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
John Philip:
I would be grateful for an explanation of your statement saying;
“So the ‘physics’ explanation is that the heat largely goes into the oceans which take years to decades to warm in response”.
Please explain how liquid water absorbs heat but does not warm until some time later. My understanding is that – in the absence of latent heat exchange – any warming would be instantaneous.
This is important for two reasons.
Firstly, the oceans have been measured to be cooling over recent years and, if my understanding is correct, then the ‘thermal inertia’ you espouse is not happening.
Secondly, there is a need for large energy storage capacity to assist smoothing of electricity grid supplies. So, a mechanism that allows water to absorb heat but not warm until later would solve a major industrial problem.
Richard
evanjones (10:09:47) :
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.
First of all, don’t use ‘old’ stuff [and note: no please here]. Second, there has been some doubt as to what degree my ideas are met with general acceptance. A very new book [Sunspots and Starspots (Cambridge Astrophysics Series) by Thomas and Weiss, 2008, ISBN 978-0-521-86003-1] summarizes the ‘textbook’ consensus as follows [page 214]:
“Reliable measurements of solar irradiance extend only over the past 30 years. The success of models involving only sunspots and faculae in reproducing these measurements has encouraged researchers to attempt to reconstruct the variations in TSI over a much longer period based either on the historical sunspot record or on the proxy record from abundances of cosmogenic isotopes, or even on models of cyclic activity in the solar photosphere (e.g. Lean 2000, Froehlich and Lean 2004, Wang. Lean, and Sheeley 2005, Krivova, Balmaceda, and Solanki 2007). The upper panel of Figure 12.3 [Figure 3 of http://www.leif.org/research/CAWSES%20-%20IMF,%20EUV,%20TSI.pdf ] shows the most straightforward reconstruction, relying only on the measured correlations between sunspot numbers and irradiance since 1978 (Froehlich and Lean 2005). Other reconstructions (e.g. Lean 2000, Wang, Lean, and Sheeley 2005) differ in the inclusion or omission of an arbitrarily varying contribution from ephemeral active regions, on on the basis of a questionable difference in Ca II emission between active and inactive stars, in assuming [emphasis added – me] that there was a long-term increase in TSI, as shown in the lower panel of Figure 12.3. In reality, sice we know that cycles persisted through the Maunder Minimum (Beer, Tobia, and Weiss 1998), it seems unlikely that the average value of TSI could have dropped significantly below its level at a normal sunspot minimum.”
Third, because of TSI = a T^4, we have dTSI/TSI = 4 dT/T, so the percentage increase in Temperature will be only 1/4 of the percentage increase of TSI, not twice as you have it
I notice the article included ENSO and AMO, representing effects from the Pacific and Atlantic Oceans. No factor from the Indian Ocean. Living in Melbourne, in South East Australia the Indian Ocean Dipole (IOD) has a major effect for temp and rainfall over SE Australia (on the other side of the continent to the Indian Ocean). I wonder if a complete model for oceanic effect on temperature can be completed without considering the Indian Ocean. (Cynically I could say that the AMO may have the greatest impact because it effects areas where the greatest amount of temp measurement is done.)
George E. Smith (14:57:31):
See the animation on:
http://www.atmosphere.mpg.de/enid/1__Oxidants___Observation/-_observation_spectroscopy_l6.html
I have the same questions.
Perhaps, I need to understand the interaction
H2O + CO2> H2CO3
in the atmosphere.
This is an interesting study.
Looking at your charts, I suspect that you may have stationarity issues with y our temperature data, which is common when working with time-series data. You should conduct some stationarity tests (such as Augmented Dickey-Fuller or Philips-Perron) and make the necessary transformations to your data if required.
Regressions using non-stationary data can be very misleading, and sometimes worthless. That’s why you should do these tests to be safe.
To Bob Tisdale,
I got your ERSST.v2 data in and working. I have to say google docs does do a mess of things so we probably shouldn’t use it again until they get the bugs out.
On the good side, this data certainly works and produces a greater coefficient for the Nino 3.4 region (lagging it by 3 months seemed to work better again). The warming signal against CO2 also drops to about 1.65C per doubling.
On the downside, the r^2 falls from 0.783 to 0.745 and the errors are a little larger and/or show more consistently above or consistently below characteristics.
But it does not look bad at all. This dataset more consistently catches the spikes for example. Here are the two charts you would want to see.
http://img265.imageshack.us/img265/3814/btisdalehadcrut3oe1.png
http://img383.imageshack.us/img383/4695/btisdalewarmingqg8.png
George E. Smith (13:15:16) :
I’m curious about your statement that the CO2 spectrum consists of a whole bunch of closely spaced lines (in the IR ?) . Do you know of any link to a high resolution spectrum for CO2. I have looked and never been able to find any good spectra for the common GHG culprits. Yet I would have thought that with all the climate interest in those gases, that the spectra would have been studied to death. The only data of much use I’ve been able to find comes from The InfraRed Handbook, from the Infrared Information Analysis Center, (ERIM) and I presume that is somewhat dated.
What is the physical basis for the many fine lines in the IR region ?
It would seem to me that in the earth atmosphere at least at ground level, that you must have a pretty continuous absorption from around 13-17 microns; but I’m puzzled as to why a molecular spectrum has many fine lines (I am not a chemist).
OK George, here goes.
The IR absorption arises because of transitions between vibrational and rotational energy levels. ( I’ve linked to some webpages below).
A molecule can vibrate and rotate but can only exist at certain energy levels, the separation between vibrational levels is much larger than rotational, in the case of the CO2 667 cm-1 vs less than 1 cm-1. A CO2 molecule in the ground state can absorb radiation by jumping up one energy level in vibration (∆v=1) while at the same time staying at the same relative rotational level (Q-branch, ∆j=0), increasing one level (R-branch, ∆j=+1) or decreasing one level (P-branch, ∆j=-1). Since there are a great many rotational energy levels there are a great many possible lines.
See here for example, down as far as isotope effects: http://www.chemistry.nmsu.edu/studntres/chem435/Lab9/intro.html
in the case of CO2 bending the Q-branch is allowed.
Here’s one specifically for CO2:
http://www.phy.davidson.edu/StuHome/jimn/CO2/Pages/CO2Theory.htm
I hope that helps?
Bill. This link
http://www.sciencemag.org/cgi/ijlink?linkType=ABST&journalCode=sci&resid=229/4716/857
Should get you to the Hansen(1985) paper without the need for a paid subscription. His results are summarised in the 2005 paper referenced earlier:
The lag in the climate response to a forcing is a sensitive function of equilibrium climate sensitivity, varying approximately as the square of the sensitivity (1), and it depends on the rate of heat exchange between the ocean’s surface mixed layer and the deeper ocean (2–4). The lag could be as short as a decade, if climate sensitivity is as small as 0.25°C per W/m2 of forcing, but it is a century or longer if climate sensitivity is 1°C per W/m2 or larger (1, 3). Evidence from Earth’s history (3–6) and climate models (7) suggests that climate sensitivity is 0.75° ± 0.25°C per W/m2, implying that 25 to 50 years are needed for Earth’s surface temperature to reach 60% of its equilibrium response (1).
http://www.sciencemag.org/cgi/content/full/308/5727/1431
I note he published a temperature forecast just a few years later which did not include any ocean absorption that we can tell of since his Scenario B forecast temps are about twice as high as they are currently.
Not so. Hansen’s scenarios were based on an early version of the NASA climate model, of course this includes the physics of ocean heat takeup. What is your source for the claim that Scenario B is currently a 100% overestimate?
The temperature trend since 1979 indicates we can never reach the 3.25C doubling level no matter how much the oceans absorbs or how much lag time there is. It would take a thousand years.
The trend in the UAH data, which shows the least warming of the various global datasets, is approx 0.17C/decade, so a linear extrapolation takes just 200 years to reach a highly dangerous 3.4C increase. 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. One example of a positive feedback: as the Arctic ice melts, the reduced albedo raises the local temperature and causes melting of the permafrosts, releasing trapped carbon. The Arctic permafrost contains twice as much carbon as the entire global atmosphere. See these recent papers
http://www.cgd.ucar.edu/ccr/dlawren/publications/lawrence.grl.submit.2008.pdf
http://www.bioone.org/perlserv/?request=get-document&doi=10.1641%2FB580807&ct=1
Moving on,
Richard Courtney: Please explain how liquid water absorbs heat but does not warm until some time later. My understanding is that – in the absence of latent heat exchange – any warming would be instantaneous.
Tell me Richard, last time you made coffee, when you switched on your kettle, applying a positive energy transfer, did the water boil
(a) Instantaneously, or
(b) A few minutes later ?
Now imagine your kettle is ocean-sized and the energy flux is measured in a few W/m2. Are you seriously arguing that a such huge body of water will warm ‘instantaneously’?
George Smith why has the earth been cooling for the last ten years? We should have had about 1/10 of the ten degrees or so predicted for the year 2100 in temperature rise, instead we have had a very sizeable temperature fall; so much for the effect of thermal lag times.
George – which of the four major global temperature indices shows a ‘sizeable temperature fall’ over the last 120 months please? Here’s the data:
http://tinyurl.com/563bmc
They all appear to show an increase. Please explain.
Bill. This link
http://www.sciencemag.org/cgi/ijlink?linkType=ABST&journalCode=sci&resid=229/4716/857
Should get you to the Hansen(1985) paper without the need for a paid subscription. His results are summarised in the 2005 paper referenced earlier:
The lag in the climate response to a forcing is a sensitive function of equilibrium climate sensitivity, varying approximately as the square of the sensitivity (1), and it depends on the rate of heat exchange between the ocean’s surface mixed layer and the deeper ocean (2–4). The lag could be as short as a decade, if climate sensitivity is as small as 0.25°C per W/m2 of forcing, but it is a century or longer if climate sensitivity is 1°C per W/m2 or larger (1, 3). Evidence from Earth’s history (3–6) and climate models (7) suggests that climate sensitivity is 0.75° ± 0.25°C per W/m2, implying that 25 to 50 years are needed for Earth’s surface temperature to reach 60% of its equilibrium response (1).
http://www.sciencemag.org/cgi/content/full/308/5727/1431
I note he published a temperature forecast just a few years later which did not include any ocean absorption that we can tell of since his Scenario B forecast temps are about twice as high as they are currently.
Not so. Hansen’s scenarios were based on an early version of the NASA climate model, of course this includes the physics of ocean heat takeup. What is your source for the claim that Scenario B is currently a 100% overestimate?
The temperature trend since 1979 indicates we can never reach the 3.25C doubling level no matter how much the oceans absorbs or how much lag time there is. It would take a thousand years.
The trend in the UAH data, which shows the least warming of the various global datasets, is approx 0.17C/decade, so a linear extrapolation takes just 200 years to reach a highly dangerous 3.4C increase. 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. One example of a positive feedback: as the Arctic ice melts, the reduced albedo raises the local temperature and causes melting of the permafrosts, releasing trapped carbon. The Arctic permafrost contains twice as much carbon as the entire global atmosphere. See these recent papers
http://www.cgd.ucar.edu/ccr/dlawren/publications/lawrence.grl.submit.2008.pdf
http://www.bioone.org/perlserv/?request=get-document&doi=10.1641%2FB580807&ct=1
Moving on,
Richard Courtney: Please explain how liquid water absorbs heat but does not warm until some time later. My understanding is that – in the absence of latent heat exchange – any warming would be instantaneous.
Tell me Richard, last time you made coffee, when you switched on your kettle, applying a positive energy transfer, did the water boil
(a) Instantaneously, or
(b) A few minutes later ?
Now imagine your kettle is ocean-sized and the energy flux is measured in a few W/m2. Are you seriously arguing that a such huge body of water will warm ‘instantaneously’?
George Smith why has the earth been cooling for the last ten years? We should have had about 1/10 of the ten degrees or so predicted for the year 2100 in temperature rise, instead we have had a very sizeable temperature fall; so much for the effect of thermal lag times.
George – which of the four major global temperature indices shows a ‘sizeable temperature fall’ over the last 120 months please? Here’s the data:
http://tinyurl.com/563bmc
They all appear to show an increase. Would you please explain why you claim a sizeable cooling?
Bill: Thanks for the update. I forgot to note earlier that the output of your model appears to generate a global temperature anomaly curve that comes much closer to the instrument data than at least one high-priced GCM. It will remain nameless so not to start a battle on this thread.
Question: At what cell did you paste in the ERSST NINO data and did you use the revised NINO data starting at Jan 1871? I want to make sure I’m looking at the same spreadsheet that you are when I include it.
Thanks again.
John Philip said:
Well, that’s one possible conclusion — if you cherry pick your beginning/ending dates.
Instead, let’s look at the big picture, from the very same UAH: click
Your claim that we’re headed for a ‘highly dangerous’ rise in temps looks silly. Why continue digging, when the planet is laughing at your hubris?
Smokey – the figure I gave is a simple linear fit to all the available UAH data,from Spencer and Christy, so there can really be no question of cherry picking. Your graph OTOH is a highly dubious polynomial fit widely condemned as misleading on this, of all, websites.
I would like to know the algorithm used to plot ‘average’ temperature vs. ‘heat’.
I can understand the logarithmic relationship between absorbance of photons with increaseing concentration; but for the life of me I have never seen an expression which explains how this energy give rise to temperature, give that we are dealing with a three phase system, ice, liquid water and vapor and the fact that changes in energy input could manifest themselves in pressure and in expansion of the atmosphere. You could for instance fill a balloon or a glass sphere with CO2 and irradiate it, the steady state temperature of the balloon would be lower than the glass sphere.
Don Keiler had asked earlier if there was any change in the rate of warming over time.
The changes are actually very hard to decifer.
My model based on ln (CO2) shows a gradual increase over time (not dissimilar to the very slightly exponential growth in Co2 levels) to where it is 0.15C per decade in the 2000s.
However, the actual observation data (after adjusting for the ENSO and the AMO) shows much more variation.
There is a very slight cooling trend from about 1890 to 1915. In the 1920s, warming jumps to about 0.2C per decade, then it falls to 0. From 1933 to 1945, it jumps to about 0.25C per decade and then falls rapidly to a negative value of -0.2C per decade from 1946 to 1955. From 1955 to 1975, warming is about 0.1C per decade. But from 1975 on, there is a gradual deceleration in the warming rate so that is very close to 0.0C per decade right now.
Complicated.
There is obviously more going on here than the model shows.
To Bob Tisdale,
The data showed it started in Jan 1854 so I pasted it into April 1854 (and Jan 1854 originally which didn’t produce as good a fit.)
I did the regression model starting in 1856 and also starting in 1871. It didn’t make much difference on the starting point.
To John Philip
The UAH unadjusted temperature trend is just 0.13C per decade (not 0.17C).
The UAH data adjusted for the ENSO and the AMO produces a warming trend of just 0.03C per decade which is probably a little low but would produce no warming to worry about at all.
John Phillip:
You ask me:
“Richard Courtney: “Please explain how liquid water absorbs heat but does not warm until some time later. My understanding is that – in the absence of latent heat exchange – any warming would be instantaneous.”
Tell me Richard, last time you made coffee, when you switched on your kettle, applying a positive energy transfer, did the water boil
(a) Instantaneously, or
(b) A few minutes later ?
Now imagine your kettle is ocean-sized and the energy flux is measured in a few W/m2. Are you seriously arguing that a such huge body of water will warm ‘instantaneously’? ”
I answer:
Yes, I am saying that water (in the kettle or in the ocean) increases its temperature as – not after – heat is added. If you don’t believe me then try turning off the kettle before the water boils and see if it does boil.
And the water cools as – not after – it looses heat. The oceans have been cooling in recent years and, therefore, the ‘thermal inertia’ you espouse is not happening.
I repeat my question to you that you have not answered: i.e.
Please explain how liquid water absorbs heat but does not warm until some time later.
And I repeat that you could make a fortune from a mechanism that would permit water to store heat without warming because it would solve the problem of needed large energy storage capacity to assist smoothing of electricity grid supplies.
I will not answer any response to this from you other than an exposition from you of the mechanism that you suggest would permit water to store heat without warming.
Richard
John Philip:
You specifically referred to UAH:
Are you actually claiming that global temperatures are continuing to rise? Is that what’s happening on your planet?
On Earth, temperatures have fallen. Unless, of course, you still believe the “adjusted” temperatures provided by the science fiction writers at GISS.
If GISS was prepared to stand behind its clearly fictional press releases, it wouldn’t be afraid to publicly archive the raw data. Would it? But the fact that GISS adamantly refuses to disclose their taxpayer-funded raw data, or the methodology they use to ‘adjust’ the temperature record ever upward, tells people all they need to know about GISS’ probity.
Bill Illis is correct: we don’t know enough about the climate. Readjusting raw data isn’t helping the science; it’s a deliberately deceptive agenda.
@ur momisugly John Philip (17:23:37) :
“Richard Courtney: Please explain how liquid water absorbs heat but does not warm until some time later. My understanding is that – in the absence of latent heat exchange – any warming would be instantaneous.
Tell me Richard, last time you made coffee, when you switched on your kettle, applying a positive energy transfer, did the water boil
(a) Instantaneously, or
(b) A few minutes later ?
Now imagine your kettle is ocean-sized and the energy flux is measured in a few W/m2. Are you seriously arguing that a such huge body of water will warm ‘instantaneously’?”
I just made some coffee in a kettle. Here are the temperatures:
start: 78°F
1 min: 94°F
2 min: 116°F
3 min: 133°F
4 min: 151°F
5 min: 170°F
6 min: 193°F (boiling at 6,200 feet above sea level)
7 min: 194°F (still boiling)
Made coffee.
From this experiment it would appear that warming was instantaneous (and measurable) after applying a “positive energy transfer.” Since the issue isn’t how long it takes the oceans to boil, but rather to warm up measurably, it would appear that warming of the oceans would be measurable without a significant lag. Considering the mass of the oceans, I would agree that it would take a considerable amount of time to warm them a given number of degrees, but such warming would be detectable as it took place as with the water to make my coffee. Although I can understand mathematically how there could be a lag in a given output with respect to a given input, I do not understand what real life physical process could take place (i.e. the “pipeline”) that could result in significant warming of the oceans that would not be measurable as it happened, as in making my cup of coffee. Please explain.
Phil. (19:44:42)
It seems that each instant after heat was applied the temperature rose. Looks pretty instantaneous to me.
====================================
kim (20:30:16)
Phil., on further reflection, it looks like I got your point.
===================================
To Phil and John Philip
Note we are talking about deep ocean temperatures here versus the sea surface (since the sea surface temps are already captured in the global temperature series.)
The data does show there is some warming in the deep oceans. There is a 0.1C increase in some latitudes (about 30% of the oceans) down to 1,000 metres and about 0.05C in some latitudes (about 15% of the oceans) down to 2,000 metres. So it appears there is, indeed, some warming of the deep oceans.
During the ice ages when surface temps fell by 5C, the deep oceans appear to have decreased in temp from their current 3C to about 0C. So the deep oceans are affected by the surface temps.
Water, however, is one of strangest chemicals around. When it solidifies as ice, it gets less dense and floats. Warmer water, however, rises to the top and colder water sinks to the bottom. If it freezes, it rises to the surface. Hence, any change in the surface sea temps takes a long, long time to influence the oceans at deeper levels. The very cold water stays at the bottom, the warmer water rises to the surface.
It almost takes a complete circulation of the oceans to make much difference at all, which can be a thousand years or more. The lag between surface temps and CO2 in the ice ages indicates we could expect 800 years for the deep ocean to be really influenced by the surface.
My issue with this is that global warmers refuse to say how much lag there is. None of Hansen’s papers that I could find (including the one linked to by John Philips) say anything about it.
More importantly, in a physical sense, the deep ocean absorption just means that more mass needs to be heated up by the increased greenhouse effect provided by increased CO2. We don’t warm to +3.25C, we only get to +1.85C and then an equilibrium is established. The deep oceans will continue to absorb energy from the surface continuously and it will start cooling again if that energy doesn’t continue rising. Once we get to a doubled CO2 level (and let’s say we stay there) there is no more warming to come once the deep oceans catch up.
That is my perspective on it. And I firmly believe the warmers need to be clear about this for once.
@ur momisugly Bill Illis (20:36:25) :
I think an important distinction needs to be made between temperatures and heat content and which of the two is being referred to by the term “warming.”
From http://climatesci.org/2008/11/25/is-global-warming-spatially-complex/:
“Unlike surface air temperature by itself (that has been the main climate metric used to assess global warming), in which there is a lag between a radiative imbalance and an equilibrium temperature …, there is no lag between a radiative imbalance and the amount of Joules in the climate system.”
From http://www.climatesci.org/publications/pdf/R-247.pdf:
“The concept encapsulated by the term “unrealized heating” more appropriately refers to storage of heat in a nonatmospheric reservoir (i.e., primarily the ocean), with the “realization” of the warming only occurring when heat is transferred into the atmosphere.”
and:
“Unlike temperature, at some specific level of the ocean, land, or the atmosphere, in which there is a time lag in its response to radiative forcing, there are no time lags associated with heat changes.”
The “warming in the pipeline” would seem to reflect heat storage in the oceans that later is or can be transferred to the atmosphere, thus affecting weather at the surface where human beings live. However, it does not seem as if heat from the Sun could be stored on Earth in a place where that heat cannot be measured as it is accumulated. Nor would it seem possible that sea levels could rise due to thermal expansion of the oceans with a lag of decades, if heat is being stored in the oceans themselves and later transferred to the atmosphere.
Bill – you’re right 0.13C/decade for UAH since 1979 is correct, I was confusing UAH with the RSS analysis, apologies for the confusion. However taking the regression analysis as published and extrapolating it forward suffers from these flaws:-
– It ignores the thermal inertia of the climate system, assuming that all the forcing applied is reflected in the temperature rise already observed. This is not the case, it is uncontroversial that the heat stored in the ocean will continue to cause a rise in surface temperatures for several decades.
– It uses detrended data for the AMO regression but (nearly) raw data for the ENSO regression. The analysis should be repeated using the same source data for both oscillations, the incorrect statement that the AMO data shows no trend should be addressed.
– It does not handle feedbacks correctly, more sophisticated models find that as water vapour increases (to choose just the most significant single feedback), the resulting greenhouse warming increases exponentially, acting to offset the logarithmic declining effetcs of additional CO2.
The question of the size of the lag between a radiative imbalance and the corresponding temperature increase does not have a simple single answer – some feedbacks, for example the disintegration of large ice sheets, operate on a scale of centuries, the IPCC’s figures for a doubling of CO2 use a definition of climate sensitivity that includes ‘fast’ feedbacks only, sometimes called the Charney sensitivity which assumes that the land surface, ice sheets and atmospheric composition stay the same.. See Chapter 10 of AR4 WG1.
Richard, later on in our thought experiment the kettle is switched off. The temperature as measured some distance from the heat source continues to rise for some time as the heat is distributed through the body of water. Same thing, but on a planetary scale.
To Bill Illis and to Phil:
Thankyou for your points. I agree with both of you, and add the following.
Bill Illis, you suggest the stored heat that may induce ‘delayed’ AGW may be in the deep ocean. In that case,
(a) As Phil says, very little of the stored heat could return to the atmosphere until the deep ocean water returned to the surface (i.e. ~800 years after the heat entered the ocean) because there is little thermal exchange across the thermocline (and please see my comment on magnitude below). This could not be a problem worthy of consideration (at least, not worthy of consideration by our civilisation). And – in the context of this debate – it is not relevant to your model (but please see my final comment below).
(b) The lack of accelerated sea level rise in recent decades suggests that such deep ocean storage of AGW heating has not been significant.
(c) The heating from AGW of the ocean is (i) mostly direct radiant IR input that is absorbed within the top few meters of the ocean, (ii) conductive heating of the ocean surface by contact with warm air, and (iii) addition of warmer water to the ocean surface layer from precipitation, rivers and runoff from the land.
Phil, I agree with you that the list of heat transfer mechanisms of AGW into the oceans that I provide in (c) indicates transfer of heat (from AGW) to the deep ocean occurs via the ocean surface layer. Therefore, in the absence of any other known thermal transfer mechanism from the surface to the deep ocean, it has to be agreed that recent cooling of the ocean surface layer indicates the transfer of heat has not occurred recently.
However, there is a possibility that heat from AGW has been conveyed to the deep ocean because warming of the ocean surface layer occurred in previous decades. This raises the issue of how much AGW will be returned to the atmosphere when that deep ocean water returns to the surface (~800 years in the future, see above).
The Second Law of Thermodynamics says that the atmospheric temperature rise induced by return to the surface of that AGW stored in deep ocean cannot be more than the temperature rise that put it into the ocean. Therefore, it threatens “our children’s children” (in ~800 years time) with no more AGW than we experienced in the twentieth century. And that warming is so small that we cannot discern it from natural variation.
Additionally, the water that went into deep ocean ~ 800 years ago is returning to the surface now. This could be affecting ocean surface layer pH, ocean surface layer temperature, and – therefore – atmospheric carbon dioxide concentration (mostly by the pH change). It could be expected to affect the magnitude of the residual trend detected in the analysis provided by Bill Illis.
Richard