Guest Post by Willis Eschenbach
The recent post here on WUWT about the Pacific Decadal Oscillation (PDO) has a lot of folks claiming that the PDO is useful for predicting the future of the climate … I don’t think so myself, and this post is about why I don’t think the PDO predicts the climate in other than a general way. Let me talk a bit about what the PDO is, what it does, and how we measure it.
First, what is the PDO when it’s at home? It is a phenomenon which manifests itself as a swing between a “cold phase” and a “warm phase”. This swing seems to occur about every thirty or forty years. The changeover from one phase to the other was first noticed in 1976, when it was called the “Great Pacific Climate Shift”. The existence of the PDO itself, curiously, was first noticed in its effects on the salmon catches of the Pacific Northwest.
Figure 1. The phases of the PDO, showing the typical winds and temperatures associated with its two phases. The color scale shows the temperature anomalies in degrees C.
Figure 1 is a clear physical depiction of the two opposite ends of the PDO swing, based on how it manifests itself in terms of surface temperatures and winds. But to me that’s not the valuable definition. The valuable definition is a functional definition, based on what the PDO does rather than on how it manifests itself. In other words, a definition based on the effect that the PDO has on the functioning of the climate as a whole.
A Functional Definition of the PDO
To understand what the PDO is doing, you first need to understand how the planet keeps from overheating. The tropics doesn’t radiate all the heat it receives. If it did the tropics would be much, much hotter than it is. Instead, the planet keeps cool by constantly moving huge, almost unimaginably large amounts of heat from the tropics to the poles. At the poles, that heat is radiated back to space.
The transportation of the heat from the equator to the poles is done by both the atmosphere and the ocean. The atmosphere can move and respond quickly, so it controls the shorter-term variations in the poleward transport. However, the ocean can carry much more heat than the atmosphere, so it is doing the slower heavy lifting.
The heat is transported by the ocean to the poles in a couple of ways. One is that because the surface waters of the tropical oceans are warm, they expand. As a result, there is a permanent gravitational gradient from the tropics to the poles, and a corresponding slow movement of water following that gradient.
The major movement of heat by the ocean, however, is not gravitationally driven. It is the millions of tonnes of warm tropical Pacific water pumped to the poles by the alternation of the El Nino and La Nina conditions. I described in “The Tao of El Nino” http://wattsupwiththat.com/2013/01/28/the-tao-of-el-nino/ how this pump works. Briefly, the Nino/Nina alteration periodically pushes a huge mass of warm water westwards. At the western edge of the Pacific Ocean, the warm water splits, and moves polewards along the Asian and Australian coasts. Finally, at the poles it radiates its heat to space. Figure 1a from my previous post shows the action of the pump.
Figure 1a. 3D section of the Pacific Ocean looking westward alone the equator. Each 3D section covers the area eight degrees north and south of the equator, from 137° East (far end) to 95° West (near end), and down to 500 metres depth. Click on image for larger size.
Figure 1a shows a stretch of the top layer of the Pacific Ocean. It runs along the Equator all the way across the Pacific, from South America (near end of illustration) to Asia (far end of illustration). During the El Nino half of the pumping cycle, which corresponds to the input stroke of a pump, warm water builds up along the Equator as shown in the left 3D section. Then in the La Nina part of the cycle, the pressure stroke, that water is physically moved by the wind across the entire Pacific, where it splits and moves toward both poles.
Now, this El Nino/La Nina pumping action is not a simple feedback in any sense. It is a complex governing mechanism which kicks in periodically to remove excess heat from the tropical Pacific to the poles. As such it exerts control over the long-term energy content of the planet.
So here’s the first oddity about the PDO. The two alternate states of the PDO look very much like the two alternate states of El Nino/La Nina. In both, heat builds up in the eastern tropical Pacific, while the poles are cool. And in both, the alternate situation is where the heat is moved to the poles, residual warmth remains along the coasts of Asia and Australia, and the eastern tropical Pacific is cool.
This is an important observation because in addition to regulating the amount of incoming energy through the timing of the onset of the clouds and thunderstorms, the planet regulates its heat content by varying the rate of “throughput”. I am using “throughput” to mean the rate at which heat is moved from the equator to the poles. When the movement of heat to the poles slows, heat builds up. And when that pole-bound movement speeds up, the heat content of the planet is reduced through increased heat loss at the poles.
The rate of throughput of heat from the tropics to the poles is controlled at different time scales by different phenomena.
On an hourly/daily scale, the variations in the amount of heat moved are all in the atmospheric part of the system. The timing and amount of thunderstorms directly regulate the amount of heat leaving the surface to join the Hadley circulation to the poles.
On an inter annual basis, the throughput is regulated by the El Nino/La Nina pump.
And finally, on a decadal basis, the throughput is regulated by the PDO.
So as a functional definition, I would say that the PDO is a another part of the complex system which controls the planetary heat content. It is a rhythmic shift in the strength and location of the Pacific currents which alternately impedes or aids the flow of heat to the poles.
The Climate Effects of the PDO
As you might imagine, the state of the PDO has a huge effect on the climate, particularly in the nearby regions. The climate of Alaska, for example, is hugely influenced by the state of the PDO.
Nor is this the only effect. The PDO seems to move in some sense in phase with global temperatures. Since the Pacific covers about half the planet, this should come as no surprise.
How We Measure the PDO
The PDO was first measured in salmon catches. Historical records in British Columbia up in Canada showed a clear cyclical pattern … and since then, a number of other ways to measure the PDO have been created. Current usage seems to favor either the detrended North Pacific temperature, or alternately using the first “principle component” (PC) of that temperature. Since the first PC of a slowly trending time series is approximately the detrended series itself, these are quite similar.
To measure the PDO or the El Nino, I don’t like these types of temperature-based indices. For both theoretical and practical reasons, I prefer pressure-based indices.
The practical reason is that we don’t have much information about the North Pacific historical water temperatures. Sure, we have the output of the computer reanalysis models, but that’s computer model output based on very fragmentary input, and not data. As a result it’s hard to take a long-term look at the PDO using temperatures, which is important when a full cycle lasts sixty years or so.
The same issue doesn’t apply as much to pressure-based indices. The big difference is that the pressure field changes much more gradually than the temperature field at all spatial scales. If you move a thermometer a hundred metres you can get a very different temperature. That is not true about a barometer, you get the same pressure anywhere in town. Indeed, they don’t suffer from many of the problems in temperature based indices, in part because the instruments used to measure pressure are not subject to the micro-climate issues that bedevil temperature records. This means that you can directly compare say the pressure in Darwin and the pressure in Tahiti. So those two datasets are used to construct the pressure-based Southern Ocean Index.
As a result, it is much easier to construct an accurate estimate of the entire pressure field from say a few hundred stations than it is to estimate the temperature field. Indeed, this kind of estimation has been used for many decades before computers to construct the weather maps showing the high and low-pressure areas. This is because the surface pressure field, unlike the surface temperature field, is smooth and relatively computable from scattered ground stations.
The theoretical reason I don’t like temperature based indices is that people always want to subtract them from the global temperature for various reasons. I see this done all the time with temperature-based El Nino indices. It all seems too incestuous to me, removing temperature of the part from temperature of the whole.
The final theoretical reason I prefer pressure-based indices is that they integrate the data from a large area. For example, the Southern Ocean Index (which measures pressures in the Southern Hemisphere) reflects conditions all the way from Australia to Tahiti.
In any case, Figure 2 shows a typical PDO index. This is the one maintained by the Japanese at JISAO. It is temperature based.
Figure 2. The temperature-based JISAO Pacific Decadal Oscillation Index. It is calculated as the leading principal component of the North Pacific sea surface temperature.
As I mentioned, for the PDO, I much prefer pressure based indices. Here is the record of one of the pressure-based indices, the “North Pacific Index”. The information page says:
The North Pacific (NP) Index is the area-weighted sea level pressure over the region 30°N-65°N, 160°E-140°W.
Figure 3. The pressure-based North Pacific Index, calculated as detailed above.
As you can see, the sense of the NP Index is opposite to the sense of the JISAO PDO Index. They’ve indicated this in Figure 3 by putting the red (for warm) below the line and the blue (for cool) above the line, but this doesn’t matter, it’s just how the index is constructed. It moves roughly in parallel (after inversion) with the JISAO PDO Index shown in Figure 2.
Now, for me, both of those charts are totally uninteresting. Why? Because they don’t tell me when the regime changes. I mean, in Figure 3, was there some kind of reversal around 1990? 1950? It’s all a jumble, with no clear switch from one regime to the other.
To answer these types of questions, I’ve become accustomed to using a procedure that other folks don’t seem to utilize much. I’ve taken some grief for using it here on WUWT, but to me it is an invaluable procedure.
This is to look at the cumulative total of the index in question. A “cumulative total” is what we get when we start with the first value, and then add each succeeding value to the previous total. Why use the cumulative total of an index? Figure 4 shows why:
Figure 4. Cumulative North Pacific Index (inverted). The data have been normalized, so the units are standard deviations. The cumulative index is detrended, see Appendix for details.
I’ve inverted the cumulative NPI to make it run the same direction as the temperature. You can see the advantage of using the cumulative total of the index—it lays bare the timing of the fundamental shifts in the system.
Now, looking at the Pacific Decadal Oscillation in this way makes it a few things clear.
First, it establishes that there are two distinct states of the PDO. It’s either going up or going down.
In addition, it shows that the shift from one to the other is clearly threshold-based. Until a certain (unknown) threshold condition is reached, there is no sign of any change in the regime, and the motion up or down continues unabated.
But once that (unknown) threshold is passed, the entire direction of motion changes. Not only that, but the turnaround time is remarkably short. After only a few months in each case the other direction is established.
Finally, to me this shows the clear fingerprint of a governing mechanism. You can see the effects of the unknown “thermostat” switching the system from one state to the other.
RECAP
I’ve hypothesized that the Pacific Decadal Oscillation (PDO) is another one of the complex interlocking emergent mechanisms which regulate the temperature and the heat content of the climate system. They do this in part by regulating the “throughput”, the speed and volume of the movement of heat from the tropics to the poles via the atmosphere and the oceans.
These emergent mechanisms operate at a variety of spatial and temporal scales. At the small end, the scales are on the order of minutes and hundreds of metres for something like a dust devil (cooling the surface by moving heat skywards and eventually polewards).
On a daily scale, the tropical thunderstorms form the main driving force for the Hadley atmospheric circulation that moves heat polewards. Of course, the hotter the tropics get, the more thunderstorms form, and the more heat is moved polewards, keeping the tropical temperature relatively constant … quite convenient, no?
On an inter-annual scale, when heat builds up in the tropical Pacific, once it reaches a certain threshold the El Nino/La Nina alteration pumps a huge amount of warm water rapidly (months) to the poles.
Finally, on a decadal scale, the entire North Pacific Ocean reorganizes itself in some as-yet unknown fashion to either aid or impede the flow of heat from the tropics to the poles.
CONCLUSION
So … can the PDO help us to forecast the temperature? Hard to tell. It is sooo tempting to say yes … but the problem is, we simply don’t know. We don’t know what the threshold is which is passed at the warm end of the scale in Figure 4 to turn the PDO back downwards. We also don’t know what the other threshold is at the cool end that re-establishes the previous regime anew. Not only do we not know the threshold, we don’t know the domain of the threshold, although obviously it involves temperatures … but which temperatures where, and what else is involved?
And most importantly, we don’t know what the physical mechanisms involved in the shift might be. My speculation, and it is only that, is that there is some rapid and fundamental shift in the pattern of the currents carrying the heat polewards. The climate system is constantly evolving and reorganizing in response to changing conditions.
As a result, it makes perfect sense and is in accordance with the Constructal Law that when the sea temperature gradient from the tropics to the poles gets steep enough, the ocean currents will re-organize in a manner that increases the polewards heat flow. Conversely, when enough heat is moved polewards and the tropics-to-poles heat gradient decreases, the currents will return to their previous configuration.
But exactly what those reversal thresholds might be, and when we will strike the next one, remains unknown.
HOWEVER … all is not lost. The reversals in the state of the PDO can be definitively established in Figure 4. They occurred in 1923, 1945, 1976, and 2005. One thing that we do NOT see in the record is any reversal shorter than 22 years (except a two-year reversal 1988-1990) … and we’re about eight years into this one. So acting on way scanty information (only three intervals, with time between reversals of 22, 31, and 29 years), my educated guess would be that we will have this state of the PDO for another decade or two. I’ve sailed across the Pacific, it’s a huge place, things don’t change fast. So I find it hard to believe that the Pacific could gain or lose heat fast enough to turn the state of the PDO around in five or ten years, when we don’t see that kind of occurrence in a century of records.
Of course, nature is rarely that regular, so we may see a PDO reversal next month … which is why I say that tempting as it might be, I wouldn’t lay any big bets on the duration of the current phase of the PDO. History says it will continue for a decade or two … but in chaotic systems, history is notoriously unreliable.
w.
PS—This discussion of pressure-based indices makes me think that there should be some way to use pressure as a proxy for the temperature. This might aid in such quests as identifying jumps in the temperature record, or UHI in the cities, or the like. So many drummers … so little time.
MATH NOTE: The shape of the cumulative total is strongly dependent on the zero value used for the total. If all of the results are positive, for example, the cumulative total will look much like a straight line heading upwards to the right, and it will go downwards to the right if the values are all negative. As a result, it cannot be used to determine an underlying trend. The key to the puzzle is to detrend the cumulative total, because strangely, the detrended cumulative total is the same no matter what number is chosen for the zero value. Go figure.
So I just calculate the trend starting with the first point in whatever units I’m using, and then detrend the result.



” It turns out that I understood what I was saying very well, and it was you and Paul who were wrong. I may be self-educated, which has its own problems, but as a result I’ve noticed a few things that the professionally trained folks such as you two might have missed in your education …
w.”
… I’ll gladly accept an apology from either or both of you. 😉
Greg Goodman says:
June 9, 2013 at 3:57 pm
I still don’t understand your point. I have done nothing but what they did, up to the point that they extracted the leading PC. At that point, I detrended the data instead of extracting the leading PC … and MY RESULTS ARE VERY, VERY SIMILAR TO THEIR RESULTS.
Which, if you recall, is what I said about the JISAO results, and what you and Paul both said was wrong, wrong, wrong …
You are correct that the final detrending doesn’t do much … but so what? I never said it did. All I required is that the time series end up detrended.
What I said was the following:
I have now tested my claim with the JISAO data. In fact, the two results are indeed quite similar.
So I’d say I was right, that for a slowing trending timeseries, the leading PC is quite similar to the detrended dataset itself. I have actually demonstrated that that is the case for the JISAO analysis, which if you recall was the reason for my comment. So do all the handwaving you want. I have experimental results on my side that show my claim was correct. You can argue about why it was correct all you want.
If you can come up with a different dataset that gives a different result, one where the leading PC of a slowly trending climate time series is greatly different from the detrended dataset, then you can make more claims. Bear in mind that the trend of the N. Pacific SST dataset is only 0.04°C per decade …
So when you come up with some kind of slowly trending timeseries, be it HadCRUT4 global average surface temperature anomalies, N. Pacific average monthly anomalies, or some other timeseries, a timeseries where the leading PC is NOT “quite similar” to the detrended dataset, then we can discuss this more.
But until then, I’ve shown my claim is true by experiment, and you’ve merely proven my point. In fact, the JISAO PDO Index is very similar to the detrended data itself.
w.
PS: From Princeton, regarding Principle Component Analysis, emphasis mine….
Note that that is the same as what I said, which you claimed was wrong:
In other words, the PC analysis involves the rotation of the axes to extract the PCs, just as I said.
Now, the key to what I’m claiming is that if there is only a very small trend in the data, the corresponding amount of axis rotation needed to extract the leading PC is also very small.
And because the result of a very small rotation is quite similar to the result of the removal of a very small trend, for a time series with a small trend (e.g. the global average surface air temperature) detrending is quite similar to extracting the leading PC … which is what I said about the JISAO PDO Index to start this off, and which is what I’ve just demonstrated with the JISAO PDO Index.
PPS: the reason that rotation by a small angle is equal to removal of a small trend is that for small angles, Tan(a) ≈ a. So removing a trend (which is Tan(a)) has nearly the same effect as a rotation by “a”. They are not identical, because trend removal is a shear transformation, and extracting the PC is a rotation. But they are quite similar.
No time for the personal crap — total waste of time.
Reminder:
http://www.billhowell.ca/Paul%20L%20Vaughan/Vaughan%20120324%20Solar-Terrestrial%20Resonance,%20Climate%20Shifts,%20&%20the%20Chandler%20Wobble%20Phase%20Reversal.pdf
More info on this for those who appear to need more than what’s minimally necessary:
http://img268.imageshack.us/img268/8272/sjev911.png
Phase units are pi radians.
[snip Paul – you don’t get to set the rules about how discussion will “advance” on my website. Feel free to resubmit sans that “personal crap”.- Anthony]
Paul Vaughan says:
June 9, 2013 at 8:13 pm
Translation: you’ve been shown to be wrong, and are unwilling to admit it …
w.
Paul Vaughan says:
June 9, 2013 at 8:21 pm
Thanks, Paul … unfortunately, that’s not the information I asked for. I asked how it was that you claim that that plot showed, what was it, hang on … oh, yeah, your claim was that that uncited, unreferenced graph, with no hint of the source of the data, showed that “The changepoints in the integral of NPI are controlled by solar activity & asymmetry”.
Since the changepoints in the integral of the NPI are in 1922, 1945, 1976, and 2005, and those dates are not readily apparent in your mystery graph, I asked what you were talking about … and in response I get an insult, and no new information.
You’re losing ground fast here, my friend.
w.
Henry@Phil.
you confuse E-UV (extreme UV) with F-UV (far UV)
As I understand, the E-UV is what forms the O3, HxOx and NxOx
The more E-UV, the less F-UV and normal UV will be coming through,
due to back radiation of this type of radiation by the increased O3, HxOx and NxOx
so, the less energy is coming into the oceans.
Just remember this: the bulk of earth’s energy from the sun is the warming of the SH oceans,
by the UV coming through. In the SH there is less ozone in the atmosphere….
(how clever is our Creator!)
Thanks, this is the most conceptually helpful article this dilettante has encountered.
Willis: “You are correct that the final detrending doesn’t do much … but so what? I never said it did. ”
“So I’d say I was right, that for a slowing trending timeseries, the leading PC is quite similar to the detrended dataset itself.”
Right ,so you have finally realised the EOF resembled the detrended because the detrending did not matter.. Of course the EOF “resembles” the data, the whole point of doing an EOF is to get something the optimally “resembles” the data.
You are now going argue until the cows come home, I have better things to do.
re. Pautl : “Translation: you’ve been shown to be wrong, and are unwilling to admit it …”
Look in the mirror , my friend.
Greg Goodman says:
June 10, 2013 at 12:16 am
So now you agree with me, that the leading PC resembles the detrended data … but you claim the detrending doesn’t matter. You provide no math and no examples to support this, but that doesn’t matter to you I guess.
You also claim that I agree that “the detrending did not matter”. I said no such thing. I said how the dataset was detrended didn’t matter. As long as it is detrended it will resemble the leading PC.
And now you seem to agree that it is quite similar … so what are you arguing about if you admit my original statement was right?
The better thing to do would be to provide either math or examples to support your argument. I’ve provided both. You’re just taking your ball and going home because you can’t stand losing.
OK, here’s what the mirror shows. I have provided both an example and the math to back up my claim that the detrended version of a dataset with a small trend is quite similar to the leading PC. You now appear to agree, but you dispute my logic … but since you agree that they are quite similar, I don’t see where you have a lot of traction.
The mirror also shows that Paul, like you, hasn’t provided a damn thing except his lip to back up his opinion, which I’ve shown to be wrong by providing both the math and the example and the citation to the paper on Principal Component Analysis. I’ve invited you both to provide a counter-example, or to show where my math is wrong.
Neither of you have provided a damn thing to back up your view. No examples. No citations. No math. I’ve provided all three. Well, to be fair, you’ve done something, so you’re ahead. You re-stated your error several times, but Paul didn’t even do that.
And finally, the mirror shows your back as you go out the door rather than admit your error …
Happy with the mirror-gazing now?
w.
At Anthony’s suggestion I’ve added an integral (cumulative) function to WFT which can partly replicate Willis’ result, but using PDO (which as he explains is inverted to the pressure data he is using). The data must be fairly symmetrical around zero because I haven’t need to detrend it.
http://www.woodfortrees.org/plot/jisao-pdo/integral
I’ll look at adding the pressure data (it looks a sensible format) when I have a bit more time!
Here’s another interesting result, though – the Atlantic (AMO) shows the same bistable oscillation, but lagging by roughly 20 years:
http://www.woodfortrees.org/plot/jisao-pdo/integral/normalise/plot/esrl-amo/integral/normalise
I’ll leave others to explain what AMO is and ponder what that means 🙂
Paul
Willis:”
Note that that is the same as what I said, which you claimed was wrong:
In any case, my understanding, and please correct me if I’m wrong, is that if you zero the “x” and “y” values of your timeseries data, draw the trend line of the dataset, and rotate the dataset around zero by the angle of the trend line, you get the first PC … no? Yes?
”
You said “please correct me if I’m wrong ” so I did.
EOF does NOT detrend the data. You clearly thought it did, so I corrected you.
The axes in the Princeton link are the “axes” in the n-space made by the all the original data time series observations not the y,t axes of the regional mean you are rotatiing or detrending about. You still have not understood what EOF is about but at least you’re starting to read up on it now.
This may help you understand SVD , rotation and orthogonality:
http://www.google.fr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDcQFjAB&url=http%3A%2F%2Fwww.ling.ohio-state.edu%2F~kbaker%2Fpubs%2FSingular_Value_Decomposition_Tutorial.pdf&ei=Ho61UYNyqq7RBf_-gagB&usg=AFQjCNGc7F5X3NTJIifUyDKlqZ3p_3o7bQ&bvm=bv.47534661,d.d2k&cad=rja
You are now hiding behind “slowly trending” and trying to confound this with insignificant trend.
Your original text was:
“Current usage seems to favor either the detrended North Pacific temperature, or alternately using the first “principle component” (PC) of that temperature. Since the first PC of a slowly trending time series is approximately the detrended series itself, these are quite similar.”
” the detrended North Pacific temperature” here refers to the regional average temperature, this is what gets detrended. This is not what get used to derive the PC. At that stage you wording implies you thought it was the PC of averaged time series.
If you chose to interpret “slowly trending” to mean negligibly small trend, then what you are noting as being similar is the first PC of the full dataset and the regional mean. Of course they are “similar” in the case where there is negligible trend , the whole point EOF is to get something that is considered to be more representative of a real time series than a regional average.
Both your method and the JISAO processing had identically removed the trend by subtracting the global average beforehand.
So to that extent noting they are “similar” is stating the obvious. It is banal.
All your discussion about centring the data rotating, shear etc. clearly shows that you thought there was some finite trend remaining and the EOF was removing this in a similar way to a simple detrending that you did.
This is where you where wrong and what I was trying explain to improve your understanding. I have now spent a lot more time than it is worth to help one man understand one small point that is not central to the point of this post.
Now since that one man seems more driven by defending his ego that learning the maths, I really have better things to do.
You have produced a valuable result in the cumulative integral of pressure as a Hurst-like detection of regime change, verified by detection of know earlier change points. Clearly identifying 2005 as a new, recent change point in the N. Pacific is a significant result
Congratulations on that result.
Oh, and someone mentioned sunspots:
http://www.woodfortrees.org/plot/sidc-ssn/integral/detrend:160000
Supplementary:
http://imageshack.us/a/img201/4995/sunspotarea.png
Data:
http://solarscience.msfc.nasa.gov/greenwch/sunspot_area.txt
http://solarscience.msfc.nasa.gov/greenwch/sunspot_area_north.txt
http://solarscience.msfc.nasa.gov/greenwch/sunspot_area_south.txt
Frequency changes of the red line here are controlled by the orange line here:
http://tallbloke.files.wordpress.com/2013/03/scd_sst_q.png
Crucial background info:
http://imageshack.us/a/img267/8476/rrankcmz.png
See section 3 here: asymmetric sun viewed from the heliosphere – Mursula (2007)
Regards
Greg Goodman (June 10, 2013 at 1:51 am) addressed Willis:
“You have produced a valuable result in the cumulative integral of pressure as a Hurst-like detection of regime change, verified by detection of know earlier change points. Clearly identifying 2005 as a new, recent change point in the N. Pacific is a significant result
Congratulations on that result.”
The value here is that there are a lot of readers here who will pay attention to this graph when Willis shows it …because it’s Willis showing it and they like Willis socially — not because they independently were able to recognize it as important when it was pointed out many times over the past several years by other WUWT commentators.
This is the second time in a few weeks that Willis has done a re-make of stuff others have shown here in the past. The exposure is welcome. The lack of acknowledgements is informative.
I think you have a point about lack of acknowledgement , I’ve noted that also. However, I’m unaware of anyone else finding such a clear indication of a change point in 2005 by this method or any other.
lgl’s claim to have “done this years ago” did not stand up to scrutiny. Apparently he did something “exactly the same” but difference 18m ago. He used a cumulative integral, did not detrend or remove the average and did not use it to point out regime changes and did not find 2005.
Are you aware of anyone having demonstrated a change point in 2005 anywhere before?
@Paul Clark
/OT
Hi Paul, since you popped in some quick requests:
ICOADS SST , hadley based datasets do funny things to frequency and have huge ‘adjustments’. It would be useful to have the original data available too.
http://climategrog.files.wordpress.com/2013/03/icoad_v_hadsst3_ddt_n_pac_chirp.png
A decent filter option. Running means are an awful choice, though popular.
http://climategrog.wordpress.com/2013/05/19/triple-running-mean-filters/
Gaussian would be nice but triple RM is very good for removing stuff like 12m cycle and would be trivial to add to your site. Just do three running means instead of one. Details in the link above.
Yes, it can be done now but it’s laborious and 99.99% of visitors will just choose from the list. Here’s why it’s needed:
http://www.woodfortrees.org/plot/rss/from:1980/plot/rss/from:1980/mean:60/plot/rss/from:1980/mean:30/mean:22/mean:17
Also is there any way to turn on the grid or at least get a straight line on a plot at y=0 for example?
I have enough knowledge to do all I need with gnuplot locally but a lot of people here seem to like linking stuff on woodfortrees so these few improvements would be helpful, should you be so inclined.
Thanks.
/OT
Greg Goodman says: June 9, 2013 at 11:01 am
” It is NASA, a satellite measured this in 2010.”
You don’t even seem to know what you’re looking at so you are probably drawing the wrong conclusions.
I had a very successful career proving people with PhD’s wrong, so I recognize your ignorant perspective.
With HADISST:
http://i863.photobucket.com/albums/ab195/weschenbach/principalcomponentvsdetrended_zps4f68b37f.jpg
With ERSSTv3b: Not so much.
Greg Goodman (June 10, 2013 at 4:56 am) wrote:
“I think you have a point about lack of acknowledgement , I’ve noted that also. However, I’m unaware of anyone else finding such a clear indication of a change point in 2005 by this method or any other. […] Are you aware of anyone having demonstrated a change point in 2005 anywhere before?”
Yes, this has been appearing in illustrations shared at WUWT for years. You missed it.
The more important point is that any sensible explorer will independently find it within seconds of having the data, so it’s a trivial “discovery”.
Those of us living in the Pacific Northwest experienced the change firsthand.
Finding it in a graph is a trivial exercise, but the change is important.
Important: The changes around 1900 & 2000 are of a different nature than the others. They correspond with 9 year solar asymmetry phase reversals, whereas the others correspond with thresholds in the phase relations of 9 year solar asymmetry and 11 year solar activity. The 9 year solar asymmetry occasionally stretches itself to 13.5 years = 9 + 9/2. The 2 times when it has done this are ~104 to 108 years apart on the record. This is a well-known period of solar activity. Don’t make the mistake of thinking the other changepoints are of the same nature — they’re not.
Can you reproduce section 3 of Mursula (2007) by a number of different methods? You should be able to get the same results with sunspot numbers (not just geomagnetic indices) using a variety of methods. The result is robust across method. Furthermore, the result is extensible to other timescales and this is crucially important to realize.
I’m severely pressed for time — that’s all I have time for today…
Steve Keohane says:
Greg Goodman says: June 9, 2013 at 11:01 am
” It is NASA, a satellite measured this in 2010.”
You don’t even seem to know what you’re looking at so you are probably drawing the wrong conclusions.
I had a very successful career proving people with PhD’s wrong, so I recognize your ignorant perspective.
Congratulations. What has that got to do with anything here? I don’t have a PhD and you didn’t prove me wrong.
Steve, you posted a totally anonymous graph (map plot) with NO information whatsoever, so I asked you what it was. You replied “it is NASA” , no it is not it’s a graph, NASA is a government agency. Then ” a satellite measured this “. Duh, which satellite , there are thousands. Yes NASA have satellites too, getting warmer. You apparently did not reply in an informative manner because you still did not even know what it was you’d posted.
So, yes I am “ignorant” of what it is and remain so because you cannot be arsed to say what it is you expected us to look at . I sure am not going to go on some paper chase trying to guess what it is.
And since you apparently didn’t know what the graph you posted was, it seemed reasonable to suggest you may be drawing the wrong conclusions.
Though I don’t usually get paid for it, I too have a long history of spotting people who don’t know what they are talking about and seeing through paper thin attempts to cover it up.
Shame I don’t get paid for it , I seem to be getting more that my fair share today.
So why did you bring this irrelevancy back into the discussion? Just for the opportunity to be insulting. Very impressive.
PV: The more important point is that any sensible explorer will independently find it within seconds of having the data, so it’s a trivial “discovery”.
Since it is so trivial and so obvious and everyone who ever picked up a dataset must obviously have found this “within seconds” probably accounts for why no one is able to point to where it has been shown already: it so obviously and trivial that no one ever thought it was worth mentioning.
Yeah, that must be it.
[ Greg, just a quick note to say here’s how to get a y=0 line:
http://www.woodfortrees.org/plot/jisao-pdo/integral/plot/jisao-pdo/scale
The rest, as my University computer support team, used to say, “Noted” 🙂 ]
Willis, if what you say is true, that the PDO is a temperature regulating mechanism, then I would not expect that there is good correlation between PDO and temperature. That’s because generally the better the regulator the smaller the correlation with the regulated effect. A perfect regulator would have zero correlation.