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The following tables were prepared to provide a reference of El Niño and La Niña events since 1900, using HADISST-based SST anomalies for the NINO3.4 region. They are based on the same definition of warm and cool ENSO events that NOAA uses for its Oceanic NINO Index (ONI). That is, all data are first smoothed with a 3-month running-average filter. An El Niño event is highlighted in red and identified with an “EN” in the far-right column if the 3-month average of NINO3.4 SST anomalies remained equal to or above 0.5 deg C for 5 consecutive 3-month periods. Likewise, a La Niña event is represented by blue and identified with a “LN” if the 3-month average of NINO3.4 SST anomalies remained equal to or below -0.5 deg C for 5 consecutive 3-month periods. And the same base years were used as the ONI Index, 1971 to 2000.
Since ENSO events typically peak in boreal winter, the second to last column reflects the two years of event evolution and decay. ENSO events typically develop during one year and decay the next—except, obviously, when an El Niño or La Niña event spans multiple years. The response of global temperatures lags the tropical Pacific and would typically be seen in the year of the decay. The same thing holds true for La Niña events. The two-year designation in the table should help avoid the confusion over an “El Niño year” or a “La Niña year” because it identifies the evolution and decay years.
Due to the length of the table, I had to break it into two parts.
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Global sea surface temperature anomalies rose from 1910 to 1944, dropped from 1945 to 1975, and rose again from 1976 to present. Keying off the development year, during the period from 1910 to 1944, there were 10 El Niño events and 6 La Niña events. (For this simple comparison, if an El Niño or La Niña event extends from one winter to the next, it would be considered two events.) From 1945 to 1975, there were only 7 El Niño events, compared to 11 La Niña events. And from 1976 to present, El Niño events dominated again. There were 12 El Niño events but only 8 La Niña events.
NOTES
The tables have been formatted similar to the ONI Index, but that doesn’t mean one should subscribe to the notion that global temperatures only respond to the 3-month average of NINO3.4 SST anomalies if they are beyond the threshold used in the tables.
SOURCE
HADISST NINO3.4 SST anomalies are available through the KNMI Climate Explorer Monthly climate indices webpage.
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The November 2010 SST data through the NOAA NOMADS website won’t be official until December 6. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for November 2010 presented by the NOMADS website. I’ve also included the weekly data through November 24, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the variations can be seen
PRELIMINARY MONTHLY DATA
Based on the preliminary data, monthly NINO3.4 SST anomalies have stopped their decline and have risen slightly. Presently they’re at -1.42 deg C.
Monthly Global SST anomalies, according to the preliminary data, have dropped another 0.04 deg C. The preliminary global SST anomaly is 0.1 deg C. As noted last month, with the step up in the South Atlantic and its effect on the North Atlantic, it will be interesting to see how much global SST anomalies will decline in response to the La Niña. Refer to the post The 2009/10 Warming Of The South Atlantic. Combine that with the typical rise in the East Indian and West Pacific Oceans that occur when El Niño events are followed by La Niña events, and one should not expect the global SST anomalies to drop to the level they reached in December 2007. http://oi51.tinypic.com/11qiaee.jpg
Monthly Global SST Anomalies
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WEEKLY DATA
The weekly NINO3.4 SST anomaly data are dropping again after their minor rebound. They are at -1.6 deg C. http://i56.tinypic.com/21kwmrn.jpg
Weekly NINO3.4 SST Anomalies
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Weekly Global SST Anomalies have stopped their rebound, and they are presently at +0.08 deg C. http://i51.tinypic.com/b4dwyd.jpg
Weekly Global SST Anomalies
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In advance of the UN negotiations next week in Cancun, the press and blogs today have included numerous elaborations on the UK Met Office press release Scientific evidence is Met Office focus at Cancun. The Australian article “Global temperature rises may be underestimated due to errors, Met Office study says” by Ben Webster includes the following statement, “The long-term rate of global warming was about 0.16C a decade in the 1970s, 1980s and 1990s but it slowed in the past 10 years to between 0.05C and 0.13C, depending on which of three major temperature records is used. The Met Office said that changes in the way ocean temperatures were measured had resulted in an under-estimate of about 0.03C in recent years.”
But what the Met Office fails to mention is that the dataset being discussed in the press release, their HADSST2 data, which is the sea surface temperature dataset used in their HADCRUT3 and HADCRUT3v global temperature products, is biased upwards by almost 0.12 deg C after 1998 due to a change in source data in 1998. I’ve illustrated and discussed this bias in two previous posts: Met Office Prediction: “Climate could warm to record levels in 2010” and The Step Change in HADSST Data After the 1997/98 El Nino.
The new source Sea Surface Temperature data was not fully consistent with the source dataset the Hadley Centre used prior to 1998. So when they merged the two datasets, the Hadley Centre failed to account for the inconsistency and created an upward bias in their HADSST2 data. This bias is easily seen when the other Hadley Centre sea surface temperature dataset, HADISST, is subtracted from the HADSST2 data, Figure 1. Note that the HADISST has relied primarily on satellite-based measurements since 1982, but the HADSST2 data is based on buoy and ship readings. The upward step is approximately 0.12 deg C. The bias created by the change in measurement methods over the past decade that was reported on in The Australian would only offset a portion of that shift.
Hopefully, when the Hadley Centre finally releases its updated Sea Surface Temperature dataset (HADSST3) they will eliminate the upward step. And for those interested, here’s a link to a Met Office Scientific Advisory Committee (MOSAC) publication, “Climate monitoring and attribution,” that provides an overview of the upcoming HADSST3 and HADISST2 datasets. Refer to page 3 under the heading of “3. Progress in development of marine datasets.” http://research.metoffice.gov.uk/research/nwp/publications/mosac/MOSAC_15.10.pdf
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The following two papers by Guan and Nigam are the most in depth and informative analyses of Sea Surface Temperature (SST) for the 20th Century that I’ve run across to date.
And the second is Guan and Nigam, 2009: Analysis of Atlantic SST variability factoring inter-basin links and the secular trend: clarified structure of the Atlantic Multidecadal Oscillation. J. Climate, 22(15), 4228-4240 http://www.atmos.umd.edu/~bguan/download/index.php?Guan&Nigam_2009.pdf
I am presenting them without further introduction. I am still studying both papers, and I will not be able to answer many of your questions. But I am interested in your impressions on what you believe to be the major points of the papers, so I won’t attempt to influence your conclusions by stating mine.
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I’ve shortened this edition of the mid-month update by including only the shorter-term NINO3.4 and global SST anomaly graphs; that is, the ones from January 2004 to present.
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NINO3.4 NINO3.4 SST anomalies for the week centered on November 17, 2010 show that central equatorial Pacific SST anomalies have dropped slightly in the past two weeks after a small rise. They’re at -1.5 deg C. http://i56.tinypic.com/es3eyq.jpg
NINO3.4 SST Anomalies - Short-Term
GLOBAL Weekly Global SST anomalies are continuing their decline. But like weekly responses to past La Niña events, there are major steps down with lesser steps up. http://i53.tinypic.com/2wbxe7a.jpg
Global SST Anomalies - Short-Term
######################### 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?
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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?”
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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.
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".
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
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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
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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 2009, 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.
################### MONTHLY SST ANOMALY MAPThe map of Global OI.v2 SST anomalies for October 2010 downloaded from the NOAA NOMADS website is shown below. The peak of the seasonal elevated SST anomalies in the North Atlantic appears to have passed. They were enhanced this season by the Arctic sea ice melt associated with the last winter’s El Niño and with the 2009/10 upward shift in South Atlantic SST anomalies. The central equatorial Pacific SST anomalies are continuing their decline, and the Pacific is showing a classic La Niña pattern: Eastern tropical Pacific SST anomalies are depressed while the Kuroshio Extension in the northwest Pacific and the South Pacific Convergence Zone east of Australia have elevated SST anomalies. http://i53.tinypic.com/30d9mhg.jpg
October 2010 SST Anomalies Map (Global SST Anomaly = +0.19 deg C)
A NOTE ABOUT GLOBAL SST ANOMALIES
Global SST anomalies, based on the weekly Reynolds OI.v2 data, have made a rebound and taken another drop over the past month. This can be seen in the graph of weekly Global SST anomalies. The present value is about where it was a month ago, 0.095 deg C. http://i53.tinypic.com/23721s.jpg
Weekly Global SST Anomalies
The next graph is a comparison of the global SST anomalies for 2010 compared to 1988, 1998, and 2007. Those were other transition years from El Niño to La Niña. In the graph, the data has been shifted so that the first weeks were all zeroed. The current transition is well within the range of past transitions from El Niño to La Niña. http://i55.tinypic.com/ih72bs.jpg
Comparison Of Transitions From El Niño To La Nina
MONTHLY OVERVIEW Monthly NINO3.4 SST anomalies are well below the -0.5 deg C threshold of a La Niña. The Monthly NINO3.4 SST Anomaly is -1.59 deg C. Weekly data has rebounded a bit and is now at -1.3 deg C.
Global SST anomalies took a good drop this month, -0.056 deg C. The decline in the Northern Hemisphere (-0.14 deg C) was partly offset by the increase in the Southern Hemisphere (+0.009 deg C). http://i51.tinypic.com/2u5xetl.jpg
Global
Monthly Change = -0.056 deg C
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NINO3.4 SST Anomaly
Monthly Change = -0.02 deg C
EAST INDIAN-WEST PACIFIC
The SST anomalies in the East Indian and West Pacific made a slight rise this month. Will they continue to rise, noticeably, in response to the La Niña as they have in the past?
I’ve added this dataset in an attempt to draw attention to what appears to be the upward step responses. Using the 1986/87/88 and 1997/98 El Niño events as references, East Indian-West Pacific SST Anomalies peak about 7 to 9 months after the peak of the NINO3.4 SST anomalies, so we shouldn’t expect any visible sign of a step change for almost 18 to 24 months. We’ll just have to watch and see. http://i56.tinypic.com/27xgqi8.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = -0.007 deg C
The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.
NOTE ABOUT THE DATA
The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from November 1981 to October 2010.
WEEKLY NINO3.4 SST ANOMALIES The weekly NINO3.4 SST anomaly data illustrate OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.32 deg C. That’s an increase of about 0.5 deg C since the minimum of about a month ago. http://i56.tinypic.com/radbnk.jpg
Weekly NINO3.4 (5S-5N, 170W-120W)
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The October 2010 SST data through the NOAA NOMADS website won’t be official until October 8. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for October 2010 presented by the NOMADS website. I’ve also included the weekly data through October 27, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the wiggles are visible.
PRELIMINARY MONTHLY DATA
Based on the preliminary data, monthly NINO3.4 SST anomalies are continuing to drop, but the rate has slowed. Presently they’re at -1.62 deg C. http://i55.tinypic.com/wa0xv5.jpg
Monthly NINO3.4 SST Anomalies
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Monthly Global SST anomalies, according to the preliminary data, have dropped considerably. The preliminary global SST anomaly is 0.15 deg C. As noted last month, with the step up in the South Atlantic and its effect on the North Atlantic, it will be interesting to see how much global SST anomalies will decline in response to the La Niña. Refer to the post The 2009/10 Warming Of The South Atlantic http://i51.tinypic.com/20pzmux.jpg
Monthly Global SST Anomalies
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WEEKLY DATA The weekly NINO3.4 SST anomaly data have risen again over the past week. They are at -1.4 deg C. http://i53.tinypic.com/4t3hax.jpg
Weekly NINO3.4 SST Anomalies
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Weekly Global SST Anomalies have increased again slightly, approximately 0.02 deg C. http://i55.tinypic.com/2n6rfdi.jpg
Weekly Global SST Anomalies
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Comments that are political in nature or that have nothing to do with the post will be deleted. #### The Smith and Reynolds SST Posts DOES NOT LIST ALL SST POSTS. I stopped using ERSST.v2 data for SST when NOAA deleted it from NOMADS early in 2009.
Please use the search feature in the upper left-hand corner of the page for posts on specific subjects. #### NOTE: I’ve discovered that some of the links to older posts provide blank pages. While it’s possible to access that post by scrolling through the history, that’s time consuming. There’s a quick fix for the problem, so if you run into an absent post, please advise me. Thanks. #### If you use the graphs, please cite or link to the address of the blog post or this website.
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