Arguments
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.
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).
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
Last updated on 10 July 2015 by pattimer. View Archives
[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).
[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.
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-16ν ≈ 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.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.
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:
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:
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:
http://i47.tinypic.com/k2g6bs.jpg
Same map for June 1975:
http://i49.tinypic.com/73040z.jpg
And it doesn’t get better as you go back in time. Here’s June 1943:
http://i49.tinypic.com/2eb8sb8.jpg
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:
The truth is a good thing, don’cha think?
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”.
http://www.pmel.noaa.gov/pubs/outstand/mcph2029/text.shtml
McPhaden writes:
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.
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.
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.
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):
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.
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:
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.
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.