Guest Post by Bob Tisdale
The new paper by McCarthy et al. (2015) Ocean impact on decadal Atlantic climate variability revealed by sea-level observations has gained some attention around the blogosphere. McCarthy et al. (2015) was discussed by Jo Nova here, at ReportingClimateScience here and LiveScience here. Also see the University of Southampton press release Global climate on verge of multi-decadal change.
As could be expected, the alarmist mainstream media have so far chosen to ignore a paper that discusses an upcoming multidecadal natural suppression of global warming…probably because indicates the slowdown in global surface warming should continue and it implies the natural variability of the North Atlantic contributed to the global warming we have seen since the mid-1970s.
QUICK OVERVIEW OF THE ATLANTIC MULTIDECADAL OSCILLATION
The Atlantic Multidecadal Oscillation (AMO) is a mode of natural variability that reveals itself in the sea surface temperatures of the North Atlantic. It is normally portrayed by detrending the sea surface temperature anomalies of the North Atlantic. See Figure 1, which includes monthly the sea surface temperature anomalies of the North Atlantic (top graph) and the detrended data, the AMO (bottom graph). The detrended (AMO) data are often smoothed with multiyear filters.
Figure 1
Note: I borrowed the graphs in Figures 1 and 2 from my upcoming book, which I’ve been working on for more than a year.
According to the NOAA Frequently Asked Questions about the Atlantic Multidecadal Oscillation webpage, the AMO can enhance global warming or suppress it.
Looking at the comparison graph of global sea surface temperatures and those of the North Atlantic, Figure 2, we can see that the sea surface temperature anomalies of the North Atlantic often run in parallel with the global data. (The data have been smoothed in that illustration.) At those times, it isn’t enhancing global warming or suppressing it.
Figure 2
During the early cooling period from the late 1870s to about 1910, the North Atlantic data dropped at about the same rate as the global data, so the North Atlantic sea surface temperatures did not enhance or suppress that cooling. Keep in mind though that the global data is sampled very poorly during that early cooling period, so the comparison may not be too realistic at those times. From 1910 to about 1920, the data show the surfaces of the North Atlantic warmed more slowly than the global data, so the North Atlantic was suppressing the global warming during that initial part of the early warming period. Then, from 1920 to about 1940, the surfaces of the North Atlantic warmed at a much faster rate than they did globally. This overcame the initial deficit and allowed the North Atlantic to enhance the global warming for the entire early warming period of 1910 to 1940.
We see similar responses during the mid-20th Century cooling period and the late warming period. That is, the North Atlantic sea surface temperatures run in parallel with the global data for the initial 10 to 15 years of those periods. It’s only after those initial periods that the North Atlantic either cools or warms more rapidly than the global data, which then enhances the cooling or warming.
INTRODUCTION TO MCCARTHY ET AL. (2015)
McCarthy et al. created a new index based on the U.S. east coast tidal-gauge measurements of sea level north and south of Cape Hatteras, from Florida to Boston. They used the new sea level based index as a proxy for variations in ocean circulation of the North Atlantic. See their “accumulated sea level index” shown in blue in their Figure 3, which is also my Figure 3.
Figure 3
McCarthy et al. confirmed the belief that (1) the North Atlantic Oscillation (a sea level pressure-based index that reflects changes in wind patterns there), (2) ocean circulation in the North Atlantic (the flow of warm tropical waters northward by the Gulf Stream) and (3) the Atlantic Multidecadal Oscillation (AMO) are linked.
The abstract of McCarthy et al. reads:
Decadal variability is a notable feature of the Atlantic Ocean and the climate of the regions it influences. Prominently, this is manifested in the Atlantic Multidecadal Oscillation (AMO) in sea surface temperatures. Positive (negative) phases of the AMO coincide with warmer (colder) North Atlantic sea surface temperatures. The AMO is linked with decadal climate fluctuations, such as Indian and Sahel rainfall1, European summer precipitation2, Atlantic hurricanes3 and variations in global temperatures4. It is widely believed that ocean circulation drives the phase changes of the AMO by controlling ocean heat content5. However, there are no direct observations of ocean circulation of sufficient length to support this, leading to questions about whether the AMO is controlled from another source6. Here we provide observational evidence of the widely hypothesized link between ocean circulation and the AMO. We take a new approach, using sea level along the east coast of the United States to estimate ocean circulation on decadal timescales. We show that ocean circulation responds to the first mode of Atlantic atmospheric forcing, the North Atlantic Oscillation, through circulation changes between the subtropical and subpolar gyres—the intergyre region7. These circulation changes affect the decadal evolution of North Atlantic heat content and, consequently, the phases of the AMO. The Atlantic overturning circulation is declining8 and the AMO is moving to a negative phase. This may offer a brief respite from the persistent rise of global temperatures4, but in the coupled system we describe, there are compensating effects. In this case, the negative AMO is associated with a continued acceleration of sea-level rise along the northeast coast of the United States9, 10.
The last two sentences are noteworthy. Good news: the surface temperatures of the North Atlantic are going to suppress global warming for the next couple of decades…after enhancing them for the past 3+ decades. The bad news: there will likely be an accelerated rise in sea level from Cape Hatteras to Boston during that time.
HEADING THE CO2-OBSESSED OFF AT THE PASS
I suspect the true blue believers in catastrophic human-induced global warming will attempt to downplay the role of the AMO by citing the curious paper Steinman et al. (2015), which clearly illustrated model failings, even though they were attempting (and failing) to make other points. See the posts:
- On Steinman et al. (2015) – Michael Mann and Company Redefine Multidecadal Variability And Wind Up Illustrating Climate Model Failings
- Another Couple of Notes about Michael Mann’s “Faux Pause” Post and Steinman et al. 2015
We’ve illustrated and discussed how poorly climate models simulate sea surface temperatures in the posts:
- Alarmists Bizarrely Claim “Just what AGW predicts” about the Record High Global Sea Surface Temperatures in 2014
- IPCC Still Delusional about Carbon Dioxide
For more information on the Atlantic Multidecadal Oscillation, refer to the NOAA Frequently Asked Questions About the Atlantic Multidecadal Oscillation (AMO) webpage and the posts:
- An Introduction To ENSO, AMO, and PDO — Part 2
- Multidecadal Variations and Sea Surface Temperature Reconstructions
[My thanks to Marcel Crok and blogger Alec aka Daffy Duck for the heads-up.]
So my prediction is that when it turns negative and the rains in the Sahel dry up, cagw will get the blame. Don’t suppose I’d get any takers in a bet against that happening.
Thanks, Bob.
I’m curious on your take on the AMO graph Dr. Bill Gray and associates produces. It apparently uses different metrics that I don’t fully understand, and showed a dip last spring and this spring.
Caleb, please provide a link to the AMO graph you’re discussing.
Thanks Bob.
The first graph shows a small upward trend in temperature. We all know that climates cycle but the frequency varies from 1000-2000 years.. So it would be nice to know the temperature data before graph start. There must be some proxy data somewhere or be discovered by some enterprising PhD student reading your posts.
“…but the frequency varies from 1000-2000 years.. ”
Says who? There are all kinds of climate cycles operating from yearly (el nino) to multi-millienial (glaciation)
Within a couple of years we’ll be treated to stories of “unprecedented” cold, “historic” snowfalls, and “extreme” frost causing “catastrophic” crop losses and famine. Of course it will be attributed to human-released carbon, and the solution will be a continued goose-step to Marxism.
Yeah, it’s no longer ‘We’re overheating the atmosphere’, now it’s ‘We’re changing the ocean currents’. Magic little molecule, that CO2. No wonder the plants lock it to their hearts.
==============
But wouldn’t that mean that CO2 is not a greenhouse gas after all and they were wrong.
Silly me what am I thinking. How could they be wrong, they know everything…just send more money!
Ha! Yep. If they want to view CO2 as the primary driver, they will continue to do so. The warmists are just working through their vast amount of cognitive dissonance at this point. At least the focus will finally be on the oceans! (..even if they do want to make the ridiculous claim that CO2 is the evilest flea in Satan’s underwear drawer 🙂 Science will eventually prevail over their ideological hatred of hydrocarbons.
No, no, no, you have to learn to think like an alarmist. The oceans the Sun and everything else just masks the global warming that has always been going on and is always caused by rising CO2, in fact if the dinosaurs didn’t have marshmallow roasts it would be a lot colder now.
“They’ll chill us, probably”.
============
What is the mechanism for rising Atlantic sea levels during the negative AMO phase? Rising sea levels were a feature of warming oceans Was the dictum of the folks decrying potential prophesied warming.
I think it is to do with the speed of the Gulf Stream forcing water further to the west and so raising the level along the US East Coast.
“Mechanism?
We ain’t got no mechanism!
We don’t need no mechanism!
I don’t have to show you any stinking mechanism!”
If anyone read the whole paper, did they ever even mention the herd of elephants in the room, that the AMO being in a warm phase previously is responsible for most of the prior warming?
Nope, they gotta holler ‘brief respite’ instead, dontcha know. The madness, and the fear, is deep and wide. Many are ill-adapted for life. Well, ‘life’ is inapt. Maybe substitute ‘dawn’.
=========
…or ‘yawn’.
Assuming the Atlantic SST’s are a major contributor to global temps and the Sun being quiet also contributes, all we need is a big bang from a volcano and I’m heading to the tropics with a boat load of freeze dried food.
hmm.. I might join you…
Ole
Pity that 3’s a crowd…
The authors may see a positive relationship between their index (blue line) and the other measures and T. I can’t. Pre 1945 seems totally the reverse of other periods.
There is always bound to be some correlation if one fiddles with data but for me their index is meaningless unless the pre 1945 is explained clearly. Have I missed something?
McCarthy et al. (2015) as far as I understand, failed to analyse the total lack of AMO-NAO correlation before 1920; that failure casts shadow over their conclusions.
I did similar analysis about 4 years ago than discussed results with Dr. Judith Curry
She responded:
Curry, Judith A Sep 7, 2011
to vukcevic
“hi, looks good! my main suggestion is to go back prior to 1920; 1850 if possible (the data in north atlantic should be good enough). when you publish this let me know so i can highlight it at climate etc. Judy”
I did follow Dr. Curry’s advice the result is here with full analysis of the total lack of AMO-NAO correlation before 1920
http://www.vukcevic.talktalk.net/AMO-NAO-relationship.pdf
Paper was published here:
http://hal.archives-ouvertes.fr/docs/00/64/12/35/PDF/NorthAtlanticOscillations-I.pdf
The hal.archives subsequently redesigned their web-archives and I was informed that a non-standard PDF file format is used, I am required to resubmit the paper in the up to date format, for the paper to be made available on line again by Le C.I.N.E.S. (Centre Informatique National de l’Enseignement Supérieur).
Will do sometime in future, but in view of number of other stuff related to the AMO and NAO I came across during the last 4 years might rewrite whole article, and that is time and patience consuming process.
Southampton University is always welcome to visit my web page again at any time.
Vuk
They confirm what I told you years ago, AMO=integral of NAO, already forgotten?
There is not supposed to be a direct or lagged AMO-NAO correlation.
Good work vukcevic,
The AMO effecting the NAO is a no brainer, almost impossible to believe otherwise knowing how each is determined. To think otherwise is to think that oceans do not play a strong role in atmospheric weather patterns close by.
How that correlation relates, phases and varies is the interesting part.
Each cycle may be different and there are other factors outside of the AMO effect that can weaken the correlation with the NAO for a period of ?? years.
Regarding data prior to 1920. I would give less weight to measurements at that time. Sometimes we feel compelled to use all data that relates, even if it pushes into a less reliable time frame, if for no other reason than to include the best data available as far back as any data was measured.
Back to CAGC (Catastrophic Anthropogenic Global Cooling)?
The net of ocean cycles explains the 64 year period of climate. When combined with the sunspot number anomaly time integral (which is a proxy for solar effect on clouds) and compared to a 5-year moving average of reported average global temperature measurements, R^2 = 0.97+ since before 1900. CO2 has no significant influence.
Proof that CO2 has no significant effect on climate and identification of the two factors that do cause climate change are at http://agwunveiled.blogspot.com
The two factors are also identified in a paper published in Energy and Environment, vol. 25, No. 8, 1455-1471
The establishment plodders (Mann and McCarthy) have been laboring hard to discovering the obvious 60 year +/- cycle that any schoolboy can see in the temperature data. How long will it take these slowpokes to notice and incorporate the quasi millennial temperature cycle which is equally obvious. See Figs 5 – 9 at
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
The same post discusses the uselessness of the IPCC models for forecasting climate and contains estimates of the timing and amplitude of the coming cooling based on the natural 60 year and millennial cycles and uses the neutron count and 10 BE data as the best measure of solar activity see
http://climatesense-norpag.blogspot.com/2014/07/climate
We have just passed the peak of the quasi -millennial cycle in about 2003 and are now 12 years into the general cooling trend which will last ( modified by the 60 year and centennial solar cycles) until the depths of the next LIA in about 2635. See
http://www.woodfortrees.org/plot/rss/from:1980.1/plot/rss/from:1980.1/to:2003.6/trend/plot/rss/from:2003.6/trend
Here is a forecast from a 30 year Climate Forecast posted on the same site in 2010 which forecasts and explains recent events in Texas and Oklahoma and elsewhere.
“There will be a steeper temperature gradient from the tropics to the poles so that violent thunderstorms with associated flooding and tornadoes will be more frequent in the USA, At the same time the jet stream will swing more sharply North – South thus local weather in the Northern hemisphere in particular will be generally more variable with occasional more northerly heat waves and more southerly unusually cold snaps. In the USA hurricanes may strike the east coast with greater frequency in summer and storm related blizzards more common.
The southern continents will be generally cooler with more frequent droughts and frost and snow in winter,
Arctic and Antarctic sea ice may react differentially to an average global cooling. We might expect sea ice to increase in the Antarctic but in the NH the Arctic Oscillation while bringing cooler temperatures further south may also occasionaly bring warmer air into the Arctic with possible relative loss of sea ice in that area during those years.”
Please ignore second, incorrect, link to climatesense-norpag site.
”The establishment plodders (Mann and McCarthy)” Spell check error? Should be plotters ?
This brings up a curiosity, if not an important point. Land and satellite-based temperature sensors are blind to areas affected by natural variation versus temperatures affected by anthropogenic increases in greenhouse gas and artificial warming due to sensor placement near encroaching flora and/or structures. In fact sensors are blind to a lot of warming unrelated to anthropogenic CO2 “greenhouse” warming. I offer a suggestion to human-related warming researchers in their search for anthropogenic warming in the temperature data set.
Through a process of elimination, sensors on land that are mostly affected by natural variation, such as those along the gulf stream and those along the west coast (there are several more), should be removed from the search for the anthropogenic CO2 warming only data set. Local warming picked up by sensors affected from encroaching flora and man-made structures that create local warming should be removed. Sensors that have been moved should be removed in order to assure a long-term data set. Sensors that have changed from one kind to another should be removed. The data set should be examined for altitude outliers. The list of sensors that should be removed is quite long so I will move on with my point.
Wait…that is my point.
More money will be needed to study the impact of sensor retirement on the data quality and the relationship of this data quality to our ability to locate the missing heat
The cryosphere chart is wrong. It shows snow extent similar to February in the Northern hemisphere.
Either climate science does not know how to deal with irregular multi-decade cycles along with their political partners, or they don’t want to know it. The former case is pathetic and latter case is not science inquiry.
Gee, now what was that little thing coming up about the next solar cycle?
Figure 3 suggests data quality differences in the early years.
Maybe in the future we will have someone reporting on the “hoax” of Climate Change as just happened with the “Dark Chocolate Diet” hoax https://www.sciencenews.org/blog/culture-beaker/attempt-shame-journalists-chocolate-study-shameful
Note that Science News is saying that is it “shameful” to fool the media but it points out that they are very easy to fool!
Bob, you have SST anomaly data going back to 1870. What baseline time period are you using for that metric?
And once again “natural variability” is found lurking around creating problems for AGW folks. Perhaps the biggest laugh I have had during “the pause, hiatus, whatever,” is that the AGW crowd have been using natural variability issues as to be the reason for the pause. I laugh because every time they say its the PDO, El Nino, the AMO, volcanoes, or whatever they choose, they don’t even realize they are saying the climate is driven by nature, not carbon dioxide. And sadly, neither does it seem to occur to many others that this is the concession they are giving without saying.
I’m not skilled enough to do it myself, but I am imagining an edit
of a scene from the Godfather movie where he says “It was Barzini all along”
but edited to say “it was the AMO all along”. Could have a few more fun edits as well
to make it even better.
This paper tells us that the oceans have a large impact on the climate of the earth. Darn! Who knew! I guess Dr. Mann will be reevaluating his scientific position any day now.
Next they might notice clouds.
Again my DSP background is making me cringe (for both “sides”). Finding a trend in a low frequency signal is fraught with peril. you don’t know the exact frequency, you don’t know the phase, and your window size is arbitrarily picked based on when the data gathering starts and ends. You are barely inside of Nyquist criteria of having a minimum of two periods. The data is also noisy with its own error bars. Furthermore your 7 year filter can have edge effects that affect the trend. It looks to me like you haven’t accounted for any of these (I’m sure the warmists haven’t either. aka “hockey stick”).
Please try running a sine wave with various phase delays, window (sample) lengths, and minor frequency changes through your trending algorithm. Then add your filtering algorithm. Then add random noise. I think you will find that your variation in trend determination is high. You could even use random phase delays and frequency changes and windows, with a variation amount judged roughly on the physical underpinnings of your data, and get an idea of the error bars on the trend line.
I’d do it, but you didn’t, AFAICT, post the source code (and if you did I find most people don’t make the code easily testable with arbitrary input signals).
I think determination of low frequency trends is not a well studied area in signal processing, and I see these significant mistakes being made all the time. Please don’t replicate the warmists’ signal processing mistakes.
I and others with DSP backgrounds have gotten Willis to fix his periodogram code (he wasn’t windowing). I hope this trend of learning from each other continues and this is fixed before you publish.
Peter
PS: Here’s An excellent thread on low frequency analysis issues with filters and windows, touches deeply in one problem area (your 7 year filter). There are more ways than this to get spurious results but this is a good start for a background on the problem, plus it’s climate related so you wont’ get confused by the weird bscure topics DSP often covers:
http://climateaudit.org/2008/10/07/are-butterworth-filters-a-good-idea-for-climate-series/
So I gotta ask, Pamela — how many sensors will be left after removing all of the ones with possible confounding non-CO_2 based anthopogenic or natural influences?
smokes & pregnant
does not smoke & pregnant
Don’t get me wrong, I get your point, but the problem with averaging is that averaging erases many things irretrievably. This is simple statistical mechanics. There are many, many possible molecular configurations that correspond to a single temperature, and the temperature represents the average energy of just one of those configurations. It tells you literally nothing about e.g. the composition or structure of the system with the temperature — the zeroth law tells you only that if you put that system (with a given temperature) in thermal contact with any other system at the same temperature, no net energy would flow between the two systems.
The temperature alone will not tell us what the cause is of its variation. The best that we can do there is try to a) accurately and honestly estimate the global average temperature; and b) see if the changes in this temperature are consistent with some physical model.
The problem there is that of course they will be, no matter what they do. There is guaranteed to be at least one correct physical model that explains the data, if one can solve the computational problem, because Nature is integrating a more or less deterministic mechanics. The problem is that there will almost certainly be multiple, quite possibly extremely disparate, physical models that explain the data, especially if the data is coarse grained and averaged and imperfectly known, let alone if the data is taken by individuals willing to put their thumbs on the scales to force the data to correspond to their favorite model(s). That and the fact that no, we cannot plausibly solve the physical model computationally by some 30 orders of magnitude and are trying to estimate global averages from absolutely implausible solutions that bear no resemblance at all to reality using statistical abomination instead of acknowledging that the problem is, so far, unsolvable.
In medicine, physicians are constantly trying to ascribe causes and detect marginal differences in highly multivariate problems — attempting (for example) to validate a hypothesis that there is a link between saturated fat in a diet and blood cholesterol levels. The problem is that this is really difficult to do because of the enormous diversity of the population studied, its genetics, and other factors that might affect cholesterol such as other elements of diet, exercise, the occurrence of certain diseases, physical size or shape or age or gender or sexual orientation and behavior (as a proxy for hormone levels) or…
In general medical studies have appallingly low N, the number of people enrolled, because of high costs, HIPAA, limited facilities, and the difficulty finding people to be studied in the first place. The enrollees are not independent and identically distributed samples pulled using a random number generator from the entire global population, so from the beginning there is a kind of selection bias, just as there is when one samples the political opinion only of people that volunteer their opinion or answer “yes” when a pollster calls and ignore all of the people who value their privacy or time too much to volunteer or don’t answer the phone when caller id reveals a pollster at the other end. But to resolve differential epidemiology in a highly multivariate population with an enormous number of possible confounding factors is not easy! It requires really large N, for one thing, as well as at least a semblance of iid sampling as opposed to biased selection criteria. That’s why it took decades for the medical research establishment to finally shoot down the hypothesis of a controlling link between dietary fats and cholesterol per se. Links between obesity and cholesterol, and between body type and cholesterol, and between individual genetics and cholesterol, were all quickly established, but even these were often confounded. Thin vegetarians can have high cholesterol. Eating saturated fats can be correlated with obesity, sure, but are they per se a factor in high cholesterol or would obesity produced by a diet with too much sugar and unsaturated fat and too little exercise (and bad genes there, as well) produce the same average cholesterol in carefully separated populations?
If you need N = 100 (say) to get a good estimate of correlation for a single factor, you might well need N = 10000 to resolve two confounding factors, and that’s if one knows what the confounding factors might be. A moderately famous example from one of my old statistics textbooks is “does smoking cause pregnancy”. One can fairly easily discover a statistically significant connection between female teens who smoke and who become pregnant. That is:
Correlation, however, is not causality, and taking away your teenage daughter’s cigarettes is not an effective contraceptive technique. Rather it is more likely that both tendency to smoke and tendency to become pregnant have one or more shared causes. It is also possible that those causes and probabilities themselves are not “universal” but may be shared only within certain sub-populations, or that still other factors modulate.
This is the problem with studying the climate. The number of confounding variables is literally uncountable. The dynamics of the climate are known to be chaotic. The pitifully inadequate number of “samples” we have, or manipulate in simulations, are not at all iid samples drawn from a stationary distribution function but are rather non-independent, non-identical samples drawn with significant biases from a non-stationary non-Markovian dynamical process. Yet this doesn’t stop climate scientists from asserting conclusions such as “we are certain that more than half of the observed temperature change in the non-stationary process is due to CO_2 increases” and publishing temperature anomaly estimates that are “predicted” with error bars in the mid-19th century so small that they would make a used car salesman blush. HadCRUT4 asserts total error bars in 1850 that are only 0.3 to 0.35 C, compared to 0.1 to 0.2 in the late 20th and early 21st century. In statistics-speak, that presumes that we have only four times as much data in 2014 from which to form an average global temperature (anomaly, whatever) than we did in the year 1850.
This is the kind of thing that drives me nuts. The variability of temperatures day to day, year to year, at any given location is pretty well known and is not small. The planet Earth is, in contrast, enormous, and 70% of its surface is ocean. Of the remaining land surface, I would guestimate that close to half of it was anywhere from sparse to entirely devoid of thermometers in 1850 — all of Antarctica, almost all of South America, Africa, huge blocks of Asia, central Australia. The sampling of the ocean was even worse, and was highly inconsistent as to methodology. El Nino — hardly unimportant as a determinant of average global temperature — wasn’t discovered and named until almost the turn of the 20th century. I am skeptical in the extreme about assertions of precision and assignments of cause in global temperature anomaly averages prior to the middle 20th century (post world-war II at least, satellite era better still, ARGO era best of all).
This.
Thanks, very excellent analysis of dealing with complex systems. Traditional science methods really aren’t suitable.
You have fleshed out my tongue in cheek comment.
Come on man. The ocean currents and ocean temperature is the tail. The sun is the dog.
Has anyone noticed that the Greenland Ice sheet summer melt is now a month behind the 20 year historical average? There is a sudden increase in Arctic multiyear ice. There is a sudden increase in Arctic ice volume. There is record Antarctic sea ice for every month of the year, starting in 2012. The purpose of looking at all of the observations at the same dang time rather than looking at a single observation at a time in separate papers or just ignoring what is happening as there is no physical explanation and/or appealing/assuming the magic wand ‘chaos’ is the cause of the anomalies, is that holistic analysis (listing the dang anomalies is step one) is the only way to solve holistic physical problems.
There is now sudden simultaneous cooling of both poles. Ocean currents cannot suddenly change in both hemispheres due to internal mechanisms and cannot change for no physical reason. An outside forcing function is required to explain what is happening to the earth.
The outside forcing function is the sun. Sudden cooling of the Atlantic ocean? Sudden increase in the frequency of La Niña events and suppressing of El Niño events? A sudden unexplained drop in the geomagnetic field intensity (ten times greater drop in intensity than is possible for a core based change) and an abrupt change in the magnetic pole location, ten times faster change in the magnetic pole drift velocity starting in the mid 1990s?
It is a fact that there is correlation of past abrupt climate changes with abrupt geomagnetic field changes. There is an interesting story to explain why there was a ten year delay in finding the correlation of the geomagnetic field changes with planetary temperature. It has assumed that cosmogenic isotope changes were caused by temperature effects on cosmogenic isotope accretion in the ocean sediment proxy, as it was assumed the geomagnetic field cannot change rapidly and was assumed the geomagnetic field cannot change cyclically. The initial ‘solution’ to make the anomaly go away was to ‘correct’ the ocean sediment proxy record for temperature.
The breakthrough in cracking the geomagnetic field/climate change puzzle was the study of ancient fired floor tiles by French scientists which enabled an accurate determination of the geomagnetic field intensity and orientation and an accurate timing of changes by a method that is not affected by ambient earth temperature. (There is an interesting Nova special that describes how the tile analysis was and is done.)
Bingo!!! It is a fact that the geomagnetic field is currently rapidly changing and has rapidly changed cyclically forced by some unknown forcer. Big surprise there is a physical explanation for past and current changes to the geomagnetic field. It’s the sun!!!
The sun and the stars are significantly different than the standard model. There are hundreds of astronomical anomalies/paradoxes that support that the assertion that the sun and stars are different than the standard model. The key to solving the solar/star puzzle is looking at the quasar anomalies/observations (roughly a couple of hundred papers that discuss twenty or so independent quasar anomalies that go away with the correct model/mechanism) which can be used to determine what happens when large bodies collapse.
Geomagnetic field changes caused the past abrupt climate changes which explain how a relatively short change in the solar cycle can cause the Younger Dryas abrupt climate change that lasted for 1200 years.
There are in addition to the smaller cyclic warming and cooling events, unimaginably large and extraordinarily rapid abrupt cooling changes in the paleo record. All of the cyclic events have the same periodicity as the smaller Little Ice age and Medieval warming type cyclic events which some may have heard of.
The logical reason for the same periodicity for all climate change events (small, medium, and super large climate events) and the fact that there is correlation of cosmogenic isotopes changes at all of the events (interesting solar gate story which explains recent monkeying with the proxy record analysis to try to make the solar cosmogenic isotope changes go away), is solar cycle changes are causing all of the cycles.
The sun changes cyclically in a manner to cause small climate changes, medium climate changes, and very, very large abrupt climate changes. The very large abrupt climate changes are modulated/amplified by the orbital position of the earth at the time of the restart of the solar cycle. We are currently in the position for maximum amplification of the solar restart’s affect on the earth. What is currently happening to the geomagnetic field is what has happened before when there was a very large climate change event.
What is currently happening to the sun has happened again and again and again and again and again, …. The current change to the solar cycle is what causes cooling similar to the 8200 Bp present abrupt cooling event.
https://ams.confex.com/ams/pdfpapers/74103.pdf
http://iopscience.iop.org/1742-6596/440/1/012001/pdf/1742-6596_440_1_012001.pdf
http://www.falw.vu/~renh/pdf/Renssen-etal-QI-2000.pdf
https://chiefio.wordpress.com/2012/12/15/d-o-ride-my-see-saw-mr-bond/
This is a very interesting article on the climate and is the correct path.
The AMO is linked to the NAO which is linked to solar activity. A sun which exhibits an AP index of less then 5 is more likely to give rise to a -NAO which will have an impact on the AMO.
The best way to see where the climate is heading is through observation and what past data tells us and then try to see which theory holds up to this the best.
AGW theory for example failing on all fronts.
A detailed investigation of the precise alignments between the lunar synodic [lunar phase] cycle and the 31/62 year Perigee-Syzygy cycle between 1865 and 2014 shows that it naturally breaks up five 31 year epochs each of which has a distinctly different tidal property. The first 31 year interval starts with the precise alignment on the 15th of April 1870 with the subsequent epoch boundaries occurring every 31 years after that:
Full Moon Epoch 1 – 15th April 1870 to 18th April 1901
New Moon Epoch 2 – 8th April 1901 to 20th April 1932
Full Moon Epoch 3 – 20th April 1932 to 23rd April 1963
New Moon Epoch 4 – 23rd April 1963 to 25th April 1994
Full Moon Epoch 5 – 25th April 1994 to 27th April 2025
[N.B. During New Moon epochs, the peak seasonal tides are dominated by new moons that are predominately in the northern hemisphere.]
[N.B. During Full Moon epochs. the peak seasonal tides are dominated by full moons that are predominately in the southern hemisphere.]
If you allow a 10 year delay for the oceans to respond to the tidal changes you get transition dates that correspond to the (approximate) years of AM) maximums and minimums:
1880 = AMO maximum
1911 = AMO minimum
1942 = AMO maximum
1973 = AMO minimum
2004 = AMO maximum
2035 = AMO minimum
This strikes me as something that is not the result of pure chance.
Peter Sable said:
“You are barely inside of Nyquist criteria of having a minimum of two periods.
@ Peter Sable – the ocean temperature data only goes back to the early 1870’s, However the 60 year cycle in the Earths climate can be seen in the:
Indian Summer Monsoon: back till 1820
Atlantic trade winds: back to 1680 (and possibly earlier)
Ref: http://astroclimateconnection.blogspot.com.au/2010/03/60-year-periodicity-in-earths-trade.html
Ian a question. According to your work on lunar and associated tidal forcing what kind of climatic trend is the lunar influence (in absence of solar) indicating for the climate for the next 20 years?
In addition can you explain (as simple as possible) the different configuration of the lunar/earth /climate connection that would promote a cooling scenario versus a warming scenario? Thanks.
However the 60 year cycle in the Earths climate can be seen in the:
Oh I agree the multi-decadal cycles are there and we have enough data to prove they exist. Determining a period (frequency) doesn’t take much, in fact that only takes 1 cycle (2 crossings).
Determining magnitude AND frequency requires Nyquist. And the bigger the error bars, the more periods it requires. To determine a trend across an entire sample requires basically a notch filter on (in this case) not enough data.. I also note his data only goes back about 2 cycles. IMHO trending any periodic data or walking data is just a bad idea in general… all you can really do is ferret out different frequencies. Usually DSP folks use wavelets with known properties to attempt to get maximum resolution of phase, magnitude, and frequency and time location at the same time, not the ad hoc stuff I see here in climate science…
I’ve often said that I’ll believe we understand something about temperature trends when we have 120 years of accurate satellite data. That’s 2 cycles of PDO, AMO, etc. since 1979. Let’s discuss climate again in the year 2099… (I wish)
Of course then there’s the 1000 year trend. Maybe we should be discussing climate in the year 3979…
“The Atlantic overturning circulation is declining and the AMO is moving to a negative phase.”
http://www.nature.com/nature/journal/v521/n7553/full/nature14491.html
That is backwards. Low AMOC events happen during negative NAO episodes, and negative increasing negative NAO drives a warm AMO mode.
This is what we will be finding out. Will both the AMO and NAO trend negative or will the NAO become more negative while the AMO remains or becomes more positive?
Salvatore – I am not sure that I can answer that question to your satisfaction as I only have a partial picture.
Clearly the Sun is the dominant source of energy that drives the climate system and so any variations in its influence upon the Earth needs to be taken into account. However, if we distinguish between lunar and solar influences, we can ask ourselves the question, what unique effects can we attribute to the Moon, if any?
I believe that both the Sun and the Moon interact with the Earth’s climate system via their influence upon the Earth’s rotation rate. However, these interactions depend upon the time scales being considered and the mechanism involved.
I will try to elaborate in a latter post.
Obviously, AMO is simply a geographically-constrained COMPONENT of the global SST average. Thus there necessarily MUST be some correlation between the two. To speak of the AMO as a mechanism “enhancing or suppressing” the global average is to miss that crucial point.
sigh
Salvatore,
I think that the lunar tides influences the El Nino/La Nina pattern in the tropics (via its interaction with the Earth’s rotation rate) and that the eventual redistribution of the absorbed solar heat from the tropics to the mid-latitudes takes ~ 10 years. Hence, the ~ 60 year AMO pattern that is phase locked to the
precise alignments between the lunar synodic [lunar phase] cycle and the 31/62 year Perigee-Syzygy cycle, but delayed by 10 years.
https://chiefio.wordpress.com/2014/01/24/the-moons-orbit-is-wrong-it-can-change-a-lot-and-tides-will-too/
Ian any commentary on this study? Thanks.
Thanks Ian I think you are on to something here.
Circulation during the winter was favorable for the Atlantic and Europe, but since 2010 AMO index falls sharply.
http://oi60.tinypic.com/1zqty55.jpg
From 1980 to 2010 it is exactly 30 years.
“For the summers (as was case for the winters) the CET data are de-trended,
showing similar trend with the annual values, except again for the 1900-1950
period (shaded in Fig.26).
There is a relative harmony between the AMO and the summer CET before
1950, this may be true relationship or result of the SST data corrections.
After 1950 the AMO lags the summer CET between 9 and 12 years,
eventually dropping to about 3 years around 1980s and since remained there.
The summer CET follows closely, with no delay the Reykjavik pressure
during the cooling periods, before 1900 and again 1940 –-1960, in a way
reminiscent of the winter’s CET – RPA relationship. This may suggests that cooling
CET summer phase is consequence of the Icelandic Low not moving enough far
north for the CET to break away from the winter’s direct teleconnection.
During the summer’s warming phases (1910-1940 and post 1980) the
Icelandic Low moves further north, following the Arctic ice retreat, the winter’s
direct atmospheric teleconnection breaks down and is replaced by the indirect
teleconnection, with some delay between the CET and the Reykjavic pressure.
The warming periods the delay indicates that the ensuing summer
teleconnection falls back onto the ocean currents induction.
it appears that cooling / warming phase onset is predetermined by some
independent factor. Although the CET does not resolve the AMO–RPA delay
dilema, it does reveal a new and important relationships.”
http://www.vukcevic.talktalk.net/AMO-NAO-relationship.pdf
McCarthy threatens
‘with a continued
acceleration of sea-level rise’.
While FIFA’s Sepp Blatter annnounces ‘Ich vergesse nie’
– ‘Ill never forgive: forget’.
Lagarde declares Greece ruined
1. Lehmann Brothers.
2. US economy.
3. The whole planet.
Obama knows of doomsday by climate change.
And Merkel neighbours with Obama.
Putin is another continent, negligeable.
Anothther glory asylum morning.
Looking at the data and eyeballing….
Warming peaks were in 1880, 1940, 2010
Cooling troughs in 1915,1977
Cycle 64 years
AMO cooling then expected 2010-2042, which also coincides with what many solar scientists are forecasting as well.
Эффективные диеты для похудания. У нас вы найдёте самые эффективные диеты, проверенные не только в теории, но и на практике