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?
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Figure 9
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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.
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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”.
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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.
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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?
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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.
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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.
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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.
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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.
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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.
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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/
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jimmi says: “A rise in evaporation cannot cause a global effect, only a local one.”
Where did I write that, outside of the tropical Pacific, a rise in SST anomalies associated with an El nino event was caused by with an increase in evaporation? In response to an El Nino event, the SST anomalies outside of the tropical Pacific rise for a number of reasons. In the tropical Atlantic for example, the paper linked in the post (Wang 2005) describes how a decrease in evaporation causes SST to rise. The decrease in evaporation is caused by a decrease in trade wind strength.
And the effect would only be local if the oceans were stagnant, but they’re not. Ocean currents redistribute waters warmed during El Nino events, or cooled during La Nina events.
Stephen Wilde says: “Until I can find a source that covers 1950 to the 1970s we have to assume…”
Big assumption. And you’ll also need a global source from the 1930s to 50s.
Bob,
What you think is only cosmetic is more than that so we have to agree to disagree.
Bob,
“So now that you’ve seen the data, if not self-recharging, what would you use instead?”
I would suggest you are seeing a cycle driven by an external source – no cycle can be 100% efficient , so ALL cycles require an external source of energy as a driver. I would suggest the obvious source of energy is the sun. However that then produces another question – if the cycles are changing, you need a change in one of the possible drivers.
sky said: “Physical systems with capacitance components can accumulate/discharge energy or other extensive variables in a manner that resembles exponentially-faded integration in the time domain. But they cannot integrate intensive variables such as temperature or their anomalies. Trenberth’s ENSO3.4 is a temperature index that is NOT centered on its long term-average. Its average obtained over any shorter interval thus is dependent upon the offset inherent in the base period. That a certain similarity is visually apparent between such an ad hoc metric and the putative “global temperature” tells us virtually nothing physical about what drives the latter. It’s simply a phenomenological curiosity.”
Physical system can accumulate energy (heat) and discharge it with exponential rise and decay, as shown by the solution of basic energy balance equations used in climate science. Correctly they integrate watts into temperature, but a strongly heated source of fluid can discharge into a sink giving the effect of integration. Thats how solar heaters work. The offset comes out as a calibration constant.
jimmi said: “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.”
It is possible to get periodic behavior driven by random perturbations, as the period depends on the limits to the natural capacity of the system to integrate those perturbations. Its like random noise setting up a swinging motion in a pendulum. No change in the external driver is required, however a slight periodicity in the driver such as orbital variation could tend to synchronize the oscillation.
Sky: You wrote, “I simply don’t buy Trenberth’s choice of 1950-1979 as a norm ‘representative’ of the 20th century, because world-wide station records unadulterated by UHI shows that interval to be distinctly cooler than the century-long mean. And the Nino3.4 index is biased upward by ~0.14K by that choice.”
I don’t follow you here. Why would SST data in the central equatorial Pacific be biased by Urban Heat Island effect? Trenberth was only establishing the base years for NINO3.4 SST anomalies in the paper.
You wrote, “And inquiring oceanographers want to know, where can they go in the South Atlantic to measure warm SURFACE currents that cross the equator?”
You may wish to start with the central South Equatorial Current. Refer to Lumpkin & Garzoli (2004). The central South Equatorial Current splits and feeds the North Brazil Current and then the Guyana Currents. See their Figure 9:
http://www.aoml.noaa.gov/phod/docs/LumpkinGarzoli05.pdf
You wrote, “This would avoid such physically unconvincing arguments as data subset NINO3.4 (which has very little low-frequency power) ‘causing’ the multi-decadal oscillations of the entire global set of data.”
Actually, the magnitude of the low-frequency component of ENSO is not that much different than the long-term variations in annual Global SST anomalies. Here’s a graph of Global SST anomalies compared to NINO3.4 SST anomalies smoothed with a 31-year filter (trailing):
http://i53.tinypic.com/33xik2c.jpg
And here’s a comparison with the NINO3.4 SST anomalies smoothed with a 21-year filter (trailing):
http://i51.tinypic.com/taptw3.jpg
And smoothed with an 11-year filter (trailing):
http://i56.tinypic.com/awyu86.jpg
But the Global SST anomalies outside of the tropical Pacific are not responding to the low frequency component; they are responding to the high frequency component:
http://i51.tinypic.com/nqz41c.jpg
You opened your reply with, “That physical causation cannot be attributed to integrals or time averages of INTENSIVE metrics is a basic tenet that no one can dispute.”
Global SST anomalies change in response to individual ENSO events, and they APPEAR to integrate the effects of ENSO for a number of reasons.
As I replied earlier, the West Pacific and East Indian Oceans warm in response to both El Nino and La Nina events, so there is a cumulative response to ENSO by a major portion of the global oceans (about 25%).
And let me add a portion of a follow-up post that I have planned: The persistence of the response of the North Atlantic SST anomalies to the El Nino-La Nina cycle also adds to the impression that the oceans are integrating the effects of ENSO. To illustrate this, here’s a graph of North Atlantic SST anomalies versus scaled NINO3.4 SST anomalies from November 1981 to present, smoothed with a 13-month filter:
http://i54.tinypic.com/k012qd.jpg
Note how the North Atlantic SST anomalies have been shifted upward by the 1997/98 El Nino. That is, there is very little response to the 1998/99/00/01 La Nina. The same thing happens in 1988/89; there’s little to no response to the La Niña. Why?
It could be argued that a portion of that is caused by the increase in trend caused by the AMO, and an AGW proponent would most assuredly argue that the other portion is explained by the “AGW trend”. So I’ll detrend the North Atlantic SST anomalies to remove the effects of the AMO and the hypothetical “AGW trend”. Note how, after detrending, the North Atlantic SST anomalies still fail to respond to the 1988/89 and 1998/99/00/01 La Niña events.
http://i55.tinypic.com/2znnz2e.jpg
The North Atlantic SST anomalies respond to the rise in NINO3.4 SST anomalies during the 1986/87/88 and 1997/98 El Niño events. But the decay of the detrended North Atlantic SST anomalies is much longer than the NINO3.4 SST anomalies, and because of the extended decay time, the North Atlantic SST anomalies don’t respond fully to the La Niña events before being driven upwards again.
Also note how, starting in 2001, the detrended North Atlantic SST anomalies rise and fall as though there was an El Niño, but none existed at the time. This additional rise and fall could be a function of Sea Level Pressure (NAO). A comparison of detrended North Atlantic SST anomalies and scaled NAO (inverted) and NINO3.4 SST anomalies shows that a change in Sea Level Pressure preceded the 2001/02 change in the North Atlantic SST anomalies. The lag between the SLP change and the response from the SST anomalies looks a little excessive though:
http://i55.tinypic.com/a3jqfd.jpg
Curiously, unlike the 1988/89 and the 1998 through 2001 La Niña events, the North Atlantic responds in full to the 2007/08 La Niña. So the North Atlantic SST anomalies respond to some La Niña events but not others. Do they respond to some El Niño events and not others? There’s no indication of that. They have risen in response to all El Niño events (over the term of the Reynolds OI.v2 dataset) that weren’t counteracted by volcanic eruptions. Maybe the 2009/10 El Niño will break the trend. I’ve never seen that subject discussed in any paper.
Why would the North Atlantic SST anomalies persist? As described in the post, Animation 2, which was the gif animation of the correlation maps of NINO3.4 SST anomalies with North Atlantic SST anomalies, showed that the response of the North Atlantic can persist far longer than the El Niño or La Niña. Also described in the post: surface waters with ENSO-induced anomalies in the South Atlantic should be transported northward into the North Atlantic by ocean currents. A third cause of the persistence was also described: El Niño events cause increases in seasonal Arctic sea ice melt during the following summer.
To conclude my reply, the fact that the oceans appear to integrate the effects of ENSO is likely do to multiple causes, with two of the major factors described as follows. The SST anomalies of the West Pacific and East Indian Oceans can and do rise in response to both El Niño and La Niña events, causing a cumulative effect that raises local SST anomalies. Since the East Indian and West Pacific Oceans are not isolated by landmass, ocean currents spread this cumulative warming into the adjoining ocean basins. The response of the North Atlantic SST anomalies to an ENSO event has been very much one sided over the past three decades. North Atlantic SST anomalies respond to El Niño events more often than they do to La Niña events. The response of North Atlantic SST anomalies to El Niño events persists due to a number of contributing factors, some of which are unknown to me at present. This multiyear persistence can and does prevent the North Atlantic SST anomalies from responding in full to the subsequent La Niña, resulting in what appear to be step changes in North Atlantic SST anomalies.
You added, “P.S. I won’t be available for further discussion until next Wednesday.”
And I’ll be available on and off on Wednesday and Thursday, one of those traditional family things. I’d prefer a prime rib but everyone else insists on turkey on Thursday.
Regards
David Stockwell said “It is possible to get periodic behavior driven by random perturbations,”
Yes, possible in theory, provided you can activate one of what I would think of as a “resonant” frequency , but regarding the system as being subjected to random time-dependent noise is a bit self-defeating if you want to build any usable model of the system. Also, unless you want to get into real trouble with the 2nd law of thermodynamics, the perturbation has to be external to the planet.
“Yes, possible in theory, provided you can activate one of what I would think of as a “resonant” frequency , but regarding the system as being subjected to random time-dependent noise is a bit self-defeating if you want to build any usable model of the system.”
Just about every statistical model in existence has a random noise component. The system in question is predictive once the initial state is defined, eg. if its at one extreme, chances are it will be at the other at half period.
“Also, unless you want to get into real trouble with the 2nd law of thermodynamics, the perturbation has to be external to the planet.”
Please dont start on the 2nd law. The energy balance models have differential equations than satisfy physical constraints, and they have solutions that include periodic oscillations – period.
David Stockwell
Nov. 22 @ur momisugly 6:17
You mentioned in the last paragraph the introduction of random noise to initiate a pendulum swing. I am used to the term Dither as applied to digital audio. Randomised noise is added to an audio signal to more accurately represent its waveform. I wonder if this process can be applied to climate data ?
In the link is interesting information as to how the term originated.
Keith, possibly there is an application of dither, but I think the following may be more applicable to the ENSO phenomenon as a source of oscillations and lags. http://en.wikibooks.org/wiki/Control_Systems/Bode_Plots. Climate scientists have no clue about system dynamics.
lgl: I found the reason for the differences in NINO3.4 SST anomaly data that you noted. For the running total, the base years were 1950 top 1979. That graph was an afterthought while I was writing the post, and I knew I needed those base years. However, when I had originally created the other graphs I did not specify the base years and the KNMI Climate Explorer spit out its default. I did a quick check and the average NINO3,4 SST anomalies for the base years of 1950 to 1979 are 0.08 deg higher than they are for the base years of 1880 to 2009, meaning there are more positive NINO3.4 SST anomalies with the base years of 1950 to 1979. In other words, it makes the El Nino events stronger and the La Nina events weaker in terms of SST anomalies.
But in the real world, looking at SST (not anomalies), the average NINO3.4 SST from 1880 to 2009 is 26.94 deg C and from 1950 to 1979 it’s 26.86 deg C.
Background fact 1:
Most people have no trouble understanding the following analogy:
If a skater is spinning with out-stretched arms and they pull their
arms closer into their body, the skater will begin to spin faster in
order to conserve angular momentum.
[The basic physical principle is that a systems angular momentum is
conserved unless it is acted upon by an outside torque (or force)]
The Earth’s and its atmosphere behaves in much the same way as this
analogy. If the rotation of the solid Earth slows down (i.e. the solid Earth’s
angular momentum decreases), then the spinning Earth/atmosphere system
must conserve or maintain angular momentum. It does this by increasing the
rotational angular momentum of the atmosphere.
Background fact 2:
The Earth’s atmosphere has a large rim of high pressure known as the
Sub-Tropical (High Pressure) Ridge (STR) which circles the Earth
between the latitudes of 20 and 40 degrees of latitude in both hemispheres.
This high pressure ridge, represents is created by the Hadley Circulation.
This is where moist unstable air rises above the Earth’s thermal equator
and moves towards the horse-latitudes located at 30 degrees north and
south of the Equator. Once it reaches the horse latitudes, the now dry air
descends and heats, creating the large high pressure cells that make up
the STR. This hot dry air makes its way back to the thermal equator
in order to complete Hadley circulation. The returning air in the southern
hemisphere is the SE trade winds and in the northern hemisphere
and the NW trade winds. The merging of these two wind systems at the
thermal equator created a strong easterly flow of air is also loosely called
the “trade winds”. It is the strength of this easterly flow of air governs
the El Nino/La Nina phenomenon in the Pacific Ocean.
Consequence of background facts 1 & 2:
One way in which the atmosphere can increase its angular momentum,
in response to a slow down in the Earth’s rotation rate, is to simply “pull
its arms in like the spinning skater” so that the atmosphere starts to spin
faster.
The atmosphere does this by increasing the Hadley circulation, so that
the mean latitude of the high pressure cells in the STR intensify and move
towards the poles. By moving the high pressure cells in the STR towards
the poles, the ring of high pressure that surrounds the Earth contracts
in radius, acting in much the same manner as the skater’s arms, and so
resulting in an increase in the atmosphere’s overall angular momentum.
The intensification of the Hadley circulation and the high pressure cells in
the STR, increases the trade-wind strength and “biases” the Pacific ocean
ENSO climate system towards a La Nina condition.
A speeding up of the rotation rate of the Earth has the opposite effect –
with the high pressure cells in the STR weakening and moving back towards
the thermal equator. This results in a slackening of the trade winds which
“biases” the ENSO climate system towards an El Nino condition.
Hence, changes in the Earth’s rotation rate is one factor that can lead
to an oscillation between El Ninos and La Ninas in the Pacific Ocean.
One important complication upon this zeroth order climate model:
The Solar/lunar tides also play a critical role in driving the Hadley
circulation and hence the intensity of the easterly equatorial trade
winds. However, you will have to wait for my paper to see the full
nature of this role.
Ninderthana, lgl, & Others,
The sign of the coupling of interannual NPI & interannual AO relates nonrandomly to LOD. (Anyone looking into this should not ignore 1st & 2nd derivatives. Bear in mind orthogonality and the effect of integrating at the temporal bandwidth of 1st & 2nd harmonics of stationary & quasistationary cycles. I recommend computing multiscale correlations in the complex plane to avoid falling victim to Simpson’s Paradox.)
After I spend a bit more time on 2.37a signals, I might take a more careful look at the 3.57a pattern (equatorial vs. polar eclipses) which lgl has pointed out a few times. There’s also a 6a year pattern in the integral of IOD. (2.37a, 3.57a, & 6a all arise in simple lunisolar beats.)
The nonstationary thermal tides (i.e. not the simple daily & annual ones) and spatial heterogeneity are the 2 areas where I suspect we have most to learn.
Another (related) matter:
I’ve seen some grumblings about early EOP records being “garbage” or “useless” or something to that effect. This thinking is misguided. For example, what if the early records are telling us something about the spatial distribution of clouds? I haven’t had time to read up on how measurements were taken, but my understanding is that before ~1960 measurements relied on observation of stars. What if spatially nonrandom diurnal cloud patterns systematically altered the geographic pattern of reporting stations? Even just by looking at the temporal pattern of the early EOP measurement error estimates, one can see patterns that show up in a variety of climate indices. Rather than grumping about what the data do not represent, I suggest we use shared patterns to get a better handle on what the measurements do represent. That the patterns relate to climate cannot be denied. Climate models should be able to reproduce these patterns. It is reasonable to expect that it may take climate scientists a few years to work this business out, but I want to strongly suggest to them that the time is ripe to tackle this business now. I also want to suggest that it is unethical to continue ignoring the problem, even if it is “too risky” to attempt a solution inside of the longest grant cycle available. I acknowledge the multifaceted nature of challenges (including funding obstacles) faced by serious climate scientists.
clarification: ground-based star observations.
also: Bear in mind the north-south continent-ocean gradient that is unique to the Indian Ocean when pondering the quasistationary 6a wave.
“What causes the Pacific trades to slow?” Bob Tisdale replies:”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.”
It does seem to be a mystery, and I’ve not found any explanation on the Internet yet. My favorite possibility is that upwelling cold water west of South America lowers the ocean and air temperatures, and consequently raises the density of the atmosphere there. At the same time, the viscosity of the surface sea water rises significantly. Humid air entering this system will precipitate, too. These factors impede the trade winds, slowing them. The wind-driven mass of water in the Western Pacific begins its Big Slosh. Once that starts, its momentum will keep it coming eastward, and El Nino has been fully triggered. Comment?
Bob,
Thanks
Paul,
Thanks, but beyond me
ninderthana,
It will be interesting to see how you determine that mass moves away from the equator because of changed rotation and not the opposite.
Bob Tisdale,
“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.”
Sure, but I guess the real issue is how to define that contribution. If you do the forcing versus temperature correlation (as Bill Illis, others, and I have all done), including the AMO as an independent variable, it looks like the cycles you have so admirably described account for perhaps 0.2 to 0.3C. of the measured variation.
It is an real contribution to link the ENSO to the AMO (this gives the AMO a more solid rational for influencing global temperatures), but it is I think unwise to suggest that ENSO driven cycles are (rather than could possibly be) responsible for most of the observed ocean surface warming since 1900.
Even if you assume a very modest warming associated with radiative forcing (say, Richard Lindzen’s estimate of ~1C per doubling of CO2), most of the 20th century warming would still have to be assigned to radiative forcing, since the current radiative forcing is in the range of 3 watts per sq. meter, and a doubling of CO2 would add ~3.7 watts per sq meter…( 3/3.7) * 1 degree/watt/M^2 = 0.81C rise from forcing.
To reiterate a point being overlooked by many:
Interannual HadSST shows stronger coupling with interannual AMO than with SOI.
Many seem to (perhaps wishfully) overlook the reality that the sign of interannual AMO coupling with SOI is not static.
lgl,
On time scales longer than about 15 years, it is the Earth’s rotation that is in the drivers seat for the simple reason that there is overwhelming evidence that changes in Earth’s rotation rate are being driven by external factors.
http://astroclimateconnection.blogspot.com/2010/03/can-we-predict-when-pdo-will-turn.html
http://astroclimateconnection.blogspot.com/2009/10/upper-graph-shows-pdo-reconstruction-of.html
http://astroclimateconnection.blogspot.com/2008/08/blog-post_02.html
On time scale shorter than about 6 years, the bulk of the rotational angular
momentum is transfered back and forth from the solid Earth to the atmosphere.
However some of it is also being externally driven by effects of the solar/lunar tides.
lgl,
On time scales longer than about 15 years, it is the Earth’s rotation that is in the drivers seat for the simple reason that there is overwhelming evidence that changes in Earth’s rotation rate are being driven by external factors.
astroclimateconnection.blogspot.com/2010/03/can-we-predict-when-pdo-will-turn.html
astroclimateconnection.blogspot.com/2009/10/upper-graph-shows-pdo-reconstruction-of.html
astroclimateconnection.blogspot.com/2008/08/blog-post_02.html
On time scale shorter than about 6 years, the bulk of the rotational angular
momentum is transfered back and forth from the solid Earth to the atmosphere.
However some of it is also being externally driven by effects of the solar/lunar tides.
Steve Fitzpatrick says: “It is an real contribution to link the ENSO to the AMO (this gives the AMO a more solid rational for influencing global temperatures), but it is I think unwise to suggest that ENSO driven cycles are (rather than could possibly be) responsible for most of the observed ocean surface warming since 1900.”
Keep in mind that the East Indian and West Pacific Oceans warm during El Nino and La Nina events and that that subset is not isolated by land mass.
Here’s a link to my post on two papers by Guan and Nigam you might find interesting and informative:
http://bobtisdale.blogspot.com/2010/11/guan-and-nigam-2008-and-2009.html
Regards
ninderthana,
Thanks, makes sense, looking forward to your paper.
Steve Fitzpatrick & Others,
See the ~30a pattern in Ninderthana’s 8 year lagged -LOD graph here:
http://1.bp.blogspot.com/_tG8JCC_Tnp0/Stp3CRsTdHI/AAAAAAAAAA4/lzlQxbj6acM/s1600-h/North_Pacific_SST_Anom_corr.JPG
Note also Ninderthana’s mention of 15 years (above). See the pattern? ~30a, ~15a, ~7.5a. We’re dealing with harmonics, derivatives, & integrals. Looking at 8a as a “lag” might not be conducive to developing deeper insight. It’s a 1/4 cycle. This is about integrals & derivatives.
At the time of the Chandler wobble phase reversal ~1920-1940, the LOD wave looks a little different. The North American Dirty 30s Drought falls in this period. Polar motion went seriously out of phase with solar barycentric radial acceleration during this interval and solar cycle acceleration went deeply negative. I’ve shared some related results at WUWT:
1) http://wattsupwiththat.com/2010/08/18/solar-terrestrial-coincidence/
2) http://wattsupwiththat.com/2010/09/04/the-north-pacific-solar-cycle-change/
3) http://wattsupwiththat.com/2010/09/11/solar-cycle-length-its-rate-of-change-the-northern-hemisphere/
After seeing the nature of the response (& lack of response) to this [ http://wattsupwiththat.com/2010/10/11/atlantic-hurricanes-the-sun/ ] later article, I realized that readers don’t have a good conceptualization of what is going on with nonrandom coupling of tropospheric interannual variations, so I started conceptualizing algorithms that would take the subjectivity out of eyeball “wiggle-matching”. What I’ve come up with is a multiscale phase-aware approach that takes the middle-man (wavelets) out of the picture. The algorithm measures 2-D correlations by looking at 0th & 1st &/or 1st & 2nd (i.e. adjacent) derivatives to extract empirical bivariate phase information at variable bandwidth. While it may not seem intuitive to many readers at this stage, this can eventually help folks understand in plain layman’s terms. Some miles to go yet, but the prototype algorithms are working. Unless someone suddenly supplies me with a heap of funding, this work is going to take awhile to finish. If serious academics are interested in this work, please feel welcome to contact me.
In case anyone is left wondering why I had to devise a means of eliminating the middle man (wavelets): It’s because of the nonstationarity. (There’s some turbulence in this machine. That’s part of the reason why folks weren’t getting very far with traditional unwindowed methods of spectral analysis. The microscope needs some local adjustments to see what is going on in particular areas and at boundaries.)
erlhapp says:
November 20, 2010 at 4:06 pm
Thanks for the invite to visit your blog, but I can only spare about half-an-hour a day for such activities. Will try to drop in during the Christmas season.
David Stockwell says:
November 22, 2010 at 6:17 pm
“basic energy balance equations used in climate science ….[c]orrectly … integrate watts into temperature, but a strongly heated source of fluid can discharge into a sink giving the effect of integration. ”
That is not physically correct. Watts time-integrate into Joules , rather than into a temperature measure. And when temperature is time-integrated, one obtains Kelvin-hours or some such dimensioned quantity, which maybe empirically useful (e.g., degree days in agriculture), but has no place in rigorous physical analysis.
BTW , because in a later comment you speak of Bode diagrams, it might interest you to know that the frequency response of ideal integration has a singularity: a pole at zero frequency. That’s what eliminates integration, without an exponentially fading impulse response function (the classic homogeneous solution of linear differential equations), as a tenable physical model.