New paper by Love et al suggests no prominent role for solar‐terrestrial interaction in global climate change. I’m providing it here for discussion.
We are not convinced that the combination of sunspot‐number,
geomagnetic‐activity, and global‐temperature data can, with
a purely phenomenological correlational analysis, be used to
identify an anthropogenic affect on climate.
Abstract
Recent studies have led to speculation that solar‐terrestrial interaction, measured by sunspot number and geomagnetic activity, has played an important role in global temperature change over the past century or so. We treat this possibility as an hypothesis for testing. We examine the statistical significance of cross‐correlations between sunspot number, geomagnetic activity, and global surface temperature for the years 1868–2008, solar cycles 11–23. The data contain substantial autocorrelation and non-stationarity, properties that are incompatible with standard measures of cross-correlational significance, but which can be largely removed by averaging over solar cycles and first‐difference detrending. Treated data show an expected statistically significant correlation between sunspot number and geomagnetic activity, Pearson ρ < 10^−4, but correlations between global temperature and sunspot number (geomagnetic activity) are not significant, ρ = 0.9954, (ρ = 0.8171). In other words, straightforward analysis does not support widely‐cited suggestions that these data record a prominent role for solar‐terrestrial interaction in global climate change.
With respect to the sunspot‐number, geomagnetic‐activity, and global‐temperature data, three alternative hypotheses remain difficult to reject: (1) the role of solar‐terrestrial interaction in recent climate change is contained wholly in long‐term trends and not in any shorter‐term secular variation, or, (2) an anthropogenic signal is hiding correlation between solar‐terrestrial variables and global temperature, or, (3) the null hypothesis, recent climate change has not been influenced by solar‐terrestrial interaction.
Citation: Love, J. J., K. Mursula, V. C. Tsai, and D. M. Perkins (2011), Are secular correlations between sunspots, geomagnetic activity, and global temperature significant?, Geophys. Res. Lett., 38, L21703, doi:10.1029/2011GL049380.
Conclusions
One of the merits of using three separate data sets in a correlational analysis is that intercomparisons can be made. After treatment for removal of autocorrelation and nonstationarity through simple averaging and differencing, we find statistically‐significant secular correlation between sunspot number and geomagnetic activity. This is expected,
and it serves as important support for our analysis method. On the other hand, after making the same treatment to the global surface temperature, correlations between temperature and either sunspot number or geomagnetic activity are not significant.
We have not, in this study, considered derived proxy metrics of relevance to climate change, such as reconstructed total‐solar irradiance [e.g., Fröhlich and Lean, 2004] or
interplanetary magnetic field [e.g., Lockwood et al., 1999]. Still, we believe that our methods are general, that they could be used for other data sets, even though our analysis, here, is tightly focused on specific data sets. [15] From analysis of sunspot‐number, geomagneticactivity, and global‐temperature data, three hypotheses remain difficult to reject; we list them.
(1) The role of solarterrestrial interaction in recent climate change is wholly contained in the long‐term trends we removed in order to reduce autocorrelation and nonstationarity. This possibility seems artificial, but we acknowledge that our method requires a nontrivial time‐dependence in the data that is different from a simple trend. Still needed is a method for measuring the significance of correlation between data sets with trends.
(2) An anthropogenic signal is hiding correlation between solar‐terrestrial variables and global temperature. A phenomenological correlational analysis, such as that used here, is not effective for testing hypotheses when the data record a superposition of different signals. Physics is required to separate their sum.
(3) Recent climate change has not been influenced by solar‐terrestrial interaction. If this null hypothesis is to be confidently rejected, it will require data and/or methods that are different from those used here.
Paper: http://www.leif.org/EOS/2011GL049380.pdf
h/t to Dr. Leif Svalgaard
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recent climate change is wholly contained in the long‐term trends we removed in order to
“We lost our watch in the dark over there, but we are looking for it under the lamp post here because the light is so much better.”
What is it with people throwing away the low frequency signal and analyzing the noise?
What about clouds? Aren’t clouds correlated to both long term temperature trends and sun spot cycles?
What about removing UHI?
Are they saying the sun does not effect climate? I’d like to see the proof of that, it could make me a warmer, or at least a luke warmer.
A phenomenological correlational analysis, such as that used here, is not effective for testing hypotheses when the data record a superposition of different signals. Physics is required to separate their sum.
I think this is an important assertion that many commenters here should heed.
Interesting paper Dr. Svalgaard, thanks.
“A phenomenological correlational analysis, such as that used here, is not effective for testing hypotheses when the data record a superposition of different signals. Physics is required to separate their sum”
Leif says:
“I think this is an important assertion that many commenters here should heed”
That statement is false imo. A hypothesis can be tested by using it to make predictions, and when(if) the predictions are observed as true, then the hypothesis is validated. For example, I can accept that gravity as defined in a testable hypothesis can be shown to exist and follow mathmatically defined laws(be true), even though no one has the physics to explain how it works.
“I think this is an important assertion that many commenters here should heed.’
Not only commenters here, but all who would seek to interpret this research as dismissing solar influence on climate.
They need to consider system heat vs surface temperature, and also system lag. It is nice to hear someone talking about stationarity and its relevance to statistical analysis. Where were these guys when statistical analysis of surface temp trends is being bandied about, in the same breath as increases in extreme events is being alleged?
Stephen Rasey says:
November 15, 2011 at 10:57 am
What is it with people throwing away the low frequency signal and analyzing the noise?
From the paper [14] “After treatment for removal of autocorrelation and nonstationarity through simple averaging and differencing”…
Differencing is an effective method of removing trend from a time series. This provides a clearer view of the true underlying behaviour of the series. More here:
http://www.duke.edu/~rnau/411diff.htm
I think that this paper is quite naive.
1) It starts assuming that there exists a simple linear relation between sunspot number, geomagnetic activity and climate. So, they assume that no physics at all exists linking these heterogeneous variables: interesting.
2) When they remove the smooth trending they claim that the correlation becomes less significant. Of course, there are the ocean oscillations, volcano signature etc, non linear responses to the solar cycles such as a frequency response and a frequency dependent time lag response that partially hide the true signal.
3)They do find that a smooth common treding exists among the records,: this is nothing really new and extremely well known in numerous works.
4) They ignore all litterature that attempts to address seriously the problem. and just calculate linear cross-correlation function between non-linearly linked raw variables.
PS: who knows why people try to reconstruct the total solar irradiance index (instead of just using sunspot numbers), use filters to isolate the components and use heat capacity models to obtain temperature signals instead of just using linear cross correlations!
By the way, a much better analysis of the data is here and in related references
N. Scafetta, “Empirical analysis of the solar contribution to global mean air surface temperature change,” Journal of Atmospheric and Solar-Terrestrial Physics 71 1916–1923 (2009), doi:10.1016/j.jastp.2009.07.007.
http://www.fel.duke.edu/~scafetta/pdf/ATP2998.pdf
N. Scafetta, “Empirical evidence for a celestial origin of the climate oscillations and its implications”. Journal of Atmospheric and Solar-Terrestrial Physics 72, 951–970 (2010), doi:10.1016/j.jastp.2010.04.015
http://www.fel.duke.edu/~scafetta/pdf/scafetta-JSTP2.pdf
N. Scafetta, “A shared frequency set between the historical mid-latitude aurora records and the global surface temperature” Journal of Atmospheric
and Solar-Terrestrial Physics, in press. DOI: 10.1016/j.jastp.2011.10.013.
http://www.fel.duke.edu/~scafetta/pdf/Scafetta-auroras.pdf
In the last two papers the climate is actually accurately reconstructed using astronomical cycles
Ed_B says:
November 15, 2011 at 11:16 am
“A phenomenological correlational analysis, such as that used here, is not effective for testing hypotheses when the data record a superposition of different signals. Physics is required to separate their sum”
Leif says:
“I think this is an important assertion that many commenters here should heed”
That statement is false imo. A hypothesis can be tested by using it to make predictions, and when(if) the predictions are observed as true, then the hypothesis is validated. For example, I can accept that gravity as defined in a testable hypothesis
The issue is not whether a given hypothesis is true of false, but has to do with a situation where many different signals are present at the same time. To separate them from each other, physics is required, because if you make a prediction based on curve-fitting of just one of those inputs and it fails, it could be that it was cancelled by the influence of one of the others.
Nicola Scafetta says:
November 15, 2011 at 11:21 am
In the last two papers the climate is actually accurately reconstructed using astronomical cycles
It is precisely that sort of papers the article warns about.
Indeed. You’re not going to get a particularly high correlation coefficient between ‘global temperature’ and any one factor (well, not one that could plausibly relate to climate anyway…). Combining and applying weighting to numerous factors (using physics and indeed chemistry and biology) might get you a lot closer to a high r-value.
If it were only that simple analyzing the correlation between temperature and CO2.
Leif Svaalgard.
“A phenomenological correlational analysis, such as that used here, is not effective for testing hypotheses when the data record a superposition of different signals. Physics is required to separate their sum. ”
Something I have taken to heart since the first time I read your comments on Climate Audit a few years ago. One of the major issues is the assumption that the magnitude of CO2 forcing is a high as estimated and as uniformly distributed as estimate. That does not appear to the the case, specifically in the Southern Extent, which means that smaller variation may have larger impacts. Something I have been looking into, the matters of scale.
http://redneckphysics.blogspot.com/2011/11/thermodynamic-layer-convergence-and.html
This is just one of the odd thermodynamic relationships that makes some solar mechanisms more plausible. Still not enough for proving any of the cyclomainia, but it does make things more interesting.
This site is always interesting to read and informative about the variables you never hear about elsewhere. So may and interactions so varied that I am in awe of the persistence of those trying to do good science to figure it out.
I need to respectfully disagree with Ed_B’s statement:
A hypothesis can be tested by using it to make predictions, and when(if) the predictions are observed as true, then the hypothesis is validated.
Actually such an event simply means that the hypothesis has not been disproven. Some other factor completely outside the model may be the actual driver of events. Given enough time, and thousands of comfirmed predictions I may begin to accept the hypothesis as true, but that’s just me.
@Leif,
“The issue is not whether a given hypothesis is true of false, but has to do with a situation where many different signals are present at the same time. To separate them from each other, physics is required, because if you make a prediction based on curve-fitting of just one of those inputs and it fails, it could be that it was cancelled by the influence of one of the others.”
I don’t think that is correct. Separating out contributing signals from a complex waveform is standard practice in many fields, such as NMR, and antenna technology we all use. Each contributing wave does not need to have some separate physics behind it to explain its contribution, especially when all componants are subject to the same physical underpinnings (thus averaging out), like with Wifi signals, or atomic spins under an external magnetic field in response to a radio frequency pulse. Thousands of waves can make up the total wave form, but some Fourier transform can break them all into separate, determinable, peaks carrying data. This, for instance, is how we can solve the atomic scale three dimensional structure of a protein.
I see no reason why that can’t be done with records here, and why there must be some separate physics for each data set to “determine its contribution”. It’s contribution is already determined in the data itself.
Now, that in no way says that there IS a signal to be found in these particular data sets, just that the logical argument you state there, in the way you stated it, is demonstrably flawed if not outright false.
The issue is the timeframe, you need to go back much further to the Maunder to realy see the forest for the trees. Of course, the problem is there was more volcanism then so the correlation may never be proven.
Questions: Did they correct for volcanism? Atmospheric aerosols?
Did you use Kelvins instead of Celsius, hope so.
Isn’t this a straw man? Svensmark’s claim is that solar cycle length, not sunspot number, is linked with climatic changes.
Leif Svalgaard says:
November 15, 2011 at 11:00 am
A phenomenological correlational analysis, such as that used here, is not effective for testing hypotheses when the data record a superposition of different signals. Physics is required to separate their sum.
I think this is an important assertion that many commenters here should heed.
_____
Exactly! And certainly the climate data record is a superposition of different signals, and as such an analysis such as the one used here is completely ineffective and so it does come back to the basic physics and detailed analysis to disentangle and accurately attribute the individual factors involved. And furthermore, what is “noise” in the signal, depends on what signal you’re looking for, as indeed, all factors are superimposed on any given slice in time, but shorter term signals will appear as noise superimposed on longer term signals, and there are many of each type existing at any moment in time.
One must understand the mechanisms by which solar changes modulate planetary cloud cover. The authors of the above paper quoted appear to have not done a research search into this subject.
Solar wind bursts create a space charge differential in the ionosphere which removes cloud forming ions. That mechanism is called electroscavenging. The solar wind burst have during the last two solar cycles of the twentieth century occurred during solar minimums. Normally during solar minimums planetary cloud cover increases which causes the planet to cool. Enric Palle published papers that measured the change in planetary cloud cover. Prior to roughly 1998 planetary cloud cover closed tracked GCR. Post 1998 it did not and there was a net reduction in planetary cloud cover.
sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf
Once again about global warming and solar activity
Solar activity, together with human activity, is considered a possible factor for the global warming observed in the last century. However, in the last decades solar activity has remained more or less constant while surface air temperature has continued to increase, which is interpreted as an evidence that in this period human activity is the main factor for
global warming. We show that the index commonly used for quantifying long-term changes in solar activity, the sunspot number, accounts for only one part of solar activity and using this index leads to the underestimation of the role of solar activity in the global warming in the recent decades. A more suitable index is the geomagnetic activity which reflects all solar activity, and it is highly correlated to global temperature variations in the whole period for which we have data.
It has been noted that in the last century the correlation between sunspot number and geomagnetic activity has been steadily decreasing from – 0.76 in the period 1868-1890, to 0.35 in the period 1960-1982, while the lag has increased from 0 to 3 years (Vieira et al. 2001). According to Echer et al. (2004), the probable cause seems to be related to the double peak structure of geomagnetic activity. The second peak, related to high speed solar wind from coronal holes, seems to have increased relative to the first one, related to sunspots (CMEs) but, as already mentioned, this type of solar activity is not accounted for by the sunspot number. In Figure 6 the long-term variations in global temperature are compared to the long-term variations in geomagnetic activity as expressed by the ak-index (Nevanlinna and Kataja 2003). The correlation between the two quantities is 0.85 with p<0.01 for the whole period studied.It could therefore be concluded that both the decreasing correlation between sunspot number and geomagnetic
activity, and the deviation of the global temperature long-term trend from solar activity as expressed by sunspot index are due to the increased number of high-speed streams of solar wind on the declining phase and in the minimum of sunspot cycle in the last decades.
Leif says “The issue is not whether a given hypothesis is true of false, but has to do with a situation where many different signals are present at the same time”
I disagree. It seems you are unable to accept that Scafettas work is proving to be true, and are looking for ways to hand wave it away. Like I said, no one has an explaination for gravity, yet I accept that the mathmatics are proven to be valid. Following your logic, should I not reject Newtons or Einsteins work on gravity?
So all of the figures and arguments presented here:
http://www.appinsys.com/GlobalWarming/GW_Part6_SolarEvidence.htm
are what, incorrect? Irrelevant? Lies? Myths? Fake?
I mean seriously, why screw around looking for second order correlations in detrended data, effectively looking to see if FLUCTUATIONS in solar activity are drivers of temperature. The FIRST ORDER correlation is already overwhelmingly convincing, especially when compared to the monotonic, boring, trivially fluctuating CO_2 concentration.
Here’s a good one. Apply precisely — and I do mean precisely — the same method of detrending to CO_2 concentration vs global temperature and look for a signal. The CO_2 is a monotonic, approximately exponential curve and by the time you remove this trend there isn’t anything left but a tiny annual fourier component.
On the other hand, there is significant direct correlation between both the length and the strength of the solar cycles and global temperature, one that stretches all the way back to the LIA. Even the primary local/chaotic climate drivers, e.g. ENSO, appear to be more or less slaved to the solar cycle.
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Leif Svalgaard says:
………………
This may be relevant to the thread. Have you had a chance to look into two variables (re the email) the way it should be done. For the moment I am looking at 1840-1960 only, leaving out disagreeable 1960-2010, or do we have to attribute it to all the coincidence?
“”Leif Svalgaard says:November 15, 2011 at 11:29 am
“Nicola Scafetta says: November 15, 2011 at 11:21 am
In the last two papers the climate is actually accurately reconstructed using astronomical cycles”
It is precisely that sort of papers the article warns about.””
Not really Leif, not really. Time series analysis and data mining are serious disciplines in all scientific fields. That paper trivializes the issue.
In my papers I decompose all natural signals (Solar, anthropogenic, volcano, ENSO, lunar) as it should be done and my models are tested on forecasting capabilities backward and forward for decades and up to centuries. Of course not all issues are fully understood yet, that is why it is called “scientific research”.
Dear Leif, I have a proposal given the fact that you are so smart. Why don’t you propose your own model and show us your performances in interpreting climate change?