Pat Frank has kindly offered another guest post this week. Since I’m completely out of time, there can be little input of my own so I am grateful for those who want to discuss interesting matters. – Jeff
Future Perfect
By
Pat Frank
22 May 2010
In my recent “New Science of Climate Change” post here on Jeff’s tAV, the cosine fits to differences among the various GISS surface air temperature anomaly data sets were intriguing. So, I decided to see what, if anything, cosines might tell us about the surface air temperature anomaly trends themselves. It turned out they have a lot to reveal.
As a qualifier, regular tAV readers know that I’ve published on the amazing neglect of the systematic instrumental error present in the surface air temperature record It seems certain that surface air temperatures are so contaminated with systematic error – at least (+/-)0.5 C — that the global air temperature anomaly trends have no climatological meaning. I’ve done further work on this issue and, although the analysis is incomplete, so far it looks like the systematic instrumental error may be worse than we thought. J But that’s for another time.
Systematic error is funny business. In surface air temperatures it’s not necessarily a constant offset but is a variable error. That means it not only biases the mean of a data set, but it is likely to have an asymmetric distribution in the data. Systematic error of that sort in a temperature series may enhance a time-wise trend or diminish it, or switch back-and-forth in some unpredictable way between these two effects. Since the systematic error arises from the effects of weather on the temperature sensors, the systematic error will vary continuously with the weather. The mean error bias will be different for every data set and so with the distribution envelope of the systematic error.
For right now, though, I’d like to put all that aside and proceed with an analysis that accepts the air temperature context as found within the IPCC ballpark. That is, for the purposes of this analysis I’m assuming that the global average surface air temperature anomaly trends are real and meaningful.
I have the GISS and the CRU annual surface air temperature anomaly data sets out to 2010. In order to make the analyses comparable, I used the GISS start time of 1880. Figure 1 shows what happened when I fit these data with a combined cosine function plus a linear trend. Both data sets were well-fit.
The unfit residuals are shown below the main plots. A linear fit to the residuals tracked exactly along the zero line, to 1 part in ~10^5. This shows that both sets of anomaly data are very well represented by a cosine-like oscillation plus a rising linear trend. The linear parts of the fitted trends were: GISS, 0.057 C/decade and CRU, 0.058 C/decade.
Removing the oscillations from the global anomaly trends should leave only the linear parts of the trends. What does that look like? Figure 2 shows this: the linear trends remaining in the GISS and CRU anomaly data sets after the cosine is subtracted away. The pure subtracted cosines are displayed below each plot.
Each of the plots showing the linearized trends also includes two straight lines. One of them is the line from the cosine plus linear fits of Figure 1. The other straight line is a linear least squares fit to the linearized trends. The linear fits had slopes of: GISS, 0.058 C/decade and CRU, 0.058 C/decade, which may as well be identical to the line slopes from the fits in Figure 1.
Figure 1 and Figure 2 show that to a high degree of certainty, and apart from year-to-year temperature variability, the entire trend in global air temperatures since 1880 can be explained by a linear trend plus an oscillation.
Figure 3 shows that the GISS cosine and the CRU cosine are very similar – probably identical given the quality of the data. They show a period of about 60 years, and an intensity of about (+/-)0.1 C. These oscillations are clearly responsible for the visually arresting slope changes in the anomaly trends after 1915 and after 1975.
The surface air temperature data sets consist of land surface temperatures plus the SSTs. It seems reasonable that the oscillation represented by the cosine stems from a net heating-cooling cycle of the world ocean.
The major oceanic cycles include the PDO, the AMO, and the Indian Ocean oscillation. Joe D’aleo has a nice summary of these here (pdf download).
The combined PDO+AMO is a rough oscillation and has a period of about 55 years, with a 20th century maximum near 1937 and a minimum near 1972 (D’Aleo Figure 11). The combined ocean cycle appears to be close to another maximum near 2002 (although the PDO has turned south). The period and phase of the PDO+AMO correspond very well with the fitted GISS and CRU cosines, and so it appears we’ve found a net world ocean thermal signature in the air temperature anomaly data sets.
In the “New Science” post we saw a weak oscillation appear in the GISS surface anomaly difference data after 1999, when the SSTs were added in. Prior and up to 1999, the GISS surface anomaly data included only the land surface temperatures.
So, I checked the GISS 1999 land surface anomaly data set to see whether it, too, could be represented by a cosine-like oscillation plus a linear trend. And so it could. The oscillation had a period of 63 years and an intensity of (+/-)0.1 C. The linear trend was 0.047 C/decade; pretty much the same oscillation but a slower warming trend by 0.1 C/decade. So, it appears that the net world ocean thermal oscillation is teleconnected into the global land surface air temperatures.
But that’s not the analysis that interested me. Figure 2 appears to show that the entire 130 years between 1880 and 2010 has had a steady warming trend of about 0.058 C/decade. This seems to explain the almost rock-steady 20th century rise in sea level, doesn’t it.
The argument has always been that the climate of the first 40-50 years of the 20th century was unaffected by human-produced GHGs. After 1960 or so, certainly after 1975, the GHG effect kicked in, and the thermal trend of the global air temperatures began to show a human influence. So the story goes.
Isn’t that claim refuted if the late 20th century warmed at the same rate as the early 20th century? That seems to be the message of Figure 2.
But the analysis can be carried further. The early and late air temperature anomaly trends can be assessed separately, and then compared. That’s what was done for Figure 4, again using the GISS and CRU data sets. In each data set, I fit the anomalies separately over 1880-1940, and over 1960-2010. In the “New Science of Climate Change” post, I showed that these linear fits can be badly biased by the choice of starting points. The anomaly profile at 1960 is similar to the profile at 1880, and so these two starting points seem to impart no obvious bias. Visually, the slope of the anomaly temperatures after 1960 seems pretty steady, especially in the GISS data set.
Figure 4 shows the results of these separate fits, yielding the linear warming trend for the early and late parts of the last 130 years.
The fit results of the early and later temperature anomaly trends are in Table 1.
Table 1: Decadal Warming Rates for the Early and Late Periods.
| Data Set |
C/d (1880-1940) |
C/d (1960-2010) |
(late minus early) |
| GISS |
0.056 |
0.087 |
0.031 |
| CRU |
0.044 |
0.073 |
0.029 |
“C/d” is the slope of the fitted lines in Celsius per decade.
So there we have it. Both data sets show the later period warmed more quickly than the earlier period. Although the GISS and CRU rates differ by about 12%, the changes in rate (data column 3) are identical.
If we accept the IPCC/AGW paradigm and grant the climatological purity of the early 20th century, then the natural recovery rate from the LIA averages about 0.05 C/decade. To proceed, we have to assume that the natural rate of 0.05 C/decade was fated to remain unchanged for the entire 130 years, through to 2010.
Assuming that, then the increased slope of 0.03 C/decade after 1960 is due to the malign influences from the unnatural and impure human-produced GHGs.
Granting all that, we now have a handle on the most climatologically elusive quantity of all: the climate sensitivity to GHGs.
I still have all the atmospheric forcings for CO2, methane, and nitrous oxide that I calculated up for my http://www.skeptic.com/reading_room/a-climate-of-belief/”>Skeptic paper. Together, these constitute the great bulk of new GHG forcing since 1880. Total chlorofluorocarbons add another 10% or so, but that’s not a large impact so they were ignored.
All we need do now is plot the progressive trend in recent GHG forcing against the balefully apparent human-caused 0.03 C/decade trend, all between the years 1960-2010, and the slope gives us the climate sensitivity in C/(W-m^-2). That plot is in Figure 5.
There’s a surprise: the trend line shows a curved dependence. More on that later. The red line in Figure 5 is a linear fit to the blue line. It yielded a slope of 0.090 C/W-m^-2.
So there it is: every Watt per meter squared of additional GHG forcing, during the last 50 years, has increased the global average surface air temperature by 0.09 C.
Spread the word: the Earth climate sensitivity is 0.090 C/W-m^-2.
The IPCC says that the increased forcing due to doubled CO2, the bug-bear of climate alarm, is about 3.8 W/m^2. The consequent increase in global average air temperature is mid-ranged at 3 Celsius. So, the IPCC officially says that Earth’s climate sensitivity is 0.79 C/W-m^-2. That’s 8.8x larger than what Earth says it is.
Our empirical sensitivity says doubled CO2 alone will cause an average air temperature rise of 0.34 C above any natural increase. This value is 4.4x -13x smaller than the range projected by the IPCC.
The total increased forcing due to doubled CO2, plus projected increases in atmospheric methane and nitrous oxide, is 5 W/m^2. The linear model says this will lead to a projected average air temperature rise of 0.45 C. This is about the rise in temperature we’ve experienced since 1980. Is that scary, or what?
But back to the negative curvature of the sensitivity plot. The change in air temperature is supposed to be linear with forcing. But here we see that for 50 years average air temperature has been negatively curved with forcing. Something is happening. In proper AGW climatology fashion, I could suppose that the data are wrong because models are always right.
But in my own scientific practice (and the practice of everyone else I know), data are the measure of theory and not vice versa. Kevin, Michael, and Gavin may criticize me for that because climatology is different and unique and Ravetzian, but I’ll go with the primary standard of science anyway.
So, what does negative curvature mean? If it’s real, that is. It means that the sensitivity of climate to GHG forcing has been decreasing all the while the GHG forcing itself has been increasing.
If I didn’t know better, I’d say the data are telling us that something in the climate system is adjusting to the GHG forcing. It’s imposing a progressively negative feedback.
It couldn’t be the negative feedback of Roy Spencer’s clouds, could it?
The climate, in other words, is showing stability in the face of a perturbation. As the perturbation is increasing, the negative compensation by the climate is increasing as well.
Let’s suppose the last 50 years are an indication of how the climate system will respond to the next 100 years of a continued increase in GHG forcing.
The inset of Figure 5 shows how the climate might respond to a steadily increased GHG forcing right up to the year 2100. That’s up through a quadrupling of atmospheric CO2.
The red line indicates the projected increase in temperature if the 0.03 C/decade linear fit model was true. Alternatively, the blue line shows how global average air temperature might respond, if the empirical negative feedback response is true.
If the climate continues to respond as it has already done, by 2100 the increase in temperature will be fully 50% less than it would be if the linear response model was true. And the linear response model produces a much smaller temperature increase than the IPCC climate model, umm, model.
Semi-empirical linear model: 0.84 C warmer by 2100.
Fully empirical negative feedback model: 0.42 C warmer by 2100.
And that’s with 10 W/m^2 of additional GHG forcing and an atmospheric CO2 level of 1274 ppmv. By way of comparison, the IPCC A2 model assumed a year 2100 atmosphere with 1250 ppmv of CO2 and a global average air temperature increase of 3.6 C.
So let’s add that: Official IPCC A2 model: 3.6 C warmer by 2100.
The semi-empirical linear model alone, empirically grounded in 50 years of actual data, says the temperature will have increased only 0.23 of the IPCC’s A2 model prediction of 3.6 C.
And if we go with the empirical negative feedback inference provided by Earth, the year 2100 temperature increase will be 0.12 of the IPCC projection.
So, there’s a nice lesson for the IPCC and the AGW modelers, about GCM projections: they are contradicted by the data of Earth itself. Interestingly enough, Earth contradicted the same crew, big time, at the hands Demetris Koutsoyiannis, too.
So, is all of this physically real? Let’s put it this way: it’s all empirically grounded in real temperature numbers. That, at least, makes this analysis far more physically real than any paleo-temperature reconstruction that attaches a temperature label to tree ring metrics or to principal components.
Clearly, though, since unknown amounts of systematic error are attached to global temperatures, we don’t know if any of this is physically real.
But we can say this to anyone who assigns physical reality to the global average surface air temperature record, or who insists that the anomaly record is climatologically meaningful: The surface air temperatures themselves say that Earth’s climate has a very low sensitivity to GHG forcing.
The major assumption used for this analysis, that the climate of the early part of the 20th century was free of human influence, is common throughout the AGW literature. The second assumption, that the natural underlying warming trend continued through the second half of the last 130 years, is also reasonable given the typical views expressed about a constant natural variability. The rest of the analysis automatically follows.
In the context of the IPCC’s very own ballpark, Earth itself is telling us there’s nothing to worry about in doubled, or even quadrupled, atmospheric CO2.
the Air Vent 
Apologies to all — I see that many of my hyperlinks did not translate true into the post. I just checked back, and they were all coded correctly in the original Word document. But apparently Word does not always translate correctly to hypertext.
Adding injuryto insult, the link to Joe D’Aleo’s excellent discussion of the AMO and PDO was also truncated during upload, and so here it is again. Also again, it’s a pdf download.
Another small error, in Table column 3, the CRU difference is missing a decimal and should be 0.029 C/decade.
In any case I hope everyone likes the analysis, and all the readers alarmed about climate change can be hereby relieved to know that Earth’s verdict on the matter is 0.4 C warmer even with quadrupled CO2. That’s half the global average T increase since 1880. All together, now: whew! 🙂
Pat,
I fixed the problems I saw in the article.
Thanks very much Jeff, and thanks for the opportunity to publish at tAV.
Koutsoyiannis link is still not working. It has some extra stuff (/”/).
Great stuff.
In figure 1, is there a hint of a 120-year wave in the residual?
In figure 4 (GISS trends) is it reasonable to have a discontinuous break unless you can explain the downward step between the two trends? It’s odd that CRU doesn’t show the same break.
In the statement “So, I checked the GISS 1999 land surface anomaly data set to see whether it, too, could be represented by a cosine-like oscillation plus a linear trend. And so it could. The oscillation had a period of 63 years and an intensity of (+/-)0.1 C. The linear trend was 0.047 C/decade; pretty much the same oscillation but a slower warming trend by 0.1 C/decade. So, it appears that the net world ocean thermal oscillation is teleconnected into the global land surface air temperatures.”
Should it actually be
pretty much the same oscillation but a slower warming trend by 0.01 C/decade?
As the whole trend is only 0.058 & 0.047 per decade.
Delicious, virtually hilarious. Getting closer to my personal null hypothesis, that CO2 forcing is zero, at most.
BTW, Frank, Anthony Watts has a current rant about submissions of Word-generated HTML. So full of garbage code it’s not worth his time to fix. Try a real HTML editor. There are lots available.
Sorry, meant to address the last comment to “Pat”, not “Frank”. No disrespect intended! 🙂
#4, Plazaeme, you’re right. It’s not in the original, but it’s there now. Jeff may get to it, but until then, anyone who wants to get to Demetris’ work will find it here
#5, Steve, there may be. I can check. But the amplitude will be so small it would be hard to say it’s not just some artifact. Remember that the data are contaminated with probably at least (+/-)0.5 C of systematic error. That could easily include some sort of periodic excursion.
The data actually fitted cover 1880-1940, and 1960-2010, with 1941-1959 left out. The lines are a little extrapolated past the fitted end-points, which is perhaps the discontinuity you’re seeing.
#6, AC Osborn, you’re right. My mistake.
#7,8, You’re right, Brian, I ought to get an HTML editor, and no offense taken. 🙂
Pat, As always thanks for an interesting article. If I could summarise to try and make sure I’ve understood correctly –
The ‘real temperature’ numbers used are GISS/CRU numbers, and as such are held up to be in effect benchmark reference numbers for climate science?
Using just the linear trend numbers you noted initially, we get 0.058 per dec. for GISS and CRU?
These and all other trends etc shown are however subject to an (at least) +- 0.5 deg systematic error as per your previous post?
If so, acceptance of even these lesser numbers requires that we ignore the systematic error noted above?
On this, I don’t recall seeing any inclusion of 2 areas in your previous posts calculations of error. Firstly, just taking the MMTS systems as an example, the manufacturers stated system accuracy is +- 0.5C, and secondly all readings are rounded/truncated to the nearest degree on capture.
It is not entirely clear whether the readings are taken in deg C and converted to deg F and whether there is single or double rounding/truncation, but all must have a severe effect on the final result?
Rats, from the title I thought this was going to be an interesting grammar lesson, but just more climate stuff . . . 🙂
Pat Frank: The AMO and PDO data cannot be combined as you’ve presented. The PDO and AMO are not similar datasets and cannot be added or averaged. The AMO is created by detrending North Atlantic SST anomalies. On the other hand, the PDO is the product of a principal component analysis of detrended North Pacific SST anomalies, north of 20N. Basically, the PDO represents the pattern of the North Pacific SST anomalies that are similar to those created by El Niño and La Niña events. If one were to detrend the SST anomalies of the North Pacific, north of 20N, and compare it to the PDO, the two curves (smoothed with a 121-month filter) appear to be inversely related:
Detrended North Pacific SST anomalies for the area north of 20N run in and out of synch with the AMO:
#10, Chuckles, Thanks, OK, and yes, yes, yes, and yes. You’ve got it. 🙂 The MMTS sensor originally used the HMP35C thermistor, and I think now has pretty much moved to the HMP45C. In either case, the accuracy of a calibrated sensor under controlled conditions is about (+/-)0.2 C. The errors are temperature dependent.
I can only infer what’s going on in the field, but from the way they treat the data it seems to me that the researchers compiling the global temperature record generally assume that the sensor errors are random, and so are the rounding adjustments, and all of that just averages away to nearly zero when they make an annual mean from ~730 measurements x thousands of surface stations.
So far as I’ve studied, no one has ever done the leg work to find out whether those assumptions are in fact statistically valid.
#11, Eric, sorry to disappoint you. 🙂
#12, Bob, I haven’t presented the PDO+AMO as summed data sets. I’ve presented the cosine-like periodicity that appears to be in the global average surface air temperature anomaly data sets. It’s extracted from the anomalies, not summed from AMO and PDO data.
I suggested that the periodicity might represent the thermal impact of the net sum of all the ocean-basin thermal oscillations. After all, the anomaly statistic is supposed to represent the net global thermal behavior of the surface air mass. One might expect that averaging the global air temperatures would also average the thermal oscillations of the ocean basins as they are coupled into the atmosphere.
Joe D’Aleo’s Figure 11 shows the AMO and the PDO oscillations, and their sum after standardizing. His curves look like your “t9zhua” detrended AMO and PDO curves, though with less smoothing. Therefore, I have no reason to doubt his sum based on comparison with your curves.
You can also look at the poster presented by Marcia Wyatt (pdf download) and their discussion here on Pielke Sr.’s blog.
In poster Figure 2 they combine the PDO and AMO as weighted averages to fit the Northern Hemisphere Temperature profile. The trend is a pseudo-oscillation with a thermal maximum around 1937 and a minimum around 1975, in close analogy to the cosine-like oscillation in my Figures 2 and 3.
So a general interpretation is not unreasonable, that the cosine-like oscillation may reflect a net heating-cooling cycle of the atmosphere by a coupling to a net thermal cycle of the world ocean.
Please note again that I am not purporting to represent a sum of data sets. I’m representing the cosine-like period as a derived thermal observable on the presumption — the stated IPCC ballpark — that the global average surface air temperature numbers have real physical meaning.
The surface sensitivity to changes in forcing (from whatever source) can be directly calculated from the energy balance at the surface, and is between about 0.095 to 0.15 DegC/W/m^2, depending on the assumptions made about changing evaporation rate with increased temperature. The lower figure assumes a constant RH and the Clausius-Clapeyron rate of change of 6.5% per DegC.
The head post is interesting from two points of view:
1. It quantifies the natural effect of short period climate oscillations, and,
2. The sensitivity found tends to support the view that increased evaporation is near 6.5% per degC. I understand that measurements indicate a figure of around 5%, but that RH is decreasing. The models assume around 2%..
Pat, the sensors may well have a spec accuracy of +/- 0.2deg C, but the manufacturers specified accuracy for the MMTS systems is +/- 0.5 deg C.
The 3200 series data sets that have been used to produce USHCN, GHCN etc. all have descriptor files that state the accuracies of the equipment, and the methodologies used to acquire the data.
The temp is measured, accurate to +/- 0.5 deg C. The MMTS then displays the measured temp to the nearest 0.1 deg F. The observer then records that reading rounded to the nearest deg F for the max and min temps. i.e. each of the readings is taken to that accuracy and rounded/truncated.
No amount of averaging, fiddling or guessing is going to change the fundamental accuracy of that raw data, or the fact that the claimed increases, trends or whatever are below the error bars of the raw data.
Pat Frank says: “Bob, I haven’t presented the PDO+AMO as summed data sets.”
Excuse my use of the word present. Let me rephrase my opening sentence: The meaningless AMO+PDO data to which you referred cannot be combined.
You continued, “Joe D’Aleo’s Figure 11 shows the AMO and the PDO oscillations, and their sum after standardizing. His curves look like your ‘t9zhua’ detrended AMO and PDO curves, though with less smoothing. Therefore, I have no reason to doubt his sum based on comparison with your curves.”
You missed the point of my earlier comment. The detrended North Pacific SST anomalies in this graph are not the PDO.
The PDO does not represent the SST anomalies of the North Pacific, north of 20N, as I illustrated with the other graph I linked in that comment:
If the PDO does not represent the SST anomalies of the North Pacific, through what mechanism are you suggesting it influences Global surface temperatures?
Additionally, a copy of the spreadsheet Joe D’Aleo used to calculate his AMO+PDO curve is linked to this comment at WUWT:
http://wattsupwiththat.com/2008/01/25/warming-trend-pdo-and-solar-correlate-better-than-co2/#comment-4549
I standardized the detrended North Atlantic and North Pacific SST anomalies from my “t9zhua” graph and compared them to Joe D’Aleo’s AMO+PDO curve in the following As you can see, Joe D’Aleo’s meaningless AMO+PDO curve does NOT look like the detrended North Atlantic or North Pacific SST anomalies. The most notable differences are the frequency of the multidecadal variations.
You wrote, “I suggested that the periodicity might represent the thermal impact of the net sum of all the ocean-basin thermal oscillations.”
I agree, but it would be best to explain the variations in land+plus sea surface temperatures as functions of Sea Surface Temperatures, not the abstract forms of sea surface temperatures. The same problem exists with the Wyatt et al discussion you linked. (Their weighted AMO+PDO data reminds me of the adage, if you torture data enough it will give you the answer you want.”
This is an interesting result. It agrees with Idso’s estimate of climate sensitivity. It agrees with an interseasonal sensitivity of about 0.1 C/W/m^2 that can be easily calculated by taking the difference between winter and summer average temperatures at one location, and dividing by the difference between ground-level winter and summer daily solar energy at the same location. NREL, as part of their efforts to support solar PV, has 30 year averages of data for many locations in the U.S.
Since blackbody sensitivity for Earth is about 0.2 C/W/m^2 and graybody sensitivity is about 0.3 C/W/m^2, there appears to be considerable additional negative feedback, even over a multi-decadal timespan.
I am curious: what would the GISS & CRU graphs look like if you extend it to the year 2100 incorporating the 60 year cycle for the Semi-empirical linear model and the Fully empirical negative feedback model? Eyeballing the graphs, it looks like 2100 would be during an increase in temperature with the temperature being about 0.6 C above 2010. (But then, my eyes aren’t what they used to be).
#14, novandilcosid, thanks and I think you should publish. 🙂
#15, Chuckles, I don’t disagree. I’m supposing, though, from the extremely low final uncertainties one can find in global average surface air anomaly papers from CRU, GISS, and wherever, that the scientists involved have all decided that the error you mention is always random. Likewise, the rounding error is considered random. All those errors are considered to average away as 1/sqrt(N) when the mean is taken of thousands of temperature numbers.
I’m not saying that approach is correct. I’m just saying that must be about what they assume given the final (+/-)0.1-0.2 C uncertainty they quote.
#16, Bob, in your “fvi92b” the PDO looks to be 180 degrees out of phase with the SST and of the same period. It’s nearly a mirror-image of SSTs. As such it looks like a pretty good proxy for SSTs.
However, more to the point, nowhere in my post did I suggest that the PDO “influences Global surface temperatures.”
All I suggested is that the oscillations I extracted from the data approximately match the period of the AMO+PDO, as represented in Joe D’Aleo’s Figure 11. E.g., I wrote, “The period and phase of the PDO+AMO correspond very well with the fitted GISS and CRU cosines…” I referred only to periods and phases.
Your “2r23g95” detrended Atlantic and Pacific SSTs also approximately match the period of Joe D’Aleo’s Figure 11. So, I don’t see the problem.
Above the quoted text, I noted that, “The surface air temperature data sets consist of land surface temperatures plus the SSTs. It seems reasonable that the oscillation represented by the cosine stems from a net heating-cooling cycle of the world ocean.” That is, the thermal signature was taken to come from inclusion of SSTs, not from inclusion of the PDO or AMO.
So, really, I don’t see what the fuss is all about.
#17, Chris those are nice corroborative points, thanks.
#18 CC, I can check that, but I’d put no physically predictive confidence in the result.
Pat Frank says: “Bob, in your “fvi92b” the PDO looks to be 180 degrees out of phase with the SST and of the same period. It’s nearly a mirror-image of SSTs. As such it looks like a pretty good proxy for SSTs.”
As illustrated in the other graph I linked above…
…the detrended North Pacific SST anomalies and the PDO appear to be inversely related. On decadal timescales, when the PDO rises, the Sea Surface Temperature of the North Pacific drops and vice versa.
You wrote, “However, more to the point, nowhere in my post did I suggest that the PDO ‘influences Global surface temperatures.’”
That statement contradicts what you wrote in the post, which was: “The period and phase of the PDO+AMO correspond very well with the fitted GISS and CRU cosines, and so it appears we’ve found a net world ocean thermal signature in the air temperature anomaly data sets.”
As discussed in my earlier comments, the PDO+AMO curve is meaningless and is, therefore, a bad example to use to illustrate the impacts of Sea Surface Temperatures on Land Surface Temperatures.
You wrote, “Your ‘2r23g95’ detrended Atlantic and Pacific SSTs also approximately match the period of Joe D’Aleo’s Figure 11. So, I don’t see the problem.”
If you can look at that graph…
…and state the “detrended Atlantic and Pacific SSTs also approximately match the period of Joe D’Aleo’s Figure 11”, there’s no reason to continue the discussion.
You concluded your reply to me with, “So, really, I don’t see what the fuss is all about.”
The “PDO+AMO” dataset is meaningless. Your reference to it detracts from your post. Your post would have been better if you had actually used Sea Surface Temperatures.
Bob, you quoted me as, “However, more to the point, nowhere in my post did I suggest that the PDO ‘influences Global surface temperatures.'”
And then wrote: “That statement contradicts what you wrote in the post, which was: “The period and phase of the PDO+AMO correspond very well with the fitted GISS and CRU cosines, and so it appears we’ve found a net world ocean thermal signature in the air temperature anomaly data sets.””
I already noted in post 19 that my original post noted inclusion of the SSTs in the GISS record after 1999, and that the thermal signature came from them. I also pointed out in #19 that my reference to the PDO+AMO referred to period and phase, but not to temperature.
But despite that, you’ve raised the same issue all over again.
According to your own 2r23g95 figure, Joe D’Aleo’s PDO+AMo has a period of about 55 years. Your own detrended north Pacific SSTs have a period of about 57 years and your north Atlantic SSTs a period of about 65 years. Although their phases are not in perfect alignment, they have overlapping negative, positive, and negative amplitudes near 1920, 1950 and 1980, respectively.
How does any of that contradict my original point that, “The combined PDO+AMO is a rough oscillation and has a period of about 55 years, with a 20th century maximum near 1937 and a minimum near 1972 (D’Aleo Figure 11). … The period and phase of the PDO+AMO correspond very well with the fitted GISS and CRU cosines, and so it appears we’ve found a net world ocean thermal signature in the air temperature anomaly data sets.”
The last sentence in the quote from the post means that the PDO+AMO are taken as a proxy for ocean thermal oscillations and their sum matched the cosine-like oscillation in the air temperature data set. The sentence under Figure 2 related that cosine-like oscillation back to inclusion of the SSTs, and was not anywhere in my post related directly to the PDO or the AMO themselves, or to their sum.
Then you go on to write that if I can look at that figure and “state the “detrended Atlantic and Pacific SSTs also approximately match the period of Joe D’Aleo’s Figure 11”, there’s no reason to continue the discussion.”
It appears you’re right. A look at your own 2r23g95 plot defeats your point, and a plain reading of my post does not yield the meaning you’re imposing on it.
You obviously have an issue about the PDO and AMO, but you’re mistaken in trying to hang it on me.
#19 “I can check that, but I’d put no physically predictive confidence in the result.” Thank you. I take everything with an extremely large pinch of sodium chloride crystals.
Apparently, great minds do think alike. Over at tallbloke’s, Roger Andews performed a similar analysis using solar cycles: Global Warming Projections Using Solar Cycles. The analysis assumed that there is no significant anthropogenic warming of the ocean, and that the ocean is warmed entirely by the sun. So, if the heating-cooling cycle of the world ocean were a direct result of solar forcing, the cosine cycles could be replaced with the more complex cycles explored in Tim Channon’s article: A cycles analysis approach to predicting solar activity. I am curious as to how this approach would affect your analysis.
#22, CC, thanks, I won’t have time immediately, but I’ll get to the analysis you suggested in #18 and will post here what happens.
Also, I looked at Roger Andrew’s analysis, but admittedly did not spend a lot of time there. His approach is interesting, and assumes for his analysis that the IPCC is approximately correct as regards the temperature effect CO2 forcing, i.e., his Figure 4. Admittedly he got a good fit.
My analysis above is strictly numerical, and so is not readily amenable to Roger’s more physical approach. From an eye-ball guess, though, projecting my linear plus cosine-like period through to 2100 might well give a total trend that looked a lot like Roger’s Figure 7.
Pat Frank: Regarding your May 29, 2011 at 8:11 pm reply, you have again missed one of the points in my earlier comments. The PDO and AMO data cannot be summed or averaged.
You ended your reply with, “You obviously have an issue about the PDO and AMO, but you’re mistaken in trying to hang it on me.”
I have no issues with the PDO or the AMO data. I am also not trying to “hang it on you.” The issue is adding the AMO and PDO data and assuming the sum has meaning. It does not.
As I’ve said in lots of places previously:
The speed of the global water cycle changes in response to any forcing that tries to make sea surface and surface air temperatures diverge.
The main symptom of a change in the speed of the global water cycle is the surface air pressure distribution which causes the jetstreams to become more meridional/zonal and/or to shift more equatorward/poleward.
So whether it is the sun, the oceans or human CO2 the system exerts the necessary negative response.
That begs the question as to what sets the equilibrium temperature that the system always seeks to return to.
The answer is atmospheric pressure and the physical characteristics of the molecular bonds between water vapour molecules and liquid water molecules.
Check out the definition of the enthalpy of vapourisation.
The atmospheric pressure dictates the energy value of the heat input required to convert liquid to vapour and vice versa. That energy value changes with atmospheric pressure. Less heat input is required to vapourise water at the top of Everst than at sea level.
It is the energy value of the enthalpy of vapourisation at sea surface level that sets the speed at which evaporation can occur for a given amount of heat input which itself sets the possible speed of energy flow (via latent heat transfer) that the system can achieve from water to air.
That is what sets the equilibrium temperature of our watery world.
The Hot Water Bottle Effect and NOT the Greenhouse Effect is what rules our planet’s temperature. The greenhouse effect is automatically negated by a shift in the air circulation systems so as not to disrupt the equilibrium set by atmospheric pressure and the differing physical properties of water and water vapour.
Everything other than atmospheric pressure is sidelined by the atmospheric response to external forcings.
So all that more CO2 does is shift the surface pressure distribution a tiny bit. Too small to notice or measure in the face of natural forcings from sun and ocean.
Stephen W;
Thanks for repeating it here. Very cogent and interesting.
“Isn’t that claim refuted if the late 20th century warmed at the same rate as the early 20th century? That seems to be the message of Figure 2.”
I think your analysis is helpful and interesting. To play the devil’s advocate, however, I suspect that the AGW crowd would point out that you have not accounted for the effects of aerosals and global dimming. While I realize that estimating the effects of aerosals is fraught with difficulty, I think that the AGW modelers would argue that aerosals have dampened the global warming effects of greenhouse gases in the latter half of the past century. Anticipating this objection to your analysis, I wonder how you would respond.
I would counter by noting the circularity of the argument. You have to assume the hypothesis of AGW is true in order to accept dimming et al. in spite of your inability to demonstrate the veracity of the latter claim.
Mark
Bob, you’re going to have to take up your “It does not.” with Joe D’Aleo and Marcia Wyatt or one of her collaborators.
As it stands now, we have your view and we have their view. I’ve not had the time or inclination to investigate the methodological details of what they, or you, have done.
It’s true that it would have been more straight-forward to use the SSTs directly. However, I took the AMO and PDO to be proxies for ocean thermal periods. Given the sizes of the Atlantic and Pacific Oceans, I further took their sum — which admittedly I presumed was properly made — to be representative of the periodicity of a net world ocean thermal cycle.
From my view, which may be shared by others, your insistence that somehow rescaling and summing the AMO and PDO is illegitimate flies in the face of D’Aleo’s and Wyatt’s confident use of that sum. It would be nice if this disparity were reconciled, so we’d all know.
I suggest, therefore, that you engage Marcia Wyatt and/or the others on her paper in an email conversation, where your objections can be discussed analytically. Post the conversation on your blog. At the end, we’ll all be wiser.
In the meantime, I note your objection and agree that using the SSTs would have provided a more direct comparison. That’s about as far as I can go until your position is reconciled with theirs, and one of you changes your views.
#27, PaulD, apart from Mark’s amusing observation in #28, and apart from Richard Lindzen’s observation that not even the sign of the aerosol effect is known for sure, the human production of aerosols is recognized to have diminished after about about 1970 when particulate emission controls were installed on coal fired power plants (that is, until India and China came on line recently).
The impact of human produced aerosols on air temperature should have steadily diminished after 1970, and nearly disappeared by 1985.
If aerosols had had the effect mentioned by the AGW folks, one should see a late-occurring turn to positive curvature in the blue line of Figure 5, as aerosols were removed from the atmosphere, until the blue line came into colinear consonance with the steady increase in GHG forcing.
When colinearity was achieved, the blue and red lines should have had tracking slopes after about 1985 through the end of the 20th century, since temperature achieved a linear increase with forcing. But we don’t see that in the data.
3-part rebuttal to Tamino’s criticism.
2,100 ppm by 2100 or Bust!
The planet’s flora have eaten themselves into near famine; time for we fauna to pick up our game.
Tamino has graduated to snipping my responses. Guess he doesn’t want to pursue his criticism. We can only wonder why. Tamino did call me an idiot, showing the deep level of his scientific criticism. Here’s the reply he snipped.
PJKlar, in my original post I wrote this: “For right now, though, I’d like to put all that aside and proceed with an analysis that accepts the air temperature context as found within the IPCC ballpark. That is, for the purposes of this analysis I’m assuming that the global average surface air temperature anomaly trends are real and meaningful.”
Towards the end, I wrote this: “Clearly, though, since unknown amounts of systematic error are attached to global temperatures, we don’t know if any of this is physically real.”
Given those explicit qualifiers, how you can see the analysis as any sort of fraud is beyond understanding.
Ray Ladbury, you wrote, “How, pray, is temperature supposed to rise unless there is a net input of energy?”
If the atmosphere and the global ocean are coupled oscillators, can thermal energy pass from one to the other without any net external energy input? We both know the answer to that question.
You also wrote, “A conservative approach is one that is 1)consistent with known physics, 2)consistent with known evidence.”
The physics of climate is not well-known and what is known is certainly not well resolved in climate models. However, there is theory that describes resonant modes in ocean basin energy flux, activated by random atmospheric stimulation.
For evidentiary precedent, Chen, et al., (2010) “Modality of semiannual to multidecadal oscillations in global sea surface temperature variability” J. Geophys. Res., 115, C03005; discuss interdecadal oscillations (IDO) in the SST record. They found four main periodicities including an interdecadal oscillation of 62.2 years; virtually identical to the period implied by the cosine fit to the CRU anomalies.
Further, McCabe, et al. 2008 did a frequency analysis of an 820 year record of drought in Montana, linked to SSTs, and found a prominent 60-year signal; see their Figure 1.
So now, here is a PSD analysis of HadCRUT3v (click over to Figure 1). It shows the same ~60 year period as found in the 800-year Montana precipitation record and as the multidecadal period noted by Chen, et al.
MartinJB, note that the unfit residuals themselves show no significant excursions away from zero over their whole length. They are both linear and of virtually zero slope everywhere. How is incorrect to conclude, therefore, that the fit accounts for the signal?
Barton, A ~60 year oscillation was noted to enter the GISS complete global anomaly set when the ocean temperatures were added into land-only temperatures. So, in the event, the fit did not represent an arbitrary cosine, but found one that exhibited the same periodicity as was induced by entry of the marine temperatures.
I’ve fit the 1999 GISS land-only temperature anomalies using the same cosine+linear strategy. The difference between the two fits , GISS (land+marine) minus GISS (land-only) produces an oscillation that goes right through the difference oscillation of the data sets themselves.
The observation that an oscillatory signal appears in the anomaly record with the marine temperatures, empirically justifies the strategy of fitting with a cosine. Look also at the similar 60-year cycles I referenced in the reply to Ray Ladbury. They also justify the result in terms of a known SST period.
Check out the current post at Judith Curry’s here. The article she is refering to is behind paywall but there is a Power Point presentation that she links to here. Pat, since you are being “scrutinized” a little right now I thought you would be interested in this.
I found Pat’s analysis here and the article which Judith cited to be interesting. Tamino, and likely others, have the view that the lack of trend in the last decade or so is due to “noise”. Some time ago, I modelled the noise of detrended UAH using auto.arima and then ran 1000 Monte Carlo like noise simulations using the modelled coeficients and determined the 10 year linear trends. A histogram of the simulated trends, along with 2 sigma confindence intervals, is here. If I have calculated correctly, I believe what the histogram shows is that the inherent red noise of UAH is not capable of overwhelming a significant underlying trend on a decadal scale.