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
KR, Philippe Chantreau, Rob Painting, Sphaerica, et al.: The basis of this discussion appears to have been this video that appeared on the WUWT-TV webcast. Since some of you have not watched the video, you would have missed the bases for it. Therefore, let’s start with satellite-era sea surface temperature data and let me then ask you to explain the following:
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.
I’ve answered those two questions in the video. Can you answer those questions? The data is available in an easy to use form through the KNMI Climate Explorer. Feel free to confirm my results in the above graphs.
[DB] To reiterate Ian's questions, so the dialogue can proceed:
1) Do you have a link to the specific dataset(s)?
2) Is the NINO3.4 data processed in anyway? and if so, how?
IanC: Excuse the delay.
You replied, “You are comparing data with a particular realization of internal variability to data with internal variability filtered out. You are effectively comparing apples to oranges, so of course they look different.”
I assume this is a discussion of the East Pacific data. The appearances are not in question. The trends are.
You replied, “To actually make a sensible analysis, you will at the very least have to look into internal variability of each model run, which entail comparing a large number individual model runs.”
Not me. I’m done with my analysis. It is the responsibility of the party wishing to dispute my results to show the effects of the point that party wants to introduce to the discussion. With that in mind, the models do such a poor job of simulating ENSO you’d be better off trying to remove the effects of ENSO from the East Pacific sea surface temperature data. Then you won’t have to analyze each of the dozens and dozens of model runs. If you don’t want to do that, that’s okay, because the “Rest of the World” data still needs to be explored.
You replied, “To answer your question, a far more plausible explanation is internal variability (e.g. PDO).”
Unfortunately, that explanation doesn’t work for a number of reasons. (a) The PDO represents the standardized leading Principal Component of the sea surface temperature anomalies of the North Pacific north of 20N after the global temperatures have been removed, not the sea surface temperature anomalies. (b) The standardization of the PDO exaggerates its actual variability by a factor of about 5.6, if memory serves. In other words, the standardization exaggerates the importance of the PDO. (c) The PDO is actually inversely related to the sea surface temperature anomalies of that portion of the North Pacific on decadal timescales. (d) The PDO is an aftereffect of ENSO and the sea level pressure of the North Pacific. The sea level pressure of the North Pacific causes the difference between the PDO and ENSO. (e) The dominant component of the PDO is the sea surface temperature of the Kuroshio-Oyashio Extension, in the western North Pacific, not the East Pacific.
You asked, “What scaling and time shifting have you applied to the NINO3.4 data?”
The scaling factor is 0.12 and there’s a 6-month lag.
You asked, “Can you provide references?”
Yup. Every study that attempts to remove the effects of ENSO from the surface temperature record by scaling an ENSO index and by subtracting the scaled and lagged ENSO index from surface temperatures assumes surface temperatures respond proportionally to El Niño and La Niña events. Examples in alphabetical order:
Foster and Rahmstorf (2011) “Global Temperature Evolution 1979–2010”
And:
Lean and Rind (2009) How Will Earth’s Surface Temperature Change in Future Decades?
And:
Lean and Rind (2008) How Natural and Anthropogenic Influences Alter Global and Regional Surface Temperatures: 1889 to 2006
And:
Santer et al (2001), Accounting for the effects of volcanoes and ENSO in comparisons of modeled and observed temperature trends
And:
Thompson et al (2008), Identifying signatures of natural climate variability in time series of global-mean surface temperature: Methodology and Insights
And:
Trenberth et al (2002) Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures (See note 1)
And:
Wigley, T. M. L. (2000), ENSO, volcanoes, and record-breaking temperatures
Note 1: Trenberth et al (2002) included the following caveat (my boldface):
“The main tool used in this study is correlation and regression analysis that, through least squares fitting, tends to emphasize the larger events. This seems appropriate as it is in those events that the signal is clearly larger than the noise. Moreover, the method properly weights each event (unlike many composite analyses). Although it is possible to use regression to eliminate the linear portion of the global mean temperature signal associated with ENSO, the processes that contribute regionally to the global mean differ considerably, and the linear approach likely leaves an ENSO residual.”
The divergences shown in brown are those ENSO residuals.
Moderator DB asked, “Do you have a link to the specific dataset(s)?”
The Reynolds OI.v2 data is available on a gridded basis through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
And through the NOAA NOMADS website:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?ctlfile=monoiv2.ctl&varlist=on&new_window=on&lite=&ptype=ts&dir=
The coordinates of the NINO3.4 region are 5S-5N, 170W-120W. The coordinates for the East Pacific is 90S-90N, 180-80W. And the coordinates for the Rest of the World are 90S-90N, 80W-180. I provided a brief introduction to the KNMI Climate Explorer here:
http://bobtisdale.wordpress.com/2010/12/30/very-basic-introduction-to-the-knmi-climate-explorer/
And DB asked, “Is the NINO3.4 data processed in anyway? and if so, how?”
The NINO3.4 sea surface temperature anomalies were scaled by a factor is 0.12, lagged 6 months, and both datasets in the graph of the detrended Rest of the World data were smoothed with 13-month running-mean filters.
Regards
[DB] If you walk away when a flaw is identified in your analysis then you shouldn't be surprised if others find your argument unconvincing. As you are challenging the mainstream scientific position, the onus is on you to show that your argument is solid. That is the way science works.
Therefore, you shouldn't ignore the moderator's advice here: "I would recommend that you do not proceed onto part 2 until we have had a chance to digest part 1 and for relevant questions to be answered".