by Judith Curry
Interpretation of statistical or dynamical predictions of future climate change needs to appropriately interpret the modes of natural internal climate variability, such as the Atlantic Multidecadal Oscillation (AMO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO) and the North Pacific Gyre Oscillation (NPGO). This interpretation is needed in the context of forced climate change (e.g. solar, greenhouse gases).
We are currently in the warm phase of the AMO (since 1995) and the cool phase of the PDO (flickering since 1999; decisively cool since 2008). The previous similar regime was in the 1950’s, which was characterized by above average rainfall in the Sahel and south Asia, drought in the southwest U.S., and many intense hurricane landfalls in the U.S. Based upon previous regime shifts, it might be anticipated that this regime will continue for at least another decade. The challenge is to predict the change points for these regimes.
Tsonis et al. 2007 and Swanson and Tsonis 2009 provide a framework for understanding climate shifts. Swanson and Tsonis 2009 write:
Interpreting past such variability and making informed projections about potential future variability requires (i) identifying the dynamical processes internal to the climate system that underlie such variability and (ii) recognizing the chain of events that mark the onset of large amplitude variability events, i.e., shifts in the climate state. Such shifts mark changes in the qualitative behavior of climate modes of variability, as well as breaks in trends of hemispheric and global mean temperature. The most celebrated of these shifts in the instrument record occurred in 1976/77. That particular winter ushered in an extended period in which the tropical Pacific Ocean was warmer than normal, with strong El Nino-Southern Oscillation (ENSO) events occurring after that time, contrasting with the weaker ENSO variability in the decades before. Global mean surface temperature also experienced a trend break, transitioning from cooling in the decades prior to 1976/77 to the strong warming that characterize the remainder of the century.
[In Tsonis et al. 2007] it was hypothesized that certain aspects of the climate system behave in a manner analogous to that of synchronized chaotic dynamical systems. Specifically, it was shown that when these modes of climate variability are synchronized, and the coupling between those modes simultaneously increases, the climate system becomes unstable and appears to be thrown into a new state. This chain of events is identical to that found in regime transitions in synchronized chaotic dynamical systems. This new state is marked by a break in the global mean temperature trend and in the character of ENSO variability. Synchronization followed by an increase in coupling coincided with all the major climate shifts of the 20th century, and was also shown to mark climate shifts in coupled ocean-atmosphere simulations.
Using a new measure of coupling strength, this update shows that these climate modes have recently synchronized, with synchronization peaking in the year 2001/02. This synchronization has been followed by an increase in coupling. This suggests that the climate system may well have shifted again, with a consequent break in the global mean temperature trend from the post 1976/77 warming to a new period (indeterminate length) of roughly constant global mean temperature.
Tsonis et al. 2007 explain the mechanism as follows:
First let’s consider the event in 1910s. The network synchronizes at about 1910. At that time the coupling strength begins to increase. Eventually the network comes out of the synchronous state sometime in late 1912 early 1913. The destruction of the synchronous state coincides with the beginning of a sharp global temperature increase and a tendency for more frequent and strong El Nino events. The network enters a new synchronization state in the early 1920s but this is not followed by an increase in coupling strength. In this case no major shifts are observed in the behavior of global temperature and ENSO. Then the system enters a new synchronization state in the early 1930. Initially this state was followed by a decrease in coupling strength and again no major shifts are observed. However, in the early 1940s the still present synchronous state is subjected to an increase in coupling strength, which soon destroys it. As the synchronous state is destroyed, a new shift in both temperature trend and ENSO variability is observed. The global temperature enters a cooling regime and El Ninos become much less frequent and weaker. The network synchronizes again in 1950. This state is followed by a decrease in coupling strength and, as was the case in 1920s no major shifts occur. Finally, the network synchronizes again in the mid 1970s. This state is followed by an increase in coupling strength and incredibly, as in the cases of 1910 and 1940, synchronization is destroyed and then climate shifts again. The global temperature enters a warming regime and El Ninos become frequent and strong. The fact that around 1910, 1940, and in the late 1970s climate shifted to a completely new state indicates that synchronization followed by an increase in coupling between the modes leads to the destruction of the synchronous state and the emergence of a new state.
Tsonis et al. (2007) speculate on climate shifts in the context of greenhouse warming in the 21st century:
The above observational and modeling results suggest the following intrinsic mechanism of the climate system leading to major climate shifts. First, the major climate modes tend to synchronize at some coupling strength. When this synchronous state is followed by an increase in the coupling strength, the network’s synchronous state is destroyed and after that climate emerges in a new state. The whole event marks a significant shift in climate. It is interesting to speculate on the climate shift after the 1970s event. The standard explanation for the post 1970s warming is that the radiative effect of greenhouse gases overcame shortwave reflection effects due to aerosols. However, comparison of the 2035 event in the 21st century simulation and the 1910s event in the observations with this event, suggests an alternative hypothesis, namely that the climate shifted after the 1970s event to a different state of a warmer climate, which may be superimposed on an anthropogenic warming trend.
Swanson and Tonis (2009) conclude:
If as suggested here, a dynamically driven climate shift has occurred, the duration of similar shifts during the 20th century suggests the new global mean temperature trend may persist for several decades. Of course, it is purely speculative to presume that the global mean temperature will remain near current levels for such an extended period of time. Moreover, we caution that the shifts described here are presumably superimposed upon a long term warming trend due to anthropogenic forcing. However, the nature of these past shifts in climate state suggests the possibility of near constant temperature lasting a decade or more into the future must at least be entertained. The apparent lack of a proximate cause behind the halt in warming post 2001/02 challenges our understanding of the climate system, specifically the physical reasoning and causal links between longer time-scale modes of internal climate variability and the impact of such modes upon global temperature.
JC’s comments: This is a post pulled together quickly, based papers that I am currently reading in an attempt to figure out how we can make some sort of sensible scenario based climate predictions on decadal time scales. My current interest in this is of an applied nature, particularly in the context of needs of military/security (more on this topic soon). I don’t have any particular insight in Tsonis’ work in terms of the chaos/nonlinear dynamics aspects (which I am not an expert on). But I find these ideas very intriguing and they make more sense to me than anything else I’ve read on trying to interpret the the climate record of the past decade and make projections a few decades hence. I would appreciate comments on this from the denizens that are knowledgeable on this topic.
In terms of any critique of this that I can provide, I am not exactly sure which of the regime indices are best to use in such an analysis. I don’t think that ENSO and PDO are independent in a multidecadal sense. On the other hand, the NPGO seems to be a mode independent to PDO. With regards to the AMO/NAO/AMOC, I wish somebody would sort these out; different communities use different indices (and on different timescales) and its not obvious which is preferred for what application. My own conceptual reasoning about this has used the AMO/PDO.
Moderation note: This is a technical thread, comments will be moderated for relevance.
Update: Tomas Milanovic provides this lucid summary and interpretation of Tsonis (2007):
As the referenced Tsonis papers are not easily readable, I will try to resume for you and the interested readers what it is actually about.
- First, Tsonis is NOT doing chaos theory, he is doing statistics.
- Second, the important paper is 2007. 2010 is a minor update with no interest in itself.
- Third, the Tsonis&al 2007 paper may be considered as minor in the field of synchronization of dynamical systems as opposed to major papers likehttp://amath.colorado.edu/faculty/juanga/Papers/PhysicaD.pdf
So now I’ll focus on the 2007 paper only. As I have written multiple times, the main reason why spatio temporal chaos is untractable is the fact that the phase space (the space of the system’s states) is uncountably infinite dimensional because of the adition of space variables to the usual time variable. That’s why all approaches of spatio temporal chaos begin with a discretization of space by taking a grid or a network which transforms the continuous spatial functions in a finite number of nodes so that the phase space becomes finite dimensional.
Tsonis acknowledges that the climate is a spatio temporal chaotic system so he looks for a spatial discretization too. Of course as the fact of being chaotic doesn’t imply that the GHG play no role (just that they play some role), he proceeds with the mandatory genuflexion to orthodox AGW by saying that “… the climate shifted after the 1970 event in another warmer state which may be superimposed on an anthropogenic warming trend.” This certainly allows him to avoid inflammatory articles by the usual suspects in the newspapers.
His discretization is to choose 4 indexes – PDO, ENSO, NAO, NPO. Why those 4?
Well as he is interested in decadal scales, those are the main ones. Shorter doesn’t exist and longer can’t be captured by the method. As a particular but important remark, one has to notice that the physical meaning of the indexes is irrelevant because what Tsonis is interesting in is their interaction. This is the first and last (weak) link to the chaos theory, after that it is just statistics.
Having a network with 4 nodes (the 4 indexes) and therefore a phase space with 4 dimensions (a huge progress to an uncountable infinity:)), he now needs a metrics. Equation (1) defines d(t), the metrics of what is called in the paper “synchronization”. It is actually just an average of the cross correlation coefficients between the 4 indexes for a gliding window 11 years wide with t in the middle of the window. If d(t)=0 then all indexes are completely correlated with each other and if d(t) = √2 then each index does what it wants. Figure 1a shows the d(t). As the values are around 1 , the indexes are rather uncorrelated (or non synchronous in Tsonis vocabulary). Nothing much interesting sofar.
Now comes the original contribution of the paper. As the purpose was not to compute correlations between time series what has been done a million of times but the coupling strengths between the indexes (and hopefully between the underlying physical processes), the paper defines a measure of “coupling strength” in Equation 2.
Also here again, like in Equation 1, the terminology is misleading because it is not really a coupling that is measured but a relevance of a predictor. Tsonis defines a “phase” for each index by considering 3 contiguous points. F.ex if the 3 points go up, the phase is 0 , if they go down, the phase is π etc. For every t there is then a 4 vector of phases Zn (n for the year) and Tsonis looks how well a least square predictor can predict Zn+1 from Zn. If the prediction is good, the “coupling “is said strong and if the prediction is bad, the “coupling” is said weak.
At this point I would criticize extremely strongly the term of “coupling”. The right use of the coupling constant and the right definition of coupling is given f.ex in the paper I linked above. Tsonis’ “coupling” is of course nothing such and yet implies that with a “good” predictor the underlying physical systems are strongly coupled (ie interacting) what is misleading. Here the strong “coupling” in Tsonis sense just means that the phases are identical, e.g if one index goes up, the otehr go up too and conversely. Anyway. The Figure 1b shows the phase predictor. The value is around 0.5 so the predictor is not specially good.
The last part of the paper is an eyeballing exercice. Tsonis added under the correlation and the predictor error figures the average temperature and the ENSO index. Now he observes that there were 4 (kind of) minima on the correlation curve. In 3 out of 4 cases the predictor error decreased (Tsonis vocabulary: “coupling” increased) and in those cases the average temperature trend as well as the ENSO variability changed significantly. In the 4th case the predictor stayed bad (weak “coupling”) and nothing special happened.
Tsonis conclusion: When the decadal oscillations synchronise and the coupling increases, then the system destroys the synchronization and jumps to a new very different (unknown) state.
My translation: When the 4 indexes are relatively strongly correlated and begin to tend to evolve all in the same direction, then they decorrelate fast .
In 3 out of 4 cases in the 20th century this decorrelation coincides with a change in the average temperature trend. The jump from this rather modest observation to a statement concerning the climate itself which can’t be resumed by an average temperature only, is daring to say the least. Of course the question whether the 4 indexes are a univoque proxy for something physical and relevant stays open.
And my conclusion. Despite the clear shortcomings of the paper (especially as far as the coupling is concerned) it suggests that the behaviour of the indexes follows correlation-decorrelation pseudo cycles. This observation has no predictive virtue so I don’t think that it could be used for your intent to elaborate a decadal scenario. All that Tsonis says, is that the behaviour (of the indexes, not of the system itself!) significantly changes when the correlation is strong and the predictor good. This situation happened in 2001 again. So according to Tsonis something will change/has changed significantly. As his paper is neither quatitative nor predictive, he cannot say WHAT will change and HOW.
However the general paradigm to consider the Earth system as a finite network of coupled (chaotic) oscillators is a good one and if one had the idea of the number of the oscillators, their average frequency and especially of the laws that govern them, it would certainly have some predictive capacity. The problem, that I have also already elaborated on, is that these oscillators are in reality not causally independent but they are ALL just emergent local manifestations of GLOBAL dynamics of the system. The coupled oscillator model is just an approximation which would be probably valid only for short term predictions (decades or so)
I cannot locate the exact papers this post refers to. I believe a paper on this subject and wonder if the 2007 paper is the same one. Can you provide a link?
hang on, let me check this, i thought i had the links correct
Links fixed
As of today, the link to Tsonis 2007 appears broken (again?). I found the paper here:
https://pantherfile.uwm.edu/aatsonis/www/2007GL030288.pdf
fixed, thx
Judith,
What happened to good old evidence?
I see many times theories trump evidence with mathematical equations.
This …
“However, comparison of the 2035 event in the 21st century simulation and the 1910s event in the observations with this event, suggests an alternative hypothesis, namely that the climate shifted after the 1970s event to a different state of a warmer climate, which may be superimposed on an anthropogenic warming trend.”
…nicely highlights something I’ve been harping on about for a while now; how can we determine the anthropogenic signature when we are still SO unsure about the NATURAL signal.
It was always my understanding that the world was warming naturally and that the anthropogenic fingerprint as it were, were added on top. Now, given the lack of understanding re: natural forcings, I ask again- how can ANY assertions be made about the anthropogenic side?
All I seem to get from reading these excerpts are a large amount of hedging and guesswork. I know this is down to my background, but I really struggle to see the merit of any of the current models.
If we can model, accurately, the natural cycles sans co2 effect, then we stand a good chance of ‘detecting’ (via model) any anthropogenic signal- though I was under the impression that this was not the case and that even the longer-term natural cycles elude the models (though I’m more than happy to be shown wrong on this one).
I do fear that I’m beginning to sound like a broken record here, continually lambasting all and sunder re: the uncertainties surrounding the natural cycles/forcings. However as a cGMP scientist, I just cannot get past this point- it underpins and undermines the entire theory- I’m hoping that I’m just missing something here; otherwise any lingering hope I had for the governmental lead-scientific process has just vanished.
Climate did shift in the 70’s.
That was when the surface salt on the oceans started to change.
Before then, many samples where taken of the oceans worldwide showing they were relatively close together in salinity.
(Gotta love reading resource books of the past!)
Indeed! The reference to “superimposed on an anthropogenic warming trend” is entirely gratuitous.
Edit: “lambasting all and sunder” — that would be “sundry”. Sunder is a verb meaning split, destroy. ;)
Ah so it is- thanks for pointing that out Brian.
Okay, I see RC provides a link to the more recent paper – which was actually written in 2009 and explains why I could not locate it. Do you have a link to the earlier paper?
ok, post is fixed, thanks for catching these
Dr. Curry,
You might also want to include and discuss the 2002 paper by Bratcher and Giese which predicted a change to a cooler climate regime in “about four years.” I think their prediction has proven true and they have not gotten enough credit for it. Amy Bratcher was a student at the time. She has married now and changed her name, but I don’t recall her married name at the moment.
Find the paper at
http://soda.tamu.edu/references/Paper_Bratcher_Giese_GRL_2002.pdf
Roy Spencer’s comment yesterday: “Twice I have testified in congress that unbiased funding on the subject of the causes of warming would be much closer to a reality if 50% of that money was devoted to finding natural reasons for climate change. Currently, that kind of research is almost non-existent.”
http://www.drroyspencer.com/2011/01/why-most-published-research-findings-are-false/
Indeed, this lack of balance merely prolongs the debate, because it leaves the known unknowns of natural variability unknown. Instead we continue to pour money into carbon and water cycle research, plus aerosols and AGW laden models. Ironically, however, there has been some movement into looking at natural variability, in order to explain the recent lack of warming. But as with the Swanson quote, this is always framed as merely a temporary disturbance in AGW.
This is partially an historical accident. When AGW was first formulated and the models first built climate was assumed to be naturally stable. That it is not only began to emerge in the late 1990’s. No one seems willing to go back to basics and rethink the problem in the context of naturally oscillating climate. Natural variability is seen as a mere add-on. Paradigms are like that, as Kuhn made clear, and AGW is a paradigm. Only a scientific revolution will set things straight at this point. Unfortunately the truth of AGW is US (and global) government policy and has been since the 1992 UNFCCC treaty was ratified.
Why did they assume the climate was stable after the work of Lorentz?
Given the huge amount of money spent on getting sun observing satellites up there and the ongoing work in quantifying solar insolation via “proxys” in particular and many of the earth observing systems, Eli finds this claim by Dr. Spencer quite amusing. Of course, there was also pretty much everything done before 1970 or so too, including the Keeling measurements. Of course, the fact that none of these could explain what was happening.
Yeah, sure bunny, the imbalance is amusing to the haves. Not so much to the have nots. What price your amusement, though?
===========
Eli, I tend to assume that Roy Spencer’s comment is based on actual knowledge. Perhaps incorrectly. If you had some numbers, I might think that your comment was based on actual and relevant knowledge, too. I would think that pre-1970 climate research funding has been dwarfed by what came later, but I’m open to changing my mind if you can quantify it. The same thing applies to the part of solar research that can be considered specifically aimed at explaining climate change.
Dr Curry:
Thankyou for providing this thread.
Like ‘Labmonkey’ I fear that any contribution I now make will “sound like a broken record” because I have been hammering the chaotic theory of climate change for many years and in many places (including threads of this blog).
It seems to me that the crux of the matter is the statement you quote from Swanson and Tonis (2009) that says:
“If as suggested here, a dynamically driven climate shift has occurred, the duration of similar shifts during the 20th century suggests the new global mean temperature trend may persist for several decades. Of course, it is purely speculative to presume that the global mean temperature will remain near current levels for such an extended period of time. Moreover, we caution that the shifts described here are presumably superimposed upon a long term warming trend due to anthropogenic forcing. However, the nature of these past shifts in climate state suggests the possibility of near constant temperature lasting a decade or more into the future must at least be entertained.”
That statement includes the sentence saying:
“Moreover, we caution that the shifts described here are presumably superimposed upon a long term warming trend due to anthropogenic forcing.”
But that “long term warming trend due to anthropogenic forcing” is merely an assumption without any supporting evidence. Indeed, the logic of the argument presented in Swanson and Tonis (2009) implies that no such long term warming trend exists. Instead, there are climatic states and the system “shifts” between them. Indeed, you quote them as saying;
“First, the major climate modes tend to synchronize at some coupling strength. When this synchronous state is followed by an increase in the coupling strength, the network’s synchronous state is destroyed and after that climate emerges in a new state.”
If the climate system is truly a chaotic system then these states are the conditions surrounding the strange atractors of the system. And, if so, the most that anthropogenic warming could do is to effect a movement from one state to another (in similar manner to Milankovitch cycles effecting a movement from one state to another).
Hence, the argument of Swanson and Tonis (2009) implies that no such long term warming trend exists, and it seems likely that they included the sentence as a method to avoid the ‘publication block’ presented to any climate paper which fails to agree there is “a long term warming trend due to anthropogenic forcing”.
Importantly, as Labmonkey says at January 4, 2011 at 11:56 am, it is not possible to discern any anthropogenic effect on global climate until this issue is resolved.
In conclusion, and for clarity, I again say that I think the climate system is a chaotic system with strange atractors. Major changes have no observed effect other than to switch the system between states defined by the system’s strange atractors. Hence, I do not agree that the maximum increase to radiative forcing of 0.4% from a doubling of atmospheric CO2 concentration could have a discernible effect on the climate system because the more than 20% increase radiative forcing from the Sun over the last 2.5 billion years has had no discernible effect on it.
Richard
Richard, obviously that was dropped in as the usual obligatory funding-protecting avowal of fealty to the Trooth. There is no other reference to cAGW in the article, AFAIK.
Such insertions are just financial CYA stuff.
Richard,
You misunderstand Swanson. He is a firm believer in AGW. Read his post on RealClimate and you will see his paper is mainly an attempt to explain the lack of new surface temp records since 1998.
Basically he is saying “We are just going through a spot of natural variability here. It will be over soon and we will be back on our way steaming towards oblivion.” Of course, that’s a paraphrase.
I would think the realization that natural cycles can over turn warming (for a decade or more) from increasing atmospheric CO2 might be a clue that CO2 isn’t as strong a driver of climate as earlier thought. Instead, the author’s thought seems to be “Just wait until all this pent up heat is unleashed in the next warm climate regime! You’re really in for it then!” Again, this is a paraphrase.
The most important implication of this newly discovered natural climate variability ought to have some impact on attribution studies. Let’s see if it will.
Ron Cram:
Thankyou for your observations. To be clear, I did not and I do not “misunderstand Swanson”, so I am especially appreciative that you have clarified a point that you observe was unclear in my post.
I agree your paraphrase of Swanson and Tonis (2009): I read it that way, too. And I most certainly agree your concluding paragraph.
But my major point (which you so cogently show lacked adequate clarity) was my statement saying;
“But that “long term warming trend due to anthropogenic forcing” is merely an assumption without any supporting evidence. Indeed, the logic of the argument presented in Swanson and Tonis (2009) implies that no such long term warming trend exists. Instead, there are climatic states and the system “shifts” between them.”
Again, thankyou for your comments that I genuinely appreciate.
Richard
Richard,
I was not successful in finding the quote you cited. I apologize. I’m trying to do too many things at once. But I can refer you to the final paragraph of the 2009 paper:
“Finally, it is vital to note that there is no comfort to be gained by having a climate with a significant degree of internal variability, even if it results in a near-term cessation of global warming. It is straightforward to argue that a climate with significant internal variability is a climate that is very sensitive to applied anthropogenic radiative anomalies (c.f. Roe [2009]). If the role of internal variability in the climate system is as large as this analysis would seem to suggest, warming over the 21st century may well be larger than that predicted by the current generation of models, given the propensity of those models to underestimate climate internal variability [Kravtsov and Spannagle 2008]”
That probably does not answer your question in full.
Ron Cram:
Thanks for that effort. Not everybody would have made it. But, sadly, it not only does not answer my question “in full”, it does not answer my question at all.
Simply, Swanson & Tsonis demonstrate the existence of the “shifts” but provide mere conjectures about possible anthropogenic effects. Indeed, the final sentence you quote says;
“If the role of internal variability in the climate system is as large as this analysis would seem to suggest, warming over the 21st century may well be larger than that predicted by the current generation of models, given the propensity of those models to underestimate climate internal variability [Kravtsov and Spannagle 2008]”
That sentence would have been equally true if had said;
“If the role of internal variability in the climate system is as large as this analysis would seem to suggest, anthropogenic warming over the 21st century may well have been zero despite the indications of the current generation of models, given the propensity of those models to underestimate climate internal variability [Kravtsov and Spannagle 2008]”
In fact, if the sentence had attempted to present the honest truth then it would have said:
“If the role of internal variability in the climate system is as large as this analysis would seem to suggest, then it is not possible to determine the degree – if any – of anthropogenic warming over the 21st century despite the indications of the current generation of models, given the propensity of those models to underestimate climate internal variability [Kravtsov and Spannagle 2008]”
Richard
Richard,
I fully agree with you. From their perspective, natural climate variability only warms in the future – not in the past. Here’s a paraphrase of their position:
“In the 20th century warming was caused by CO2. Oh, but now that natural climate variability has kicked in… well, just you wait! It’s really going to get hot for you now!”
It is not a viable position, but it is the position they have taken.
Okay, let me modify that somewhat. They recognize that the El Nino of 1998 was from natural climate variability, but they seem to think the trend from 1976 to 1997 was almost completely driven by CO2. The assumption is not based on any evidence. Its complete arm waving.
Just a lay person here, but I somewhat disagree. The trend they do not show, but hint at, is the trend CAGW people theorize is there, which would be something like 1975 to 1998. What they are saying is the true AGW trend is around .1C per decade, and that a natural warming phase lifted that trend up to the expectations of CAGW’ers: just like they are also saying the trend from 1999 to 2008 is suppressing the real trend of .1C per decade. At some future date they expect that suppression of the AGW trend to end.
Vaughan Pratt has inspired me to try to learn to use Wood for Trees. This my amateurish attempt to graph the above:
http://tinyurl.com/33qfa5y
CAGW trend – purple*
AGW trend – green
Mr. Natural’s puts the beat down on AGW – blue (natural cooling phase)
(*Mr. Natural gives AGW an assist – purple {natural warming phase})
JCH, I’m a lay person also and have not had much time to spend with the article. The interpretation you put on it makes sense. If that is what they are saying, then the paper would be more reasonable than I thought.
My problem though is the 0.1C per decade is below the IPCC range and would not be catastrophic. Their prose is obviously warning of a coming catastrophe. So now I have a difficult time resolving a non-catastrophic trend with their catastrophic verbiage.
You might find this interesting:
Like Prof Latif, Prof Tsonis is not a climate change ‘denier’. There is, he said, a measure of additional ‘background’ warming due to human activity and greenhouse gases that runs across the MDO cycles.
But he added: ‘I do not believe in catastrophe theories. Man-made warming is balanced by the natural cycles, and I do not trust the computer models which state that if CO2 reaches a particular level then temperatures and sea levels will rise by a given amount. – http://climateresearchnews.com/2010/01/latif-tsonis-up-to-three-decades-of-cooling-starts-here/
Ron Cram and JCH:
Thank you for your comments that I find interesting and useful on two levels.
Firstly, I think there is a semantic difference between yours views which – if I understand each of you correctly – needs to be explicitly stated.
Ron Cram says of Swanson & Tsonis (2009);
“They recognize that the El Nino of 1998 was from natural climate variability, but they seem to think the trend from 1976 to 1997 was almost completely driven by CO2. The assumption is not based on any evidence. Its complete arm waving.”
I completely agree with the statement of Ron (as I repeatedly said above).
JCH says he “somewhat disagrees” (and elegantly illustrates his point with excellent graphs) saying;
“The trend they do not show, but hint at, is the trend CAGW people theorize is there, which would be something like 1975 to 1998. What they are saying is the true AGW trend is around .1C per decade, and that a natural warming phase lifted that trend up to the expectations of CAGW’ers: just like they are also saying the trend from 1999 to 2008 is suppressing the real trend of .1C per decade. At some future date they expect that suppression of the AGW trend to end.”
As I see it, we all agree that – to use Ron’s words – the “assumption is not based on any evidence. Its complete arm waving” but JCH quantifies the assumption and elegantly displays that quantification with his graphs.
My interpretation is supported by the statement of Swanson in his article at RC (yes, I went there and I have had a bath afterwards) that says;
“We hypothesize that the established pre-1998 trend is the true forced warming signal, and that the climate system effectively overshot this signal in response to the 1997/98 El Niño. ”
And, as I say in my comment below at January 4, 2011 at 5:05 pm, I can only interpret that statement to be saying:
“they conjectured a “true forced warming signal” and they conjectured that the climate system “effectively overshot” that signal in 1997/98.”
Simply, the findings of Swanson & Tsonis (2009) are:
(a) The climate experienced a “shift” from one state to another around the turn of the century.
(b) Such shifts between climate states are natural and occur when the major climate modes tend to synchronize at some coupling strength and this is followed by an increase in the coupling strength.
(c) The existence of such shifts indicates that climate variability is so large that it can overwhelm – and since ~2000 it has overwhelmed – any anthropogenic global warming.
But they hypothesize (n.b. ‘hypothesize’ is Swanson’s word) that the anthropogenic warming does exist and will become significant in future.
And this brings us to the second level of importance of your comments that I think is clearly expressed by Ron when he says.
“My problem though is the 0.1C per decade is below the IPCC range and would not be catastrophic. Their prose is obviously warning of a coming catastrophe. So now I have a difficult time resolving a non-catastrophic trend with their catastrophic verbiage.”
Indeed, that value of a recent about 0.1K per decade variation from anthropogenic influence would fit with my interpretation that the system is chaotic and, therefore, any AGW would be too small to be discernible (as I summarise in my brief explanation in the final paragraph of my above post at January 4, 2011 at 12:56 pm).
And “their catastrophic verbiage” does jibe with that 0.1K per decade value (as I also said in my post at January 4, 2011 at 12:56 pm).
But JCH solves that riddle for Ron and me when he reports that Tsonis has written;
“‘I do not believe in catastrophe theories. Man-made warming is balanced by the natural cycles,
In other words, Tsonis says he believes the climate system operates in the manner that I have been asserting for several years in many places (including on this blog) and which I have been vilified for promoting (including on this blog). Who would have thought it?
But Tsonis published with Swanson who – by his own admission – is a catastrophist. So, the paper by Swanson & Tsonis (2009) provides their findings of climate shifts while adding some mention of catastrophist assumptions – which do not affect their findings – to satisfy Swanson.
And if I were Tsonis then I would have agreed to those additions because they assist getting the findings past peer review.
Richard
Internal variability does not make a system more sensitive to random forcing.
Again, consider the model of a playground swing as an oscillator. An 60 pound child is on the swing, pumping with very little force in phase with the motion. Stand to the side of the swing and with one hand apply slowly increasing pressure in a forward direction, such as might happen with CO2 forcing.
You will not appreciably change the amplitude of the swing. What you will do is just slightly shift the whole motion forward. Thus, the argument that an increased forcing from CO2 with increase variability is not founded in physics, unless the forcing is in phase with the oscillation.
ge0050:
You say:
“Thus, the argument that an increased forcing from CO2 with increase variability is not founded in physics, unless the forcing is in phase with the oscillation.”
That would be true if the system were simple, but may not be true if the system is complex because complex systems respond to changes in ways that are often not intuitively obvious..
For example, consider the complex system of your body. It maintains a narrow range of sugar content despite variable inputs of sugar (unless you are diabetic).
Richard
Can I ask a couple of vocabulary questions?
Synchronization – does this refer to cycles with varying lengths coming into ‘alignment’ for a certain period?
Coupling – does this refer to the cycles reinforcing themselves during these periods?
Thanks
Chip:
Yes and yes.
Richard
The best description of synchronization can be found on scholarpedia.This is a peer reviewed format so hence can be citied.
http://www.scholarpedia.org/article/Synchronization
useful link, thx
This could be more convincing.
Yes, I’ve seen this before. Amazing, isn’t it?
So, Vukcevic,
Your following a rythm and not the currents of ocean movement?
Think of the PDO and other climate systems not as currents, but as oscillations. Like the daily tides. There is an incoming phase and an outgoing phase. There is a warming phase and a cooling phase.
Thanks,
You just about put me to sleep with your hypnotic powers.
It is voodoo science though. Has absolutely no meaning to planetary weather.
My heart beats too but does that regulate my heat?
Nicola Scafetta provides the math for coupled oscillators converging, and applies it to climate. See Fig. 15 in his paper:
Empirical evidence for a celestial origin of the climate oscillations and its implications
http://www.fel.duke.edu/~scafetta/pdf/scafetta-JSTP2.pdf
Am I nitpicking when I suggest that those sentences are back to front?
Was the moderately strong El Nino of 09-10 due to higher Ts or the higher Ts due to El Nino?
Likewise, has the current cool(er) Ts caused the strong La Nina or did this strong La Nina cause cool(er) Ts?
The distinction is important IMHO, else, how can we predict future ENSOs?
Reconstructions of AMO/PDO pre 1960 are not reliable. Post 1960 AMO/PDO are synchronised (on 10 year m.a.) except (for 3 years post Pinatubo).
It appears that the North Atlantic is the synchronising factor.
http://www.vukcevic.talktalk.net/CD.htm
vukcevic,
Why do you say the pre-1960 data are not reliable?
AMO/PDO synchronisation pre 1960 is weak and sporadic.
over the ocean, the sea surface temps aren’t reliable, i agree. I discussed this a little bit on one of the detection and attribution threads. the main problem is missing data. to fill in missing data, they do an EOF analysis based on 1960-1990, and fill in from that, thereby missing the main multidecadal modes of variability. This EOF fill in manages to corrupt even places where there is real data. a redo is in order, more on this sometime soon.
Dr. Curry
there are too many assumptions made about AMO.
http://www.vukcevic.talktalk.net/ANOMALIES.gif
This is another reason why the AMO amplitude and periodicity reconstructions may be questioned. Hence I repeat my previous observation: AMO is not an independent natural temperature drive, it is a result of the temperature’s oscillations, due to some other natural variable with known physical properties, capable of initiating such oscillations.
Vukcevic, this may be true, but until you explain your ideas about what this mysterious reason is, allow us to assume that it is unforced internal variability.
While the data is bad in the pacific, there is more than ample data in the north atlantic to document the AMO observationally back to the latter part of the 19th century.
There are no mysterious reasons in science, including climate science. There are laws of physics, and most of the observed events can be explained within those limits, providing sufficient information and data are available.
I provided a link demonstrating Atlantic side of the equation:
http://www.vukcevic.talktalk.net/ANOMALIES.gif
Simple logic shows that if, for the last 150 years, September and November are highly correlated with AMO but October and December are in the anti phase, than AMO cannot be driving the temperatures, it is the AMO reconstruction that has to be questioned.
It is not me who introduces mystery into AMO and PDO, it is climate scientist who have no idea why the temperatures oscillate, but if these oscillations are removed it suits the AGW conjecture.
Further error is to consider that these oscillations have no trend, i.e. it is manipulated with ‘de-trended’ data. Available temperature records show that fastest ever temperature rise was during short period 1690-1740.
Source of any mystery is simply lack of understanding.
Vukcevic,
Every square inch of this planet has different energy actions happening that are unique at this single point in time. The atmospheric pressure and rotation smooths out these actions. Following temperature oscillations has been done back in the 70’s for the human body called bio-rythms. Not very successfully.
Another paper that is relevant to this discussion is Carvalho et al (Tsonis is also a co author)
Anti-persistence in the global temperature anomaly field
Abstract. In this study, low-frequency variations in temperature anomaly are investigated by mapping temperature anomaly records onto random walks. We show evidence that global overturns in trends of temperature anomalies occur on decadal time-scales as part of the natural variability of the climate system. Paleoclimatic summer records in Europe and New-Zealand provide further support for these findings as they indicate that anti-persistence of temperature anomalies on decadal time-scale have occurred in the last 226 yrs. Atmospheric processes in the subtropics and mid-latitudes of the SH and interactions with the Southern Oceans seem to play an important role to moderate global variations of temperature on decadal time-scales.
http://www.nonlin-processes-geophys.net/14/723/2007/npg-14-723-2007.html
My analysis would suggest that AMO from 1750 to 1900 would have been weak with the average period of oscillation about 47 years.
There is evidence of a 45 year ccyle in the AMO:
A storminess record in geomorphic (that is, physical) form is preserved in a “staircase” of 184 isostatically uplifted beach lines on Hudson Bay (Fairbridge and Hillaire-Marcel” 1977, Nature. Vol. 268), which date back to more than 8,000 years. Their extraordinary regularity is duplicated in other parts of the Arctic, which denies any theory of randomness in storminess cycles. Their mean periodicity is about 45 years, but secondary modulation appears at 111 years, 317 years, and longer intervals.
http://www.crawfordperspectives.com/Fairbridge-ClimateandKeplerianPlanetaryDynamics.htm
The 45 year cycle is of LUNISOLAR origin. Pay attention to what Piers Corbyn is saying (filtering off his hyperpartisan political injections) about eclipse cycles & jet-stream blocking and then look at the rate of change of daily-timescale length of day to see with your own eyes. Of course there is confounding of lunisolar variables with solar system variables, as the solar system has played a role (over a very long period of time) in defining Earth-Moon relations. Although many researchers have fallen victim to the confounding (drawing misguided/misleading conclusions), it is certainly worthwhile to investigate which of their insights might transfer to a lunisolar framework that has undisputed physical relevance.
Judith,
We’ve failed in the lost art of 50,000 questions from every angle to come up with a valid answer.
In the case of solar due to distances, we have 3 other planets close by to see if changes are happening to know if the sun is changing it’s radiation output.
I’m an advent believer that our atmopshere is the cause only because I have done so much researching to answer my questions.
It seems to me that Swanson and Tsonis and now Soares all point to the best correlation between CO2 rise and temperature rise being in the last quarter of the last century, and that natural cycles predominate, though with CO2’s effect unknown, and likely small.
==========
kim:
You say:
“It seems to me that Swanson and Tsonis and now Soares all point to the best correlation between CO2 rise and temperature rise being in the last quarter of the last century, and that natural cycles predominate, though with CO2′s effect unknown, and likely small.”
Please explain why it seems to you that “Swanson and Tsonis and now Soares all point to the best correlation between CO2 rise and temperature rise being in the last quarter of the last century”.
As I read Swanson and Tsonis I understand their findings to show no “correlation between CO2 rise and temperature “. Indeed, they say;
“the shifts described here are presumably superimposed upon a long term warming trend due to anthropogenic forcing”. And they demonstrate the existence of “the shifts” but they do not demonstrate the existence of the “long term warming trend due to anthropogenic forcing”.
What have I missed?
Richard
Richard,
Read Swanson’s blog post on RealClimate that Dr Curry provided. You might also look at my comments to you above.
Ron Cram:
I read your post to me above but fail to see the relevance to my question. So, I will grit my teeth and lower myself into RC for the first time in years in hope that it will give me the info. I seek (I can take a bath afterwards).
Richard
Oops. I see you have already responded above. I will try to respond to that question.
Ron Cram:
OK. I have now scanned the linked item at RC but have not yet studied it in detail. But one sentence in that article screamed at me. It says;
“We hypothesize that the established pre-1998 trend is the true forced warming signal, and that the climate system effectively overshot this signal in response to the 1997/98 El Niño. ”
So, they conjectured a “true forced warming signal” and they conjectured that the climate system “effectively overshot” that signal in 1997/98.
This leaves me with my understanding that I stated above; i.e.
“And they [i.e. Swanson & Tsonis (2009)] demonstrate the existence of “the shifts” but they do not demonstrate the existence of the “long term warming trend due to anthropogenic forcing”.
I will study the RC article in depth, but – at this moment – I still have no answer to my question, “What am I missing?”, unless that answer is, ‘nothing’.
Richard
Ron Cram:
I have noticed your additional comment (far above) that provides additional answer to my question and I have responded to it there.
Richard
It was the only time that CO2 and temperatures rose in concert. During the last quarter of the last century, and not before and not since.
===================
And from this lovely Co-Incidence just before the Turn of the Millenium, comes the correlation of global warming and anthropogenic CO2 that is the greatest example yet of the Post Hoc, Ergo Propter Hoc logical fallacy.
================
Kim:
Thankyou for those answers.
Please see my discussion (far above) with Ron Cram and JCH. It seems that you, I and they have much agreement with each other (and with Tsonis!)
Richard
Raichard – I’m not agreeing or disagreeing with Tsonis. On a substantive level, I don’t have the knowledge set to be agreeing or disagreeing with anybody.
I was just trying to understand what he’s saying.
As Swanson admits, thing look very different if you use GISTEMP to make the graph, which I also tried (as well as UAH and RSS.) I’ve read two articles recently that indicate to me HadCRUT could be making an adjustment to their series in the near future to bring it into closer alignment with GISTEMP. I could be very wrong there, but they keep saying they’re underestimating things.
One thing, I think the warmth of both 2009 and 2010 somewhat dings their shift theory. The shift might happen, but if NASA is right it doesn’t appear to me it has happened yet. I’m not big enough argue with an ocean experiencing a cooling phase, but 108.59 ppm plus growth of anthropogenic atmospheric CO2 doing just that is the CAGW bet.
Kim:
I sincerely apologise if I have misrepresented you and/or your views. That was not my intention, and I would not wish to do it because – as I think my comments (far above) demonstrate – I have found your contribution to the discussion to be helpful to me personnally.
I agree with you that all the climate data is dubious. But I do not agree with you when you say;
“I think the warmth of both 2009 and 2010 somewhat dings their shift theory”.
The temperature has remained stable (within 95% confidence) for the last 15 years (according to all the global temperature data sets) and the most recent 2 years do not change that.
Richard
Scenarios: 2010-2040
The global temperature record shows the (inaccurate) sum result of the variation of terrestrial climates (note plural). The only longterm records we have which show the causes of these variations are the sunspot record, the ice cores with their co2 and 10Be results, and tree ring plaeodendroclimatology (for what it’s worth), plus a bunch of other proxies which are more or less difficult to calibrate.
Co2 variations lag behind changes in temperature at all timescales so whatever the validity of the arguments about the supplemental effect they may have, they are not prime movers in climate terms.
Solar variation is invoked by both sides of the debate to explain terrestrial temperature variation before co2 variation became noticeable enough to have made a difference (if it does).
Oceanic cycles seem to be regarded by both sides of the debate as causing temperature variation on multi-decadal timescales, but consideration of the possibility that they may make a difference on a centennial scale seems neglected. This is strange, as the oceans have by far the biggest heat capacity of all the terrestrial sinks which might account for ‘internal natural variability’.
Currently, the deep ocean is being used as the last refuge for ‘heat in the pipeline’ due to co2 ‘forcing’, because there is nowhere else it could be hiding. Von Schuckmann thinks he’s found an increase in heat content there.
But the deep ocean could just as easily be a repository for solar energy accumulated over a very long timescale, hundreds and thousands and millions of years. If Von Schuckmann is right and temperature at 2000m has increased in the last decade, this might be because solar energy sequestered even further down in the abyss has started upwards again.
When I ask oceanologists about the mechanisms and pathways for energy storage in the oceans below the mixed layer (of 50m or so) I get as many different answers as there are oceanologists to ask. This is not an area of settled science so far as I can tell. Having performed many experiments myself in the design and construction of calorifying devices for water, I have some appreciation for the amazing mobility and complex response of water to heat.
It is counter intuitive that warm water should get forced down into the abyss, form stratified layers, and re-appear when the forcing is reduced, but that is what happens. I made a small study of the logic which reduces to this:
1) Empirical evidence shows that when the sun is more active than average, ocean heat content increases, and when it is less active than average, ocean heat content decreases (see Craig Loehle’s stud of ARGO data from 2003).
2) When the sun is highly active, more heat-energy is going into and staying in the ocean than is escaping (Steric sea level rise, OHC measurement). If all that extra heat energy were concentrated in the mixed layer, it would have got hotter than observed, and if it had escaped to atmosphere that would have got hotter than observed given the stability of OLR, so the heat-energy has to have gone downwards into the ocean. It can’t go sideways, it can’t go up faster than the atmosphere and it’s clouds allows, so it must have gone downwards.
3) The ocean equilibrium value from phenomenological analysis seems to be around 40SSN. The sunspot number has been above this value most of the time since 1835. SST from 1840 shows a fairly consistent rise apart from a dip around 1890-1917 and again around 1942-1975. Both these time periods were presaged by sunspot cycles lower than those preceding them.
4) When the sun goes quiet, excess energy sequestered in the oceans has a chance to escape. On the decadal scale, this manifests as big el nino’s shortly after solar minimum (see last five solar minima). On the multi-decadal scale, the drop in solar activity from 1870 was followed by big el nino’s and la nina’s which caused large swings in SST at the end of the C19th and during the 1900-1910 period. These map almost perfectly to what has happened since 1998 too. Same solar cycle phases, same ENSO swings.
This leads me to believe that the prognosis for the 2010-2040 period is as follows:
(i) If solar cycle 24 and 25 resemble the Dalton minimum period 1804-1824:
A roller coaster of El Ninos and La Ninas as the oceans lose sequestered energy, with the SST trend heading downwards until the sun perks up again in 2035. A concomitantly lower surface air temperature on average but the average drowned by big swings leading to plenty of exciting weather worldwide, quieting down as the ocean lose energy towards 2030. Then a lull. Then an upswing as the sun wakes up.
(ii) If the sun gets really sulky and we have a Maunder type grand minimum, then as above to 2030, followed by calm cold weather out to 2070.
(iii) If the Sun does something really unexpected and bursts back into life at late C20th levels, then temperature will remain at a stable level until 2020 then rise gently. I think this is pretty unlikely.
In terms of the climate science debate:
Once the influence of the sun has been amply demonstrated over the next 8 years, the co2 temperature scare will abate, to be replaced by an ocean acidification scare, based on even more uncertain data, and of course computer models to which their progenitors and proponents will be ardently attached.
Returning to the beginning of my comment, the Earth’s climates (note the plural) will continue to show a variety of trends, as the magnetic anomalies move around as they always have. These will probably cause variations smaller than the ENSO component for a decade or more, but will dominate once the quieter phase is reached.
By assuming a century-long manmade trend they implicitly contradict the IPCC (that the last 50 years show a separable, identifiable manmade warming) so the solar influence must perforce come back into consideration.
Josh Willis had also previously suggested the entire century had a clear century long manmade signal (in a guest email to Revkins dotearth piece on the pdo cycle). When pressed by Pielke Snr, Willis on his own blog, trotted out the long-discredited MBH98 hockey stick as evidence of this signal.
The upshot is that they just cannot make their minds up when our influence started, and natures finished, but they are nevertheless all so darn certain that our influence is there somewhere; by turns cooling and warming. I sometimes jokingly say that this current temperature plateau should have happened in 1960, when the sunspots stopped increasing, but it didn’t because our manmade aerosols prevented it happening. While a joke, it contains no more armwaving than the typical standard climate science explanations for natural cooling/warming/stasis events.
If I’m correct about the timescales on which heat-energy can be stored in the oceans, then the correct way to interpret solar activity level data (sunspot numbers in the longer term) is to integrate them rather than simply peering at the peak amplitudes. This is because the effect of solar activity is clearly cumulative. It seems to work quite well.
http://tallbloke.wordpress.com/2010/07/21/nailing-the-solar-activity-global-temperature-divergence-lie/
I have made a 2010-2040 update to that graph which I will reveal on my blog in due course.
Good deep analysis Tallbloke!
I cannot see the heat perameters of the ocean coming back due to other physical factors not included due to salinity changes and pressure build-up.
Wish we could trust the ARGO data considering they have to play with the data and mathematics due to the raw data all showing a temperature drop.
Yes, your analysis is pretty close to my understanding of the solar climate connection. I favor your Dalton minimum scenario over the next 20 years. I am certain a CO2 signature will be in there but likely masked by the exceptionally quiet sun leading to lower ocean temps over time.
If I’m right, then the question becomes:
“How much room is left for a ‘co2 signature’ once natural variations are accounted for?”
I don’t know the answer to that quantitatively, but my gut feeling is:
“Not much”
That’s a lot of caveats on “nostramdamus” type “predictions.
Put some numbers on those prognostications. Hard numbers.
(i) If solar cycle 24 and 25 resemble the Dalton minimum period 1804-1824:
Numbers please…..
as well for (ii) and (iii) ect
Hi Mosh.
How hard are the numbers provided by the GCM model output spaghetti graphs?
Point me to some hard numbers for sunspots and OHC from 200 years ago to now and I’ll give you exact outputs from my quantitative model.
Until then I reserve the right to be at least as vague as the people who get paid big money to spin their carboncentric GIGO.
I did make a specific prediction for the near term on WUWT yesterday.
Global SST to be -0.32C +/- 0.05C by September according to Dr Roy Spencer’s metric.
Hi Mosh.
How hard are the numbers provided by the GCM model output spaghetti graphs?
##########
they are hard enough for Lucia to test them.
they are hard enough for willis to criticize them
they are hard enough Santer to Defend and mcintyre to audit.
they are hard enough for me to go to a database and pull down.
If they are not hard enough for your liking then WHY would you commit to NO numbers whatsoever. You do not impress me by pointing to the flaws of others. Weak move. You have no made a “prediction” What would prove you wrong?
“Point me to some hard numbers for sunspots and OHC from 200 years ago to now and I’ll give you exact outputs from my quantitative model.”
I looked at your model. Not very impressed. You have not published a replicatable method, have not audited the data and it’s uncertainties.
By my read you have no physical mechanism to explain things.
“Until then I reserve the right to be at least as vague as the people who get paid big money to spin their carboncentric GIGO.”
The issue is you are being MORE VAGUE. what does it mean to say that cycle 24 and 25 RESEMBLE the dalton minimum. Because you use the term dalton minimum, what EXACTLY are you pointing to?
the SSN? , i suppose for your approach it would have to be the “integrated” sunspot number? mashed together in someway with an estimate of the sunspot areas? Very simply, When you say like the dalton minimum, what will you look at to decide “likeness”. what test will you use? how close does it have to be for it to count as “RESEMBLE”. That’s just basic. I can do that for a GCM. I can say for example, that a GCM can reproduce trends of the 20th century global temp that RESEMBLE the real thing. I can say this means:
1. You take HADCRUT as the target to match.
2. You take the GCM output and calculate the trends in the same way.
3. RESEMBLE means that trends for the modesl and the trends for the real world pass a statistical test with 90% confidence.
Now, you may suggest a different test. You may suggest a tighter criteria for RESEMBLE, you may object to a whole host of things. BUT, you can object only because I am able to make a definitive testable statement. If you cannot do this for your exmaple, then you havent said anything scientific. You have not engaged in scientific behavior. For example, if the GCMs only came with 50% of actual and THEN I told you that “resemble” means being within 50%, you’d be fustrated in your attempt to dialogue with me. We’d be arguing about the definition of resemble and NOT about my prediction. So until you describe HOW you can be proven wrong, you have not said anything meaningful. And futher to the extent that others do this, they likewise have not said anything meaningful.
“I did make a specific prediction for the near term on WUWT yesterday. Global SST to be -0.32C +/- 0.05C by September according to Dr Roy Spencer’s metric.”
Great. And how will your theory be impacted/modified when you are wrong.
“how will your theory be impacted/modified when you are wrong.”
You haven’t waited long enough to see if it’s wrong yet. :-)
We’ve given the AGW theory 20 years so far and we’re still waiting, can’t you give me nine months?
Thanks for the crit, it’s all meat and drink to me. Since none of the historical data is that great, I’m really not too bothered about introducing meaningless false impressions of precision and concepts like 95% confidence at this stage. Phenomenological analysis and engineering estimate is sufficient to the task until we have a better handle on mechanism.
Something that people who believe co2 managed to warm the ocean 0.5C at the surface and on average 0.25C to 700m in the last 50 years would do well to think about.
Cheers, and check your mail.
Hi Mosh,
A call for maths is interesting but we already know that our solar equations are incomplete. Most climate scientists agree that during the Maunder and Dalton minimums the oceans cooled significantly. Current best estimates of TSI as studied between solar max/min in the 11 yr sunspot cycle do not account for this potent climate driver. And yet… it happened. The oceans cooled.
If the oceans cooled .3C during the Dalton Min. (wild guess because we don’t really know with precision) then it is reasonable to expect that under similar solar conditions, the oceans will cool .3C again. Changes in manmade GHGs effectively raise the overall temperature of the climate but as far as we know do not change the interaction between the sun and the oceans. The result of 100 yrs of rising manmade GHGs AND a 100k year solar grand maxima event has resulted in higher temps today (+1.5C?) than in 1804. If the oceans cool.3C from a Dalton min. event over the next 30 years, they started at a higher temp than in 1804 so it will not get as cold but the change (-.3C) can be expected to be consistent with a historical Dalton Min. event.
We still have some pretty big holes in our equations so our collective maths in this area are pretty weak. This weakness is accounted for in AR4 under solar forcing “still poorly understood”. Even though we don’t understand all the mechanisms involved doesn’t mean that oceanic cooling as a result of a quiet sun didn’t happen in the past, or that it won’t happen now. It is very likely that it will happen now.
By the way Mosh, I did expound the physical basis for my prognostication so I resent the ‘Nostradamus’ slur. Leif Svalgaard leveled that one at me too, when two years ago I predicted a solar cycle 24 with a peak amplitude of 35-50SSN, based on my understanding of the effect of planetary motion on solar activity levels.
His prediction of 72SSN based on his understanding of his solar dynamo theory isn’t looking too good right now.
What’s your prediction?
Tallbloke,
Something nobody is considering yet is staring everyone in the face is that the pressure systems are slowing on this planet.
Last year I had a massive high up to Greenland that gave our area nice weather for 8 long months.
Now this year, we have a system stalled along the Atlantic corridor which is picking up huge amounts of precipitation and record cold temperatures unprecidented in length of time it is sticking around.
Crap, it’s not math so it don’t count. Right Steven?
By the way Mosh, I did expound the physical basis for my prognostication so I resent the ‘Nostradamus’ slur. Leif Svalgaard leveled that one at me too, when two years ago I predicted a solar cycle 24 with a peak amplitude of 35-50SSN, based on my understanding of the effect of planetary motion on solar activity levels.
########
The “nostradamous” reference is not a slur. Take a look at your predictions. You will find that it is impossible to tie them to any physical reference with a physical measure. As they stand it is impossible for them to be wrong, which means they cannot be meaningfully tested. WRT you method I’d like to see its out of sample performance to begin with, but even then a method that only predicts one metric of the climate is not of much use in planning for the future.
“His prediction of 72SSN based on his understanding of his solar dynamo theory isn’t looking too good right now.
What’s your prediction?”
That’s a funny diversion. Personally, I’m with Leif on the relative unimportance of the sun and SSN. Until and unless those who claim it’s importance can put the effect on a measurable quantifiable basis, it’s at best a culdisac of spurious, half baked, ill documented, “correlations” and resemblances.
What’s your prediction for the Martian summer?
See two can play the diversion game. The real questions remain as I asked them. What are your predictions actually. numbers.
I’m with Leif on the relative unimportance of the sun and SSN. Until and unless those who claim it’s importance can put the effect on a measurable quantifiable basis, it’s at best a culdisac of spurious, half baked, ill documented, “correlations” and resemblances.
Yes, it’s a pity billions has been spent on studying co2 while solar research has been cut back don’t you think? In the UK, the solar research program has all but been cut off at the knees while atmospheric research gets all the pie.
Yet it’s clear that the Sun affects the chemistry of the atmosphere in ways we understand only poorly, and that greater research investment would help us find out why the Sun has a much stronger effect on climate than small variation in overall TSI can account for in raw energy terms. Not all joules are the same.
Please drop me an email, I have some info for you which could be to your advantage, but I’m not getting a response from the address I have for you at h-mail.
I would say Nostradamus of the old was probably far superior to the present day ‘experts’ who prognosticate ‘hell and fire’ from the burnt ashes of fossil fuels.
Vukcevic,
When food supplies dwindle due to the “weather” and there are riots and starvation, I believe we may see hell.
Tallbloke,
I believe the ARGO raw data to be correct.
Grants flow when AGW is warming and changing the computer programs and models to show this.
Why did they not just recalibrate or follow a buoy to find out if it is accurate?
No, just change the programs to a warming trend.
Reminds me of biorhythms, which, wikipedia reminds me, is pseudoscience but then handily directs me to the real science of Chronobiology, described thusly;
“….a field of biology that examines periodic (cyclic) phenomena in living organisms and their adaptation to solar- and lunar-related rhythms.”
Back to the Sun and Moon then…apparently it’s ok to study the solar and lunar influence on organisms despite the lack of an identified mechanism. Of course it used to be ok in climate science too, before the arrival of the dogma that humans must be more important.
Dr. Curry,
This begs the question of ‘where is the cliamte crisis?’
Judith, there’s lots of confusion about ocean cycle data. As you’re aware, the AMO according to the NOAA ESRL webpage is simply detrended North Atlantic SST anomaly data.
http://www.esrl.noaa.gov/psd/data/timeseries/AMO/
The PDO, on the other hand, is not. The PDO is calculated as the leading PC of the North Pacific SST anomalies north of 20N, where each 5X5 deg grid is detrended. That’s the classical definition presented by JISAO based on the methods used by Zhang et al (1997) in ENSO-like Interdecadal Variability: 1900–93. PDO is identified as NP in the paper:
http://www.atmos.washington.edu/~david/zwb1997.pdf
The PDO over decadal periods is actually inversely related to detrended North Pacific SST anomalies. This can be seen if the two datasets are smoothed with a 121-month running-average filter (same filter the ESRL uses for the AMO data) and when the PDO is scaled by a factor of 0.2:
http://i52.tinypic.com/25hcxg7.jpg
And you’re aware that one has to use caution when evaluating any long-term dataset since there’s so little source data and so much infilling.
If I understand the papers written by Di Lorenzo, the NPGO is the second PC of the North Pacific SST anomaly data, and it’s associated with Central Pacific ENSO events (El Niño Modoki).
The amplitude of the multidecadal variations in the AMO (detrended North Atlantic SST anomalies) and detrended North Pacific SST anomalies are not that different (if the data are smoothed with the same 121-month filter). The AMO varies a little more from peak to trough. The cycles based on HADISST data align themselves occasionally. Also note the variability of the scaled NINO3.4 SST anomalies in the background:
http://i56.tinypic.com/t9zhua.jpg
That graph is from the following post:
http://bobtisdale.blogspot.com/2010/09/introduction-to-enso-amo-and-pdo-part-3.html
The PDO has also become a generalized term, which adds to the confusion. That is, researchers also use PDO to describe a number of variables, not only the definition presented by the JISAO.
If you’re discussing multidecadal changes in temperature on this thread, then I would suggest using the detrended North Pacific SST anomaly data in place of the PDO, and using the detrended North Atlantic SST anomaly data (AMO), and using NINO3.4 SST anomalies, which basically have a flat trend from 1900 to present.
The Hadley Centre reinserts the “raw” source data back into the HADISST data after interpolation, where ERSST.v3b does not, so you’re likely better off using the HADISST as a base SST dataset if you’re going to do some wiggle matching.
Regards
Bob, thanks much for this clarification
Bob,
Just using anomalies misses the big picture of weather system movement. A system can stay in an area for an extended period of time yet the recorded temperature anomaly would be minimal.
Since ONLY temperatures are being followed, this misses the record breaking precipitation that is occuring as precipitation is not a temperature.
Here in the pacific north west our major climate driver is rain. A warm winter means we had a lot of rain and clouds. A cold winter means we had a lot of clear day. When we get snow it is because we had a lot of clear days before.
I missed the beginning of your series of posts on Scenarios 2010-2040 over the holidays. Your posts touch on a big new effort by the IPCC to provide regional climate predictions. One IPCC official who testified at an IAC hearing emphasized that governments (ie, the IPCC’s customers) are more interested in the regional projections made by WGII than any other part of the report. This is particularly true for governments of less developed nations that have few resources to deal with climate change and limited expertise in the field. (Environmental activists also need fresh “scary scenarios”.) The UEA emails included a 2008 email from J Skula entitled “Future of the IPCC” that focuses on the same theme. (http://www.eastangliaemails.com/emails.php?eid=861&filename=1202939193.txt). “I would like to submit that the current climate models have such large errors in simulating the statistics of regional (climate) that we are not ready to provide policymakers a robust scientific basis for “action” at regional scale … It is urgently required that the climate modeling community arrive at a consensus on the required accuracy of the climate models to meet the ‘greater demand for a higher level of policy relevance’.” More information can be found in “Proposal for an IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections” from Knutti, Santer et al (http://www.ipcc.ch/meetings/session30/doc11.pdf)
Given the substantial disagreements between current models concerning regional climate change and the lack of major changes to models, is any scientific breakthrough likely to occur between AR4 and AR5 that will increase the validity of regional climate projections? The solution appears to be a political one: Phasing out the concept of “model democracy” and ignoring uncertainty. As the first paragraph of the proposal states (but an SPM is unlikely to disclose): “Nevertheless, these intercomparisons are not designed to yield formal error estimates and remain ‘ensembles of opportunity.’”
If we want scientifically valid projections of regional climate change, we should be trying to understand why different models make different projections and to learn which models are making the right projections. But there apparently isn’t any confidence that an acceptable scientific answer to these questions can be identified and no one wants to “invalidate” some of the the models that led to the IPCC’s consensus on future climate change.
Actually the AR5/CMIP5 is using an interesting experimental design for the decadal simulations, and we should learn something from these simulations, see http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf
Perhaps we will learn simply learn that climate models are chaotic and therefore we can’t accurately initialize them for decade-long high resolution hindcasts and forecasts (or we can obtain whatever result we want for hindcasts from “proper” initialization). Sorry for being cynical.
When researching “Amazongate”, I found that an intermodel comparison study of the Amazon done by your GT colleagues for AR4 found that 5 models predicted increased rainfall, 3 predicted unchanged rainfall in the Amazon, 3 predicted reduced rainfall, and one of those three predicted a dramatic seasonal decrease followed by “savannization”. (http://climate.eas.gatech.edu/dickinson/publications/Li-jgr2006-rainfall.pdf) The Amazon has survived many seasonal droughts. It doesn’t appear to me that contradictory results like these are likely to be scientifically resolved without significant changes in some models. The SPM for WGII resolved this contradiction politically by citing results from only the most pessimistic study. “By mid-century, increases in temperature and associated decreases in soil water are projected to lead to gradual replacement of tropical forest by savanna in eastern Amazonia.” http://www.ipcc.ch/publications_and_data/ar4/wg2/en/spmsspm-c-11-latin-america.html
The pressure to produce “useful” regional climate predictions from these CMIP5 studies could lead to more politics and poorer science.
I suspect the Swanson and Tsonis work on climate shifts is interesting for multidecadal climate forecasting, but is not the main game.
As I read it they are arguing that significant events in the climate record can be attributed to the internal variation in the climate itself. Unfortunately for forecasting this result is basically descriptive and not particularity quantitative.
Two comments in this regard:
First it doesn’t address whether there are definable external precursor events that might give predictability to these events arising (but the result is obviously suggestive that a bit of a look here might be worthwhile). Without this incorporating these sequences into predictions is problematic.
Second, nor does it tell us if the new state post perturbation is fundamentally the same (say in average global temperature terms) as it was when the sequence started. One can imagine an internally initiated temperature rise that then has the effect of changing susceptibility to external forcings that in turn leads to a permanent change in the climate.
However the presence of these events in unforced GCM runs with long-term stable climates is suggestive that the perturbations don’t lead to a new state (although the fact that the sequences get extinguished over time in these models possibly suggests either these sequences do change the climate in someway or alternatively are dependent on external precursor forcings to trigger them. The results from GCMs is something I’ll return to).
Again whether these sequences change the climate in a permanent way is something that calls out for more empirical work.
The really useful thing for forecasting that I took out of this work when I first read it was that if the complete sequence was internally driven and basically neutral in terms of its impact then this was a whole chunk of variability that could be put aside (and partialed out) of the climate history – particularly if you are viewing the atmosphere as a black box.
We mightn’t (yet) be able to predict when one of these events might trigger, but if we can be confident that we’ll end up where we started from (as it were) we can look through them. In this way it’s equivalent of the impact of volcanoes in predictions. I stress this view relies heavily on the assumption that the sequences are benign, an assumption that is not demonstrated by Swanson and Tsonis in my reading.
Finally a comment on the GCMs and this phenomena. Swanson and Tsonis test GCMs against their observation of these sequences in nature. They compare with non-forced GCMs and find the sequences are only seen in runs during the second century. This is suggestive that “run ups” to initialise these models for longer than this period may not be modeling the real climate accurately, and it rather places a limit on the period that GCMs can reliably be used as comparators without demonstrating that this aspect of the climate is not important to the comparison.
After years of dispute it was finally recognized that the Milankovitch cycles drive the ice ages. Why do we assume that the natural variations between the ice ages has a different source? The obvious driver for variations in earth’s climate is orbital mechanics. The 60 year cycle does a much better job of predicting the climate than current CO2 models.
http://www.heliogenic.net/2010/03/26/scafetta-on-the-60-year-temperature-cycle/
We have a very effective theory of gravity, that does very well at prediction, yet we have almost know understanding of the cause of gravity. We may not know what causes the 60 year cycle in climate, but that should not prevent us from using it to make useful prediction.
“governments (ie, the IPCC’s customers) are more interested in the regional projections made by WGII than any other part of the report”
Subtract 60 from the year you want to know about, and look at the historical ecords for that time. Odds are the result will be more accurate than any climate model looking forward.
“Moreover, we caution that the shifts described here are presumably superimposed upon a long term warming trend due to anthropogenic forcing.”
I would be fascinated to see if this statement was in their original draft or whether an editor or reviewer at GRL forced the issue.
Bob:
Please see my discussion with Ron Cram and JCH (far above). It addresses your point.
Richard
I don’t have any particular insight in Tsonis’ work in terms of the chaos/nonlinear dynamics aspects (which I am not an expert on).
What RDS does is to identify persistence or residence time around a set of fluctuating qualities ie to identify the attractor and its domain of influence,whether it explores other regions (outside of its points of attraction) eg bifurcations where velocity inversions may occur etc.
Alexander Ruzmaiken does a nice simplification of the problem.
” Linear and non-linear systems respond differently to external forcing. A classical example of a linear system response is the Hooke’s law of elasticity that states that the amount by which a material body is deformed is linearly proportional to the force causing the deformation. Earlier climate change studies used this linear approximation to evaluate the sensitivity of the global temperature change caused by external forcing. However the response of non-linear systems to external forcing is conceptually different; the issue is not a magnitude (sensitivity) of the response. Non-linear systems have internally defined preferred states (called attractors in mathematics) and variabilities driven by residence in the states and transitions between them. The question is what is the effect of an external forcing: change of the states, residence times or something else?
Answer to this question is critical to our understanding of climate change.
Based on the model studies mentioned above we can formulate the following, updated conjecture of the climate system response to external forcing: external effects, such as solar, the QBO and anthropogenic influences, weakly affect the climate patterns and their mean residence times but increase a probability of occurrence of long residences. In other words, under solar or anthropogenic influence the changes in mean climate values, such as the global temperature, are less important than increased duration of certain climate patterns associated say with cold conditions in some regions and warm conditions in the other regions “
Rather then get into the alphabet soup problem eg NAO,PDO etc it is more simple to look at the two dominant modes the NAM and SAM and whether these are applicable to climate dynamics and over what temporal scales, do they have local or global effects,and are there mechanisms we can understand or are these merely random excursions exhibiting historical behaviour without being recurrent.
thx, NAM/SAM is a different way to approach this
I wish somebody would sort all these indices out in a coherent way. Not entirely sure i buy the argument that everything being discussed by Tsonis can be collapsed to NAM/SAM?
From the CSU site http://www.atmos.colostate.edu/ao/introduction.html
What’s the difference between the NAM, the Arctic Oscillation, and the North Atlantic Oscillation? And what’s the difference between the SAM, the Antarctic Oscillation, and the high-latitude mode?
The short answers : none and none.
Through most of the 20th century, the NAM was referred to exclusively as the North Atlantic Oscillation. That’s because both the data coverage and the amplitude of the NAM are largest over the Atlantic sector of the hemisphere. When the polar scale of the northern pressure center of the NAO was identified in the late 1990s, the name Arctic Oscillation was introduced to highlight the fact that the pressure anomalies associated with the NAO span most of the Arctic. But ‘oscillation’ is something of a misnomer, as neither the northern or southern annular modes oscillate regularly in time (see text on What are the annular modes?, above). For this reason, in recent years the NAO/AO nomenclature has been increasingly supplanted in the dynamical literature by the phrase Northern Annular Mode (NAM). Annular denotes the longitudinal scale of the pattern, and annular mode suggests the NAM reflects dynamical processes that transcend a particular hemisphere or, for that matter, planet.
Likewise, the leading mode of variability in the Southern Hemisphere has been referred to as the High Latitude Mode and the Antarctic Oscillation, but is most commonly labeled the Southern Annular Mode in the recent literature.
annular mode suggests the NAM reflects dynamical processes that transcend a particular hemisphere or, for that matter, planet
Most systems exhibit interference and mode locking due to external forcing, in enso this is the annular or seasonal mode eg Ghil et al 2008,if there is natural variation in response to external forcing it makes little sense to say average externalities such as TSI etc to identify a centennial forcing of say ghg 3.7 wm^2 when annular variations at the TOA are around 120 wm^2.
This has implifications for seperating the asymmetry into hemispherical climatologies and say zonal climatologies.
.
I wish somebody would sort all these indices out in a coherent way. Not entirely sure i buy the argument that everything being discussed by Tsonis can be collapsed to NAM/SAM?
Trenberth 2004 suggest it is a legitimate line of enquiry.
Using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) for 1958 to 2001, adjusted for bias over the southern oceans prior to 1979, an analysis is made of global
patterns of monthly mean anomalies of atmospheric mass, which is approximately conserved globally. It differs from previous analyses of atmospheric circulation by effectively area weighting surface or sea level pressure that diminishes the role of high latitudes. To examine whether global patterns of behavior exist requires analysis of all seasons together (as opposite seasons occur in each hemisphere). Empirical orthogonal function (EOF) analysis, R-mode varimax-rotated EOF analysis, and cyclostationary EOF (CSEOF) analysis tools are used to explore patterns and variability on interannual and longer time scales.
Clarification is given of varimax terminology and procedures that have been previously misinterpreted. The dominant global monthly variability overall is associated with the Southern Hemisphere annular mode (SAM), which is active in all months of the year. However, it is not very coherent from month to month and exhibits a great deal of natural unforced variability. The third most important pattern is the Northern
Hemisphere annular mode (NAM) and associated North Atlantic Oscillation (NAO), which is the equivalent Northern Hemisphere expression. Neither of these is really a global mode, although they covary onlong time scales in association with tropical or external forcing. For monthly data, the second mode is coherent with Niño-3.4 sea surface temperatures and thus corresponds to El Niño–Southern Oscillation (ENSO), which is truly global in extent. It exhibits more coherent evolution with time and projects strongest onto the interannual variability, where it stands out by far as the dominant mode in the CSEOF analysis. The CSEOF analysis extracts the patterns phase locked with annual cycle and reveals their evolution throughout the year. Standard EOF and varimax analyses are not able to evolve with time of year unless the analysis is stratified by season. Varimax analysis is able to extract the SAM, NAM, and ENSO modes very well, however.
…The results provide a rationale for focusing on the NAM, SAM, and ENSO, and with the fourth pattern related to the North Pacific index that in turn is linked to the PDO. Nevertheless, Pacific variability features patterns of decadal SST variability similar to those for
ENSO, so it is not independent. The VEOF4 features strong wave train patterns in the Southern Hemisphere as well as the North Pacific center of action, and this also suggests that the Tropics may be involved, but this aspect also likely requires stratification by season to bring further clarification.
This framing would allow a better understanding of what is the causal mechanisms for the redistribution of mass,and its accompanying excursion of sign.eg Roy and Haigh 2010
We investigate an apparent inconsistency between two published results concerning the temperature of the winter polar stratosphere and its dependence on the state of the Sun and the phase of the Quasi-Biennial Oscillation (QBO).We find that the differences can be explained by the use of the authors of different pressure levels to define the phase of the QBO.
We identify QBO and solar cycle signals in sea level pressure (SLP) data using a multiple linear regression approach. First we used a standard QBO time series dating back to 1953. In the SLP observations dating back to that time we find at high latitudes that individually the solar and QBO signals are weak but that a temporal index representing the combined effects of the Sun and the QBO shows a significant signal. This
is such that combinations of low solar activity with westerly QBO and high solar activity with easterly QBO are both associated with a strengthening in the polar modes; while the opposite combinations coincide with a weakening. This result is true irrespective of the choice of QBO pressure level. By employing a QBO dataset reconstructed back to 1900, we extended the analysis and also find a robust signal in the surface SAM;though weaker for surface NAM.
Our results suggest that solar variability, modulated by the phase of QBO, influences zonal mean temperatures at high latitudes in the lower stratosphere and subsequently affect sea level pressure near the poles. Thus a knowledge of the state of the Sun, and the phase of the QBO might be useful in surface climate prediction.
http://www.atmos-chem-phys-discuss.net/10/30453/2010/
thx for this, v. helpful
Re: above
See the lower graph (email) 1870-2010.
The paper is worthy of a very careful read:
Trenberth, K.E.; Stepaniak, D.P.; & Smith, L. (2005). Interannual variability of patterns of atmospheric mass distribution. Journal of Climate 18, 2812-2825.
http://www.cgd.ucar.edu/cas/Trenberth/trenberth.papers/massEteleconnJC.pdf
It’s both refreshing & encouraging to see promotion of varimax rotation. Figure 3 & Table 3 are true gems.
Interesting. NAM makes sense but only if the Arctic Oscillation is separated out since it influences or is influenced by the PDO. This is behind a pay wall so I have read it. It may of be of interest is someone has a subscription.
http://www.springerlink.com/content/p5431824r1452037/
Should say “haven’t read it”
Careful with that one. NPI is usually correlated with AO, but not always positively.
“… an attempt to figure out how we can make some sort of sensible scenario based climate predictions on decadal time scales.”
I’ve yet to find a study that presents all of the oscillations in a holistic way. A majority of the better studies relate to the fishing industry where the oscillations and species related forecasts have been documented for decades.
Complex and fascinating topic but the only sensible scenario is to model the specifics before making climate predictions and I doubt this has been done in sufficient detail. Example: some oscillations occur within the context of others and some act like switches.
Decadal climate cycles and declining Columbia River salmon
Published in Sustainable Fisheries Conference Proceedings (1998)
http://www.cbr.washington.edu/papers/jim/victoria.html
Fish and climate patterns
Multidecadal fluctuations in fish stock abundance have been observed for centuries (Rothschild 1995), but an appreciation of the importance of climate-fish fluctuations is relatively recent. The longest record of fish population fluctuations was obtained from a 2000 year sedimentary record off California. A surrogate record for the abundance of Pacific sardines and northern anchovy, inferred from scales in sediment cores, exhibited strong fluctuations over two millennia. A spectral analysis of the records revealed a peak at periods of about 60 years (Smith 1978, Baumgartner et al. 1992, Sharp 1992). The contribution of climate to these types of fluctuations was inferred from a similar pattern of climate indicators and sardine (Sardinops) catches around the Pacific basin (Kawasaki, 1984). One of the earliest papers on fishery oceanography documented the impact of the 1972-73 El Niño on the crash of the Peruvian anchovy fishery (Valdivia 1978). The general significance of climate on fisheries variability has also been the focus of other treatises (Smith 1978, Gantz 1992, Beamish 1995, and others).
A Pacific interdecadal climate oscillation with impacts on salmon production
Published in the Bulletin of the American Meteorological Society, June, 1997 (Vol 78, pp. 1069-1079)
http://www.atmos.washington.edu/~mantua/REPORTS/PDO/pdo_paper.html
Our synthesis of climate and fishery data from the North Pacific sector highlights the existence of a very large scale, interdecadal, coherent pattern of environmental and biotic changes. It has recently come to our attention that Minobe (1997) has compiled a complementary study of North Pacific climate variability that includes SST indices from the coastal Japan and Indian Ocean-maritime continent regions. Especially relevant to our work is the fact that Minobe used instrumental records to independently identify the same dates we promote for climatic regime shifts (1925, 1947, and 1977). Also intriguing is Minobe’s analysis of (tree-ring) reconstructed continental surface temperatures that suggest PDO-like climate variability has a characteristic recurrance interval of 50 to 70 years, and that these fluctuations are evident throughout the past three centuries.
Stability of the Atlantic meridional overturning circulation in a zonally-averaged ocean model: the effects of freshwater flux, wind stress, and diapycnal diffusivity
http://earth.geology.yale.edu/~avf5/publications_pdf/SevellecFedorov.AMOC.2D.2010.pdf
The goal of this paper is to construct a simple, yet realistic zonally-averaged (latitude-depth) model of the AMOC, which can be used to explore in a broad parameter range the stability and sensitivity of AMOC to various forcings.
While the history of two-dimensional ocean models is quite rich (Marotzke et al., 1988; Wright and Stocker, 1991; Drbohlav and Jin, 1998; Marchal et al., 2007, just to name few examples), a number of important processes were left unaccounted for, including the impacts on overturning and ocean stratification of the wind stress and the entire dynamics in the Southern Ocean (as described by Marshall and Radko, 2006, for instance).
EOF Representations of the Madden–Julian Oscillation and Its Connection with ENSO
http://journals.ametsoc.org/doi/pdf/10.1175/1520-0442%282001%29014%3C3055%3AEROTMJ%3E2.0.CO%3B2
MJO–ENSO association is unambiguous, with each of the El Nino events since 1979 clearly associated with an eastward extension of the MJO envelope, and the La Nina periods with a westward retreat. This suggests that a complete understanding of the evolution of the ENSO cycle depends on understanding the role of the MJO in it, and the question will not be represented as a simple change in global MJO activity.
yes, the fish seem to have sorted out the pacific for us. the atlantic still seems murky
Why do you think the Dolphins were grateful for them? Simple nourishment?
============
The last thing the fishes had to say was: ‘Thanks for all the plankton’.
H/t anna v.
=======
“El Nino–Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices” (2006) I. I. Mokhov and D. A. Smirnov and “Analysis of the cause and effect relationships between El Niño in the Pacific and its analog in the equatorial Atlantic” (2009) S. S. Kozlenko, I. I. Mokhov and D. A. Smirnov suggest the fish may have sorted it for the Atlantic as well.
I’m not obsessive just systematic, honest.
ok this is interesting i will check it out
Pacific salinity is one of the forcings on the Arctic and Atlantic:
Revising the Bering Strait Freshwater Flux into the Arctic Ocean
Rebecca A Woodgate and Knut Aagaard
http://psc.apl.washington.edu/HLD/Bstrait/BSFWpaper.html
Abstract
The freshwater flux through the Bering Strait into the Arctic Ocean is important regionally and globally, e.g. for Chukchi Sea hydrography, Arctic Ocean stratification, the global freshwater cycle, and the stability of the Atlantic overturning circulation. Aagaard and Carmack [1989] estimated the Bering Strait freshwater flux as 1670 km3/yr (relative to 34.8 psu), assuming an annual mean transport (0.8 Sv) and salinity (32.5 psu). This is ~ 1/3rd of the total freshwater input to the Arctic. Using long-term moored measurements and ship-based observations, we show that this is a substantial underestimate of the freshwater flux. Specifically, the warm, fresh Alaskan Coastal Current in the eastern Bering Strait may add ~ 400 km3/yr. Seasonal stratification and ice transport may add another ~ 400 km3/yr. Combined, these corrections are larger than the interannual variability observed by near-bottom measurements and near-surface measurements will be necessary to quantify this flux and its interannual variability.
from the study:
The throughflow has a profound influence not only on the adjacent Chukchi Sea [C75; Wetal], but also on the thermal structure, the nutrient loading, and the fresh water budget of the Arctic Ocean [e.g. Shimada et al., 2001; Steele et al., 2004; Walsh et al., 1989; Aagaard and Carmack, 1989, henceforth AC89]. Furthermore, theoretical and modeling studies indicate that the fluxes through the Bering Strait play an important role in the salt and fresh water cycles of the world oceans [AC89; Wijffels et al., 1992; Huang and Schmitt, 1993], to the extent of having a controlling influence on the Atlantic meridional overturning circulation [e.g. Reason and Power, 1994; Goosse et al., 1997; Wadley and Bigg, 2002], the strength of the deep western boundary currents, the separation point of the Gulf Stream from the American coast [Huang and Schmitt, 1993], and possibly also world climate [DeBoer and Nof, 2004].
You are wanting to model what is probably the strange attractor of ocean dynamics. I wonder how many dimensions it alone, apart from the rest of the climate system, would have?
When climate models replicate ocean oscillations in a control run, it is tempting to call this “internal” but it could also be that an external kick could entrain to switch a cycle like the PDO. That is, the fact that decadal excursions can be generated by GCMs does not prove the excursions are strictly random/internal. The work of Scafetta showing correlations of PDO cycles with solar orbital variations suggests to me that the ocean cycles are subject to entrainment. This entrainment need not look like a one-to-one correlation with a forcing at all. There are known chaotic attractors that result from a periodic kick, and this could be that type of pattern. For these reasons I think Tsonis is on the right track, but the work needs further development.
Craig, interesting comment. What are some of these known chaotic attractors you mention? Can you give me some examples? And if they result from a periodic kick, how are they chaotic?
They are a chaotic attractor in the sense of never exactly repeating their previous path. I ran across this some years ago but don’t have time to dig it out right now. But the basic idea is that an outside forcing could initate or entrain a system that has a tendency to internal oscillation, which would seem to fit here nicely, and would suggest that we need not find a continuous forcing that gives the PDO or toehr cycles.
I have some ideas shown here:
http://www.vukcevic.talktalk.net/NPG.htm
I would hazard to say that Scafetta may got it wrong.
He based his estimates on only about 150 years records, but if the 350 year long CET record is considered than 47/8 year long cycle is more evident. This orbital cycle may be (if one is so inclined) reflected in the North Atlantic temperatures not via gravitation forces but by geo-centric magnetic configuration of heliosphere:
http://www.vukcevic.talktalk.net/CETpr.htm
whereby frequency of the Arctic being subjected to the solar magnetic impact is a function of the heliospheric magnetic configuration. http://www.vukcevic.talktalk.net/MF.htm
vukcevic, the current framing of the natural oscillations in terms of SAM, NAM may be relevant to what you are doing. These oscillations seem to work from the top down (i.e. they are first noticed in the stratosphere).
There is also indication that ENSO may have a link to events as high as ionosphere.
Here is a short overview of such possibility:
http://www.vukcevic.talktalk.net/LFC20.htm
Judith,
The good challenge of planetary science is every square inch of this planet has a different energy and action happening. The atmospheric pressure sort of blends all these actions thanks to planetary rotation.
I think this question is relevant because it is not a catastrophe-based predictions of doom: Shall prospects of global cooling be considered a disaster too?
While a claimed consensus of opinion holds that global warming is of paramount concern, AGW heresy is the natural process of anyone with the required intellectual courage and necessary scientific integrity to make the conscientious decision to apply reason and logic to the facts.
“The partial forecast indicates that climate may stabilize or cool until 2030-2040. Possible physical mechanisms are qualitatively discussed with an emphasis on the phenomenon of collective synchronization of coupled oscillators.” ~Nikola Scafetta
And, Note: “… a long-term global cooling starting around 2002 is expected to continue for next five to seven decades…” ~Lu, Q.
And, what if humans actually averted an ice age–still a disaster?
“If man made global warming is indeed real, and it helps to prevent another ice age, this would be the most fortunate thing that has happened to our species since we barely escaped extinction from an especially cold period during the last ice age some 75,000 years ago.” ~Walter Starck
And, what if global warming were to continue for 100 years? But, what if as throughout the 10,000 years of the Holocene, the global warming had nothing to do with humans–still a disaster?
In answering the above questions, we all must consider all of those pesky little problems we sometimes refer to as, reality, e.g., “Fossil fuels will run out well before any drastic effects on climate are possible.” (Ibid.)
Additionally, we all should keep in mind when answering these questions that everyone knows global warming has been much better for humanity than global cooling. “The net result of a projected doubling of atmospheric CO2 is most likely to be positive.” (Ibid.)
I’m just waiting for the manner in which man will be blamed for the coming cooling. C’mon, we clearly need this supernaturalism in our lives; the phenomenon is persistent. Reality is so mundane.
============
Sorry, did I miss the free video game exchange on this post? Isn’t speaking in the 3rd person about our species a bit odd when the world is thinking in 1st person?
It has just come to me, that the real power-madness of the cAGW cult is the desire for near-total control over Nature. This will be achieved with the Magic Themostat, CO2.
But to control CO2, they must first control the entirety of human energy generation and utilization, which means all economies and all lifestyles. But that’s just the means to the Glorious Goal — thermostatic control of the planet!
Oscillation. Does it measure precipitation?
An Ice Age is pure precipitation and a light cooling thrown in.
typo: Magic Thermostat (not themostat) ;)
While a claimed consensus of opinion holds that global warming is of paramount concern, AGW heresy is the natural process of anyone with the required intellectual courage and necessary scientific integrity to make the conscientious decision to apply reason and logic to the facts.
Well Said!
s/b
Well Said Wagathon!
Judith,
Not to insult you but climate science seems to have been designed from “quackery”.
No physical evidence is being followed such as precitation patterns and durations, atmospheric pressure patterns and durations, physical history of planetary change.
Temperatures are not physical and are all over the place in model recreations, oscillations are like bio-rythms rather than ocean current changes. So where is the science? Co2 theory is currently taking a nose dive.
Sadly, Joe Lalonde (5 Jan @ 8:33 am) hit the nail on the head.
It appears that Professor Curry has not grasped that future predictions of climate change for 2010-2040 will not be trusted to those who failed so miserably in the past.
Unless Presidents of the once-trusted US National Academy of Sciences and the UK’s Royal Society step forward and accept responsibility for the errors of 1980-2010, there will be no public funds available in 2010-2040 for “statistical or dynamical predictions of future climate change.”
With kind regards,
Oliver K. Manuel
Oliver,
All they had to do was follow the ice recession to have some idea of how climate was behaving.
Soil depth would have given a general idea of how old plant growth started in the area.
Some trees in the near Arctic are still in their first growth from ice recession.
Consensus Climate Scientists who talk about climate and earth temperature frequently talk about chaotic and instabilities.
I look at a temperature plot of temperature from ice core data for the past 800,000 years and I see a very stable system. I look at a temperature plot of the past 10,000 years and I see an extremely stable system with tight temperature control.
What these Consensus Climate Scientists see is instability in their flawed climate model output that diverges because they have modeled flawed theory.
Some proper statistical analysis of the data will show a very stable system that cannot be upset with one molecule of manmade CO2 for every ten thousand molecules of other gases. We are well inside the normal range of the Earth’s Temperature over the past ten thousand years. We have a lot of data for modern times, but you cannot extrapolate a small time frame of data and get valid long term answers.
That is designed by having a system that uses pressure in an enclosed bioshere to regulate weather changes.
Judith
As the referenced Tsonis papers are not easily readable, I will try to resume for you and the interested readers what it is actually about.
First, Tsonis is NOT doing chaos theory, he is doing statistics.
Second, the important paper is 2007. 2010 is a minor update with no interest in itself.
Third, the Tsonis&al 2007 paper may be considered as minor in the field of synchronization of dynamical systems as opposed to major papers like http://amath.colorado.edu/faculty/juanga/Papers/PhysicaD.pdf
So now I’ll focus on the 2007 paper only.
As I have written multiple times, the main reason why spatio temporal chaos is untractable is the fact that the phase space (the space of the system’s states) is uncountably infinite dimensional because of the adition of space variables to the usual time variable.
That’s why all approaches of spatio temporal chaos begin with a discretization of space by taking a grid or a network which transforms the continuous spatial functions in a finite number of nodes so that the phase space becomes finite dimensional.
Tsonis acknowledges that the climate is a spatio temporal chaotic system so he looks for a spatial discretization too.
Of course as the fact of being chaotic doesn’t imply that the GHG play no role (just that they play some role), he proceeds with the mandatory genuflexion to orthodox AGW by saying that “… the climate shifted after the 1970 event in another warmer state which may be superimposed on an anthropogenic warming trend.”
This certainly allows him to avoid inflammatory articles by the usual suspects in the newspapers.
His discretization is to choose 4 indexes – PDO, ENSO, NAO, NPO.
Why those 4?
Well as he is interested in decadal scales, those are the main ones. Shorter doesn’t exist and longer can’t be captured by the method.
As a particular but important remark, one has to notice that the physical meaning of the indexes is irrelevant because what Tsonis is interesting in is their interaction.
This is the first and last (weak) link to the chaos theory, after that it is just statistics.
Having a network with 4 nodes (the 4 indexes) and therefore a phase space with 4 dimensions (a huge progress to an uncountable infinity:)), he now needs a metrics.
Equation (1) defines d(t), the metrics of what is called in the paper “synchronization”.
It is actually just an average of the cross correlation coefficients between the 4 indexes for a gliding window 11 years wide with t in the middle of the window.
If d(t)=0 then all indexes are completely correlated with each other and if
d(t) = √2 then each index does what it wants.
Figure 1a shows the d(t). As the values are around 1 , the indexes are rather uncorrelated (or non synchronous in Tsonis vocabulary).
Nothing much interesting sofar.
Now comes the original contribution of the paper.
As the purpose was not to compute correlations between time series what has been done a million of times but the coupling strengths between the indexes (and hopefully between the underlying physical processes), the paper defines a measure of “coupling strength” in Equation 2.
Also here again, like in Equation 1, the terminology is misleading because it is not really a coupling that is measured but a relevance of a predictor.
Tsonis defines a “phase” for each index by considering 3 contiguous points.
F.ex if the 3 points go up, the phase is 0 , if they go down, the phase is π etc.
For every t there is then a 4 vector of phases Zn (n for the year) and Tsonis looks how well a least square predictor can predict Zn+1 from Zn.
If the prediction is good, the “coupling “is said strong and if the prediction is bad, the “coupling” is said weak.
At this point I would criticize extremely strongly the term of “coupling”.
The right use of the coupling constant and the right definition of coupling is given f.ex in the paper I linked above.
Tsonis’ “coupling” is of course nothing such and yet implies that with a “good” predictor the underlying physical systems are strongly coupled (ie interacting) what is misleading. Here the strong “coupling” in Tsonis sense just means that the phases are identical, e.g if one index goes up, the otehr go up too and conversely.
Anyway. The Figure 1b shows the phase predictor. The value is around 0.5 so the predictor is not specially good.
The last part of the paper is an eyeballing exercice.
Tsonis added under the correlation and the predictor error figures the average temperature and the ENSO index.
Now he observes that there were 4 (kind of) minima on the correlation curve.
In 3 out of 4 cases the predictor error decreased (Tsonis vocabulary: “coupling” increased) and in those cases the average temperature trend as well as the ENSO variability changed significantly. In the 4th case the predictor stayed bad (weak “coupling”) and nothing special happened.
Tsonis conclusion: When the decadal oscillations synchronise and the coupling increases, then the system destroys the synchronization and jumps to a new very different (unknown) state.
My translation: When the 4 indexes are relatively strongly correlated and begin to tend to evolve all in the same direction, then they decorrelate fast .
In 3 out of 4 cases in the 20th century this decorrelation coincides with a change in the average temperature trend. The jump from this rather modest observation to a statement concerning the climate itself which can’t be resumed by an average temperature only, is daring to say the least.
Of course the question whether the 4 indexes are a univoque proxy for something physical and relevant stays open.
And my conclusion.
Despite the clear shortcomings of the paper (especially as far as the coupling is concerned) it suggests that the behaviour of the indexes follows correlation-decorrelation pseudo cycles.
This observation has no predictive virtue so I don’t think that it could be used for your intent to elaborate a decadal scenario.
All that Tsonis says, is that the behaviour (of the indexes, not of the system itself!) significantly changes when the correlation is strong and the predictor good.
This situation happened in 2001 again.
So according to Tsonis something will change/has changed significantly.
As his paper is neither quatitative nor predictive, he cannot say WHAT will change and HOW.
However the general paradigm to consider the Earth system as a finite network of coupled (chaotic) oscillators is a good one and if one had the idea of the number of the oscillators, their average frequency and especially of the laws that govern them, it would certainly have some predictive capacity.
The problem, that I have also already elaborated on, is that these oscillators are in reality not causally independent but they are ALL just emergent local manifestations of GLOBAL dynamics of the system.
The coupled oscillator model is just an approximation which would be probably valid only for short term predictions (decades or so).
Thanks for that nice deconvolution.
“The problem, that I have also already elaborated on, is that these oscillators are in reality not causally independent but they are ALL just emergent local manifestations of GLOBAL dynamics of the system. The coupled oscillator model is just an approximation which would be probably valid only for short term predictions (decades or so)”
well put
Tomas, thank you very much for your summary
I’ll add this summary to the main post
Wow, I do believe I actually understood a fair bit of that.
Thanks Tomas. :)
Thanks AnyColourYouLike :)
Actually this exercice and your comment remind me just how strongly the vocabulary impacts the understanding (and I am not even a native english speaker).
Many scientists, and I am not always an exception, tend to say very simple sometimes almost trivial things with words which make these things unnecessarily utterly obscure and incomprehensible.
Note that the converse is not true.
There are some domains in science which are really difficult and incomprehensible for 99% of people or even scientists (f.ex AdS/CFT correspondance to just mention one case) but my guess is that 80% of the non quantum science and certainly 90% of the current climate “science” is really easy and accessible for everybody with at least a basic math/stat/thermo training.
What would be great now is for Andy Lacis or Chris Colose (or dare I say Gavin Schmidt) to comment on this and provide their views. Judith…get on the blog phone and call ’em in!! Fascinating stuff.
Actually I probably know more nonlinear dynamics than lacis, colose or schmidt (schmidt is more of a dynamicist than the others tho), which isn’t saying much. This is why i think it is so rich having experts from outside the climate field comment on these topics.
but I want to know the answer and I seem to be no closer to it…
Is this comment designed to lure Lacis, Colose or Schmidt into dialog? I notice that Swanson has not succumbed to your allure.
The one thing I would add is that the 2007 paper seems to lack the warning language of coming catastrophe the 2009 paper concludes with. It was striking to me to have Swanson conclude that “no comfort to be gained by having a climate with a significant degree of internal variability.” I think there is a great deal of comfort to be gained from the realization ALL 20th century warming is not driven by CO2.
Tomas,
Hate to disagree with you but climate science has NEVER looked to the past beyond 150 years.
Soil depth would give a good approximation of the areas plant growth to the receeding ice that was in the area.
How many generation of tree did the scientists look at?
Some trees in the near Arctic are in their first generation.
Simple system of melting and freezing going back billions of years.
Just current science caught up on the end when pressure HAD to build in order to physically change to trigger the safety system of water.
It did occur to me that one way to introduce quantification would be to add a binary state variable reflecting the presence or absence of phase alignment at time t into simply models of the temperature and then seeing if it (and any memory of it to cover the recovery period eg the decay period might be a function of length of time in the state) gave any significant increment in explanatory power.
However I take from your comments that you don’t hold out much hope for a positive result (and quite possibly that direct inclusion of ENSO state might do just as well?)
Tomas Milanovic:
I like your good summary of Tsonis’ work that I find understandable, and it does go a long way to answering my request for clarifications of points in your post on the ‘Climate Feedbacks: Part I’ thread. But it is not a complete answer.
In terms of my original questions on that other thread, I remind that my post there included this:
“Indeed, as you say, Tsonis adopts an approximation which attempts to ‘get around’ these practical limitations which you state. As you say of Tsonis;
“By selecting N (N finite) most “important” spatially extended objects considered as being only time dependent (temporal chaos) and by coupling them among themselves with different coupling constants, he achieves both spatial extension AND finite phase space dimension what would be impossible in the “full” theory.”
It seems to me that Tsonis’ approximation is a useful contribution to the problems. If so, then the problems’ affects may only provide inaccuracies concerning the positions of the attractors (which Tsonis call “climate states”) because the Tsonis ‘solution’ is an approximation, and the only real problem is a determination of those inaccuracies.”
You have now written in this thread;
“However the general paradigm to consider the Earth system as a finite network of coupled (chaotic) oscillators is a good one and if one had the idea of the number of the oscillators, their average frequency and especially of the laws that govern them, it would certainly have some predictive capacity.
The problem, that I have also already elaborated on, is that these oscillators are in reality not causally independent but they are ALL just emergent local manifestations of GLOBAL dynamics of the system.
The coupled oscillator model is just an approximation which would be probably valid only for short term predictions (decades or so).”
I interpret that to be an agreement with my view (at least a partial agreement). So, it seems to me that there are two remaining issues if the Tsonis approach is to be supported as one potentially useful way forward.
The first of those issues is stated by you when you say;
“So according to Tsonis something will change/has changed significantly.
As his paper is neither quatitative nor predictive, he cannot say WHAT will change and HOW.”
OK, I think that can be agreed although it is not capable of verification because “something” in climate is always changing. However, if it is true then the Tsonis analysis suggests that changes consist of transitions between what he calls “climate states”. Given sufficient time then those ‘states’ can be observed and the probable transitions between adjacent ‘states’ could be determined. Hence, if Tsonis is right then his method is a small step towards a true predictive model of climate behaviour which would not require determination of each and every climate mechanism both known and unknown.
However, that possibility brings me back to my final request to you for clarification that I posed on the other thread which was as follows.
“But I may be misunderstanding you because you say the existing mathematical theory;
“deals with dynamical systems in a finite dimensional phase space with variables depending ONLY on time”.
Hence, for clarification, I ask if you are suggesting that variable inputs (e.g. altered radiative forcing) alter the system so require a different model if a chaotic model of the climate system is adopted. If so, then that is not how I interpret Lorenz 2005 paper
(ref. Lorenz EN, Designing Chaotic Models. Journal of the Atmospheric Sciences: Vol. 62, No. 5, pp. 1574–1587 (2005) ).
So, I would be grateful for a clarification.”
If a different model is required each time a change of ‘state’ occurs then the ‘Tsonis approach’ is very likely to be a ‘dead end’.
In conclusion, I stress that I find your comments informative and helpful so I am iterating my remaining unanswered request for clarification because of that. I am very grateful for your posts to date and I would much appreciate this final clarification.
Richard
Richard Courtney
Hence, for clarification, I ask if you are suggesting that variable inputs (e.g. altered radiative forcing) alter the system so require a different model if a chaotic model of the climate system is adopted. If so, then that is not how I interpret Lorenz 2005 paper
(ref. Lorenz EN, Designing Chaotic Models. Journal of the Atmospheric Sciences: Vol. 62, No. 5, pp. 1574–1587 (2005) ).
So, I would be grateful for a clarification.”
If a different model is required each time a change of ‘state’ occurs then the ‘Tsonis approach’ is very likely to be a ‘dead end’.
Sorry, I have been afk for some 2 weeks and have missed your post.
I think that the sought clarification involves a clear, very clear understanding of the difference between temporal (e.g Lorenzian) and spatio-temporal chaos.
In the mind of most people, there is only “chaos” and for those better informed there are also things like attractors, bifurcations etc.
Nothing could be more incomplete than this view.
What is called temporal, deterministic chaos deals with a system of non linear ODEs. And ODE=variability with time only.
The Lorenz system, dripping faucet, electronic oscilators, planetary orbits etc are physical systems described by a system of non linear ODEs.
What you call “model” is finding a solution of such a system.
Every variable of the ODE system (X1(t) … Xn(t)) is a degree of freedom of the physical system and the states of the system are perfectly defined by giving n numbers. The evolution of the vector (X1(t) … Xn(t)) in an n dimensional space (phase space) describes an orbit which univoquely defines the dynamics of the system.
What is called chaos theory is the study of these vectors. One finds attractors (strange or not), limit cycles and all kinds of geometrical properties of this finite n dimensional space and the dynamical orbits within.
THIS (temporal) chaos theory is nothing new, it didn’t begin with Lorenz, it began with Poincare studying the 3 body gravitational problem and discovering that the planetary orbits were chaotic 100 years ago.
But then there is also spatio temporal chaos.
To easily understand what is that thing, is too observe cigarette smoke.
You see wildly fluctuating spatial structures.
Then you could focus just on a small spatial region and look how it varies with time. There would be places where the variation is small (just above the cigarette end) and places where the variation is large (farther up the smoke stream).
These instabilities and fluctuations keep on even if there is no air movement. Now you perturb the system by putting a finger in the smoke.
You will see all kinds of new fluctuations and then the system adopts a new smoke structure which you can clearly distinguish from the previous one onsome places (just above the finger) and which looks just as chaotic as before on other places.
Well this system is described by a system of non linear PDEs (mainly Navier Stokes).
But PDEs are something completely different than ODEs.
There where the solutions of an ODE system form a finite dimensional vector with components depending only on time, the solutions of a PDE system form a FIELD.
They are functions f1(x,y,z,t) … fn(x,y,z,t). Space matters.
So in this case you have no more a phase space, you have no more orbits, you have no more “attractors” and most importantly you have even a difficulty to define what the state of the system is.
Because of that you LOST all the tools of the (temporal) chaos theory.
So what you need is to invent a new theory (what you would probably call “model”) which deals with this new qualitatively completely different physical system. This full theory doesn’t exist today. What exists is what I have written several times, are numerous approaches(“models”) which all have in common some spatial discretization in order to get back to finite dimensional spaces. This gives then coupled lattices, chaotic spatio-temporal networks etc etc.
All this is cutting egde science in opposition to the (temporal) chaos theory which is 100 years old.
And the climate in all that?
Well everybody will agree that it is a spatio-temporal system (space matters).
So, like Al Thekaski has very rightly written on a thread sopmewhere, it is a field theoretical problem (e.g a problem of non linear coupled PDEs).
On top it is chaotic too (weather is as sensible to initial conditions as the cigarette smoke or a dripping faucet are).
So clearly a correct dealing with weather/climate should be as cutting edge science as the spatio-temporal chaos study is.
Unfortunately it is not.
It is mostly either classical statistics (like Tsonis) or numerical simulations of primitive models. Of course there are a few exceptions (Nicolis, Fraedrich etc) who tried to apply some of the tools of the chaos theory but as they stay in the temporal domain only, with mixed and uncertain results.
Tomas Milanovic:
Please accept my sincere thanks for the time and great effort you have expended in providing your very useful and informative answer to me.
However, I fail to find a specific answer to my specific question in that answer. Perhaps that is because I did not make the question sufficiently succinct or that I am failing to read your answer correctly.
My question, bluntly put, was:
“Hence, for clarification, I ask if you are suggesting that variable inputs (e.g. altered radiative forcing) alter the system so require a different model if a chaotic model of the climate system is adopted.”
And I am pressing the point because – as I said – I see approximations of the type suggested by Tsonis affording the possibility of a predictive climate model that does not require quantification of all climate processes both known and unknown.
However, if each change of ‘state’ requires a new and different formulation then such a predictive model (developed from Tsonis’ approximation) would probably not be possible.
I am sure you intended to answer my question because, for example, I do notice your statement that says;
“So what you need is to invent a new theory (what you would probably call “model”) which deals with this new qualitatively completely different physical system. This full theory doesn’t exist today. ”
However, Tsonis does not provide a new theory of chaos: he suggests an approximation based on existing chaos theory. My interest is in whether the use of such approximations is known to have limitations that prevent them having practical usefulness. And, specifically, I want to know if the approximation adopted by Tsonis does have such a theoretical limitation that I have failed to discern.
Again, I am truly grateful for the information you have posted here to date and, therefore, I hope you will be so kind as to answer this additional post from me.
Richard
Richard,
I can’t speak for Tomas, the following information might be useful.
The following two papers might address your question. I’m not at all sure, however. I have not been successful in locating online copies.
Pielke Sr. has a paper on the subject:
And here’s another paper:
http://eapsweb.mit.edu/research/Lorenz/Irregularity_A_Fundamental_Property_1984.pdf
http://eapsweb.mit.edu/research/lorenz/Can_chaos_90.pdf
(second attempt to post so apologies is it repeats)
Richard,
I give my own interpretation of the situation. I think that it is in most respects in agreement with, what Tomas has written.
The atmospheric system (including interactions with oceans) is chaotic in the sense that very small differences in the initial conditions may develop to large differences at a later time. Many models of atmosphere are deterministic chaotic systems of the type studied by the chaos theory, but this applies only to the models, not to the real atmosphere. It is, however, possible that some results of chaos theory have similarities with, what happens in the real atmosphere and oceans. The papers of Tsonis and Swanson describe such an idea and test it with the empirical data.
Most people agree that there are oscillatory phenomena in the oceans in interaction with the atmosphere. Two possibilities for having such oscillations are:
1. traditional periodic motion (with possibly variable period). There could be some currents of that type in the oceans.
2. The atmospheric system might have two or more states which are favored in the way that the atmosphere stays typically a for a while in such a state before moving relatively rapidly to another state. This has a similarity with attractors of chaos theory, but these atmospheric states may be much more loosely defined than attractors of a well defined theory.
The idea of Tsonis and Swanson belongs to this second alternative. They propose furthermore that the transition from one state to another may be triggered by a suitable combination of phenomena identified by the oscillatory indexes, and that a particular combination is exceptionally interesting from the point of view of the climate.
Whether they are right or not, there are oscillations and understanding them would be a great step forward in understanding the climate systems and might lead to significantly better climate models. If there are separate preferred states of climate, the models and the climate itself might behave somewhat differently in each such state, but this is not likely a reason for having separate models for each preferred state.
===
As a separate comment. To me the justification that Tsonis and Swanson present for their results is not very convincing. They use quite little independent data and use it in such a way that some of their results might be consequences of their data manipulation rather than tell something new about the climate. The idea is still interesting and worth more study. Perhaps that will produce more convincing and useful results.
Richard
“Hence, for clarification, I ask if you are suggesting that variable inputs (e.g. altered radiative forcing) alter the system so require a different model if a chaotic model of the climate system is adopted.”
My admittedly long previous answer meant NO.
Pekka has said something similar in his comment.
I also must say that, indeed, with the way you formulate the question I possibly misunderstand what your problem is.
Let’s decompose your question.
if a chaotic model of the climate system is adopted.
That means for me that you adopted a field theoretical treatment of the spatio-temporal chaos which is the only correct way.
It can NOT be that you adopted a deterministic chaos (Lorenzian) theory because as I think having adequately explained, these 2 chaotic theories (models) are completely different and apply on completely different systems.
So already by writing only “chaotic model” your question is for me undefined and that’s why I explained that there is a HUGE difference between the temporal and spatio-temporal chaos.
OK so now having clarified this, I suppose that you wanted to say “spatio temporal chaotic model”.
From there the answer is then trivial.
As you are already using the right theory (model) then variable “inputs” as well the whole dynamics of all fields are just a detail inside of the theory. You don’t need to change anything.
However if you use a wrong theory (f.ex deterministic temporal chaos or equilibrium thermodynamics) then your results are either wrong everywhere or they may be close to (approximate) the correct dynamics somewhere and/or under some conditions and/or for some time.
However, Tsonis does not provide a new theory of chaos: he suggests an approximation based on existing chaos theory. My interest is in whether the use of such approximations is known to have limitations that prevent them having practical usefulness. And, specifically, I want to know if the approximation adopted by Tsonis does have such a theoretical limitation that I have failed to discern.
I have covered this issue in the first 3 bullets of my post that Judith attached to her leading post.
Tsonis doesn’t propose a “new” chaos theory because there are only 2 possible – the temporal which is known for 100 years and the spatio-temporal which will not be known for 100 years (or more).
So what he does is not an “approximation” of these 2 theories either.
Actually it has nothing to do with coupled dynamical systems and their synchronisation. If you want to see what has to do with these matters and what gives a MAJOR result, read please the paper I linked.
You will see that it has nothing to do with what Tsonis does.
Again and I apologize as I am repeating myself, what Tsonis does is statistics on 4 indexes and analyzes their cross correlations and something that he calls “phase” and which qualitatively represents the variation trend of each index (up , down , up then down etc).
There is nothing predictive at all. There is no model or equation which would allow to say “these 4 indexes are that today so they will be that tomorrow”.
All he says is “when the 4 indexes were in this particular mode, then something unusual happened”.
So it is approximation of nothing, it is just a kind of generalised correlation result expressed in a conditional way “if X does that then Y seems to change unusually”.
It doesn’t say WHAT Y does, it doesn’t say WHEN will this condition happend and it doesn’t say WHY Y does what it does.
That’s why I called it a minor result.
Pekka has written
They propose furthermore that the transition from one state to another may be triggered by a suitable combination of phenomena identified by the oscillatory indexes, and that a particular combination is exceptionally interesting from the point of view of the climate.
which is the same thing in other words.
And last, is this suggestion believable?
Well from my point of view based on non linear dynamics, it is on the right track.
Indeed spatio-temporal (and also temporal) chaos is typically a kingdom where complex coupled oscillatory behaviours reign.
Fast and brutal switches between different states are also a feature.
Unpredictability and unclear cause-effect relations are another.
Now, Is the choice of precisely these 4 indexes relevant in any way? Aren’t the results just artefacts or coincidences?
It is impossible to answer because precisely Tsonis doesn’t offer a quantitative predictive model.
I would say that as the particular configuration detected by Tsonis happened in 2001 again, if something unusual began to happen in 2001 and that will appear statistically significant in 5 or 10 years, then he might have found something.
But then others, more theoretically knowledgeable people would have to really explain why happens what is happening.
And it is only then that some predictive virtue would appear.
It is not the case today.
I must also add something that I consider extremely important.
If Tsonis is right in the sense that his observation holds, then ALL climate models without exception are completely wrong.
Indeed to my knowledge no single model has predicted that something significantly unusual will happen around 2001 what means that they all miss one of the major dynamical features of the system.
The part of the 2007 paper that demosntrates that are the Figures 4.
Tsonis has used a climate model (GFDL CM2) simulation for the 21st century with the CO2 “effect” of 2°C/century “removed”.
Figure3 does the same thing but for the preindustrial conditions.
Yet “removing” CO2 “effect” or using preindustrial conditions should give almost exactly the same behaviour for the synchronisation and “coupling” Tsonis parameters.
It does not, Figures 3 and 4 are very different.
This proves that if Tsonis is right then the models miss something essential.
Tomas Milanovic, Dan Hughes, HAS and Pekka Pirilä:
I write to thank each of you for your responses to my question. Just so you know, I had read all the items cited and linked in your responses, but I appreciate your efforts in providing them, and those efforts may encourage others to read the items, too.
I especially thank Tomas Milanovic and Pekka Pirilä for their answers.
I agree with Tomas Milanovic when he says;
“If Tsonis is right in the sense that his observation holds, then ALL climate models without exception are completely wrong.”
However, I suspect he and I part company when I say that I think the climate models are all “wrong” (for several reasons that are off-topic here).
Pekka Pirilä picked up on the reason for my interest when he wrote;
“2. The atmospheric system might have two or more states which are favored in the way that the atmosphere stays typically a for a while in such a state before moving relatively rapidly to another state. This has a similarity with attractors of chaos theory, but these atmospheric states may be much more loosely defined than attractors of a well defined theory.
The idea of Tsonis and Swanson belongs to this second alternative. They propose furthermore that the transition from one state to another may be triggered by a suitable combination of phenomena identified by the oscillatory indexes, and that a particular combination is exceptionally interesting from the point of view of the climate.”
I completely agree. Indeed, my repeated use of the word “approximation” when relating Tsonis’ argument to chaos theories pertains to what Pekka Pirilä says when he writes “these atmospheric states may be much more loosely defined than attractors of a well defined theory”.
However, it seems to me that the most cogent points in all the responses to my queries are provided by the final statements of Tomas Milanovic; i.e.
“Tsonis has used a climate model (GFDL CM2) simulation for the 21st century with the CO2 “effect” of 2°C/century “removed”.
Figure3 does the same thing but for the preindustrial conditions.
Yet “removing” CO2 “effect” or using preindustrial conditions should give almost exactly the same behaviour for the synchronisation and “coupling” Tsonis parameters.
It does not, Figures 3 and 4 are very different.
This proves that if Tsonis is right then the models miss something essential.”
This may not be damning of Tsonis’ ideas because – as I said – I lack any trust in the performance of the models. However, it does demonstrate that much more work is needed before Tsonis’ ideas can be accepted.
I remain hopeful that the concept of ‘climate states’ may resolve the problem that our present methods for climate prediction require quantification of all climate mechansisms both known and unknown together with a full undestanding of all their interactions. But this conversation has convinced me that much, much more work is required before that hope can be realised or rejected.
Again, I offer my sincere thanks to all of you.
Richard
The Earth’s climate is not random. But it sure is chaotic. How shall we model it? Can we model it?
From our Earthly point of view, it is impossible to determine the future effect of solar activity — the independent variable — on the Earth’s climate. And constructing an Earthly climate model that captures the holistic interaction of the myriad dependent variables is naturally daunting. But, that is what we must do to make long range climate change predictions.
Moreover, the mathematics of modeling what for us is a chaotic system is an earthbound mathematical challenge. The mathematics of chaos makes the selection of a starting point a key element in the outcome of the modeling process.
Let’s assume we had adequate data, knew how all of the variables were related, and could predict the future effects of the sun and the moon and the energy from the cosmos on the Earth’s climate. Still we must choose some starting point to initialize our Earthly climate model.
Do we start our model from the last interglacial warm period, the last ice age, or shall we start a couple of ice ages back? Do we go back a millennium or a thousand millenniums? And that is a problem: Using the mathematics of chaos, our Earthly climate model will give us a different long term prediction of the future for every initial starting point we choose.
The problem with long term climate prediction is that climate science is in its infancy. Some may believe we know more than we do and that is a problem too. “If we knew what it was we were doing, it would not be called research, would it?” (Albert Einstein)
On what basis has this differentiation been demonstrated for the Earth’s climate systems?
But do we not have but a single trajectory for the Earth’s climate. So in order to get the bundle of trajectories do we have only the models on which to rely? Then, on what basis can it be determined that the models are in fact reliable, high-fidelity representations of the Earth’s climate? On to what template, for example, could we map all the continuous equations of the models so as to demonstrate that those equations are in fact consistent with the mathematical properties / requirements of spatial-temporal chaotic response? Are there other options? The properties and characteristics for low-order temporal ( ODE ) chaotic response are well known and are used to construct generalizations of the Lorenz equations.
Corrections for incorrectos will be appreciated.
Thanks for all info.
oops, landed in the wrong place. Should follow Wagathon and Tomas.
Dan Hughes
But do we not have but a single trajectory for the Earth’s climate. So in order to get the bundle of trajectories do we have only the models on which to rely? Then, on what basis can it be determined that the models are in fact reliable, high-fidelity representations of the Earth’s climate?
These are relevant and important questions.
Yes, as we don’t know the governing equations and those we know (Navier Stokes) , we cannot solve, there are only numerical computer models for computing the bundles.
And of course there is no way to determine that the states computed have anything to do with some “solutions”.
This is because a numerical model does <b<NOT and will NEVER solve any dynamical equation because there is not enough computing power in the Universe to do so numerically.
It always boils down to the only interesting question which is whether there are some invariant statistical properties of the trajectories which would allow to predict statistically the distributions of future states.
And the answer on that question can’t be given by a computer either.
You know very well that it can be rigorously proven that any numerical solution even of the “simple” Lorenz ODE system is an artefact beyond a certain time T regardless of the accuracy of the solving alogorithm and of the (finite) power of the computer.
So what do you think that a computer can do about an infinitely more complex chaotic system like the climate?
Right, artefacts too.
Believable and possibly even consistent artefacts but artefacts all the same.
Do we start our model from the last interglacial warm period, the last ice age, or shall we start a couple of ice ages back? Do we go back a millennium or a thousand millenniums? And that is a problem:
No, that is not a big problem.
And if the system is chaotic AND ergodic then it is not a problem at all.
You must not confuse 1 single trajectory which is per definition unpredictible if the system is chaotic and a bundle of trajectories starting at an infinite number of places that you can arbitrarily choose.
In the second case this (infinite) number of trajectories will follow a nice probability distribution which will not depend on how you choose your bundle … if the system is ergodic.
F.ex the Lorenzian chaotic system is ergodic and you can perfectly predict the probability that he system will take a certain state in the future and this probability is independent of initial conditions.
Of course if the system is not ergodic, then you are in a deep …
There is also the problem that people take the common use of the tern “chaotic” to mean wild swings. An orbit can be chaotic while still closely following a particular path: it is chaotic only if you measure it closely rather than having unbounded oscillations. The dripping of a faucet can follow a chaotic attractor. Thus the impression from looking at the ice core record or any other of stability or chaos of the internal dynamics of the earth system reflects purely our visual intuition and not mathematics. Periods of wild fluctuation could be purely forced from outside and periods that appear stable, such as the Holocene, could follow a chaotic orbit. Or not. The visual impression provides no evidence.
It all has to do with the recession of ice. 2000 years ago most of the northern parts of the hemisphere were still covered with millions of tons of the stuff.
What happens when you run out of ice to melt?
You build up pressure from the added heat and lack of anything else to melt.
Following the depth of soil gives a general idea of the age of the growth in the area from the ice melt.
“What happens when you run out of ice to melt?”
Also, a dimunition of ice advection and an increased temperature of runoff into high latitude oceans. This can be time-dated by sediments in ocean floor cores and observing the shift in the ratio of calceriferous to aglutinated floraminifera.
Our planet hit that plateau in the 1970’s.
Physical Planetary changes started occuring to cool the planet.
Since you mentioned soil depth and age of growth as a means to put a date on final ice recession (2000 yrs ago, etc.) I thought it would be useful if I pointed to an additional way of examining this.. that’s all.
Hmm – I misspelled foraminfera.
This Winter already had record cold and snow, not predicted by the consensus climate models.
Many things affect climate, but on this Earth, there is nothing more stabilizing than ICE and WATER! When the Earth is cold and the Arctic Ocean is frozen, there is no source for Arctic Ocean Effect Snow and the ice on the Earth retreats, Albedo decreases and the equilibrium temperature of earth increases. When the Earth is warm and the Arctic Ocean is thawed, there is a source for Arctic Ocean Effect Snow and the ice on the Earth advances, Albedo increases and the equilibrium temperature of the earth decreases.
Look at the history of the temperature of the earth. Especially look at the ice core data for the past half eight hundred thousand years. It has gotten warm, time and time again. Every time that it got warm, it then got cold. When it got very warm it then got very cold. When it got a little warm it then got a little cold. Come on people! Look at the data. Warm melts Arctic ice. Exposed Arctic Water causes Ocean Effect Snow and that makes it cold. Ice and Albedo changes resulting from the waxing and waning of the Arctic Ice is what keeps the earth in our comfort zone.
Last September had a record, second lowest Arctic Sea Ice Extent since satellite data started keeping track and sure enough, it got cold and snowed.
Science did not do their homework and strictly focused on one area temperature and CO2. Failing to look at the past and follow the history of each individual area.
Grants went to anyone that agreed Global Warming without looking at the history to this point in time.
The climate cycles are well predicted by orbital mechanics. Much better then by CO2 models.
http://www.warwickhughes.com/agri/fairbridge_rhodes.pdf
Each 179 years the sun begins a new cycle of the epitrochoid
family of barycentric orbits; the most recent of these began in
1996 with Sunspot Cycle No. 23. Whilst the sun is in the
beginning phase of the new epitrochoid cycle, solar output of all
types is understood to decline and the climate on the earth cools.
The four previous epitrochoid cycles began in about 1790, 1620,
1430 and 1270 respectively. Solar activity diminished during the
first several decades of each of these epitrochoid cycles, resulting
in a cooling of the earth. For example, Europe between the 1620s
to the 1710s (the Maunder Minimum) was a time of intense cold,
causing extensive havoc and misery. The Thames froze each
winter and the alpine glaciers grew deep into the valleys. Between
the 1790s and 1820s (the Dalton Minimum) was also a time of
intense cold throughout Europe, with 1816 being considered one
of the coldest of the last 250 years.ix All of the cold intervals have
been well documented in both the standard climatological records
and the broader historical record (FAGAN, 2000).
http://www.crawfordperspectives.com/Fairbridge-ClimateandKeplerianPlanetaryDynamics.htm
http://www.canada.com/nationalpost/news/story.html?id=bfeddc8e-90d7-4f54-9ca7-1f56fadc7c2b
The concept of variability as is being employed in (some of the ) remarks is a sham.
‘Variability’ is being used as a proxy for ‘ignorance’.
The facts debunk global warming alarmism. I am not sure if the mathematics of chaos does. If it were that simple, then, e.g.,
“In 2001, Russian geologist Sergey Kotov used the mathematics of chaos to analyse the atmospheric temperature record of the past 4000 years from a Greenland ice core. Based on the pattern he recognised in the data, Kotov extrapolated cooling from 2000 to about 2030, followed by warming to the end of the century and 300 years of cooling thereafter.” ~Bob Carter
This equation has been source of irritation to number of solar scientists, led by Dr. Hathaway, followed by Dr. Svalgaard et al since its inception and publication in 2004.
http://www.vukcevic.talktalk.net/LFC2.htm
It predicted severe reduction in solar activity at the time when Dr. Hathaway, NASA’s top solar man, was predicting highest solar activity since the records began in 1620’s by Galileo.
Ergo: many scientists are top experts in describing known, but not so good in predicting complex events, and that goes for the climatologists too.
http://www.crawfordperspectives.com/Fairbridge-ClimateandKeplerianPlanetaryDynamics.htm
The chaotic orbits of the planets are not truly chaotic. Over time the very slight interaction between the obits sets up a resonance – they planets align their orbits on integer beat frequencies. This stabilizes the orbits (Laplace).
Is it a co-incidence that the solar cycle is beat frequency aligned with the orbits of the planets? Is it a co-incidence that many climate cycles on earth are similarly aligned?
Again consider a playground swing. If you randomly push on it (forcing) there will be very little movement, even it you do this repeatedly. However, if you push on it repeatedly in-phase with its natural frequency, the motion will be large.
Consider the oceans of the world. They have a natural frequency, given by their dimensions. This frequency will be different in different directions, depending on the dimensions of the oceans. A small, regular forcing due to orbital mechanics will discover these frequencies and amplify them over time (millennia) those that are in phase, leading to multi-decadal oscillations as we see for example in the PDO.
See post above:
http://judithcurry.com/2011/01/04/scenarios-2010-2040-part-iii-climate-shifts/#comment-28164
“In 2001, Russian geologist Sergey Kotov used the mathematics of chaos to analyse the atmospheric temperature record of the past 4000 years”
Those findings match closely with the predictions of R.W. Fairbridge and others from orbital mechanics. Like Milankovitch, their work was largely ridiculed/ignored by mainstream.
I would like to thank Dr Curry for her courage and integrity in speaking up in the scientific community.
http://www.griffith.edu.au/conference/ics2007/pdf/ICS176.pdf
Journal of Coastal Research, Special Issue 50, 2007
AN EARLY TEST OF THE SOLAR INERTIAL
MOTION HYPOTHESIS IS POSSIBLE DURING
2007 TO 2011
The solar inertial motion hypothesis predicts that the period
from about 2010 to 2040 will be one of relatively severe cold
throughout the world. The hypothesis predicts that the emergent
Sunspot Cycle No 24 will be quieter than Sunspot Cycle No 23
and just like Sunspot Cycle No 14, the weakest cycle in the last
100 years, which began in February, 1902 and ended in August,
1913
It will get cold only because time is up for an Ice Age.
Sun never did have an alarm clock.
You can expect to see weather you have never dreamed of as this planet has got quite the imagination where weather is concerned.
We all doubtless all could debate whether mathematics is a necessary or an optional tool in the field of climatology. The debate over the AGW hypotheses, I think, favors the view that without a ‘mathematical realism’ that is independent of the intuition and beliefs of researchers, all of the conclusions about global warming amount to nothing but dogma.
For me that is why the mathematics of McShane and Wyner should be thought of as the chalkboard squeak heard ’round the world. The M&S paper sets out to debunk yet again MBH98/99/08 (aka, the `hockey stick’ graph). M&S probably also wanted to inspire other statisticians to examine the ‘science’ of Mann and his sycophants.
And, that is where I believe mathematics of M&S and other inspired statisticians make their greatest contribution. These statisticians do not even have to take sides.
There are so many ways to kick this cat it should become a fun and favorite pastime for geeks. That global warming is a hoax is not even the issue because the math shouldn’t be a debate. Just pick up the chalk and outline the dead bodies.
The “Medium is the Message,” and that message is now very clear. There is absolutely no ‘signal’ in Mann’s proxy data. The ‘consensus’ is shot to hell. Global Warming Theory is essentially a ‘science’ without mathematics: sort of like the sun without the heat and vice versa.
AGW is not a problem it is a symptom of a problem. A claimed ‘consensus’ concerning global warming is really just the opinion of those who have been destroying the culture, society and science all along and all who sign on to the AGW bandwagon are really outing themselves as science pariahs.
“Until recently it was generally doubted that the solar variability in the 11 year sunspot cycle (SSC), as measured by satellites, has a significant influence on weather and climate variations. Bur several studies, both empirical and modelling, have in recent years pointed to probable and certain influences. Different observations indicate that the mean meridional circulation systems, like the Brewer-Dobson Circulation and the Hadley Circulation are influenced by the 11 year solar cycle. Today, there is general agreement that the direct influence of the changes in the Ultra Violet part of the spectrum (6% to 8% between solar maxima and solar minima) leads to more Ozone and warming in the upper stratosphere (around 60 km) in solar maxima. This lead to changes in the thermal gradients and thus in the wind systems, which in turn lead to changes in the vertical propagation of the planetary waves that drive the global circulation. ” ~Labitzke (2006)
A 45 year cycle for the AMO and a 60 year cycle for the PDO will repeat on a 180 year cycle (4:3 beat). This matches almost exactly the 179 year cycle of the sun around the barycenter. Co-incidence?
The only things that seem clear so far regarding a 30 year horizons in climate is that:
1) climate science is not going to offer meaningful predictions on what to expect
2) there is no climate crisis underway.
http://plasmaresources.com/ozwx/wilson/Syzygy.pdf
Reading Wilson I see we have a resonance on Earth, Venus, Jupiter every 44.8 years, very close to the AMO cycle. We have an inferior alignment every 22.4 years, very close to the Hale cycle. Add to that the resonance of Saturn and Jupiter every 60 years, very close to the PDO cycle. All co-incidence.
The major forcing for AGW is not CO2, it is funding.
You have identified the problem.
Government funding for climate studies in 2010-2040 will disappear if constitutional government (by the people) survives the climate scandal.
Much is made here and elsewhere of the significance of climate cycles as potential modifiers of long term trends, including trends attributed to anthropogenic greenhouse gas emissions. For me, this raises a general question: “which identified cycles reflect genuine mechanisms operating on a systematic and cyclic basis, and which reflect instead the coincidence of independent mechanisms that by chance mediate a climate variation of given length at one time, while unrelated mechanisms create the same cycle interval during a subsequent cycle?” In fact, could the coincidence of unrelated phenomena explain a series of even three or more cycles operating as though they obeyed a predetermined pattern?
It might seem counterintuitive to suggest that several cycles of very similar length (e.g., 60 +/- a few years) might be the result of nothing but coincidence, with each cycle determined by a different set of mechanisms. Two perhaps, but three or four? Doesn’t that strain the bounds of probability? My suggestion is that it may or may not, and the distinction depends on what process led to the identification of the particular cycle interval.
As a rough analogy, consider a series of ten thousand tosses of a “fair” coin, with the series divided into one thousand groups of ten tosses. What is the probability that in group 41 (to pick a group at random), each toss will turn up heads? By the definition of a fair coin, p = 1/2^10, which is 1/1024. It is certainly a highly improbable outcome, and if observed, might cause an observer to wonder whether the coin was actually “fair”. On the other hand, suppose that we ask the question: What is the probability that at least one of our 1000 groups will yield ten consecutive heads? The answer is easily calculated as 1 minus the probability that none will perform that way, which is 1 – 0.999^1000, and for which the probability is about 0.63 – i.e., there is a better than even chance that we will find at least one group of 10 heads. The probability would be even higher if we allowed the run of heads to occur independent of grouping.
In each scenario, we are considering an event due exclusively to chance. The difference between one positive outcome in 1024 and 63 in a hundred is that in the first instance, we specified the improbable occurrence we were seeking, while in the second, we examined the data to see whether they might harbor an improbable occurrence somewhere.
I will speculate that the same principle applies to climate cycles if we substitute the choice of cycle length for the choice of a particular group of ten tosses in my analogy. If, for example, we develop of reason, a priori, to believe that cycles of length 47 years should be discernible in the climate record, and subsequently find that to be the case, we will have uncovered evidence unlikely to be due to coincidence. On the other hand, if we merely scrutinize the record for any possible cycle length, and if in addition, we allow a margin of a few percent on either side of a detected signal, it would not be surprising to find more than a few consecutive cycles of very similar length even in the absence of a common mechanism for each interval.
An obvious example of an interval specified in advance, based on reasonable speculation, would be to seek 11-year terrestrial climate cycles based on the known 11-year solar cycles. When these are apparent, we can reasonably consider the correlation to represent more than coincidence. My question relates to the large variety of other cycles that have been identified. Which of them were based on a priori hypotheses, and which derived from scanning the climate record? Note that “after the fact” mechanisms to explain a result derived from scanning don’t count, because it is almost always possible to explain something retrospectively.
This is not to say that cycles derived from scanning are not “real” – if they are cyclic and repetitive, one can’t quarrel with the facts. The more relevant question is to the issue of coincidence versus a common mechanism for each repetition of the cycle. Note also that cycles derived from the scanning of records, while not probative of a common mechanism, can nevertheless serve as hypotheses for future testing. If one observes a quartet of 47 year cycles from scanning, it would be legitimate to ask, “what will happen over the next 47 years?” If the cycle is repeated, an underlying mechanism acquires some confirmation. If not, perhaps we were witnessing the principle that given enough opportunity, rare coincidences are inevitable.
I will be interested in the history of the climate cycles that have already been identified, as they relate to the above analysis.
I’ve been trying to figure out where to put this, so I’ll inflict it on Fred.
You say three of four is improbable. Everybody has a theory they pet too often and I’m no exception. I believe one of the 4 shifts is the cooling that commenced in 1940. I think that cooling had an anthropogenic contributor: WW2, which I suspect rolls up, in a “little strokes fell great oaks” type of way, to a climate altering event something like a volcanic eruption. This is likely a bad theory.
OT: Fred knows that I am a guitar player. I was watching the youtube video up above of all the metronomes and voila, I had a brainstorm: two pie rollers and a board will allow me to keep better time (I begrudgingly admit I needs a bass player.) Well, guitar okay; butt very sore.
Only planetary orbits could generate regular cycle (harmonic oscillations), but I do not think there is a direct link. Interesting you should pick on 47 year cycle. On this graph
http://www.vukcevic.talktalk.net/CETpr.htm
I picked max numbers from the Excel columns, didn’t bother to find out how many are of the same value, but as it happens they gave same average period within a decimal point. I would assume more of coincidence than some kind of precise synchronisation. I am not convinced that either PDO or AMO type oscillations are true cycles, more likely subject to some kind of undulation, as is the train of solar cycles. Even solar cycles length varies from under 9 up to 13 years, and there is no correlation between strength and length. http://www.vukcevic.talktalk.net/SSCsl.htm
You wrote- “Only planetary orbits could generate regular cycle”
If you think about it you know that is not correct regarding climate change. Without a bounding of the term “regular”, it would certainly be possible to have “periodic” cycles that seemed regular to the observer. An example would be changes in deep ocean flow patterns, or periodic warming or the ocean due to regular deep volcanic events.
My point is it is better not to jump to conclusions of “only” when dealing with any system where one does not yet understand all the variables, much less the specific impact of the driving variables
Blame my lack of ever taking a single lesson in the English language. It was meant in a sense that the planetary orbits are sufficiently regular (but not perfect) to produce ‘only possible harmonic oscillations’ within solar system if such natural oscillations are found, be it solar cycles, climatic effects say PDO or whatever. Only proper oscillations I can think of are tides (well moon and sun, not planets exactly, you know what I mean), and Jupiter’s radio emissions (moon again).
Fred,
I think there are two parts to the quandary you’ve presented.
First, the physical models of climate are sets of several dozen coupled differential equations for which there is no known analytical solution. In order to get to a point where solutions are tractable, several well-known and, hopefully, well-understood approximations are invoked. Therefore, any a priori information that would be gained from this theoretical perspective must be gleaned with the help of supercomputers, which I am positive you already know. These simulations of physical climate models must also be approximated given the algorithms and computational tools available to make the programs work in the first place. I think the community is still trying to understand exactly what they are missing from these approximations, but it’s likely they understand some of what they lose.
Prof. Curry would likely be able to fill in a bit on that note I’m sure.
So because models must be approximated before simulations are undertaken, which then necessitates more approximation, it’s to know what to expect a priori in terms of our understanding. These approximations cost the community some information, but hopefully that information was worth missing to begin with, as is the hope with any approximation.
Second, so much of what we know about climate is based on field observations. Climatic effects like the trade winds, jet streams and current systems of the planet laid the foundation for how we understand the physical processes in play and were all first thought up after observation. Even in fields as esoteric as my own, we cannot always head toward what we know from theory, but must be willing to ‘look’ for something new and then attempt to explain its inner workings after the data are sufficiently flushed out. I’m not sure if any of the oscillations discussed above were determined a prior and would even claim that the solar cycle was discovered via observation and not theoretical postulation. It might even have been Galileo.
Given that so much of what we know comes from ‘looking’, I don’t hold out much hope we’re going to be able to theorize our way out of this one. I love theoretical physics, but I also appreciate its limitations.
Maxwell – I agree with what you say about the need simply to “look”. My main point is that on those occasions when we observe something suggestive of a pattern modulating climate behavior without having sought it in advance, we should consider that outcome to have generated a hypothesis rather than a conclusion. Real patterns certainly exist, which is why hypotheses do get confirmed from time to time.
Fred Moolten:
You rightly say;
“Real patterns certainly exist, which is why hypotheses do get confirmed from time to time.”
But I would wish to add;
“And sometimes real patterns occur by chance but – also by chance – they fail to be sustained.”
I think the repeated experiences of economic modelling show the importance of my addition.
Richard
Fred,
‘My main point is that on those occasions when we observe something suggestive of a pattern modulating climate behavior without having sought it in advance, we should consider that outcome to have generated a hypothesis rather than a conclusion.’
I completely agree with this sentiment. I think it’s much more true than promoters of specific results in every field want their fellow researchers to think. If we’re discussing something that was been recently published or its replication status is awaiting, we should treat conclusions by authors and speakers with caution and skepticism.
That can be hard in the face of types of promotion present in all fields. But no one said science was easy.
Thanks, ge0050 for your comments above.
Of course the Sun influences Earth’s climate.
It is beyond comprehension to assume that Earth’s changing climate is immune to cyclic changes in the variable star that heats Earth and sustains our very lives.
The political basis for this unscientific assumption were explained in “Earth Heat Source – The Sun” [Energy and Environment 20 (2009) pp. 131-144] http://arxiv.org/pdf/0905.0704
With kind regards,
Oliver K. Manuel
Former NASA Principal
Investigator for Apollo
Oliver,
I realized long ago that the sun did not have an alarm clock to generate an Ice Age at a specific time on schedule. If it did ALL planets in this solar system would be in one at the same time. 10,000 year intervals to distance on our further planets would mean they would be constantly frozen from period to period.
So, it had to be an individual planetary event.
We cannot ignore the possibility that solar variability on a millenial time scale may be involved, as well as decadel and centinnial time scales.
“It is well known that the Sun plays the fundamental role as our energy source. However, it is still an open question what role the Sun plays in climate change. Direct measurements of the total (TSI) and the spectral (SSI) irradiance using satellite born radiometers show relatively small changes in accordance with the solar magnetic activity. To answer this question, we have (1) to understand the physical mechanisms responsible for the irradiance changes, (2) to reconstruct the solar variability over the past millennia, (3) to derive from this variability record, reliable estimates of the TSI and SSI changes, and (4) to determine the response of the climate system using appropriate models. As far as the second topic is concerned we can rely on a variety of solar activity proxies based on direct observations (sunspots, aa-index, aurorae). However, all of them are limited to periods ranging from a few decades to a few centuries. To date, the only proxy providing information about the solar variability on millennial time scales are cosmogenic radionuclides stored in natural archives such as ice cores. They clearly reveal that the Sun varies significantly on millennial time scales and most likely plays an important role in climate change.” (J. Beer, et al. Solar variability over the past several millennia. 11-Nov-2005)
It seems possible that some sort of regional climate shift occurred in 2007 affecting north west Europe (and probably eastern North America). Those interested in UK weather and climate used to talk about the modern climate (UK) of mild winters and BBQ summers. This changed in 2007, to be replaced by late summer “monsoon” and “arctic” winters, and is popularly termed the postmodern climate (UK). The synoptic symptoms of this seem to be the “flabby”, southerly and amplified jet stream resulting in winter the strongly negative AO and NAO.
The experience on the ground, is a stark change in our expected weather that is now “common knowledge” and that experts (MetO) are struggling to describe (exemplified in the transport winter resilience paper of the summer where they expressed a belief there was no pattern and chances of a harsh winter remain approx 1 in 20); was mentioned on the BBC news tonight as we recorded the coldest December on record, much talk now of whether there is a pattern and what the cause is.
I think there was a paper in the the early 2000’s with Schmidt and Hansen as co-authors where low solar activity in the maunder minimum was linked to negative NAO and AO causing the LIA anomaly. I suspect current solar minima is a factor but in concert with non-linear shifts between phase states.
To fully appreciate the depth of the immorality to which the secular, socialist government science authoritarians have descended, you have to understand that because ‘global warming’ per se does not really exist, not in the heavens nor in the seas, the AGW True Believers are now left hoping for weather-related catastrophes to punctuate their otherwise droll doomsday drumbeat of disastrous climate change calamity.
Understanding multidecadal variations requires an understanding of semi-annual variations. The following paper is seminal:
Le Mouël, J.-L.; Blanter, E.; Shnirman, M.; & Courtillot, V. (2010). Solar forcing of the semi-annual variation of length-of-day. Geophysical Research Letters 37, L15307. doi:10.1029/2010GL043185.
Detailed elaboration & extension:
Vaughan, P. (2010). Semi-Annual Solar-Terrestrial Power.
http://wattsupwiththat.com/2010/12/23/confirmation-of-solar-forcing-of-the-semi-annual-variation-of-length-of-day/
It’s a small step from there to a coarse understanding of multidecadal oscillations – e.g.:
a) http://wattsupwiththat.files.wordpress.com/2010/09/scl_northpacificsst.png
b) http://wattsupwiththat.files.wordpress.com/2010/09/scl_0-90n.png
c) http://wattsupwiththat.files.wordpress.com/2010/08/vaughn_lod_amo_sc.png
My current focus (which includes the development of new metrics) is on the nature of multi-index interannual relations. These relations are independent of neither solar nor lunisolar variables.
—
Cautionary Notes:
1)Those of you posting about planets need to be mindful of confounding, understanding clearly that all of the main solar system beats can be found in lunisolar variations.
2) The PDO does NOT represent average North Pacific SST – see here:
http://wattsupwiththat.files.wordpress.com/2010/09/northpacificsst_pdo.png
In climate blog discussions, MUCH clearer thinking is needed about what PDO is and what it is NOT.
—
The topic of this blog post is the most refreshing one I’ve seen in a long time – much appreciated.
Very interesting comment. Thanks Paul.
>The topic of this blog post is the most refreshing one I’ve seen in a long time – much appreciated.
Agree, and here’s the link to a fitting, recent ‘three-worlds’ shot for the occasion:
http://legault.perso.sfr.fr/eclipse110104_solar_transit.html
I should clarify that SCL’ = rate of change of solar cycle length, which can alternately be conceptualized as solar cycle deceleration. There is a ratcheting across terrestrial seasons, QBO phase, etc. which is not independent of variables such as summer-winter SOI contrast.
http://www.bccr.no/acdc/filer/242.i3yGAl.pdf
This is the best summary of knowledge on the AMO I could find.
this is VERY good, thanks much for this link
Looking at the 4 indices I notice that for the PDO, NAO and the AMO that post 1950 or so, the ocillations become more regular and more pronounced.
Could this be because the earlier data is less precise or because there is a pronounced CO2 forcing in the later part of the 20th century.
The NPGO is a bit more regualar but does not go back as far according to this http://horizon.ucsd.edu/miller/download/NPGO/NPGO.pdf
And it is certainly possible that increase CO2 is driving the NAO to stay in a mostly positive state according to http://www.pnas.org/content/98/23/12876.full
If it can be shown that CO2 does have an effect on the modes of natural variability then making any long term predicitions without taking that into account is folly.
Or how can one tell the difference between a linear trend with a noisy sinusoidal component superimposed and a step change function with a lot of noise.
Which is it and does it matter?
Before attempting to deal with Earthly climate idiosyncrasies caused by a holistic interplay of myriad natural phenomena beyond contemplation we should first understand our limitations. Before the modeling and mathematics begin let us all remember that fear of global warming is a phenomena of Western civilization. And accordingly we in the West have ample reason to question our collective sanity in the area of climate change.
Western Europe, NZ and Australia preceded America down the dead end road of anti-capitalism by way of the AGW pseudoscience/CO2 fearmongering detour from sanity. Thanks primarily to the steadfastness of George Bush, America was the last industrialized country of the West to be dragged down economically by the secular, socialist AGW eco-whackpot juggernaut of AGW Big government science authoritarianism.
And perhaps because it was last, America also was the last to have raised its collective IQ above the nihilism of the global warming alarmist herd of the Left. NZ is now openly antagonistic to the AGW hypothesis; and, there is no longer a consensus of opinion in Australia.
The UK has had its nose rubbed in the fraud and collusion that marks the AGW Climate-Man Cult. Canada has begun to realize that growing wheat may no longer be possible there. The insane trading schemes of the Scandinavians were thoroughly routed in Corruptenhagen. And, all of the anti-America nations of dead and dying old Europe have learned to live through frigid cold winters for years now and have begun to accept the reality of global cooling for perhaps many more decades to come.
Hi Judith.
I rarely stick my head up above the parapet of these technical threads as it will take little of the hurly burly to reveal my lack of scientific knowledge. However, I thought I would risk it briefly to say that I see little utility in temperature forecasting over, say, a 30 yr timescale because the likely change in average temperature is going to be small anyway (lets say a degree maximum in either direction if we include natural variability?). Rather, what we are really interested in are any adverse effects in regional weather – storms, drought, flooding etc. To my mind, we should be focussing on likely weather changes across a range of temperature changes. The results could be expressed for policy makers with appropriate uncertainties to permit a risk assessment to be performed. Thus the focus shifts from trying to calculate a temperature change to one of forecasting significant changes in the weather. That should be the focus IMHO, but sitting in UK where the Met Office has a particular poor record of forceasting more than a month or two ahead, a 30 year forecast may seem a tall order!
RobB:
You touch on a hobby horse of mine.
Climate is the range of weather that can be expected, but weather is what occurs.
So, what use is a climate prediction unless the limits of the range change significantly ?
For example, the UK has recently been disrupted by a severe cold spell that is not outside the range of previous climate. The effects of that cold spell could have been mitigated by a long-range weather forecast but a climate forecast would not have predicted it.
Similarly for all other severe weather events, not only severe temperature events. For example, a region prone to hurricanes needs to be able to cope with hurricanes. If a region has systems to cope then a doubling of hurricane frequency is a minor nuisance compared to the fact that hurricanes happen. But if the region has no systems to cope then it will be unable to recover from any hurricane before the next one strikes whether or not hurricane frequency doubles.
We need long range weather forecasts. But is there any use for climate forecasts (or predictions, or ‘projections’) ?
Richard
Gary Moran | January 5, 2011 at 7:00 pm | Reply
I think there was a paper in the the early 2000′s with Schmidt and Hansen as co-authors where low solar activity in the maunder minimum was linked to negative NAO and AO causing the LIA anomaly.
That would be an interesting paper to read…..
Well, read it:
http://pubs.giss.nasa.gov/docs/2001/2001_Shindell_etal_1.pdf
Concluding sentences: ”
These results provide evidence that relatively small solar forcing may play a significant role in century-scale NH winter climate
change. This suggests that colder winter temperatures over the NH continents during portions of the 15th through the 17th centuries
(sometimes called the Little Ice Age) and
warmer temperatures during the 12th through
14th centuries (the putative Medieval Warm
Period) may have been influenced by longterm solar variations”
Don’t you just love “sometimes called” and “putative”?
LOL
So, I think you are saying that the use of language like “sometimes called” and “putative,” aka, inferring “maybe yes-maybe no,” leaves us giving tremendous credit for their presentation of the narrowest view of an already narrow view of an otherwise grim reality, given the hard hand concerning the view of people from whom they must seek approval, in an extraordinary effort to provide a report that would not automatically require a willing suspension of disbelief, e.g., an example of the Left having turned English into a liars language, which is why the scientific method is so easily abandoned — i.e., because where there is political correctness there is no respect for truth for its own sake — and, that is why the West is dying.
What’s the truth?
Was the Little Ice Age an ice age?
An “ice age” or, more precisely, “glacial age” is a generic geological period of long-term reduction in the temperature of the Earth’s surface and atmosphere, resulting in the presence or expansion of continental ice sheets, polar ice sheets and alpine glaciers.
Sounds like not. It’s sort of like the technical virgin. Some often say “the ‘so-called’ wore white, scandalous!”
Is the period of warming commonly called the Medieval Warm Period, and less commonly called the Medieval Warm Epoch or the Medieval Climate Optimum or the Medieval Climate Anomaly? Perhaps the more names you know for the medieval warming years, the more scientific papers you will find.
Their language is actually pretty accurate.
<>
If polar bears really do fall from the skies as a result of less than 0.6 °C of global warming per century of global warming since 1870, then we say so. Otherwise, we should not continue to reward deniers of facts — like Michael Mann for example who only recently admitted that, ‘Yes, Virginia there really was a MWP’ — by permitting the language to be abused to the point that meaningful discussion of simple facts is impossible. It’s bad enough that there is no accountability for instances of good old fashioned fraud.
Thank you for that. Doing a search for Medieval Warm Epoch, I found a paper co-authored only recently by Michael E. Mann in 1999.
So where are we? Tsonis et al 2007 is not definitive but it does indicate that natural events, excluding solar variation, have a strong impact on climate.
Solar variation from maximum to minimum of about .1% is unlikely to cause dramatic climate change, excluding 47K and approximately 100K year cycles that seem to have just enough umph to trigger a lower quasi stable temperature range.
The major oscillations can synchronize (correlate) for relatively short periods of time, but if past temperature reconstructions are to be believed, century long climate shifts are probable without AGW. (Don’t go dragging out Lean 2000, “Climate was solar driven until 1950 when AGW took over.” is nonsense based on a study that has been revised several times.)
Climate shifts do occur though their duration, magnitude and direction are not reasonably predictable. Warm phases, assuming warm means high latitudes, appear to have greater impact on average global temperature than the reverse. (The poles can warm a bunch while the equator can’t.)
If you assume that the small variation in solar TSI can drive climate then a similar change in convection can also drive climate (I may be wrong here but the average hurricane releases about 600 trillion Watts per day. More frequent and more powerful hurricanes would mean more heat loss, quite possibly on the order of a 0.1 % change in TSI.) The solar TSI variation from average is about 0.5 watts per meter squared TOA. Oh, the 600 trillion Watts is based solely on precipitation, wind energy, tidal energy and increased solar reflection by Gulf of Mexico size storms are a couple orders of magnitude lower.
Question: The MWP was just regional and assumed to have had little impact on global average temperature. What impact on global temperature would a red blob over Greenland, eastern North America and Western Europe have if it persisted say 25 years?
. What impact on global temperature would a red blob over Greenland, eastern North America and Western Europe have if it persisted say 25 years?
Wouldn’t that depend on what was happening on the rest of the globe for those 25 years?
. What impact on global temperature would a red blob over Greenland, eastern North America and Western Europe have if it persisted say 25 years?
“Wouldn’t that depend on what was happening on the rest of the globe for those 25 years?”
That is the question. If that area of the high latitudes warmed some other areas would cool. The oscillations are just moving heat around. Unless something else changes because of the shift, there should be no change in the global temperature average. Would the albedo change around Greenland be more significant than the precipitation changes there and elsewhere? Could some of the warming or cooling be an artifact of the method of determining global average temperature?
I think is has to do with how the weather patterns change, rather than measuring hots versus cold spots. The neg AO and NAO are bending the jetstream which allows cold arctic air to stream south. The blocking high over Greenland being a major cause. Greenland will be warmer but most of the southern hemisphere should experience lower temperatures this winter.
That should read “northern hemisphere”…although we are having a poor southern summer so far here in Australia. The decade long drought has been replaced with record floods. Penny Wong’s mantra of the Murray Darling Basin seems to have disappeared.
I too had been contemplating your rhetorical question “So where are we?”
Remembering that we are looking to forecast climate in 30 years time how much does all this help and how?
One of the interesting things I’ve taken from the thread is how preoccupied we are about complexity and what we can or cannot predict out of it.
For my money I’d be interested in a someone easier problem, namely if our mission is predicting climate in 30 years time how much of this complexity can we ignore (or at least at what scale can we ignore it). For me the starting point is to find that lens through which the complexity melts away.
The more I’ve dug around in just a small bit of the literature dealing with datamining and causality in atmospheric systems, the more the biblical injunction to “lift thine eyes to heavens” (or at least the top of the atmosphere) is apposite. Only once you leave the atmosphere behind do you begin to see something akin to the hard smooth billiard ball of Newtonian mechanics.
From this point of view all the complexity of the atmosphere is reduced by nature down to a small number of key phenomena dealing with how the globe (including atmosphere) absorbs, reflects and radiates energy (and how this changes in time and across the globes surface).
It’s this system I’d be looking to forecast 30 years ahead with (even give limited time periods of direct measurement). I’d only add complexity where needed to better describe those phenomena. No doubt this has been done in some shape or form so poking around here seems the next thing to do.
Some may protest that describing what’s happening at the top of the atmosphere ain’t good enough for people who live on the surface of the planet. A response to think about is if the atmosphere is such a complex beast that might be as close as we are ever going to get. Only at the boundary does the system get predictable (as opposed to describable).
Possibly what happens up there might be climate while what we get down here is weather.
That’s “a somewhat easier problem” in para 4. I need that editor.
“Hence, the big question: if in the Middle Holocene, when dung and firewood were the only fuels used by Mankind and the world population was at least two orders of magnitude smaller than today’s, there were temperatures and sea levels considerably higher than the present ones; if the atmospheric warming at the end of the Younger Dryas 11,600 years ago and the sea level rise between 18,000 and 6,000 years ago were much faster than the observed since the 19th century; so, where are the evidences that would allow us to point to the “human fingerprint” in the small variations of the latest 140 years, against the background of the much wider and faster natural oscillations of the historical and geological past?
The answer is: there aren’t any (for evidences I mean, obviously, hard facts observed in the physical world, not mathematical concoctions extracted from the climate models).” ~Geraldo Luís Lino
One of the themes of this post is the distinction between climate oscillations and long term trends – in particular, the modulation of an anthropogenic warming trend, driven by greenhouse gases, by natural climate variations of a cyclic or quasi-cyclic oscillatory nature, not to mention variations in other anthropogenic forcings such as those mediated by aerosols.
An important element in disentangling these phenomena is interval length – high frequency cycles are relatively easy to separate out (e.g., ENSO events), and on the other extreme, variations of extreme length (tens of thousands of years or more) are unlikely to contaminate the signal of centennial scale trends. The most vexing oscillations are those of length not radically different from the interval over which a trend is evaluated.
Many of the analytical methods for accomplishing the separation are somewhat arbitrary. An interesting approach designed to be tailored to a given dataset rather than applied in formulaic fashiong is described in
Trends and Detrending . In part, it entails the converse of standard detrending operations designed to identify cycles, in that it seeks to correct for the oscillations in order to identify and quantify a trend within the same data.
The findings of McShane and Wyner were that, for example, there is absolutely no ‘signal’ whatsoever in Mann’s proxy data. So, how can you tease-out humanity’s contribution to a non-existent warming trend?
Just looking at the results of the analysis, my conclusion is that it helps very little in determining the strength of anthropogenic influence. The method produces multidecadal oscillations, which are a possible interpretation, but the shape and strength of the last oscillatory phase is largely undetermined. The same problem is present in the last parts of the two following components.
When the last part of the data is not bound by some model, many different combinations fit equally well in particular, if we consider it possible that this part of the data may behave differently from earlier years.
In concordance with the authors, I interpret figures 2, 3, 6, and 7 to strongly suggest a long term warming trend over the past century, together with a separate oscillation of about 65 years. C6 (the trend statistic) is statistically significant. The paper does not address the cause of the warming, but both the trend and its magnitude are consistent with climatology that attributes much of the warming (particularly after mid-century) to anthropogenic greenhouse gas emissions. That attribution has been extensively discussed elsewhere, and for that reason, my comment was focused on the question of how to define, identify, and quantitate trends, and disitnguish them from internal oscillations. The extent of the anthropogenic contribution is not likely to cease to be argued, but as the authors point out, the concepts addressed in the paper deserve attention separate from the question of causality.
Fred,
My problems concern the final period of, say, 20 years, which is essential for estimating the strength of AGW. When the phase of every mode is free at the end of the period in 2003, using cubic splines is very weakly constrained at each step. Looking at Fig. 2, C1 oscillates strongly over the last 10 years. A different choice in determining C1 would have influenced strongly C2 (which is now almost flat) and C3, which ends in opposite phase than C1. The components C4 and c5 could also be quite different over the last 20 years. This part of the solution is not at all model independent, but very much affected by the details of the procedure.
My claims are not based on performing an alternative analysis, but I am confident in claiming that there is a strong model dependence in the results of last 20 years.
It would be interesting to see, how much the results change with the addition of years 2004-2010.
It seems to me that you have identified uncertainties about the oscillations that would be critical if they were the focus of the analysis. The trend statistic (C6) doesn’t seem to be equally vulnerable. Regarding a point made below by Tomas Milanovic, I’m not sure which figures in the paper he is referring to as inadequate to document the conclusions. However, I would agree that the statistics by themselves won’t identify a process or physical mechanism, or determine future behavior – at best, they can generate a hypothesis. Mechanisms are the province of the scientists. The authors agree.
Fred,
My main point is that far from the ends of the interval the trend is pretty well determined by a loose requirement of smoothness, but close to the ends the details of implementing this requirement affect significantly the result, which could be expressed as the annual the rate of increase.
In the AGW we know that the emissions have grown rapidly over last few decennia. Therefore the signal of most interest is the annual rate of increase over a relatively short recent interval or essentially the rate of increase of the trend component at the end of the period. This is the number about which the analysis is least reliable of everything that it tells about the trend.
Identifying trends does need some work if regional forecasts on a decade timescale is going to be anything but another joke. Give it some cool name like quasi-trend indicator, but if it takes 15 years to determine there is a trend you are approaching the next climate shift trend before you can say you are in a trend. A scientific wild ass guess would be more timely than statistical correctness.
So let us nickname Tsonis et al. 2007, SWAG Climate Shift Prediction Method. The 2020 prediction in the paper makes more sense than the GCM’s. Heck, the 3 degree C for doubling is based on a compromise, not math. Hanson estimated 4, what’s his name before Hanson estimated 2 and as if by magic a third paper very eloquently proved that 3 is the average of 2 and 4,so therefore; 3 degrees C is the climate sensitivity of a doubling of carbon dioxide. Seriously, I don’t have a link to the paper, but I started becoming skeptical after reading it. It was actually linked on RC along with the Lean 2000 TSI reconstruction that “proved” AGW took charge after circa 1950.
So since Judith is looking for some insight into predicting regional climate for the next decade, how would she convince policy makers that the SWAG method has greater predictive potential than GCM’s?
Manabe was what’s his name. Charney was the magician. Subsequent papers were remarkably close to the magic numbers. Ain’t statistics wonderful! If you have a statistical tool box and a number in mind you can normally find it. Arrhenius started with with 4 or 5(what he was looking for) and a decade later settled with 1.6 C. My money is on the famous dead guy.
Fig. 6 has some undesirable properties. Repeat narrow-boxcar smoothing with iterative end-correction is a sensible way to isolate a nonlinear trend. If one knows the dominant high-frequency modes (e.g. the day & the year for many terrestrial variables), they can be used to set the boxcar width. If not, repeat 3-point smoothing with iterative end-correction is a sensible starting point for a pilot investigation.
When the last part of the data is not bound by some model, many different combinations fit equally well in particular, if we consider it possible that this part of the data may behave differently from earlier years.
I completely agree.
D.Koutsoyianis has published several papers about these issues.
It is absolutely impossible to distinguish by only statistical methods whether a time series is generated by a stationary process with trend or a non stationary process.
Statistics will find an equally good past fit for both hypothesis but will then give 2 radically different predictions.
So the 2 hypothesis correspond to 2 completely different physical processes.
Statistics alone are helpless to answer these physical questions, one needs to have a physical understanding of what is really happening.
Personnaly I find that statistical methods are abused in climate studies and people try to make them answer questions on which they are unable to answer.
“Personally I find that statistical methods are abused in climate studies and people try to make them answer questions on which they are unable to answer.”
I am not sure if they are abusing statistics or just being abused by statistics.
Meanwhile, there has been a cooling trend for a decade, the oceans have been cooling since 2002 with no end to the cooling in sight, according to Dr. Spencer, and as Dr. Pielke, Senior observed, in a period when the oceans are cooling there is no warming during that period.
I’ve developed multiscale multi-index summaries that are more intuitively digestible (for a layman audience), but since I’m presently a long way from having a write-up, I’ll share this alternative to Tsonis, Swanson, & Kravtsov (2007):
Schwing, F.B.; Jiang, J.; & Mendelssohn, R. (2003). Coherency of multi-scale abrupt changes between the NAO, NPI, and PDO. Geophysical Research Letters 30(7), 1406. doi:10.1029/2002GL016535.
http://www.spaceweather.ac.cn/publication/jgrs/2003/Geophysical_Research_Letters/2002GL016535.pdf
The modeling community is left appearing to have skipped thorough data exploration as a prerequisite to sensible modeling.
Hi Paul,
When and if it becomes possible to show that there is a good reason for the oceanic oscillations (as suggested here http://www.vukcevic.talktalk.net/NPG.htm )
then some in the plethora of indices may be given more meaningful interpretation, while fate of others may not be that certain. A note of caution to software modellers; if such time does come, then as with the volcano eruptions, it may not be possible to factor them in.
vukcevic, it is the distribution of mass, including that of water in its various states (including ice that depresses continents and clouds that control spatiotemporal insolation [not to be confused with irradiance]), that demands our attention. All it takes is strategic changes of pressure patterns to set up rearranging deflection.
Suggested: Include the following variables in multiscale correlation studies:
http://omniweb.gsfc.nasa.gov/
For example, at interannual timescales, nonrandom relations of some of these variables with AO & NPI are easily detected. Those with NPI are particularly striking.
I continue to contemplate how/why it is that the mainstream has overlooked these nonrandom relations. Excessive focus on grain, coupled with insufficient awareness of extent, has produced exploratory stagnation. Lack of application of complex numbers, coupled with lack of attention to higher derivatives, has exacerbated the innovative gridlock.
Please be wary of (a) confounding and (b) Simpson’s Paradox (particularly the spatiotemporal version) in your studies of geomagnetism.
Those who are familiar with the Russian literature (e.g. Barkin, Sidoreknov, etc.) and have understood the recent French work do not need to imagine mysterious forces and mythically stationary “60 year” cycles.
It may take 5 years (or more) before the mainstream fully appreciates the implications of the recent French scholarship. Much longer delays are not uncommon following seminal publications.
” It is actually just an average of the cross correlation coefficients between the 4 indexes for a gliding window 11 years wide with t in the middle of the window.”
OH no ! Not the firggin’ runny means again. Doesn’t any one in climate science understand that this is an awful , error-inducing filter?
http://oi41.tinypic.com/nevxon.jpg
http://i44.tinypic.com/351v6a1.png
To make it worse they chose a window size of 11y that corresponds to a spectral peak right across climate history. This the worst thing to do with a runny mean, that you should not be using in the first place.
Any influence of the 11y cycle will probably get inverted!
I think the basic idea is very intersting, shame they can’t find a proper filter, their phase relationships might look a lot better if they did.
Thanks to Thomas Milanovic for that excellent explanation and correction of the terminology. It all makes a lot more sense in those terms.