UPDATE: Chris de Freitas responds to comments with an addendum below – Anthony
Dr. Judith Curry called the paper “mind blowing”
Now there’s another paper that reaches a similar conclusion:
Update of the Chronology of Natural Signals in the Near-Surface Mean Global Temperature Record and the Southern Oscillation Index
de Freitas and McLean, 2013, p. 237 (Int J Geosciences – open access):
“All other things being equal, a period dominated by a high frequency of El Niño-like conditions will result in global warming, whereas a period dominated by a high frequency of La Niña-like conditions will result in global cooling. Overall, the results imply that natural climate forcing associated with ENSO is a major contributor to temperature variability and perhaps a major control knob governing Earth’s temperature.”
Time series for the Southern Oscillation Index and mean global near surface temperature anomalies are compared for the 1950 to 2012 period using recently released HadCRU4 data. The method avoids a focused statistical analysis of the data, in part because the study deals with smoothed data, which means there is the danger of spurious correlations, and in part because the El Niño Southern Oscillation is a cyclical phenomenon of irregular period. In these situations the results of regression analysis or similar statistical evaluation can be misleading.
With the potential controversy arising over a particular statistical analysis removed, the findings indicate that El Nino-Southern Oscillation exercises a major influence on mean global temperature. The results show the potential of natural forcing mechanisms to account for mean global temperature variation, although the extent of the influence is difficult to quantify from among the variability of short-term influences.
Since the paper is open access, and available here: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=27382
Here is the link to the PDF:
This figure is interesting:
Figure 1. Four-month shifted SOI anomalies with monthly MGT anomalies shown for periods 1950 to1970 (a), 1970 to 1990 (b) and 1990 to June 2012 (c), where the Y-axis scale is identical in each case. The dark line indicates SOI and light line indicates MGT. Periods of volcanic activity are indi-cated (see text).
Discussion and Conclusions
The results show that, by and large, the Southern Oscilla- tion has a consistent influence on mean global tempera- ture. Changes in temperature are consistent with changes in the SOI that occur about four months earlier. The rela- tionship weakens or breaks down at times of major volcanic eruptions. Since the mid-1990s, little volcanic activity has been observed in the tropics and global average temperatures have risen and fallen in close accord with the SOI of four months earlier; although with the unexplained divergence of NH and SH average temperature anomalies modifying the earlier relationship.
The strength of the SOI-MGT relationship may be indicative of the increased vigor in the meridional dispersal of heat during El Niño conditions and the delay in the temperature response is consistent with the transfer of tropical heat polewards. The mechanism of heat transfer is likely the more vigorous Hadley Cell Circulation on both sides of the Intertropical Convergence Zone distributing warm air from the tropical regions to higher lati- tudes. The process of meridional heat dispersal weakens during La Niña conditions and is accompanied by a lower than normal MGT. Hadley Cell Circulation is weakened when the Southern Oscillation is in a state associated with La Niña conditions (i.e. positive Troup SOI values), but strengthens as the Southern Oscillation moves to a condition consistent with El Niño conditions (that is negative SOI values) [6,7].
The precision of the 4-month lag period is uncertain, but the credibility of a lag of some length is not in dispute. Researchers  found that mean tropical temperatures for a 13-year record lagged outgoing longwave anomalies by about three months, while  found warming events peak three months after sea surface temperature (SST) in the Niño-3.4 region. On the same theme,  found lags between 1 – 3 months with SST in the Niño-3.4 region for the period 1950-1999. Along the same lines  determined that the correlation between SST in the Niño-3 region and the MGT anomaly was optimum with a time lag of 3-6 months. The sequence of the lagged relationship indicates that ENSO is driving temperature rather than the reverse. Reliable ENSO prediction is possible only to about 12 months , which implies that improved temperature forecasting beyond that period is dependent on advancements in ENSO prediction.
The reason for the post-1995 period shift in the SOI- MGT relationship illustrated in Figure 1(c) is puzzling. An explanation may lie in changes in global albedo due to changes in lower-level cloud cover. In an analysis of Australian data,  found positive values of SOI anomalies to be associated with increased cloudiness and decreased incoming solar radiation. Data from the International Satellite Cloud Climatology Project (ISCCP) indicate that, from 1984 to 2005, mid-level cloud cover in the tropics was relatively constant but both lower and upper level cloud cover declined slightly. In the exotropics (latitude > 20 degrees, low-level cloud progressively decreased from 1998 onwards. It is not clear whether the change is a cause or an effect of a parallel temperature change . The post-1995 shift appears unrelated to carbon dioxide increase because it occurred long after atmospheric CO2 was known to be rising. It is important to see the shift as more of discrete (i.e. step) change rather than a divergence, with the relationship reestablished after 2 – 3 years. Another possibility is that there are problems with the HadCRUT4 1.1.0 data. For example, we note that the published monthly average global temperature anomalies are not equal to the mean of the two published corresponding hemispheric values.
The approach used here avoids a focused statistical analysis of the data, in part because the study deals with smoothed data, which means there is the danger of spu- rious correlations, and in part because the ENSO is a cyclical phenomenon of irregular period. In these situations, the results of regression analysis or similar statisti- cal evaluation can be misleading. With the potential con- troversy arising over a particular statistical analysis re- moved, the findings reported here indicate that atmos- pheric processes that are part of the ENSO cycle are col- lectively a major driver of temperature anomalies on a global scale. All other things being equal, a period dominated by a high frequency of El Niño-like condi- tions will result in global warming, whereas a period dominated by a high frequency of La Niña-like condi- tions will result in global cooling. Overall, the results imply that natural climate forcing associated with ENSO is a major contributor to temperature variability and per- haps a major control knob governing Earth’s temperature.
UPDATE: 9/5/13 4:15PM PDT Chris de Freitas asked for this addendum to be posted in response to comments/discussion – Anthony
I understand concerns of the global warming alarmists. I too have been looking high and low for evidence that human-caused carbon dioxide increase is a major driver of mean global temperature. Our current is not part of that quest.
The intention of the work reported in the paper (de Freitas and McLean, 2013) was to stay as far away as possible from statistical massaging of the data. The reason is that, in our earlier 2009 work (McLean, de Freitas and Carter – references below), we were roundly criticised for the statistical methods we used. It detracted from the main finding of the work (i.e. Fig 7), which was free from statistical massaging; namely, that ENSO accounted for a great deal of the variability in mean global temperature; similar to that reported in the more recent paper in Nature (Kosaka and Xie, 2013).
In de Freitas and McLean (2013) we also stayed away from looking for trends. Determining trends and implementing detrending procedures can be important steps in data analysis. However, there is no precise definition of ‘trend’ or any ‘correct’ algorithm for extracting it. Consequently, identification of trend in a time series is subjective because a trend cannot be unequivocally distinguished from low frequency fluctuations. For this reason, a variety of ad hoc methods have been used to determine trends and to facilitate detrending methods (which are also subjective). As regards the correlation routine (Table 2 of our IJG 2013 paper), the idea there was to look for guidance in aligning the X-axis of Figures 1 and 3. It could have (even) been done by eye.
The overriding message is this. Climate is never constant; it is always cooling or warming. Various things cause these trends. Ever since I began studying climate 40 years ago I have been looking for patterns along with possible mechanisms and explanations. I have not had great success; if fact nobody has, and we have all been wrong once or twice. Notwithstanding that, our IJG (2013) paper shows that ENSO correlates well with global temperature. A possible reason (as described) is enhanced (or reduced) Hadley circulation, which increases (or decreases) the effectiveness of meridional heat transfer from the vast tropical zone of surplus towards the poles. It could be that the same process causes vast amounts of stored ocean heat to be fed into the atmosphere over extended periods (or moved back into the ocean over lengthy periods) The result is planet-wide warming (or cooling). If this persists, we get decadal scale global warming (or cooling) trends.
Like the work of Kosaka and Xie (2013), our IJG (2013) and earlier work (2009) shows that the current (or past hiatus), or multi-decadal-scale cooling or warming (‘climate change’), are possibly a reflection of natural climate variability tied specifically to ENSO decadal-scale processes. I assume these are superimposed upon what seems for the moment to be the less potent CO2-caused warming, and likely other less potent mechanisms as well.
Whether the ENSO-caused multi-decadal trends are internal or forced is unknown. My guess is that cooling and warming trends we see, or hiatus, are probably due to natural internal variability rather than a forced response. But we don’t know.
Chris de Freitas
de Freitas, C.R. and McLean, J.D., 2013. Update of the chronology of natural signals in the near-surface mean global temperature record and the Southern Oscillation Index. International Journal of Geosciences, 4(1), 234-239.
Open access at:
McLean, J. D., C. R. de Freitas, and R. M. Carter, 2009b. Correction to ”Influence of the Southern Oscillation on tropospheric temperature”, Journal of Geophysical Research, 114, D20101, doi:10.1029/2009JD013006. ISSN 0148-0227
McLean, J. D., C. R. de Freitas, and R. M. Carter, 2009a. Influence of the Southern Oscillation on tropospheric temperature, Journal of Geophysical Research, 114, D14104, doi:10.1029/2008JD011637. ISSN 0148-0227