the same caveats discussed here.
Pamela Gray says:
July 26, 2014 at 10:54 am
The increase in Ocean heat has been calculated to be measurable but not significant. See the following post Judith Curry likes as well. How does this increase compare to the present thesis being considered here?
http://motls.blogspot.com/2013/09/ocean-heat-content-relentless-but.html
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I used the Levitus et al. data as posted on the NOAA web site. My best fit to the heat content data yielded about 16% more calculated heat absorbed than Lubos obtained. The way Lubos calculated the temperature change involved an integration of the temperature gradient over the depth which he never completed in detail. Nevertheless, he computed an average rate of required heating that he said was less than 0.5 watt/m^2. I started at zero in 1965 and ramped it up linearly for 45 years. My average rate would be 0.69 watt/m^2. Because of differences in the way our calculations were done, I don’t think that the results are completely comparable. When all is said and done, my calculations matched both the heat content changes to 700 meters and the surface temperatures. I might go back and average the temperature changes over the 700 meters, but I would guess that I would get a smaller mean increase because the temperature changes are much larger at the surface than at greater depths where most of the mass is located.
Pamela Gray says:
July 26, 2014 at 7:38 am
However, over very long time spans we might have a convincing argument related to a building imbalance (as in more heat going in than going out) leading to long noisy cycles of warmth.
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Pamela, I had noticed when looking at one of Dr Svalgaard,s solar charts that there is a distinct change in the length of time at solar minimum. In regards to that I had similar thoughts to what you express above. The chart shows the last minimum as having a 4+/- year period. The prior 6 minima have a period at minimum of 2+/- years. The 6 minima prior to that all have 4 +/- years at minimum. The chart starts in 1875. I would love to see that chart extended back a few more cycles to see if the minimum reverts back to 2+/- years. Cycle 17 is the pivot point between 4+/- and 2 +/- years. Is this two iterations of a 6 cycle pattern? Cycle 17 is the first above average cycle, which is then followed by the 2 strongest cycles on the 139 year chart. My first impression 6 years ago when I first looked at the ssn charts available was “why wouldn’t that be considered as the main component of the current warming?”. I made comments to that effect at newsvine. My next thought was “look at how the solar cycles appear to fit with the Pacific Northwest 9 year flood pattern. A pattern which shifted in the mid 1970s and now ranges around 11+ years, to almost 12 years. That set the hook and I have been wriggling at the end of the line ever since.
Anyhow, my original thought on the difference of a solar minimum spending less time at minimum was “would that lead to an additional accumulation in the energy budget of Earth, and thus the warming which the records show?”. Is the reversion back to a 4+/- base in the last minimum the reason that we now see a slight cooling?
A terrific chart on data coverage for Ocean Heat Content from
Judith Curry, Ocean Heat Content Uncertainties, Jan. 21, 2014.
http://curryja.files.wordpress.com/2014/01/presentation6.jpg?w=1500&h=1158
Note the 0-700 meter curve shows. 20% global coverage in 1990, 40-30% from 1995-2003, and then rises to 70% with the advent of Argo.
Further note the definition of “coverage”: At least one temperature reading in each 1 x 1 deg bin per year, like measuring January, April, July, or October makes no difference when looking of 0.01 deg C changes to the world’s oceans. I think the source is Levitus 2012.
My speculation is that Earth’s own atmospheric variations undergoing oscillations that put more or less clouds in the sky over long periods of time determines heat build up or loss from the greatest store of all, the oceans in the equatorial band, and in particular the Pacific equatorial band. Solar variations at sea surface would be completely buried in the much more noisy and oscillating intrinsic factors that let in or keep out solar heating.
M Simon says:
I might also mention that some years back Climate Audit looked at SST measurements and covered a lot of the same caveats discussed here.
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That was in relation to HadSST2 which was based on Folland’s folly: a 0.5 deg drop inserted in 1946. McIntyre was heavily critical of this kludge and it seems to have been heeded to some extent.
HadSST3 came out with a whole new raft of “corrections” in a jolly array of 100 permutations and the median of the 100 frigs which is what is usually taken to be “HadSST3”
This new version basically phased the same 0.5 C in over about 25 years so this it did not look so obviously wrong. The excuse being Folland was “right for the wrong reason”. In order to achieve the nice smooth slide in they ignored the ships records of what was engine-room intake or buckets and allocated their bucket and ERI bias “corrections” on a random basis to acheive what they considered to be the “correct” proportion of ERI vs buckets in each grid cell for a particular year.
ie. they ignored the written record and made it up as they saw fit.
I discussed all this in detail in the post at Curry’s blog, that I linked above.
george e. smith says:
July 26, 2014 at 11:39 am
. . . .Seems to me that Leif’s calculation is simply the small delta version of the Stefan-Boltzmann 4th power calculation, that presumes black body circumstances. And BB radiation laws tell us that the BB limit is the worst case. So I don’t know what your gripe with Leif’s number is.
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While Leif’s calculations are exact, they would apply only at the unit optical depth height in the atmosphere from which outgoing infrared exits at about 255 K and not at the sea surface. And only then if nothing more than average TSI plus its small variations over the solar cycle were passing on down to sea level.
Consider a planet in a solar system with a rock solid constant TSI, but unfortunately, the planet’s atmosphere is infested with invisible cloud weevils that cyclically eat holes in the cloud cover. If the holes are large enough they can be detected by the variations of sea surface temperatures that they cause. The holes periodically let more of the steady TSI reach the surface. Using orbiting satellites to look for incoming TSI variations would yield nothing in this case. Either surface temperature measurements or satellite measurements of outgoing thermal infrared should show cyclical oscillations even though incoming TSI at the top of the atmosphere shows none.
Since earth shows such surface temperature oscillations, Leif needs some outgoing IR observations to prove that we do not have cloud weevils. Saying that they don’t exist on the basis of an inapplicable calculation is not good enough.