Guest post by Lance Wallace
The carbon dioxide data from Mauna Loa is widely recognized to be extremely regular and possibly exponential in nature. If it is exponential, we can learn about when it may have started “taking off” from a constant pre-Industrial Revolution background, and can also predict its future behavior. There may also be information in the residuals—are there any cyclic or other variations that can be related to known climatic oscillations like El Niños?
I am sure others have fitted a model to it, but I thought I would do my own fit. Using the latest NOAA monthly seasonally adjusted CO2 dataset running from March 1958 to May 2012 (646 months) I tried fitting a quadratic and an exponential to the data. The quadratic fit gave a slightly better average error (0.46 ppm compared to 0.57 ppm). On the other hand, the exponential fit gave parameters that have more understandable interpretations. Figures 1 and 2 show the quadratic and exponential fits.
Figure 1. Quadratic fit to Mauna Loa monthly observations.
Figure 2. Exponential fit
From the exponential fit, we see that the “start year” for the exponential was 1958-235 = 1723, and that in and before that year the predicted CO2 level was 260 ppm. These values are not far off the estimated level of 280 ppm up until the Industrial Revolution. It might be noted that Newcomen invented his steam engine in 1712, although the start of the Industrial Revolution is generally considered to be later in the century. The e-folding time (for the incremental CO2 levels > 260 ppm) is 59 years, or a half-life of 59 ln 2 = 41 years.
The model predicts CO2 levels in future years as in Figure 3. The doubling from 260 to 520 ppm occurs in the year 2050.
Figure 3. Model predictions from 1722 to 2050.
The departures from the model are interesting in themselves. The residuals from both the quadratic and exponential fits are shown in Figure 4.
Figure 4. Residuals from the quadratic and exponential fits.
Both fits show similar cyclic behavior, with the CO2 levels higher than predicted from about 1958-62 and also 1978-92. More rapid oscillations with smaller amplitudes occur after 2002. There are sharp peaks in 1973 and 1998 (the latter coinciding with the super El Niño.) Whether the oil crisis of 1973 has anything to do with this I can’t say. For persons who know more than I about decadal oscillations these results may be of interest.
The data were taken from the NOAA site at ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt
The nonlinear fits were done using Excel Solver and placing no restrictions on the 3 parameters in each model.
Ferdinand:
re. your post at June 5, 2012 at 9:30 am
The ‘background’ CO2 level is meaningless. An IR photon interacting with a CO2 molecule does not ‘know’ if the molecule is ‘background’ or not. So, the total number of CO2 molecules in the atmosphere is all that matters, especially when CO2 is “well mixed” in the air.
At issue is to determine how the total amount of CO2 in the atmosphere has varied with time. Local measurements can do that for their localities. We use Mauna Loa as a proxy for the global total but, in principle, anywhere would do.
Richard
Robert Brown:
Please forgive my ignorance of your local idiom. What does “Damn skippy” mean, please?
And, while I am asking, I take this opportunity to say I agree everything else you said in that post (I did understand that).
Thanking you in anticipation.
Richard
Robert Brown says:
June 5, 2012 at 10:56 am
“However, one would have to look at the raw data to see if in fact this is what is happening, and I have not done so.
Do so. Here, I take the smoothing level down to 12 months (you have to average out the yearly variation). You’ve still got a 6 month advance because of the WoodForTrees centering of the average, but your spurious leads have vanished.
“Second, because sometimes dCO_2/dt leads T, and sometimes T leads dCO_2/dt (or they are closely synchronized) it is also important to bear in mind that inferring causality from correlation is weak in both directions.”
No, it isn’t. The variables of interest are the temperature and the total CO2, not the derivative. A change in temperature shows up in overall CO2 concentration at a later time. The temperature leads.
richardscourtney says:
June 5, 2012 at 10:45 am
“Hence, almost any model with two or more variables can be tuned to match the Mauna Loa data to within the measurement error (n.b. to a perfect fit to each datum).”
No, not perfect. The word perfect has a very specific meaning. Only within the arbitrary bounds you have set as a threshold.
“Why don’t the natural sequestration processes sequester all the emissions (natural and anthropogenic) when it is clear that they can? “
They very nearly do. If the system were in equilibrium, they would.
Please forgive my ignorance of your local idiom. What does “Damn skippy” mean, please?
It means “yes, I most emphatically agree”. I have no idea where it comes from, but of course Google has some ideas and at least one definition.
http://en.wiktionary.org/wiki/damn_skippy
So you could have replied to me with “Damn skippy to your damn skippy!” yourself instead of the more sedate “I agree (with) everything else you said in that post”:-).
rgb
richardscourtney says: @ur momisugly June 5, 2012 at 10:45 am
…I repeat the important question is
Why don’t the natural sequestration processes sequester all the emissions (natural and anthropogenic) when it is clear that they can?….
_______________________________________
SWAG.
#1. CO2 is not evenly distributed throughout the atmosphere.
#2. CO2 has not remained more or less constant for eons but fluctuates a great deal more than the Warmists want us to know.
#3 There is competition between C3 and C4 plants. C3 plants do not take CO2 down below around 300 ppm based on the open field wheat study. If there is abundant CO2 the C3 plants have an advantage over the C4 plants and crowd them out. (SWAG)
#4 WIND – Trade winds and the jet streams. We have seen a change in the trades (El Nino, La Nina and a change in the location of the jets)
#5 The temperature has stopped rising and maybe falling (see Beck’s comment about 1941 blip)
In 2000 the mean annual air -sea flux for CO2 http://www.ldeo.columbia.edu/res/pi/CO2/carbondioxide/image/annfluxgmm2u2windmap.jpg
Models again but they mention that the air-sea gas transfer rate is a function of WIND SPEED.
From the Air Vent, a comment by Ernest Beck:
various references:
Carbon starvation in glacial trees recovered from the La Brea tar pits, southern California
http://www.co2science.org/subject/b/summaries/biodivc3vsc4.php
plant response to CO2: http://i32.tinypic.com/nwix4x.png
CO2: stomata http://www.geocraft.com/WVFossils/stomata.html
CO2 Aquittal: http://www.scribd.com/doc/31652921/CO2-Acquittal-by-Jeffrey-A-Glassman-PhD
ON WHY CO2 IS KNOWN NOT TO HAVE ACCUMULATED IN THE ATMOSPHERE &
WHAT IS HAPPENING WITH CO2 IN THE MODERN ERA: http://www.rocketscientistsjournal.com/2007/06/on_why_co2_is_known_not_to_hav.html#more
Satellite Data:
http://www.jaxa.jp/press/2009/10/20091030_ibuki_e.html
http://chiefio.wordpress.com/2011/10/31/japanese-satellites-say-3rd-world-owes-co2-reparations-to-the-west/
CO2 Flux Estimated from Air-Sea Difference in CO2 Partial Pressure: http://www.ldeo.columbia.edu/res/pi/CO2/carbondioxide/pages/air_sea_flux_2000.html
Robert Brown and Bart:
Robert, thankyou. As you say, I should have Googled it. Sorry.
Bart, I strongly object to your saying
The fit was perfect in that each datum from each model matches each corresponding datum in the Mauna Loa data set to within the measurement error. That IS a perfect fit. And the measurement error is not “arbitrary bounds” I made. I told you its value (Ferdinand has said the same above) and I provided you with a link to the Mauna Loa Lab.’s own explanation of how they derive it.
I do NOT make arbitrary choices (but I sometimes make mistaken ones). In this case I used the only valid “threshold”.
Richard
richardscourtney says:
June 5, 2012 at 10:45 am
“Only within the arbitrary bounds you have set as a threshold.”
The measurement error is a wideband process. The “signal” you are looking for is at low frequency. As a result, you can apply a low pass filter to remove high frequency noise, revealing the low frequency signal hiding in it. This is especially the case for numerically differentiated data, as the differentiation process amplifies noise at high frequency.
In general, there is a particularly strong yearly signal you want to remove. A twelve month average will do this, though it does not have particularly good passband characteristics (gain falls off from unity at dc fairly rapidly). A better filter can be designed using standard packages, but that requires specialized knowledge.
A succession of 12 month averages really clobbers noise in a derivative, as you can see in this plot.
Now, in that plot, you will see that I divided the temperature series up into segments, because there appears to be a step change around 1990. In the analogous model I suggested above, this could result from a step change in the variable To, the equilibrium temperature. This suggests that those arguing above on this thread for deep ocean upwelling as the source of the temperature differential needed to drive the levels of CO2 to their current levels may be on the right track. Around 1990 or so, there might have been a sudden shift in the thermodynamic state of the upwelling water.
On the other hand, the Hadley SST here may not be very precise, or have been subject to various “adjustments” which are not readily available to us.
Bart:
Your response to my post is a travesty. It quotes your words as being mine when yhose were the very words I had explained are a lie. It then waffles on with complete misunderstanding of what measurement error indicates when I have previously explained this basic science to you in a previous thread.
I see no point in answering you further whatever you post unless it is to refute another misquotation of me.
Richard
Richard, “Why don’t the natural sequestration processes sequester all the emissions (natural and anthropogenic) when it is clear that they can? “
The annual cycle in the Arctic is a clue. The cold water and biological activity could be considered a great sink limited only by how fast the CO2 is transfered to the water surface. During much of the year most of the surface is covered with ice. As the ice closes the sink drain in the fall, the CO2 concentration rises as CO2 continues to be delivered from the south. The concentration reaches a maximum around the middle of February when most of the ocean is covered with ice. It reaches a minimum when the area of exposed cold water is a maximum. Also, the partial pressure difference between air and water is a factor (transfer rate a function of concentration). Another factor to consider is the length of time any extra absorbed CO2 takes to get back to the equator where it is readmitted into the atmosphere. Think about the life and death cycle of phytoplankton.
Bart says:
June 5, 2012 at 9:07 am
You cannot arbitrarily detrend the data. The slope of the temperature is what produces the curvature in the accumulated CO2, and it matches exactly. That leaves no room for a significant human influence.
It is not only about the slope of the temperature, it is mainly about the offset. That is what “matches” the accumulated CO2, but both are simply chosen to match the CO2 data when integrated. That is curve fitting, which in this case is easely fitted, because the underlying trend in the data is quite linear and the temperature variability matches the variability in
CO2 increase rate, because that is a clear cause-effect relationship.
Where it goes wrong is that the slope and the offset are as good (or even better) fitted by the emissions, which are twice as high as the observed rate of change. Thus your slope and offset are completely arbitrary and can be replaced by 0-55% of the emissions, the upper bound does leave only a little room for the influence of temperature.
Indeed we have had many discussions on this topic, but besides the mass balance argument, the main problem with your theory is that you made the human emissions and the temperature too interdependent, while the influence of both on CO2 levels is (near) completely independent of each other. Therefore there is no need for a rapid sequestration of human (or any other) CO2, which anyway is not what is observed.
But again, there is a simple proof that your formula does or doesn’t work if you use the same coefficient and offset for the past periods (1900-1960 and LIA-1900).
And still I am waiting for any knowledge of a physical process that delivers 70 ppmv CO2 over 50 years only from a continuous small elevated CO2 level…
fhhaynie:
Thankyou for your post at June 5, 2012 at 12:46 pm.
Yes, but none of that is quantified. In fact almost nothing in the carbon cycle is.
I again post the processes which we considered most important.
Short-term processes
1. Consumption of CO2 by photosynthesis that takes place in green plants on land. CO2 from the air and water from the soil are coupled to form carbohydrates. Oxygen is liberated. This process takes place mostly in spring and summer. A rough distinction can be made:
1a. The formation of leaves that are short lived (less than a year).
1b. The formation of tree branches and trunks, that are long lived (decades).
2. Production of CO2 by the metabolism of animals, and by the decomposition of vegetable matter by micro-organisms including those in the intestines of animals, whereby oxygen is consumed and water and CO2 (and some carbon monoxide and methane that will eventually be oxidised to CO2) are liberated. Again distinctions can be made:
2a. The decomposition of leaves, that takes place in autumn and continues well into the next winter, spring and summer.
2b. The decomposition of branches, trunks, etc. that typically has a delay of some decades after their formation.
2c. The metabolism of animals that goes on throughout the year.
3. Consumption of CO2 by absorption in cold ocean waters. Part of this is consumed by marine vegetation through photosynthesis.
4. Production of CO2 by desorption from warm ocean waters. Part of this may be the result of decomposition of organic debris.
5. Circulation of ocean waters from warm to cold zones, and vice versa, thus promoting processes 3 and 4.
Longer-term process
6. Formation of peat from dead leaves and branches (eventually leading to lignite and coal).
7. Erosion of silicate rocks, whereby carbonates are formed and silica is liberated.
8. Precipitation of calcium carbonate in the ocean, that sinks to the bottom, together with formation of corals and shells.
Natural processes that add CO2 to the system:
9. Production of CO2 from volcanoes (by eruption and gas leakage).
10. Natural forest fires, coal seam fires and peat fires.
Anthropogenic processes that add CO2 to the system:
11. Production of CO2 by burning of vegetation (“biomass”).
12. Production of CO2 by burning of fossil fuels (and by lime kilns).
Several of these processes are rate dependent and several of them interact.
At higher air temperatures, the rates of processes 1, 2, 4 and 5 will increase and the rate of process 3 will decrease. Process 1 is strongly dependent on temperature, so its rate will vary strongly (maybe by a factor of 10) throughout the changing seasons.
The rates of processes 1, 3 and 4 are dependent on the CO2 concentration in the atmosphere. The rates of processes 1 and 3 will increase with higher CO2 concentration, but the rate of process 4 will decrease.
The rate of process 1 has a complicated dependence on the atmospheric CO2 concentration. At higher concentrations at first there will be an increase that will probably be less than linear (with an “order” <1). But after some time, when more vegetation (more biomass) has been formed, the capacity for photosynthesis will have increased, resulting in a progressive increase of the consumption rate.
Processes 1 to 5 are obviously coupled by mass balances. Our paper assessed the steady-state situation to be an oversimplification because there are two factors that will never be “steady”:
I. The removal of CO2 from the system, or its addition to the system.
II. External factors that are not constant and may influence the process rates, such as varying solar activity.
Modeling this system is a difficult because so little is known concerning the rate equations. However, some things can be stated from the empirical data.
At present the yearly increase of the anthropogenic emissions is approximately 0.1 GtC/year. The natural fluctuation of the excess consumption (i.e. consumption processes 1 and 3 minus production processes 2 and 4) is at least 6 ppmv (which corresponds to 12 GtC) in 4 months. This is more than 100 times the yearly increase of human production, which strongly suggests that the dynamics of the natural processes here listed 1-5 can cope easily with the human production of CO2. A serious disruption of the system may be expected when the rate of increase of the anthropogenic emissions becomes larger than the natural variations of CO2. But the above data indicates this is not possible.
The accumulation rate of CO2 in the atmosphere (~1.5 ppmv/year which corresponds to ~3 GtC/year) is equal to almost half the human emission (~6.5 GtC/year). However, this does not mean that half the human emission accumulates in the atmosphere, as is often stated. There are several other and much larger CO2 flows in and out of the atmosphere. The total CO2 flow into the atmosphere is at least 156.5 GtC/year with 150 GtC/year of this being from natural origin and 6.5 GtC/year from human origin. So, on the average, ~3/156.5 = ~2% of all emissions accumulate.
The above qualitative considerations suggest the carbon cycle cannot be very sensitive to relatively small disturbances such as the present anthropogenic emissions of CO2. However, the system could be quite sensitive to temperature. So, our paper considered how the carbon cycle would be disturbed if – for some reason – the temperature of the atmosphere were to rise, as it almost certainly did between 1880 and 1940 (there was an estimated average rise of ~0.5 °C in average surface temperature).
Clearly, much more needs to be known if we are to answer my question.
And, as you point out, ocean circulation is but one of several variables additional to those we considered.
Richard
Allan MacRae says: June 5, 2012 at 5:15 am ADDENDUM – ADDED POINT 4. BELOW.
The question is what primarily causes what – does atmospheric CO2 drive temperature or does temperature drive CO2? Do current humanmade CO2 emissions significantly increase atmospheric CO2, or are they “lost in the noise” of the much larger dynamic natural system?
My contention is, adapting Ferdinand’s wording:
“ the bulk of the rate of change is NOT caused by human emissions, BUT IS A RESULT OF ONE OR MORE LONGER-TIME NATURAL TEMPERATURE CHANGE CYCLES, CONSISTENT WITH the SHORT-TIME-CYCLE variability of the rate of change THAT IS ALSO caused by temperature changes.”
I prefer my hypo because
1. My hypo is more consistent with Occam’ s Razor – whereas Ferdinand’s hypo requires opposing trend directions at different time scales in the system, mine does not, such that all trends are consistently in the same direction (temperature drives CO2) at all time scales.
2. My hypo is consistent with the fact that CO2 lags temperature at all measured time scales, from an ~800 year lag on the longer time cycle as evidenced in ice cores, to a ~9 month lag on the shorter time cycle as evidenced by satellite data.
3. I have yet to see evidence of a major human signature in actual CO2 measurements, from the aforementioned AIRS animations to urban CO2 readings ( although I expect there are local data that I have not seen that do show urban CO2 impacts, particularly in winter and locally in industrialized China.)
[new point 4]
4. My hypo is more consistent with the Uniformitarian Principle.
Richard, I awoke very early this morning and have had little sleep – accordingly, I may tackle your excellent question in more detail later.
My high-risk, sleep deprived response is that Jan Veizer probably has it mostly right in his landmark 2005 GSA Today paper.
In my own words, the CO2 cycle “piggybacks” on the water cycle, and is a huge, DYNAMIC, DISPERSED (global in area) and HETEROGENEOUS system that is condemned to chase equilibrium in time and space into eternity.
Obviously, I need some sleep.
Best personal regards, Allan
Robert Brown says:
June 5, 2012 at 11:10 am
The raw data, including the data that they throw away because of the direction the wind blows etc, might tell a different story because accepting or rejecting any part of the data according to external considerations forces the conclusion away from anything that omitted data might confound.
Look for yourself if throwing out some of the data at MLO or other stations has any effect on the trend and/or slope of the “cleaned” data. For four baseline stations, the raw (hourly averaged) data are available at:
ftp://ftp.cmdl.noaa.gov/ccg/co2/in-situ/
I have plotted the raw data and the “cleaned” averages from MLO and SPO here:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/co2_mlo_spo_raw_select_2008.jpg
But mind the CO2 scale!
richardscourtney says:
June 5, 2012 at 11:10 am
The ‘background’ CO2 level is meaningless. An IR photon interacting with a CO2 molecule does not ‘know’ if the molecule is ‘background’ or not. So, the total number of CO2 molecules in the atmosphere is all that matters, especially when CO2 is “well mixed” in the air.
The influence of CO2 on radiation effects is over the whole air column. Over the oceans, the CO2 levels are rather uniform up to 20 km height. Over land you can have enormous differences in the first few hundred meters, due to huge local sources and sinks. But if you look at the radiation, even if you have 1,000 ppmv in the first 1,000 meters, its effect would be minimal.
Thus measurements in the first few hundred meters over land shouldn’t be used for possible local effects (which are negligible), neither for trends, although in average the trends may resemble what happens in the bulk of the atmosphere.
Ferdinand:
Please read the final paragraph in my post you answered. You seem to have missed the point of my post in your answer.
Richard
richardscourtney says:
June 5, 2012 at 12:34 pm
I quoted words so you would know where in the conversation I was picking up. Anyone following the thread knows who said what.
And, you are just plain wrong about the measurements. I have tried to explain basic filtering theory to you, but you just plug your ears and shout “nah, nah, nah!”
Your sheltered arrogance is astounding. You are denying an entire field of research from such giants as Fisher and Kalman, and ubiquitous application. How do I reach through to you to do even a modicum of research, or just try what I have explained you need to do?
FerdiEgb says:
June 5, 2012 at 2:31 pm
“It is not only about the slope of the temperature, it is mainly about the offset.”
No, it is not. I have explained this at length. I am tired of explaining.
richardscourtney says:
June 5, 2012 at 12:21 pm
“The fit was perfect in that each datum from each model matches each corresponding datum in the Mauna Loa data set to within the measurement error. That IS a perfect fit.”
No, it is not perfect. Perfect means PERFECT.
The measurement error is distributed in frequency, much of it in the high frequency region in which we are not interested. That error can be filtered out.
Here is a very simple example. Suppose I tell you a signal is known to be a constant plus uncorrelated zero mean noise with standard deviation of S. By your logic, I can never determine the constant value to less than +/-S.
But, if I take N samples and average them, my estimate will have an uncertainty of +/- S/sqrt(N). As the number of points N goes to infinity, I asymptotically approach perfect (really perfect, not just pretend perfect) knowledge of the constant.
A constant is very low frequency, uncorrelated noise is evenly distributed across all frequencies, and an average is a low pass filter. It’s the same general deal.
richardscourtney says:
June 5, 2012 at 12:34 pm
“I see no point in answering you further whatever you post unless it is to refute another misquotation of me.”
In that case, richardscourtney said above, and I quote, “I enjoy molesting puppies.”
Now, you have to respond 😉
FerdiEgb says:
June 5, 2012 at 2:31 pm
“But again, there is a simple proof that your formula does or doesn’t work if you use the same coefficient and offset for the past periods (1900-1960 and LIA-1900).”
A) we do not have reliable measurements from those times
B) different operating conditions mean different parameters. This is typical of linearized approximations of nonlinear systems – they only hold in a local neighborhood of the time in which the linearization is performed. Right now, in the modern era, these parameters hold, and they explain the last several decades of atmospheric CO2 concentration and rule out singificant human contribution to it.
“And still I am waiting for any knowledge of a physical process that delivers 70 ppmv CO2 over 50 years only from a continuous small elevated [temperature] level…”
Deep ocean upwelling, as I and others have commented.
Shyguy says:
June 3, 2012 at 12:26 am
Looks to me like the co2 records got corrupted just like everything else the ipcc get it’s hands on….
___________________________
All I had to do is read what Mauna Loa said. The “selection process” at Mauna Loa Observatory.
“Nough said.
Lucy Skywalker says: @ur momisugly June 3, 2012 at 1:52 am
….Now think. CO2 lags temperature by 800 years, according to Caillon et al. What happened 800 years ago?? Anyone?? And what cycle takes 800 years to happen?? Anyone??
___________________________________
Here is the Graph guys ( “Correction to: A 2000-Year Global Temperature Reconstruction Based on Non-Tree Ring Proxies,” ~ Loehle and McCulloch (2008))
http://www.econ.ohio-state.edu/jhm/AGW/Loehle/
I have plotted the raw data and the “cleaned” averages from MLO and SPO here:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/co2_mlo_spo_raw_select_2008.jpg
But mind the CO2 scale!
Now that’s very interesting. They both look reasonable (raw to cooked), but there is impressive variability at MLO compared to SPO (not to mention significantly higher baseline values). In fact, the SPO data you can’t tell the difference between raw and cooked, where at MLO cooked looks like it (if anything) underestimates the true variance of the data, especially on the low side of things. But this makes the correlation between even smoothed CO_2 and global temperature more suspicious. MLO is clearly not particularly representative of the entire globe (given two samples, both completely different in their fluctuation properties). As one might expect, it suggests that we would be far better served by an entire globe spanning set of CO_2 concentration monitoring stations than by “just one”, sitting on an active volcano that is used as if it is representative of the entire atmosphere at 4200 meters above sea level at all temperatures and latitudes.
Just a thought.
rgb
Robert,
I have been statistically analyzing global CO2 and 13CO2 data (monthly averages, raw flask, and continuous instrument) for years. You should expect significant differences between Mauna Loa and the South Pole. The Arctic ocean is a big sink with a seasonally adjusted drain valve while the circumpolar current sink that travels around an Antarctic elevated land mass (that is neither source nor sink) is never closed. I think this difference results in the amplitude of the seasonal cycle increasing with latitude (but not longitude) in NH and not nearly so in SH. The Scripps column 10 monthly averages not only factors out these seasonal differences but also excludes observed spikes in the raw flask data. I have just completed a global, “background”, statistical model for CO2 and the 13CO2 index. I think this needs to be refined and published. I haven’t written anything for peer review publication since I retired from EPA 21 years ago. I live in Cary. If you could find a smart graduate student with good math and programming skills, that is interested; I would gladly share what I have done and work with them (no charge). You can judge my analytical approach at http://www.retiredresearcher.wordpress.com.
PS. Of all those that comment here, I rank you as being nearest to “scientifically correct”.
Repeating my above statement, to correct those who repeatedly insist on misrepresenting my position, either through illiteracy or malice:
Allan MacRae says: June 3, 2012 at 8:44 am
“For the record, I have no problem with CO2 measurement accuracy. The CO2 measurements at Barrow, Mauna Loa, the South Pole and many other sites correlate well and make sense.”
This observation does NOT require that CO2 is always well-mixed. It is clear that much time and effort is devoted to taking many CO2 measurements and rejecting many “outliers”.
The AIRS animation proves the point. Repeating it, yet again:
http://svs.gsfc.nasa.gov/vis/a000000/a003500/a003562/carbonDioxideSequence2002_2008_at15fps.mp4
“Nough said.
Indeed. You really do have to wonder if anybody in the world understands statistics and why rejecting outliers in an unknown distribution is insane. But then, locating “the” CO_2 observatory for the world on an active volcano is insane. Having just one (or just five, or just ten) for the world is insane. From a statistical point of view.
It is not impossible that 100% of the Mauna Loa increasing CO_2 “signal” is due to a steady, occult, increase in CO_2 outgassing due to volcanic processes within Mauna Loa itself and surrounding islands. I don’t suggest that this is the mostly likely/plausible explanation, only that the only way one could check is with an observatory on top of Mount Everest, another on Kilimanjaro, ten thousand (or a hundred thousand) more moored on weather balloons at 20,000 feet in some sort of regular grid covering the planetary surface. Or performing some very complex and dubious geophysical research (since even if you excluded ML itself, there would be outgassing from vulcanism on the surrounding pacific floor to consider, and still more confounding factors). Expecting MLO to generalize to “the Earth” is a bit egregious.
rgb
richardscourtney says:
June 5, 2012 at 10:45 am
Thanks, Richard. It seems to me that the sequestration processes, following Le Chatelier’s Principle, will push the system back towards equilibrium. How hard they push, however, is a function of how far the atmospheric levels are from equilibrium. As a result, I would not expect them to sequester all the emissions, and I am puzzled why you think that they would, could, or should sequester it all …
What am I missing?
w.
Robert Brown says:
June 5, 2012 at 6:16 pm
Actually, we do have a bunch of stations, one in Samoa, one in Barrow, Alaska, and the like. They lead to things like this:
My best to you as always,
w.