Source: Mantua, 2000
The essay below has been part of a back and forth email exchange for about a week. Bill has done some yeoman’s work here at coaxing some new information from existing data. Both HadCRUT and GISS data was used for the comparisons to a doubling of CO2, and what I find most interesting is that both Hadley and GISS data come out higher in for a doubling of CO2 than NCDC data, implying that the adjustments to data used in GISS and HadCRUT add something that really isn’t there.
The logarithmic plots of CO2 doubling help demonstrate why CO2 won’t cause a runaway greenhouse effect due to diminished IR returns as CO2 PPM’s increase. This is something many people don’t get to see visualized.
One of the other interesting items is the essay is about the El Nino event in 1878. Bill writes:
The 1877-78 El Nino was the biggest event on record. The anomaly peaked at +3.4C in Nov, 1877 and by Feb, 1878, global temperatures had spiked to +0.364C or nearly 0.7C above the background temperature trend of the time.
Clearly the oceans ruled the climate, and it appears they still do.
Let’s all give this a good examination, point out weaknesses, and give encouragement for Bill’s work. This is a must read. – Anthony
Adjusting Temperatures for the ENSO and the AMO
A guest post by: Bill Illis
People have noted for a long time that the effect of the El Nino Southern Oscillation (ENSO) should be accounted for and adjusted for in analyzing temperature trends. The same point has been raised for the Atlantic Multidecadal Oscillation (AMO). Until now, there has not been a robust method of doing so.
This post will outline a simple least squares regression solution to adjusting monthly temperatures for the impact of the ENSO and the AMO. There is no smoothing of the data, no plugging of the data; this is a simple mathematical calculation.
Some basic points before we continue.
– The ENSO and the AMO both affect temperatures and, hence, any reconstruction needs to use both ocean temperature indices. The AMO actually provides a greater impact on temperatures than the ENSO.
– The ENSO and the AMO impact temperatures directly and continuously on a monthly basis. Any smoothing of the data or even using annual temperature data just reduces the information which can be extracted.
– The ENSO’s impact on temperatures is lagged by 3 months while the AMO seems to be more immediate. This model uses the Nino 3.4 region anomaly since it seems to be the most indicative of the underlying El Nino and La Nina trends.
– When the ENSO and the AMO impacts are adjusted for, all that is left is the global warming signal and a white noise error.
– The ENSO and the AMO are capable of explaining almost all of the natural variation in the climate.
– We can finally answer the question of how much global warming has there been to date and how much has occurred since 1979 for example. And, yes, there has been global warming but the amount is much less than global warming models predict and the effect even seems to be slowing down since 1979.
– Unfortunately, there is not currently a good forecast model for the ENSO or AMO so this method will have to focus on current and past temperatures versus providing forecasts for the future.
And now to the good part, here is what the reconstruction looks like for the Hadley Centre’s HadCRUT3 global monthly temperature series going back to 1871 – 1,652 data points.
I will walk you through how this method was developed since it will help with understanding some of its components.
Let’s first look at the Nino 3.4 region anomaly going back to 1871 as developed by Trenberth (actually this index is smoothed but it is the least smoothed data available).
– The 1877-78 El Nino was the biggest event on record. The anomaly peaked at +3.4C in Nov, 1877 and by Feb, 1878, global temperatures had spiked to +0.364C or nearly 0.7C above the background temperature trend of the time.
– The 1997-98 El Nino produced similar results and still holds the record for the highest monthly temperature of +0.749C in Feb, 1998.
– There is a lag of about 3 months in the impact of ENSO on temperatures. Sometimes it is only 2 months, sometimes 4 months and this reconstruction uses the 3 month lag.
– Going back to 1871, there is no real trend in the Nino 3.4 anomaly which indicates it is a natural climate cycle and is not related to global warming in the sense that more El Ninos are occurring as a result of warming. This point becomes important because we need to separate the natural variation in the climate from the global warming influence.
The AMO anomaly has longer cycles than the ENSO.
– While the Nino 3.4 region can spike up to +3.4C, the AMO index rarely gets above +0.6C anomaly.
– The long cycles of the AMO matches the major climate shifts which have occurred over the last 130 years. The downswing in temperatures from 1890 to 1915, the upswing in temps from 1915 to 1945, the decline from 1946 to 1975 and the upswing in temps from 1975 to 2005.
– The AMO also has spikes during the major El Nino events of 1877-88 and 1997-98 and other spikes at different times.
– It is apparent that the major increase in temperatures during the 1997-98 El Nino was also caused by the AMO anomaly. I think this has lead some to believe the impact of ENSO is bigger than it really is and has caused people to focus too much on the ENSO.
– There is some autocorrelation between the ENSO and the AMO given these simultaneous spikes but the longer cycles of the AMO versus the short sharp swings in the ENSO means they are relatively independent.
– As well, the AMO appears to be a natural climate cycle unrelated to global warming.
When these two ocean indices are regressed against the monthly temperature record, we have a very good match.
– The coefficient for the Nino 3.4 region at 0.058 means it is capable of explaining changes in temps of as much as +/- 0.2C.
– The coefficient for the AMO index at 0.51 to 0.75 indicates it is capable of explaining changes in temps of as much as +/- 0.3C to +/- 0.4C.
– The F-statistic for this regression at 222.5 means it passes a 99.9% confidence interval.
But there is a divergence between the actual temperature record and the regression model based solely on the Nino and the AMO. This is the real global warming signal.
The global warming signal (which also includes an error, UHI, poor siting and adjustments in the temperature record as demonstrated by Anthony Watts) can be now be modeled against the rise in CO2 over the period.
– Warming occurs in a logarithmic relationship to CO2 and, consequently, any model of warming should be done on the natural log of CO2.
– CO2 in this case is just a proxy for all the GHGs but since it is the biggest one and nitrous oxide is rising at the same rate, it can be used as the basis for the warming model.
This regression produces a global warming signal which is about half of that predicted by the global warming models. The F statistic at 4,308 passes a 99.9% confidence interval.
– Using the HadCRUT3 temperature series, warming works out to only 1.85C per doubling of CO2.
– The GISS reconstruction also produces 1.85C per doubling while the NCDC temperature record only produces 1.6C per doubling.
– Global warming theorists are now explaining the lack of warming to date is due to the deep oceans absorbing some of the increase (not the surface since this is already included in the temperature data). This means the global warming model prediction line should be pushed out 35 years, or 75 years or even 100s of years.
Here is a depiction of how logarithmic warming works. I’ve included these log charts because it is fundamental to how to regress for CO2 and it is a view of global warming which I believe many have not seen before.
The formula for the global warming models has been constructed by myself (I’m not even sure the modelers have this perspective on the issue) but it is the only formula which goes through the temperature figures at the start of the record (285 ppm or 280 ppm) and the 3.25C increase in temperatures for a doubling of CO2. It is curious that the global warming models are also based on CO2 or GHGs being responsible for nearly all of the 33C greenhouse effect through its impact on water vapour as well.
The divergence, however, is going to be harder to explain in just a few years since the ENSO and AMO-adjusted warming observations are tracking farther and farther away from the global warming model’s track. As the RSS satellite log warming chart will show later, temperatures have in fact moved even farther away from the models since 1979.
The global warming models formula produces temperatures which would be +10C in geologic time periods when CO2 was 3,000 ppm, for example, while this model’s log warming would result in temperatures about +5C at 3,000 ppm. This is much closer to the estimated temperature history of the planet.
This method is not perfect. The overall reconstruction produces a resulting error which is higher than one would want. The error term is roughly +/-0.2C but the it does appear to be strictly white noise. It would be better if the resulting error was less than +/- 0.2C but it appears this is unavoidable in something as complicated as the climate and in the measurement errors which exist for temperature, the ENSO and the AMO.
This is the error for the reconstruction of GISS monthly data going back to 1880.
There does not appear to be a signal remaining in the errors for another natural climate variable to impact the reconstruction. In reviewing this model, I have also reviewed the impact of the major volcanoes. All of them appear to have been caught by the ENSO and AMO indices which I imagine are influenced by volcanoes. There appears to be some room to look at a solar influence but this would be quite small. Everyone is welcome to improve on this reconstruction method by examining other variables, other indices.
Overall, this reconstruction produces an r^2 of 0.783 which is pretty good for a monthly climate model based on just three simple variables. Here is the scatterplot of the HadCRUT3 reconstruction.
This method works for all the major monthly temperature series I have tried it on.
Here is the model for the RSS satellite-based temperature series.
Since 1979, warming appears to be slowing down (after it is adjusted for the ENSO and the AMO influence.)
The model produces warming for the RSS data of just 0.046C per decade which would also imply an increase in temperature of just 0.7C for a doubling of CO2 (and there is only 0.4C more to go to that doubling level.)
Looking at how far off this warming trend is from the models can be seen in this zoom-in of the log warming chart. If you apply the same method to GISS data since 1979, it is in the same circle as the satellite observations so the different agencies do not produce much different results.
There may be some explanations for this even wider divergence since 1979.
– The regression coefficient for the AMO increases from about 0.51 in the reconstructions starting in 1880 to about 0.75 when the reconstruction starts in 1979. This is not an expected result in regression modelling.
– Since the AMO was cycling upward since 1975, the increased coefficient might just be catching a ride with that increasing trend.
– I believe a regression is a regression and we should just accept this coefficient. The F statistic for this model is 267 which would pass a 99.9% confidence interval.
– On the other hand, the warming for RSS is really at the very lowest possible end for temperatures which might be expected from increased GHGs. I would not use a formula which is lower than this for example.
– The other explanation would be that the adjustments of old temperature records by GISS and the Hadley Centre and others have artificially increased the temperature trend prior to 1979 when the satellites became available to keep them honest. The post-1979 warming formulae (not just RSS but all of them) indicate old records might have been increased by 0.3C above where they really were.
– I think these explanations are both partially correct.
This temperature reconstruction method works for all of the major temperature series over any time period chosen and for the smaller zonal components as well. There is a really nice fit to the RSS Tropics zone, for example, where the Nino coefficient increases to 0.21 as would be expected.
Unfortunately, the method does not work for smaller regional temperature series such as the US lower 48 and the Arctic where there is too much variation to produce a reasonable result.
I have included my spreadsheets which have been set up so that anyone can use them. All of the data for HadCRUT3, GISS, UAH, RSS and NCDC is included if you want to try out other series. All of the base data on a monthly basis including CO2 back to 1850, the AMO back to 1856 and the Nino 3.4 region going back to 1871 is included in the spreadsheet.
The model for monthly temperatures is “here” and for annual temperatures is “here” (note the annual reconstruction is a little less accurate than the monthly reconstruction but still works).
I have set-up a photobucket site where anyone can review these charts and others that I have constructed.
http://s463.photobucket.com/albums/qq360/Bill-illis/
So, we can now adjust temperatures for the natural variation in the climate caused by the ENSO and the AMO and this has provided a better insight into global warming. The method is not perfect, however, as the remaining error term is higher than one would want to see but it might be unavoidable in something as complicated as the climate.
I encourage everyone to try to improve on this method and/or find any errors. I expect this will have to be taken into account from now on in global warming research. It is a simple regression.
UPDATED: Zip files should download OK now.
SUPPLEMENTAL INFO NOTE: Bill has made the Excel spreadsheets with data and graphs used for this essay available to me, and for those interested in replication and further investigation, I’m making them available here on my office webserver as a single ZIP file
Downloads:
Annual Temp Anomaly Model 171K Zip file
Monthly Temp Anomaly Model 1.1M Zip file
Just click the download link above, save as zip file, then unzip to your local drive work folder.
Here is the AMO data which is updated monthly a few days after month end.
http://www.cdc.noaa.gov/Correlation/amon.us.long.data
Here is the Nino 3.4 anomaly from Trenbeth from 1871 to 2007.
ftp://ftp.cgd.ucar.edu/pub/CAS/TNI_N34/Nino34.1871.2007.txt
And here is Nino 3.4 data updated from 2007 on.
http://www.cpc.ncep.noaa.gov:80/data/indices/sstoi.indices
– Anthony
Richard Sharpe: “They contain, as far as I can tell, all sorts of ad-hoc forcings to get them to conform to the actual temperature record.”
Sorry, that’s above my pay grade, but if you think you have a case, write it up and see how it runs.
Smokey: “Because the challenge to formally debate AGW in a neutral setting has been out there for a lo-o-o-ng time now.”
A while back I watched a television debate between warmers and sceptics. My local newspaper features pro and con AGW views when the issue arises. There’s plenty of debate across all sorts of media, from scientific journals to the MSM to internet venues such as this one.
But ultimately it’s the scientists who will decide for or against AGW, so the scientific journals and related media are the ultimate arbiter of the facts about human-induced climate change.
Richard S Courtney (15:01:43) :
Says,
” John Philip:
The discussion of a hypothetical ‘ocean skin’ is a distraction from the purpose of this debate; viz. the analysis by Bill Illis.
Believe in the existence of this mythical ’skin’ if you want. But do not expect others to believe in it until there is some – any – empirical evidence for its existence.
Such evidence is not provided by papers that say,
“The existence of the surface skin layer can be demonstrated both in theory (Hinzpeter, 1967, 1968) and in observations (Ewing and McAlister, 1960; Saunders, 1967; Clauss et al., 1970; Schluessel et al., 1990) by the need to regulate the long wave radiation and the sensible and latent turbulent heat fluxes across the sea surface.”
They merely state the hypothesis in the absence of knowledge of how the sea/air interface really operates.”
You are wrong about that. The basis is actual experiments. In my post there is a link to a graph,
http://www.realclimate.org/images/Minnett_2.gif
which shows the temperature difference between the surface of the ocean, and 5cm below the surface, to demonstrate how the flow of heat toward the surface of the ocean from below responds to the IR radiation balance at the surface.
” I have had my say in this distraction concerning the hypothetical ‘ocean skin’ and will say no more on it whatever ‘hooks’ are dangled.”
That is OK. Then I have had the last word.
Richard
28 11 2008
TomVonk (01:32:30) :
The temperature stays constant for a given concentration – that is the LTE condition .
Indeed when the CO2 concentration changes , the equilibrium temperature changes but very slightly .
Yes the atmosphere will absorb more but it will also RADIATE more . Absorption and radiation always go hand in hand .
The Planck’s distribution of the energy levels has a very low sensibility to temperatures – around the room temperature only about 5 % of the CO2 molecules are excited . At lower temperatures even less .
The main point which Phil has got wrong is that he imagines that for a given CO2 concentration CO2 only absorbs and transfers the absorbed energy by collisions to N2&O2 .
Or in other words that the CO2 doesn’t radiate in the troposphere .
Of course that is not what happens because :
a) CO2 radiates as any tropospheric spectrum shows .
b) The collisional reaction CO2* + N2 CO2 + N2* where the symbol * means high energy species (it can be either translationnal energy or vibro/rotationnal energy) is an equilibrium . From that follows that for every CO2* that transfers energy to N2 , there is an N2* that transfers energy to CO2 .
Obviously if it was not the case , the reaction would not be an equilibrium .
The constraint is that the energy distribution of both CO2 and N2 must stay constant because by definition we are in LTE and the temperature is constant .
That’s the problem Tom, you’re discussing a physical abstraction rather than a real atmosphere. Your circular argument says “it’s in LTE (your initial assumption) therefore the atmosphere can’t heat up”. The point is that the atmosphere does heat up therefore your assumption in your model is not valid!
So the naive vision in which CO2 is a kind of “pump” which absorbs IR , never radiates and heats the air is simply wrong .
What CO2 absorbs is radiated away (and reabsorbed and reradiated etc) .
If the CO2 concentration changes , the processes described above don’t change but the equilibrium temperature will slightly change with negligible impact on the energy distribution .
It’s not that it never radiates, just that it’s far below equilibrating with the absorbed radiation. Tom’s atmosphere would never warm up, also he forgets about the other gases present, for example if water has a shorter emission lifetime than CO2 then it will be the preferential emitter (check out the HeNe or CO2 lasers). The IR is absorbed by CO2 (and H2O) the gas heats up until either it gets hot enough so that emission (by either or both species) equals absorption, or convection occurs the gas expands and the temperature drops. Either way you get a lapse rate, on our planet the convective lapse rate predominates in the troposphere.
Richard Sharpe
I believe that was von Neumann.
Tom Vonk, Phil
If I understand the argument, the truth is somewhat between the two sides arguing:
If you increase the CO2 concentration, the radiation balance of Tom Vonk will be temporarily disturbed such that the air heats up, until a new, higher steady-state temperature is reached where the radiation balance is once again restored.
So, in many ways, both are correct: the radiation balance is usually valid, but can be temporarily disturbed, so as to attain a new steady-state temperature.
To all posters: This has been great debate – it has brought out the best in the bunch as far as technical debate. Both sides have done good job presenting their points…. without either side clearly serving a death blow to the other side. ….. which is of course the basic point of the skeptic community – the science is not even close to settled & solid scientific research & debate (vs political debate) needs to continue. Why are skeptics skeptical? Because they know enough about the science to know there is a lot we don’t know & aren’t arrogant enough to pretend that we do know.
Smokey (17:24:06) : asks : What are they afraid of?
A debate exactly like this one is what they are afraid of – because when it all comes out, what is clear is we really don’t have all the answers & the science isn’t settled – and that is a very hard position on which to sell major sacrifices to the general public.
Along those same lines, if there are any journalist types out there lurking – who are objectively looking at this – please report on this specific debate because the public deserves to know what’s really going on behind the scenes.
Norm posted the following earlier, however because Norm is a geophysicist with some experience his opinion was felt by some to be authoritative. Here’s some data to refute his handwaving.
There is only a single vibration mode of CO2 that resonates within the thermal spectrum radiated by the Earth (and Mars). This bend vibration resonates with a band of energy centred on a wavelength of 14.77microns (wavenumber 677cm-1) and the width of this band is quite narrow as depicted on the spectra from Earth and Mars.
Here it is for earth conditions:
http://www.spectralcalc.com/calc/plots/1227930543.png
It only takes a minute amount of CO2 to fully “capture” the energy at the resonant wavelength, and additional CO2 progressively captures energy that is further and further from the peak wavelength. At the 280ppmv CO2 preindustrial level used as reference in the forcing parameter, about 95% of the energy bandwidth that could possibly be captured by CO2 has already been captured. There is only 5% of this limited energy available within the confines of this potential “capture” band left to be captured.
The greenhouse effect from CO2 is generally stated as 3°C, so an additional 100ppmv above the 280ppmv level is only capable of generating a maximum 5% increase or 0.15°C. The forcing parameter is based on a full 0.6°C which is four times the 0.15°C absolute physical limit of warming from CO2.
Furthermore if this 0.15°C increase has used up the full 5% of the remaining possible energy as the concentration reached 380ppmv, there is zero warming possible from further increases in CO2.
Here are the spectra for a single line at 380ppm and 760ppm, note the absorbance increases (transmission decreases).
http://www.spectralcalc.com/calc/plots/1227931967.png
http://www.spectralcalc.com/calc/plots/1227932570.png
This is why the CO2 notch is virtually identical in the two spectra; the CO2 band was virtually saturated at the 325ppmv concentration level, so even nine times more CO2 has almost no appreciable effect.
Norm K.
Here’s the spectral line for Martian conditions:
http://www.spectralcalc.com/calc/plots/1227934327.png
A bit more than “almost no appreciable effect”!
Just one more thought on that ocean skin.
If the water skin is warmed up so as to slow down the release of heat energy from ocean to atmosphere will that not reduce the heat energy available to the atmosphere which will then cool down so as to terminate the process and reinstate the ‘normal’ flow of energy from ocean to atmosphere to space ?
I think this is relevant to the initial article from Bill because it deals with processes that may affect the data used there.
Apologies to all for pursuing this but Eric says
You might want to revise your belief that the surface skin mechanism is not working on the basis of real data.
Eric
This from the links you provide
The slope of the relationship is 0.002ºK (W/m2)-1. Of course the range of net infrared forcing caused by changing cloud conditions (~100W/m2) is much greater than that caused by increasing levels of greenhouse gases (e.g. doubling pre-industrial CO2 levels will increase the net forcing by ~4W/m2), but the objective of this exercise was to demonstrate a relationship.
Note the “object of the exercise was to demonstrate a relationship” because it certainly did not demonstrate that the forcing ( ~1.6 w/m2 ) from CO2 increases to date had resulted in a warming of the world’s oceans. Although, of course, the ocean “skin” temperature might be 0.003 K warmer than it was in 1850.
So No, Eric, I don’t wish to revise my belief. In fact, I’m staggered that someone is getting away with this nonsense.
Brendan H:
You assert:
“Scientific hypotheses/theories are deductive. A general case is stated, then particular observations/tests made for or against the general claim. These observations/tests are evidence. Climate models can act as evidence because they test the theory.”
Sorry, but No!
Climate models describe the theory: they do not “test” it.
Comparison of the model’s output with empirical data is a test of the theory. If the output and the data do not agree then this indicates
(a) the theory is incorrect,
Or
(b) the description of the theory (i.e. the model) is incorrect
Or
(c) both the theory and the description of the theory are incorrect.
These indications remain true until the data is shown to be wrong.
A major problem with climatology is modelers who seem to think ‘models can act as evidence because they test the theory’. Such a thought as tantamount to a claim that the climate does what a climate model says.
A theory is an idea, a model is a representation of an idea, and reality is something else.
Richard
I feel proud to be in the company of lay-persons here. Sorry to post so late. Bill Illis, Fellow of the Royal Society of Amateurs (those who do it for the love of it); Willis Eschenbach, another FRSA who has been demolishing the shiny new hockeystick; Jeff Id, FRSA (if I remember right) who has demolished the very mathematical basis of all hockeystick lookalikes; Stephen Wilde FRSA; who else? I’m another layperson, another blog-contributor noob; the thing I couldn’t find was an adequate primer on Real Climate Science for my needs, so I studied the science, wrote my own, and try to keep on improving its clarity for lay readers as well as its scientific adequacy.
Jeff L (26/11, 06:43:41) : “This little exercise here is a good example of collaborative science – not unlike the concept behind linux. As a community, there should be some consideration of a way to formalize this concept”.
I’ve recently had thoughts, again, along these lines, and if Anthony would like, I would be happy to draft an article for this blog. But meanwhile, to catch bright ideas now, I’ve set up a thread on our forum here. Jeff L please get in touch if this speaks to you!
Bill Illis (10:59:02) :
Thanks for your hard work, and for the trended AMO plot.
Just to confirm, is this from the link John Philip (13:53:42) posted corrected for “climo”?
Would be great to add that link to the “Resources” page. I’ll post it over on the comments of that page if you can confirm.
Thanks again.
Okay, I have solved my problem with modelling the Southern Hemisphere temperatures and have a better global temperature reconstruction now.
Based on my thought from above that there is a third active region where the Oceans are exchanging energy with the atmosphere, the Antarctic Downwelling region, about where the Weddell Sea is, I have created an new index from the Smith and Reynolds ocean SST dataset. This region is effectively the southern version of the AMO where the warmer ocean water, cools and sinks to become part of the deep ocean circulation.
Bob Tisdale always uses this dataset, so I thought I would as well. It goes back to 1854 on a monthly basis and is updated to October 2008. I downloaded the monthly anomalies for this box which is the Antarctic downwelling region. It has similar characteristics to the AMO with some longer cycles but only a +/-0.6C variation.
http://maps.google.ca/maps/ms?hl=en&ie=UTF8&msa=0&ll=33.137551,-49.921875&spn=164.593939,360&z=1&msid=110686680343951250375.00045cd66a477c08c6d08
The data then provides a pretty good reconstruction for the southern hemisphere – not perfect but certainly covering the changes. The Nino region now ceases to provide any info for this reconstruction (but it is at least not negatively correlated as it was before). The AMO stays significant (coefficient is 0.216) and the Antarctic DownWelling region is 0.545.
Southern Hemisphere
http://img127.imageshack.us/img127/2889/shantdwmodelim8.png
Putting this new index into the global reconstruction results in an overall better model in my opinion but the r^2 falls to 0.724. The Nino coefficient rises now to 0.07 (providing +/- 0.2C to the reconstruction), the AMO coefficient rises to 0.59 (providing +/- 0.36C to the reconstruction) and the ANT DW coefficient is 0.36 (providing up to +/- 0.2C to the reconstruction).
Global Temp Reconstruction
http://img240.imageshack.us/img240/749/antdwhadcrut3modelid5.png
Its a little hard to see what is going on here because the Red Line model is covering up the Blue Line Hadcrut3 temp anomaly for lots of the time period – which would be the goal I guess.
Global Warming now falls to 1.35C per doubling and there is a better match to the residual over the record.
http://img78.imageshack.us/img78/9020/antdwwarmingjm8.png
Any thoughts?
To John M,
Yes the data for the AMO raw trended data is from John Philips link. It comes from the exact same dataset and page I was using.
The description of the data on that page is a little clumsy and you can’t really tell it is the raw data which is why I didn’t look at it before.
Phil,
I was really keen to try to understand what you are saying, but I found two problems:
1. I found it hard to differentiate between what you wrote and what you were quoting,
2. The links you provided do not work.
Can you try to use <blockquote> and </blockquote> around material you are quoting?
Bill Illis:
You ask:
“Global Warming now falls to 1.35C per doubling and there is a better match to the residual over the record.
http://img78.imageshack.us/img78/9020/antdwwarmingjm8.png
Any thoughts?”
I offer a few.
Firstly, congratulations. This is a remarkable analysis that deserves publication.
On face value, the analysis could be accused of assuming ‘correlation indicates causation’, but it is not guilty of that and any such accusation should be rejected.
The analysis removes known natural effects from the time series to reveal a residual trend. Of course, there could be other natural effects that may be contributing to the time series. And one assumption of the analysis is that all natural effects are providing a positive contribution to the trend: this assumption may not be correct: for example, volcanism lowers temperatures (at least, for temporary periods).
However, it can be said that the residual trend of your analysis shows the warming that has happened independent of AMO, ENSO and Antarctic Downwelling.
And it can be assumed that this residual indicates a maximum of the warming that may have happened as a result of AGW over the analysed time period. Using this assumption the analysis suggests that climate sensitivity has a maximum value of 1.35 deg.C for a doubling of atmospheric carbon dioxide.
The obtained maximum climate sensitivity of 1.35 deg.C is less than half the IPCC “best estimate” of 3 deg.C for a doubling of atmospheric carbon dioxide, and well below the range of the IPCC AR4 estimates (2 to 4.5 deg.C) for a doubling of atmospheric carbon dioxide. Indeed, 1.35 deg.C is below 1.5 deg.C, and the AR4 says the climate sensitivity is “very unlikely” to be below 1.5 deg.C for a doubling of atmospheric carbon dioxide.
But, of course, 1.35 deg.C is more than three times the 0.4 deg.C that Sherwood Idso obtained from his 8 “natural experiments” to determine the climate sensitivity for a doubling of atmospheric carbon dioxide.
A very fine piece of work and I look forward to seeing it in print.
Richard
To Richard
Just a short comment about volcanoes. The large ones clearly impact temperatures but for whatever reason, the impact is picked up by the ocean indices I am using.
I had charted these before (linked below) with the previous model and have now zoomed into the specific periods in question with the newer one as well and I can’t see there is an adjustment required for the large volcanoes.
http://img372.imageshack.us/img372/3685/volcanoehadleyxs2.png
Bill,
As you may know, I’ve done a lot of analysis of the HadCRUT3 series from the standpoint of its spectral characteristics. It would be interesting to examine how well your model preserves the spectral characteristics of the raw data. If you are interested, let’s take this up in email. I’ll send you what I’ve done, and tell you what I’d need from you to do a comparable spectrum analysis.
If interested, email me at blcjr2 at gmail dot com, and I’ll respond.
Basil
Bill Illis (09:51:19) :
Thanks Bill. I’ll add a comment to the Resources thread.
Bill Illis, don’t you have an implicit assumption that the “heat in the pipeline” from start to finish of your data is similar to the heat that was put into the systems from X(i) pipelines in Y(i) years starting with x(i), y(i) before your data starts? I only point it out for completeness. I do not know of any proof of “heat in the pipeline” since such a phenomena should be measurable.
Just to maybe convince myself further (I am a natural skeptic after all) and from the comment by Richard Courtney about volcanoes where I had to squeeze down the period covered in the charts to have a look, I created little 10 year chucks of the chart so that we can see if it is actually working or not.
And I’ll be ___. There is no way this is a fluke. Now keep in mind there is little 0.1C to 0.2C and even 0.3C errors in this reconstruction, but this model really does follow the Hadcrut3 trend/cycles very closely.
There are a few periods where it is off by too much for my liking – 1955 to 1957, some parts of 2000-2003, and a section from 1920 to 1926 where the reconstruction is consistently about 0.1C below the temps, but other than that it is not bad.
Have a look, (there is a lot of them but in order I believe.)
http://img509.imageshack.us/img509/4858/7180modelxb6.png
http://img142.imageshack.us/img142/9788/8090modelzt4.png
http://img142.imageshack.us/img142/1627/0010modelpe4.png
http://img142.imageshack.us/img142/535/1020modelfa6.png
http://img366.imageshack.us/img366/8895/2030modelgs9.png
http://img366.imageshack.us/img366/6363/3040modelqt1.png
http://img142.imageshack.us/img142/4554/4050modelva1.png
http://img366.imageshack.us/img366/8308/5060modelaf9.png
http://img366.imageshack.us/img366/2155/7080modelft9.png
http://img509.imageshack.us/img509/9771/198090modelci1.png
http://img366.imageshack.us/img366/4530/19902000modeloy1.png
http://img366.imageshack.us/img366/5065/20002008modelsq1.png
Bill Illis:
This is an interesting piece of research.
I hate to sound like a nagging statistics prof, but I haven’t read anything that suggests that you have examined the issue of stationarity in your time-series data. As I mentioned in an earlier comment, if your data is not stationary but that you haven’t corrected for this problem, your estimated coefficients could be severely biased or outright wrong.
Most of the comments on the paper have focused on issues related to climate theory. This is well and good. But if you are applying a statistical method such as regression analysis, you also must make sure that the method is applied properly. Regression analysis only works if your data respect specific conditions. Stationarity tests are a must in this case.
Otherwise, you might end up like the infamous Michael Mann and his “hockey stick”, which resulted from an incorrect application of statistical techniques that he did not fully understand.
All the best.
Richard: “If the output and the data do not agree then this indicates
(a) the theory is incorrect,
Or
(b) the description of the theory (i.e. the model) is incorrect
Or
(c) both the theory and the description of the theory are incorrect.”
A fourth possibility is that the data is faulty.
“Comparison of the model’s output with empirical data is a test of the theory.”
Yes, I was speaking in shorthand. Climate models are part of the procedure that is used to test the theory, so in that sense form part of the body of evidence. In principle the procedure is similar to the use of laboratory experiments to test a theory in other fields.
“Such a thought as tantamount to a claim that the climate does what a climate model says.”
If a climate theory is a claim about what the climate does, and if the model describes the theory, then by your own logic the model attempts to show what the climate does.
Bill Illis – I agree with Bob Tisdale that the treatment of ENSO ignores the physical reality of the situation. I’ve documented how the oceans have responded to ENSO here: http://climatechange1.wordpress.com/2008/11/29/how-enso-rules-the-oceans/
Tom Vonk is missing an important point. When a local thermodynamic equilibrium (LTE) is established the amount of radiation absorbed per second by greenhouse gases (GHG) at a point in the atmosphere equals that emitted. If the amount of GHGs at that point increases, the amount of IR light absorbed increases and then the amount of radiation from the GHGs must increase to reestablish the LTE. In order for that to happen the temperature of the atmosphere at that point increases.
It is only through this increase in temperature that the average kinetic energy of collisions can increase and thus the rate of exciting the greenhouse gases GHGs so that they can radiate (The energetic increase could also be in the average vibrational/rotational energy of the collision partners, but again, the only way to raise that is by increasing the temperature).