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
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
In thinking about it overnight, I came to the same conclusion that some of the other posters did. What you have done is residualized the long wavelength trend not directly related to ENSO / AMO out of the signal. Over the time scale of investigation, a linear trend or logarithmic trend could be fit – I am guessing with similar r^2’s. I would agree that temps & CO2 have a logarithmic trend, but there isn’t enough spread in the data to say the residual has a logarithmic trend. SO, with that being said, if you make the ASSUMPTION that the residual trend is purely due to CO2 – the 1.85C per doubling of CO2 is a MAXIMUM effect end member – assuming any other forcing mechanisms at play are positive & not negative.
Question for the group to consider : What other long wavelength forcings are out there that could drive this residual (ie positive forcings, with CO2 being an even smaller positive forcing) & what other possible long wavelength negative forcings are out there that would make this an underestimate of CO2 forcing?
One more IMPORTANT comment for group: 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 (not that I have time to do this, but someone reading might). I think the over-riding concern with the “skeptics” community is that we want the science done right – science as science, with everything considered, not as dogmatic political science. I would bet that if a web-based mechanism was set up for collaborative research, that scientifically sound progress could be made on many different aspect of climate change by the skeptic community. It could have different threads investigating specific questions, a compilation of all important publicly available datasets, a compilation of pertinent publications, as well as all research done by the group to date for others to build upon. Bill’s paper above would be a good example of a starting point for a thread of research. Questions brought up by posters could be investigated further, the model / hypothesis refined with those answers. New questions such as the causation behind the residual could start as a new thread of research. As long as no one lets their egos get in the way (looking for glory) & the goal is simply getting the right answer, it could be a powerful tool.
Anthony, still having problems opening the excel sheets, even in zip format. Says they are corrupted.
REPLY: I tried a download from another machine that I didn’t write the post from and had the same result. I’ll see if I can figure out this nuance. – Anthony
Maybe we just have warm or cold air blowing on our temp gauges. What patterns does the jet stream take on with oceanic oscillations? At least in the upper part of the US we either freeze or save on fuel when the jet stream dips or not. The winter of 07/08 experienced plenty of cold temps because the jet stream looped down into our territory many times. The more times in a season it stays up above the 45 parallel the more fuel we save. Is there an oscillation to the predominant jet stream pattern that coincides with oceanic cycles? And what do we know about the jet stream in relation with solar cycles? Plus, wouldn’t the jet stream move water vapor along to somewhere else?
Chris S. (06:44:32)
Whose reconstruction of solar activity did your speakers use? Where’s Leif to comment on this latest?
========================================
Uh, that’s (06:32:44) for Chris S.
===========================
re: web based research collaboration. Scientists need to feed their families too. Who will fund such an effort? Scientists don’t make a product that you can buy at Walmart. What funding sources could be accessed for such an endeavor that does not have, or can set aside, a vested interest in the outcome? And how long would they be willing to fund before answers are forthcoming?
Damn it, everybody! I am in the middle of writing several articles, a policy paper and a book, and I can’t make time for this post……but will have to! Thank you Bill and all other contributors – it is one of the most informative and challenging posts yet – and very hard for me to digest because I am really bad at stats.
Others have picked up the issue of long term trends and recovery from the Little Ice Age, PDO cycles, and even longer ones – and not assuming the divergence is doe to CO2 until these are factored in – and the post on CO2 saturation is great stuff – I need to revisit that….and so little time!
One request – of the IPPC’s 0.6C, how much are you knocking off? Bill – You said it was a large amount, but I can’t see it on the graphs. What simple proportion do you ascribe to carbon dioxide?
Bill Illis, one last thing. With respect to your statement and graph about the NINO3.4 anomaly data trend or lack thereof, there is a significant difference between the Trenberth NINO3.4 SST anomaly data (The ultimate source is HADSST) and the Smith and Reynolds (ERSST.v2) version. Here’s a comparative graph of the monthly data:
http://i36.tinypic.com/2s76q02.jpg
Here’s a graph of the difference with a linear trend line:
http://i37.tinypic.com/2q33dcz.jpg
That trend is substantial and the dip in the early 20th century is consistent with the ERSST.v2 version of the Pacific Ocean. I don’t think the Trenberth (HADSST) NINO3.4 data has been detrended, though it looks like it might have been, because the difference also shows up in the annual NINO3.4 SST data of the two data sets:
http://i33.tinypic.com/orl1tc.jpg
And the difference in the annual SST data:
http://i38.tinypic.com/20zw7bb.jpg
I discussed it here:
http://bobtisdale.blogspot.com/2008/11/nino34-data-comparison-hadsst-and.html
Who knows, maybe the guys at the Hadley Centre didn’t like the pesty SST dip in the early 20th century, so they smoothed it out. Stranger things have happened.
Regards
Chris Shoeneveld: “Yet they realized that the changes in solar forcing are not enough to cause the temperature fluctuations, but they didn’t want to speculate about the possible causes. Obviously some other mechanism must explain the amplified effect on temperature.”
I’m not convinced there even has been an “amplified effect on temperature” any more than normal.
Thank you, Bill Illis, for this very nice post.
May I, nevertheless, ask the following questions:
1) the weight of ENSO is considerably less than that of AMO (0.06 or +/- 0.2 Celsius variation vs 0.5-0.7 or +/- 0.3-0.4 C variation).
ENSO represents tropical pacific, while AMO represents northern atlantic – not including tropical atlantic. The total area of pacific ocean is approx. 180 million km2, thereof tropical/subtropical area approx. 90 million km2. The atlantic ocean has 80 million km2, thereof the AMO area is approx. 30 million km2. The ratio is 3:1 while the weight factors ENSO vs AMO have roughly 1:2.
Why do you not use pacific decadal oscillation instead of AMO? PDO represents the total pacific, with, maybe, too little weight for the tropical pacific, which then would be compensated by ENSO.
2) The logarithmic dependence on CO2 concentration. Could it be replaced by just a linear dependence on time, without spoiling the agreement? After all, it can be claimed according to IPCC that 1/3 of the global warming arises from solar influence, which to a very first approximation has been linear in time (probably no longer).
3) The Hadcrut global data CO2 logarithmic prefactor of 2.73 yield 0.1 C per decade warming (since 1958), while the RSS global data give a CO2 logarithmic prefactor of 1(or 0.046 C/decade, since 1978). The difference to some extend seems to arise from the heavier weight of the last decade in the RSS analysis, but mainly may arise from inadequate land surface data (see UHI etc discussion a few days ago). Do you share this view?
A logarithmic ansatz for the CO2 dependence ignores any negative feedback due to enhanced latent heat transport at higher temperatures (the Lindzen argument).
Bill: As soon as the glitch in the download is fixed, I’ll try to replace the Trenberth data with ERSST.v2 and see what happens with your model. I’ll post the results.
BTW, how’d you answer my comment about the differences in the data sets before I posted it?
Pamela Gray (07:14:29) :
“Scientists need to feed their families too. Who will fund such an effort?”
That is why I made the Linux analogy – built collaboratively by a community of intensely interested individuals – with nothing more than sweat equity. I assume Bill Illis did not get paid for his little research work – yet he has put together a potentially very interesting piece of science. Anthony puts this blog together, which furthers the science and doesn’t get paid. This is not unrealistic to think that it could work. We clearly have a large group of technically competent people out here with the skills & the knowledge to address many of these problems. Just as with Linux, one person didn’t write the whole OS, the community did it together. Same could apply here – those who have the skills to contribute can contribute as they are able. As a side benefit, no one could say it is an effort funded by an agenda – no grant money funding the AGW’s, no “big oil” money (as the AGW’s like to think the “skeptics” are funded by) – just people motivated by finding the truth.
Bill Illis,
Very nice article. On my first reading I am impressed that the climate signal at least occurs in the visually correct time frame for CO2.
Please be careful not to over conclude that because you don’t have other explanations for the temp rise it must be CO2. IMHO you should strike those comments from the article or make strong caveats. Your graph makes a convincing enough argument for it by itself I think.
Also, although you were careful to point out data quality issues in your comments I need to mention that the corrections in the GISS data are nearly as large as your signal. While the corrections may (or may not) be entirely reasonable, errors in the data will have a large effect on the total rate of warming in your results. The same is true for the ENSO and AMO measurements.
I will spend some time over the next two days reviewing the rest of your work. It really is an interesting calculation. Tamino did something similar but didn’t publish any of his calculation methods so it was impossible to review. Also, he is stuck on the idea that climate change was linear for a hundred years and that makes it hard to take him seriously.
Great stuff though.
Have you, or anyone, attempted to apply a Fourier transform to either the raw data or the residual error data?
“Eyeballing” residual frequencies in a graph like that is fraught with observation errors. If there are regularities in the data, a Fourier transform will exhibit spikes at various frequencies, and you can then go looking for things that happen on those time scales.
Regards,
Ric
As I have stated before, if it is known what forces cause change, then change can be predicted. If the forces which cause change are not known, then projecting future changes from historical data is nothing more than extrapolation, and extrapolation is sure to be wrong, and sometimes far wrong.
It is clear from historical data that the equation (increased CO2 levels) = (global warming) is simply not true. What these people doing the extrapolation of historical data keep saying is, “If it weren’t for all of these factors which happen quite often and naturally, it would have gotten warmer.” But these events, the oscillation of the Atlantic and Pacific Oceans, volcanoes erupting, sunspots changing, varying amounts of water vapor in the atmosphere and the like occur frequently and are quite natural events.
It is much like the old sort of joke, “If the dog wouldn’t have stopped to take a crap, the dog would have caught the rabbit.” But if the dog always stops to take a crap, the dog will never catch the rabbit.
We humans simply can’t affect the volcanic eruptions, nor the oscillations of the oceans, nor the appearance (or not) of sunspots, nor the amount of water vapor in the atmosphere. Thus, we humans can’t have any sort of control over the “climate”. The climate has never been under any sort of human control, and isn’t going to be under control of humans in the future.
All this “smoothing” of historical temperature data is simply ignoring what actually happened in history. All this “adjusting” of historical temperature data is nothing more than changing actual data to fit a hypothesis which clearly has failed the test of verification by actual observation.
It is long since time to toss out this hypothesis which has clearly failed, stop using extrapolation of historical temperature data (which itself has been adjusted that is, finagled to fit a false hypothesis) and stop using this pseudo science and begin using scientific method again. If one doesn’t know the actual causes, and the proportion of effect of each cause, then there are no such things as “trends”. These “trends” exist only in the minds of imaginative people.
Pamela Gray wrote: “re: web based research collaboration. Scientists need to feed their families too. Who will fund such an effort? Scientists don’t make a product that you can buy at Walmart. What funding sources could be accessed for such an endeavor that does not have, or can set aside, a vested interest in the outcome? And how long would they be willing to fund before answers are forthcoming?”
Of course the funding has to be from “proper” sources, otherwise the results are suspect for some unknown reason.
Pamala, re # 26,
And I thought they were all being funded by the big oil companies!
Can that be an error?
Willi
Kim,
De Jager considered both the equatorial and the polar activities. The latter is, apparently, often neglected.
He referred to the “smoothed maximum sunspot numbers; a proxy for the maximum toroidal field strength over the centuries” and the “smoothed values of the geomagnetic aa index at sunspot minimum; a proxy for the maximum poloidal field strength” (Duhai& De Jager, 2008). Since 1000 AD they named the following minima: Oort, Wolf, Sporer, Maunder and the Dalton.
By the way, they predict the next solar cycle, #24 to have a maximum strength of 68 +/-17 sunspots to be reached in 2014.
Kim,
Sorry, it is Duhau & de Jager
Title:
The Solar Dynamo and Its Phase Transitions during the Last Millennium
in: Solar Physics, Volume 250, Issue 1, pp.1-15.
Duhau, S.; de Jager, C.
Equally important is: what temperature proxy data did they use?
For the 400 years tropospheric temperature oscillations they used Moberg et al. 2005. Nature, 433: 613-617. Obviously, they didn’t use the hockey stick.
To the posters who are suggesting trying other series etc., that is why I have done this. Just to show that it can be done. Like I wrote, it could certainly be improved on.
When we get the spreadsheets up and running, they are set-up so that one could try just about any other variation. All the charts etc. are in there as well.
I really did this so we could actually start adjusting for these things rather than just noting “if you adjust for …”
That and it was clear to me that increased temperature trend of the 1980s, 1990s and 2000s was in part just a reflection of the ENSO and AMO and was not global warming. realclimate even has a GISS temp chart up right now showing temps going straight up like it will reach the moon. This analysis method says the GISS trend per decade since 1979 is only 0.058C per decade far, far less than 0.2C per decade it is projected to be.
http://i463.photobucket.com/albums/qq360/Bill-illis/GISS1979Warming.png?t=1227720573
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.
Is it 0.058 or a typo for 0.58?
Great post, Bill, and some great comments, too. I agree with some others that a natural warming since the LIA could explain the gradual increase in temperatures; it doesn’t have to be CO2-related. We know there are some very long-term cycles at play. The current lull in solar cycle 24 will probably help us understand the effects of the sun better. I’m buying more long-johns.
Some of those outside factors might include other ocean-atmospheric cycles. From 1976 – 2001 not only the PDO and AMO, but also the NAO, IPO (which overlap), and the AO and AAO went from cool phase to warm. Could that possibly fill in the gap ascribed to CO2?
Then there are McKitick, Michaels, LaDochy, etc., who claim the recent historical trend has been been exaggerated. I would sure like to know why the raw data is adjusted upwards when what common sense I can bring to bear tells me it should be adjusted downwards.
And, by definition, the sun is the primary power source. What is at issue is if CHANGES in the sun might be affecting ocean, atmosphere, etc. My current take on that is that the small stuff may not matter, but the grand minimums probably do matter – a lot. And if they don’t, then some other natural force (quite apart from Milankovic cycles) would be able to create “little” minimums and optimums.
Leif tells us that TSI seems more constant than previously believed. Using the questionable old figures, one will note that TSI increased c. 0.02% over the last century while temperatures (using questionable NOAA or GISS historical figures) increased c. 0.04% from absolute zero. Just a side-by-side look, and using old figures.