It Isn’t How Climate Scientists Communicated their Message; It’s the Message

Over the past few months, there have been a number of articles about how the climate science community could have presented their message differently, or responded differently, so that they could have avoided the problem they’re now facing with the halt in global warming.  Example: the problems with communications by climate scientists to the public were the subject of a recent editorial, and linked webpages, at Nature Climate Change titled Scientist communicators.  In reading it, you’ll find the editorial is really nothing more than a rephrasing of manmade-global-warming dogma.

One of the climate science community’s primary problems was a very basic message…an intentionally misleading message.  That is, it wasn’t how it was communicated; it was the message itself.  I ran across that message again as I was searching for links for a chapter on atmospheric temperatures for my upcoming book The Oceans Ate My Global Warming.  It appeared on the Remote Sensing Systems (RSS) Climate Analysis webpage. That webpage includes data that runs through 2013 in many cases, so it’s relatively new. Under the heading of TROPOSPERIC TEMPERATURE, RSS write (my boldface):

Over the past decade, we have been collaborating with Ben Santer at LLNL (along with numerous other investigators) to compare our tropospheric results with the predictions of climate models.  Our results can be summarized as follows:

  • Over the past 35 years, the troposphere has warmed significantly.  The global average temperature has risen at an average rate of about 0.13 degrees Kelvin per decade (0.23 degrees F per decade).
  • Climate models cannot explain this warming if human-caused increases in greenhouse gases are not included as input to the model simulation.
  • The spatial pattern of warming is consistent with human-induced warming.  See Santer et al 2008, 2009, 2011, and 2012 for more about the detection and attribution of human induced changes in atmospheric temperature using MSU/AMSU data.

The message from the climate science community has been and continues to be:

  • If climate models are not forced by manmade greenhouse gases, then the models cannot simulate the warming from the mid-1970s to the turn of the century, and
  • If climate models are forced by manmade greenhouse gases, then the models can simulate the warming from the mid-1970s to the turn of the century,
  • Both of which lead to the stated conclusion that only manmade greenhouse gases can explain the observed warming from the mid-1970s to the turn of the century.


The IPCC was blatant in their presentation of that misleading message in the 4th Assessment Report.  It appeared in the AR4 Summary for Policymakers.  The fourth bullet-pointed paragraph on their page 10 reads (my boldface):

It is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica (see Figure SPM.4). The observed patterns of warming, including greater warming over land than over the ocean, and their changes over time, are only simulated by models that include anthropogenic forcing. The ability of coupled climate models to simulate the observed temperature evolution on each of six continents provides stronger evidence of human influence on climate than was available in the TAR. {3.2, 9.4}

Figure SPM.4 from AR4 is presented as my Figure 1.

Figure 1 - AR4 Figure SPM.4

Figure 1 (Figure SPM.4 from AR4)

They then further reinforced that message with their Figure 9.5 of AR4’s Chapter 9.  The accompanying text, under the heading of “ Simulations of the 20th Century” reads:

Figure 9.5 shows that simulations that incorporate anthropogenic forcings, including increasing greenhouse gas concentrations and the effects of aerosols, and that also incorporate natural external forcings provide a consistent explanation of the observed temperature record, whereas simulations that include only natural forcings do not simulate the warming observed over the last three decades.

Figure 9.5 from AR4 is presented as my Figure 2.

Figure 2 - AR4 Figure 9.5

Figure 2 (AR4 Figure 9.5)


The IPCC continued with their misleading presentation of climate models (with and without anthropogenic forcings) in AR5.  It was presented as Figure TS.9 on page 60 of the Full Working Group 1 AR5 Report (Caution 357MB). The IPCC writes:

Observed GMST anomalies relative to 1880–1919 in recent years lie well outside the range of GMST anomalies in CMIP5 simulations with natural forcing only, but are consistent with the ensemble of CMIP5 simulations including both anthropogenic and natural forcing (Figure TS.9) even though some individual models overestimate the warming trend, while others underestimate it. Simulations with WMGHG changes only, and no aerosol changes, generally exhibit stronger warming than has been observed (Figure TS.9). Observed  temperature trends over the period 1951–2010, which are characterized by warming over most of the globe with the most intense warming over the NH continents, are, at most observed locations, consistent with the temperature trends in CMIP5 simulations including anthropogenic and natural forcings and inconsistent with the temperature trends in CMIP5 simulations including natural forcings only. A number of studies have investigated the effects of the Atlantic Multi-decadal Oscillation (AMO) on GMST. Although some studies find a significant role for the AMO in driving multi-decadal variability in GMST, the AMO exhibited little trend over the period 1951–2010 on which the current assessments are based, and the AMO is assessed with high confidence to have made little contribution to the GMST trend between 1951 and 2010 (considerably less than 0.1°C). {2.4, 9.8.1, 10.3; FAQ 9.1}

My Figure 3 is the IPCC Figure TS.9 from AR5.

Figure 3 - AR5 Figure TS.9

Figure 3 (AR5 Figure TS.9)

Then the IPCC added a new wrinkle…they shifted focus.  Instead of stating that the warming is “only simulated by models that include anthropogenic forcing”, they use the misleading model comparisons as proof that the “human influence has been detected”.

The Summary for Policymakers for their 5th Assessment Report (AR5) reads:

D.3 Detection and Attribution of Climate Change

Human influence has been detected in warming of the atmosphere and the ocean, in changes in the global water cycle, in reductions in snow and ice, in global mean sea level rise, and in changes in some climate extremes (see Figure SPM.6 and Table SPM.1). This evidence for human influence has grown since AR4. It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. {10.3–10.6, 10.9}

Their Figure SPM.6 from AR5 is presented as my Figure 4.

Figure 4 - AR5 Figure SPM.6

Figure 4 (Figure SPM.6 from AR5)

The IPCC continues on page 74 of the full AR5 WG1 report.  The simulations with anthropogenic and natural forcings are described as “emerging anthropogenic and natural signals”, while simulations with only natural forcings are being described as “the alternative hypothesis of just natural variations”:

The coherence of observed changes with simulations of anthropogenic and natural forcing in the physical system is remarkable (Figure TS.12), particularly for temperature-related variables. Surface temperature and ocean heat content show emerging anthropogenic and natural signals in both records, and a clear separation from the alternative hypothesis of just natural variations. These signals do not appear just in the global means, but also appear at regional scales on continents and in ocean basins in each of these variables. Sea ice extent emerges clearly from the range of internal variability for the Arctic. At sub-continental scales human influence is likely to have substantially increased the probability of occurrence of heat waves in some locations. {Table 10.1}

My Figure 5 is AR5 Figure TS.12.

Figure 5 - AR5 Figure TS.12

Figure 5 (Figure TS.12 from AR5)

The IPCC then presents a series of similar graphs on page 930 in their Figure 10.21, and continues with their misrepresentation of climate model capabilities. On page 927, under the heading of “10.9.2 Whole Climate System”, they write (my boldface), again using the “emerging anthropogenic and natural signals” and “alternative hypothesis of just natural variations”:

To demonstrate how observed changes across the climate system can be understood in terms of natural and anthropogenic causes Figure 10.21 compares observed and modelled changes in the atmosphere, ocean and cryosphere. The instrumental records associated with each element of the climate system are generally independent (see FAQ 2.1), and consequently joint interpretations across observations from the main components of the climate system increases the confidence to higher levels than from any single study or component of the climate system. The ability of climate models to replicate observed changes (to within internal variability) across a wide suite of climate indicators also builds confidence in the capacity of the models to simulate the Earth’s climate.

The coherence of observed changes for the variables shown in Figure 10.21 with climate model simulations that include anthropogenic and natural forcing is remarkable. Surface temperatures over land, SSTs and ocean heat content changes show emerging anthropogenic and natural signals with a clear separation between the observed changes and the alternative hypothesis of just natural variations (Figure 10.21, Global panels). These signals appear not just in the global means, but also at continental and ocean basin scales in these variables. Sea ice emerges strongly from the range expected from natural variability for the Arctic and Antarctica remains broadly within the range of natural variability consistent with expectations from model simulations including anthropogenic forcings.

My Figure 6 is the IPCC’s Figure 10.21 from AR5.

Figure 6 - AR5 Figure 10.21

Figure 6 (Figure 10.21 from AR5)

The IPCC must like those model-data comparisons, because they certainly do like to offer variations of them.

Unfortunately for the IPCC, the models they show with only natural forcings (the blue curves) do not present natural variability.  The climate models employed by the IPCC cannot simulate naturally occurring, coupled, ocean-atmosphere processes that cause multidecadal variations in surface temperatures.  These variations are most evident in the surface temperatures of the Northern Hemisphere, and they are driven by the naturally occurring multidecadal variations in North Atlantic sea surface temperatures (known as the Atlantic Multidecadal Oscillation) and the naturally occurring multidecadal variations in North Pacific sea surface temperatures (not represented by the Pacific Decadal Oscillation/PDO data).  See the post Multidecadal Variations and Sea Surface Temperature Reconstructions.

Figures 7 and 8 are model-data comparisons for the sea surface temperature anomalies of the North Pacific and the North Atlantic for the period of Jan 1870 to Feb 2014. The model outputs and data have been detrended.  The models are represented by the multi-model ensemble-member mean of the CMIP5-archived model simulations of sea surface temperature for the respective ocean basins.  Those are the models used by the IPCC for their 5th Assessment Report. The model mean represents the forced-component of the climate models, or, in other words, the model mean represents how the sea surface temperatures would vary if they varied in response to the anthropogenic and natural forcings used to drive the climate models. (For further information about that topic, see the post On the Use of the Multi-Model Mean.)  The data is the ERSST.v3b sea surface temperature data used by GISS and NCDC in their global land+sea surface temperature products. The detrended data and model outputs have been smoothed with 61-month running-average filters to minimize the annual variations, thereby highlighting the decadal and multidecadal variations.

As illustrated, the forced component of the models (the model mean) fails to produce the multidecadal variations in the sea surface temperatures of the North Pacific and North Atlantic Oceans. This indicates the sea surface temperatures of the North Pacific and North Atlantic are capable of varying over decadal and multidecadal timeframes without being forced to do so by manmade greenhouse gases and aerosols.

Figure 7

Figure 7

# # # #

Figure 8

Figure 8

Keeping in mind that we’re looking at detrended data, the models do not simulate the cooling that took place from the late-1800s to the 1910s, and they failed to simulate the warming from the 1910s to the early-1940s. Likewise, the models failed to simulate the cooling from the early-1940s to the mid-1970s, and they do a poor job of simulating the warming from the mid-1970s to the turn of the century…even though the models are tuned to the late warming period. (See Mauritsen, et al. (2012) Tuning the Climate of a Global Model [paywalled]. A preprint edition is here.)

It’s hard to imagine how the IPCC can claim that the climate models with only natural forcings could somehow represent “the alternative hypothesis of just natural variations”, when the models with natural and anthropogenic forcings cannot simulate the “natural variations”.

Let’s return to the quote from the Technical Summary about the Atlantic Multidecadal Oscillation.  They wrote:

A number of studies have investigated the effects of the Atlantic Multi-decadal Oscillation (AMO) on GMST. Although some studies find a significant role for the AMO in driving multi-decadal variability in GMST, the AMO exhibited little trend over the period 1951–2010 on which the current assessments are based, and the AMO is assessed with high confidence to have made little contribution to the GMST trend between 1951 and 2010 (considerably less than 0.1°C). {2.4, 9.8.1, 10.3; FAQ 9.1}

First, the Atlantic Multidecadal Oscillation is represented by detrended North Atlantic sea surface temperature anomalies, using the coordinates of 0-70N, 80W-0.  Refer again to the model-data comparison in Figure 8.

Second, it’s of little importance if the Atlantic Multidecadal Oscillation contributed little to the global mean surface temperature from 1951-2010.  What is important is that the IPCC is overlooking the fact that they tuned their models to naturally occurring upswings in the sea surface temperatures of the North Atlantic and North Pacific, and extended their projections from those upswings…without considering the likelihood that the upswings would be followed by a naturally occurring downturns in the surface temperatures of both basins.  In other words, they did not tune the models to the long-term trends of the Northern Hemisphere sea surface temperature datasets, which account for the multidecadal variations; they tuned the models to the recent high-trend period that represents only one-half of “cycles”.  See Figures 9 and 10.

Figure 9

Figure 9

# # # #

Figure 10

Figure 10

Yet the climate science community somehow seems surprised that global surface temperatures have stopped warming.  They look more and more foolish with every passing year and with each new IPCC assessment report.


As I have presented on numerous occasions over the past 5 years, ocean heat content data and satellite-era sea surface temperature data both indicate that naturally occurring processes are responsible for the warming of the global oceans, not manmade greenhouse gases.  If this topic is new to you, please refer to the free illustrated essay “The Manmade Global Warming Challenge” (42MB) for an introduction.  The discussions and documentation are much more detailed in my ebook Who Turned on the Heat?


You may wish to continue to read the RSS Climate Analysis webpage because they then go on to write (their boldface):


The troposphere has not warmed as fast as almost all climate models predict.

And then RSS present three model-data comparisons that show the models failing to simulate lower troposphere temperatures globally and in the tropics and that only Arctic lower troposphere temperatures are warming as predicted by models.


As I was writing this, it occurred to me that this post would make a good supplement to my ebook Climate Models Fail.  I’ll try to prepare a pdf edition of this post for those who are collecting them.  Please check back in a couple of days.

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in Atlantic Multidecadal Oscillation, CAGW Proponent Arguments, Climate Model Failings, Model-Data Comparison SST. Bookmark the permalink.

8 Responses to It Isn’t How Climate Scientists Communicated their Message; It’s the Message

  1. Thanks, Bob. Excellent article, deserves a link.
    You write:
    It’s hard to imagine how the IPCC can claim that the climate models with only natural forcings could somehow represent “the alternative hypothesis of just natural variations”, when the models with natural and anthropogenic forcings cannot simulate the “natural variations”.

    I think they tried to confuse the problem so they could claim to be the only ones who understand it. This is dishonest, so I think you are correct; It is the message itself that has a problem, a problem of attempting to deceive and being caught in the deception.

  2. Bob, I have linked to this article from my climate and weather pages, right after the links to New Book: “Climate Models Fail” (Bob Tisdale, September 24, 2013).

  3. Bob Tisdale says:

    Thank you, Andres.

  4. goldminor says:

    @ Bob Tisdale…I posted this a little while ago at WUWT. I also wanted to share this with you, as I believe that you should be able to assess this and fit it into your expertise on the oceans
    “OK, here goes a long thought from the unusual one. I am still quite a bit tired. I have not slept well for weeks, but with some nice fresh coffee I should be able to get the main thrust of this argument laid out in a reasonable fashion. I also forgot about daylight savings time, which always affects
    me adversely for a few days.

    My first epiphany in my climate change study came early on. The first time that I looked at a solar min max chart, it only took a minute or so to realize that the 9 year flood cycle in the Pacific Northwest was linked to the solar minimums. The realization sparked my thoughts as I realized that I might do well connecting the dots as I progressed in the study, and I became hooked into following this path to see where it would lead to. There have been further small successes along the path, and two nights ago I believe that I may have found the “key” to the Great Climate Change Argument. So, here we go. This is my 6th year putting my mind into this endeavour. Strange the 6th of anything has often had significance in my life.

    The core to this has to do with the Sun, and an obvious thought that probably first came to me around 4 years ago when looking at the Solar cycle charts, and prior to coming here to WUWT to read further. The question arose inside ‘why wouldn’t the high strong solar maximums that started in the 1940s and then continued up to 2003 be the reason for the warming trend that has been experienced since the late1970s. Everyone seemed to agree that the Sun is a very stable entity, and that the relatively small change of 0.1% from max to min could not possibly account for the observed warming. On top of that the Sun cycles clearly did not appear to fit in with the pattern of the warming trend. Several times I looked at temps charts and the solar cycle chart to eke out a possible link. Nothing came to mind, zero links. I also looked multiple times at ENSO and solar, and at ENSO, temps, and solar. Still there was no way to link or wiggle match any of the charts. I even made a comment some 3 weeks ago through my Disqus account at The Telegraph site, where I once again brought up the argument that the Sun must be influencing the oceans and that there had to be some ocean offset that allowed for the heat to arise at a later date in time. I was arguing with one ‘Blathra’ who occasionally joins the conversation there. I have a suspicion that ‘Blathra’ could be Ed Davies. Still, I wasn’t able to find a proper answer when he asked ‘how long does the heat hide, before reemerging?’. That all changed 2 nights ago.

    Late Friday evening, as I finished the reading for the day at WUWT, I had the thought to straighten up a few folders where I save stuff. As I was in the process of doing that, once again I found myself comparing several charts to refresh my thoughts. I took the chart of the Multivariate ENSO Index and set it on the desktop. Then I put a solar cycle chart from pics into the preview so that I could then compare the two. I could not find the copy of Dr Svalgaard,s great high resolution chart at the time. The other solar charts which I had were of a coarser image. I went online and saved a recent solar chart from Dr Hathaway, which had a better resolution and current data. As I perused the combination of the two charts and puzzled over where to start to find a first puzzle piece connection, the first connection came into view. My thought had been to use the grand max of 1959 as the first piece. That should have been the easiest one to fit into some other piece on the MEI. And then I saw a fit. The grand max of 1959 fit with the El Nino of 1990, which began right at the end of 1989. The connection was a spacing of 30 years +/-1. The reason why no ones connect the Sun with the warming is that the warming from the Sun enters into the oceans and then comes out of the oceans 30 years later. Then I started examining the MEI for further connections, and there they were. I started with El Ninos and solar maxs. Every one was there, solar max…El Nino starts. I quickly glanced at a few of the minimums and sure enough, solar minimum…La Nina starts. I started writing down the sequences and improving my approach to the exercise. Then I noticed that there were a few events that did not readily connect with the La Nina. All of the major El Ninos were looking good though. I knew that I had found something. Inspiration grew! Then I thought that I should look once more for Dr Svalgaard,s higher res chart. I had a little trepidation with that thought as his chart had refuted a previous ‘connect the dots’ idea that I had. Plus I had already left my cryptic message up above saying that ‘I found something’. Yet, I knew full well that I had to use Dr Svalgaard,s work, or I would be deceiving myself. I found his chart and went to work, and BINGO. It went way beyond my expectations. Every move and tweak on the MEI had the right 30 year phase offset pattern, and I do mean every little move. Connections that I could not make with Dr Hathaway,s chart were completely verified with Dr Svalgaard,s work. Next step, here is the data connections. I use the prefixes ‘pre’ and ‘post’ to denote a shift which occurs before or after the top of a max or the bottom of a min.
    Also note that, Note that the use of Nino and Nina only implies the changes in the MEI and not that the conditions for Nino or Nina were actually fulfilled.
    SSN pre Min-1919/20 Nina-1949/50
    SSN Min -1924/25 Nina-1954/55
    SSN Max -1927/29 Nino-1957/58
    SSN pre Min-1929/30 Nina-1959/60
    spike-up-1933 Nino-1964
    SSN min -1934/35 Nina-1964/65
    SSN pre Max-1935/36 Nino-1965/66
    SSN postMin-1936/37 Nina-1967/68
    SSN Max -1938/39 Nino-1968/69
    SSN pre Min-1940/41 Nina-1970/71
    spike-up-1942 Nino-1972
    SSN Min -1943/44 Nina-1973/74
    SSN Max -1947/48 Nino-1977/78
    1948-spike-down Nina-1978
    SSN postMax-1948/49 Nino-1978/79
    1950/51-spike down Nina-1981
    spike up-1951 Nino-1981
    1952-spike down Nina1982
    spike up-1952 Nino-1982
    1952.1/2-spike down Nina-1982.1/2
    SSN post spike 1951/52 Nino-1982/83
    1954-spike down Nina-1984
    spike up-1954 Nino-1984
    SSN Min-1954/55 Nina-1984/85
    SSN pre Max-1957 Nino-1986/87
    1958/59 spike down Nina-1988/89
    SSN Grand Max-1959/60 Nino+ -1990/95
    SSN postMin-1966/67 Nina-1996/97
    SSN Max-1967/68 Nino-1997/98 El Grande
    1968 spike down Nina-1998/99
    spike up-1970 Nino-2000
    1970-spike down Nina-200/01
    SSN Max end-1971 Nino-2001
    SSN pre Min-1972 Nina-2002
    SSN post Max-1972/73 with continued up spikes Nino-2002/03/04/05
    1974 spike down Nina-2004
    SSN Min-1976 Nina-2006
    SSN pre Max-1977 Nino-2007
    SSN post Min-1977 Nina-2007
    SSN Max 1978-itty bitty Nino-2008-itty bitty
    1978 spike down Nina-2008
    SSN Max-1979 Nino-2009/10
    SSN pre Min-1981 Nina-2010/11
    SSN Max-1982 Nino-2012
    SSN pre Min-1983 early Nina-2013
    spike up-1983 Nino-2013
    SSN pre Min-1983 Nina-2013 late
    spike up-1983 Nino-2013 late
    SSN Min-1984 Nina-2014
    and that is all she wrote for now, as the saying goes. That is every twist and turn of the MEI as correlated with Dr Svalgaard,s great work in his high res solar cycle chart.
    Further, as I consider this to be accurate that means that I should now be able to make a prediction for future El Nino and La Nina. Here it is. It looks like a definite la Nina for now. That is an easy prediction, See I am already spot on with that prediction. The first swing back towards an El Nino will be early next year, but that should be an El Nado and short. After that it should be a strong La Nina all the way till late 2016 and then another short small El Nado. Late 2016 should be the beginning of a true El Nino that will go through 2018, and then back to La Nina. The winter of 2016/17 is very probable for a very heavy rain for the Pacific Northwest. I will leave my prediction there for now. I am tired, and my eyes are bugging out from trying to follow the year by year chart by Dr Svalgaard, which has no larger indicators to show where one might be such as 1970, 1980, 1990, etc etc.

    Here are some other impressions from this exercise.The orbital shifts of the Earth with the consequent changes in w/m2 probably influences the size of Nino and Nina. Is this connected with the stadium wave theory? The 30 year pattern could be further influenced by planetary configurations and lunar tidal forcings. The MEI shows 1/4, 1/8, and 1/16 cycles very well. That reminds me of the conversation with Greg Goodman on the ‘Why Reanalysis Isn’t….” post from May of last year. In discussing that post, I had noted that there appeared to be a short cyclical pattern of approximately 3.5 years. Greg Goodman showed some of his great work, and that information of his added to my thoughts. Bob Tisdale is the obvious choice to flesh out what happens in the ocean with oceanic cyclical patterns after the heat enters therein. How does it end up to be a 30 year pattern? Dr Svalgaard, Dr Norman Page, and Vukvecic should be able to confirm and augment solar, gcr, lunar, and/or planetary influences to further flesh out the full details of how and why. That, of course, will be dependent on how they view this information that I am relating. Overall, this is for everyone who has contributed here at this site. Obviously there are other dynamics at play here. I could go on for several more pages I think, but I am going to stop here. I am tired and looking at all of those tiny little lines on the solar chart has me close to seeing tiny little objects everywhere.

    This should allow for anyone to predict future MEI conditions, and also hindcast MEI to ssn and vice versa.”

  5. Bob Tisdale says:

    Goldminor: People have been trying unsuccessfully to link ENSO to the solar cycle for years.

  6. goldminor says:

    How is it that the above fits so exactly in to Dr Svalgaard,s solar chart? That shows the Sun,s energy entering into the oceans and coming out 30 years later. Every move blue to red on the MEI can be seen with this. Let me point out some key connections. Let’s start with the ssn max at 1967/68. The main rise is in 1967 to the beginning of 1968. From the beginning of 1968 to the end of 1969 the ssn count drops sharply. So there we have the Nino 1997/98 followed by the Nina 1998 into the very beginning of 2000. Here is a key part. Early in 2000 there is a blip of Nino. That Nino was sparked by a sharp uptick around April 1969. I call that a spike movement. It generates the little uptick of the Nino in early/mid 2000. Right at the end of 1969 there is a sharp down tick. That continues the Nina after the little Nino uptick in early 2000. There is maybe a delay of 6 months after movement. At the end of 1969 into 1970 there is a sharp move back up that ends the ssn max. That leads to the 6 month Nino at the beginning of 2001. From mid 1970 to mid 1971 is predominate down in ssn. 2001 ends with a 6 month Nina.

    At the end of 1971 into 1972 there is a sharp uptick, another one follows late 1972, again in early and mid 1973, again in early and mid 1974, final is after mid 1975. This is the Nino from 2002 to late 2005, the sharp upticks in the 70s dominate despite the approaching 70s minimum. Yet during that 4 year period of 2000s Nino, look where it almost drops back to Nina twice, because the driver events were close to the 70s minimum. Looking at the 75/76 minimum there is a lot of upward movements during that period of time. That minimum is not flat based. This also right before the upcoming warming of the late 70s. In mid 1975 a last sharp up spike and 4 little ones. This is the Nino from late 2006 to mid 2007. Then the 1975/76/early77 minimum shows itself with the Nina at mid 2007 through 2008. The four little up spikes during the 70s minimum could be why the little Nino in early 2008. The last part to this is the late 1977 to late1978 sharp upward move that would account for the early 2009 into early 2010 Nino. Then at the end of 1979 and during that max there are a series of 4 sharp down turns through mid 1980. That could be the Nina driver of early 2010 till early 2012.

    This looks like the Rosetta Stone of the climate. This would explain why we see climate as around 30 year periods. There are way too many connecting points between the two charts. Look at the end of 1947 into 1948. There is a sharp drop off the middle of the max. In early 1978 there is a little Nina right in the middle of a long Nino. It is these little connections that strike me the most as showing how the fit works. All of the major obvious mins and maxs are easy to see that they point to the start of a Nino or Nina. Hathaway,s chart only shows a partial fit. Only Svalgaard,s high res chart gives the details to see these fine points. I just wish that he had put in better marks to denote 10 year intervals. It would make it much easier to see. I even did a hindcast to the longer version of the MEI with moderate success with his chart.

  7. Bob Tisdale says:

    goldminor: To see why they do not agree as well as you believe, plot them on the same graph.

  8. goldminor says:

    Ok, thanks for your thought on it. I,ll keep my thoughts open on it.

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