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Making sense of the slowdown in global surface warming

Posted on 26 May 2015 by Kevin C

The slowdown in global warming is a subject of intense study. Is it a real physical effect, or a few chance cool years, or something more complex? Could it have been predicted? Can we understand it in retrospect? The following lecture and commentary from the Denial101x course attempt to summarize recent work on the subject. However it is a very fast-moving field, so this summary can only cover a small fraction of the material and will quickly become out-of-date (if it is not already so).

View on YouTube

Making Sense of the Slowdown: Commentary

The term 'hiatus' is often applied to describe a slowdown in the rate of global warming since the late 1990s or the early 2000s. However there are two separate questions which are often confused in discussion of the hiatus. The first is whether there has been a change in the rate of warming, while the second concerns whether the rate of warming is in line with model projections.

When looking at the rate of warming, the year-to-year variability makes it hard to draw conclusions from short periods, especially if we are allowed to cherry-pick a start date. Separating a change in the rate of warming from a few chance cool years is hard, however careful analysis of climate models suggest that recent changes in the rate of warming can occur naturally, but are uncommon (Roberts et al 2015).

However the interesting scientific question is not whether changes in the rate of warming can be explained by chance, but rather how they affect our understanding of the climate system. Given that one way we understand the drivers of climate is by doing experiments in climate models, any difference between the models and observations is interesting.

Climate models

Climate model projections are based on predictions of what the drivers of climate will be over future years - which involves making educated guesses of not only human activity, but also solar cycles, and volcanic eruptions. The problem is that if those guesses are wrong, the climate models will give the wrong results, even if their simulation of the physics is perfect.

Climate model projections for temperature have been higher than the observations for most model runs over the last decade - see for example IPCC (2013) figure 9.8. That could mean that there is a problem with the models, but it could also be a problem with the inputs.

The problem is further complicated by internal variability - the long term climate signal is hidden under chaotic short term variations - which we can loosely describe as weather. If the same model is run several times with the same inputs and almost identical starting points, it will give different results. Any difference triggers a 'butterfly effect' leading to wildly divergent results. We can retrieve the climate signal from a climate model by running it lots of times and averaging the results.

The real world shows chaotic behaviour too, and we can't average that out because we only have one reality. But to determine whether the models are giving realistic results over periods of a decade or two, we have to first eliminate the chaotic part of the signal.

So comparing models to observations is hard. First we must correct the inputs to match what actually happened since the models were run, then we have to correct for the effects of internal variability. A number of recent papers have attempted exactly this, and we will look at some of them in detail.

Schmidt et al (2014): Reconciling warming trends

Schmidt and colleagues estimate the effect of updating the inputs to the models, including the effects of increased volcanic activity (stratospheric aerosols), a weak solar cycle, the cooling effect of pollution (tropospheric aerosols), and also higher than expected greenhouse gas emissions (although this last term is tiny). They adjust the model outputs to match the observed variability in the Pacific (the El Niño Southern Oscillation or ENSO) by estimating the contribution of El Niño to global temperatures in previous years

When they compare the adjusted model results with global temperature series from NASA GISTEMP, and from Cowtan & Way (2014) they obtain a good agreement. The largest contributor to the hiatus by this method is the increase in volcanic activity, followed by increased pollution, and then the weak solar cycle and a recent trend towards cool La Nina events.

The animated comparison of the model outputs to the data shown in the lecture is based on this paper.

Huber and Knutti (2014) Natural variability, radiative forcing and climate response in the recent hiatus

Huber and Knutti perform a similar analysis: Like Schmidt and colleagues, they adjust the model outputs to account for the difference between the predicted volcanic and solar contributions and what actually happened. Unlike Schmidt and colleagues they do not include any adjustment for an increased cooling effect due to human pollution. The biggest difference in their approach is that rather than estimating the temperature impact of El Niño cycles from the historical record, they select segments from existing climate model runs which show similar El Niño behaviour to what actually happened, and use these to estimate the impact on global surface temperatures.

In contrast to Schmidt and colleagues, Huber and Knutti find the the trend towards the cool phase of the El Niño cycle is the biggest contributor to the hiatus, with the solar and volcanic contributions each being about half the size of the El Niño contribution. However they also find good agreement with the UK Met Office temperature data once it has been extended to global coverage.

Risbey et al (2014) Well-estimated global surface warming in climate projections selected for ENSO phase

In an approach similar to that of Huber and Knutti, Risbey and colleagues select segments from climate model runs which show a similar trend in El Niño over overlapping 15 year periods. They do not however update the volcanic or solar contributions. When they compare these selected model segments to the observed trends from global temperature series from NASA GISTEMP and from Cowtan & Way (2014) they obtain a good agreement. This supports the suggestion of Huber and Knutti that the El Niño contribution is the most significant.

Saffioti et al (2015) Contributions of Atmospheric Circulation Variability and Data Coverage Bias to the Warming Hiatus

All of the preceding papers have examined the contribution of the tropical Pacific, and in particular the El Niño cycle to the slowdown in warming. However this presents a conundrum: The slowdown in warming is not a global phenomena. In fact, most of the planet has continued to warm, however this warming has been masked by a much larger localised cooling of the northern mid-latitudes, particularly over Asia. Furthermore, the cooling is primarily a winter phenomena (Cohen et al, 2012).

Saffioti et al examine air pressure data for the northern hemisphere excluding the tropics, and find two modes of internal variability (one being the North Atlantic oscillation, also investigated by Trenberth and Fasullo 2013) which have contributed to a cooling over the hiatus period, with a similar geographical pattern to the observed cooling. These variations, along with the omission of the Arctic from observational datasets, explain the majority of the slowdown in warming.

Evidence for and against the different contributions to the hiatus

In the lecture, the different contributions to the hiatus were summarised in this graphic, illustrating how the greenhouse warming has been offset by cooling effects from some combination of the El Niño cycle, volcanic activity, the solar cycle, pollution, and incomplete coverage of the observations.


Unpicking the precise impact of the different contributions is clearly not a solved problem. We can however look at the evidence for the existence of each effect in turn.

El Niño

The influence of the El Niño cycle has long been known, and can be seen by simply comparing the El Niño cycle against the global temperature record over various time spans, demonstrated by papers including Lean and Rind (2009) and Foster and Rahmstorf (2011). However the fact that the cycles agree does not necessarily mean that one causes another - additional evidence is required.

Kosaka and Xie (2013) (blog post) used an ensemble of climate model runs and constrained the ocean surface temperatures over a small region of the Pacific to match observations, and found that the resulting global temperatures tracked observations well, suggesting that El Niño does indeed play a driving role in global temperature, and that the El Niño trend plays a dominant role in the hiatus.

England et al (2014) (blog post) find a possible mechanism for the El Niño trend in a strengthening of easterly winds in the tropical Pacific, driving heat down into the western pacific ocean, but with impacts in the Indian ocean as well. When replicated in models this mechanism explains a large part, but not all of the hiatus.

The El Niño cycle is not the only natural cycle in the climate system. Some have questioned whether other cycles might be involved in the hiatus. Steinman et al (2015) (blog post) address this question by examining the effect of both Atlantic and Pacific oscillations. They develop a method to isolate internal climate variability by subtracting out the human contribution from climate model runs, then look for the same signature in the observations to determine the impact of internal variability in the real temperature record. They find a small contribution from the Atlantic to the current warmth, but a major role for the Pacific in the slowdown in warming.

These studies provide a range of evidence for a role of internal variability, particularly in the tropical Pacific, in the slowdown in global warming. The results typically explain between half and most of the slowdown.


Climate projections assume an 'average' frequency of volcanic eruptions, and while the 2000's have not featured any massive eruptions like the 1991 Pinatubo eruption, there have been an increasing number of modest eruptions. Our graphic shows all of the volcanic eruptions with a Volcanic Explosivity Index (VEI) of 4 or greater, however in reality the type and location of the eruption are also important.

One of the primary sources of evidence comes from satellite observations of the transparency of the earth's atmosphere: Vernier et al (2011) detected the impact of volcanic particles using three different satellite instruments. Santer et al (2015) also detect the volcanic influence in surface observations, including  incoming solar radiation, air temperatures, tropical and global sea surface temperatures and water vapour observations.

The impact of volcanic emissions is also demonstrated through a number of modelling studies, for example Fyfe et al (2013) who find that volcanic emissions could have cooled the earth by 0.07°C compared to projections. Other studies include Solomon et al (2011), Ridley et al (2014), and Haywood et al (2014) with the last study finding a smaller effect.

Solar cycle

The influence of the solar cycle on recent climate is in some respects the simplest to understand, since the intensity of incoming solar radiation has been directly measured by satellites since 1978 (e.g. Fröhlich 2000). The last solar cycle has featured a prolonged minimum followed by a rather weak maximum, with a corresponding cooling effect on the climate. However while the amount of incoming radiation is measured, there are possible secondary effects from the impact of variations in ultraviolet radiation on atmospheric chemistry which could amplify the direct impact of the weak solar cycle (e.g. Shindell et al 1999).


The Schmidt et al study found a cooling contribution to the hiatus from industrial pollution particles (aerosols) emitted into the atmosphere, with the principal change being rapid industrialisation in Asia. This was also suggested by Kaufmann et al in 2011. In our graphic the factory symbols track the growth of the Chinese economy. This is probably the most contentious contribution to the hiatus, with recent studies like Regayre et al (2014) and Gettelman et al (2015) finding little or no impact from pollution aerosols over the hiatus period. However Schmidt et al do include the effect of nitrates, whereas the other studies are restricted to sulphate pollution.


The widely used HadCRUT4 temperature data from the UK Met Office only covers about five sixths of the planet, with significant areas of missing coverage over the Arctic, Antarctica and central Africa. Cowtan and Way (2014) examined the effect of missing coverage, and found that if the observations are extended to cover the whole globe either by infilling from nearby weather stations, or using satellite data to fill the gaps, the temperature trend over the hiatus period substantially increased.

About half of the increase comes from the Arctic, which although comprising a rather small region has been warming many times faster than the rest of the planet. The rapid warming of the Arctic is well established from observations (e.g. Berkeley Earth), weather models (Simmons and Poli 2014, Saffioti et al 2015), and infrared observations from satellites (Comiso and Hall 2014), and is of course implicated in the decline in Arctic sea ice.

The other half of the coverage bias is split between Antarctica and the rest of the world. For the rest of the world, coverage is reasonable and the agreement between Berkeley Earth, NASA GISTEMP and Cowtan and Way are good. Antarctica is more difficult with very few stations covering a massive area. GISTEMP and Cowtan and Way show similar trends, however Berkeley Earth shows slower warming and better agreement with the weather models. Therefore while most of the coverage bias is well founded, the Antarctic contribution is more uncertain.


There is substantial evidence that pacific variability, volcanic cooling, the solar cycle, and incomplete data coverage have all contributed to offsetting greenhouse warming since the late 1990s. Pollution may also have played a role, but is more contentious. In size, the effects of El Niño, volcanoes, and coverage are all substantial. The impact of the weak solar cycle is smaller unless it has been significantly amplified through effects on atmospheric chemistry. If all the effects reviewed here are as large as the authors suggest then the hiatus probably over explained, however this assumes they are independent - some of the effects could be different ways of looking at the same phenomenon.

So we have a good idea what factors are causing the hiatus. The size of each of the effects is less certain - together they may fall a little short of explaining the slowdown in warming, or they may overshoot. Estimates for the different cooling influences over recent years will continue to be improved, giving us a clearer picture of how climate changes over short as well as longer timescales.

The hiatus has not changed our understanding of the fundamentals of climate science in terms of the greenhouse effect and amplifying feedbacks. However the desire to understand short term trends, rather than the long term changes on which climate science has traditionally focussed, has driven significant new work into internal variability, volcanic impacts and observational biases. In other words it has forced us to improve our understanding of the climate system, but so far those changes have not affected our expectations concerning future climate change.


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  • Comiso, J. C., & Hall, D. K. (2014). Climate trends in the Arctic as observed from space. Wiley Interdisciplinary Reviews: Climate Change, 5(3), 389-409.

  • Cowtan, K., & Way, R. G. (2014). Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society, 140(683), 1935-1944.

  • England, M. H., McGregor, S., Spence, P., Meehl, G. A., Timmermann, A., Cai, W., ... & Santoso, A. (2014). Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Climate change, 4(3), 222-227.

  • Haywood, J. M., Jones, A., & Jones, G. S. (2014). The impact of volcanic eruptions in the period 2000–2013 on global mean temperature trends evaluated in the HadGEM2?ES climate model. Atmospheric Science Letters, 15(2), 92-96.

  • Huber, M., & Knutti, R. (2014). Natural variability, radiative forcing and climate response in the recent hiatus reconciled. Nature Geoscience.

  • Regayre, L. A., Pringle, K. J., Booth, B. B. B., Lee, L. A., Mann, G. W., Browse, J., ... & Carslaw, K. S. (2014). Uncertainty in the magnitude of aerosol?cloud radiative forcing over recent decades. Geophysical Research Letters.

  • Ridley, D. A., Solomon, S., Barnes, J. E., Burlakov, V. D., Deshler, T., Dolgii, S. I., ... & Vernier, J. P. (2014). Total volcanic stratospheric aerosol optical depths and implications for global climate change. Geophysical Research Letters, 41(22), 7763-7769.

  • Risbey, J. S., Lewandowsky, S., Langlais, C., Monselesan, D. P., O’Kane, T. J., & Oreskes, N. (2014). Well-estimated global surface warming in climate projections selected for ENSO phase. Nature Climate Change, 4(9), 835-840.

  • Santer, B. D., Solomon, S., Bonfils, C., Zelinka, M. D., Painter, J. F., Beltran, F., ... & Wentz, F. J. (2014). Observed multivariable signals of late 20th and early 21st century volcanic activity. Geophysical Research Letters.

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Comments 1 to 22:

  1. Thanks interesting article and video.

    It is going to be interesting to see where 2014, 2015, 2016 put things as the current El-Nino plays out.

    Do wonder whether the global temperatures might migrate towards the higher end of predictions again?

    The last 12 months was the hottest to date again (April 2014, May 2015).

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  2. Were global temps at the higher end of predictions at some point?

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  3. @2 "Were global temps at the higher end of predictions at some point?"

    This Sks post (by Dana) contains the following statement;

    The observed trend for the period 1998–2012 is lower than most model simulations. But the observed trend for the period 1992–2006 is higher than most model simulations. Why weren't Curry and McIntyre decrying the models for underestimating global warming 6 years ago?

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  4. 1. You make no mention of the change in forcing due to the reduction in CFC emissions. The impact of this on the rate of increase in anthropogenic forcings can be seen in the GISS forcing data. I would have expected an effect on the warming rate as large as those you report. Were you unable to find relevant literature that considered CFC's as a cause.

    2. I also think that in making sense of the slowdown it is important to consider views like Taminos. I view from his pieces is that there is no slowdown unless you compare the trend using short time periods (15 years) instead of decent intervals of 30 years. With short intervals the apparant differences are meaningless due to inherent uncertainty and we must conclude that warming continues at the same pace.

    A key point in making sense of the slowdown is that it is an illusion we inflict on ourselves by trying to see a pattern in short term data. If there has been a slowdown since 2000 we need to wait another 15 years to have a reasonable chance of detecting it. Past experience should warn us that the most likely explanation is that we seeing just another step of the down escalator. The explanations given above being reasons these steps can appear but they are not explanations of a real slowdown.

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  5. I've read many discussions of the supposed slowdown or hiatus, including (of course) Tamino's posts.  But no one I've read has discussed it in the rather different way I've been analyzing it.  I'm in the middle of writing it up, but in short, if you look at the longest temperature record, the one from 1850 to present (e.g., HADCRUT4), you can see a number of features including a 30-year rise from about 1910 through 1940 and a 30-year flat period from about 1940 to 1970.  I find that you can reproduce the entire 160+ year record surprisingly well with the sum of a smooth trend (e.g., quadratic fit), a 66.5-year sine wave, and a 21.3-year sine wave.  Except for frequencies with period less than about 10 years, and so excluding  ENSO and volcanos, this simple sum reproduces all the main features of the data.  BTW, there is no trace of the sunspot cycle that I can find in the HADCRUT4 data.

    A slowdown since around year 2000 is clear in this reconstruction, being the destructive interference of the sine waves combining with the trend, and is just ending. 

    Now, this may be only numerology, but the components are so few and so simple that the approach is attractive.  One set of causes - two ongoing oscillations continuing for more than 150 years - interacting with a smooth, simple, concave upwards temperature trend.  No need to invoke unpredictable variations in ENSO, no need to bring in special cases for other features, no mystery about the halt in warming between 1940 and 1970.

    As to what these two sinusoids represent, that remains to be seen.  But note that a current of only 1 km/day over a size typical of ocean basins would give times in the right ballpark.  And you would think that the oceans would have to be involved in oscillations with such long periods.  

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  6. This style of curve-fitting is common. Nicola Scafetta has published on this many times. The trouble is with quadratic and two sine curves you can fit any time series well. See here for more statistical discussion and specifically on Scafetta here. Unless you have a physical basis for the curve, what are we to make of it?

    Of course you can do curve fitting with the actual physical factors (eg Schmidt and Benestad, which was a counter to another Scafetta wild claim). Compare that with yours for same period. If you want to postulate some "undiscovered natural cycle", then where is the heat coming from (ie your proposal must respect conservation of energy), and what is your explanation for the measured forcings have so little effect if you think the natural cycle is important?

    If you insist on curve fitting, then a better way to do it, is use part of the data set for training (eg first 1/2 to 3/4) and then see how well it predicts the rest of the dataset, or do it in reverse.

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  7. You might also like to run EXACTLY the same curve-fitting analysis, (quadratic and 2 sine) but with say monthly Dow-Jones average and see how good the fit is.

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  8. @scaddenp: "This style of curve-fitting is common. Nicola Scafetta has published on this many times. The trouble is with quadratic and two sine curves you can fit any time series well.... Unless you have a physical basis for the curve, what are we to make of it?"

    Yes, of course.  Not only that, but the data are too noisy to be able to discriminate between variations on the theme, or even between very different models.  Yet you can't necessarily fit just any time series with two sine waves (plus a slow, nearly DC component, in this case).  This analysis didn't show me that the data *is* caused by a couple of sine waves.  It's in no way adequate for that.  It just showed that many of the really pronounced features of the temperature record can be reduced to just a few.  That's usually worthwhile.

    As for physical causes, I don't know about that (I'll add: "yet").  It would be better to know.  I'm a physicist and engineer, I always want physical causes.  You know, it's something like the tides.  The causes are well known - the modification in the Earth's iso-gravitational contours caused by the sun and the moon, approximated by a dipole moment - but the way in which they combine over time, together with the detailed shapes of the ocean basins and local undersea topography and weather lead to very complex details of the time series at any given point.  The details of all this may not really be known, but the general picture still gives us quite a bit of understanding even so. 

    Maybe existing climate models actually crank out a 66-year oscillation.  That would be interesting to know about, although I don't at the moment.

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  9. @scaddenp: "You might also like to run EXACTLY the same curve-fitting analysis, (quadratic and 2 sine) but with say monthly Dow-Jones average and see how good the fit is."

    That would be interesting, wouldn't it?  I recall from many years ago that someone published an analysis of extinctions (of species) that purported to identify a particular periodicity.  I think it was around 55 million years, if memory serves.  The method was fairly complex, and of course the data was pretty sketchy.

    Some years later, someone else tried repeating the work, and found that when fed any pseudo data that looked roughly like the real ones, even data with some other periodicity, that the original method always cranked out that 55 million year periodicity.  Somehow the method just baked it in.  I remember reading the second paper with a lot of enjoyment.

    In this case, it's easy to show in several ways that there's a lot of power at the 66-year period, so whatever the reason for it, I'm confident it's really there.  Is it "real", in the sense that somehow heat actually is sloshing around in the oceans with that period?  II would seem to be generically plausible.  ENSO has heat sloshing around with periods of a year or a few years.  The PDO and AMO (Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation) seem to be accepted phenomena, and they are generally speaking the right kind of thing (whatever may be driving them).  The current indices of the PDO don't correlate too closely with the temperature anomaly record.  The AMO correlates better, if I recall correctly from looking at it last year. (Actually, these indices are arrived at by subtracting off some version of the temperature trend from the data, then doing some variation of a principal component analysis.  So using them to support the idea of a long-period heat sloshing may seem like a bit of circular reasoning, but at least I'm not the only one).

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  10. For the shorter term predictions, is the problem with the accuracy and precision of the data or is it a problem with the precision of the climate models?

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  11. _rand15_ @ 9

    "so whatever the reason for it, I'm confident it's really there."

    What does that even mean? You are describing a shape, nothing more, nothing less. The shape you are decribing is really the shape that it is?

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  12. Extracting a 66yt periodicy from 160 years of data is extremely fraught. And yes, I am pretty sure you will get around same value from output of climate models over same period since its source 1940s temperature depression.

    Beware Von Neumann's elephant: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk."

    You havent provided details but fitting 2 sines and a quadratic sounds like 9 parameters to me. (contant, linear and quadratic terms for the quadratic; amplitude, phase, and frequency for each sine).

    I dont doubt that ocean cycles provide pseudo-periodic signal to the decadal noise in climate signal and that this is part of "slow down" - a series of La nina's while PDO negative. While interesting in terms of understanding causes of internal variability, they dont say much about climate.

    As I understand it, it is not clear whether AMO index represents ocean noise or whether it is forced.

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  13. Wryan - in the short term, the temperature record is dominated by internal variability - ENSO in particular. This is chaotic behaviour that defies prediction even a few months in advance. Models have no skill at decadal level prediction because of this. Noone can tell you when PDO will switch from -ve to +ve. It is also extremely unclear what effect a warming world will have on ENSO, if any. However, the range of behaviour is bound by the long-term energy balance and this is what emerges from climate models.

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  14. WRyan - Climate models are boundary condition solutions, where expected variation is contained within the range of possibilities. As a result, they do not predict exact trajectories of a particular set of initial conditions. We can expect the weather to vary within the range of climate model ensembles, if the predictions are good, but there is no way, really, to project exactly how internal variations like El Nino, volcanoes, the PDO, etc will track within that range.

    Climate is about averages and ranges, not about predicting whether it will rain in Buffalo NY on May 9th twenty years from now. For short term solutions, weather models take very specific starting conditions and see how they might develop - with a useful range of about a week. But we can still predict that summers will be warmer than winters, and that 30 years from now average global temperatures will have risen something like 0.48C (0.16C/decade) +/- variations if nothing changes.

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  15. _rand15_

    I worked for many years with material scientists in a commercial lab, and I learned never to let statistics and nice mathematical constructs trump physics, no matter how simple, well-fitting and tractable the models seemed.

    "All models are wrong, but some are useful" (George Box) and "Let models be simple, but not too simple" (Einstein) are good precepts to the modeller or analyst.

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  16. The article above mentions Steinman, Mann, and Miller (2015), but does not explicitly mention what I think is one of their most interesting contributions to the idea of multidecadal *internal* (rather than external) variability, which is the multidecadal NMO, which is more general than either the AMO or the PMO and thus I think should be the focus rather than either the AMO or PMO. (The PMO and NMO are explained further below.) The article links to the abstract of the paper (which requires payment to read), but does not link to that summary Mann wrote which contains a nice graph of the NMO for all to see. See here
    for a good summary by Mann, this summary found also at the Huffington Post and Ecowatch sites. This article giving a nice graph of the NMO, and here is a link to the graph:

    As for the PMO and NMO: Mann says, "We focused on the Northern Hemisphere and the role played by two climate oscillations known as the Atlantic Multidecadal Oscillation or "AMO" (a term I coined back in 2000, as recounted in my book The Hockey Stick and the Climate Wars) and the so-called Pacific Decadal Oscillation or "PDO" (we a use a slightly different term-Pacific Multidecadal Oscillation or "PMO" to refer to the longer-term features of this apparent oscillation). The oscillation in Northern Hemisphere average temperatures (which we term the Northern Hemisphere Multidecadal Oscillation or "NMO") is found to result from a combination of the AMO and PMO.

    Here is a very recent study the article above did not mention, A. Dai, Fyfe, Xie, and X. Dai (2015):
    "Decadal modulation of global surface temperature by internal climate variability"
    This article below contains quotes from the authors:
    "Scripps Study Explains Recent Pause in Global Warming"
    "A National Science Foundation-supported study co-authored by Shang-Ping Xie, a climate scientist at Scripps Institution of Oceanography, UC San Diego, attributes nearly the entire difference between observations and simulations to a climate cycle known as the Inter-decadal Pacific Oscillation (IPO)........"The new study extends this earlier modeling study by relying on observations that go back to 1920," said Xie, "We show that over nearly 100 years, the observed deviations in global mean temperature from the anthropogenically forced climate response are nearly all due to IPO."....... "Recent history suggests that the IPO could reverse course soon. Should that happen, we may see accelerated global warming rates in the coming decades," said Dai."

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  17. A recently published paper states “we use lidar, Aerosol Robotic Network, and balloon-borne observations to provide evidence that currently available satellite databases neglect substantial amounts of volcanic aerosol between the tropopause and 15km at middle to high latitudes and therefore underestimate total radiative forcing resulting from the recent eruptions.”

    Has anyone seen a graph that takes this into consideration? 

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  18. After reading Schmidt et al (2014) from the link above they dont use Ridley et al (2014) data which found a lot more volcanic cooling. If you included his findings I think the models are running low, so more than 3 degrees should be expected.. 

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  19. You have fallen for the old Conservative trick of letting them define the language.Stop using the term “hiatus” and start calling it what it is: “The latest attempt by science deniers to cherry pick the last strong el-Nino year while ignoring that 2014 was the hottest year on record.”

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    Moderator Response:

    [JH] To whom is your comment directed?

  20. Who was my comment directed to?

    Everyone and anyone who keeps falling for the old Conservative trick of letting them define the language.

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    Moderator Response:

    [JH] Per the SkS Comments Policy, "The purpose of the discussion threads is to allow notification and correction of errors in the article, and to permit clarification of related points."

  21. It is a related point. If facts were the only thing required, there would be no discussion.

    When was the last time one of these sociopaths accepted the science? Point out the GRACE data to them that shows that the Antarctic is losing ice mass, they just turn and pretend that the increase in the sea ice, which is nothing compared to the loss of land ice, still magically not only cancels out the much larger loss in land ice but also proves that the ice pack is growing.

    There is no hiatus/pause. This is just the same old 1998 BS repackaged.

    Over at a denier site they are using the existence of this thread as proof that there is a hiatus.

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    Moderator Response:

    [JH] The residents of Deniersville say and do strange things. Generall speaking, it's best to let them stew in their own pseudo-science poppycock.

  22. The point I am trying to make is that we need to also include other disciplines, than just climate science, when combating those who are conspiring, for whatever motivations, to commit mass murder on a global scale with AGW.

    Taker Easterbrook’s lasted BS for example:

    Whoever is across from him on the stage at a debate/symposium/conference/whatever needs to use Marketing/Advertising techniques to create a single simple easy to understand by most people sentence sound bite that deals with what Easterbrook did when he used his bogus model output.

    The rest of the allotted time should then be used to hammer home the fact that Easterbrook is a sociopathic terrorist, who for whatever dark twisted reason, is conspiring to murder, using the WMD known as AGW, the children and grandchildren of everyone present.

    Pointing out the fact that a sociopath is a sociopath is not a personal attack, it is merely a statement of fact.

    Pointing out the fact that when it comes to global warming that science has spoken and that there is no debate, there is no discussion, and there is no opinion. There are those who want to commit mass murder on a global scale with global warming, and those who do not want to commit mass murder on a global scale; is not engaging in political activity, it is merely pointing out a simple fact.

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    Moderator Response:

    [JH] Inflamatory rhetoric and diatribes against individuals are not welcome on this website. 

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