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/
Re Fig 10: assume that the 2 warming periods are each 22 years long; and contrast it with what Piers Corbyn says here in a comment:
http://climaterealists.com/index.php?id=6658
“Indeed we (eg WeatherAction) know the Earth’s weather and climate is controlled by solar activity specifically the magnetic – ie 22yr cycle […]”
Just a coincidence?
Bob, thanks a million
and that La Niña events dominated from 1945 to 1975.
=========================================
Exactly when the coming of the next ice age was predicted.
Great job.
Any chance you can turn this into scientific paper for peer review?
DirkH says: “Re Fig 10: assume that the 2 warming periods are each 22 years long; and contrast it with what Piers Corbyn says here in a comment…”
Figure 10 shows trends for two periods: from 1916 to 1946 and from 1974 to 2004. Both are spans of 31 years. I’m not sure where you arrive at “2 warming periods are each 22 years long.”
Regards
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.
R. de Haan says: “Any chance you can turn this into scientific paper for peer review?”
Writing a scientific paper would be work. I’m retired from work–a four-letter word. I blog to entertain myself. But feel free to write a paper about the topic. Just remember where you saw this first.
So El Nino (directly) & La Nina (indirectly) both cause warming essentially.
So, hypothetically, how would it be possible for global cooling start to start and last for a similar time period (~130 years)?
Seems like there is not mechanism to cool the earth then? ENSO apparently doesn’t…
sky says: “Trenberth’s ENSO3.4 is a temperature index that is NOT centered on its long term-average.”
Agreed. Trenberth’s NINO3.4 SST anomaly data has base years of 1950 to 1979, because, as Trenberth writes in “The Definition of El Niño” (1997), “it is representative of the record this century.”
ftp://grads.iges.org/pub/kjin/BADGER/1021/Trenberth-1997-BAMS(ElNino.Define).pdf
The full quote: “Figure 1 shows the 5-month running mean SST time series for the Niño 3 and 3.4 regions relative to a base period climatology of 1950–79 given in Table 1. The base period can make a difference. This standard 30-year base period is chosen as it is representative of the record this century, whereas the period after 1979 has been biased warm and dominated by El Niño events (Trenberth and Hoar 1996a).”
And those base years, which Trenberth describes as, “representative of the record this century,” for the NINO3.4 SST anomaly data, establishes the right ratio of positive to negative NINO3.4 SST anomalies that permit the scaled running total to portray the global SST anomalies.
You wrote, “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.”
Newman et al (2004) appears to contradict your opinion. Link:
http://courses.washington.edu/pcc587/readings/newman2003.pdf
They write. “Deep oceanic mixed layer temperature anomalies from one winter become decoupled from the surface during summer and then ‘reemerge’ through entrainment into the mixed layer as it deepens the following winter (Alexander et al. 1999). Thus, over the course of years, at least during winter and spring, the North Pacific integrates the effects of ENSO.”
Regardless of whether or not the oceans integrate ENSO and portray it in sea surface temperature anomalies, 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. This then could be the ultimate cause of the apparent integration.
Regards
“…Tropical North Atlantic SST anomalies rise during an El Niño event because the trade winds there weaken and there is less evaporation….”
But why do the trade winds weaken?
SS says: “So El Nino (directly) & La Nina (indirectly) both cause warming essentially.”
The warming during a La Nina is direct for the East Indian and West Pacific Oceans. During a La Nina, “leftover” warm water from the El Nino is pushed to the west and carried to the higher latitudes by the western boundary currents. And some works its way into the Indonesian Throughflow and on into the East Indian Ocean. Also, during the La Nina, the strengthened trade winds shift cloud cover to the west, allowing more downward shortwave radiation the warm the tropical Pacific ocean, and like the “leftover” warm water, it is carried to the Kuroshio Extension, the SPCZ, and the eastern tropical Indian Ocean.
You wrote, “So, hypothetically, how would it be possible for global cooling start to start and last for a similar time period (~130 years)?”
The East Indian and West Pacific Oceans warmed to the combination of specific El Nino and La Nina events, not all. The effect is apparent with the 1986/87/88 El Nino combined with the 1988/89 La Nina and with the 1997/98 El Nino combined with the 1998/99/00/01 La Nina. So the combined El Nino/La Nina warming there requires significant El Nino events followed by significant La Nina events. To answer your hypothetical question, if there was a period with minor El Ninos and when La Ninas dominated due to a higher frequency and magnitude, global SST anomalies should drop. The global effects of the La Nina events would outweigh any warming taking place in the West Pacific and East Indian Oceans.
Thanks. I hadn’t addressed that point.
jorgekafkazar says: “But why do the trade winds weaken?”
Due to changes in Hadley and Walker Circulation associated with the relocation of tropical Pacific convection during the El Nino. That is, during the El Nino, the warm water from the West Pacific Warm Pool shifts east, bringing the convection with it. The relocation of the convection alters normal Hadley and Walker Circulation globally and one of the results is that the trade winds in the tropical North Atlantic weaken.
From this post I get the impression the climate scientists measuring the average conditions of weather at discreet time intervals and following the change in the average over time is a very limited approach seeking to identify causes and effects, when we have known for a long time the major inputs in the climate such as insolation, orbital characteristics, evaporation, condensation and etc.. Perhaps a more useful approach is the one taken by Bob and looking at the climate as the integral of the eather would provide a more useful model of the climate.
Jeez Bob, if this is how you entertain yerself in retirement, you musta been a hell of a worker! Just absolutely one of the most fascinating posts i have ever seen. Kudos to you!
Bob,
31 years, 31 years……? Where have I heard that time period before…..?
This period ensures a close alignment between lunar perigee and solar/lunar syzygy
(i.e. New or Full Moon). It also involves a close agreement between the lunar perigee/syzygy and BOTH the perihelion of the Earth’s orbit and the time of passage of the Moon through one of its nodes.
It is actually a half cycle, flipping between Full and New Moon. The full cycle is 62.01371 years, and it is known to be a significant luni-solar cycle in producing enhanced gravitational force on the Earth as a result of large lunar parallaxes and close lunar distances at perigee.
7 x (lunar evectional period) + 3 x (Saros cycle) = 62.0134 years
7 x (1.131778) + 3 (18.030331) = 62.0134 years
This is the other variable that you have been missing.
Bob, you said:
Where is the cause and effect? It would seem counter intuitive to say it “just happens”.
Now we’re talking!
Fantastic analysis as ever Bob. Now all we need is for Ninderthana to confirm the lunar-solar-LOD connection over a couple of centuries in his forthcoming paper and we can put a new and better theory of climate variation together.
Interesting, Bob, as this region being relatively shallow with close island chains, would provide some resistance to flow compared to deeper water, restricting the volume entering the eastern Indian Ocean area, also this mechanism may be reason the Indian Ocean Dipole is negative (warm) and together with La Nina, bringing welcome rain to SE Australia.
A lot to read and think about. Thanks for this cogent analysis. I could not find any flaws in logic or claims not supported by the data.
“To answer your hypothetical question, if there was a period with minor El Ninos and when La Ninas dominated due to a higher frequency and magnitude, global SST anomalies should drop. The global effects of the La Nina events would outweigh any warming taking place in the West Pacific and East Indian Oceans.
Thanks. I hadn’t addressed that point.”
Bob, you are further proof that the ‘retired’ Elders among us are an under-appreciated and underutilized goldmine of both experience and insight…thank you.
Could I trouble you for a SWAG at what you think the general weather outlook for Central/Northern CA might look like over the next handful of years?
I have a few Farming neighbors here who might want to know if things are getting wetter/colder or not (and by what ~rough~ order of magnitude).
I’m also curious about things like trends in increasing amounts of cloud-cover and decreasing levels of brightness as it might relate to some of the larger solar farms going up (20-200MW) and what it might mean for, for example, for the 25 year trend in average kilowatt-hours per square meter per day (kW·h/(m2·day) hitting the ground in Fresno, CA (constant or declining?) as I know for a fact that no business model today accounts for this. Since 1996, visible brightness is off less than a percent, but in some non-visible UV bands, it is off ~6%.
As time and the solar/climactic cycles march on, natural changes ahead might end up putting a -not insignificant dent- in the harvested energy (measured in kwh/yr) of a larger 20MW PV system over a quarter century.
Bottom line is your SWAG on the above is probably more meaningful than anything NASA or other more mainstream sources could put out these days…and I’d be privileged and appreciative of any thoughts you might share on the above.
Too bad we couldn’t distill your intuition into the Solar/Farmers Almanac 2.0.
Great stuff above….thank you for sharing.
When looking at set after set of temperatures in many places, one of the enduring features is a hot 1914 year. If you add not 31 but 28 years again and again to that, you get the series 1914, 1942, 1970 and 1998.
If you then go back to your first graph of Annual Global SST anomalies HADISST, you will find most of the major hot years appearing in this sequence, +/- a year or so. The biggest swings from hot to cold (or vice versa) year on year also happen at about these periods of 28 years. Might your figures work better on a 28 year cycle?
It puzzles me that these hot SST years are commonly hot land temperature years as well. Whatever the mechanism, it has to be quite fast to transfer sea temperature properties to air over land so rapidly.
Do you believe that the El Nino/La Nina effect causes SST changes because of the time lag between them, or because of a to-be-postulated mechanistic effect? My intuition would say that changes in SST promoted climate like El Nino. That would leave me with a need to explain where the heat goes to and comes from in a closed system. (If the system is closed). I’l read your essay more closely now.
Excellent work as usual, Bob, but you won’t be surprised that I’m still trying to see how your ENSO material can be worked into the climate cycling from MWP to LIA to date without some other force altering the relative strengths of El Nino and La Nina over longer timescales than the multidecadal. I would have though that absent any such additional forcing the El Nino and La Nina would indeed cancel out over enough time but as you point out they clearly do not cancel out on multidecadal timescales.
My thinking continues to be that the relative strengths must indeed be altered by variations in solar insolation to the oceans given that the oceans have such a profound effect on the temperature of the air masses above them.
The way it could work is by variations in solar activity altering the size and intensity of the polar vortexes. I have explained elswhere how that could work.
The size and intensity of the polar vortexes then has an effect on the latitudinal position of the jetstreams which then alters total cloud quantities (and reflectance) so as to alter global albedo and thereby alter solar energy input to the oceans.
Thus when more energy enters the oceans the strength of El Nino will be enhanced relative to La Nina and the effect will be cumulative over time for so long as the sun is sufficiently active with the jets sufficiently poleward. The opposite when the sun is less active.
That would produce the step like upward progression that we have seen over the past century or so in tropospheric temperatures because the sun did get more active during the period. The external forcing would become more apparent with each phase ENSO cycle. Stepping upwards during a rising solar cycle and stepping downward during a falling solar cycle.
It would also deal with the observation that from time to time such as during solar cycle 20 there was a slight cooling period despite the fact that solar activity was still high in historical terms (though 20 was a little weaker than 19 and 21). The La Nina part of the cycle would have been temporarily more than offsetting the fact that even cycle 20 was still adding energy to the oceans albeit at a lower rate than cycles 19 and 21.
What predictive capability does this explanation yield?
The AGW crowd have repeated their mantra a sufficient number of times to create a paradigm (a belief among the general population such as a portion of the news media and a portion of the general public). The paradigm has been accepted as fact by the believers such that those who question the mantra can be labeled by the believers as skeptics or deniers. (idiots or bad guys)
Part of the preparer work (propaganda) for the AGW movement was to eliminate past cyclic changes from the paleoclimatic record. If planetary temperature increased and decreased in the past there must be some unexplained cyclic forcing function. For example the sun. Few people have read paleo-climatology text books, are aware of the glacial/interglacial cycle, are aware that the paleoclimatic record has unequivocal evidence of cyclic gradual changes and cyclic abrupt climate events, are aware that the abrupt climate change events such as the abrupt termination of the last 22 interglacial periods lacks an explanation, are aware that all of the past interglacial periods are short (roughly 12,000 years) and that they have ended abruptly, and so on. The complete climate change scientific facts are not part of the public discussion. (The observations are filtered and manipulated to support an agenda.)
The sun was at its highest activity level in 10,000 years during the last 40 years of the 2oth century. It appears that most of the 20th century warming has due to changes in the solar cycle. (There are papers noting a decade by decade reduction in planetary cloud cover during the warming period. There are also papers explaining the mechanisms by which specific solar cycle changes could reduce planetary cloud cover and there is observational evidence that shows sub cycles of warming and cooling with the sub cycles correlating to the specific solar mechanisms during the 20 year period at which time there was satellite measurement of planetary cloud cover. (There is more than one solar mechanism. i.e. The theory/hypothesis to explain the 20th century warming was due to solar cycle changes not atmospheric CO2 increases is advanced and logically supported by the observations and the paleo record. There is a cycle in the past of warming and cooling with correlation of cosmogenic isotopes. There is smoking gun evidence that it left at the past cyclic climate changes gradual and abrupt that points to the sun. The question is not if the sun is responsible for the observations but rather how.)
Solar cycle 24 appears to be an interruption to the solar cycle. If let say 0.4C of the 0.5C of the 20th century warming was due to the high solar cycle, then we can expect a -0.8C change (0.4C high forcing is removed and -0.4C due to the interrupt in solar cycle 24.)
The paleoclimatic record was cycles of 1470 years (Bond cycle. Gerald Bond has able to tracked 30 cycles through the Holocene interglacial and into the glacial period. Bond found cosmogenic isotope changes at each of the climate change events which it is accepted are due solar magnetic cycle changes but was not able to provide at the time of publishing of his results (2000) an explanation as to mechanism.) of warming and cooling (roughly 0.8C warming and cooling followed occasionally by (with a super cycle of 2400 years and 8000 years) of abrupt cooling events of 2C to 4C. The complete set of cycles appears to be solar driven however there are different solar mechanisms involved which explains the differences in the cycle times and the differences in the amount of climate change for the different solar events. What happened cyclically before, happened for a reason. There was a physical cause.
The AGW supporters are rightly concerned about climate change, however, the problem is abrupt cooling (the -2C to -4C events) not gradual warming. It will be interesting to watch how the paradigm will change, if there is observed gradual cooling (0.4C to 0.8C).
Atmospheric CO2 increases are positive to the biosphere not negative. Plants eat CO2. It is odd (surreal) that those who purport to support environmental protection have aligned with the AGW paradigm and the corruption group that is now pushing it. The entire scientific premise is incorrect.
Weather change observations precede climate change.
http://i53.tinypic.com/14j50et.jpg
http://www.osdpd.noaa.gov/data/sst/anomaly/2010/anomnight.11.18.2010.gif
http://www.accuweather.com/video/432724657001/extreme-winter-travel-conditions-thanksgiving-week.asp
http://www.accuweather.com/video/681364180001/the-cold-train-rides-roughshod-over-europe.asp?channel=vbbastaj
Michael says:
Your comment is awaiting moderation.
November 19, 2010 at 8:25 pm
Now there’s art, and then there’s blog smart.
Oh come on, can’t you give me some credit for this quote?
Ninderthana, but are the tidal changes powerful enough to produce the effect, and how do they produce the effect? The co-incidence is interesting, but for an hypothesis you need to show -how- that can work.
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.
Brilliant, just brilliant!
Thanks Dr. Tisdale
(see http://www.oarval.org/ClimateChange.htm and http://www.oarval.org/meteorologFL.htm)
(in Spanish at http://www.oarval.org/CambioClima.htm and http://www.oarval.org/meteorolog.htm)
William,
Fine post, very thought-provoking. The Bond event troughs are truly scary.
The real concern is global freezing, not beneficial warming.
Smokey says: wrote
November 19, 2010 at 9:03 pm
William,
“Fine post, very thought-provoking. The Bond event troughs are truly scary.
The real concern is global freezing, not beneficial warming.”
Now that’s scary.
The date in the middle of the large paragraph under the heading
WHAT DOES THE RUNNING TOTAL IMPLY?
in this line: . . . Ocean. In January 1999, I began illustrating . . .
should be 2009, not 1999
Bob Tisdale says:
November 19, 2010 at 4:49 pm
R. de Haan says: “Any chance you can turn this into scientific paper for peer review?”
“Writing a scientific paper would be work. I’m retired from work–a four-letter word. I blog to entertain myself. But feel free to write a paper about the topic. Just remember where you saw this first.”
Excellent post Bob. I would think there might be a few people here interested in writing a joint paper with you. If not “Tisdale and de Haan, 2010” I’m thinking “Tisdale and Eschenbach”? Or “Tisdale and Maue”?
John F. Hultquist says: Thanks for finding the typo. Yes, it should read January 2009. I’ve corrected it on the original at my blog.
Phil’s Dad says: “What predictive capability does this explanation yield?”
Little. It would require the ability for climate models to predict the frequency and strengths of future ENSO events, and none have displayed any ability to do that in the short term, let alone long term. Will the 60-year cycle repeat? Don’t know.
Geoff Sherrington says: “Might your figures work better on a 28 year cycle?”
Thanks for noting the 28-year peaks. I’ve looked at other periods based on 10year spans but not 28 years.
You wrote, “It puzzles me that these hot SST years are commonly hot land temperature years as well. Whatever the mechanism, it has to be quite fast to transfer sea temperature properties to air over land so rapidly.”
Keep in mind that the land surface temperatures are also changing due to changes in atmospheric circulation, not due to a direct transfer of heat. It takes well less than a year for the changes in atmospheric circulation to migrate their way east and circle the globe. The Trenberth et al paper linked in the post does a good job of explaining it, once one comes to term with all of the scientific jargon.
You asked, “Do you believe that the El Nino/La Nina effect causes SST changes because of the time lag between them, or because of a to-be-postulated mechanistic effect?”
There are known changes in atmospheric processes that cause the SST anomalies outside of the tropical Pacific to vary in response to the ENSO event.
Tesla_X says: Sorry. I don’t make predictions. Maybe Anthony can help along those lines.
Keith Minto says: “Interesting, Bob, as this region being relatively shallow with close island chains, would provide some resistance to flow compared to deeper water, restricting the volume entering the eastern Indian Ocean area…”
The Indo-Australian landmass is why the warm water pools in the west Pacific. The Pacific trade winds can’t push the warm surface waters any farther. It pools there and all the convection associated with it establishes rainfall patterns around the globe. But some of the warm water squeeks through.
There is a natural process going on in the Pacific, with all necessary attributes to generate long term oscillations.
http://www.vukcevic.talktalk.net/PDOc.htm
But of course, there are many other factors involved acting on much shorter scale.
Hello Bob,
is it possible toget Your post as a pdf-file?
regards
Phil’s Dad says:
November 19, 2010 at 8:22 pm
What predictive capability does this explanation yield?
Predictions are for Gypsy Rose Lee. No-one can see the future and particularly the future of a non-linear system as vast as the climate.
Incidently BOB, I’m getting really tired of saying this but WELL DONE. As someone has already said you must have been one hell of a worker!!
tallbloke says:
November 19, 2010 at 7:40 pm
…….Now all we need is for Ninderthana to confirm the lunar-solar-LOD connection…
Hi Rog
Here is my take on the planet’s wobble-temperature possible relationship.
I made an attempt to separate, what one could consider to be natural oscillations, from an apparent man-made input, result is shown in this graph:
http://www.vukcevic.talktalk.net/CETng.htm
GP ‘Global precursor’ (green line, based on the JPL ephemeredes, kind of a ‘mini Milankovic’ effect ) shows a promising result. If large volcanic eruptions (Katla 1755, then in the early 1800’s Mayon and Tambora) are taken into account, than GP ‘proxy’ has a good track with the CETs for nearly 350 years span.
Period of significant divergence starts in the 1950s, when various man made influences (CO2, UHI, CFCs etc) may have come into play.
For time being GP is just an interesting correlation, energy is miniscule, while NAP has all the necessary attributes to do that, but again indirectly via N. Atlantic currents.
However, I might work out something in the future.
A reminder that Tsonis et all using a neural net model to simulate chaos and the behavior of ocean currents, give predictions for the next century.
HelmutU says: “is it possible toget Your post as a pdf-file?”
Sorry. I only have Adobe reader.
tallbloke says:
November 19, 2010 at 7:40 pm
Now we’re talking!
“”
Between Bob, Ian and Erl Hap there is a sufficient, coherent supply of silver bullets to kill the CAGW vampire(s)
^sorry Erl^
Dear Mr Happ, please forgive my lack of Ps and Qs.
Andres Valencia says: “Thanks Dr. Tisdale”
You’re welcome, but there’s no Dr. before my last name, just Bob.
Great effort Bob!
This is possibly the most profound piece of climate analysis ever posted on any blog!
Those who support a solar-climate link will of course now concentrate on how the solar cycle might influence the El Nino/ La Nina magnitudes and frequencies.
Now, how to get the IPCC models adjusted ? Perhaps send a copy to Gavin over at Real (CO2) Climate ?
Hi Vuk,
interesting graph.
“Period of significant divergence starts in the 1950s, when various man made influences (CO2, UHI, CFCs etc) may have come into play.”
Or non-linearity due to high OHC. Or diddling of the record. Or….
“For time being GP is just an interesting correlation, energy is miniscule, while NAP has all the necessary attributes to do that, but again indirectly via N. Atlantic currents.”
After my chat with some experts on the geomagnetic field, I think there is still a lot we don’t know about LOD and the global electromagnetic circuit. Ocean circulation still holds secrets.
Cirrius Man says: “Now, how to get the IPCC models adjusted ? Perhaps send a copy to Gavin over at Real (CO2) Climate ?”
The GISS Model E does not model the oceans and therefore has no ENSO capabilites.
Hi Bob
I have recorded post as a .pdf file, it is Ok except for the animation (only work via link). It is too big to upload on my website, but I could email it to you.
Great job Bob,
but I have a problem with fig. 13. The flatness between 1940 and 1980. Did you use the +0.2 scaling like in fig. 7 ? Could you please link to the numbers behind fig. 9?
“Cirrius Man says:
November 20, 2010 at 3:32 am
Those who support a solar-climate link will of course now concentrate on how the solar cycle might influence the El Nino/ La Nina magnitudes and frequencies. ”
Already done Cirrius, see here:
http://climaterealists.com/index.php?id=6645&linkbox=true&position=7
“How The Sun Could Control Earth’s Temperature”
Just shift the clouds latitudinally to change solar input to the oceans and thereby skew the relative intensities of El Nino and La Nina.
RE:
vukcevic says:
November 20, 2010 at 4:12 am
Hi Bob
I have recorded post as a .pdf file, it is Ok except for the animation (only work via link). It is too big to upload on my website, but I could email it to you.
That or I have it stored as a PDF on my webspace if Bob prefers to DL from there.
Regards,
Lee
Earth’s wobble 1650-2040
http://www.vukcevic.talktalk.net/EW.htm
Bob, you might not be Dr, but WUWT should award you an honorary chair as professor emeritus of oceanography and climate.
This is indeed a game changer. It correlates nicely with the post some months ago by Roy Spencer who “trained” a relationship between ENSO and global temps from the early 20th century and showed that the rest of the 20th century temperatures were accurately predicted by ENSO.
This link must now be firmly established. The next question has to be – what drives ENSO. This is an important juncture in climate science. It is important also that, like Einstein’s e=mc2, it has happened outside the entrenched academic establishent.
lgl says: “I have a problem with fig. 13. The flatness between 1940 and 1980. Did you use the +0.2 scaling like in fig. 7 ? “
Figure 7 includes the 30-year changes in global SST anomalies based on the GISS maps. It also includes the smoothed (31-year) NINO3.4 SST anomalies that had been scaled by a factor of 2.5 and shifted up 0.2 deg C before the smoothing. Figure 13 is a totally different beast. It includes standard old “raw” global SST anomalies, and a scaled (0.06) running total of NINO3.4 SST anomalies.
You asked, “Could you please link to the numbers behind fig. 9?”
Better. I’ll give you a link to the source data:
http://climexp.knmi.nl/selectindex.cgi?someone@somewhere
The second line under ENSO shows NINO indices based on HADISST data. Select NINO3.4 and it will change pages. Scroll down to “Manipulate this time series” and enter 1900 and 2010 under “Select Years”, then hit select. On the next page, there are three plots. Below the third (anomalies) are two fields adjacent to “Redisplay the anomalies using the years”. That’s how you set the base years. Enter 1950 and 1979, click on select. On the next page, scroll down to the second plot, which is monthly HADISST NINO3.4 SST anomalies from 1900 to present with the base years of 1950 to 1979, which is the dataset in Figure 9. Select raw data above the plot. All you have to do then is load it in a spreadsheet and convert it to annual data (or use the monthly data). If all you’re interested in is the trend, you don’t have to redo the base years.
vukcevic says: “I have recorded post as a .pdf file, it is Ok except for the animation (only work via link). It is too big to upload on my website, but I could email it to you.”
Thanks. Unfortunately, I also have no means to upload it to my website. Maybe HelmutU could provide you with his email address in a comment at your website. Then you could email it to him directly.
Regards
Lee Kington says: “That or I have it stored as a PDF on my webspace if Bob prefers to DL from there.”
Thanks, Lee.
vukcevic: Please feel free to work with Lee Kington on a place to store the pdf. Thanks to both of you.
HelmutU says:
November 20, 2010 at 1:34 am
Hello Bob,
is it possible toget Your post as a pdf-file?
regards
Helmut,
I suggest downloading a .pdf printer. It’s probably not as good as having the original as a .pdf (as printed from a word processor for example), but if you download one of these, it becomes a windows printer. You simply select that printer, print it, and it will create a .pdf of it that you can save where you like. Works for any printable document. I use this one:
http://www.primopdf.com/download.aspx
I’ve also used this one:
http://www.cutepdf.com/
There are probably others out there too… Mike S.
About pdf printing / saving. Among the best is a Microsoft downloadable utility at Office downloads for making pdfs – unlike some pdf freeware it saves at full quality, and on my computer works quicker and better even than the Adobe Acrobat MS Office add-in. Plus its free.
Lee Kington
My email address is visible here:
http://www.vukcevic.talktalk.net/LFC-CETfiles.htm
Thanks Bob,
Then fig. 13 is very sensitive to base years. Another selection could have given an accumulated ENSO with a negative trend. Have you justified using 1950-1979 anywhere in the text? That period is almost the low period 1945-1975 so it seems a bit strange. I think what you are actually doing is assuming that the 1900s ENSO is around 0.2 higher that some longer term average. That may be true but I’m missing the justification.
Bob, I really think WUWT and some of it’s posters have all the expertise to produce peer review ready reports that really stand out and could make a difference.
It only takes some organizing, coordination and team work to get this started.
In my humble opinion this is only a natural development as WUWT is such a cluster of talent and exertise and so much crappy science is send into the world stamped as “settled science”.
Your own work begs for admission within the scientific world and it would really be a shame if we wouldn’t have a closer look at this concept.
Just stating my opinion but I am quite sure many other posters here will underwrite this view.
Someone up above asked why ice age/glacial periods end so abruptly. I think this can be explained mechanically (there are several large continental sized geological sites that show evidence of this). While the ice build up is likely slow due to decreasing temps, the eventual increasing temperature begins to melt massive ice dams slowly at first with little change down stream. I’ve seen this on a small scale in river systems. The ice build-up is slow, starting from the bottom of the stream and eventually rising to the top. Then water and ice builds up behind the river. When spring arrives, the ice begins to slowly melt, much as it did when it began to grow. But suddenly the ice dams will break up and flow downstream, releasing a deluge of water and ice. On a larger scale, there will come a breaking point to these massive dams that will result in sudden and catastrophic release of massive amounts of fresh water into global drainage basins and out to sea. I think it is possible that this sudden huge mechanical event after a period of slow warming leads to the rapid decline of ice build-up instead of repeating the same slope you see at the beginning of ice ages.
Spectacular job, Bob.
About Figure 9 and conclusion.
In 100 years. (1900/2009)
y = 0.0001 x + 0.2985 ~ 0.2985 ~ 0.3 ° C
approaching …. ln [390 ppm / 290ppm] = 0.296 ~ 0.3
a = 1
Soon.
We will have negative values.
I ask, It is possible to withdraw in 1998 (only) in this series?
Pamela Gray says:
November 20, 2010 at 9:13 am
………….
I would like to add following:
Climate warming increases evaporation and rainfall. Ice melt is more accelerated by rainfall than by initial small increase in the temperature. Simple physical explanation is in the huge difference in heat content of water and air which may be in contact with icesurface. Direct sunlight is of cause effective, even if lot of it reflected back, but again it is not sunny all the time; latitude is also a factor. More ice melts, more temperatures go up, more evaporation, more rain, i.e. very strong positive feedback at work. Eventually ice retreats to the polar region where precipitations and sunlight are much lower and equilibrium is established.
lgl says: “Have you justified using 1950-1979 anywhere in the text? That period is almost the low period 1945-1975 so it seems a bit strange.”
Nope. The justification is in a comment above, but I’ll copy and paste it for you.
Regarding the base years of 1950 to 1979, Trenberth writes in “The Definition of El Niño” (1997), “it is representative of the record this century.”
ftp://grads.iges.org/pub/kjin/BADGER/1021/Trenberth-1997-BAMS(ElNino.Define).pdf
The full quote: “Figure 1 shows the 5-month running mean SST time series for the Niño 3 and 3.4 regions relative to a base period climatology of 1950–79 given in Table 1. The base period can make a difference. This standard 30-year base period is chosen as it is representative of the record this century, whereas the period after 1979 has been biased warm and dominated by El Niño events (Trenberth and Hoar 1996a).”
And those base years, which Trenberth describes as, “representative of the record this century,” for the NINO3.4 SST anomaly data, establishes the right ratio of positive to negative NINO3.4 SST anomalies that permit the scaled running total to portray the global SST anomalies.
Regards
For PDF creation, storage, and access, register (free) at FileJumbo.com. You can upload anything from your own computer and pass on the access URL.
Actually, I do the “create” part with web2pdfconvert.com , then upload to filejumbo.com .
Works fine.
RE:
vukcevic says:
November 20, 2010 at 8:00 am
I have sent an email to you with a URL for the PDF.
vukcevic, tallbloke, & others,
~1952 there was a phase reversal in the conditional relationship between interannual geomagnetic aa index and NPI (North Pacific Index). I have a lot of work on my plate…
Geoff Sherrington & others,
The 28 year thing relates to LOD.
To all interested:
You can download Bob’s post as .pdf file from here:
http://www.vukcevic.talktalk.net/ENSO-BTisdale.pdf
It will be there for next 12-14 hours only (since I had to take some of my own stuff down, my web space is full)
Bob, have you ever isolated the interannual components of AMO & HadSST for direct comparison with SOI? Inerannual HadSST relates much more consistently with interannual AMO than with SOI. We’ve got to be careful as follows: Linear correlations can’t finish the job. Not only do we need multi-timescale analysis (such as that which Bob has presented here), but we also need complex (i.e. with both real & imaginary parts) correlations that can see differences (derivatives) & sums (integrals) [i.e. correlations with the capacity to see sign switching, to avoid the hazards of Simpson’s Paradox]. I’m on the job…
I would agree that rain is increased after the temperature begins to climb and is a big player in ice melt. This could possibly warm ice and water behind those huge dams and result in the beginning of undercutting at the bottom part of the ice dam, as well as at the top. Once the bottom of the dam has been breached, the result is much like an explosion of any kind of dam if done at the bottom. Melting the top away would result in slow release. But topographical evidence says that the release was sudden and powerful. I think these massive ice dams melted from the bottom as the warmer water behind them carved out and undercut the seam between ice and river/lake bottom.
So the Niño and Niña events as well as clouds seem to fall into the category of “stuff that just happens.”
Amazing….. arm chair quarterbacking does work…. or is it just that communicating openly and taking into account differing points of view and thought might be the key to REAL SCIENCE?
Home Run Bob…..
Now if Galactic radiation, solar output, and Cloud formations due to changes in those “flows” can be determined to force change in Ocean Surface temps the drive train for our climate may be found…
Imagine that, magnetic waves from the sun is driving our climate…..
Bob, I have an interesting Excel macro I wrote that will use a least squares error method to solve for the best fit x-axis offset and magnitude of a sine wave, in case you want to describe your cycles in terms of amplitude and offset. I wrote it to solve for the best fit rotation angle and amplitude of runout data. I think it will also do the slope. If not, it would be an easy add. Let me know if you want it, I can polish it up a little and send it over. Maybe you could send me some data and I’ll make it work for that, then I could send it over. The figure 12 data would be ideal. It will plot the sine wave on the chart as well. I’ll see if I can find a link on your page to send my email address…
Bob, here’s another way… If you want to send some data per previous post, you can deduce the email from the above link… Thanks.
Bill H says:
November 20, 2010 at 10:36 am
Bob,
Disagree. You can’t choose the base period that way. Changing the base period of SST only moves the graph up or down the y-axis, but changing the base period of ENSO totally alters the graph after integration. It’s not true that 50-79 is “representative of the record this century,” like you have shown in fig. 14. that period is far below average. The most representative of the record this century is the whole century, but even that is not a good base period because it covers two high periods but only one low period (again fig.14). You need to cover at least two whole cycles, 1880-2000 for instance. I tried that: http://virakkraft.com/SST-ENSO-integral.png I’m not saying this version is more correct, maybe your base period is closer to the long term average, but it is more in accordance with the available data. And you have still done a great job.
Here is a link to a PDF of Bob’s article here, I just created it from Word 2010 which has pdf as one of the available save file types:
https://docs.google.com/viewer?a=v&pid=explorer&chrome=true&srcid=0B9p_cojT-pflYjYyMTdkYzItMDMwOS00MjFjLWJmYTAtMzdjYjM1YjhhMmFj&hl=en_GB
Thanks for the interesting article:)
Just to show how extremely sensitive this is to ENSO average. Same as last, base 1880-2000, but offset 0.05 deg. http://virakkraft.com/SST-ENSO-integral-offset.png
lgl & Bob, there’s a simple way to work around your differences: Integrate raw data rather than anomalies. (Also suggested: Include on the plot of the raw integral a 1 year boxcar-smooth of the raw integral.)
R. de Haan says on November 19, 2010 at 4:37 pm:
Great job.
Any chance you can turn this into scientific paper for peer review?
He doesn’t have to do this. Major websites and blogs that post articles on global warming, climate change, weather and related topics are now well known to scientists and researchers and have a world-wide audience, many of which are pretty picky reviewers. Indeed, some articles posted here receive scathing reviews over at RealClimate, Tamino’s Open Mind, etc.
Many of these articles disclose interesting and useful information and data that are on par with traditional scientific papers. For example, the late John Daly’s “Still Waiting for Greehouse” was well known to the CRU and HAD climate crowd. In the Climategate emails, one of these guys mentioned he was “elated” when he learned that John Daly had suddenly did of a heart attack. You only wish death on your most mortal enemies.
Bob a very nice cogent post.
However I have one big problem. I know that computer programmers preparing their pieces for publication in a science magazine or in the hope that it will be included in an IPCC report need data to work with. However the inconvenient truth is that there is no sort of reliable record of SST’s going back to 1880 and no amount of data torturing can remove that basic fact
tonyb
lgl says: “It’s not true that 50-79 is ‘representative of the record this century,’ like you have shown in fig. 14. that period is far below average.”
I beg to differ with you. You referred to Figure 14 and gave your impression that the period of 1950 to 1979 is well below average. Since you have apparently downloaded the NINO3.4 SST anomalies with the 1950 to 1979 base period (my guess since you’re looking at other base periods), average the SST anomalies for the period of 1950 to 1979 and let me know if you get something significantly more or less than zero. It should be just about zero because 1950 to 1979 are the base years and the anomalies are referenced to that period.
You wrote, “You need to cover at least two whole cycles, 1880-2000 for instance.”
My question to you, does the data over that period present two whole cycles?
You’re assuming that NINO3.4 SST anomaly data before the opening of the Panama Canal is of any value. Keep in mind there is very little source data when you go that far back, especially for an area as small as the NINO3.4 region. Most of the data before 1950 has been infilled. And the further back in time you go, the more you’re relying on the infilling. (Or in the case of the HADSST2 data your graph says you’re using, you’re relying on the very sparse data, since it’s not infilled.) That’s why many scientific studies of ENSO don’t analyse data before 1950.
Regards
Michael D Smith: Thanks for the offer. You’re assuming I could interpret the output of your program. With respect to Figure 12, I’ve just posted that data on Google Spreadsheet:
https://spreadsheets0.google.com/ccc?key=tV5MlisM71Kss52ff6CafGw&hl=en#gid=0
Let me know if the link works. It should be three columns: years and two data columns that I’ve identified.
Bob Tisdale says:
November 19, 2010 at 5:32 pm
That physical causation cannot be attributed to integrals or time averages of INTENSIVE metrics is a basic tenet that no one can dispute. I suspect that when Newman et al. write that “over the course of years, at least during winter and spring, the North Pacific integrates the effects of ENSO,” they are using the term in the sense of “incorporates” rather than performs a time-integration of index anomalies.
The question of proper centering of index anomalies lies at the core of the instability of numerical results obtained by cumulative sums or running averages. 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. If you run a cumulative sum based on the 20th century mean, you’ll find that the result no longer resembles HADCRUT3 or GISTEMP at all in its low frequency components. In other words, the resemblence is an artifact of the arbitrary choice of “norm.”
Bob, I would suggest that the considerable time and effort that you invest in dealing with SST data would be more rewarding if you anchored your conceptions not in purely numerical data relationships but in the physical processes of which the data are but a recorded manifestation. 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. And inquiring oceanographers want to know, where can they go in the South Atlantic to measure warm SURFACE currents that cross the equator?
pochas says: “So the Niño and Niña events as well as clouds seem to fall into the category of ‘stuff that just happens.'”
Parts of the mechanics of ENSO are pretty well understood. Unfortunately, the ENSO-related subsurface ocean data outside of the tropical Pacific is very sparse. With ARGO, it’s getting there and there may be some solid long-term data in a few decades.
And the models are improving but they have a long way to go. What initiates the ENSO event process seems to vary per ENSO event (and many times there’s debate as to what initiated specific events), so there are hurdles the researchers need to overcome.
lgl says: “Just to show how extremely sensitive this is to ENSO average. Same as last, base 1880-2000, but offset 0.05 deg.”
Yup. As you’re discovering, regardless of the base years, you can get the effect to work if you find the right offset. There’s probably a very simple explanation, but I’ve known about this effect for a couple of years and I haven’t found it–yet.
Thanks for looking into it.
P.S. I won’t be available for further discussion until next Wednesday.
Bill H says:
November 20, 2010 at 10:36 am
Imagine that, magnetic waves from the sun is driving our climate…..
Maybe…
http://tallbloke.wordpress.com/2010/01/05/my-simple-solar-planetary-energy-model/
Bob, a very enlightening post, as usual. My two comments:
1. On the 3rd last sentence in CLOSING: “the global oceans could rise and fall ….”, which is well reminiscent of the Moses’ miracle, should be “the global oceans could warm and cool” or “the surface temperature of global oceans could rise and fall.”
2. In Figure 2, the violet curve is sort of a differential (derivative) of the blue curve. Can I then understand that in later Figures you make “comparisons between derivatives” of SST and related variables?
Bob,
I’m quite sure you have to use the longest possible base period, ideally a few centuries, but the average of that period may very well be the 1880-2000 average minus 0.05 so I’m convinced you are right.
sky
Take a look this: http://virakkraft.com/sst-deriv-enso.png
SST had to increase a lot between 77 and 98 because SST always increases when ENSO is strong positive.
A comment addressed to ‘Sky’
“if you anchored your conceptions not in purely numerical data relationships but in the physical processes of which the data are but a recorded manifestation.”
That is what I am trying to encourage at http://climatechange1.wordpress.com
And your interest would be most welcome.
Excellent data supports the proposition that the distribution of atmospheric mass is externally driven. From that is derived the variations in wind strength that are aligned with change in SST according to the dominant physical processes (evaporation, change in cloud cover) that vary with latitude.
When atmospheric mass moves from polar regions it is usually the Antarctic that provides, but quite frequently the Arctic contributes. There is a push pull relationship between them. The latitudes that gain in atmospheric mass lie in the main in the Northern Hemisphere.
There has been a lot of interest in the relationship between ENSO and the AAO with better correlation in some seasons than others but little realization that the northern trades and the southern exhibit very different behaviors, that the Arctic contributes, that the flux in ozone comes from the Arctic more than the Antarctic, this affecting mid latitude high altitude cloud cover. Too much reliance on sophisticated statistics. Not enough interest in simple physical principles.
So, the AAO and the AO are worthy of study, varying with the stratosphere and mesosphere manifestly reacting to solar influences. I will document the link when the time is ripe. Meanwhile, I want to encourage participation. I don’t want this work to disappear in the ether of the internet.
Google ‘the puzzle erl happ’
In response to comments by Bob, sky, & lgl:
Some light can be shed on this discussion of anomalies & base years via 3-D plotting (with month on the y-axis and using color on a z-axis). Running conditional integrations (by month & by combination of months) in parallel sheds further light on the nature of pattern sensitivity. The relationships one will note are most certainly not random.
Bob Tisdale replies: [the trade winds weaken] “Due to changes in Hadley and Walker Circulation associated with the relocation of tropical Pacific convection during the El Nino. That is, during the El Nino:
[A] the warm water from the West Pacific Warm Pool shifts east,
[B] bringing the convection with it.
[C] The relocation of the convection alters normal Hadley and Walker Circulation globally and one of the results is that
[D] the trade winds in the tropical North Atlantic weaken.
But in your link, I find (among other useful stuff) this statement:
[E] “During the El Niño phase, the trade winds first slow, then reverse. Since the trade winds are no longer “holding” the water in place in the western Pacific, gravity causes the warm water to slosh to the east.”
So……[A] causes [B] which causes [C] which causes [D] which causes [E] which turns out to be…the same as [A]. Looks circular to me, Bob. Somewhere in this sequence, there must be a primary trigger that starts the process, that shoves the pendulum the other direction. What is it?
This is nifty stuff, by the way, Bob. Great threads! Keep it coming.
tokyoboy says: “In Figure 2, the violet curve is sort of a differential (derivative) of the blue curve. Can I then understand that in later Figures you make ‘comparisons between derivatives’ of SST and related variables?”
Yes, Figures 3, 7, and 12 are then comparing those “differential (derivative)” curves to NINO3.4 SST anomalies smoothed with a 31-year filter.
tonyb says: “However the inconvenient truth is that there is no sort of reliable record of SST’s going back to 1880 and no amount of data torturing can remove that basic fact.”
Which is why I have noted in a number of places about the early portion of the data that, considering the sparsity of data in those early years, the apparent relationships were quite remarkable.
Bob,
The following table shows the times where the Moon is at or near
Full/New Moon (within +/- 10 degrees) at a time of closest perigee
(distance ~ 357,000 km) at the same time as Earth/Moon system is
closest to the date of perihelion (which occurs on roughly January 3rd) .
Close inspection of the table shows there transitions or slipages
from one sequence of lunar phases to the next as the time of closest
perigee/New/Full Moon slips past the time of perihelion. These slipages
occur in the year:
1903
1934
1965
and 1995
If you compare these years to the curve that you have plotted in figures 5
and 6 of your presentation, you will see that there is a close match. Proof of
the pudding will be an ending the cooling trend in sea-surface
temperatures around about 2026.
YEAR DATE OFFSET DAYS FROM
FROM PERIHELION
NEW/FULL
1895 Jan 12 F+17hrs +9
1899 Jan 12 N+02hrs +9
1903 Jan 13 F-11h +10
1908 Jan 04 N+14h +1
1912 Jan 04 F+00h +1
1916 Jan 04 N-14h +1
1920 Dec 26 F+11h -8
1924 Dec 26 N-02h -8
1928 Dec 26 F-17h -8
1934 Jan 15 N-12h +12
1939 Jan 06 F+13h +3
1943 Jan 06 N-00h +3
1947 Jan 06 F-15h +3
1951 Dec 28 N+11h -6
1955 Dec 29 F-03h -5
1959 Dec 29 N-17h -5
1965 Jan 17 F-13h +14
1970 Jan 08 N+13h +5
1974 Jan 08 F-01h +5
1978 Jan 08 N-15h +5
1982 Dec 30 F+10h -4
1986 Dec 30 N-03h -4
1990 Dec 30 F-18h -4
1995 Dec 22 N+07h -12
2001 Jan 10 F+12h +7
2005 Jan 10 N-01h +7
2009 Jan 10 F-16h +7
2014 Jan 01 N+09h -2
Bob,
Sorry about the lack of formating of the table. The columns in the table
should read:
1st column –> the Year
2nd column –> date of closest perigee that occurs within +/- 10 deg of New/Full Moon
3rd column –> Number of hrs of closest perigee from New/Full Moon
4th column –> Separation in days from perihelion on January 3rd.
re – Paul Vaughan says:
November 20, 2010 at 10:01 am
Geoff Sherrington & others,
The 28 year thing relates to LOD.
We have a common chord as I’ve been beating the drum about adjusted records for many years. If you have some concrete examples that might be too esoteric to post here, please feel free to email me at sherro1 at optusnet dot com dot au.
My background was in mineral exploration, where we grabbed anomalous features and mined them for extra mechanisms. In climate work the emphasis seems to be on smoothing the interest out of short anomalies. Wrong way to go, sometimes.
jorgekafkazar says: “[D] the trade winds in the tropical North Atlantic weaken.
“But in your link, I find (among other useful stuff) this statement:
[E] ‘During the El Niño phase, the trade winds first slow, then reverse. Since the trade winds are no longer ‘holding’ the water in place in the western Pacific, gravity causes the warm water to slosh to the east.'”
The trade winds in [D] are the trade winds in the North Atlantic, while the trade winds in [E] are those in the Pacific.
Bob, the link did not work. It says I don’t have access to the spreadsheet. I requested access from you (via gmail), so if you grant it, I should get an email back. You probably have a gmail asking for permission from me… Thanks, Mike S.
Here is my WUWT blog entries to one of Bob’s earlier articles posted on July 23rd 2009.
“Surge in global temperatures since 1977 can be attributed to a 1976 climate shift in the Pacific Ocean”
Ninderthana says:
July 25, 2009 at 1:25 am
I think that everyone here [and on the alarmist side] is missing a very important point.
These authors may have used a technique that effectively removed linear long term trend in temperature from their data but they did show that short term (sub-decadal) fluctuations in temperature (as measured by the temperature anomaly) are mainly due to ENSO events. In other words, I think almost every one agrees that El Nino’s are associated with short-term inceases in the world mean temperature, while La Nina’s are associated with short-term decreases in the world mean temperature. [I am not ruling out the the moderating influences of the PDO and AMO have on mutidecadal time scales]
The important point being made by their paper is that long-term temperature changes could be produced by a a simple change in the relative frequency of El Nino and La Nina events.
Between 1940 and 1976, La Nina’s were relatively more common than El Nino’s (a condition that exists if there is a negative PDO) , producing an overall cooling of the planet.
Between 1976 and 2006, El Nino’s were relative more common than La Nina’s (a condition that exists if there is a positive PDO), producing an overall warming of the planet.
The question is, is the warming produced by the change in relative frequency of EL Nino/La Nina events in 1976 sufficient to explain the bulk of the warming between 1976 and 2006? If it is then it leaves little room for either
solar or Anthropogenic CO2 to play a role unless it can be shown that they
directly influence the relative frequency of the ENSO events.
I have evidence that the onset of El Nino events over the last 400 years are synchronized with extreme proxygean spring tide indicating that the Solar/Lunar tides may play a [note that I am using the indefinite article here] crucial role is setting the timing of El Nino events.
Hence, it is quiet possible that it is the Lunar/Solar tides, and not Anthropogenic CO2 nor the Sun, that plays the most important role
in setting the world’s mean temperature in the long-term.
Bob,
I couldn’t get to your data, so I just downloaded some NINO3.4 data and took a 31 year moving average. On the sheet below, see the NINO3.4 tab. You’ll see that I was able to find the best fit sine wave equation, plus the slope equations by backsolving to minimize the sum-squared-errors of my prediction versus the actual 31 year MA. I don’t have my cool backsolver macro here, it’s at the office, but you can still make it work with Excel’s goal seek tool, which is pretty clunky but can work. Or not. The backsolver will find the prediction parameters to minimize the SSE, but it won’t always succeed and might overshoot into oblivion. You may have to manually tweak things until the chart looks close, then backsolve each parameter several times to get the best fit. It can only iterate one variable at a time, and you need to solve for 5. I can write a better solver and customize it so it works every time on this sort of thing, but in the mean time, it’s worth playing with. You’ll see some other tabs where I was trying to find cycle information on PDO, AMO, Hoyt & Schatten TSI, etc, even with multiple frequencies. The cycle length of NINO3.4 actually matches TSI frequency pretty well (83.7 vs 89 years).
Maybe you can find it useful for items like figure 12 where you might want to find a lag relationship between the two variables. Right-click below and save-as…
http://home.comcast.net/~naturalclimate/SineWaveSolver.xls
If anyone else wants to have a look, go ahead. I haven’t attempted to make it pretty, but it can be a handy tool. Of course, you can use the same technique on any function, or multiple functions, not just sine waves… Mike S.
Bob, one more thing on the sheet I think is interesting (it’s been a while since I looked through the original analysis). Check out the Hoyt.S. TSI 1900 tab. I used 3 waveforms there to get a decent match. The light blue line is the sum of 3 frequencies and a trendline. 86 years, 10.5, and 36.3 make a remarkable match. I don’t know whether these correspond to any underlying phenomena (except the 10.5), but it’s pretty interesting that you can add the 3 waveforms and get such a good match. Something’s going on there. Does it match anything you’re seeing?
Michael D Smith: Thanks for the spreadsheet. I’ve changed the NINO3.4 dataset without hiccup and I’ll see where it goes from there. Thanks again.
Bob
Your 5.30
You will appreciate I wasnt having a go at your article, merely obsereving that data analysts need data to analyse and the fact it is often very thin makes no difference to them
tonyb
Ninderthana says: “Here is my WUWT blog entries to one of Bob’s earlier articles posted on July 23rd 2009.”
I didn’t write the WUWT post on July 23rd 2009.
Michael D Smith says: “Bob, one more thing on the sheet I think is interesting (it’s been a while since I looked through the original analysis). Check out the Hoyt.S. TSI 1900 tab….”
Michael, the Hoyt and Schatten TSI data is considered obsolete. They created it in an effort to explain the warming in the early part of the 20th century. The newer TSI reconstructions are much flatter; that is, they don’t have the major long-term variations in solar minumums.
Bob,
Sorry, I was referring to the post that was written on the 23rd of July 2009
by Anthony:
“Surge in global temperatures since 1977 can be attributed to a 1976 climate shift in the Pacific Ocean”
It discussed the paper:
McLean, J. D., C. R. de Freitas, and R. M. Carter (2009), Influence of the Southern Oscillation on tropospheric temperature, Journal of Geophysical Research, 114, D14104, doi:10.1029/2008JD011637.
My musings back then about the Moon have since been backed up more substantial
research evidence. However, like you I have little time to get these results into print, so it will be a few more months before I am ready to publish.
However, my work on the Moon’s influence upon climate would not be accepted by
the wider scientific community if it were not for the brilliant research of people like you.
Your fantastic post has laid the ground work for a greater understanding of the main factors that drive climate and I think that this particular piece of work will cited by
many researchers in the field.
I also believe that it is important that you join up with someone to get your ideas into the literature, as your findings are so important that they should be presented to the
wider scientific community.
Well Done!!
phlogiston: Thanks for the converting the post to a pdf and for posting it. Kind of makes it look official.
Bob,
“does the data over that period present two whole cycles?”
Don’t know. 60 years cycle time just seems close. If you are suggesting 62 years it wouldn’t make much difference I guess.
lgl says: “Don’t know. 60 years cycle time just seems close. If you are suggesting 62 years it wouldn’t make much difference I guess.”
Sorry. I should have been more clear with my question. Does the poor coverage and poor representation of NINO3.4 SST anomalies before the early 1900s allow you to conclude that the cycles continue before the early 1900s? Also, if we look at the graphs above with the NINO3,4 SST anomalies smoothed with a 31-year filter (Figures 3, 7 and 12), there aren’t two complete cycles in the data that runs from 1880 to 2009.
HelmutU;
You can pretty much make your own pdf of any post or page; use the web2pdfconvert.com site.
PDF access for all: save at FileJumbo.com (free) and give the link to the world. Always on, no passwords, no space limits.
Bob,
To me it is sufficient that the curve drops from 1880 to 1910. It’s probably not correct from year to year but we have to use what’s available. Not sure I understand your last sentence. You have used the data from 1880 to 2009. The filtering is cutting the result to one and a half cycle. I don’t see the problem. The base period is not an issue with the figures you mention because the scaling takes care of that.
lgl: It’s immaterial since we’ve already established that regardless of the base period, we can still get the running total to work by shifting the data. But the point I was trying to make was that you were looking for 2 full cycles from 1880 to 2000, but the data was only giving one and one-half cycles from 1880 to 2010.
The interesting aspect for me from Bob’s work is the chicken and egg process with respect to ENSO and PDO.
I think Erl is on the ball when mentioning that there are external influences on the atmospherical processes that coincide with PDO positive and negative events. Past neg PDO events occur when solar EUV output is low which might suggest that varying ENSO events could drive the PDO cycle. BUT the fit is loose as seen in the 1945 change to neg PDO which has the Solar output at a record high.
This influences me to think the PDO is driven by a yet unknown mechanism which may be linked to Solar velocity (Scafetta) which in turn influences ESNO. Neg PDO has stronger and more frequent La Nina episodes with positive events being the reverse. The PDO could be the main driver which when further supported by low EUV and the following pressure pattern changes (NAO, AAO etc) takes the ocean SST’s to their lowest points. This is observed in the 1945 to 1970’s timeframe.
Ninderthana, you list dates spaced according to the 6th harmonic of the 179.3a lunisolar harmonic spectrum. I need to once again bring up confounding, as SCL” (acceleration of solar cycle length) gives an even better phase match with the patterns illustrated here by Bob (which have been illustrated by others in the past, btw).
As Corbyn, Lindzen, & others (directly &/or indirectly) have demonstrated over the decades, it is the 2.37 year timescale at which lunisolar variations are most evident [if one knows how to deal methodologically with the linear-correlation-crushing tropospheric turbulence that masks spatiotemporally].
So we arrive at the following question:
Are we seeing this particular harmonic of 179.3a in recent decades due to resonance related to the (multidecadal) frequency of solar frequency modulation (interannual timescales) of the 2.37 year lunisolar framework? (i.e. solar thermal tides strumming on the gravitational lunisolar guitar via wind/evaporation/clouds as Erl Happ seems to suggest…?)
It would seem that things are moving along as a result of these discussions.
Credibility demands that confounding not be ignored. The possibility of intermittent resonance is an interesting one. SCL’ & SCL” (-1/4 cycle shift from SCL’) do not have stationary periods over the 1749+ sunspot record, but they’ve recently hit ~60 years for 2 consecutive cycles.
Linear factor analysis methods (like PCA), that – in their traditional forms – ignore the deterministic orthogonality of derivatives & integrals, appear to have tripped climate science right into the deep & embarrassing pit of Simpson’s Paradox.
Cautionary note: For work on multidecadal timescales, repeat 1 year smoothing is (for many data exploration purposes, not all) superior to use of wide boxcar kernels.
We all have work to do. I want to suggest that the highest priority at this stage is to study the nature of sign switching in nonrandom multivariate coupling of the interannual components (& their 1st 2 derivatives) of variables such as the following: NPI, AMO, QBO, SOI, PDO, aa, LOD, GLAAM, SAM, AAO, AO, NAO, NLR, NOR, IOD, PWP, CO2, GSST, SOSST, SEPSST, etc. I also suggest throwing in your local interannual TMin, TMax, & PPT to bring the story closer to home.
Best Regards.
The 1940s to 1970s timeframe was a period of more equatorward jets just like the period we are entering now.
It was coincident with the slightly weaker cycle 20 too just as the current spell is linked to a weaker cycle 23.
And both periods show more clouds and higher albedo than the late 20th century warming spell.
So jet stream positioning and consequent changes in solar input to the oceans could swing the ENSO phenomenon in favour of dominant El Nino or dominant La Nina subject additionally to possible modulation from internal oceanic effects.
Erl Happ wrote, “There has been a lot of interest in the relationship between ENSO and the AAO […]”
I agree that this is important. When I have time I hope to write a story called “The Tale of SAM & SOI” in the form of an analogy with human coupling. (It will have to be rated at least PG-13.) One can read the story from a pair of graphs derived via wavelet & multiscale correlation methods. Not enough hours in the day…
Stephen Wilde says:
November 21, 2010 at 3:23 pm
The 1940s to 1970s timeframe was a period of more equatorward jets just like the period we are entering now.
I would be interested to see some data on this if you have it. If we look a recent trends it would suggest jet stream changes are occurring at the end of the 1940/70’s neg PDO period. The opposite would be expected in the early stages of this period due to high solar activity
Stephen Wilde, have you carefully studied the work of Karin Labitzke?
http://strat-www.met.fu-berlin.de/labitzke/
You can refine your ideas about spatiotemporal variation of insolation by taking into consideration what she points out about QBO-timescale (2.37 years) conditional dependencies. If one digs around (in the literature, the data, & NASA Horizons output), these nonrandom, nonchaotic interannual variations appear to be of lunisolar gravitational origin (has to do with how lunar nodal crossings time relative to harmonics of the terrestrial year). The interannual variations are much larger than the decadal ones, so you have here an opportunity to engineer large gains in your narrative with little effort (as per the Pareto Principle).
For those who have not previously considered Labitzke’s work, one pdf on her site is “a summary”.
Additional note for those interested in the nuts & bolts of multiscale, phase-aware, conditional data exploration:
Detection of related signals in the troposphere cannot be adequately accomplished with traditional linear approaches. I am developing new methods that are not blind to interannual-timescale regional/hemispheric-spatial-scale phase-reversals in nonrandom multivariate coupling patterns. I’m at a juncture where I’m going to briefly need substantial funding to free my focus from other financial pursuits for a few weeks so that I can focus deeply on programming the algorithms (which are already blueprinted conceptually).
Stephen Wilde says: “And both periods show more clouds and higher albedo than the late 20th century warming spell.”
If this refers to the period of the 1940s to the 1970s, what cloud cover data on you relying on for this statement?
Geoff Sharp says: “The interesting aspect for me from Bob’s work is the chicken and egg process with respect to ENSO and PDO.”
I did not include this graph in the post but it was included in the longer version of the video. I used 31-year smoothing in the post for the NINO3.4 data. If we then compare it to the PDO smoothed with a 31-year filter…
http://i51.tinypic.com/2wqbxgn.jpg
…the PDO clearly lags ENSO. The PDO is an aftereffect of ENSO.
The correlations observed in this work are interesting but there is a problem with one part of it. The author asks,
“Someone is bound to ask, how could the global Sea Surface Temperatures rise over multidecadal periods without an increase in radiative forcing?”
and then suggests that no forcing is needed. However some forcing is needed, in fact essential otherwise the proposal breaks either the 1st or the 2nd laws of thermodynamics, or both. If the sea surface temperature rise is correctly observed, as the paper assumes, and if it is truly global, as is stated, then a large amount of energy has been added to the top layer of the ocean. This cannot be explained by oscillatory effects. Ocean oscillations and currents could (and do) cause local effects as warm and cold water interchanges, but they cannot cause a global effect as they just move energy around rather than increasing it. Vertical movement of water, and evaporation cannot by themselves explain a global effect either as these all need a net energy input.
Furthermore there is a problem with all assumed oscillatory cycles – they all require a driver. Ultimately this comes down to the 2nd law of thermodynamics, and its implication that no process can ever be 100% efficient. The consequence is that in the natural world all oscillatory behavior is damped, and without an external driver the oscillations would die out – it is unlikely that these processes began in 1880, so why are they still there without forcing?
To get forcing you need either and increase in solar energy, or a change in the earth’s reflectivity (and that would need a verifiable reason) or a change in absorption of solar energy (which could be provided by GHG’s). Which you chose is up to you, provided you have evidence, but a choice is needed.
Bob Tisdale says:
November 21, 2010 at 5:42 pm
Thanks Bob, your graph does show some deviation but the turning points at 1925 1960 and around 1992 show both data sets coming together. It may require more research as well as a proven mechanism before the chicken or egg question is solved?
More chicken and egg….The JISAO PDO values for October have been released today. A small positive trend is noticed which is prior to a similar position of the La Nina 2 weeks later. The Australian BOM reports the slight upward trend in La Nina is a result of MJO activity which is now waning, but the warming PDO could also be the cause.
Graph HERE and HERE.
Bob Tisdale says: “The trade winds in [D] are the trade winds in the North Atlantic, while the trade winds in [E] are those in the Pacific.”
Thanks, that makes sense, if the trades are not coupled N-S. But the question remains, just looking at [E] ‘During the El Niño phase, the trade winds first slow, then reverse. Since the trade winds are no longer ‘holding’ the water in place in the western Pacific, gravity causes the warm water to slosh to the east’. What causes the Pacific trades to slow?
jorgekafkazar says: “What causes the Pacific trades to slow?”
Dunno. That’s one of the unanswered questions in climate science. I’m not being elusive by saying it varies. It’s one of the reasons that past ENSO variability is so hard to model.
Geoff Sharp says: “More chicken and egg….The JISAO PDO values for October have been released today. A small positive trend is noticed which is prior to a similar position of the La Nina 2 weeks later. The Australian BOM reports the slight upward trend in La Nina is a result of MJO activity which is now waning, but the warming PDO could also be the cause.”
The PDO does not represent SST in the North Pacific, just the pattern. The rise in the PDO could be caused by an increase in the SST in the east or by a decrease in the central and west. The SST anomalies in the north Pacific as a whole could be rising or falling, but the PDO will be postitive just as long as the eastern North Pacific is warmer than it is in the central and western North Pacific.
The PDO pattern is “ENSO-like” because ENSO creates it. The pattern can persist longer than (or shorter than) ENSO events or can vary month-to-month because it is also impacted by variations in sea level pressure. There’s no chicken and egg. ENSO is a process and the PDO is one of its aftereffects.
jimmi says: “If the sea surface temperature rise is correctly observed, as the paper assumes, and if it is truly global, as is stated, then a large amount of energy has been added to the top layer of the ocean.”
The rise in SST anomalies outside of the equatorial Pacific is caused by a reduction in evaporattion. Exampe: A decrease in surface wind speed will cause less evaporation and sea surface temperture will rise.
You wrote, “Furthermore there is a problem with all assumed oscillatory cycles – they all require a driver.”
ENSO has existed for as far back as paleoclimatology can reach. It is a self-charging oscillation (discharge/recharge) caused by the interdependence of the coupled ocean-atmosphere processes in the tropical Pacific. The recharging takes place during La Nina events when stronger trade winds decrease cloud cover and allow more downward shortwave radiation to warm the tropical Pacific. The trade winds “pile up” this warm water in the western tropical Pacific in an area called the West Pacific Warm Pool. When there is a relaxation of trade winds, the warm water in the West Pacific Warm Pool sloshes to the east and speads across the surface and there’s an El Nino event, which is the discharge mode.
jimmi: An afterthought: as mentioned in the post and as discussed and illustrated in detail in the linked posts, the both El Nino and La Nina events warm the East Indian and West Pacific Oceans, and these warmings can be and are cumulative. This is visible in SST data and is very obvious once you know it’s there.
Bob Tisdale asked:
“If this refers to the period of the 1940s to the 1970s, what cloud cover data on you relying on for this statement?”
It is necessary to link two separate sources because pre satellite cloudiness data is very sparse and usually regional in nature.
Firstly we have this, admittedly limited to the US:
http://europa.agu.org/?uri=/journals/gl/GL017i011p01925.xml&view=article
2) Fewer clouds were present during the 1930s and early 1950s.
3) There has been a tendency toward increased since 1948.
So a decrease during the 1930s warmth then the start of an increase as the mid century cooling spell began.
Until I can find a source that covers 1950 to the 1970s we have to assume that the increase which began around 1948 continued until another decrease began around 1980 as per this from the Earthshine project:
http://bbso.njit.edu/Research/EarthShine/literature/Palle_etal_2006_EOS.pdf
in Figs 1 and 2 which show reducing cloudiness during the late 20th century warming and the start of an increase in the last ten years. The increased cloudiness is in parallel with albedo changes and of course the jets now seem to be more equatorward again as they were during the mid 20th century cooling.
Geoff Sharp asked about jet stream latitudinal positioning but no one has been tracking changes beyond normal seasonal variation so we need largely to rely on anecdotal evidence. However the jets certainly moved poleward during the late 20th century warming spell as per this:
http://www.msnbc.msn.com/id/24228037/
“From 1979 to 2001, the Northern Hemisphere’s jet stream moved northward on average at a rate of about 1.25 miles a year, according to the paper published Friday in the journal Geophysical Research Letters. The authors suspect global warming is the cause, but have yet to prove it.”
So one can assume that the jets were more equatorward pre 1979 and we now see them more equatorward post 2001. I first noted the start of the reversal in 2000.
So putting all that together the latitudinal shifting of the jets is associated with total cloudiness, albedo changes and changes in tropospheric temperature trend and likely as not also responsible for changes in solar energy into the oceans and changes in the relative strengths of El Nino and La Nina.
An interesting feature is that reduced cloudiness is linked to warming spells and reduced albedo whereas AGW theory proposes more clouds with increased warmth.
As to the cause of the jet stream shifts I have been investigating that separately as some here will be well aware.
Bob,
A rise in evaporation cannot cause a global effect, only a local one – the evaporated water has to condense and return to the ocean somewhere so this process represents a redistribution of existing energy , not a global increase.
There is no such thing as a self-charging oscillation – that would give you a perpetual motion machine and the 2nd law of thermodynamics forbids it – it needs an external energy source. You are shifting the cause to a decrease in cloud cover, or a change in wind patterns, but those both require causes themselves – just because something is ‘natural’ does not mean it has no cause. The description of ENSO may well be correct phenomenologically, but that does not imply that the components (sea temperature, trade wind, cloud cover) are causes – they may all be due to external factors. Also if we are seeing a change in something it is required that at least one of the causes (drivers) is changing in frequency or magnitude.
jimmi says:
November 22, 2010 at 3:09 am
There is no such thing as a self-charging oscillation
A change in cloud is all that is needed, there is more than one theory that would cover this from an external viewpoint. It may also be generated internally, the doors need to be left open.
Bob,
Of course your result only shows 1.5 cycles when your method cuts the period down to 1895 to 1994, but there are still two cycles in the raw data. Lets leave this detail and focus on the crucial point. Your fig.3 and the likes show SST increases when Nino3.4 is above average, but they do not show that all of the increase is caused by Nino. In fact your fig.13 contradicts fig.3. The SST derivative in fig.3 is centered around 0.1, not around 0, giving the SST a positive trend. So to align the curves you had to give Nino a 0.1 deg offset. In fig.13 you fix the problem by choosing a base period where Nino is below average. Again, I’m not saying fig.13 is wrong, but I’m sure future peer-reviews will hang you for this. The available data gives a Nino integral with only a weak positive trend (my first version http://virakkraft.com/SST-ENSO-integral.png). To go beyond that you have to justify adding 0.05 to the 1880-2000 base. Perhaps you can use this http://astroclimateconnection.blogspot.com/2010/03/60-year-periodicity-in-earths-trade.html as part of your argument. It seems to indicate the trade winds did in fact decrease since 1700.
Regardless, even with the “true” base of 1880-2000 about 70% of the SST rise since 1910 is caused by ENSO related factors.
Geoff,
“A change in cloud is all that is needed, there is more than one theory that would cover this from an external viewpoint.”
Yes, but that then would not be self charging, which was my point.
Jimmi,
I (and others) have suggested that ENSO is driven by the moon, or actually by the bodies of the solar system because they put the moon where it is. http://virakkraft.com/hadcrut-eclipse.png
Erl Happ has shown that the equatorial SLP increases during periods of weakened trade winds so the mechanism may simply be that the moon is periodically pulling the atmosphere towards lower latitudes. Or perhaps the planets periodically accelerates the Earth changing it’s rotation. There’s a quite good correlation between Ju-Ea-Ve line-ups and temperature too. http://virakkraft.com/Ju-Ea-Ve-hadcrut.png. Not perfect but then there are more planets. Hope Ian will soon pin this down.
jorgekafkazar says: “What causes the Pacific trades to slow?”
Bobs reply,
Dunno. That’s one of the unanswered questions in climate science. I’m not being elusive by saying it varies. It’s one of the reasons that past ENSO variability is so hard to model.
Ninderthana adds:
It’s the variation in the Solar/lunar tides resonantly interacting with the seasonal variations in the atmosphere that are being driven by the Sun.
A Californian group of researchers (I cannot divulge their names at present) have found vigorous bi-weekly (i.e 14.7 day = half the synodic (phase) cycle of the Moon) reversal of cross-equatorial currents and winds. Tropical Instability Waves extracted from altimetry observations show Mixed-Rossby-Gravity Waves with a highest frequency of 14.7 days.
The researchers conclude that since 1850, climate records indicate that adding the 18.6 year lunar Nodic cycle to the 11 year solar activity cycle is helpful in explaining the decadal modulations of the ENSO indices.
My own research indicates that past researchers have been looking for the wrong tidal signature in the climate data. They have been looking for a signal that varies in the same manner as that seen in the strength of the absolute peak tides. What they should have been looking for in climate record are variations that match the periods of the peak tides that repeat at the same point in time in the seasonal cycle.
The reason for this change of paradigm is simple. The Moon is not the main driver of climate variables. It only becomes a player when it acts in resonance with the solar driven seasonal cycles.
And yes, the Lunar driving cycles are not effective all of the time, since they slowly drift in and out of phase over the centuries. So the 20th century may have been an unusual period where the influence of the Lunar tides kicked in and added to the normal solar influence.
Watch this space.
The group finds
My investigations of the properties of the lunar orbit indicate that the strengths of
extreme proxigean spring tides are affected by two main alignment periods that reoccur on distinctly different time scales.
The first period is the time required for the syzygies of the Earth/Moon/Sun (i.e. the Lunar Synodic month) to realign with the closest lunar perigees. These alignments reoccur at intervals of 10.1466 and 20.2933 (269 anomalistic months = 251 synodic months) Julian years.
The second period is the time required for the syzgies of the Earth/Moon/Sun (i.e. the Lunar Synodic month) to realign with the line of nodes of the Lunar orbit. These alignments reoccur at intervals of 1.8980 and 3.7958 (51 draconic months = 47 synodic months) Julian years.
Combining these forcing periods, you would expect that, over extended periods of time, the effects of extreme proxigean spring tides should manifest themselves in the climate indices on time scales that are set by the beat periods between these two fundamental time scales.
[10.1466 X 1.8980] / [10.1466 – 1.8980] = 2.335 yrs ~ 28.0 months
Interestingly, this time period is almost exactly the same as that of the Quasi-Biennial
Oscillation (QBO). The QBO is a quasi-periodic oscillation in the equatorial stratospheric zonal winds that has an average period of oscillation of 28 months, although it can vary between 24 and 30 months (Giorgetta and Doege 2004).
It represents the beat period between:
a) the time scale associated with the synchronization of the line-of-apsides with the the syzygies of the Earth–Moon–Sun system (i.e. the Lunar Synodic month)
and
b) the time scale associated with associated with the synchronization of the line-of-nodes with the the syzygies of the Earth–Moon–Sun system (i.e. the Lunar Synodic month)
Bob,
Can you clarify one thing?
Are you saying that the accumulated effects of the El Nino cycle *could* account for all (or nearly all) of the observed warming of the last 100 years, or are you saying that the accumulated effects of the El Nino cycle definitely are responsible for all (or nearly all) of the observed warming? If it is the later case, then why do think El Nino has dominated La Nina during this period?
I think your efforts are very helpful in understanding the evolution of sea surface temperatures over the last century, especially the causal link between ENSO and the AMO, since this explains why there is a cyclical appearance in the temperature record which is closely correlated with the AMO index. I also agree that solar cycle’s and volcanic aerosol effects account for much of the shorter term variation. But I do not think that the analysis can be used to exclude the possibility of a contribution of increased GHG forcing.
A regression of AMO index, Nino 3.4, solar cycle, and total GHG forcing against the Hadley global temperature record shows very good overall correlation (R^2 of about 0.9) as well, and suggests both strong correlation of temperature to the AMO index and a low sensitivity to radiative forcing (about 1.2C per doubling of CO2). But low sensitivity does not mean zero sensitivity. Are you really suggesting zero sensitivity to radiative forcing?
I think the temperature of the water itself is the driver of this whole system.
And it does oscillate up and down all on its own based on the way the water circulates in the central pacific. Warm water piles up against New Guinea, where the deep oceans ends and some of it is forced down because the water flow is restricted by the now shallow ocean and islands.
http://www.srh.noaa.gov/jetstream/tropics/images/la.jpg
Much of it is forced down and it flows back to the east at 200 metres depth and when the warm water surfaces at the Galapogos Islands in 9 months (replaceing the water which is flowing east-west at the surface), it starts to slow down the Trade Winds because of the convection effect. Then we have an El Nino.
http://www.srh.noaa.gov/jetstream/tropics/images/el.jpg
Meanwhile back at New Guinea, the cold water left over from the previous La Nina is now being forced down and it enters the subsurface circulation and in about 9 months, it will surface at the Galapagos Islands, speed up the Trade Winds and we have a La Nina.
It is an oscillation which reflects the predominant ocean circulation patterns and the temperature of the water itself.
The Trade Winds lag a few months behind the temperature of the water itself.
http://img152.imageshack.us/img152/7366/ensoeuhctradesoct10.png
And right now, we are seeing the La Nina starting to reach its peak. By about July or so, we will be in an El Nino.
Surface circulation.
http://www.osdpd.noaa.gov/data/sst/anomaly/anomalyps_2m.gif
Subsurface circulation.
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ocean/anim/wkxzteq_anm.gif
Steve Fitzpatrick says: Are you saying that the accumulated effects of the El Nino cycle *could* account for all (or nearly all) of the observed warming of the last 100 years…”
I have presented nothing in this post that would allow me to state that “the accumulated effects of the El Nino cycle definitely are responsible for all (or nearly all) of the observed warming.”
You asked, “…then why do think El Nino has dominated La Nina during this period?”
There a low-frequency component to ENSO and we’ve been luck to catch two of them when El Nino events dominated.
You asked, “Are you really suggesting zero sensitivity to radiative forcing?”
I’m suggesting that ENSO represents more of the rise in global temoperatures than its linear component, which is what studies like Thompson et al (2008) would like us to believe. How much more? Dunno. Could the accumulated affects of ENSO in the East Indian and West Pacific Oceans represent a major portion of the rise in global surface temperatures? Yes. And regardless of whether or not the AMO is driven by THC/AMOC or by ENSO, it’s still a natural form of variability and it also contributes significatly to the overall rise in global temps from the trough in the early 1900s to present.
Bob, can I suggest to you that there are better ways of observing ENSO than using Nino3.4? It is only a proxy, sits there in the middle of the equatorial countercurrent and watches El Nino waves go by. They carry warm water from the Indo-Pacific warm pool to South America. When they get there they run up the coast and spread out. The large area of warm water exposed thereby warms the air, warm air rises, interferes with trades, mixes with global circulation, and raises its temperature by half a degree. It is that temperature rise that counts, and it is present in all global temperature curves. Unfortunately stupidity interferes because so-called “climatologists” get rid of it since nothing less than thirty years means anything to them. But one HadCRUT3 version somehow escaped being smoothed out and you can get it in Junk Science. Click Global Temperatures, Contemporary Time Series, and HadCRUT3. It is the graph on the lower right. It covers the period from 1850 to 2008. They give you the URL and you can download the data yourself if you don’t like their version.
lgl says: “Your fig.3 and the likes show SST increases when Nino3.4 is above average…”
Nope. It shows that the 31-year change in global SST anomalies increases when the NINO3.4 SST anomalies rise and the opposite when the smoothed NINO3.4 SST anomalies drop. Sometimes it’s easier to think about 31-year trends (Figure 7) increasing as 31-year average NINO3.4 SST anomalies increase.
You continued, “…but they do not show that all of the increase is caused by Nino.”
The curves show that when the 31-year average NINO3.4 SST anomalies were at their peak in 1926 (representing the period from 1911 to 1941) global SST anomalies rose about 0.43 deg C, and when the 31-year average of NINO3.4 SST anomalies were at their lowest point in 1960, global SST anomalies dropped about 0.22 deg C.
You wrote, “In fact your fig.13 contradicts fig.3…”
In no way do the two figures contradict one another. The reason the NINO3,4 data was shifted up 0.1 deg C in Figure 3 was for cosmetic reasons, simply to align the wiggles. Same thing with Figure 13. The running total was shifted down 0.26 deg C to align the two curves. By doing so, it made the relationship easier to see.
The rest of your comment continued to express your concerns about the fact that the running total depends on the ratio of positive to negative anomalies. That’s really a minimal requirement when one considers the contortions climate modelers go through in order to reproduce the curve of the 20th century surface temperature anomalies. They use outdated TSI reconstructions with rises in solar minimums during the first half of the 20th century in order to reproduce the rise in temperature at the time. They use manufactured aerosol datasets to create the mid century drop in temperature, and they rearrange the aerosols depending on the dataset they’re trying to reproduce. Last, who knows what they tweak with all of the adjustments so that the average of the ensemble resembles the temperature anomaly curve.
And thanks for the link to the post about the trade wind reconstruction.
Arno Arrak says: “Bob, can I suggest to you that there are better ways of observing ENSO than using Nino3.4? It is only a proxy, sits there in the middle of the equatorial countercurrent and watches El Nino waves go by.”
Unfortunately and fortunately, NINO3.4 SST anomalies and the CTI) are widely used and to incorporate something else would require additional justification. It’s best for me to use commonplace proxies.
Jimmi says: “There is no such thing as a self-charging oscillation.”
It was a poor choice of words. ENSO is a discharge/recharge oscillation. Refer to the graph of NINO3.4 SST anomalies (proxy for ENSO events) and tropical Pacific Ocean Heat Content (OHC):
http://i36.tinypic.com/eqwdvl.png
El Nino events discharge heat from the tropical Pacific and most times the subsequent La Nina recharges only part of the heat lost during the El Nino. That’s why there are the decadal and multidecadal declines in Ocean Heat Content–the typical La Nina recharge doesn’t make up for the typical El Nino discharge. Then there are the oddities. The 1973/74/75/76 La Nina persisted so long that tropical Pacific OHC rose considerably. And then there was the 1995/96 that also caused the unusual rise in tropical Pacific OHC that fueled the 1997/98 El Nino, which has been describes as the El Nino of the century. That rise in OHC is associated with unusually strong trade winds.
So now that you’ve seen the data, if not self-recharging, what would you use instead?
Regards
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 @ 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.
November 22, 2010 at 6:21 pm
The reason I referred to station records unadulterated by UHI is because
they provide the best available indication of multidecadal temperature
variations for the entire past century. (As you’re aware, pre-satellite
era SST data is not only very spotty, but fails to maintain consistent
datum levels due to inconsistent measurement methods.) The vetted station
records on average show the 1950-1979 period to be ~.15K below the 20th
century mean. The Nino3.4 index is no exception.
Getting a time-series well centered is essential for analytic work. It is
not a discretionary choice. Otherwise, one gets misleading estimates of
correlation functions, power spectra, etc. This is no less important in
integration over indefinite time. Only zero-mean functions yield
well-bounded integrals that do not increase or decrease idefinitely. What
you are doing by accepting Trenberth’s chosen–and clearly biased–“norm”
is introducing a positive trend into the cumulative sum of the otherwise
trendless record. That bogus trend is inversely proportional to the bias
of the “norm.”
Integration (cumulative summation) is a linear operator. Ipso facto, it is
incapable of producing multidecadal oscillations that are not there in the
record. The entire idea of higher-frequency components (the power spectrum
of the Nino3.4 record peaks at ~5.5a periods) producing multidecadal
oscillations upon integration is mathematically impossible. And the
striking thing is that cross-spectrum analysis with the (properly
recentered) HADCRUT3 series shows virtually no coherence at mutidecadal
periods and only marginal coherence at the Nino spectral peak. This is
also evident in your 31-yr smoothing comparison.
Why, then, does the cumulative sum of the miscentered Nino3.4 series so
strongly resemble HADCRUT3 visually over the available years? Can’t say
for sure, but I suspect that it’s a combination of a) strong coherence at
high frequencies b) bogus trend introduction and c) intra-record offsets in
both series, which are suspect on the basis of certain tests that I won’t
describe here. In other words, it’s a numerical oddity.
On the side-issue of cross-equatorial surface currents in the Atlantic, I read Lumpkin and Garzoli when they first published. There are two major weaknesses to their study: Eulerian inferences are made from Lagrangian (drifter) data and the effect of Amazonian discharge upon the dynamic topography along the NE facing coast of Brazil is not taken into account. Even so, if you read their paper carefully, you’ll find the Guyana Current questioned as a “true” current, with eddy ring shedding at the retroflection of the NBC into the ECC being offered as the tentative explanation. Inasmuch as both of these currents circulate primarily North Atlantic water in a narrow equatorial loop, this scarcely qualifies as the great conveyor of heat from SH to NH that you seem to suggest.
Thanks for the invite to continue the discussion tomorrow, but I have other commitments. Have a good Thanksgiving!
Cut and paste dropped Bob Tisdale’s name from the header of my last comment.
sky,
How can you say “producing multidecadal oscillations upon integration is mathematically impossible” when the integration is clearly showing multidecadal oscillations? (also when using Nino-data centered around zero)
lgl, I think sky is saying (data quality issues aside) that the low frequency component is present in the data. So in practical terms, integration is just helping to see it (for example if observers were blinded by the magnitude of interannual variations). In short, integration doesn’t create low-frequency oscillations that weren’t there in the first place. I again encourage everyone, including sky, to graph SOI (both raw & base-period-dependent anomaly) by month and to look at integrals by month (& by combination of months). It is very important to be aware of variations in the seasonal cycle. It appears to me that in their focus on the 1st moment, many folks are overlooking the second moment. It should not be assumed that a cycling average has random homoskedastic variance. Even worse than not stating assumptions, researchers are often not aware of their assumptions.
Bob, thanks for delivering posts that drive quality commentary.
sky: I’ll be addressing your comments in a follow-up post. Thanks.
Regards
Bob, l found time to read the 2 papers to which you linked here:
http://bobtisdale.blogspot.com/2010/11/guan-and-nigam-2008-and-2009.html
I remember a down-to-earth (even a bit red-neck) academic statistician once telling me that a new algorithm could secure a warm welcome if it was “cute”. I’m confident the EEOF thing would fit his “cute” criteria. While I’m a fan of factor analysis generally speaking, it is important to recognize that it runs straight into dead ends in many applications in nature (including terrestrial climate) due to (context-specific) untenable assumptions. Putting more bells & whistles on an algorithm (e.g. the upgrade from EOF to EEOF) cannot overcome obstacles stemming from fundamentally flawed (context-specific) assumptions. (Worse than not stating assumptions, many researchers are not even aware that they are making assumptions.)
Despite misapplication (to data for which assumptions are untenable), the EEOF algorithm yields worthwhile (but insufficient on its own) information and the articles (particularly the 2008 one [on the Pacific]) are stimulating. If I was running a graduate level university course, I would consider assigning the 2008 paper as required reading for an in-depth (2-3 hour) discussion.
Thanks for sharing the finds.
Bill Illis posted:
http://a.imageshack.us/img832/3174/newesthadsst3.png
These characters clearly do not pay attention to EOP (Earth orientation parameters) and the evolution of seasonal patterns in geomagnetic aa index. Worse than that, it’s like they don’t realize that clouds affect insolation – and that there is a relationship between wind & clouds. It is the responsibility of sensible people to find an efficient way to arrest the errant behavior of these vandals.
lgl says:
November 25, 2010 at 5:41 am
All linear operators are strictly frequency-preserving. An elementary example is the cosine function, which produces upon integration the sine function of EXACTLY the same frequency. That’s why I can say that integration of HIGH-FREQUENCY components of ENSO cannot produce MULTIDECADAL components. Only NONLINEAR operators can do that. Hope this clarifies matters for you.
sky says: “All linear operators are strictly frequency-preserving.”
And your assumption with respect to ENSO is?
Bob, I’m not making any dynamic assumptions whatsoever about what PHYSICALLY produces ENSO or how its effects may be redistributed around the globe. What I’m saying is that the strictly linear MATHEMATICAL operation of integration–continuous or discrete–cannot produce signal components at frequencies other than those already present in the signal. Incredible mathematics is hardly a good foundation for sound physics. Let’s leave it at that.
sky: in response to the relationship between forcing and temperature.
Given a forcing in units of Joules per second, acting on a mass with a heat capacity in units of Kelvin per Joule, the result of integration over time will be temperature in Kelvin.
“What I’m saying is that the strictly linear MATHEMATICAL operation of integration–continuous or discrete–cannot produce signal components at frequencies other than those already present in the signal.”
You should read Roe 2009. He says on pp106: “… variability at long periods can be the natural result of physical processes whose timescales are much shorter.” and again on pp108 “the vast majority of the variance in the PDO can be explained by simple integrative physics with a perhaps surprisingly short timescale.”
sky,
Thanks but it does not clarify matters. Running summation is low-pass filtering and the result clearly shows a low-frequency component. I don’t understand why you included the paragraph containing “it is incapable of producing multidecadal oscillations that are not there in the record” when there is a multidecadal oscillation in the record (other that to confuse of course).
sky is correct. It’s not the frequency content that changes with differentiation & integration, but rather the relative power (comparatively between timescales before & after differentiation &/or integration). [Also, note that sky has acknowledged the spatial dimension (“may be redistributed”), while restricting mathematical comment to the temporal dimension.]
I recommend that readers compare seasonal SOI integrals, noting in particular summer-winter divergences before 1959. This should shed some light on early 20th century differences between the integrals of NPI/ALPI & PDO. It may also help establish a framework for overcoming misconceptions about PDO vs. North Pacific SST.
Strongly suggested:
Carefully study & compare the following:
a) http://icecap.us/images/uploads/AMOTEMPS.jpg
b) Figure 10:
Carvalho, L.M.V.; Tsonis, A.A.; Jones, C.; Rocha, H.R.; & Polito, P.S. (2007). Anti-persistence in the global temperature anomaly field. Nonlinear Processes in Geophysics 14, 723-733.
http://www.uwm.edu/~aatsonis/npg-14-723-2007.pdf
http://www.icess.ucsb.edu/gem/papers/npg-14-723-2007.pdf
Anyone reproducing Bob’s 31-year-difference series should compare their results with the differenced series (plain monthly differences) (a) smoothed at 31 year bandwidth and (b) repeat 1 year smoothed (don’t hesitate to jack the repetition way up…)
This discussion has raised many items which have only peripherally received the attention they deserve. I look forward to Bob’s follow-up post.
David Stockwell linked to:
Roe, G. (2009). Feedbacks, timescales, and seeing red. Annual Review of Earth and Planetary Sciences 37, 93-115. doi: 10.1146/annurev.earth.061008.134734.
http://sheridan.geog.kent.edu/geog41066/7-Roe.pdf
‘Irreducible complexity’ slogans are too lazy. We don’t yet know enough about terrestrial climate for this approach to avoid killer problems with Simpson’s Paradox. The era of meaningful statistical inference will necessarily follow the era of careful data exploration.
David Stockwell:
You overlook the fact that Tisdale is integrating not the forcing, but its effect–the temperature. As for the frequency-preserving property of integration, that is a mathematical theorem. If Roe (2009) knows what he is talking about (always an open question in climate science), he will not make any claims in violation of it. In my earlier response to you, I already mentioned the impulse response function of linear systems that acts as a convolution kernel in the integral defining the response. Not all integration is the same! He may have in mind that sort of integration or NONlinear systems that indeed can create harmonics and subharmonics. Unless Roe can provide a concrete model, however, his words are just vague hand-waving.
lgl:
See additional comments above about frequency preservation by linear operators. The Nino3.4 index, of course, does have some multidecadal components (accounting for ~8% of its total variance) but they are incoherent with those of HADCRUT3 (where they account for ~75%), and no LINEAR operation will make them coherent. I don’t know how to make this crucial point any clearer. P.S. My time is much too valuable for me to ever waste it on generating confusion.
sky,
Wrong again, after 1900 they are coherent too. Both peak around 1940 and 2000 and trough around 1910 and 1975. The different variance is interesting but there is no rule saying the high-frequency components can not be more damped than the low-frequency on a global scale.
sky says: “You overlook the fact that Tisdale is integrating not the forcing, but its effect–the temperature. ”
I believe I know where our views differ. You’re looking at NINO3.4 SST anomalies as numbers. I view them as a proxy for a process. That process releases warm water from below the surface of the PWP, shifts it to the central and eastern equatorial Pacific, releases heat there through evaporation, which causes changes in atmospheric circulation, in turn causing SST outside of the tropical Pacific to vary. The process continues when a Rossby wave returns leftover warm water from the eastern to the western tropical Pacific during the subsequent La Nina……….
lgl says:
December 1, 2010 at 3:15 am
Cross-spectral coherence has little to do with when extreme values are achieved in a wide-band stochastic process such as ENSO. Having already demonstrated extraordinary difficulty in grasping signal analysis basics (you seem to think that low-pass filters CREATE low-frequency components) , it comes as no surprise that you totally misunderstand the concept of frequency-dependent coherence. I recommend Papoulis’ monograph “Signal Analysis” for getting a solid grasp. Good luck!
Bob Tisdale says:
December 1, 2010 at 3:43 pm
Bob, it is you who cumulatively summed NINO3.4 temperature anomalies (miscentered at that) and concluded on the basis of numerical similarity that they “cause” the global temperature variations seen in the HADCRUT3 anomalies. That scarcely conveys viewing ENSO as a physical process. All I did was point out the insurmountable logical lapses in drawing that conclusion on that basis.
sky
Cross-spectral coherence has little to do in this discussion. Cross-spectral coherence would mean SST is driven by ENSO alone and nobody ever claimed that of course.
The main point is the 60 years cycle in both ENSO and SST. SST detrended looks very similar to the integral of ENSO. Do you think that’s just a coincidence?
My “Running summation is low-pass filtering and the result clearly shows a low-frequency component … there is a multidecadal oscillation in the record”
can’t possibly be turned into “you seem to think that low-pass filters CREATE low-frequency components” If you don’t have an argument, leave it.
Bob Tisdale wrote (addressing sky), “I believe I know where our views differ. You’re looking at NINO3.4 SST anomalies as numbers. I view them as a proxy for a process.”
In following the exchange, that has been my instinct as well. To express it one way, Bob is treating SOI (an indicator of equatorial Pacific wind & cloud) as a proxy for the rate of change of global temperature. It’s no surprise that the integral of f'(x) equals f(x) plus a constant. Similarly, the integral of global atmospheric angular momentum will reveal the 1976 climate shift. I disagree strongly with sky’s decision to view ENSO as a largely stochastic process. However, sky is correct to point out that in a strictly temporal domain, integration does not create the low frequency pattern from high frequency components. How circulation aliases high frequency equatorial Pacific components onto nonstationary spatiotemporal modes elsewhere and what pattern variations emerge with variable aggregation criteria is another matter.
lgl:
The entire point of Tisdale’s presentation is to show a COHERENT relationship between the cumulative sum of Nino3.4 anomalies and global SST anomalies, not just the presence of UNRELATED multidecadal oscillations in both series. But that involves series with different units– in other words, a numerological/phenomenological rather than a physical comparison. Yet he does claim a causal relationship. That’s where I, who usually applauds his presentations, try to get him to consider the issue more rigorously. Cross-spectral coherence underlies the essence of his claims. See more below.
Paul Vaughn:
Nino3.4 is a localized temperature index, not a proxy for anything else. And your reservations about treating ENSO as stochastic processes are misplaced. Physical processes that involve turbulence or other deterministic chaos DO produce time-histories that are stochastic in their variability. A commonplace example is wind-driven ocean waves. Analyzing the records via spectral methods is eminently sensible in unraveling the complexities and–in particular–seeking out DETERMINISTIC physical relationships between pairs of series. That’s where cross-spectral coherence becomes absolutely crucial. Without strong coherence, no claimed physical relationship is credible.
As an empirical matter, the multidecadal components of Nino3.4 are INCOHERENT with those of HADCRUT3, which I use as an overall temperature index in cross-spectrum analysis. This tells us that, no matter how important ENSO is as a mechanism for redistributing the excess heat of the equatorial Pacific, it is NOT the mechanism that drives the multidecadal oscillations of global temperatures. Integration or cumulative summation cannot change that.
Sorry, gentlemen, I simply cannot take more time for this discussion.
Paul:
As a footnote, if ENSO were indicative of the differential of global temperature, then the cross-spectral phase (Nino3.4 – HADCRUT3) would be 90 degrees. In fact it is not, even at the 3-4yr periods where the coherence is strongest.
sky: “Sorry, gentlemen, I simply cannot take more time for this discussion.”
I’ll second that. Time to move on to additional ways to illustrate the cumulative effects of ENSO.
Bob,
can’t stop the fun now
sky
Thanks for your time, sorry you left but I have to add a few comments.
“Nino3.4 is a localized temperature index, not a proxy for anything else”
It’s localized but still probably a proxy for a global phenomena. The wind anomalies revers all over the globe when switching from Nino to Nina
http://www.crces.org/presentations/dmv_ipwp/images/figure7.gif from http://www.crces.org/presentations/dmv_ipwp/
and because this phenomena is not coherent around the globe, North-atlantic SST lags North-pacific SST about one year for instance (Bob can tell you all about other lags) , a cross-spectral coherence is impossible. In addition things like large volcanic eruptions are not visible in Nino3.4 but very visible in global SST.
“then the cross-spectral phase (Nino3.4 – HADCRUT3) would be 90 degrees. In fact it is not, even at the 3-4yr periods where the coherence is strongest.”
For the strong 3.6 yr period it is very close to 90 degrees (again, it can’t be expected to be exact) SST’ usually leads Nino3.4 about one year. http://virakkraft.com/SST-deriv-ENSO-1880-2010.png
I suspected some readers might misinterpret my last comment.
sky, I appreciate your knowledge, intellect, & notes. I also encourage you to:
1) run more windowed analyses (varying window parameters).
2) think more about conditioning variables, coupling switches, & how to engineer related detection algorithms.
I agree with you that the multidecadal oscillation is not an export of the Nino 3.4 region (all by itself). That’s what I was hinting at with the strong suggestion included here (comparison of 2 maps).
I look forward to future exchanges (at a manageable pace).
Something to think about / audit in the meantime:
White, W.B.; & Liu, Z. (2008). Non-linear alignment of El Nino to the 11-yr solar cycle. Geophysical Research Letters 35, L19607. doi:10.1029/2008GL034831.
https://www.cfa.harvard.edu/~wsoon/RoddamNarasimha-SolarENSOISM-09-d/WhiteLiu08-SolarHarmonics+ENSO.pdf
lgl, you’ll likely note the focus on the nonstationary 3rd solar cycle harmonic (~3.6 years). During some parts of the records, there is phase-confounding with the polar-equatorial eclipse cycle. There’s also intermittent phase-confounding of the nonstationary 5th solar cycle harmonic with the QBO and the 2nd subharmonic of the Chandler wobble. I’m just getting started on a new line of investigation…
I’ve drafted some notes to share in Bob’s next thread…
Thanks Paul,
interesting, but I think White was a bit over-creative when constructing the DAO by transforming the 9 (or 10) years ESST period to match the solar cycle. Why not use the 9 yrs moon cycle directly (or was it 8,84 yrs)
http://tenaya.ucsd.edu/~dettinge/solarposter.pdf