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Tuesday, May 27, 2008

Annual and Long-Term Impacts of El Nino/Southern Oscillation (ENSO)

Keep in mind when reading this post that the NINO3.4 temperatures shown in many of the graphs have been scaled drastically; the changes in NINO3.4 temperature are, in fact, more than 10 times greater than illustrated. Also keep in mind that the changes in NINO3.4 temperature precede the changes in global temperature anomaly; global temperature is responding to the El Nino or La Nina, not vice versa.


Figure 1 is a typical illustration of global temperature anomaly from 1850 to 2007. I’ve used Hadley Centre global temperature data (HadCRUT3GL) for this post.

Figure 1

Annual changes in anomaly, Figure 2, are calculated by subtracting the prior year anomaly value from the current year value, then repeating the calculation over the range of 1850 to 2007. Temperatures are rising whenever the “Annual Change” values are above zero, and dropping when they are below zero.

Figure 2

Figure 3 illustrates the magnitude of these annual variations without the visual skewing of the anomaly data. For the most part, these annual changes are driven by El Nino and La Nina events, large and small. Doubt that? Read on.

Figure 3

The El Nino/Southern Oscillation (ENSO) data used are NINO3.4 from NCDC, available here:

Note that the NINO3.4 data begins in 1871, so the global temperature data prior to that year will be excluded.

NINO3.4 SST and anomaly curves are shown in Figures 4 and 5, where the base period for the anomaly data is 1950 to 1979, the same base period used by Trenberth and Stepaniak in “Indices of El NiƱo Evolution”, J. Climate, 14, 1697-1701. Refer to: http://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/index.html#Sec5

Figure 4

Figure 5

Working backwards in time, the significant 1997/98, 1982/83, 1939/40/41/42, and 1878/79 El Nino events clearly stand out. The 1939 to 42 El Nino appears to be the source of that bump in the global temperature anomaly, Figure 1, that’s centered around 1940. It’s then followed by the two major La Nina events in 1950/51 and 1954/55/56/57, which appear to have created or enhanced the global temperature dip in the 1950s.

To put the annual changes in global temperature and NINO3.4 into perspective, Figure 6 illustrates the raw data. Please click on the TinyPic links for the full-sized graphs. The correlation between the annual variations in NINO3.4 area SST and global temperature is obvious. Changes in NINO3.4 temperature are in many cases more than 10 times larger than the changes in global temperature anomaly.

Figure 6

In “Evolution of El Nino–Southern Oscillation and Global Atmospheric Surface Temperatures”, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D8, 10.1029/2000JD000298, 2002, Trenberth et al identified the global temperature reaction to the 97/98 El Nino: “The regression coefficient based on the detrended relationship is 0.094 deg C per N3.4 and is deemed more appropriate. The N3.4 contribution is given in Figure 3. It shows that for the 1997–1998 El Nino, where N3.4 peaked at ~2.5 deg C, the global mean temperature was elevated as much as 0.24 deg C (Figure 2), although, averaged over the year centered on March 1998, the value drops to ~0.17 deg C.” Note: The figures referenced within the quotation are the figures in the Trenberth study, not this post.


I’m using annual data for this comparison, and the NINO3.4 data sources are different. The following will be used to verify the scaling factor: The Trenberth et al annual average value of 0.17 deg C global temperature and the annual NINO3.4 anomaly value centered on December 1997 (assumes a 3-month lag between NINO3.4 and global temperature) from the calculated NINO3.4 anomaly data illustrated in Figure 6, which is 1.823 deg C. The calculated annual scaling factor is 0.093 (0.17/1.823). The difference from the Trenberth et al regression coefficient is insignificant.

Figure 7 illustrates the correlation of annual changes in global temperature and NINO3.4 data, where the NINO3.4 data has been adjusted by the scaling factor of 0.093. Again, please use the TinyPic link to view the full-sized graph. And again, keep in mind that changes in NINO3.4 temperature precede global temperature and that the NINO3.4 data in Figure 7 has been reduced in scale by a factor of more than 10. It’s easy to see that ANNUAL changes in NINO3.4 temperature drive ANNUAL global temperature variations.

Figure 7

Why was the NINO3.4 data scaled with the value of 0.093 in Figure 7, when a larger value would have provided a better comparison? The scaling factor of 0.093 has a role in the long-term comparison.


Recall: To create the graphs of the annual changes in global temperature that were used in Figures 2, 3, 6, and 7, the anomaly from the prior year was subtracted from the anomaly of the year being calculated, and the process was repeated over the term of the data. To return the data to its original long-term state, the annual values would need to be added to each other, from year to year, using a running total. The data will shift because the base period for the anomaly data was eliminated in the first calculations, but the shape and magnitude of the curve will remain the same.

This also allows a side-by-side comparison of the long-term effects of the scaled NINO3.4 on global temperature. A running total can also be applied to that data. Refer to Figure 8.

Figure 8

The correlation between the running totals infers that ENSO drives long-term change in global temperature, in addition to the annual variations. From the early 20th Century on, temperatures rise in parallel until the 1950s, when they dip until the mid-1970s, then rise again, all a function of El Nino/Southern Oscillation. Using the NINO3.4 data, there was no need to magically apply a non-existent tropospheric aerosol forcing in the 1950s through 1970s in order to slow the warming.


I created the following graph, Figure 9, to show that the magnitude and frequency of ENSO events rose with the increase in the annual TSI changes. These annual changes in TSI were calculated in the same way used to calculate the annual changes in temperature; that is, the prior year TSI value was subtracted from the year being calculated, with the process repeating each year for the term of the data. With a few exceptions, the graph does infer that ENSO event frequency and magnitude do increase with rising variations in solar irradiance.
Figure 9

Then I noticed that a few of the data points appeared to correlate but were offset by a few years, so I shifted the TSI data four years. Refer to Figure 10. It looks nice, but it still only illustrates an implied relationship between the amplitude of TSI and the frequency and magnitude of ENSO events.

Figure 10


NINO3.4 and global temperature anomaly data correlate annually and over a long term. This provides a stronger case for cause and effect than if they had correlated in only one way.


Arnost said...

Bob, compliments again, you are doing some very interesting stuff.

I did a similar exercise a little while ago myself – when I was musing about the Great Pacific Climate Shift and the flattening of the global temperature trend on another forum.

There are two observations (which you may find interesting) that I would like to share. One is that the role of atmospheric (and specifically volcanic) aerosols should not be underestimated. The other is that the Atlantic Multidecadal Oscillation (AMO) plays a similar role to ENSO WRT global temperatures.

Here’s a step through my logic (which is a cut from what I posted elsewhere) to explain why I think they are important:

If we know that Pinatubo cooled the globe by 0.5C, one can use the forcings to adjust the temperature series to status quo ante up to 1880. Here is the HadCRU temperature series from 1950 and the GISS model forcings (thick black line) - note that the scale has been adjusted such that the max amount for Pinatubo’s effect is about 0.4C (less than what research suggests):


And this is what the temperature series (thick yellow line) would / should(?) have been if the three major volcanic eruptions, Agung (1963/64), El Chichon (1982) and Pinatubo (1991) did not take place:


One can immediately see that the adjustments bring up the global temperature significantly at the time of El Chichon and Pinatubo. Why? Is this realistic? I think yes. At the time of these eruptions, there were significant El Ninos occurring. Knowing there is a strong correlation between ENSO and global temps, then a simple test would be to see if the correlation improves. If one plots the Multivariate ENSO Index (MEI) against the adjusted HadCru temperature, this is the result:


It fits… the expected correlation with ENSO is demonstrated. It is entirely possible that if the El Chichon and Pinatubo eruptions did not take place that there would have been significant temperature spikes in the early 80's and 90's. (The red lines in the graph above are what I used to show the Climatic Shift). And that without the cooling effects of El Chichon and Pinatubo, the flattening of the global temperatures would have been over a period approaching 30 years.

Given the above, if you then take the HadCRU Global temp for the last 57 years, subtract a decadal trend of about 0.1C, and plot this against the MEI plus AMO adjusted by the volcanic aerosol forcing - this is what you get:


Simply averaging the monthly MEI and AMO indexes gives a correlation of 0.65. Slightly higher correlations are achievable if the indexes are independently tweaked and optimised – very high correlations are achievable if you smooth the data. Note: the AMO/MEI composite leads the temps by 5-6 months (which is adjusted for in above).



The Stratospheric Aerosols forcing are the ones used in GISS models - from here:
StratAer: http://data.giss.nasa.gov/modelforce/RadF.txt
The Multivariate ENSO Index is from here:
MEI: http://www.cdc.noaa.gov/people/klaus.wolter/MEI/table.html
The Atlantic Multidecadal Oscillation Index is from here:
AMO: http://www.cdc.noaa.gov/Correlation/amon.us.data
And the long AMO version (not used): http://www.cdc.noaa.gov/Correlation/amon.us.long.data

P.S. There are some discrepancies between the long Nino3.4 versions and the shorter versions at NOAA – they do not tie up. Have you noticed this?

Bob Tisdale said...

Arnost: A few comments before I go back and look at some earlier investigations I did regarding AMO and volcanic aerosols. See if I want to post the graphs I have that are similar to yours. I didn't document some of my early investigations very well.

Most of the short-term ENSO data (ONI, MEI, etc.) is smoothed, normalized, and/or mixed with other variables, which is why I use the NCDC NINO3.4 SST data. It takes out all the unknowns.

I've been using the SATO index for volcanic aerosols and their effects on global temperature. It was tough to divide it up into monthly and annual data. It appears realistic, but I haven't used the GISS “StratAer” data yet. The claimed effects of Mount Pinatubo appear to vary greatly. I've seen reports that peg it at 0.5 deg C for a high, and 0.2 deg C for a low. I've been using 0.3 deg C as a "midpoint".

The AMO adds then subtracts over the term of the instrument temperature record. I'll look for the paper, I believe it was by Knight, that has the basis for a coefficient of global and hemispheric effects per deg C change of the AMO. If memory serves me well, the global impact was 0.1 deg C for the peak-to-peak AMO and 0.2 deg C for the Northern Hemisphere. I started working on a post that uses some more recent data on the regional effects of the AMO. I was going to pull out my old HADCRUT2 data and play with that.

Thanks for the input.



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