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Global warming and the El Niño Southern Oscillation

What the science says...

The El Nino Southern Oscillation shows close correlation to global temperatures over the short term. However, it is unable to explain the long term warming trend over the past few decades.

Climate Myth...

It's El Niño

"Three Australasian researchers have shown that natural forces are the dominant influence on climate, in a study just published in the highly-regarded Journal of Geophysical Research. According to this study little or none of the late 20th century global warming and cooling can be attributed to human activity. The close relationship between ENSO and global temperature, as described in the paper, leaves little room for any warming driven by human carbon dioxide emissions. The available data indicate that future global temperatures will continue to change primarily in response to ENSO cycling, volcanic activity and solar changes." (Climate Depot)

The paper claiming a link between global warming and the El Niño Southern Oscillation (ENSO)  is Influence of the Southern Oscillation on tropospheric temperature (McLean 2009). What does the paper find? According to one of it's authors, Bob Carter,

"The close relationship between ENSO and global temperature, as described in the paper, leaves little room for any warming driven by human carbon dioxide emissions."

In other words, they claim that any global warming over the past few decades can be explained by El Niño activity.

How do they arrive at this conclusion? They begin by comparing satellite measurements of tropospheric temperature to El Niño activity. Figure 1 plots a 12 month running average of Global Tropospheric Temperature Anomaly (GTTA, the light grey line) and the Southern Oscillation Index (SOI, the black line).

Figure 1: Twelve-month running means of SOI (dark line) and MSU GTTA (light line) for the period 1980 to 2006 with major periods of volcanic activity indicated (McLean 2009).

The Southern Oscillation Index shows no long term trend (hence the term Oscillation) while the temperature record shows a long term warming trend. Consequently, they find only a weak correlation between temperature and SOI. Next, they compare derivative values of SOI and GTTA. This is done by subtracting the 12 month running average from the same average 1 year later. They do this to "remove the noise" from the data. They fail to mention it also removes any linear trend, which is obvious from just a few steps of basic arithmetic. It is also visually apparent when comparing the SOI derivative to the GTTA derivative in Figure 2:

Figure 2: Derivatives of SOI (dark line) and MSU GTTA (light line) for the period 1981–2007 after removing periods of volcanic influence (McLean 2009).

The linear warming trend has been removed from the temperature record, resulting in a close correlation between the filtered temperature and SOI. The implications from this analysis should be readily apparent. El Niño has a strong short term effect on global temperature but cannot explain the long term trend. In fact, this is a point made repeatedly on this website (eg - here and here).

This view is confirmed in other analyses. An examination of the temperature record from 1880 to 2007 finds internal variability such as El Nino has relatively small impact on the long term trend (Hoerling 2008). Instead, they find long term trends in sea surface temperatures are driven predominantly by the planet's energy imbalance.

There have been various attempts to filter out the ENSO signal from the temperature record. We've examined one such paper by Fawcett 2007 when addressing the global warming stopped in 1998 argument. Similarly, Thompson 2008 filters out the ENSO signal from the temperature record. What remains is a warming trend with less variability:

Figure 3: Surface air temperature records with ENSO signal removed. HadCRUT corrections by Thompson 2008, GISTEMP corrections by Real Climate.

Foster and Rahmstorf (2011) used a multiple linear regression approach to filter out the effects of volcanic and solar activity and ENSO.  They found that ENSO, as measured through the the Multivariate ENSO Index (MEI), had a slight cooling effect of about -0.014 to -0.023°C per decade in the surface and lower troposphere temperatures, respectively from 1979 through 2010 (Table 1, Figure 4).  This corresponds to 0.045 to 0.074°C cooling from ENSO since 1979, respectively.  The results are essentially unchanged when using SOI as opposed to MEI.

Table 1: Trends in  °C/decade of the signal components due to MEI, AOD and TSI in the regression of global temperature, for each of the five temperature records from 1979 to 2010.

table 3

Figure 7

Figure 4: Influence of exogenous factors on global temperature for GISS (blue) and RSS data (red). (a) MEI; (b) AOD; (c) TSI.

Like Foster and Rahmstorf, Lean and Rind (2008) performed a multiple linear regression on the temperature data, and found that although ENSO is responsible for approximately 12% of the observed global warming from 1955 to 2005, it actually had a small net cooling effect from 1979 to 2005.  Overall, from 1889 to 2005, ENSO can only explain approximately 2.3% of the observed global warming.

Ultimately, all the data analysis shouldn't distract us from the physical reality of what is happening to our climate. Over the past 4 decades, oceans all over the globe have been accumulating heat (Levitus 2008; Nuccitelli et al. 2012, Figure 5). The El Niño Southern Oscillation is an internal phenomenon where heat is exchanged between the atmosphere and ocean and cannot explain an overall buildup of global ocean heat. This points to an energy imbalance responsible for the long term trend (Wong 2005).

Fig 1

Figure 5: Land, atmosphere, and ice heating (red), 0-700 meter OHC increase (light blue), 700-2,000 meter OHC increase (dark blue).  From Nuccitelli et al. (2012),

Data analysis, physical observations and basic arithmetic all show ENSO cannot explain the long term warming trend over the past few decades. Hence the irony in Bob Carter's conclusion "The close relationship between ENSO and global temperature leaves little room for any warming driven by human carbon dioxide emissions". What his paper actually proves is once you remove any long term warming trend from the temperature record, it leaves little room for any warming.

Intermediate rebuttal written by dana1981

Update July 2015:

Here is a related lecture-video from Denial101x - Making Sense of Climate Science Denial

Last updated on 10 July 2015 by pattimer. View Archives

Printable Version  |  Offline PDF Version  |  Link to this page

Argument Feedback

Please use this form to let us know about suggested updates to this rebuttal.

Further reading

NOAA have a very useful resource ENSO Cycle: Recent Evolution, Current Status and Predictions which features recent ENSO activity as well as model predictions of ENSO activity in the near future.


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Comments 126 to 150 out of 189:

  1. KR @124, The ENSO Southern Oscillation Index (SOI) is calculated from the pressure differences between Tahiti and Darwin (in Northern Australia) by the formula:
    SOI= 10*[Pdiff-Pdiffav]/SD(Pdiff) where Pdiff = (average Tahiti MSLP for the month) - (average Darwin MSLP for the month), Pdiffav = long term average of Pdiff for the month in question, and SD(Pdiff) = long term standard deviation of Pdiff for the month in question.
    (Source) IMO the SOI is a superior measure of ENSO activity to purely temperature based measures such as Nino 3.4 (and Nino 3, Nino 4, etc) in that it measures the cause of the El Nino Southern Oscillation, the pressure differences across western Pacific, rather than the consequences. It also has the advantage of having a direct instrumental record going back to the 19th century.
  2. Tom Curtis - Quite correct, my error, I clearly was thinking about NINO3.4 while typing... the SOI is from Darwin vs. Tahiti air pressure. While more complex, and with a shorter history, the MEI is IMO a good measure as it encompasses a great many of ENSO variables. However, given the high correspondence between the MEI, the ONI, and the SOI, I believe all of the three are reasonable indices to use when examining ENSO effects. Foster and Rahmstorf 2011 ran their regression analysis with both MEI and SOI - they did not find significant differences.
  3. Mods, I *still get 403 and 404 errors when I click on the first two links under this thread....?
    Response: [JH] Exactly where are these two broken links located.

    [Sph] The links cannot be fixed, only removed, as the external source documents have been removed from the Internet (and we have no control over that).
  4. I found the source pages elsewhere - links fixed.
  5. vrooomie, I am unable to find either the Tamino article or the Atmoz article on the web, so the links appear to be permanently dead. The wayback machine is currently not loading for me, so I am unable to check there. Tamino's critique was widely discussed on the web at the time, including by by Greenfyre and deepclimate, and a similar critique was made by James Annan. Tamino has a later blog discussing some of the fallout. Most importantly, an expanded version of Tamino's crtitique later appeared in a peer reviewed paper of which he was the lead author (discussed here).

    [DB] The Atmoz article is available here.

    The Tamino article is available here, via the Open Mind Archives.

    This link to the web archive is functional.

  6. So, on the basis of Bob’s comments, I’ve been looking at figure 13, shown below. Bob asked us to explain the features of the graph. The aim is to understand the features of the rest-of-the-world SST anomalies in terms of Nino34 and volcanoes. Now, as a starting point we don’t have a hypothesis for a mechanism. That means that we’re data mining. That’s OK, but it means we have to be very careful. If we already has a model we would be very constrained in testing fit to the data. With no model, we’re free to make up any sort of model, and as a result it is very easy to fool ourselves. This is a particular source of concern to me with your argument, since data mining has produced only two events, and as we will see the chances of making spurious connections as a result of artifacts of the method is rather high. So I started out by setting some rather stringent ground rules: 1. Any parameter added (including hidden parameters) must decrease the AIC of the model by at least 10. 2. Any parameter must be statistically significant at the 95% level after taking into account autocorrelation. (Preferably 99%, but I reduced that because it drew out something which may be of future interest.) I apologise that I haven’t had time to illustrate every step graphically, but I’m sure you can follow along with the analysis. So the first step was to fit the detrended rest-of-the-world SST data (henceforth Rest) with the Nino34 data, allowing a lag, a constant offset and a linear trend. Your eyeballed lag of about 30 weeks gives an R2 of ~10%, which is pretty poor. Surprisingly, you can get a rather better R2 with a lag of minus 30 weeks, which is of course nonsense, but highlights the problems of very poor models. The worse our model, the more noise we will have and the more easily we can be fooled by spurious features. The issue of course is that we’ve got a big nuisance variable - the volcanoes. So next I picked up tau_line.txt from, and calculated a term aod=0.4*NHem+0.6*SHem to take into account the proportion of ocean in each hemisphere. Including this makes a huge difference straight away, and makes the 30 week lag a clear winner over the nonsense one. Now - a physics aside: Lagging terms in a regression is a pretty unphysical thing to do. It requires a pretty funky mechanism to achieve, e.g. a delay line in electronics. A much more common mode of response to an input is an exponential decay, e.g. an RC network in electronics, the temperature response of a heat reservoir to energy input in physics, or a 1-box model in climate science. And indeed adding an exponential decay term to aod again dramatically improves the model. It looks like this:
    f(t) = exp(t/τ) {t<0} f(t) = 0 {t>0} g(t) = f(t) / Σ f(t) aodlag = aod ⊗ g
    where ⊗ denotes convolution. t is in years. τ = 0.85. (The sign of t may be reversed depending on your convolution convention, obviously the effect of the volcano must lag it’s cause.) So now the model (using R notation in which the coefficients are inferred) is: Rest ~ constant + trend + Nino34 + aodlag But we also need to take into account the the effect of Nino34 also need not be a simple lag. One of the things I try when attempting to understand a time series response to an input is feed in multiple lags simultaneously to the regression. That is a profligate use of parameters and can be misleading, but you can sometimes learn something from it. Often you see coefficients which decay over time, and you know to replace the multiple lags with an exponential response function as above. However in this case something more interesting occured. If you replace the 30 week lag with two lags of 15 and 45 weeks, you get a much better fit. So next I tried Gaussian and top hat smooths to cover the two lags and everything in between. Surprisingly, it gives a much worse model. In fact you can drop a 30 week lagged term in the middle, and it comes back weaker than the other two. The system really does seem to respond to Nino34 with two different lags. That’s really interesting, and a kick-off point for further study. With your knowledge of teleconnections and experience with the data I’m hoping you can use this as a kicking off point for some serious investigation, although I should stress that further work is required to ensure there is not a simpler explanation. I tried fitting three arbitrary lags, and got a best model with lags at 0, 9 and 46 weeks. The 0 week term is interesting, because the coefficient is negative. It’s not huge - it fails to meet my 99% criterion, but is significant at the 95% level. It doesn’t make a visual impact on the model, but I’ve kept it in case it offers a further insight into mechanism. So the model is now: Rest ~ constant + trend + lag(Nino34,0) + lag(Nino34,9) + lag(Nino34,46) + aodlag So what do the model stats look like? Here they are: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.130818 0.476369 19.168 <2e-16 *** sst$Nino34a -0.029659 0.003410 -8.699 <2e-16 *** sst$Nino34b 0.049365 0.003374 14.630 <2e-16 *** sst$Nino34c 0.034237 0.001911 17.920 <2e-16 *** sst$Year -0.004543 0.000238 -19.085 <2e-16 *** sst$aodlag -3.495310 0.095644 -36.545 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06831 on 1555 degrees of freedom Multiple R-squared: 0.5086, Adjusted R-squared: 0.5071 F-statistic: 321.9 on 5 and 1555 DF, p-value: < 2.2e-16 ν ≈ 11, so the t-values can be scaled down by a factor of 3.3, but they’re all hugely significant except lag(Nino34,0), which has a p-value of ~98%. R2 is now up to 0.5. That’s a model we can begin to draw conclusions from. What does it look like? See the following figure. Red is Rest, blue is the model, both with a 13 month smooth. We can still see a divergence around 1998-2001, although it doesn’t stand out like it did. However the 1989-1991 event has all but vanished. In eliminating the nuisance variables to obtain a better model whose parameters are therefore vastly more significant, one of the two events you identified has disappeared. That’s why I cautioned about the dangers of data-mining. When the model is poor it is far too easy to over-interpret the results. What about the 1998-2001 divergence which is still present? It’s magnitude is much reduced, but the ends are now very sharply defined. It could be down to a number of causes. It could be of genuine climatic significance. It could be noise - some more data would help. An observational issue is not impossible - we could look at other datasets to see if they show anything similar. It may be an artifact of the model - if we were doing real rather than blog science we’d either want to characterise the filter analytically (maybe some frequency domain analysis), or do extensive trials with synthetic data to see if it there is any possibility that the feature is an artifact of the method. (Note especially that nearby lags with opposite coefficients have a high pass filter effect.) Discussion: Improving the model to better fit the observations reduces the noise significantly. As a result the 1989-1991 feature largely disappears. The most convincing explanation for this feature is that it is an artifact of a poor model. The 1998-2001 feature is still present at much reduced magnitude, although the endpoints are now very clearly defined. However we now only have one event. You can’t fit a pattern to a single event. Also it gives us no grounds for determining the frequency or likely scales of such events. We really need more data. There are a number of issues requiring further study: 1. How unique is the 2 / 3 lag solution found here? Is there any less physically exotic model which would produce an equally good fit? 2. Is there any physical mechanism which can explain this behaviour? 3. Can we find similar events by extending the analysis further back in time? How unusual is the 1998 event in terms of size and shape? Do past events show any connection with surface temperature changes? 4. Is the same behaviour seen in other datasets?
  7. Tom Curtis, in your comment 80, you presented your findings about the significance (the lack thereof) of the lack of warming in the East Pacific sea surface temperature data versus the IPCC model hindcasts/projections. A recent post at Niche Modeling titled East Pacific Region Temperatures: Climate Models Fail Again found precisely the opposite, as you may have guessed from the title of the post.

  8. michael sweet says at 122: My point is still valid, you must compare to the range of estimates, not the average.”

    NCAR and Gavin Schmidt disagree with you.

    The National Center for Atmospheric Research (NCAR)’s Geographic Information Systems (GIS) Climate Change Scenarios webpage has a relatively easy-to-read description. This quote appears on their Frequently Asked Questions webpage:

    Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.

    On the thread of the RealClimate post Decadal predictions, a visitor asked the very basic question, “If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?”

    Gavin Schmidt replied:

    Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will [be] uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).

    We’re interested in the forced component, michael, not the noise, hence my use of the multi-model ensemble mean.

    michael sweet says: “Many other data sets exist that expand the time period of your analysis. It has been shown by others in this thread that you cherry picked the data set you used.”

    I cherry-picked my dataset? Since I must have missed the comments you’re referring to, let me answer your and their comments now. Maybe you’re referring to a comment by IanC. I replied to him, Why aren’t we looking at the sea surface temperature data prior to the satellite era? Because there’s little source data south of 30-45S. Here’s a map that illustrates the ICOADS sampling locations six months before the start of the Reynolds OI.v2 dataset:

    Same map for June 1975:

    And it doesn’t get better as you go back in time. Here’s June 1943:

    And to further respond to your accusations of cherry-picking, michael sweet, are you aware that HADSST2 and HADSST3 are spatially incomplete during satellite era? Are you aware that NOAA’s ERSST.v3b has to be infilled during the satellite era because it does not use satellite data? Are you aware that Smith and Reynolds called the Reynolds OI.v2 dataset the “truth”? Refer to Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997). It is about the Reynolds OI.v2 data we’ll be using as the primary source of data for this book:

    Although the NOAA OI analysis contains some noise due to its use of different data types and bias corrections for satellite data, it is dominated by satellite data and gives a good estimate of the truth.

    The truth is a good thing, don’cha think?

  9. KR says at 124: “If as you say La Nina's absorb more heat (due perhaps to changes in cloudiness or other effects) than El Nino releases, how can this have driven warming since the 1970's? There has been a preponderance of El Nino events over that period (fewer than average La Nina events to raise total climate energy, esp. late 1970's-1998).”

    You need to look at surface temperature and ocean heat content separately, because they respond differently to ENSO.

    Let’s discuss surface temperatures first:

    KR, we agree on something. “There has been a preponderance of El Niño events over that period…” Glad you confirmed that ENSO has been skewed toward El Niño since the late 1970s. This means that more warm water than normal has been released from the tropical Pacific and redistributed, and it means that more heat than normal has been released to the atmosphere. That answers your question, “how can this have driven warming since the 1970's?”

    Now let’s address the ocean heat content portion of your question: “If as you say La Nina's absorb more heat (due perhaps to changes in cloudiness or other effects) than El Nino releases…”

    This makes itself known in the Ocean Heat Content for the tropical Pacific. The 1973-76 La Niña created the warm water that served as the initial fuel for the subsequent 1982/83 through the 1994/95 El Niño events, with the La Niña events that trailed those El Ninos replacing part of the warm water. That’s why the tropical Pacific OHC trend is negative from 1976 until the 1995/96 La Niña.

    The 1995/96 La Niña was a freak, and discussed in McPhaden 1999. “Genesis and Evolution of the 1997-98 El Niño”.

    McPhaden writes:

    For at least a year before the onset of the 1997–98 El Niño, there was a buildup of heat content in the western equatorial Pacific due to stronger than normal trade winds associated with a weak La Niña in 1995–96.

    In other words, there are parts of ENSO that cannot be accounted for with an ENSO index.

    The impact of the 1995/96 La Niña stands out like a sore thumb in the graph of tropical Pacific OHC. Then, moving forward in time, there’s the dip and rebound associated with the 1997/98 El Niño and 1998-01 La Niña.

    KR asks: “Why now? What has changed? The ENSO has been an existent pattern for perhaps hundreds of thousands of years. Why would it suddenly change behavior in recent years, when it hasn't in the past?”

    Paleoclimatological studies find evidence of ENSO back millions of years ago—not just hundreds of thousands of years. See Watanabe et al (2011). Your second question (“Why would it suddenly change behavior in recent years, when it hasn't in the past?”) is an assumption on your part. El Niño events were also dominant during the early warming period of the 20th Century, and global temperatures warmed in response then, too.

    KR asks: “Finally, what about the greenhouse effect?”

    Downward longwave radiation appears to do nothing more cause a little more evaporation from the ocean surface, which makes perfect sense since it only penetrates the top few millimeters.

  10. In response to KevinC comment at 131:

    Thanks for your efforts. As you noted, it’s a great place to start a discussion. You seem to have created a great fit for the Mount Pinatubo eruption and the lesser ENSO events.

    KevinC says: “However we now only have one event. You can’t fit a pattern to a single event.”

    The 1997/98 El Niño was the strongest event and should have the cleanest signal, which makes your model versus Rest of the World graph look very awkward and unrealistic. (The fact that it should be the cleanest signal is why I keyed off the leading edge of the 1997/98 El Niño in my illustration.) Note how the other larger El Niño events in your model-data graph also don’t fit that well. If you would, please subtract the ROW data from your model to show how significant the residuals are.

    Therefore, for a really detailed analysis you’re attempting to perform, where you’re keying off all events, it’s likely you’ll need to isolate the East Pacific El Niño events like the 1986/87/88 and 1997/98 events and their trailing La Niña events. Why? The global temperature response to East Pacific El Niños (large events) is different from Central Pacific El Ninos (lesser events). That was the basis for the Ashok et al (2007) paper El Niño Modoki and its Possible Teleconnection.

    The reasons for the divergences in the Rest-Of-the-World data during the 1988/89 and 1998-2001 La Niñas are physical, KevinC. You can try to eliminate or minimize them using models, but they exist. East Pacific El Niños like the 1986/87/88 and 1997/98 El Niños release vast amounts of warm water from below the surface of the west Pacific Warm Pool. Much of that warm water spreads across the surface of the central and eastern tropical Pacific. For the East Pacific El Niño events, like those in 1986/87/88 and 1997/98, that warm water impacts the surface all the way to the coast of the Americas (while with Central Pacific El Niño events it does not). The El Niños do not “consume” all of the warm water. At the conclusion of an El Niño, the trade winds push the leftover warm surface water back to the West Pacific. Additionally, there is left over warm water below the surface that’s returned to the west Pacific and into the East Indian Ocean via a Rossby wave or Rossby waves. This animation captures a Rossby wave returning warm water to the West Pacific and East Indian Oceans after the 1997/98 El Niño. Watch what happens when it hits Indonesia. It’s like there’s a secondary El Niño taking place in the Western Tropical Pacific and it’s happening during the La Niña. It’s difficult to miss it. (The full JPL animation is here.) Gravity causes that warm water to rise to the surface with time. The leftover warm water exists and it cannot be accounted for with a statistical model based on an ENSO index. You can see—you can watch it happen—the impacts of that warm water in this animation. There are no ENSO indices that account for the leftover warm water.

  11. John Hartz says at 123: “Please explain in one or two succinct paragraphs why you do not agree with the above statement.”

    Easy to do. I’ll cut and paste my opening comment on this thread, where the East Pacific data agrees with the WMO Secretary-General Michel Jarraud’s quote, but the Rest-Of-The World data does not:

    The East Pacific Ocean (90S-90N, 180-80W) has not warmed since the start of the satellite-based Reynolds OI.v2 sea surface temperature dataset, yet the multi-model mean of the CMIP3 (IPCC AR4) and CMIP5 (IPCC AR5) simulations of sea surface temperatures say, if they were warmed by anthropogenic forcings, they should have warmed approximately 0.42 to 0.44 deg C. Why hasn’t the East Pacific warmed?

    The detrended sea surface temperature anomalies for the Rest of the World (90S-90N, 80W-180) diverge significantly from scaled NINO3.4 sea surface temperature anomalies in 4 places. Other than those four-multiyear periods, the detrended sea surface temperature anomalies for the Rest of the World mimic the scaled ENSO index. The first and third divergences are caused by the eruptions or El Chichon and Mount Pinatubo. Why does the detrended data diverge from the ENSO index during the 1988/89 and 1998/99/00/01 La Niñas? According to numerous peer-reviewed papers, surface temperatures respond proportionally to El Niño and La Niña events, but it’s obvious they do not.

  12. IanC says at 120: “Seriously, do you see yourself being wrong on the issue? do you see a possibility that your analyses are wrong?”

    IanC, with respect to my understanding of ENSO, I have investigated, discussed, illustrated, and animated the process of ENSO and its effects on global surface temperatures, ocean heat content and lower troposphere temperatures for almost 4 years. I have presented the effects of ENSO on sea surface temperature, sea level, ocean currents, ocean heat content, depth-averaged temperature, downward shortwave radiation, warm water volume, sea level pressure, cloud amount, precipitation, the strength and direction of the trade winds, etc. I have presented the multiyear aftereffects of ENSO on sea surface temperature, land-plus-sea surface temperature, lower troposphere temperature and ocean heat content data. I have created numerous animations. Everything I’ve investigated confirms my understanding of ENSO and its long-term effects.

    ENSO is a process. It cannot be accounted for by an ENSO index. Compo and Sardeshmukh (2010) “Removing ENSO-Related Variations from the Climate Record” seems to be a step in the right direction. They write (my boldface):

    An important question in assessing twentieth-century climate is to what extent have ENSO-related variations contributed to the observed trends. Isolating such contributions is challenging for several reasons, including ambiguities arising from how ENSO is defined. In particular, defining ENSO in terms of a single index and ENSO-related variations in terms of regressions on that index, as done in many previous studies, can lead to wrong conclusions. This paper argues that ENSO is best viewed not as a number but as an evolving dynamical process for this purpose.

    Compo and Sardeshmukh have not accounted for the left over warm water associated with major El Niño events, like the 1986/87/88 and 1997/98 El Niños. In time, maybe they will.

  13. Bob, in all your focus on one region of the Earth, you have apparently neatly dodged important questions with relation to global warming and your unusual conjectures: 1: Where's the heat coming from? The oceans, globally, are warming, the atmosphere is warming, and yet the Sun is not getting any brighter. What's your energy source? 2: What's the physical mechanism involved, if it's not the rise in greenhouse gases? Sloshing water about the oceans does not appear adequate if the oceans, as a whole, are warming. 3: Why is this mechanism unidirectional, when ENSO is oscillatory? 4: Assuming that a new unidirectional process must be a recent or temporary occurrence, otherwise we would have boiled or frozen awfuly quickly ... Why is your proposed physical mechanism a recent occurrence, when ENSO has been around for millennia, perhaps hundreds of thousands of years? 5: Why is the well-understood mechanism of an enhanced greenhouse effect from long-lived GHGs not operating according to their physics, despite this physics neatly explaining both present climate and palaeoclimate changes? To date I have not seen you directly tackle these absolutely crucial questions, despite several of them being asked on this thread.
  14. At comment 107, Composer99 quoted one of my earlier comments: “Are you aware that the global oceans can be divided into logical subsets which show the ocean heat content warmed naturally?”

    Composer99 replied, “No, they can't. Ocean heat has to come from somewhere.”

    Apparently you have never divided OHC data into subsets, because if you had, you would not make such a statement. Dividing the oceans into subsets shows the ocean heat comes from somewhere, but it’s not CO2.

    For the sake of discussion, I’m going to borrow some graphs from an upcoming post. Here’s a comparison graph of Global ocean heat content and the ocean heat content for the Pacific Ocean north of 24S, which captures the tropical Pacific and the extratropics of the North Pacific (24S-65N, 120E-80W). The Pacific OHC (North of 24S) shows similar but somewhat noisier warming. That is, the decadal variations are similar. The warm trend of the Pacific subset is about 72% of the global trend, but that’s to be expected since the excessive warming of the North Atlantic OHC skews the global data. All in all, both datasets give the impression of a long-term warming that’s somewhat continuous. People might assume the warmings of both datasets were caused by CO2.

    We’re going to separate the tropical Pacific (24S-24N) from the extratropical North Pacific (25N-65N), looking at the tropical Pacific first, but that requires a brief overview of how La Niña events produce the warm water that fuel El Niño events.

    El Niño and La Niña events are part of a coupled ocean-atmosphere process. Sea surface temperatures, trade winds, cloud cover, downward shortwave radiation (aka visible sunlight), ocean heat content, and subsurface ocean processes (upwelling, subsurface currents, thermocline depth, downwelling and upwelling Kelvin waves, etc.) all interact. They’re dependent on one another. During a La Nina, trade winds are stronger than normal. The stronger trade winds reduce cloud cover, which, in turn, allows more downward shortwave radiation to enter and warm the tropical Pacific.

    If you’re having trouble with my explanation because it’s so simple, refer to Pavlakis et al (2008) paper “ENSO Surface Shortwave Radiation Forcing over the Tropical Pacific.” Note the inverse relationship between downward shortwave radiation and the sea surface temperature anomalies of the NINO3.4 region in their Figure 6. During El Niño events, warm water from the surface and below the surface of the West Pacific Warm Pool slosh east, so the sea surface temperatures of the NINO3.4 region warm, causing more evaporation and more clouds, which reduce downward shortwave radiation. During La Niña events, stronger trade winds cause more upwelling of cool water from below the surface of the eastern equatorial Pacific, so sea surface temperature to drop in the NINO3.4 region, in turn causing less evaporation. The stronger trade winds also push cloud cover farther to the west than normal. As a result of the reduced cloud cover, more downward shortwave radiation is allowed to enter and warm the tropical Pacific during La Niña events.

    To complement that, here’s a graph to show the interrelationship between the sea surface temperature anomalies of the NINO3.4 region and cloud cover for the regions presented by Pavlakis et al.

    That discussion explains why the long-term warming of the Ocean Heat Content for the tropical Pacific was caused by the 3-year La Nina events and the unusual 1995/96 La Niña. First, here’s a graph of tropical Pacific Ocean Heat Content. It’s color coded to isolate the data between and after the 3-year La Niña events of 1954-57, 1973-76 and 1998-2001. Those La Niña events are shown in red. Note how the ocean heat content there cools between the 3-year La Niña events. Anyone who understands ENSO would easily comprehend how and why that happens. It’s tough to claim that greenhouse gases have caused the warming of the tropical Pacific when the tropical Pacific cools for multidecadal periods between the 3-year La Niñas, Composer99.

    As you can see, the warming that took place during the 1995/96 La Niña was freakish. Refer to McPhaden 1999 Genesis and Evolution of the 1997-98 El Niño”.

    McPhaden writes:

    For at least a year before the onset of the 1997–98 El Niño, there was a buildup of heat content in the western equatorial Pacific due to stronger than normal trade winds associated with a weak La Niña in 1995–96.

    Based on the earlier description, that “build up of heat content” resulted from the interdependence of trade winds, cloud cover, downward shortwave radiation and ocean heat content. Simple. As you can see in the above graph, the upward spike caused by the 1995/96 La Niña skews the trend of the mid-cooling period, and if we eliminate the data associated with it and the 1997/98 El Niño, then the trend line for the mid-period falls into line with the others.

    So far, there’s no apparent AGW signal.

    Let’s move on to the extratropical North Pacific. That dataset cooled significantly from 1955 to 1988, more than 3 decades. Where’s the CO2 warming signal there, Composer99? Then in 1989 and 1990, there was an upward shift. It’s really tough to miss, because the North Pacific was cooling before the sudden 2-year warming and then it warmed after it. As you’ll note, the cooling trend before the shift is comparable to the warming trend after it. BUT, big but, the cooling period lasted for 34 years, while the warming period only lasted for 22 years. That means the North Pacific (north of 24N) would have cooled since 1955 if it wasn’t for that 2-year upward shift.

    In summary, the ocean heat content data for the Pacific Ocean north of 24s (the initial graph)  gives misleading impression of a relatively continuous warming; it’s misleading because, when the data is broken down into two logical subsets, tropics versus extratropics of the North Pacific, the data clearly shows that factors other than greenhouse gases were responsible for the warming.

  15. Skywatcher says at 138: “To date I have not seen you directly tackle these absolutely crucial questions, despite several of them being asked on this thread.” I believe I’ve answered your questions. Please review my comments on this thread, including the ones I posted while you were writing yours. As opposed to my answering your questions by cutting and pasting those comments, here’s a link to a video that also answers all of your questions:
    Response: [Sph] I'm sorry, but no, they have not been answered, and they are simple, straightforward questions that deserve simple, straightforward and clear answers.

    I think Skywatcher's request deserves a non-evasive response (a link to an hour long video is hardly a response to a question).

    I would suggest, for clarity, posting one comment for each question, and sticking to the question being addressed, to avoid any confusion. Please answer the questions directly and succinctly.
  16. @Bob Tisdale: You have not responded to the question I posed to you in #121. For everyone's convenience, I will repost it here. Do you believe the following graph to be a valid representation of Jan-Oct global land & and surface temperature anomalies with respect to the 1961-1990 base period for calendar years 1950 through 2012? Image and video hosting by TinyPic Source: 2012: Record Arctic Sea Ice Melt, Multiple Extremes and High Temperatures, WMO Press Release No 966, Nov 28, 2012
  17. I'd rather not add another question to the heap here but if we're to accept the description Bob provides us we need to ask how the reversal of entropy works in his system. In order for Bob's scenario to work there needs to be some kind of heat pump added to the picture. Bob, where's the heat pump? How does it function?
  18. Bob, So in short, you are very certain that you are correct, and see no possible reason for your analysis to be flawed? By the way, I'm mainly referring to PDO and decadal temperature trends.
  19. Bob: You are claiming that the difference between Nino34 and the rest of the world temperature is real. Well, yes, I don’t dispute that. However, in attaching meaning to that difference, you are implicitly assuming a model. Just because your model is very simple, doesn’t mean it isn’t there. And you conclusions are totally dependent on the validity of that model. You implicit model seems to be something like this: Ignoring volcanoes, the rest of the world temperatures track Nino34 with a single lag, except for some significant deviations which require another explanation. There’s a huge assumption here. (In fact there are several, but one is relevant.) You have assumed that normally the whole of the rest of the world SST follow Nino34 with a single lag. Using your knowledge of teleconnections, do you consider that to be a safe assumption? Now, you may have been led to make this assumption because others have made it before, notably Foster and Rahmstorf. That doesn’t make it true. It needs to be checked to see if it affects the conclusions for a particular question. I’ll be looking into that for F&R in the light of what we’ve done here, but that’s not the subject at hand. So let’s do the check for your problem. This is a much simpler calculation. Let us create a hypothetical system in which two lags are presents, so that the mean SST over the rest of the globe is the sum of two equal Nino34 terms with lags of 9 and 46 weeks. Now suppose we try to fit the SST with just a single lag. Here’s what the true SST and best single lag model look like: Note that the model has some serious deviations, especially around 1998-2001, but also 1985 and 1989. These deviations are solely due to the failure of the model to reflect the known behaviour of our hypothetical system. I presume you will therefore accept that if we were to attempt to draw conclusions from these divergences, those conclusions would be fantasy? If so, then the question we have to ask is how does the real system behaves. In order to justify your conclusions, you need to establish that your single lag model is realistic. I made no such assumption - rather I asked the data what lags were present, using very conservative statistical tests to avoid overfitting. There are a number of other tests of various kinds we can apply to see whose is the better model - I suspect we will be working through them over the next few weeks. However the whole discussion is missing the really interesting bit of the science. The 2-lag (and possibly 3-lag) solution is potentially interesting from the point of view of ENSO and teleconnections. We’ve got the first piece in the puzzle and there are obvious ways to explore this further on a spatial as well as temporal level. It’s your project, there may be a paper in it, and I really don’t want to do it for you - I’ve got more unwritten papers than I can handle already.
  20. Like Doug I have no desire to pile on to Tisdale, although his persistent skirting of the hanging questions makes it difficult not to do so when entering the fray. However, he made a comment at #134 that I'd like to have clarified:
    Downward longwave radiation appears to do nothing more cause a little more evaporation from the ocean surface, which makes perfect sense since it only penetrates the top few millimeters.
    Specifically, I would like to know by exactly how much Tisdale believes that "[d]ownward longwave radiation" does or does not contribute to warming of the planet in terms of a particular DLR flux, and by what mechanisms that warming does - or indeed does not - does not occur. Numbers and primary references would assist to make an objective and nuanced case.
  21. Bob Tisdale @134 claims that:
    "El Niño events were also dominant during the early warming period of the 20th Century, and global temperatures warmed in response then, too."
    That is a breath taking claim, but he appears to support it with a graph showing the average SST anomaly in the ENSO 3.4 region in the period 1912-1944 was 0.02 C warmer than that in the period from 1976 to 2011. He neglects to mention that the later half (1999-2011) of the second period is La Nina dominated, significantly bringing down its average temperature. Using the Reynold's data from Nov 1981 to the end of 1998, the average anomaly is 0.22 degrees C, whereas that from November 1981-Dec 2011 is only 0.08 C, the difference being because of because the average anomaly from Jan 1999 to Dec 2011 is -0.11 C. This raises two questions. Why did Tisdale include a La Nina dominated period in his final period, when he must know that doing so will distort the comparison with the earlier period? And if La Nina dominated periods cause cooling, why has there been no cooling in the La Nina dominated period from Jan 1999 to the present? Tisdale's response cannot be that El Nino dominated periods cause warming, but La Nina dominated periods do not cause cooling, for as he says, the El Nino Southern Oscillation has been in existence for millions of years. If it acts to warm in El Nino dominated periods, but does not equally act to cool in La Nina dominated periods, it must act as a ratchet on global temperatures, pushing them always higher, which of course, has not occurred over those millions of years. That, however, is not the issue I wish to pursue in this post. Interestingly, in his comment @137 Tisdale quotes Compo and Sardeshmuk (2010) as saying:
    "In particular, defining ENSO in terms of a single index and ENSO-related variations in terms of regressions on that index, as done in many previous studies, can lead to wrong conclusions. This paper argues that ENSO is best viewed not as a number but as an evolving dynamical process for this purpose."
    Tisdale emphasized that point, which is odd given that he persistently uses just one, temperature based index of ENSO activity. While he wants to drive the point home, he appears also to want to ignore its implications regarding his methods. Indeed, Compo and Sardeshmuk expanded further on this theme:
    "One immediate difficulty with using such single-index definitions of ENSO is that no ENSO-unrelated variations can occur in that index. If one uses the Nino-3.4 SST index, for example, then no ENSO-unrelated 'global warming' signal can ever occur in the Nino-3.4 region - by definition."
    (Original emphasis) It should not need saying that other sources of SST fluctuation are also excluded by definition. Therefore simply taking the SST anomaly in the Nino 3.4 region over vastly different time periods cannot plausibly be considered a measure of ENSO activity. It in no way allows for the effects of other factors which we know will cause changes in the SST in the Nino 3.4 region as much as anywhere else. As I have previously indicated, I dislike the Nino 3.4 index for exactly this reason. While suitable for short term comparisons, it is not suitable for comparisons across the span of a century. A far more suitable measure in the Southern Oscillation Index (SOI): Even a brief glance shows that the period 1912-1944 is nowhere near as dominated by El Nino Events as that between 1976-1998. The cumulative sum of the inverted SOI for the former period is negative 10.34, while that of the latter is positive 87.23. Even including the full 1976-2011 interval results in a cumulative sum of 56.01, despite the very negative 32.21 cumulative sum for 1999-2011. So, contrary to Tisdale, the period of rapid temperature increase in the early Twentieth century was ENSO neutral, and leaning towards La Nina dominance rather than the reverse.
  22. Bob, Your claim here that Gavin Schmidt at Realclimate says you can compare a single realization (what happened) to the model average and not to the model envelope is simply untrue. The single realization includes all the noise. Your conclusion that temperatures are not what was predicted relies on this noise alone. It is clear that you are not on a reality based program. I do not have time to exchange comments with such clap trap. I am withdrawing from the exchange.
  23. Bob Tisdale #140: You did not answer my questions. That's why I asked them. And your video does not answer them either. I'll echo Sphaerica's request for nice clear answers that would fill the crucial, enormous holes in your vague conjectures. Without them your conjectures are, to me, utterly worthless.
    [From Tisdale's #139:] Composer99 replied, “No, they can't. Ocean heat has to come from somewhere.” [Tisdale responded:] Apparently you have never divided OHC data into subsets, because if you had, you would not make such a statement. Dividing the oceans into subsets shows the ocean heat comes from somewhere, but it’s not CO2.
    That's a truly staggeringly nonsensical statement. Apparently you think that you can increase the quantity of something by dividing it up, that's an interesting recipe for a perpetual motion machine. Try it with an orange sometime? sigh, where's the heat coming from, Bob, and why only within the last century or so? You are presently magicking an enormous amount of energy into existence with an alacrity that would truly alarm Einstein and Hawking...
  24. Actually, I think I've misrepresented the cause of the artifacts in Bob's figure 13. I only get odd minutes to work on this, so I've only just got to the point of picking my model apart again to see what is happening inside. It now looks like the cause of Bob's artifacts is even simpler than I have suggested. The multiple lags thing is real and very interesting scientifically, but it's not the dominant factor in the features of figure 13. I'll try and give a fuller analysis over the next few days. Then move on to strict validation tests to confirm or refute my hypothesis next weekend.
  25. Bob Tisdale
    KR asks: “Finally, what about the greenhouse effect?” [more than sufficient to cause observed warming without asymmetric ENSO] Downward longwave radiation appears to do nothing more cause a little more evaporation from the ocean surface, which makes perfect sense since it only penetrates the top few millimeters.
    I'm afraid this rather glib dismissal of the greenhouse effect fails to substitute for science. Despite the short penetration depth of longwave radiation, it is more than sufficient to greatly warm the oceans by changing the skin layer temperature gradient, the top millimeter of the oceans. There's an excellent article on it here, including the experimental evidence - one of the better discussions I've seen on the topic, in fact. Again - the radiative greenhouse effect more than accounts for the observed warming of the last 50 years. Forcings during the early 20th century (in particular high insolation and quite low volcanic aerosols) account for early 20th century warming, too, despite your statements. Claims of asymmetric ENSO forcings (in addition to lacking a physical mechanism for asymmetry) simply have no support from the full temperature record. To be blunt, I consider both your (a) assertion of (recently) asymmetric ENSO's and (b) dismissal of the accumulated evidence for the radiative greenhouse effect to be equally glib handwaving. You've failed to support either with clear, testable hypotheses, or to even discuss the (multiple) lines of evidence contradicting your hypotheses.

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