Thompson et al (2009) – High-Tech Wiggle Matching Helps Illustrate El Nino-Induced Step Changes
Guest Post by Bob Tisdale
INTRODUCTION
In “Identifying signatures of natural climate variability in time series of global-mean surface temperature: Methodology and Insights”, Thompson et al (2009) remove the effects of three natural variables from the Global Surface Temperature record (January 1900 to March 2009). Those three natural variables are El Nino-Southern Oscillation, stratospheric aerosols emitted by explosive volcanic eruptions, and “variations in the advection of marine air masses over the high latitude continents during winter”, which they condense to “dynamically induced variability” or Tdyn in the paper. Thompson et al use “a series of novel methodologies to identify and filter out of the unsmoothed monthly-mean time series of global-mean land and ocean temperatures the variance associated with ENSO, dynamically-induced atmospheric variability, and volcanic eruptions.”
Thompson et al (2009) Link:
http://ams.allenpress.com/perlserv/?request=get-abstract&doi=10.1175%2F2009JCLI3089.1
Preprint Version:
http://www.atmos.colostate.edu/ao/ThompsonPapers/TWJK_JClimate2009_revised.pdf
Thompson et al (2009) also provided a link to five of the datasets they used and created while preparing the paper. The webpage is identified as “Data for Thompson, Wallace, Jones, Kennedy”:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/
OVERVIEW
This post briefly discusses the data made available by Thompson et al (2009), it illustrates the ENSO and volcanic aerosol residuals that remained in the global temperature anomaly data after the effects of ENSO, volcanic aerosols, and dynamically induced variability were said to be removed, and it illustrates the El Nino-induced step changes that resulted from the significant El Nino events that occurred since 1976.
The post does not discuss the erroneous assumption made by Thompson et al (2009), which is that the relationship between ENSO and global temperature is linear. It is not. The non-linear relationship between ENSO and global temperatures was discussed in the following three posts, which all cover the same subject, fundamentally, though there are differences in the presentation:
1. The Relationship Between ENSO And Global Surface Temperature Is Not Linear
2. Multiple Wrongs Don’t Make A Right, Especially When It Comes To Determining The Impacts Of ENSO
3. Regression Analyses Do Not Capture The Multiyear Aftereffects Of Significant El Nino Events.”
The data furnished by Thompson et al actually reinforces the fact that the global temperature response to El Nino events is not linear.
THOMPSON ET AL (2009) DATA
INITIAL NOTE: The title block of the graphs in this post use the nomenclature from the “Data for Thompson, Wallace, Jones, Kennedy” webpage linked above.
Figure 1 illustrates the residual global temperature time-series data after the effects of ENSO, volcanic aerosols, and dynamically induced variability were removed. It is identified throughout this post as “Tdyn/ENSO/Volcano residual global mean”. ENSO continues to make its presence known in the “Tdyn/ENSO/Volcano residual global mean” data in Figure 1, indicating that Thompson et al failed to remove all of the effects. Note the spike from the 1997/98 El Nino and the dip due to the 2007/08 La Nina. Both are reduced in magnitude, but they are still quite visible. There are other El Nino event residuals in the data, as will be illustrated later.
http://i37.tinypic.com/4r8apz.png
Figure 1
Figure 2 is a comparative graph of the raw “Tdyn/ENSO/Volcano residual global mean” data and Global Surface Temperature anomalies (listed as “Global mean” on the “Data for Thompson, Wallace, Jones, Kennedy” webpage linked above). Thompson et al (2009) identifies the “Global mean” data as HadCRUT3, which is the Hadley Centre’s combined land surface temperature and SST data.
http://i38.tinypic.com/jacsaw.png
Figure 2
Smoothing both datasets with 13-month filters, Figure 3, helps to highlight the ENSO residuals left in the “Tdyn/ENSO/Volcano residual global mean” data. It also illustrates how well the methods used by Thompson et al (2009) appear to have removed the effects of volcanic aerosols. Note the differences in the datasets immediately following the 1982 and 1991 volcanic eruptions of El Chichon and Mount Pinatubo.
http://i38.tinypic.com/2ed8voh.png
Figure 3
Thompson et al (2009) uses Cold Tongue Index data [5S-5N, 180-90W] as the base for its “ENSO fit” data, Figure 4. Link to “ENSO fit” data:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/TGlobe1900March2009_ENSOfit
NOTE: The methods used by Thompson et al (2009) to create the “ENSO fit” (“Volcano fit” and “Dynamic fit”) datasets will not be discussed in this post. Refer to the paper for further information.
http://i34.tinypic.com/f53lme.png
Figure 4
Figure 5 is comparative graph of scaled HADSST Cold Tongue Index data (downloaded through the KNMI Climate Explorer) and the “ENSO fit” data. The model used by Thompson et al (2009) exaggerated the Cold Tongue Index data in some months and suppressed it in others.
http://i38.tinypic.com/10rikb4.png
Figure 5
A time-series graph of the “Volcano fit” data is presented in Figure 6. Link to “Volcano fit” data:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/TGlobe1900March2009_VOLCANOfit
http://i34.tinypic.com/2n0n5p0.png
Figure 6
The data source for volcanic aerosols in Thompson et al (2009) is the Sato Stratospheric aerosol optical depth data available though GISS:
http://data.giss.nasa.gov/modelforce/strataer/
Specifically:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt
Figure 7 compares the “Volcano fit” data and the inverted and scaled Sato Mean Optical Thickness data. There are minor differences in the month-to-month variations between the source data and the model output.
http://i36.tinypic.com/2qcp7ar.png
Figure 7
The “Dynamic fit” dataset, Figure 8, is unique to Thompson et al (2009). As noted above, it accounts for the “variations in the advection of marine air masses over the high latitude continents during winter.” They provide a detailed description of the dataset starting on page 9 of the paper. Link to “Dynamic fit” data:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/TGlobe1900March2009_TDYNfit 
http://i38.tinypic.com/amfxup.png
Figure 8
Figure 9 is a comparative graph of the “ENSO fit”, “Volcano fit”, and “Dynamic fit” datasets for those who are interested in seeing their relative magnitudes. The data have been smoothed with 13-month running average filters.
http://i36.tinypic.com/2rc4prl.png
Figure 9
DETRENDED “Tdyn/ENSO/Volcano residual global mean” DATA
To provide an alternate view of the “Tdyn/ENSO/Volcano residual global mean” data, I’ve excluded the first 11 years of data, a short period of declining temperatures, and divided the remainder into three epochs, Figure 10: January 1912 to December 1943 (period of temperature increase), January 1944 to December 1975 (period of flat to declining temperatures), and January 1976 to March 2009 (period of temperature increase). I then detrended the “Tdyn/ENSO/Volcano residual global mean” data during those periods and compared them to the “ENSO fit” and “Volcano fit” datasets, the two major climate variables, to illustrate how much of the ENSO signal remained after the effects of the three variables were removed.
http://i38.tinypic.com/2i0c0mx.png
Figure 10
Figure 11 covers the period of Jan 1976 to March 2009. It compares detrended “Tdyn/ENSO/Volcano residual global mean” data to “ENSO fit” and “Volcano fit” data. There appears to be very little of the 1982 volcanic eruption left in the “Tdyn/ENSO/Volcano residual global mean” data, while the some of the 1991 eruption remains.
http://i35.tinypic.com/2n0ei8.png
Figure 11
In Figure 12, I’ve deleted the “Volcano fit” data, leaving a comparison of detrended “Tdyn/ENSO/Volcano residual global mean” and “ENSO fit” data for the period of January 1976 to March 2009. As illustrated there are very large ENSO residuals remaining in the detrended “Tdyn/ENSO/Volcano residual global mean”. Note how the lag varies with each ENSO event. It is apparent that Thompson et al failed to capture and remove a significant portion of ENSO during this period.
http://i35.tinypic.com/2znmols.png
Figure 12
Figure 13 illustrates the detrended Thompson et al (2009) “Tdyn/ENSO/Volcano residual global mean”, the “ENSO fit” and the “Volcano fit” data for January 1944 to December 1975. The major drop in detrended “Tdyn/ENSO/Volcano residual global mean” from 1943 to 1947 represents the discontinuity in the HadCRUT3 data that was first discussed in Thompson et al (2008) “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature,” Nature, 453, 646–650, doi:10.1038/nature06982.Link:
http://www.nature.com/nature/journal/v453/n7195/abs/nature06982.html
http://i36.tinypic.com/2uy1zpz.png
Figure 13
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SIDE NOTE
The Hadley Centre appears to be using both papers to justify changes they are making to the HADSST dataset. In the concluding remarks of Thompson et al (2009), they write, “THE SST DATA CORRECTED FOR INSTRUMENT CHANGES IN THE MID 20TH CENTURY ARE EXPECTED TO BECOME AVAILABLE IN 2009, and it will be interesting to see how the corrections affect the time history of global-mean temperatures, particularly in the middle part of the century.” [Emphasis added.]
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Figure 14 compares detrended “Tdyn/ENSO/Volcano residual global mean” and “ENSO fit” data from January 1944 to December 1975 to illustrate, again, that there are sizable residual ENSO effects in the dataset. Note that during this period there is little to no lag between the “ENSO fit” and the detrended “Tdyn/ENSO/Volcano residual global mean” data, while there were considerable lags between the two datasets from 1976 to present. Is this a function of the magnitude of the ENSO events, where there are longer lags with larger ENSO events?
http://i35.tinypic.com/fc09jc.png
Figure 14
Figures 15 and 16 are the comparative graphs of detrended “Tdyn/ENSO/Volcano residual global mean” and “ENSO fit” data from January 1912 to December 1943. Figure 15 includes the “Volcano fit” data; Figure 16 does not. Note how there is little agreement between the multiyear variations of the ENSO data and the detrended “Tdyn/ENSO/Volcano residual global mean” data.
http://i38.tinypic.com/wb9z80.png
Figure 15
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http://i33.tinypic.com/2zrgwtj.png
Figure 16
The lack of agreement between the two datasets during this period is likely the result of the uncertainties in the datasets, especially the Cold Tongue Index data. Figures 17 and 18 illustrate the number of SST observations for the Cold Tongue region from 1845 to 1991 and from 1900 to 1950. Note how few observations were made in the early part of the Thompson et al (2009) data compared to more recent numbers. Also note that the number of observations between 1900 and 1950 was influenced by the opening of the Panama Canal in 1914 and by the two World Wars. JISAO link:
http://jisao.washington.edu/data/cti/
http://i33.tinypic.com/sfcks1.png
Figure 17
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http://i38.tinypic.com/144a981.png
Figure 18
Would the model used by Thompson et al (2009) have better determined the relationship between ENSO and global temperature during the last two epochs had they excluded the data before 1943? In other words, did the uncertainties in the Global Surface Temperature and Cold Tongue Index data prior to 1943 skew their model so that it failed to identify the true relationship between the datasets in later years. Figures 19, 20, and 21 are comparative graphs of detrended “Tdyn/ENSO/Volcano residual global mean” data and detrended “Global mean” data, which is the unadjusted global surface temperature data, for the three periods. The model used by Thompson et al (2009) appears to have removed little of the effects of ENSO.
http://i37.tinypic.com/302xfuo.png
Figure 19
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http://i33.tinypic.com/2zs0fg1.png
Figure 20
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http://i37.tinypic.com/2eexyrq.png
Figure 21
A CLOSER LOOK AT THE RESIDUAL DATA FROM 1976 TO PRESENT REVEALS STEP CHANGES DUE TO SIGNIFICANT EL NINO EVENTS
Like many climate bloggers I have removed the linear effects of ENSO and volcanic aerosols from a number of different TLT and surface temperature datasets. While doing so, I’ve noted a curious effect in the data since 1976 but I’ve been hesitant to post the results because of the possibility of claims that I’d somehow manipulated the data to create the effect. Since the data was created by Thompson et al (2009) there should be no way for others to accuse me of misrepresenting the data. They may not agree with my results or how I segmented the data, but that is something else entirely. For them, in the closing, I’ve provided links to my posts that illustrate El Nino-induced step changes in TLT and SST data. Additionally, I would anticipate that someone will note that El Nino (and La Nina) events are not official ENSO events unless NINO3.4 SST anomalies equal or rise above (or fall below) 0.5 deg C. For that someone, global temperatures do not respond only to variations in eastern equatorial Pacific SST anomalies when the ENSO event is official; they respond to the entire ENSO signal.
Figure 22 is a comparison graph of the “ENSO fit” and “Tdyn/ENSO/Volcano residual global mean” data from 1976 to present. Neither dataset has been smoothed. What struck me was, after the initial warming from 1976 to early in 1982, the majority of the rises in the “Tdyn/ENSO/Volcano residual global mean” data occurred during the significant El Nino events of 1982/83, 1986/87/88 and 1997/98. I’ve highlighted the months when the “ENSO fit” data crosses zero for those El Nino events.
http://i37.tinypic.com/2nqxgm1.png
Figure 22
If the “Tdyn/ENSO/Volcano residual global mean” data during those El Nino events is eliminated, Figure 23, something else emerges. Note how there appears to be little to no rise in the “Tdyn/ENSO/Volcano residual global mean” data after the 1997/98 El Nino. The data looks flat. Also note how little the “Tdyn/ENSO/Volcano residual global mean” data rose between the 1986/87/88 and 1997/98 El Nino events. There’s the decline in the data between the 1982/83 and 1986/87/88 El Nino events. Last thing to note, the most substantial rise in “Tdyn/ENSO/Volcano residual global mean” data occurred from 1976 to early in 1982.
http://i34.tinypic.com/s2zhvs.png
Figure 23
If we ignore the short period between the 1982/83 and 1986/87/88 El Nino events when temperatures fell, the “Tdyn/ENSO/Volcano residual global mean” data has not been accelerating; it has been decelerating. This can be seen quite clearly if the trends during the “non-significant El Nino periods” are determined and added to the illustration. Refer to Figure 24 for those trends.
http://i33.tinypic.com/2i7o3dj.png
Figure 24
In Figure 25, keying off the trend lines, I’ve listed the rises in the “Tdyn/ENSO/Volcano residual global mean” data that occurred during the significant El Nino events of 1982/83, 1986/87/88, and 1997/98. It appears that most of the rise in “Tdyn/ENSO/Volcano residual global mean” data after early 1982 was the direct result of those El Nino events.
http://i38.tinypic.com/10nt7w3.png
Figure 25
CLOSING
Step changes in TLT anomalies and SST anomalies that resulted from significant El Nino events are discussed in detail in:
RSS MSU TLT Time-Latitude Plots…
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 1
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2
Bob,
The ~1945 discontinuity (discussed in earlier threads) shows a huge spike in the EOP (Earth orientation parameter) records, so in their efforts to rewrite climate history these guys are up against some pretty bright folks in the astronomy/geodesy community. Even just based on Stat 101 they have gone wrong in so many ways with their decomposition that I’m not even going to waste my time commenting. I am actually quite angry.
If these changes go through, this interferes with future efforts to understand natural variations. This mischievous act of vandalism must be resisted with severity.
Bull Hockey
So much spaghetti so little sauce. (And no amount of Chianti can fix it…)
Note to Phil Jones: make the raw data used to cooking HADCRUT available now.
Wouldn’t the 11 year solar cycle need to be removed as well?
Although its strange that Svensmark’s cloud seeding theory – if correct of course – is not more pronounced in Thompson’s filtered data?
can someone superimpose Thompsons filtered data on the 11 year cycle…
[thanks, noted but way off topic]
Bob, FYI, in the news today if you haven’t seen it.
Global warming creates different El Nino
http://www.news.com.au/story/0,27574,26120863-401,00.html
While I have only the vaguest idea what Thompson et al are trying to achieve here, it seems to this non-scientist that they are attempting to remove any meaningful data that might upset their warmist objectives. And using a now discredited temperature dataset to boot.
Please feel free to correct/educate me…
O/T sort of. Apologies.
I would like to nominate this for a possible Quote of the Week:
“Climate Change – The only tipping point to come is the tipping point of public opinion against the alarmist falsity of Climate Change Policy.”
The quote is sourced from an article on the estimable Pier’s Corbyn’s website, WeatherAction:
http://www.weatheraction.com/displayarticle.asp?a=86&c=1
Regardless of the validity of the removal of the effects the resultant 13 month smoothed graph appears to show that, other than the sudden drop in 1945, the temperature has been rising steadily since about1910 which rather messes up the CO2 link
Antonio San: The raw data is available. It’s the COADS dataset.
http://i34.tinypic.com/t0j1v6.png
Here’s a comparison of the COADS and the recently obsoleted ERSST.v2 to put things into perspective:
http://i38.tinypic.com/15d42nb.jpg
Philip_B: The news stroy “Global warming creates different El Nino” has been making the rounds. Discovery has one:
http://blogs.discoverychannel.co.uk/discovery-news/2009/09/pacific-ocean-develops-new-el-nino.html
I signed up for the Discovery blog but they wouldn’t let me leave a comment. And the UK Telegraph had a similar story:
http://www.telegraph.co.uk/earth/earthnews/6223794/Climate-change-causing-new-El-Nino-weather-pattern-to-form-known-as-Modoki.html
My El Nino Modoki posts have been getting a lot of hits as a result:
http://bobtisdale.blogspot.com/2009/07/there-is-nothing-new-about-el-nino.html
http://bobtisdale.blogspot.com/2009/07/comparison-of-el-nino-modoki-index-and.html
UK Sceptic: You wrote, “…they are attempting to remove any meaningful data that might upset their warmist objectives.”
But they didn’t do a very good job of it. Eyeballing the curves, one can do a better job of removing the linear effects of ENSO from the global temperature record.
But then there are the residuals. Compo and Sardeshmukh in “Removing ENSO-related variations from the climate record” estimated the ENSO residuals represent ~40% of the rise of the global SST anomalies from 1871 to 2006.
http://www.cdc.noaa.gov/people/gilbert.p.compo/CompoSardeshmukh2008b.pdf
This report hardly supports the idea of GW.
http://www.news.com.au/story/0,27574,25348657-401,00.html
“Pierre Gosselin (03:53:42) :
This report hardly supports the idea of GW.”
We already know facts do not matter, the mindset of the unwashed masses has been, well, set. But hang on (Esp in the UK), “Big Brother” is on, call back in an hour. There *is* warming and that little jolt from ~220ppm CO2 to ~385PPM over 150 years, *is* the driver of the (non) increase in *cough* “bad weather”.
I am overly curious how CRU will handle that truly “anthropogenic” step-down in SST in 40ties, causing similar step in all global datasets. In no one ground record is such step visible. I think the reason was to provide that except the single step, temperature to be rising since WWII. If done properly, temps should peak in 50ties-60ties (when extra strong 19th sun cycle peaked) and there should be inconvenient cooling trend from 50ies to end of 70ties.
I tried once to replicate CA findings about the changes in SST sampling and produced such a graph: http://blog.sme.sk/blog/560/195013/hadsstssn.jpg
Now combine such SST with 70% weight with ground stations and the correlation with SSN will be much better. Also correlation with CO2 will not point to any catastrophic warming. Those rats exactly knew what they did..
“If we ignore the short period between the 1982/83 and 1986/87/88 El Nino events when temperatures fell, the “Tdyn/ENSO/Volcano residual global mean” data has not been accelerating; it has been decelerating.”
Huh!?! Did I read this wrong? If we ignore the fact that my car is a Ford, then we can clearly see that it’s a Rolls-Royce?
Bob Tisdale-great work. It reaaly needs some digesting though.
It appears at first reading that ‘known’ factors have been removed, although ‘unknown’ factors, such as the jet stream, or little known factors- such as solar activity (sorry Leif) have not even been considered in the first place.
The paucity of observations in the earlier years seems on a par with using temperature information from a tiny number of unrepresentative global stations, or believing three northern hemisphere tidal gauges -whose long term data is interpolated- should be consdered representative of global trends.
Temperature has been rising- unsurprisingly-since the depths of the LIA in the 1660’s, after dipping following the peak of the MWP ( although even then there there have been peaks and trouighs). Natural oscillations- whether of current, or air, or solar, (or whatever) over the short term cycle are surely what this study is picking up-although it doesn’t go far enough back to see everything in a broader historic context and pick up longer term cycles.
tonyb
Decomposing natural/periodic and AGW components of trend is the hot issue of the ‘debate’ right now; however the waters are being muddied by AGW attempts to establish high-order dependencies of periodic components on AGW [ see Vecchi, Cai and the oddest of the lot Meehl;
http://ams.confex.com/ams/88Annual/techprogram/paper_133611.htm ]
and the Modoki as a proxy for AGW component of temperature trend is making a comeback;
http://www.agu.org/pubs/crossref/2009/2009GL037885.shtml
http://www.nature.com/nature/journal/v461/n7263/full/nature08316.html
But as a matter of the 2nd law of thermodynamics if AGW components are causing variations in periodic components then that must reduce the direct effect of the AGW component on temperature otherwise the total imput will exceed 100%. In the first instance what is required is a comprehensive comparison of periodic/stationary and AGW/non-stationary/model components to establish what dominates trend [and I believe David Stockwell is attempeting such an analysis]; if it is the case that periodic components do dominate trend then all that is left for AGW is the fanciful notion of ‘possession’ of periodic components.
I think the most significant point is that air temp lags ocean temps. Extremely significant in that most of the energy of our climate system is in the oceans, not the air. Here’s the basic math:
1) The heat capacity (unit storage of energy per unit mass) of water is 4 times larger than air.
2) The density of water is 773 times greater than air ( 1 g/cc vs 0.001293 g/cc).
3) Standard atmospheric air pressure at the surface is 14.696 psi.
4) Standard water density is 0.43353 psi/ft (strange density units for some, but commonly used in geoscience)
5) How much ocean water is equal to the weight of of the atmosphere? ==> 14.696 psi / 0.43353 psi/ft = 33 ft
6) Roughly 3/4 of the earth is covered by water, so 33/ (0.75) = 44 ft of ocean = weight of entire atmosphere
7) So, the weight of the top 44 ft of the oceans is equal to the entire weight of our atmosphere – BUT – we still have to take into account heat capacity and water is 4x air so : 44 /4 = 11 ft.
8) The basic conclusion is the top 11 FT OF THE OCEANS CONTAIN THE SAME AMOUNT OF ENERGY AS THE ENTIRE ATMOSPHERE. (assuming long term equilibrium between the two ).
9) In general, the upper 200 meters (660 ft) of ocean is considered to be well mixed & in long term temperature equilibrium with atmosphere. That implies that the ocean’s influence on atmospheric temperature is 60 times greater (660/ 11 = 60) greater than the air’s effect – from a heat capacity standpoint.
10) So, how can CO2 have a significant effect on atmospheric temps when it is hugely buffered by the heat capacity of the ocean? It can’t. Even if CO2 causes air temps to rise, that energy would be transfered into the ocean & not noticed – at a long term ratio of roughly 60 to 1 – ie – if CO2 caused a 1 deg rise is air temp , it would equilibrate with the oceans resulting in an actual 1/60th of a degree rise in air temps.
11) The lag observation between ocean temps & air temps proves all of this – the ocean heats the air – not the other way around. Yes, global air temps are all about the oceans, not CO2.
So, since we have now solved the CO2 problem, l need to get to work & find some oil ;))
Everybody is asking the wrong questions. Removing the effect of ENSO is not the right idea. ENSO temps are part of the data, not errors in the data.
The proper thing to measure is not temperature, but heat energy. Energy is measured in joules or in BTUs, not degrees. As just one example, the phase change from ice to water as it melts absorbs energy but does not change the temperature. Recording only temperature would lose information.
More importantly, the ocean is part of the Earth, the “Globe”, and heat energy from the sun can be stored in the air or the ocean, and when it moves back and forth from one to the other, it is called ENSO, AMO, etc. But moving back and forth is neither warming nor cooling.
El Nino does not cause global warming and La Nina does not cause global cooling. El Nino causes atmospheric warming and La Nina causes atmospheric cooling, but that is secondary.
All these calculations would be easier if the temperatures were first converted to heat.
cohenite: Thanks for the links. In the last link, Sang-Wook Yeh et al (2009)ended their Abstract with, “When restricted to the six climate models with the best representation of the twentieth-century ratio of CP-El Niño to EP-El Niño, the occurrence ratio of CP-El Niño/EP-El Niño is projected to increase as much as five times under global warming. The change is related to a flattening of the thermocline in the equatorial Pacific.”
This is good thing, since it’s the significant traditional El Nino (their EP-El Niño), that cause global TLT and LST to rise in steps. So if there are fewer of those EP-El Niño events, global temperatures should rise less.
Fred2: You wrote, “El Nino does not cause global warming and La Nina does not cause global cooling. El Nino causes atmospheric warming and La Nina causes atmospheric cooling, but that is secondary.”
Significant traditional El Nino events also redistribute heat within the Pacific and Indian oceans, causing upward step changes in the Eastern Indian and West Pacific oceans:
http://bobtisdale.blogspot.com/2009/01/can-el-nino-events-explain-all-of.html
http://bobtisdale.blogspot.com/2009/01/can-el-nino-events-explain-all-of_11.html
Counterintuitively, they also cause step increases in Ocean Heat Content:
http://bobtisdale.blogspot.com/2009/09/enso-dominates-nodc-ocean-heat-content.html
Quick summary of post:
1. El Nino, volcanoes, and other things complicate the temperature trend.
2. Warming appears to have peaked at end of 20th century.
3. According GW theory, it should be the other way around (i.e., slow warming leading to faster warming) as CO2 concentration increases.
4. In my opinion, most of the 20th century warming is due to a) land use changes, 2) less aerosol emissions, and 3) solar effects. Regarding a) and b), at some point you reach diminishing returns. I believe this happened in the 90’s (in other words, additional land-use changes and cleaner air over NA, Europe, and Eastern Europe/Russia only provide marginal increases in surface temps). Brown soot from China has increased surface temps as well since the 90’s.
5. Given the above (and a quiet sun), I would be betting on cooler (versus hotter) temps going forward.
6. The true CO2 signal will be seen in the 21st centurty, not the last century (due to the complicating factors mentioned above). In other words, the climate scientists are studying the wrong century.
H.R.: You wrote, “Huh!?! Did I read this wrong? If we ignore the fact that my car is a Ford, then we can clearly see that it’s a Rolls-Royce?”
Would you have preferred…
We’ll ignore the anomalous period with the negative trend from April 1984 to August 1986 because it is dominated solely by one La Nina event. If we then compare the trends of periods between significant El Nino events, for those periods longer than ~2 years, that is from January 1976 to March 1982 (0.41 deg C/decade), from August 1988 to May 1997 (0.1 deg C/decade), and from November 1998 to March 2009 (0.01 deg C/decade), the trends of the “Tdyn/ENSO/Volcano residual global mean” data have decreased with time. In other words, it has not been accelerating; it has been decelerating.
Does that ring truer for you?
Juraj V: You wrote, “I am overly curious how CRU will handle that truly ‘anthropogenic’ step-down in SST in 40ties, causing similar step in all global datasets.”
What always struck me odd was that they were basing their claim of a discontinuity in 1945 for their entire SST dataset on the fact that there were no corresponding ENSO events or volcanic eruptions to cause the drop. Did they consider that maybe they overlooked a significant El Nino/La Nina event during the hand-off between U.S. and British sampling periods. Sure does look like there’s a missing significant El Nino/La Nina around 1945 to me.
http://i38.tinypic.com/nwzpj9.png
Which is more likely, that the SST data for 95% of the global oceans are wrong, or that SST data for an area that represent 5% of the global oceans are wrong?
“”” Jeff L (06:28:51) :
I think the most significant point is that air temp lags ocean temps. Extremely significant in that most of the energy of our climate system is in the oceans, not the air. Here’s the basic math:
1) The heat capacity (unit storage of energy per unit mass) of water is 4 times larger than air.
2) The density of water is 773 times greater than air ( 1 g/cc vs 0.001293 g/cc).
3) Standard atmospheric air pressure at the surface is 14.696 psi.
4) Standard water density is 0.43353 psi/ft (strange density units for some, but commonly used in geoscience)
5) How much ocean water is equal to the weight of of the atmosphere? ==> 14.696 psi / 0.43353 psi/ft = 33 ft
6) Roughly 3/4 of the earth is covered by water, so 33/ (0.75) = 44 ft of ocean = weight of entire atmosphere
7) So, the weight of the top 44 ft of the oceans is equal to the entire weight of our atmosphere – BUT – we still have to take into account heat capacity and water is 4x air so : 44 /4 = 11 ft.
8) The basic conclusion is the top 11 FT OF THE OCEANS CONTAIN THE SAME AMOUNT OF ENERGY AS THE ENTIRE ATMOSPHERE. (assuming long term equilibrium between the two ). “””
Well I followed your stick in the sand math up through #7 which you say gives the relative heat capacities of atmosphere and 11 feet of ocean water.
But so far; not a jot of energy anywhere in either ocean or atmosphere; yet suddenly comes #8 out of the blue and decleares the two energies to be equal.
So it is like you just showed that the heat capacity of the air in my kitchen is equal to the heat capacity of one ice tray cell in my freezer; and suddenly they each have to contain the same energy; how is that ?
Jeff L (06:28:51) :
True, a 1 degree transient air temperature response from GHG heating (or any other source) will lead to an equilibrium temperature response of 1/60 degree after a new air/ocean equilibrium is obtained. That is assuming real world physics apply. However, this is “climate science”, so that assumption is wrong.
Climate science postulates that the relationship between the transient response and equilibrium response is different: Transient response + Positive feedback/heat in the pipeline = Equilibrium response. This means that an atmosphere heated 1 degree from increased CO2, will create a hidden energy storage in the deep oceans, that decades or centuries later will come back and be released into the atmosphere again, creating a temperature rise of 3 degrees. This is settled science.
George E. Smith (09:30:22) :
“yet suddenly comes #8 out of the blue and decleares the two energies to be equal.”
– the key is
“assuming long term equilibrium between the two” as I said at the at the end of (8)
Do you have reason to believe that the two should not be in long term thermal equilibrium (ie contain the same amount of energy) ?
With no insulator between the two, there is no reason they would not be in equilibrium over the long term (heat transfer & mixing will take some period of time )
Chris (08:27:37) :
Quick summary of post:
5. Given the above (and a quiet sun), I would be betting on cooler (versus hotter) temps going forward.
You should contact James Annan. He will be happy to take your bet.
JAN; “settled science”?! Perhaps you would like to comment on what OHC is doing; clue: it is going down, so where is that equilibrium response heat being stored?
Bob Tisdale (08:32:18) :
Thanks for expanding (and a little rewording). I had read the text below Fig 23 and stopped. After your response I went back and found it helpful (to me, at least) to go right through from Fig 23 to 24 to 25.
Thanks again.
When the PDO changes phase, as it did in the mid-1940s, global precipitation south of 55N tends to kick up a fuss. On top of the PDO shift, we see AMO plummet coincident with this:
http://www.sfu.ca/~plv/NutationObliquity.png
Add all of this to Bob’s earlier post:
“The Large 1945 SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data”
http://bobtisdale.blogspot.com/2009/03/large-1945-sst-discontinuity-also.html
I wrote to the ICOADS people and they told me the cloud observations have nothing to do with the SST measurements.
Additionally, the authors have made untenable assumptions about shared variance. This is a particularly serious error. They should be well-aware of links between NAM/AO & ENSO/SOI. (I’ll post some more graphs if/when I can find time to help illustrate the lack of independence [which has been falsely assumed in the decompositions].)
Did we say more intensity? We just meant more…of everything.
Sorry for the confusion.
JAN: You wrote, “This is settled science”, and used “heat in the pipeline” with respect to Ocean Heat Content.
It’s my experience that bloggers who use those phrases base their understanding of climate variability on GCMs and not on the instrument temperature record.
First, many of the Global Circulation Models employed by the “settled science” of the IPCC do not model ENSO. Those that do model ENSO model it poorly. This is caused by the modelers misunderstanding of ENSO. They treat it as noise, like Thompson et al (2009), the topic of this post. ENSO is a process.
I’ve illustrated the SST anomalies for 25% of the global oceans rise in steps in response to significant ENSO events, while the remaining 75% of the oceans mimic the rises and falls of ENSO. When averaged together, those two portions of the global oceans give the impression of a gradual rise in SST anomalies that is mistaken for anthropogenic warming. These step changes were discussed in the posts I linked at the end of this post. Here they are again:
http://bobtisdale.blogspot.com/2009/01/can-el-nino-events-explain-all-of.html
http://bobtisdale.blogspot.com/2009/01/can-el-nino-events-explain-all-of_11.html
As I wrote above in a reply to Fred2 above, they also cause step increases in Ocean Heat Content:
http://bobtisdale.blogspot.com/2009/09/enso-dominates-nodc-ocean-heat-content.html
Not negative step changes, positive step changes.
Oops, almost forgot, those significant El Nino events also cause upward step changes in TLT anomalies for the mid-to-high latitudes of the Northern Hemisphere, while the rest of the world mimics the variations of ENSO. Same thing happens when you mix the two. It gives the impression of AGW. The 1997/98 El Nino and the decade afterwards offer a rare opportunity to see this because there wasn’t a major volcanic eruption to add noise and spoil the effect. Refer to the following graph:
http://i38.tinypic.com/k3ndzn.png
This is discussed further in my post:
http://bobtisdale.blogspot.com/2009/06/rss-msu-tlt-time-latitude-plots.html%5B
If and when GCMs can properly model the multiyear and decadal aftereffects of ENSO, they might be believable. At present they cannot, and are, therefore, not credible sources of forecasting or hindcasting.
cohenite (17:02:17) :
“JAN; “settled science”?! Perhaps you would like to comment on what OHC is doing; clue: it is going down, so where is that equilibrium response heat being stored?”
Where, indeed. It is my understanding the “settled science” of AGW claims that the “heat in the pipeline” is being stored in the deep oceans below the thermocline. Hence, it will not show up as OHC. AFAIK though, there isn’t even a hypothesis on the mechanisms in play for transfer of the GHG warming of the atmosphere into the deep oceans, neither is there one for the amplification of the energy in storage and subsequent release of this energy back to the atmosphere decades or centuries later. Yet, this is being propagated by the AGW community as settled science. Go figure.
Bob Tisdale (19:04:58) :
“JAN: You wrote, “This is settled science”, and used “heat in the pipeline” with respect to Ocean Heat Content.
It’s my experience that bloggers who use those phrases base their understanding of climate variability on GCMs and not on the instrument temperature record.”
Bob, I didn’t necessarily relate “heat in the pipeline” to OHC. As I understand it, AGW science suggests that the extra heat is stored in the deep oceans, without showing up in OHC measurements. How that is possible, I am sure I don’t know.
I think you are correct that the concept of “heat in the pipeline/stored heat” is a product of the GCM’s need to produce alarming scenarios of future warming, not a result of understanding physical processes. Since GCM’s cannot even handle effects of ENSO or other ocean oscillations, they are pretty useless in forecasting, as you say. Therefore, the concept of “heat in the pipeline/stored heat” and the “equilibrium response” resulting from this concept is only “settled science” in the AGW consensus community. Among rational people, it is no science at all.
Regards
H.R.: You replied, “Thanks for expanding (and a little rewording). I had read the text below Fig 23 and stopped. After your response I went back and found it helpful (to me, at least) to go right through from Fig 23 to 24 to 25.”
I’m glad it helped. I’ve gone back and revised that language at the version on my website. Thanks for pointing it out. I credited you for the find.
http://bobtisdale.blogspot.com/2009/09/thompson-et-al-2009-high-tech-wiggle.html
As you are aware, it’s difficult to edit your own work, because you read what you want it to say, not necessarily what it says. Thanks again.
Adding my perspective on the exchange of Bob & H.R.:
http://www.sfu.ca/~plv/CumuSumPDO(76,88,98).png
Some of you may find it interesting to blink between these two:
http://www.sfu.ca/~plv/CumuSumAMO.png
http://www.sfu.ca/~plv/CumuSumPDO.png
Note where they differ — and compare with:
http://www.sfu.ca/~plv/NutationObliquity(6a).png
http://www.sfu.ca/~plv/ChandlerPeriod.PNG
http://www.sfu.ca/~plv/1931UniquePhaseHarmonics.png
Bob has pointed out on his blog how Hadley used the 1998 El Nino as a convenient point at which to switch data sources:
http://bobtisdale.blogspot.com/2008/12/step-change-in-hadsst-data-after-199798.html
If you look here, you will discover that the wide 1940 El Nino has also been exploited:
Thompson, D.W.J.; Kennedy, J.J.; Wallace, J.M.; & Jones, P.D. (2008). A large discontinuity in the mid-twentieth century in observed global-mean surface temperature. Nature 453, 646-650. doi:10.1038/nature06982.
I think it’s time these authors pause to consider that the timing of human decisions is affected by weather/climate. Here’s another index showing a ~1945 turn:
http://www.sfu.ca/~plv/CumuSumALPI.png
Related: Some readers may find the arguments here ‘interesting’:
Bernaerts, A. (2007). Can the “Big Warming” at Spitsbergen from 1918 to 1940 be explained? PACON 2007 Proceedings 325-337.
http://www.arctic-heats-up.com/pdf/Submitted_conference_paper.pdf
What is causing what?
Here’s an excerpt from Sidorenkov (2005):
“… Our prediction methodology differs fundamentally from all the methodologies used by weather forecasters. It enables one to make weather forecasts with a daily discreteness for a 1 year period. About 75% of such forecasts proved to be correct.
We believe that this methodology can also be used to predict natural and social phenomena, such as seismicity, volcano eruptions, economic crises, epidemics, population explosions, political coups and even wars.”