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
“Would you please explain why you claim a sizeable cooling?”
That’s all only a play with the starting point. I just did the same with the last 80 months and here we go with a sizable cooling:
http://www.woodfortrees.org/plot/hadcrut3vgl/last:80/plot/hadcrut3vgl/last:80/trend/plot/gistemp/last:80/plot/gistemp/last:80/trend/plot/uah/last:80/plot/uah/last:80/trend/plot/rss/last:80/plot/rss/last:80/trend
Bill – another little feature of the analysis, in the spreadsheet you are using the natural log of the CO2 concentration, in fact the forcing due to an increase in CO2 is proportional to the log of the difference between starting and ending concentrations, in fact it is estimated to be
Delta F = 5.3 x ln(C1/C0) where C1 is the end concentration and C0 the start concentration.
By taking the log of the total concentration you will get a curve that is proportional to the theoretical forcing that would have occurred if concentrations were zero at the start of the period!
Phil (22:58:19) That’s an excellent explication of a nice nuance. This is why the Jason measure of sea level are so important. Since Argos buoys only go down about two miles, if Trenberth’s ‘extra heat’ is being stored deep in the oceans, there should still be thermal expansion of volume of the oceans.
I particularly worry about the halt in the reported Jason data stream until very lately, and the attempt to jigger the Argos readings of the last four years back to warming. The evidence that the oceans are cooling is truly of more import than the recent atmosphere cooling.
===============================================
If I may offer a critique – I don’t know who chose it but the image at the top of the page is…well…IMO, a poor choice. As I see it, yes, it does show a El Nino and a La Nina – however – it is showing an El Nino during a PDO warm phase and the La Nina is shown during a PDO cool phase. Your apples & oranges are getting mixed together. The ENSO cycle (12-18 months or so) does not switch with the PDO cycle (20-30 years) & the image is not an accurate representation of the Pacific-wide basin during an ENSO cycle…correct?
*very* interesting write-up though.
Reguards,
Jeff
Earlier John Philips had linked to the unadjusted untrended AMO data. I have put this data into the model now. It is exactly the same data I used before except it has very slight trend in it, about 0.002C per year or about 0.02C per decade GW impact potential.
It produces some interesting results.
The coefficient for the AMO goes back up to 0.75 as we has seen in other times. The r^2 falls somewhat to 0.74 but the F-statistic jumps to by far the highest number I have seen at 738.
More importantly the warming residual left over falls to the infamous 1.24C per doubling (which the actually physics calculations say the number should be.) Interesting.
It also allows one to see better some of the changing over time warming signals I was seeing before. There is certainly a peak then fall-off starting in the late 1970s for example and other variations at other times.
Here is the Warming modeled chart.
http://img73.imageshack.us/img73/4331/hadwarmusinguntramorr6.png
I will have to think about if it is valid to use an AMO index which has a slight trend in it. The point about this regression method is to remove the natural variation from the climate. if the AMO is rising due to warming, then it can’t really be used for this purpose (although the untrended data could be). Other studies have shown that the AMO is a natural climate cycle that even has longer cycles lasting hundreds of years.
Any thoughts?
John Phillip,
“In fact the warming is more likely to be exponential, due to thermal inertia and the impact of positive feedbacks, notably absent from this analysis.”
Positive feedback is a bit of a pet peeve of mine which is a regular theme of the AGW guys. The earth clearly has a large number of negative feedback mechanisms as well which are poorly understood. I can make that statement because our climate would have gone over this huge evil tipping point dozens of times by the ice core data and it hasn’t happened. Also there is endless data that the ice has melted well beyond today’s levels in the last 6000 years and yet there was still no massive overwhelming flood. The tipping point is a theory with no foundation in the data.
Most of the tipping point arguments are based on massive releases of CO2, melting ice, increased CO2 release until the earth turns into whatever horror story they can make up. What Bill Illis has shown shifts the equations for the limit to the amount of warming created by CO2 to a much lower level so even if there is massive amount of CO2 released the oceans are less likely to flood the earth.
When you say warming is more likely to be exponential, you are going way too far.
The quote from the Hansen 1985 paper includes “Evidence from Earth’s history (3–6) and climate models“. Since when have climate models been able to produce evidence? I always thought evidence came from measurements, not from predictions!
Phil.
No, in our atmosphere up to the tropopause or so virtually all of the energy absorbed by CO2 is converted to the thermal motion of colliding molecules, primarily N2 & O2. The emission lifetime of the excited CO2 is much longer than the mean time between collisions and so is rapidly quenched.
That is only a half of the story and therefore wrong .
While vibrationnaly excited CO2 transfers energy to N2&O2 by collisions , so N2&O2 transfers energy to CO2 exciting its vibrationnal levels by collisions .
As we are in LTE , the rates are equal .
The Planck’s distribution of the excited vibrationnal levels of CO2 demands that the proportion of excited levels stays CONSTANT for a given temperature .
Consequence is that CO2 simply must radiate away what it absorbs because else we have no more LTE .
The proof is trivial : look at a CO2 spectrum anywhere in the troposphere .
The CO2 radiates as expected .
So it is not because the relaxation time is much longer than the mean time between collisions that CO2 only absorbs , collides and never radiates .
That’s why the quenching that works both directions does NOT say that the CO2 is “heating the atmosphere” because it’s doing simultaneously both – cooling by radiation AND heating by absorption .
In equilibrium because of the already mentionned Planck’s distribution constraint both actions are equal .
Remember , in equilibrium one has always “Anything that is absorbed must be emitted but not necessarily by the same molecule .”
On the other hand the original poster you commented on got most of the QM processes wrong (like the dipolar momentum , energy “storage” , “saturation” etc) .
To John Philipp
There is no “pipeline” .
Thermal inertia indeed exists but I am afraid that you did not understand what it means .
Like R.Courtney says , any heating is instantaneous by definition .
Any molecule that increases its energy by absorption or collision (aka it “heats”) does so instantaneously .
And it doesn’t matter if you take them by trillions of trillions . The bulk will still heat as the sum of heated molecules which do so instantaneously .
Those points about the interplay between sun, atmosphere, ocean surface and ocean depths are critical to the whole issue.
I have heard it said that the ocean height continues to rise despite the ocean surfaces cooling and so the global system is still warming even though SST and atmosphere seem to be cooling.
My explanation would be that when the ocean oscillations are negative then less heat energy is being released to the atmosphere but solar input to the oceans continues so that there is an increase of energy in the oceans despite a cooling of atmosphere, land and so climate. During such periods solar energy gets tucked away in the depths and is denied to the atmosphere so that energy radiates to space faster than it is replenished by energy released from the oceans.
Conversely a period of positive oscillations causing atmospheric warming would normally involve enough release of energy from the oceans to warm the atmosphere but reduce the total energy in the system. That should result in a fall in ocean height and normally would.
However it appears that even during the 25 year warming spell from 1975 to 2000 there was nevertheless a slow increase in ocean height which appears to falsify the above BUT at the same time there was a so called grand solar maximum. Thus it is possible that the energy in the system continued to increase during the warming spell despite a full set of positive ocean oscillations. All that is needed for that to happen is for the historically high solar input (not necessarily reflected solely by TSI) to be putting more into the system than the positive oscillations are releasing.
Any consideration of multidecadal movements of energy between ocean surfaces and depths and variable and intermittent releases of that energy to the atmosphere is currently missing from the whole debate but in my view it is critical.
Combine those energy flows with solar changes over several cycles and I suspect that all the changes we have observed so far will be fully explained without involving CO2 at all.
The strong point of Illis’ study is the demonstration that a linear combination of AMO and ENSO indices can account for much of the variance seen in the standard “global temperature anomaly” compilations. The weak point is the naked presumption that the remnant is a physical “climate signal,” to which the logarithmic increments of temperature seen in vitro with rising CO2 concentrations can be applied . Given the multiple “adjustments” of actual data made by compilers such as GISS and Hadley and their failure to avoid UHI effects, any trend in their anomaly series is of dubious validity. And the surface temperature control in actual climate is provided by moist convection adjusting the vertical lapse rate in the atmosphere, not by the marginal radiative effect of trace gas concentrations.
John S says:
This brings to mind something I cannot understand with respect to AGW claims about the marginal effect of CO2.
That is, what mechanism do they propose will actually cause thermal runaway as CO2 levels increase?
They seem to hint that increasing CO2 will cause more of the outgoing LWR to be absorbed, thus heating up the atmosphere, and then? Causing more water to be evaporated and thus heating up the atmosphere more?
However, for that to occur, the extra heat/energy in the atmosphere has to be transferred to water, ie the oceans. But this would depend very much on timing, since hot air rises, and only the small portion in contact with the sea can transfer that energy by conduction. Of course, radiative transfer could also occur, but that would depend on the mean free path at the frequencies involved, and the probability that a molecule of CO2 transfers its energy to another molecule in the atmosphere before it radiates that extra energy away.
So, it would seem to me that the effect of an increase in CO2 would be to increase the convection effect that John S refers to, although only marginally.
I still ask:
–What about the other cycles? NAO, IPO, AO, AAO, and the Indian Ocean temperatures (which follow 20th century variations fairly well).
–What about the Mckitrick and LaDochy papers which indicate that global temperature trends are exaggerated?
Leif: Thanks. I’ll have to be content with the 30-year measures, then.
To John Philip,
Regarding the proper log formulas, I am really modeling all the GHGs here (using CO2 as a proxy for all of the them.) And I am modeling Temperature response.
The 5.35 ln (CO2/CO2o) is the formula for the “Forcing” of CO2 only (and there is a bunch of other formulas for the other GHGs and other forcings such as aerosols as well). In the most recent IPCC, the coefficient for CO2 was changed from 5.35 to 5.0.
With the 5.0 or 5.35 formula, one then gets a 3.45 Watts/m^2 impact from the CO2 forcing only which when multiplied by 0.75C /w/m^2 estimated temperature response to a forcing results in 2.6C per doubling of CO2 only.
When you add up all the forcings from all the GHGs and other feedbacks – the estimated forcing per doubling is 4.33 w/m^2 * 0.75C /w/m^2 = 3.25C per doubling.
In effect I am just regressing to find out how much forcing change there has really been to date (assuming forcings result in 0.75C per watt per metre although it really doesn’t matter what the number is). In effect, I am skipping all these steps and just regressing for the actual temperature response.
The warming models are really based on (using the anomaly C basis rather than Kelvin):
— 4.7 * ln(280) – 26.9 = -0.42C
— 4.7 * ln(560) – 26.9 = +2.84C
and the change is +3.25C per doubling
The regression returns
— 2.7 * ln(280) – 15.8 = -0.42C
— 2.7 * ln(560) – 15.8 = +1.44C
and the change is +1.85C per doubling
I was hoping I wouldn’t have to get into this discussion because it is very messy. I fell for this trap too a couple of times when I was designing it (as Anthony Watts can attest to). There are a lot of coincidences in these numbers.
And perhaps the modelers, in trying to nail down every little impact in “forcings” which may be based on calculations that are not correct to start with (where did the 5.35 come from anyway) , they have lost a little perspective on what they are trying to do which is model a Temperature response. Is the climate actually responding in “Temperatures C” rather than in “minute forcings in watts per metre squared”, the way it is supposed to.
I got quite a few “Watts” into that post.
Fernando:
“Perhaps, I need to understand the interaction
H2O + CO2> H2CO3 in the atmosphere”
That is a key question:That reaction is endothermic.
Tom Vonk says:
Bear with me here as I am trying to understand this, and may have to go away and read a physics text book.
However, what says that the temperature must stay CONSTANT? When the atmosphere, via CO2 increases, absorbs more energy, wouldn’t its temperature shift upwards and reach a different LTE point?
And I ask yet again, what about the influence of other cycles?
Here are my links:
PDO:
ftp://ftp.atmos.washington.edu/mantua/pnw_impacts/INDICES/PDO.latest
AMO:
http://www.cdc.noaa.gov/Timeseries/AMO/
Arctic Oscillation:
http://jisao.washington.edu/data/aots/
Antarctic Oscillation:
http://www.lasg.ac.cn/staff/ljp/data-NAM-SAM-NAO/SAMI1948-2007.ascii
North Atlantic Oscillation:
http://www.cgd.ucar.edu/cas/jhurrell/indices.data.html#naostatann
http://www.cgd.ucar.edu/cas/jhurrell/indices.data.html#nam
Indian Ocean Temp.Anomalies:
http://www.jisao.washington.edu/data/indiansat/
And what about the possibly spurious adjustments to the historical record?
All that might make the curve fit even better.
Richard Sharpe (10:02:53) :
Tom Vonk says:
The Planck’s distribution of the excited vibrationnal levels of CO2 demands that the proportion of excited levels stays CONSTANT for a given temperature .
Consequence is that CO2 simply must radiate away what it absorbs because else we have no more LTE .
Bear with me here as I am trying to understand this, and may have to go away and read a physics text book.
I don’t know what context this was said but if it’s intended to describe the situation in the earth’s troposphere it’s wrong. It should read:
Consequence is that CO2 simply must radiate away or lose by collisional transfer what it absorbs because else we have no more LTE.
In the stratosphere where CO2 is responsible for cooling and collisions are few then CO2 does predominantly lose energy by radiation.
To evanjones
I’ll try these out tonight. It would be preferable to have a series which is continuously updated with monthly data, a series which is a natural cycle unrelated to global warming and one that has a long enough time series (back to at least 1900 for example.)
Bill,
I’m impressed. I also haven’t had the time to look into it carefully. One thing that stands out to me, though, is your 4th figure, where you show the “warming” in the divergence between the temperature series, and the Nino+AMO only model, after ~1970.
I wonder if the “warming” would not be as dramatic, or even noticeable, if you used a tropics only temperature series, rather than the global series. I’ve done enough temperature trend analysis to know that most of the “warming” we’ve seen in the past 2-3 decades occurred in the northern extra-tropics, and over land. Before I attributed the gap you see in your 4th figure to rising CO2, I’d want to see what kind of gap exists just in the tropics. Then, if it is indeed localized primarily to northern extratropics, over the Asia land mass, I cannot help but wonder how much of that is due to quality issues with respect to the surface stations in that part of the globe.
I have raised the question several times, but have never received a cogent answer: why would warming from CO2 be localized in the northern extratropics, and over land? (And, for that matter, primarily over Asia; the USA has not been warming.)
While your results are intriguing, I think you should see how robust they are to hemispheric/tropical differences in long term temperature trends. If the “warming gap” diminishes, or goes away, if you use only tropical temperature trends, how would that affect your analysis?
I’m sure you know, but you can get the HadCRUT series specifically for tropics (it is 30N-30S, rather than the more common 20N-20S, but it will do for what I’m thinking about) here:
http://hadobs.metoffice.com/hadcrut3/diagnostics/regional/30-30/
Basil
@ur momisugly kim (04:03:59) :
Jason 2 has now been handed over to NOAA:
“The handover is a major step in Jason-2 operations. NOAA will now carry out routine operations on the satellite and, by the end of November, process the operational data received by its ground stations and interface with users.”
See http://www.eumetsat.int/home/main/media/news/708051
To Basil,
I did do the RSS tropics and the RSS northern hemisphere. (There is a nice fit to the Tropics).
http://img151.imageshack.us/img151/1222/rsstropicslz2.png
http://i463.photobucket.com/albums/qq360/Bill-illis/RSSNorthHemisphere.png
The warming in the Tropics for RSS is about 0.045C per decade which is basically the same as the global number. The Northern Hemisphere is higher at about 0.07C per decade.
I, as well, have not seen a very good explanation for why the temperature in the northern hemisphere is rising faster.
One little explanation might be that the AMO has been cycling upward since 1975. The reconstruction shows the AMO is much more important in the northern hemisphere than it is for the global temp series or for the Tropics. In the Tropics, the ENSO becomes dominant and the ENSO doesn’t really have long cycles of consistently up or down – just a lot of short sharp swings.
And thanks for the link to the regional Hadcrut3 data series. I have not used these specific breakdowns yet and will try them out.
Can someone help me understand something?
How is it that the global warming issue became a liberal vs conservative issue? I am blown away by how is sometimes seems more a political issue than a scientific one. Everyone with a political ideology has self-appointed himself a scientific expert on the topic. Does anyone really think he is smarter or knows more than the real experts? I say leave the scientific debate to the scientists and out of politics.
Personally I am more concerned with the truth than my beliefs. It seems that (other than people who don’t know or care about the issue) everyone has a prejudiced belief and will only pursue evidence of their viewpoint.
I have liberal leanings (though I am much too independently minded to agree with most Democratic or American liberal perspectives). I believe global warming is occurring, but not because of anything any pundit or politician has said, but because of what scientists have written. That being said, I am not afraid to look at evidence from the “opposition”, because I am so much more interested in the truth than in being right.
I am busy, so I don’t have time to read everything that interests me, but I plan on reading “Red Hot Lies” soon. I only hope that everyone, liberal or conservative, will keep an open mind and not be afraid or looking at evidence from both sides.
Moderator:
Sorry, I clicked on the wrong page before I submitted this. I reposted on the correct page. Please remove the above comment as it is not on topic here.
REPLY: It’s the holiday, not to worry – Anthony
“How is it that the global warming issue became a liberal vs conservative issue? ”
The scientists who were/are pushing the theory of global warming started pushing liberal ideology and solutions, while talking about global warming.
“I believe global warming is occurring, but not because of anything any pundit or politician has said, but because of what scientists have written.”
I don’t think I have ever read a post here that stated global warming is non-existant.
Also, your statement is vague. Do you believe man-made global warming exits? That would be one of the points of discussion.
What about the scientists that do not think man has caused the planet to warm or that man’s effect is small? Being scientists does there opinion influence you?
“I only hope that everyone, liberal or conservative, will keep an open mind and not be afraid or looking at evidence from both sides.”
That would be my hope also. I would argue that you have just described the far majority of people at this site. I don’t think anyone here believes others should be prosecuted or jailed for disagreeing with another over a scientific issus as complex as climate change.
I trust the Moderator will not post this if it is straying too far from the important point of Bill Illis’s analysis, but it concerns the natures of ‘evidence’ and ‘modelling’ (the analysis is a model).
Phillip Bratby says:
“The quote from the Hansen 1985 paper includes “Evidence from Earth’s history (3–6) and climate models“. Since when have climate models been able to produce evidence? I always thought evidence came from measurements, not from predictions!”
I agree. One my peer review comments for the IPCC AR4 (which it is not surprising was ignored) said the following:
Page 2-47 Chapter 2 Section 2.6.3 Line 46
Delete the phrase, “and a physical model” because it is a falsehood.
Evidence says what it says, and construction of a physical model is irrelevant to that in any real science.
The authors of this draft Report seem to have an extreme prejudice in favour of models (some parts of the Report seem to assert that climate obeys what the models say; e.g. Page 2-47 Chapter 2 Section 2.6.3 Lines 33 and 34), and this phrase that needs deletion is an example of the prejudice.
Evidence is the result of empirical observation of reality.
Hypotheses are ideas based on the evidence.
Theories are hypotheses that have repeatedly been tested by comparison with evidence and have withstood all the tests.
Models are representations of the hypotheses and theories.
Outputs of the models can be used as evidence only when the output data is demonstrated to accurately represent reality.
If a model output disagrees with the available evidence then this indicates fault in the model, and this indication remains true until the evidence is shown to be wrong.
This draft Report repeatedly demonstrates that its authors do not understand these matters. So, I provide the following analogy to help them. If they can comprehend the analogy then they may achieve graduate standard in their science practice.
A scientist discovers a new species.
1.
He/she names it (e.g. he/she calls it a gazelle) and describes it (e.g. a gazelle has a leg in each corner).
2.
He/she observes that gazelles leap. (n.b. the muscles, ligaments etc. that enable gazelles to leap are not known, do not need to be discovered, and do not need to be modelled to observe that gazelles leap. The observation is evidence.)
3.
Gazelles are observed to always leap when a predator is near. (This observation is also evidence.)
4.
From (3) it can be deduced that gazelles leap in response to the presence of a predator.
5.
n.b. The gazelle’s internal body structure and central nervous system do not need to be studied, known or modelled for the conclusion in (4) that “gazelles leap when a predator is near” to be valid. Indeed, study of a gazelle’s internal body structure and central nervous system may never reveal that, and such a model may take decades to construct following achievement of the conclusion from the evidence.
Richard