Guest Post by Willis Eschenbach
I thought I was done with sunspots … but as the well-known climate scientist Michael Corleone once remarked, “Just when I thought I was out … they pull me back in”. In this case Marcel Crok, the well-known Dutch climate writer, asked me if I’d seen the paper from Nir Shaviv called “Using the Oceans as a Calorimeter to Quantify the Solar Radiative Forcing”, available here. Dr. Shaviv’s paper claims that both the ocean heat content and the ocean sea surface temperature (SST) vary in step with the ~11 year solar cycle. Although it’s not clear what “we” means when he uses it, he says: 
“We find that the total radiative forcing associated with solar cycles variations is about 5 to 7 times larger than just those associated with the TSI variations, thus implying the necessary existence of an amplification mechanism, though without pointing to which one.” Since the ocean heat content data is both spotty and incomplete, I looked to see if the much more extensive SST data actually showed signs of the claimed solar-related variation.
To start with, here’s what Shaviv2008 says about the treatment of the data:
Before deriving the global heat flux from the observed ocean heat content, it is worth while to study in more detail the different data sets we used, and in particular, to better understand their limitations. Since we wish to compare them to each other, we begin by creating comparable data sets, with the same resolution and time range. Thus, we down sample higher resolution data into one year bins and truncate all data sets to the range of 1955 to 2003.
I assume the 1955 start of their data is because the ocean heat content data starts in 1955. Their study uses the HadISST dataset, the “Ice and Sea Surface Temperature” data, so I went to the marvelous KNMI site and got that data to compare to the sunspot data. Here are the untruncated versions of the SIDC sunspot and the HadISST sea surface temperature data.
Figure 1. Sunspot numbers (upper panel) and sea surface temperatures (lower panel).
So … is there a solar component to the SST data? Well, looking at Figure 1, for starters we can say that if there is a solar component to SST, it’s pretty small. How small? Well, for that we need the math. I often start with a cross-correlation. A cross-correlation looks not only at how well correlated two datasets might be. It also shows how well correlated the two datasets are with a lag between the two. Figure 2 shows the cross-correlation between the sunspots and the SST:
Figure 2. Cross-correlation, sunspots and sea surface temperatures. Note that they are not significant at any lag, and that’s without accounting for autocorrelation.
So … I’m not seeing anything significant in the cross-correlation over full overlap of the two datasets, which is the period 1870-2013. However, they haven’t used the full dataset, only the part from 1955 to 2003. That’s only 49 years … and right then I start getting nervous. Remember, we’re looking for an 11-year cycle. So results from that particular half-century of data only represent three complete solar cycles, and that’s skinny … but in any case, here’s cross-correlation on the truncated datasets 1955-2003:
Figure 3. Cross-correlation, truncated sunspots and sea surface temperatures 1955-2003. Note that while they are larger than for the full dataset, they are still not significant at any lag, and that’s without accounting for autocorrelation.
Well, I can see how if all you looked at was the shortened datasets you might believe that there is a correlation between SST and sunspots. Figure 3 at least shows a positive correlation with no lag, one which is almost statistically significant if you ignore autocorrelation.
But remember, in the cross-correlation of the complete dataset shown back in Figure 2, the no-lag correlation is … well … zero. The apparent correlation shown in the half-century dataset disappears entirely when we look at the full 140-year dataset.
This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …
Now, in Shaviv2008, the author suggests a way around this conundrum, viz:
Another way of visualizing the results, is to fold the data over the 11-year solar cycle and average. This reduces the relative contribution of sources uncorrelated with the solar activity as they will tend to average out (whether they are real or noise).
In support of this claim, he shows the following figure:
Figure 4. This shows Figure 5 from the Shaviv2008 paper. Of interest to this post is the top panel, showing the ostensible variation in the averaged cycles.
Now, I’ve used this technique myself. However, if I were to do it, I wouldn’t do it the way he has. He has aligned the solar minimum at time t=0, and then averaged the data for the 11 years after that. If I were doing it, I think I’d align them at the peak, and then take the averages for say six years on either side of the peak.
But in any case, rather than do it my way, I figured I’d see if I could emulate his results. Unfortunately, I ran into some issues right away when I started to do the actual calculations. Here’s the first issue:
Figure 5. The data used in Shaviv2008 to show the putative sunspot-SST relationship.
I’m sure you can see the problem. Because the dataset is so short (n = 49 years), there are only four solar minima—1964, 1976, 1986, and 1996. And since the truncated data ends in 2003, that means that we only have three complete solar cycles during the period.
This leads directly to a second problem, which is the size of the uncertainty of the results of the “folded” data. With only three full cycles to analyze, the uncertainty gets quite large. Here are the three folded datasets, along with the mean and the 95% confidence interval on the mean.
Figure 6. Sea surface temperatures from three full solar cycles, “folded” over the 11-year solar cycle as described in Shaviv2008
Now, when I’m looking for a repetitive cycle, I look at the 95% confidence interval of the mean. If the 95%CI includes the zero line, it means the variation is not significant. The problem in Figure 6, of course, is the fact that there are only three cycles in the dataset. As a result, the 95%CI goes “from the floor to the ceiling”, as the saying goes, and the results are not significant in the slightest.
So why does the Shaviv2008 result shown in Figure 4 look so convincing? Well … it’s because he’s only showing one standard error as the uncertainty in his results, when what is relevant is the 95%CI. If he showed the 95%CI, it would be obvious that the results are not significant.
However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets. Their common period goes from 1870 through 2013, so there are many more cycles to average. Figure 7 shows the same type of “folded” analysis, except this time for the full period 1870-2013:
Figure 7. Sea surface temperatures from all solar cycles from 1870-2013, “folded” over the 11-year solar cycle as described in Shaviv2008
Here, we see the same thing that was revealed by the cross-correlation. The apparent cycle that seemed to be present in the most recent half-century of the data, the apparent cycle that is shown in Shaviv2008, that cycle disappears entirely when we look at the full dataset. And despite having a much narrower 95%CI because we have more data, once again there is no statistically significant departure from zero. At no time do we see anything unexplainable or unusual at all
And so once again, I find that the claims of a connection between the sun and climate evaporate when they are examined closely.
Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.
What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.
So, for what I hope will be the final time, let me put out the challenge once again. Where is the climate dataset that shows the ~11-year sunspot/magnetism/cosmic rays/solar wind cycle? Shaviv echoes many others when he claims that there is some unknown amplification mechanism that makes the effects “about 5 to 7 times larger than just those associated with the TSI variations” … however, I’m not seeing it. So where can we find this mystery ~11-year cycle?
Please use whatever kind of analysis you prefer to demonstrate the putative 11-year cycle—”folded” analysis as above, cross-correlation, wavelet analysis, whatever.
Regards,
w.
My Usual Request: If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH. This prevents many flavors of misunderstanding, and lets us all see just what it is that you think is incorrect.
Subject: This post is about the quest for the 11-year solar cycle. It is not about your pet theory about 19.8 year Jupiter/Saturn synoptic cycles. If you wish to write about them, this is not the place. Take it to Tallbloke’s Talkshop, they enjoy discussing those kinds of cycles. Here, I’m looking for the 11-year sunspot cycles in weather data, so let me ask you kindly to restrict your comments to subjects involving those cycles.
Data and Code: I’ve put the sunspot and HadISST annual data online, along with the R computer code, in a single zipped folder called “Shaviv Folder.zip“
Willis
Since the oceans are 70% of the surface, of course the global temperature and the oceanic SST are very closely correlated … and? What does that have to do with the ~11-year sunspot cycle? What am I missing here?
If it is not the various sun cycles that drive the 60-70 year atmosphere cycle then it is likely the Ocean cycles as Bob Tisdale has cleared shown . I tend to agree with Pamela Gray’,s post . For example the extra cold temperatures in the CET records during past major solar minimums may not be due to solar minimums at all but due to valleys in the North Atlantic SST due to stronger MOC which causes more upwelling of colder water. driven by the oceans conveyor belts . AMO has gone negative now for many months and the past 60-70 year SST pattern may again be repeating it self.
ren says
what do you think of Dansgaard-Oeschger cycle?
henry says
the evidence seems reasonable to me
http://epic.awi.de/13582/1/Bra2005e.pdf
but [I think] you would have to try and link the 1470 year cycle to some special configuration of the solar system,
just like I did with the 87 year Gleiszberg cycle
(predict dead end strop in the middle of the cycle around 2015 or 2016)
Happy hunting!
David L. Hagen says:
June 7, 2014 at 6:15 am
Sorry, no joy with that one either. The cumulative sum (integral) of sunspots starts up high in 1880, drops steadily to a valley in 1940, and then increases steadily to 2013. No correlation at all to SST.
Guys, again I encourage you to actually look at the data before you uncap your electronic pen … don’t take some random guy’s word for it just because he got it published somewhere. I’ve met Nir Shaviv, he seems a good guy, I don’t think he has bad motives or anything … but I don’t take anyone’s word for anything. “Nullius in verba”, as the saying goes …
w.
Dear Willis,
A reader on my infrequently updated blog pointed me out to your post here. Here are my thoughts about it.
You write: “… however, I’m not seeing it. So where can we find this mystery ~11-year cycle?”. So, let me start by referencing earlier work detecting the 11-year solar cycle in the land or sea surface temperature records. The following examples (and quite a few more I didn’t mention) indicate that the peak to peak variations of either SST or land surface temperature are around 0.08 to 0.1°C between solar minimum and solar maximum:
– Douglass and Clader (2002) found a pear to peak variaton of 0.11 ± 0.02K
– White et al. (1997) found a = 0.10 ± 0.02K (looking at eigenmodes in the ocean).
– Shaviv (2005) found 0.09±0.03K (using 300 years of surface data)
Douglass, D. H., and B. D. Clader (2002), Climate sensitivity of the earth to
solar irradiance, Geophys. Res. Lett., 29(16), 1786, doi:10.1029/
2002GL015345.
White, W. B., J. Lean, D. R. Cayan, and M. D. Dettinger (1997), Response of global upper ocean temperature to changing solar irradiance, J. Geo- phys. Res., 102(C2), 3255–3266.
Shaviv, N., On climate response to changes in the cosmic ray flux and radiative budget, Journal of geophysical research, 110(A8), 8105–8105, 2005.
Given these previous detections it is thus worthwhile to ask whether you expect to see a statistically significant signal in the dataset you used. i.e., you should place an upper limit and then compare to previous determinations of about 0.1°C. If the upper limit is bellow this value, then you have something interesting to say, otherwise, the result is isn’t interesting, it just means that the dataset you used doesn’t prove or disprove anything.
When inspecting your fig. 6, the signal you see there does in fact have the right amplitude and phase. So the question is not whether you see a signal or not, it should be whether the signal you see is statistically significant.
To answer this question you have to do some minimal statistical analysis which you haven’t done. For example, you should estimate the probability that the null hypothesis would give the blue line apparent in fig. 6, namely that a large fraction of the points would be as far as they are from the null signal. (e.g., by calculating the chi 2 and the effective number of degrees of freedom). I did this and found that the null hypothesis can be ruled out at better than the 99% confidence level. (and it doesn’t matter if I plotted 1-sigma or 2-sigma error bars on the points).
The two possible answers that you could get are that a) the null hypothesis can be ruled out, implying that this dataset supports the idea that you see a solar signal. b) the null hypothesis cannot be ruled out, which would then just mean that this dataset does not support the idea, but it doesn’t rule it out either. Now, given that there are other data sets that clearly show the solar signal, I couldn’t care less what the answer is.
Next, you don’t see a signal when using the SST from 1870. However, your conclusions don’t consider the following points:
a) The SST over long time scales is very poor, not much signal and a lot of noise. How much of the southern pacific was sampled by boats in the first 50 years of that time interval? In fact, even today it is poorly sampled.
b) You assume that the solar cycle is strictly periodic (say 11 years?) but in fact it isn’t. As a consequence, any analysis such as Fourier transforming, or folding over the period is smeared out from the non-constant period. (You have to fold the data while keeping the right phase within the solar cycle, which I don’t think you did).
c) Like with the previous case, you don’t estimate what is the statistical significance of the null result you find. Are the errors so large that the expected signal drowns in noise?
Last, my biggest grievance about your criticism is that the analysis in the paper you considered was of 3 datasets. You chose only the SST, and disregarded the tide gauge records which shows the solar signal with a very high statistical significance. See http://www.sciencebits.com/calorimeter .Its problem is that it may have some “contaminant” from variations in the amount of ice, but that doesn’t matter if you want to prove that you see a climatic signal over the solar cycle.
You could also ask whether 0.1°C is large or small, and the answer is that because of the large heat capacity of the oceans it corresponds to about 1W/m^2 variations (when averaged over Earth’s surface). So in fact, the small temperature variations observed are at leaf 6 times larger than what the IPCC is willing to admit.
Steven Mosher says:
June 7, 2014 at 9:14 am
Thanks, Mosh. Most folks seem to know that, but instead they agree with Nir Shaviv when he says:
Me, not.
w.
Pamela
“The Earth, with its many ways of storing and belching heat, seems quite capable of hourly, daily, seasonally, and long termally (backdoor alliteration is more fun than plain ol’ alliteration) changing the temperature all by itself-ally.”
The solar variation is so minor that I do the following thought experiment.
I make a chart of TSI versus time.
I label it c02.
I make a chart of temperature versus time
I label it temperature.
Then I imagine what a skeptic would say If I claimed that chart one helped to explain chart two.
nir shaviv
http://wattsupwiththat.com/2014/06/06/sunspots-and-sea-surface-temperature/#comment-1656610
henry says
good comment there, Nir!!
kadaka (KD Knoebel) says:
June 7, 2014 at 12:46 pm
Good find. Thanks for your excellent sleuthing, of Jimbo caliber.
Now everyone can have it.
Thanks!
PS: The references are also useful, whether you’re convinced by their proposed “bottom up” (as opposed to “top down”) mechanism for solar irradiance (or insolation) influence on climate or not.
@nir shaviv
btw
my results [on the Gleiszberg cycle alone] suggest a variation of ca. +0.5 degree C max. in the warming period of 44 years and ca.-0.5 degrees C min in the cooling period.
On average this suggests an average difference of [0.125K] between each of 8 succussive Schwabe solar cycles .[I think] this compares well with your own result.
I am just a small unpaid hobbyist, you know….
Willis – “However, I wouldn’t say that the ocean surface temperatures are “controlled almost entirely by windspeed”. Particularly in the tropics, the clouds regulate the amount of energy entering the system. If you get a day with no clouds, SSTs will climb. On the other hand, a cloudy day followed by a clear night will lead to falling SSTs.”
No that is incorrect, and to me very surprising also. I have been measuring it (in the tropics too) and it simply doesn’t work that way. What does get warmer is the layer just below the surface. What happens is that the top warm layer gets thicker during sunny days and thinner during cloudy days and nights, but the surface stays the same temperature as long as there is a warm layer reservoir below it.
I know i sound like a snake oil salesman, but wind speed (via evaporation and conduction) really does control the surface temperature. And it explains everything else too 🙂 Higher wind speeds = lower temperatures as long as there is moisture to evaporate of course, which is why it explains UHI, Climate change, climate warming, cooling etc. Wind is also self regulating, higher temps means increased evaporation which creates lows which increases the wind speed which lowers the surface temperature.
Unless changes in radiation levels can change the wind-evaporation loop changes in radiation levels won’t control the climate. That is also why everyone is searching in vain for correlations with sunspots, CO2, albedo, etc. they don’t exist. But humans are great at spotting patterns : )
Genghis says:
June 7, 2014 at 2:45 pm
You might be interested in the conclusions of Zhou & Tung (2013).
“4. Conclusions
“We have established statistically the existence of the 11-yr solar cycle signal in temperature throughout the troposphere. There is a robust heating center located over the tropics below the tropopause in all seasons, which is statistically significant. It cannot be interpreted as heating due to ozone absorption of solar UV radiation, since tropospheric ozone concentration is extremely
small. This heating is situated above a minimum in warming over the tropical ocean surface, suggestive of vertical convection caused by surface heating and evaporative feedback (which reduces surface warming). There are two vertical strips of warming outside the edge of the tropics in the troposphere that could be a result of a poleward shift of an expanded Hadley circulation.
The evidence we present here is suggestive of a ‘‘bottom up’’ mechanism for the tropospheric and
surface response similar to that for greenhouse warming, as discussed in Cai and Tung (2012): Most of the solar forcing reaching the surface in the tropics does not go directly into warming the ocean but into evaporating water and heating the upper troposphere through convection and latent heating. From there large-scale transport carries the heat poleward, resulting in a global
warming pattern. Because the tropical ocean is not warmed appreciably, only a small fraction of the heat is transferred into the ocean mixed layer. This may explain why the lag in the surface and tropospheric response is almost nonexistent, smaller than expected based on the thermal inertia of the entire mixed layer, and why the amplitude of the response is close to that estimated at
equilibrium.
“Previously, there have been several general circulation modeling studies with fixed sea surface temperature (SST) (Shindell et al. 1999; Haigh 1996, 1999; Larkin et al. 2000; Matthes et al. 2004; Balachandran et al. 1999) to isolate the ‘‘top down’’ effect. However, given that the visible/near-infrared solar heating in these experiments still penetrates to the surface, evaporates water,
and causes vertical convection, the calculated circulation change could still be, at least partly, from the bottom-up mechanism that we proposed. Since the change in the tropical SST is small in both the observation shown here and in the model of Cai and Tung (2012) where the SST was allowed to vary, fixing the SST in the important tropics in these experiments does not present a condition so different as to prevent the bottom-up mechanism from acting. Therefore, the results from these fixed SST experiments cannot be interpreted as arising only from the top-down mechanism.”
Not sure I consider model tests as experiments, but the approach is better than making a lot of assumptions not in evidence.
Nir, you do realize that papers that include “changes in solar irradiance” based on the uncorrected SSN values places you on shaky ground in terms of supporting literature.
A case in point: I wonder if Judith Lean still considers the paper linked below an adequate examination of solar drivers given what she now knows of problems with solar data (IE Leif’s group working on reconciling weighting issue with raw SSN). If my memory serves me, she no longer subscribes to solar reconstructions used in several papers she has co-authored in the distant past. Whether that includes this one I don’t know.
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0CD0QFjAC&url=http%3A%2F%2Ftenaya.ucsd.edu%2F~dettinge%2Fwhite1.pdf&ei=C4yTU6bWE8KsyATo1ICQBQ&usg=AFQjCNFTzLHIf_TIYRtnt2ksydCO4C2uJw&sig2=TSSOFLnWhguk9aKa8ZSqxA&bvm=bv.68445247,d.aWw
The Sun is about to enter a hibernation phase into solar cycle #25 and that will usher in global cooling, which I have been forecasting for years is coming. It is closer now and will begin officially in December 2017.
As for ENSO, it is driven, like all climate change on Earth, by the Sun and planetary angular relationships to the Sun and Earth.
However, there will be no ENSO this year, next year, or the year after that.
The next ENSO will be a powerful La Nina, that will impact 2020-2022, with the worst winter season in the northern hemisphere in 2021-2022, and it will rival the last brutal winter of 2014 and set new weather records for cold temperatures, heavy snowfall and ice.
In my climate forecast, there will be no ENSO until 2020, and that one will be La Nina – a very strong one at that.
Until 2020, we will see strange cool plumes in worldwide sea surface temperatures; a lack of hurricanes; along with the continued growth and expansion of the Arctic and Antarctic sea ice extents.
This is the trending to Global Cooling, which I have forecasted to begin officially in December 2017 and last approximately 36 years.
ENSO events are solar-planetary forced and occur every 10-11 years. The last ENSO, which I forecasted, was a El Nino in mid-2009 that was followed by a La Nina in 2010-11.
Think of ENSO as climate change in action.
You are seeing what amounts to a large scale variability in the circulatory system, and when you take out ENSO you are removing a climate mechanism where the thermal/kinetic exchange to equilibrium is achieved.
ENSO is externally forced through the polar annular modes/AAM, and ENSO is climate change in action. What confounds the computer modellers about ENSO’s cycle is that the thermodynamic response to perturbation is not linear.
ENSO responds to fluctuations by the external forcing from the Sun.
Understand at the dynamics of ENSO and what forces it.
ENSO is forced by the Sun externally because the strength of the trade winds, that’s Walker Cell dynamics, and the AAM integral come before ENSO SST variation.
Now, the atmosphere is the less energetic body, so by definition there has to be an ‘external’ perturbation present.
Evidence of such Solar forcing exists and the relationship is significant:
Corotating coronal holes of the Sun induce fluctuations of the solar wind speed in the vicinity of the Earth.
These fluctuations of solar wind speed are closely correlated with geomagnetic activity and the resultant geophysical climate and weather effects on Earth.
It is basic to Astrometeorology. That is what I do.
Now, solar wind speeds have been observed and monitored by orbiting Earth satellites since the mid-1960s. The long-term series of solar wind speed clearly reveals enhanced amplitudes at the solar rotation period of 27.3 days and at its harmonics 13.6 and 9.1 days.
The amplitude series are modulated by a quasi-biennial oscillation (QBO) that has a period of 1.75a (that’s 21 months) as bispectral analysis reveals.
A 1.75a QBO component is also present in the equatorial, zonal wind of the stratosphere at 30 hPa, in addition to the well-known QBO component at the period 2.4a (at 29 months.)
The solar wind QBO influences the stratospheric QBO, the global electric circuit, and cloud cover by modulation of ionospheric electric fields, cosmic ray flux and particle precipitation.
And the series of solar wind speed fluctuations are bandpass-filtered at the period 1.75a. The filtered series provide the amplitude of the solar wind QBO as function of time.
The maxima of the solar wind QBO series correlate with those of the ENSO Index. Analysis confirms that the solar wind QBO helps to trigger ENSO activity.
The solar forcing of ENSO is done by changes in meridional flux through the NAM/SAM and that ties directly right back into planetary wave action.
In volume 36, issue 17, of the September 2009 Geophysical Research Letters, Rodrigo Caballero and Bruce T. Anderson state that:
“Stationary planetary waves are excited in the mid-latitudes, propagate equatorward and are absorbed in the subtropics. The impact these waves have on the tropical climate has yet to be fully unraveled.
“Previous work has shown that interannual variability of zonal-mean stationary eddy stress is well correlated with interannual variability in Hadley cell strength. A separate line of research has shown that changes in midlatitude planetary waves local to the Pacific strongly affect ENSO variability.
“Here, we show that the two phenomena are in fact closely connected. Interannual variability of wave activity flux impinging on the subtropical central Pacific affects the local Hadley cell. The associated changes in subtropical subsidence affect the surface pressure field and wind stresses, which in turn affect ENSO.
“As a result, a winter with an anomalously weak Hadley cell tends to be followed a year later by an El Niño event.”
Moreover, there is a link from the Pacific Meridional Mode to ENSO, as Ping Chang and Link Ji from Texas A&M University at College Station, Texas wrote in late 2008:
“The occurrence of a boreal spring phenomenon referred to as the Pacific Meridional Model (MM) is shown to be intimately linked to the development of El Niño–Southern Oscillation (ENSO) in a long simulation of a coupled model.
The MM, characterized by an anomalous north–south SST gradient and anomalous surface circulation in the northeasterly trade regime with maximum variance in boreal spring, is shown to be inherent to thermodynamic ocean–atmosphere coupling in the intertropical convergence zone (ITCZ) latitude, and the MM existence is independent of ENSO.
“The thermodynamic coupling enhances the persistence of the anomalous winds in the deep tropics, forcing energetic equatorially trapped oceanic waves to occur in the central western Pacific, which in turn initiate an ENSO event. The majority of ENSO events in both nature and the coupled model are preceded by MM events.”
Now, the reasons why NOAA/NWS and every other conventional climate center on Earth, along with climatologists and their computer models cannot forecast ENSO; is that their computer models are shit.
ENSO is an *astronomically-caused* climate event.
And clearly the algorithms in their overblown and error-filled computer models are not programmed to understand ENSO.
That is why they cannot forecast it and every single year they come out with forecasts on ENSO and they fail.
They did it last time when I forecasted the 2009-2011 ENSO three years in advance, from 2006.
Rather, what conventional modellers do is that they take an initial condition and then they apply their own perturbation theories to attempt to get a future projection – and those projections are always wrong, wrong, wrong.
In truth, in the real world of climate, ENSO is NOT an internally driven or a chaotic phenomenon.
ENSO is a solar and planetary magnetically-driven event that forces upper stratospheric U-flow/QBO and you can witness the results and impact on the N/S annular modes.
Reports from the CFS project on the 2011 La Nina that I forecasted fell to -4C because those expensive computer models are founded on absolutely useless methods on the given boundary conditions that they use to project from.
It means that they are essentially using a system dynamic that *drives* the system state, rather than the other way around. They have it ass backwards.
For instance, if you subtract ENSO, then you also have to subtract the poleward migration of Hadley cells/expansion of the Ferrel cells seen since solar year 1976.
Now, once you do that, you will lose the 3-4 percent decrease that’s observed in tropical cloud cover. Therefore, you lose essentially all of the warming that has occurred since the 1970s and that relates to about 3.5W/m^2 of loss since 1982.
NOAA/NWS and every other climate forecast center do not successfully produce accurate seasonal forecasts.
Again, that’s because their models are only programmed to the general governing equations that are put into them.
For years now, with all that money they’ve wasted, the computer climate modeling world is a total disaster and they have to know it after busting every season, every year, year in and year out.
Again, there will be no ENSO until 2020. We will see signatures by mid-2019 when things really begin to get interesting, but by 2020 there will a full blown La Nina that will be in force for 2.5 years according to my calculations.
The worst of it will be during the winter of 2021-2022 – a really bad and long winter season followed by a cold, wet spring and cool summer of 2022.
ENSO is climate change in action and that climate change is to GLOBAL COOLING, which officially begins in December 2017. That’s been my forecast and people had better prepare for it too.
~ Theodore White, Astromet
milodonharlani says:
June 7, 2014 at 11:43 am
Pass. Their previous claim was Zhou-Tung-Camp 2008, where they claimed to find strong solar cycle / temperature signals in various temperature datasets.
Perhaps you could read that one, and tell us all how many complete solar cycles were included in their whiz-bang analysis? Because once I did that, I gave up entirely on Mssrs. Tung and Zhou. Not to mention the thrashing they gave to their data, cutting out chunks they didn’t like … sorry, milodon, but those guys are on my junk list.
HOWEVER: If you think Z&T2013 is valid, how about you do what I suggested? I mean, you’ve got the paper and I don’t. Go get their numbers and see if you can replicate their results.
Again, folks, let me invite you to do your homework. If you think something is valid, hows about YOU take the time to see if you are recommending garbage BEFORE tossing it on the table? I’m getting tired and bored with shoveling the muck.
w.
Greg Goodman says:
June 7, 2014 at 12:01 pm
Say what? I DID quote you, in context, and exactly. Let’s start with my whole quote, for clarity. It’s from my comment here. This is what I said AFTER quoting your words.
I had quoted your exact words about the 170-year cycle, immediately above my comment in the same post. You had said, and I had quoted:
So I’m sorry, but your complaint has no merit. I did quote what you said.
w.
Willis Eschenbach says:
June 7, 2014 at 3:39 pm
How do you know it’s garbage if you haven’t read it? Ignoring others’ work or dismissing it out of hand doesn’t inspire confidence in your conclusions. Even if you don’t want to consider it, their references include relevant papers.
I don’t think it’s up to me to reproduce their results. It’s your quest to find a signal for the 11-year cycle, not mine. On its face, their methodology looks OK to me.
Courtesy of Kadaka, there’s now a link to Zhou & Tung (2013) in the comments above. I copied their conclusion section.
Greg Goodman says:
June 7, 2014 at 12:32 pm
Not true in the slightest. If you take e.g. data that has only one large peak or one major trough in the data, it will have NO 11-year periodicity. This kind of single-peak or single-trough result is quite common in red noise.
But if you run a cross-correlation of that non-cyclical data with the sunspot data, you’ll get a strong 11-year cycle.
In other words, the procedure you are using will very frequently have an 11-year cyclicality REGARDLESS of whether such a cycle exists in the other dataset or not.
Which means it is useless for the purpose you are putting it to.
w.
milodonharlani says:
June 7, 2014 at 3:51 pm
I don’t “know that it’s garbage”, that’s why I suggested that you investigate it and see if it is or not.
What I was trying to say (apparently unsuccessfully) was that in my opinion, those guys are not worth wasting my time on. I’m simply following the first rule of holes.
If you are citing their results and commending them to my attention, you are saying that you think that they are true and worthwhile of my spending time on.
If you think that the paper is worthwhile, go for it. But don’t try to bust me for being once-bitten, and therefore twice-shy. When two guys make ludicrous claims like they did in Z&T2008, they go to the bottom of my list of things to spend time on.
w.
PS—I just read the first line of the Z&T2013 paper’s abstract. It says:
Milodon, they haven’t even looked at any data. Instead, they’ve looked at computer climate model output. What they call “reanalysis data” is not data in any sense of the word. It is the output of a computer climate model which is forced in part with solar data.
Now, I’ve shown many times that computer model temperature outputs are simple linear transforms of their inputs. Given that fact, it is obvious that all reanalysis “data” is very likely to show a solar signal …
… and if you truly think that finding a solar signal in computer climate model output means anything about the real world, you are nowhere near as smart as I believe you to be.
I don’t think it’s “true”, but their proposed mechanism makes sense on its face. I brought it to your attention because they find the signal & try to explain it. I don’t rule out reanalysis on its face. I went to the paper they cite, Labitzke et al. (2002), & it looked OK, but I didn’t do any statistical analysis of my own.
I’m OK without an 11 year signal, since the sun’s irradiance & insolation as modulated by earth’s orbital & rotational mechanics (& other terrestrial & ET effects) are IMO clearly implicated in climatic periodicities on the scale of multiple decades, centuries, millennia, myriads, hundreds of thousands, millions, tens of millions, hundreds of millions & billions of years.
Would it be worth renewing the analysis, using cycle-length as the primary criteria?
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my point as well. there is no 11 year solar cycle. that is simply a mathematical average.
look for correlation between rate of warming and inverse length of cycle before claiming it doesn’t exist. Otherwise you are simply sailing south from England, claiming the America’s don’t exist, simply because you didn’t find it.
Theodore White says:
June 7, 2014 at 3:19 pm
“The Sun is about to enter a hibernation phase into solar cycle #25 and that will usher in global cooling, which I have been forecasting for years is coming. It is closer now and will begin officially in December 2017.”
I tend to agree with your comment and have been going naked on hurricane insurance in SW Florida since 2009, on the bet the Sun is going into a quiet period, saving a ton of money.
I alluded to this question in a previous solar thread and suggested the Earth acts like a battery, recharging and discharging its absorbed energy. Such a perfect equilibrium from one solar cycle to the next has been enjoyed without noticeable affects, except for current solar cycle 24 in modern times.
Instead of folding, try compressing and stretching like a Slinky the records to correlate each recharge discharge cycle from solar cycle to cycle. Depending on amplitude and duration of any given solar cycle our planet will have a net gain, net neutral, or net loss of energy from one cycle to the next. Maybe my comment yesterday on time lag effects of solar influence nudged Willis Eschenbach also to post an excellent discussion on this matter. I enjoy these discussions. Perhaps an understanding of battery technology recharge discharge cycles, can add to our understanding of how the whole climate of the planet works.
I’m OK without an 11 year signal
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So am I, because there is no 11 year solar cycle. The 11 year cycle is a mathematical average of the true solar cycle. It is an imaginary construct. It does not exist. Read Briggs about using averages for anything. It is garbage in, garbage out.
Willis, you say “However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets.“. But then you say “This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …“.
Given that Earth’s climate system is complex, coupled and non-linear [as per the latter part of your statement above], and given that any effect of the “11-year” solar cycle must if it exists be weak and/or inconsistent because otherwise it would have been quite easily found already, it follows that your argument based on it not being visible in the full SST record is invalid. ie, if it does exist then the full SST record is not the place to look.
So where should one look? Other commenters have suggestions, which I haven’t yet checked, but I would suggest looking in selected regional data. Maybe rainfall or diurnal temperature range or whatever data does exist, in various individual locations. Maybe some commenters here have already found something valuable – I confess I haven’t followed them all up.
NB. I’m not saying that the sunspot signal exists, just that if you are serious about looking for it then instead of looking in the places (eg. full SST) where you have already failed to find it, it would be a good idea to start looking in other places. Please understand that this is not meant as a criticism but as a helpful comment.
Greg Goodman says:
June 7, 2014 at 8:09 am
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“I suspect this may be correct but I’ve yet to see the “proof”.”
Greg, happy to oblige –
http://i42.tinypic.com/2h6rsoz.jpg
The best way to understand is to build and run the experiment for yourself. Start with 40C water samples under the strong and weak LWIR sources. You should notice little or no divergence in the cooling rate of the water samples. Now repeat the experiment but float a square of LDPE film (cling wrap) onto the surface of each sample. This allows conductive and radiative cooling but prevents evaporation. You should now record a distinct divergence in the cooling rate of the samples.
I have run a number of these type of experiments since 2011. I can assure you that LWIR does not effect liquid water that is free to evaporatively cool in the same manner as it would a “near blackbody”
Posts by others who have also taken in situ IR measurements of the ocean also support these results – Genghis says: June 7, 2014 at 10:22 am