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
Tom Vonk says:
Does this mean that at any one instant, only 5% of the CO2 molecules are able to heat the atmosphere by transferring energy to other species?
I’ve made the position quite clear elswhere.
There is a small error in that I was a student member from 1968 to 1971 but I have been a Fellow since then.
I cannot use the letters FRMetS since a rule change in 1973 but I can continue to refer to myself as a courtesy title within the rules of the RMetS.
I make my position as an amateur enthusiast perfectly clear in the Contributors section at CO2sceptics.com and in my introduction to my part of the forum there.
Hmmmm … a seems the RMS rules changed in 2003, the new stringent requirements were introduced, and Fellows elected before that date may continue to describe themselves as a such purely as a courtesy title but not use the formal FRMetS appellation as a sign of professional competence.
Still, most people are not aware of this distinction and would be bound to conclude that someone describing themselves as a Fellow of the RMS was indeed a FRMetS and a professional meterologist, rather than an enthusiastic amatuer.
Incidentally, the Code of Conduct for Fellows (FRMetS) states they must use the name of the Society only when duly authorised.
Whoops, I meant a rule change in 2003.
As regards my article it is expressed to be a discussion piece, not a definitive exposition.
It provides a starting point from which lay readers can consider the facts and relate them to different theories.
The points I raised have never been answered to my satisfaction and the ocean skin theory remains unproven on a global scale.
Even if it happens the scale of the phenomenon may be insignificant in the face of natural forces.
The ocean skin theory is at present merely a helpful speculation for the AGW lobby which has problems convincing anyone that slightly warmer air can heat oceans on a meaningful time scale.
I take it as a compliment that some consider my article good enough to justify attacking me on personal grounds.
Mr Wilde – Thanks for clearing that up, as I said, it was quite possibly a misunderstanding – which turns out to be the case. Most people will find it a little odd that someone with no professional meteorological qualifications, publications or experience, and who is ineligible to use the title FRMetS can legitimately describe themselves as a Fellow of the Royal Meteorological Society, but I accept that this is indeed the case.
Anyway, this is drifting both off-topic and ad-hom, as it is meant as a ‘discussion piece’ I may add a few thoughts and corrections later if I get time, but probably not here. …
JP (B.Sc) 😉
John Philip,
Of course most people would not be aware of the distinction which is why the first sentence of my first article made it clear that my Fellowship predated the requirement for a professional qualification.
Please do be more careful in jumping to conclusions.
By now my true status is widely known and unlikely to mislead anyone who is interested enough in my stuff to actually read it.
After examining the AMO index issues more thoroughly, I have decided it is still valid to use the AMO index as a natural climate variable which is not related to global warming.
It is apparent the untrended index should be used, however, given the AGW community would not accept using the raw untrended data (given it does have a trend.)
Earlier in developing this model, I had downloaded a Long-Term AMO reconstruction by Stephen Gray et al which goes back to 1572. I decided not to use it since it is just annual data and has greater variability than the current AMO index method. There are a few inconsistencies in the time periods when the indices overlap but agree on the general up and down swings.
Here it is.
http://img510.imageshack.us/img510/1639/ltamoindexxr2.png
This reconstruction shows that the AMO appears to be a natural climate cycle that even has greater swings in temperatures in the past than the current index shows. Some of these swings match up with the climate changes we know about in history such as the onset of the Little Ice Age for example.
Here is what the Raw Untrended AMO Index looks like.
http://img357.imageshack.us/img357/900/trendedamoindexkq9.png
While it does have what seems to be a pretty rapid trend upward, some of this is just a result of the scale and the time period covered. The trend upward over the past 140 years would not be inconsistent with what is seen in the longer reconstruction.
The increase is only 0.024C per decade and, in terms of the regressed coefficient for the AMO, it would have just a 0.018C per decade GW impact (the models predict about 10 times as much).
Other studies conclude the AMO is a natural cycle, so therefore, I believe it can continue to be used.
Furthermore, it clearly impacts monthly temperatures so any reconstruction should use it. The untrended data does not contain a global warming signal. What may be a completely natural increase in the AMO over the past 140 years, can be left for the global warming residual.
Oh, and by the way…
The ocean skin theory is at present merely a helpful speculation for the AGW lobby which has problems convincing anyone that slightly warmer air can heat oceans on a meaningful time scale.
is not an accurate description of the process. The heating occurs as a result of solar irradiance penetrating the surface, the IR radiation reduces the temperature gradient at the surface layer slowing the release of that heat back to the atmosphere. In fact on average the ocean surface is warmer than the air above so there is no proposal that the ocean warming is a result of heating from ‘slightly warmer air’.
HTH
I kept saying “Raw Untrended” in the above and that should say “Raw Trended”.
Richard Sharpe:
You ask me:
Richard S Courtney says:
“The assertion assumes little mixing of the ocean surface layer. Indeed, it assumes a “surface skin layer” that is “less than 1 mm” thick and is independent of the underlying water. But the entire surface layer is turbulent and it is very turbulent near the surface.”
Doesn’t that then provide a mechanism for the transfer of energy (heat) from the surface layer to lower layers, and thus reduce the opportunity for its removal via evaporation?”
I agree, it must do that. However, I made no mention of evaporation because I think is not pertinent to the assertion that was made.
I was addressing a specific assertion of John Philip; viz.
“Actually, although IR only penetrates less than 1 mm an important part of the mechanism by which GHG forcing heats the ocean is IR absorbtion in the surface skin layer which reduces the temperature gradient across the layer, causing more of the heat from sunlight absorbion to be retained in the water below.”
My statement that you quoted says that such a “surface skin layer” is very unlikely to exist and, therefore, the hypothesis of that “surface skin layer” inhibiting upward loss of “sunlight absorbion” (sic) is very unlikely.
And I concluded from this saying:
“While the possibility of the assertion cannot be rejected, it is so implausible as to be worthy of rejection until supporting evidence is provided.”
(Since then no such supporting evidence has been provided in this discussion, but an attempt at “argument from authority” was attempted.)
We could discuss evaporation elsewhere if that were desired. (Evaporation provides a much greater thermal transport from ocean surface than IR which is involved in the GH effect). However, such discussion would be a distraction from the important assessment of Bill Illis’s analysis which is the subject of this debate.
Richard
is not an accurate description of the process. The heating occurs as a result of solar irradiance penetrating the surface, the IR radiation reduces the temperature gradient at the surface layer slowing the release of that heat back to the atmosphere.
JP
what about the mixing? or if there’s no mixing – evaporation?
Though we’ve been talking about 1 mm penetration isn’t the true value more like 1/20 mm, i.e. ~99% is absorbed within 0.05mm.
I too find the AGW explanation for ocean warming (or lack of cooling) totally implausible – and certainly within the timeframe of decades.
The question of physical process and causality was my concern with this work, after all, you seem to be saying that global temperature can be expressed predominantly as the sum of two localised temperature measurements. I don’t think this ought to be a great surprise. (given the accepted effect on weather that ENSO and AMO have)
After reflecting a little, I now wonder if, given the presence of this coupling, can any model which does not incorporate the coupling be relied upon to demonstrate the underlying forcing effect?
Sean
To Sean
Underlying the ENSO and the AMO is the thermohaline ocean circulation.
The AMO is where the once warm ocean water sinks to the depths and becomes part of the deep ocean. The energy transfer between the ocean and the atmosphere as a result of this process creates the forcing/temperature change – sometimes warm, sometimes less warm.
The ENSO is not normally thought to be a node of the ocean circulation but has similar characteristics in that deeper and colder ocean water is sometimes brought to the surface when the Trade Winds are stronger than normal for an extended period of time. Colder water wells up and there again is an energy exchange. When the Trades slow down for an extended period of time, there is less up-welling and more surface heating from the Sun in the Nino region and again there is then a warm energy exchange rather than a cooling energy exchange.
Now if there were an ocean circulation index, we wouldn’t the two measures.
John Finn
says,
“JP
what about the mixing? or if there’s no mixing – evaporation?
Though we’ve been talking about 1 mm penetration isn’t the true value more like 1/20 mm, i.e. ~99% is absorbed within 0.05mm.”
If the surface skin, which absorbs the downwelling radiatio,n is mixed into the ocean below, than the bulk of the ocean below is certainly being heated by the downwelling radiation.
Actual measurements have been made supporting this specific mechanism by which the downwelling radiation absorbed in the surface skin suppresses the transmission of heat from the ocean bulk to the surface. The temperature gradient between the surface and 5 cm below the surface has been shown to depend on the difference between downwelling and upwelling radiation.
http://www.realclimate.org/index.php/archives/2006/09/why-greenhouse-gases-heat-the-ocean/
http://www.realclimate.org/images/Minnett_2.gif
You might want to revise your belief that the surface skin mechanism is not working on the basis of real data.
For those unconvinced about the existence of the ocean surface skin layer and its role in the increase in OHC here’s another blatent appeal to authority, NASA this time. It’s got figures and charts and references and everything …
Over the surface of the ocean, there frequently exists a very thin layer called the surface skin layer in remote sensing sciences (Schluessel et al., 1990) (Figure 2). 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. Above and below the thin skin layer, turbulent eddy fluxes enhance heat flux in the ocean and/or atmosphere across the interface. However, the eddy cannot transport heat across the ocean surface by itself. The heat balance in the skin layer must be accomplished by molecular processes, hence the thin skin layer. The actual thickness of the skin layer depends on the local energy flux of the molecular transports, which is usually less than 1 mm thick and can persist at wind speed up to 10 m/s. For stronger winds, the skin layer is destroyed by breaking waves. Observations indicate that the skin layer can re-establish itself within 10 to 12 seconds after the dissipation of the breaking waves (Ewing and McAlister, 1960; Clauss et al., 1970).
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.
I have had my say in this distraction concerning the hypothetical ‘ocean skin’ and will say no more on it whatever ‘hooks’ are dangled.
Richard
Bill, I don’t have a southern Ocean index, but one of those links is to the Indian Ocean temps, monthly, going back well before 1900.
It’s not a IO dipole index, though, just temperature anomaly.
John,
The paper your referenced is AGW extremeism based on pre-existing AGW work. It is an on topic reference for your point but it is a weak paper because of it’s simplified method for calculation of water feedback and overreaching conclusion. Here’s a quote which rubbed me wrong.
“We use a conventional definition of the strength of
the water-vapor feedback:
Soden et al. [2008] provide pre-computed values of
@R/@q(x, y, z). We then multiply @R/@q(x, y, z) by the
observed Dq(x, y, z)/DTs between two climate states and
then sum over latitude, longitude, and altitude to obtain an
estimate of lq. Soden et al. also provide @R/@q(x, y, z)
broken down into longwave (LW) and shortwave (SW)
components, allowing us to separately compute the LW
and SW water-vapor feedbacks, lq,LW and lq,SW.”
They use previous estimates to calculate the water vapor feedback. These calcs are based on further estimates of the feedback mechanisms for water. Again, I don’t make the claim that AGW is false, just that this kind of work does not help.
The paper itself references numbers from 0.94 to 2.69 W/m^2. This doesn’t do a good job supporting you claims of exponential out of control warming and I think scientists would do well to examine our real knowledge of historic temperatures before making such claims.
We know for certain that people and plants are being uncovered from glaciers that lived only a few thousand years ago yet there is no evidence of floods. We also have the distinct possibility that temps “globally” may have spiked above today’s temps only 1000 years ago. Again, there was no major flood which would indicate exponential temperature rise!
Where I have my problem with this study, is in the reliance on simplistic equations which are fine but they are followed with unreasonable conclusions. The authors give their motives away completely with this over-conclusion:
“The existence of a strong and positive water-vapor feedback means that projected business-as-usual greenhousegas emissions over the next century are virtually guaranteed to produce warming of several degrees Celsius. The only way that will not happen is if a strong, negative, and currently unknown feedback is discovered somewhere
in our climate system”
The feedback magnitude is clearly not correct as demonstrated by Bill Illis’s work above amongst other things. But the real evidence should be the multiple climate reconstructions such as Crag Loehle which use reasonable methods and demonstrate significantly warmer temps only 1000 years ago with no great flood.
My final point is NOT that you cannot be right, you might be. But rather that you cannot claim you are correct with the current state of science.
We just don’t know!
It is an amazing thing in this science where so many scientists make overreaching conclusions from their calculations. Such simple stuff too, Bill removed known factors from GISS and made the conclusion the rest is CO2 warming (without presenting evidence). Not that you are wrong Bill but no evidence was presented to support this conclusion. I am very pleased with the rest of the work, you are on the right track.
Mann 08 makes a complete disaster (IMHO intentionally) of the math in their paper and makes the conclusion that we are warmer than ever.
Dessler 2008 does simplistic calcs and determines exponential growth is a done deal yet it is unsupported by any historic temperature measurement.
Will it ever stop?
How backward is this science, Here is a quote from CA by Esper 2003 paper
“this does not mean that one could not improve a chronology by reducing the number of series used if the purpose of removing samples is to enhance a desired signal. The ability to pick and choose which samples to use is an advantage unique to dendroclimatology.”
Really gorgeous quote I think.
If we don’t know history, we don’t know the future.
Richard Courtney: “4.From (3) it can be deduced that gazelles leap in response to the presence of a predator.”
What you are describing is induction, not deduction. In this instance you have argued from the particular to the general.
“Gazelles are observed to always leap when a predator is near” is a particular observation, or observations, that is, limited to a finite set.
“Gazelles leap in response to the presence of a predator” is a general conclusion. Therefore, the argument is inductive. A deductive argument would be:
– Gazelles leap in response to the presence of a predator
– This animal leaps in response to the presence of a predator
– This animal is a gazelle.
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.
To Richard Courtney
Apologies for wasting your time with peer-reviewed papers and irrelevancies about Fellows of learned societies who turn out to be amateurs, and so forth.
But perhaps I could crave your indulgence for just one more moment and ask you to remind us – what was the title of your PhD thesis?
Thanks.
Brendan H says:
That would be the case if climate models faithfully implemented the theory, however, that would seem to be far from the case.
They contain, as far as I can tell, all sorts of ad-hoc forcings to get them to conform to the actual temperature record.
Who was it who said give me five parameters and I can model an elephant.
Moderator –
WUWT recently saw fit to post about Tom Karl’s honourary doctorate.
[snip]
John while I agree with you in principle, you’ve missed an important distinction. People such as Karl who are public servants who abuse such titles have no expectation of privacy by virtue of their public employment.
Private citizens do have an expectation of privacy.
I will not allow you to turn this blog into a PERSONAL LEGAL LIABILITY FOR ME by posting such things as your personal opinion. I do not have time to verify such things, for all I know the letter could be fabricated. By allowing you to post such things on my blog the liability shifts to me.
Cease and desist or be banned. Your welcome is just about worn out. No dissent, no further discussion, just stop. Not one more peep from you on this issue.
– Anthony Watts
Brendan H:
Is that why climate alarmists won’t defend their ‘runaway global warming/CO2/AGW is gonna getcha’ hypothesis in a formal, moderated debate?
Or are they, like, too busy modeling to defend their [repeatedly falsified] AGW hypothesis?
Because the challenge to formally debate AGW in a neutral setting has been out there for a lo-o-o-ng time now.
What are they afraid of?
Adolfo Giurfa (09:48:27)
Thank you, but remember, I am speaking of an open system.
I think that any model that considers CO2 and H2O, as noble gas, is doomed to failure.
…. 2 H2O> (H2O)2 dimer
…. 3 H2O> (H2O)3 trimer
…. 2 CO2> (CO2)2 dimer
…. H2O + CO2> H2CO3
…. H2CO3> H+ + HCO3 –
…. H2CO3> 2H+ + CO3 —
I can get more exotic structures:
…. 3 H2O + 2 CO2> (H2O)3(CO2) 2
I can imagine any structure to 4ºC and pressure equal to 100 atm. (In the deep ocean)
FM
To evanjones,
I put the Indian Ocean SST index into the reconstruction and it certainly helps with the southern hemisphere reconstruction and it also helps with the overall global temp reconstruction.
Three problems, however. The data ends in 2004 and doesn’t seem to be updated anymore. Second, I tried other Indian Ocean Indices including the dipole and these other ones don’t provide an improved reconstruction. Thirdly, the Indian Ocean SST index covers the whole Indian Ocean and I don’t want to use indices that cover really large sections of the oceans – the complete ocean index would be the best reconstruction of course but then it would be like trying to reconstruct a dataset when you are already using 70% of the dataset as your independent variable. It is one of the reasons I used just the Nina 3.4 regions.
So, back to the drawing board again.
I’ve been thinking about the question Sean Houlihane asked and what is the underlying forcing (or let’s say rationale) with the ENSO and the AMO which makes them good choices to reconstruct climate variations.
And the answer really is that these two regions are the most active regions where the Oceans are exchanging energy (heat and cold) with the Atmosphere. There is far, far more energy being transferred back and forth in these two regions than any other …
… Except for the third big region which is the opposite of the AMO in the southern hemisphere and that is the downwelling region in the Weddell Sea off Antarctica.
http://upload.wikimedia.org/wikipedia/commons/4/4c/Thermohaline_Circulation_2.png
So this is the missing piece of the puzzle. No index for this region however. Any ideas by anyone? Bob Tisdale or David Smith still around?