Guest post by Bob Tisdale
Longer Title: Do Multidecadal Changes In The Strength And Frequency Of El Niño and La Niña Events Cause Global Sea Surface Temperature Anomalies To Rise And Fall Over Multidecadal Periods?
############
UPDATE (November 19, 2010): I’ve added a clarification about the running total of scaled NINO3.4 SST anomalies and its implications. I changed a paragraph after Figure 13, and added a discussion under the heading of “What Does The Running Total Imply?”
############
OVERVIEW
This post presents evidence that multidecadal variations in the strength and frequency of El Niño and La Niña events are responsible for the multidecadal changes in Global Sea Surface Temperature (SST) anomalies. It compares running 31-year averages of NINO3.4 SST anomalies (a widely used proxy for the frequency and magnitude of ENSO events) to the 31-year changes in global sea surface temperature anomalies. Also presented is a video that animates the maps of the changes in Global Sea Surface Temperature anomalies over 31-year periods, (maps that are available through the GISS Map-Making web page). That is, the animation begins with the map of the changes in annual SST anomalies from1880 to 1910, and it is followed by maps of the changes from 1881 to 1911, from 1882 to 1912, etc., through 1979 to 2009. The animation of the maps shows two multidecadal periods, both containing what appears to be a persistent El Niño event, one in the early 1900s and one in the late 1900s to present, and between those two epochs, there appears to be a persistent La Niña event.
INTRODUCTION
A long-term (1880 to 2009) graph of Global Surface Temperature anomalies or Global Sea Surface Temperature (SST) anomalies (Figure 1) often initiates blog discussions about the causes of the visible 60-year cycle. The SST anomalies rise from early-1910s to the early-1940s, drop from the early 1940s to the mid-1970s, then rise from the mid-1970s to present. Natural variables like the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) are cited as the causes for these variations.
http://i51.tinypic.com/119z1ux.jpg
Figure 1
Note: HADISST data was used for the long-term SST anomaly graphs in this post. The exception is the GISS SST data, which is a combination of HADISST data before the satellite era and Reynolds OI.v2 SST data from December 1981 to present.
THE PDO CANNOT BE THE CAUSE
The SST anomalies of the North Pacific region used to calculate the PDO are inversely related to the PDO over decadal periods. This was shown in the post An Inverse Relationship Between The PDO And North Pacific SST Anomaly Residuals. This means that the SST anomalies of the North Pacific contribute to the rise in global SST anomalies during decadal periods when the PDO is negative and suppress the rise in global SST anomalies when the PDO is positive. The PDO, therefore, cannot be the cause of the multidecadal rises and falls in global SST anomalies. That leaves the AMO or another variable.
MULTIDECADAL CHANGES IN GLOBAL SST ANOMALIES
If we subtract the annual global SST anomalies in 1880 from the value in 1910, the difference is the change in global SST anomalies over that 31-year span. Using this same simple calculation for the remaining years of the dataset provides a curve that exaggerates the variations in global SST anomalies. This dataset is identified as the “Running Change (31-Year) In Global SST Anomalies” in Figure 2. The data have been centered on the 16th year.
http://i55.tinypic.com/2cndnq1.jpg
Figure 2
Why 31 years? A span of 31 years was used because it is approximately one-half the apparent cycle in the datasets, and it should capture the maximum trough-to-peak and peak-to-trough changes that occur as part of the 60-year cycle. Using 31 years also allows the data to be centered on the 16th year, with 15 years before and after.
The curve of the “Running Change (31-Year) In Global SST Anomalies” is very similar to the curve of annual NINO3.4 SST anomalies that have been smoothed with a 31-year filter. Refer to Figure 3. (NINO3.4 SST anomalies are commonly used to illustrate the frequency and magnitude of El Niño and La Niña events. For readers new to the topic of El Niño and La Niña events, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.) Both datasets are centered on the 16th year. Considering how sparse the SST measurements are for the early source data, the match is actually remarkable at that time.
http://i55.tinypic.com/zmgv9l.jpg
Figure 3
Let’s take a closer look at that relationship. The purple curve represents the running 31-year average of annual NINO3.4 SST anomalies, and it shows that, for example, at its peak in 1926, the frequency and magnitude of the El Niño events from 1911 to 1941 were far greater than the frequency and magnitude of La Niña events. The blue curve, on the other hand, portrays the change in global SST anomalies based on a 31-year span, and it shows, at its peak in 1926 that global SST anomalies rose more from 1911 to 1941 than it did during the other 31-year periods in the early 20th century. Skip ahead a few decades to 1960. Both curves reached a low point about then. At 1960, the purple curve indicates the frequency and magnitude of La Niña events from 1945 to 1975 outweighed El Niño events. And over the same period of 1945 to 1975, annual global SST anomalies dropped the greatest amount. Afterwards, the frequency and magnitudes of El Niño events increased (and/or the frequency and magnitude of La Niña events decreased) and the multidecadal changes in global SST anomalies started to rise, eventually reaching their peak around 1991 (the period of 1976 to 2006).
Since Global SST anomalies respond to changes in NINO3.4 SST anomalies, this relationship implies that the strengths and frequencies of El Niño and La Niña events over multidecadal periods cause the multidecadal rises and falls in global sea surface temperatures. In other words, its shows that global sea surface temperatures rose from 1910 to the early 1940s and from the mid-1970s to present because El Niño events dominated ENSO during those periods, and it shows that global sea surface temperatures dropped from the early 1940s to the mid 1970s because La Niña events dominated ENSO.
This apparent relationship contradicts the opinion presented by some climate studies that ENSO is only noise, that ENSO is only responsible for the major year-to-year wiggles in the global SST anomaly curve. Refer back to Figure 1. Examples of these studies are Thompson et al (2009) “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”.
Link (with paywall) to Thompson et al (2009):
http://journals.ametsoc.org/doi/abs/10.1175/2009JCLI3089.1
Link to Trenberth et al (2002):
http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf
Keep in mind, when climate studies such as Thompson et al (2009) and Trenberth et al (2002)attempt to account for El Niño and La Niña events in the global surface temperature record they scale an ENSO proxy, like NINO3.4 SST anomalies, and subtract it from the Global dataset, removing the major wiggles. They then assume the difference, which is a smoother rising curve, is caused by anthropogenic greenhouse gases.
The relationship in Figure 3 (that the multidecadal variations in strength and frequency of ENSO events are responsible for the rises and falls in global sea surface temperature) also contradicts the basic premise behind the hypothesis of anthropogenic global warming, which assumes that the rise in global sea surface temperatures since 1975 could only be caused the increase in anthropogenic greenhouse gases.
The first question that comes to mind: shouldn’t a multidecadal rise in Sea Surface Temperatures require an increase in radiative forcing? The answer is no, and I’ll discuss this later in the post. Back to Figure 3.
Once more, the relationship in Figure 3 illustrates that multidecadal variations in the frequency and magnitude of El Niño and La Niña events cause the multidecadal changes in SST anomalies. But how do I verify that this is the case, and how do I illustrate it for those without science backgrounds? Again, for those who need to brush up on El Niño and La Nina events, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.
THE ANIMATION OF MULTIDECADAL CHANGES IN SST ANOMALIES
The Goddard Institute of Space Studies (GISS) Global Map-Making webpage allows users to create maps of global SST anomalies and maps of the changes in global SST anomalies (based on local linear trends) over user-specified time intervals. Figure 4 is a sample map of the changes in annual SST anomalies for the 31-year period from 1906 to 1936. In the upper right-hand corner is a value that represents the change in annual SST anomalies over that time span. GISS describes the value as, “Temperature change of a specified mean period over a specified time interval based on local linear trends.” And as far as I can tell, these local linear trends are weighted by latitude. I downloaded the GISS maps of the changes in annual global SST anomalies, starting with the interval of 1880 to 1910 and ending with the interval of 1979 to 2009, with the intent of animating the maps, but the data they presented was also helpful.
http://i56.tinypic.com/21ou8lg.jpg
Figure 4
Figure 5 shows the curve presented by the GISS Multidecadal (31-year span) Changes In Global SST anomalies for all those maps, with the data centered on the 16th year. Comparing it to the “Running Change (31-Year) In Global SST Anomalies” data previously calculated, Figure 6, illustrates the similarities between the two curves. The GISS data from the maps presents a much smoother curve.
http://i53.tinypic.com/14j50et.jpg
Figure 5
###############
http://i51.tinypic.com/9hq83s.jpg
Figure 6
And if we compare the curve of the GISS Multidecadal (31-year span) Changes In Global SST anomalies from those maps to the NINO3.4 SST anomalies smoothed with a 31-month filter, Figure 7, we can see that the multidecadal changes in Global SST anomalies lag the variations in strengths and magnitudes of ENSO events. The lag prior to 1920 appears excessive, but keep in mind that the early source SST measurements are very sparse. The fact that there are similarities in the curves in those early decades says much about the methods used by researchers to infill all of that missing data.
http://i54.tinypic.com/9gvyh0.jpg
Figure 7
THE VIDEO
The animations are presented in two formats in the YouTube video titled “Multidecadal Changes In Global SST Anomalies”. The first format is as presented by GISS, with the Pacific Ocean split at the dateline. That is, the maps are centered on the Atlantic. Refer back to Figure 4. The second format is with the maps rearranged so that the major ocean basins are complete. Those maps are centered on the Pacific. With the maps centered on the Pacific, the animation shows what appear to be two (noisy) multidecadal El Niño events separated by a multidecadal La Niña event.
As noted in the video, the long-term El Niño and La Niña events appear in the patterns, not necessarily along the central and eastern equatorial Pacific. For those not familiar with the SST anomaly patterns associated with ENSO, refer to Figure 8. It is Figure 8 from Trenberth et al (2002) “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures”. Link to Trenberth et al (2002) was provided earlier.
Figure 8 shows where Sea Surface Temperatures warm and cool during the evolution (the negative lags) of an ENSO event, at the peak of an ENSO event (zero lag), and during the decay of ENSO events (the positive lags). The reds indicate areas that are positively correlated with ENSO events, and the blues are areas that are negatively correlated. That is, the red areas warm during an El Niño and the blues are the areas of that cool during an El Niño. During a La Niña event, the reds indicate areas that cool, and the blues indicate areas that warm.
http://i55.tinypic.com/z1f6o.jpg
Figure 8
And for those wondering why the ENSO events don’t always appear along the equatorial Pacific in the animated maps, keep in mind that the maps are showing the multidecadal changes in SST anomalies based on linear trends. The long-term linear trend of the equatorial Pacific SST anomalies are incredibly flat, meaning there is little trend. Refer to Figure 9, which shows the annual NINO3.4 SST anomalies and linear trend from 1900 to 2009.
http://i56.tinypic.com/2ag0u2u.jpg
Figure 9
#############################
http://www.youtube.com/watch?v=O_QopFYSyGE
Video 1
And here’s a link to a stand-alone version of the video. The only difference is that the following version includes a detailed introduction, discussion, and conclusion, which are presented in this post. It’s about 5 minutes longer.
http://www.youtube.com/watch?v=SMKA_uG3zK0
Link To Stand-Alone Version Of Video
DOES THE VIDEO AND DATA PRESENT MORE THAN MULTIDECADAL VARIABILITY IN GLOBAL SST ANOMALIES?
Yes. This has actually been stated a number of times, but the following explanation may be helpful.
One of the arguments presented during discussions of multidecadal variations in global SST anomalies is that the Atlantic Multidecadal Oscillation (AMO) is detrended and that it strengthens or counteracts the basic long-term rise in global SST anomalies. However, the data associated with the GISS maps used in the video are based on linear trends. And Figure 7 shows that the Global SST anomalies rose from 1910 to 1944 and from 1976 to 2009 because El Niño events dominated, and dropped from 1945 to 1975 because La Niña events dominated.
That is, the animation of the GISS maps and the data GISS provides with those maps show that the trends in global sea surface temperature are driven by the multidecadal variations in the strengths and magnitudes of El Niño and La Niña events. The “GISS Multidecadal (31-year span) Changes In Global SST anomaly” data peaked in 1931 at 0.39 deg C. Refer back to Figure 5. That is, from 1916 to 1946, global SST anomalies rose 0.39 deg C (based on local linear trends). That equals a linear trend of 0.13 deg C per decade. And the “GISS Multidecadal (31-year span) Changes In Global SST anomaly” data peaked in 1989 at 0.41 deg C, and that equals a trend of 0.137 deg C per decade from 1974 to 2004. Let’s look at the “Raw” Global SST anomaly data. The linear trends of the “Raw” Global SST Anomalies for the same periods, Figure 10, are approximately 0.12 deg C per decade. Again, the peaks in the “GISS Multidecadal (31-year span) Changes In Global SST anomaly” data represent the periods with the greatest linear trends, and, as shown in Figure 7, they lag the peaks of the multidecadal variations in NINO3.4 SST anomalies.
http://i56.tinypic.com/343r903.jpg
Figure 10
Note: The highest trend in the later epoch of the GISS-based “change data” is about 5% higher than the highest trend in the earlier warming period. And that’s not unreasonable considering the early period was so poorly sampled. Again, the similarities in trends between the two epochs speaks highly of the methods used by the researchers to infill the data
A NOTE ABOUT THE NORTH ATLANTIC
Oceanic processes such as Atlantic Meridional Overturning Circulation (AMOC) and Thermohaline Circulation (THC) are normally cited as the cause of the additional multidecadal variability of North Atlantic SST anomalies. This additional variability is presented in an index called the Atlantic Multidecadal Oscillation or AMO. The AMO data are simply North Atlantic SST anomalies that have been detrended. As discussed in the post An Introduction To ENSO, AMO, and PDO — Part 2, the NOAA Earth System Research Laboratory (ESRL) Atlantic Multidecadal Oscillation webpage refers readers to the Wikipedia Atlantic Multidecadal Oscillation webpage for further discussion. And Wikipedia’s description includes the statement, “While there is some support for this mode in models and in historical observations, controversy exists with regard to its amplitude…” The phrase “some support” does not project or instill a high level of confidence.
Early in this post we prepared a dataset that illustrated the “Running Change (31-Year) In Global SST Anomalies” by subtracting the annual SST anomalies of a given year from the SST anomalies 30 years later and repeating this each year for the term of 1880 to 2009. We can prepare the “Running Change (31-Year) In North Atlantic SST Anomalies” using the same simple method. Those two datasets (based on global and North Atlantic SST anomalies) are shown in Figure 11. The “Running Change (31-Year) In North Atlantic SST Anomalies” dataset appears simply to be an exaggerated version of the “Running Change (31-Year) In Global SST Anomalies”.
http://i52.tinypic.com/72yjwj.jpg
Figure 11
And comparing the “Running Change (31-Year) In North Atlantic SST Anomalies” to the NINO3.4 SST anomalies smoothed with a 31-year filter, Figure 12, shows that the NINO3.4 SST anomalies lead the multidecadal changes in North Atlantic SST anomalies.
http://i54.tinypic.com/besbvb.jpg
Figure 12
Putting Figures 11 and 12 into other words, the AMO appears to simply be the North Atlantic exaggerating the cumulative effects of the variations in the frequency and magnitude of ENSO. During epochs when El Niño events dominate, the SST anomalies of the North Atlantic rise more than the SST anomalies of the other ocean basins, and when La Niña events dominate, the North Atlantic SST anomalies drop more than the SST anomalies for the rest of the globe.
Why? The South Atlantic (not a typo) is the only ocean basin where heat is transported toward the equator (and into the North Atlantic). So warmer-than-normal surface waters in the South Atlantic created by the changes in atmospheric circulation during an El Niño should be transported northward into the North Atlantic (and vice versa for a La Niña). This effect seems to be visible in the animation of Atlantic SST anomalies from September 23, 2009 to November 3, 2010, Animation 1. (Note: By the start of the animation, September 2009, the 2009/10 El Niño was well underway.) Unfortunately, there is a seasonal component in those SST anomaly maps, and it’s difficult to determine whether the seasonal component is enhancing or inhibiting the appearance of northward migration of warm waters. Rephrased as a question, is the seasonal component in the SST anomalies creating (or detracting from) an illusion that makes it appear that the warm SST anomalies are migrating from the South Atlantic to the North Atlantic?
http://i55.tinypic.com/jzbdqe.jpg
Animation 1
The northward migration of warm waters from the South Atlantic to the North Atlantic also appears to be present in the following animation of the correlation of NINO3.4 SST anomalies with Atlantic SST anomalies at time lags that vary from 0 to 12 months, Animation 2. Again the correlation maps show areas that warm (red) or cool (blue) in response to an El Niño and the positive lags represent the number of months following the peak of the El Niño. Three month average NINO3.4 and Atlantic SST anomalies were used.
http://i52.tinypic.com/2gtai6d.jpg
Animation 2
Another reason the North Atlantic exaggerates the effects of ENSO is because the North Atlantic is open to the Arctic Ocean. El Niño events cause increases in seasonal Arctic sea ice melt during the following summer. It would also seem logical that El Niño events would increase the seasonal Greenland glacial melt as well. Refer again to Animation 2. Starting around the 9-month lag, positive correlations (warm waters during an El Niño) migrate south from the southern tip of Greenland, and starting around the 4-month lag from the Davis Strait, along the west coast of Greenland. Is that from glacial ice melt in Greenland and Arctic sea ice melt, with the melt caused by the El Niño? They’re correlated with NINO3.4 SST anomalies.
Regardless of the cause, in the North Atlantic, there are significant positive correlations with NINO3.4 SST anomalies 12 months after the peak of the ENSO event, and for at least 6 months after the ENSO event has ended. And this means that the El Niño event is responsible for the persistent warming (or cooling for a La Niña event) in the North Atlantic.
MYTH: EL NIÑO EVENTS ARE COUNTERACTED BY LA NIÑA EVENTS
One of the common misunderstandings about ENSO is that La Niña events are assumed to balance out the effects of El Niño events.
The fact: correlations between NINO3.4 SST anomalies and global sea surface temperatures are basically the same for El Niño and La Niña events; that is, El Niño and La Niña events have similar effects on regional sea surface temperatures; they are simply the opposite sign.
But that does not mean the effects of the El Niño event will be counteracted by the La Niña event that follows. First problem with that logic: La Niña events do not follow every El Niño event. That’s plainly visible in instrument temperature record. Refer to the Oceanic Niño Index (ONI) (ERSST.v3b) table. Also an El Niño event may be followed by a La Niña event that lasts for up to three years. And sometimes there are multiyear El Niño events, like the 1986/87/88 El Niño.
The easiest way the show that La Niña events do not counteract El Niño events is by creating a running total of annual NINO3.4 SST anomalies. If La Niña events counteracted El Niño events, a Running Total would return to zero with each El Niño-La Niña cycle. Refer to the Wikipedia webpage on Running total. The running total of NINO3.4 SST anomalies (to paraphrase the Wikipedia description) is the summation of NINO3.4 SST anomalies which is updated each year when the value of a new annual NINO3.4 SST anomaly is added to the sequence, simply by adding the annual value of the NINO3.4 SST anomaly to the running total each year. I’ve scaled the NINO3.4 SST anomalies by a factor of 0.06 before calculating the running total for the comparison graph in Figure 13.
http://i53.tinypic.com/29fcjl2.jpg
Figure 13
And what the Running Total shows is that El Niño and La Niña events do not tend to cancel out one another. There are periods (from 1910s to the 1940s and from the mid 1970s to present) when El Niño events dominated, and a period when La Niña events dominated (from the mid-1940s to the mid-1970s). And with the scaling factor, the running total does a good job of reproducing the global SST anomaly curve. Global temperature anomalies can also be reproduced using monthly NINO3.4 SST anomaly data. This was illustrated and discussed in detail in the post Reproducing Global Temperature Anomalies With Natural Forcings.
UPDATE– The original paragraph has been crossed out and the updated version follows.
Figure 13 implies that 6% of each El Niño and La Niña event remains within the global surface temperature record and that it is this cumulative effect of ENSO events that raises and lowers global Sea Surface Temperatures.
Figure 13 appears to imply that 6% of each El Niño and La Niña event remains within the global surface temperature record and that it is this cumulative effect of ENSO events that raises and lowers global Sea Surface Temperatures. Let’s examine that later in the post.
So that’s two ways, using sea surface temperature data, that the multidecadal rises and falls in global sea surface temperatures appear to be responses to the frequency and magnitude of El Niño and La Niña events.
HOW COULD THE OCEANS WARM WITHOUT AN INCREASE IN RADIATIVE FORCING?
Someone is bound to ask, how could the global Sea Surface Temperatures rise over multidecadal periods without an increase in radiative forcing? The answer is rather simple, but it requires a basic understanding of why and how, outside of the central and eastern tropical Pacific, sea surface temperatures rise and fall in response to ENSO events. Refer back to Figure 8, which includes the correlation maps from Trenberth et al (2002), and note that there are areas of the global oceans outside of the central and eastern equatorial Pacific that warm and cool in response to ENSO events. During an El Niño event, the warming outside of the eastern and central equatorial Pacific is greater than the cooling, and global SST anomalies rise.
But why do global SST anomalies rise outside of the eastern and central tropical Pacific during an El Niño event?
There are changes in atmospheric circulation associated with ENSO events, and these changes in atmospheric circulation cause changes in processes that impact surface temperatures. Let’s look at the tropical North Atlantic as an example. Tropical North Atlantic SST anomalies rise during an El Niño event because the trade winds there weaken and there is less evaporation. This is discussed in detail in the paper Wang (2005), “ENSO, Atlantic Climate Variability, And The Walker And Hadley Circulation.” Wang (2005) link:
http://www.aoml.noaa.gov/phod/docs/Wang_Hadley_Camera.pdf
Reworded, the reduction in trade wind strength due to the El Niño causes less evaporation, and since there is less evaporation, tropical North Atlantic sea surface temperatures rise. The weaker trade winds also draw less cool water from below the surface. So there are two effects that cause the Sea Surface Temperatures of the tropical North Atlantic to rise during El Niño events. And, of course, the opposite would hold true during La Niña events.
Again for example, during multidecadal periods when El Niño events dominate, the tropical North Atlantic trade winds would be on average weaker than “normal”, there would be less evaporation, less cool subsurface waters would be drawn to the surface, and tropical North Atlantic sea surface temperatures would rise. The western currents of the North Atlantic gyre would spin the warmer water northward. Some of the warm water would be subducted by Atlantic Meridional Overturning Circulation/Thermohaline Circulation, some would be carried by ocean currents into the Arctic Ocean where it would melt sea ice, and the remainder would be spun southward by the North Atlantic gyre toward the tropics so it could be warmed more by the effects of the slower-than-normal trade winds. Similar processes in the tropical South Atlantic also contribute to the warming of the North Atlantic, since ocean currents carry the warmer-than-normal surface waters from the South Atlantic to the North Atlantic.
Refer again to the correlation maps in Figure 8. Those are snapshots of monthly SST anomaly correlations. If those patterns were to persist for three decades due to a prolonged low-intensity El Niño event, global SST anomalies would rise. And the opposite would hold true for a prolonged La Niña event.
Let’s look at the average NINO3.4 SST anomalies during the three epochs of 1910 to 1944, 1945 to 1975, and 1976 to 2009. As shown in Figure 14, the average NINO3.4 SST anomalies were approximately +0.15 deg C from 1910 to 1944; then from 1945 to 1975, they were approximately -0.06 deg C; and from 1976 to 2009, the NINO3.4 SST anomalies were approximately 0.2 deg C. This is a very simple way to show that El Niño events dominated the two periods from 1910 to 1945 and from 1976 to 2009 and that La Niña events dominated from 1945 to 1975.
http://i56.tinypic.com/zxmsg8.jpg
Figure 14
Figure 15 compares annual Global SST anomalies to the average NINO3.4 SST anomalies for those three periods. Global SST anomalies rose from 1910 to 1944 because El Niño events dominated, and because the SST anomaly patterns (caused by the changes in atmospheric circulation) associated with El Niño events persisted. Because La Niña events dominated from 1945 to 1975, and because the SST anomaly patterns associated with La Niña events persisted, Global SST anomalies dropped. And Global SST anomalies rose again from 1976 to 2009 because El Niño events dominated, and because the SST anomaly patterns associated with El Niño events persisted.
http://i55.tinypic.com/33cwt4j.jpg
Figure 15
The fact that the rise in global Sea Surface Temperature anomalies since the early 1900s can be recreated without an increase in radiative forcing implies a number of things, one being that anthropogenic greenhouse gases do nothing more than cause a little more evaporation from the global oceans.
UPDATE – The following discussion (What Does The Running Total Imply?) has been added.
WHAT DOES THE RUNNING TOTAL IMPLY?
Earlier I wrote, Figure 13 [which was the comparison graph of global SST anomalies versus the running total of scaled NINO3.4 SST anomalies] appears to imply that 6% of each El Niño and La Niña event remains within the global surface temperature record and that it is this cumulative effect of ENSO events that raises and lowers global Sea Surface Temperatures. But is that really the case?
Keep in mind that the running total is a simple way to show the rise in global SST anomalies can be explained by the oceans integrating the effects of ENSO. It does not, of course, explain or encompass many interrelated ENSO-induced processes taking place in each of the ocean basins. Each El Niño and La Niña event is different and the global SST anomalies responses to them are different. For example, the South Atlantic SST anomalies remained relatively flat for almost 20 years, but then there was an unusual warming Of The South Atlantic during 2009/2010. Why? I have not found a paper that explains why South Atlantic SST anomalies can and do remain flat, let alone why there was the unusual rise. In this post, the gif animation of NINO3.4 SST anomaly correlation with North Atlantic SST anomalies, Animation 2, showed that the response of the North Atlantic can persist far longer than the El Niño or La Niña, but if I understand correctly, this type of analysis will emphasize the stronger events. What happens during lesser ENSO events? And there’s the East Indian and West Pacific Ocean. In January 1999, I began illustrating and discussing how the East Indian and West Pacific Oceans (60S-65N, 80E-180 or about 25% of the global ocean surface area) can and does warm in response to El Niño AND La Niña events. The first posts on this cumulative effect were Can El Nino Events Explain All of the Global Warming Since 1976? – Part 1, and Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2. And the most recent post was La Niña Is Not The Opposite Of El Niño – The Videos. The Eastern Pacific Ocean is, of course, dominated by the ENSO signal along the equator. However, because of the North and South Pacific gyres, the East Pacific also influences and is influenced by the West Pacific, which can warm during El Niño and La Niña events. And there’s the Indian Ocean with its own internal variability, represented in part by the Indian Ocean Dipole (IOD). The decadal variability of the IOD has been found to enhance and suppress ENSO, and, one would assume, vice versa.
HOW MUCH OF THE RISE IN GLOBAL TEMPERATURES OVER THE 20TH CENTURY COULD BE EXPLAINED BY THE GLOBAL OCEANS INTEGRATING ENSO?
As shown in Figure 13 and as discussed in detail in the post Reproducing Global Temperature Anomalies With Natural Forcings, virtually all of the rise in global surface temperatures from the early 1900s to present times can be reproduced using NINO3.4 SST anomaly data. The scaled running total of NINO3.4 SST anomalies establishes the base curve and would represent the integration of ENSO outside of the eastern and central equatorial Pacific. Scaled NINO3.4 SST anomalies are overlaid on that curve to represent the direct effects of ENSO on the eastern and central equatorial Pacific. Add to that scaled monthly sunspot data to introduce the 0.1 deg C variations is surface temperature resulting from the solar cycle and add scaled monthly Stratospheric Aerosol Optical Depth data for dips and rebounds due to volcanic eruptions, and global surface temperature anomalies can be reproduced quite well. Refer to Figure 16, which is Figure 8 from the post Reproducing Global Temperature Anomalies With Natural Forcings.
http://i42.tinypic.com/2zqufzp.jpg
Figure 16
Basically, that was the entire point of this post. One of the mainstays of the anthropogenic global warming hypothesis is that there are no natural factors that could explain all of the global warming since 1975. But this post has shown that ALL of the rise in global sea surface temperatures since 1900 can be explained by the oceans integrating the effects of ENSO.
CLOSING
This post presented graphs and animations that showed Global SST anomalies rose and fell over the past 100 years in response to the dominant ENSO phase; that is, Global SST anomalies rose over multidecadal periods when and because El Niño events prevailed and they fell over multidecadal periods when and because La Niña events dominated. Basically, it showed that the oceans outside of the central and eastern tropical Pacific integrate the impacts of ENSO, and that it would only require the oceans to accumulate 6% of the annual ENSO signal (Figure13) in order to explain most of the rise in global SST anomalies since 1910. And the post provided an initial explanation as to why and how the global oceans could rise and fall without additional radiative forcings. It also showed that the Atlantic Multidecadal Oscillation (AMO) appears to be an exaggerated response to the dominant multidecadal phase of ENSO. Hopefully, it also dispelled the incorrect assumption that La Niña events tend to cancel out El Niño events.
SOURCES
The HADISST data used in this post is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
The maps used in the video are available from the GISS map-making webpage:
http://data.giss.nasa.gov/gistemp/maps/
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
Stephen Wilde says: “And both periods show more clouds and higher albedo than the late 20th century warming spell.”
If this refers to the period of the 1940s to the 1970s, what cloud cover data on you relying on for this statement?
Geoff Sharp says: “The interesting aspect for me from Bob’s work is the chicken and egg process with respect to ENSO and PDO.”
I did not include this graph in the post but it was included in the longer version of the video. I used 31-year smoothing in the post for the NINO3.4 data. If we then compare it to the PDO smoothed with a 31-year filter…
http://i51.tinypic.com/2wqbxgn.jpg
…the PDO clearly lags ENSO. The PDO is an aftereffect of ENSO.
The correlations observed in this work are interesting but there is a problem with one part of it. The author asks,
“Someone is bound to ask, how could the global Sea Surface Temperatures rise over multidecadal periods without an increase in radiative forcing?”
and then suggests that no forcing is needed. However some forcing is needed, in fact essential otherwise the proposal breaks either the 1st or the 2nd laws of thermodynamics, or both. If the sea surface temperature rise is correctly observed, as the paper assumes, and if it is truly global, as is stated, then a large amount of energy has been added to the top layer of the ocean. This cannot be explained by oscillatory effects. Ocean oscillations and currents could (and do) cause local effects as warm and cold water interchanges, but they cannot cause a global effect as they just move energy around rather than increasing it. Vertical movement of water, and evaporation cannot by themselves explain a global effect either as these all need a net energy input.
Furthermore there is a problem with all assumed oscillatory cycles – they all require a driver. Ultimately this comes down to the 2nd law of thermodynamics, and its implication that no process can ever be 100% efficient. The consequence is that in the natural world all oscillatory behavior is damped, and without an external driver the oscillations would die out – it is unlikely that these processes began in 1880, so why are they still there without forcing?
To get forcing you need either and increase in solar energy, or a change in the earth’s reflectivity (and that would need a verifiable reason) or a change in absorption of solar energy (which could be provided by GHG’s). Which you chose is up to you, provided you have evidence, but a choice is needed.
Bob Tisdale says:
November 21, 2010 at 5:42 pm
Thanks Bob, your graph does show some deviation but the turning points at 1925 1960 and around 1992 show both data sets coming together. It may require more research as well as a proven mechanism before the chicken or egg question is solved?
More chicken and egg….The JISAO PDO values for October have been released today. A small positive trend is noticed which is prior to a similar position of the La Nina 2 weeks later. The Australian BOM reports the slight upward trend in La Nina is a result of MJO activity which is now waning, but the warming PDO could also be the cause.
Graph HERE and HERE.
Bob Tisdale says: “The trade winds in [D] are the trade winds in the North Atlantic, while the trade winds in [E] are those in the Pacific.”
Thanks, that makes sense, if the trades are not coupled N-S. But the question remains, just looking at [E] ‘During the El Niño phase, the trade winds first slow, then reverse. Since the trade winds are no longer ‘holding’ the water in place in the western Pacific, gravity causes the warm water to slosh to the east’. What causes the Pacific trades to slow?
jorgekafkazar says: “What causes the Pacific trades to slow?”
Dunno. That’s one of the unanswered questions in climate science. I’m not being elusive by saying it varies. It’s one of the reasons that past ENSO variability is so hard to model.
Geoff Sharp says: “More chicken and egg….The JISAO PDO values for October have been released today. A small positive trend is noticed which is prior to a similar position of the La Nina 2 weeks later. The Australian BOM reports the slight upward trend in La Nina is a result of MJO activity which is now waning, but the warming PDO could also be the cause.”
The PDO does not represent SST in the North Pacific, just the pattern. The rise in the PDO could be caused by an increase in the SST in the east or by a decrease in the central and west. The SST anomalies in the north Pacific as a whole could be rising or falling, but the PDO will be postitive just as long as the eastern North Pacific is warmer than it is in the central and western North Pacific.
The PDO pattern is “ENSO-like” because ENSO creates it. The pattern can persist longer than (or shorter than) ENSO events or can vary month-to-month because it is also impacted by variations in sea level pressure. There’s no chicken and egg. ENSO is a process and the PDO is one of its aftereffects.
jimmi says: “If the sea surface temperature rise is correctly observed, as the paper assumes, and if it is truly global, as is stated, then a large amount of energy has been added to the top layer of the ocean.”
The rise in SST anomalies outside of the equatorial Pacific is caused by a reduction in evaporattion. Exampe: A decrease in surface wind speed will cause less evaporation and sea surface temperture will rise.
You wrote, “Furthermore there is a problem with all assumed oscillatory cycles – they all require a driver.”
ENSO has existed for as far back as paleoclimatology can reach. It is a self-charging oscillation (discharge/recharge) caused by the interdependence of the coupled ocean-atmosphere processes in the tropical Pacific. The recharging takes place during La Nina events when stronger trade winds decrease cloud cover and allow more downward shortwave radiation to warm the tropical Pacific. The trade winds “pile up” this warm water in the western tropical Pacific in an area called the West Pacific Warm Pool. When there is a relaxation of trade winds, the warm water in the West Pacific Warm Pool sloshes to the east and speads across the surface and there’s an El Nino event, which is the discharge mode.
jimmi: An afterthought: as mentioned in the post and as discussed and illustrated in detail in the linked posts, the both El Nino and La Nina events warm the East Indian and West Pacific Oceans, and these warmings can be and are cumulative. This is visible in SST data and is very obvious once you know it’s there.
Bob Tisdale asked:
“If this refers to the period of the 1940s to the 1970s, what cloud cover data on you relying on for this statement?”
It is necessary to link two separate sources because pre satellite cloudiness data is very sparse and usually regional in nature.
Firstly we have this, admittedly limited to the US:
http://europa.agu.org/?uri=/journals/gl/GL017i011p01925.xml&view=article
2) Fewer clouds were present during the 1930s and early 1950s.
3) There has been a tendency toward increased since 1948.
So a decrease during the 1930s warmth then the start of an increase as the mid century cooling spell began.
Until I can find a source that covers 1950 to the 1970s we have to assume that the increase which began around 1948 continued until another decrease began around 1980 as per this from the Earthshine project:
http://bbso.njit.edu/Research/EarthShine/literature/Palle_etal_2006_EOS.pdf
in Figs 1 and 2 which show reducing cloudiness during the late 20th century warming and the start of an increase in the last ten years. The increased cloudiness is in parallel with albedo changes and of course the jets now seem to be more equatorward again as they were during the mid 20th century cooling.
Geoff Sharp asked about jet stream latitudinal positioning but no one has been tracking changes beyond normal seasonal variation so we need largely to rely on anecdotal evidence. However the jets certainly moved poleward during the late 20th century warming spell as per this:
http://www.msnbc.msn.com/id/24228037/
“From 1979 to 2001, the Northern Hemisphere’s jet stream moved northward on average at a rate of about 1.25 miles a year, according to the paper published Friday in the journal Geophysical Research Letters. The authors suspect global warming is the cause, but have yet to prove it.”
So one can assume that the jets were more equatorward pre 1979 and we now see them more equatorward post 2001. I first noted the start of the reversal in 2000.
So putting all that together the latitudinal shifting of the jets is associated with total cloudiness, albedo changes and changes in tropospheric temperature trend and likely as not also responsible for changes in solar energy into the oceans and changes in the relative strengths of El Nino and La Nina.
An interesting feature is that reduced cloudiness is linked to warming spells and reduced albedo whereas AGW theory proposes more clouds with increased warmth.
As to the cause of the jet stream shifts I have been investigating that separately as some here will be well aware.
Bob,
A rise in evaporation cannot cause a global effect, only a local one – the evaporated water has to condense and return to the ocean somewhere so this process represents a redistribution of existing energy , not a global increase.
There is no such thing as a self-charging oscillation – that would give you a perpetual motion machine and the 2nd law of thermodynamics forbids it – it needs an external energy source. You are shifting the cause to a decrease in cloud cover, or a change in wind patterns, but those both require causes themselves – just because something is ‘natural’ does not mean it has no cause. The description of ENSO may well be correct phenomenologically, but that does not imply that the components (sea temperature, trade wind, cloud cover) are causes – they may all be due to external factors. Also if we are seeing a change in something it is required that at least one of the causes (drivers) is changing in frequency or magnitude.
jimmi says:
November 22, 2010 at 3:09 am
There is no such thing as a self-charging oscillation
A change in cloud is all that is needed, there is more than one theory that would cover this from an external viewpoint. It may also be generated internally, the doors need to be left open.
Bob,
Of course your result only shows 1.5 cycles when your method cuts the period down to 1895 to 1994, but there are still two cycles in the raw data. Lets leave this detail and focus on the crucial point. Your fig.3 and the likes show SST increases when Nino3.4 is above average, but they do not show that all of the increase is caused by Nino. In fact your fig.13 contradicts fig.3. The SST derivative in fig.3 is centered around 0.1, not around 0, giving the SST a positive trend. So to align the curves you had to give Nino a 0.1 deg offset. In fig.13 you fix the problem by choosing a base period where Nino is below average. Again, I’m not saying fig.13 is wrong, but I’m sure future peer-reviews will hang you for this. The available data gives a Nino integral with only a weak positive trend (my first version http://virakkraft.com/SST-ENSO-integral.png). To go beyond that you have to justify adding 0.05 to the 1880-2000 base. Perhaps you can use this http://astroclimateconnection.blogspot.com/2010/03/60-year-periodicity-in-earths-trade.html as part of your argument. It seems to indicate the trade winds did in fact decrease since 1700.
Regardless, even with the “true” base of 1880-2000 about 70% of the SST rise since 1910 is caused by ENSO related factors.
Geoff,
“A change in cloud is all that is needed, there is more than one theory that would cover this from an external viewpoint.”
Yes, but that then would not be self charging, which was my point.
Jimmi,
I (and others) have suggested that ENSO is driven by the moon, or actually by the bodies of the solar system because they put the moon where it is. http://virakkraft.com/hadcrut-eclipse.png
Erl Happ has shown that the equatorial SLP increases during periods of weakened trade winds so the mechanism may simply be that the moon is periodically pulling the atmosphere towards lower latitudes. Or perhaps the planets periodically accelerates the Earth changing it’s rotation. There’s a quite good correlation between Ju-Ea-Ve line-ups and temperature too. http://virakkraft.com/Ju-Ea-Ve-hadcrut.png. Not perfect but then there are more planets. Hope Ian will soon pin this down.
jorgekafkazar says: “What causes the Pacific trades to slow?”
Bobs reply,
Dunno. That’s one of the unanswered questions in climate science. I’m not being elusive by saying it varies. It’s one of the reasons that past ENSO variability is so hard to model.
Ninderthana adds:
It’s the variation in the Solar/lunar tides resonantly interacting with the seasonal variations in the atmosphere that are being driven by the Sun.
A Californian group of researchers (I cannot divulge their names at present) have found vigorous bi-weekly (i.e 14.7 day = half the synodic (phase) cycle of the Moon) reversal of cross-equatorial currents and winds. Tropical Instability Waves extracted from altimetry observations show Mixed-Rossby-Gravity Waves with a highest frequency of 14.7 days.
The researchers conclude that since 1850, climate records indicate that adding the 18.6 year lunar Nodic cycle to the 11 year solar activity cycle is helpful in explaining the decadal modulations of the ENSO indices.
My own research indicates that past researchers have been looking for the wrong tidal signature in the climate data. They have been looking for a signal that varies in the same manner as that seen in the strength of the absolute peak tides. What they should have been looking for in climate record are variations that match the periods of the peak tides that repeat at the same point in time in the seasonal cycle.
The reason for this change of paradigm is simple. The Moon is not the main driver of climate variables. It only becomes a player when it acts in resonance with the solar driven seasonal cycles.
And yes, the Lunar driving cycles are not effective all of the time, since they slowly drift in and out of phase over the centuries. So the 20th century may have been an unusual period where the influence of the Lunar tides kicked in and added to the normal solar influence.
Watch this space.
The group finds
My investigations of the properties of the lunar orbit indicate that the strengths of
extreme proxigean spring tides are affected by two main alignment periods that reoccur on distinctly different time scales.
The first period is the time required for the syzygies of the Earth/Moon/Sun (i.e. the Lunar Synodic month) to realign with the closest lunar perigees. These alignments reoccur at intervals of 10.1466 and 20.2933 (269 anomalistic months = 251 synodic months) Julian years.
The second period is the time required for the syzgies of the Earth/Moon/Sun (i.e. the Lunar Synodic month) to realign with the line of nodes of the Lunar orbit. These alignments reoccur at intervals of 1.8980 and 3.7958 (51 draconic months = 47 synodic months) Julian years.
Combining these forcing periods, you would expect that, over extended periods of time, the effects of extreme proxigean spring tides should manifest themselves in the climate indices on time scales that are set by the beat periods between these two fundamental time scales.
[10.1466 X 1.8980] / [10.1466 – 1.8980] = 2.335 yrs ~ 28.0 months
Interestingly, this time period is almost exactly the same as that of the Quasi-Biennial
Oscillation (QBO). The QBO is a quasi-periodic oscillation in the equatorial stratospheric zonal winds that has an average period of oscillation of 28 months, although it can vary between 24 and 30 months (Giorgetta and Doege 2004).
It represents the beat period between:
a) the time scale associated with the synchronization of the line-of-apsides with the the syzygies of the Earth–Moon–Sun system (i.e. the Lunar Synodic month)
and
b) the time scale associated with associated with the synchronization of the line-of-nodes with the the syzygies of the Earth–Moon–Sun system (i.e. the Lunar Synodic month)
Bob,
Can you clarify one thing?
Are you saying that the accumulated effects of the El Nino cycle *could* account for all (or nearly all) of the observed warming of the last 100 years, or are you saying that the accumulated effects of the El Nino cycle definitely are responsible for all (or nearly all) of the observed warming? If it is the later case, then why do think El Nino has dominated La Nina during this period?
I think your efforts are very helpful in understanding the evolution of sea surface temperatures over the last century, especially the causal link between ENSO and the AMO, since this explains why there is a cyclical appearance in the temperature record which is closely correlated with the AMO index. I also agree that solar cycle’s and volcanic aerosol effects account for much of the shorter term variation. But I do not think that the analysis can be used to exclude the possibility of a contribution of increased GHG forcing.
A regression of AMO index, Nino 3.4, solar cycle, and total GHG forcing against the Hadley global temperature record shows very good overall correlation (R^2 of about 0.9) as well, and suggests both strong correlation of temperature to the AMO index and a low sensitivity to radiative forcing (about 1.2C per doubling of CO2). But low sensitivity does not mean zero sensitivity. Are you really suggesting zero sensitivity to radiative forcing?
I think the temperature of the water itself is the driver of this whole system.
And it does oscillate up and down all on its own based on the way the water circulates in the central pacific. Warm water piles up against New Guinea, where the deep oceans ends and some of it is forced down because the water flow is restricted by the now shallow ocean and islands.
http://www.srh.noaa.gov/jetstream/tropics/images/la.jpg
Much of it is forced down and it flows back to the east at 200 metres depth and when the warm water surfaces at the Galapogos Islands in 9 months (replaceing the water which is flowing east-west at the surface), it starts to slow down the Trade Winds because of the convection effect. Then we have an El Nino.
http://www.srh.noaa.gov/jetstream/tropics/images/el.jpg
Meanwhile back at New Guinea, the cold water left over from the previous La Nina is now being forced down and it enters the subsurface circulation and in about 9 months, it will surface at the Galapagos Islands, speed up the Trade Winds and we have a La Nina.
It is an oscillation which reflects the predominant ocean circulation patterns and the temperature of the water itself.
The Trade Winds lag a few months behind the temperature of the water itself.
http://img152.imageshack.us/img152/7366/ensoeuhctradesoct10.png
And right now, we are seeing the La Nina starting to reach its peak. By about July or so, we will be in an El Nino.
Surface circulation.
http://www.osdpd.noaa.gov/data/sst/anomaly/anomalyps_2m.gif
Subsurface circulation.
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ocean/anim/wkxzteq_anm.gif
Steve Fitzpatrick says: Are you saying that the accumulated effects of the El Nino cycle *could* account for all (or nearly all) of the observed warming of the last 100 years…”
I have presented nothing in this post that would allow me to state that “the accumulated effects of the El Nino cycle definitely are responsible for all (or nearly all) of the observed warming.”
You asked, “…then why do think El Nino has dominated La Nina during this period?”
There a low-frequency component to ENSO and we’ve been luck to catch two of them when El Nino events dominated.
You asked, “Are you really suggesting zero sensitivity to radiative forcing?”
I’m suggesting that ENSO represents more of the rise in global temoperatures than its linear component, which is what studies like Thompson et al (2008) would like us to believe. How much more? Dunno. Could the accumulated affects of ENSO in the East Indian and West Pacific Oceans represent a major portion of the rise in global surface temperatures? Yes. And regardless of whether or not the AMO is driven by THC/AMOC or by ENSO, it’s still a natural form of variability and it also contributes significatly to the overall rise in global temps from the trough in the early 1900s to present.
Bob, can I suggest to you that there are better ways of observing ENSO than using Nino3.4? It is only a proxy, sits there in the middle of the equatorial countercurrent and watches El Nino waves go by. They carry warm water from the Indo-Pacific warm pool to South America. When they get there they run up the coast and spread out. The large area of warm water exposed thereby warms the air, warm air rises, interferes with trades, mixes with global circulation, and raises its temperature by half a degree. It is that temperature rise that counts, and it is present in all global temperature curves. Unfortunately stupidity interferes because so-called “climatologists” get rid of it since nothing less than thirty years means anything to them. But one HadCRUT3 version somehow escaped being smoothed out and you can get it in Junk Science. Click Global Temperatures, Contemporary Time Series, and HadCRUT3. It is the graph on the lower right. It covers the period from 1850 to 2008. They give you the URL and you can download the data yourself if you don’t like their version.
lgl says: “Your fig.3 and the likes show SST increases when Nino3.4 is above average…”
Nope. It shows that the 31-year change in global SST anomalies increases when the NINO3.4 SST anomalies rise and the opposite when the smoothed NINO3.4 SST anomalies drop. Sometimes it’s easier to think about 31-year trends (Figure 7) increasing as 31-year average NINO3.4 SST anomalies increase.
You continued, “…but they do not show that all of the increase is caused by Nino.”
The curves show that when the 31-year average NINO3.4 SST anomalies were at their peak in 1926 (representing the period from 1911 to 1941) global SST anomalies rose about 0.43 deg C, and when the 31-year average of NINO3.4 SST anomalies were at their lowest point in 1960, global SST anomalies dropped about 0.22 deg C.
You wrote, “In fact your fig.13 contradicts fig.3…”
In no way do the two figures contradict one another. The reason the NINO3,4 data was shifted up 0.1 deg C in Figure 3 was for cosmetic reasons, simply to align the wiggles. Same thing with Figure 13. The running total was shifted down 0.26 deg C to align the two curves. By doing so, it made the relationship easier to see.
The rest of your comment continued to express your concerns about the fact that the running total depends on the ratio of positive to negative anomalies. That’s really a minimal requirement when one considers the contortions climate modelers go through in order to reproduce the curve of the 20th century surface temperature anomalies. They use outdated TSI reconstructions with rises in solar minimums during the first half of the 20th century in order to reproduce the rise in temperature at the time. They use manufactured aerosol datasets to create the mid century drop in temperature, and they rearrange the aerosols depending on the dataset they’re trying to reproduce. Last, who knows what they tweak with all of the adjustments so that the average of the ensemble resembles the temperature anomaly curve.
And thanks for the link to the post about the trade wind reconstruction.
Arno Arrak says: “Bob, can I suggest to you that there are better ways of observing ENSO than using Nino3.4? It is only a proxy, sits there in the middle of the equatorial countercurrent and watches El Nino waves go by.”
Unfortunately and fortunately, NINO3.4 SST anomalies and the CTI) are widely used and to incorporate something else would require additional justification. It’s best for me to use commonplace proxies.
Jimmi says: “There is no such thing as a self-charging oscillation.”
It was a poor choice of words. ENSO is a discharge/recharge oscillation. Refer to the graph of NINO3.4 SST anomalies (proxy for ENSO events) and tropical Pacific Ocean Heat Content (OHC):
http://i36.tinypic.com/eqwdvl.png
El Nino events discharge heat from the tropical Pacific and most times the subsequent La Nina recharges only part of the heat lost during the El Nino. That’s why there are the decadal and multidecadal declines in Ocean Heat Content–the typical La Nina recharge doesn’t make up for the typical El Nino discharge. Then there are the oddities. The 1973/74/75/76 La Nina persisted so long that tropical Pacific OHC rose considerably. And then there was the 1995/96 that also caused the unusual rise in tropical Pacific OHC that fueled the 1997/98 El Nino, which has been describes as the El Nino of the century. That rise in OHC is associated with unusually strong trade winds.
So now that you’ve seen the data, if not self-recharging, what would you use instead?
Regards