Guest post by Lon Hocker
Abstract
Differentiating the CO2 measurements over the last thirty years produces a pattern that matches the temperature anomaly measured by satellites in extreme detail. That this correlation includes El Niño years, and shows that the temperature rise is causing the rise in CO2, rather than the other way around. The simple equation that connects the satellite and Mauna Loa data is shown to have a straight forward physical explanation.
Introduction
The last few decades has shown a heated debate on the topic of whether the increase of CO2 in the atmosphere is causing rising temperatures. Many complex models have been made that seem to confirm the idea that anthropological CO2 is responsible for the temperature increase that has been observed. The debate has long since jumped the boundary between science and politics and has produced a large amount of questionable research.
“Consensus View”
Many people claim that anthropological CO2 is the cause of global warming. Satellite temperature data, http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt, and Mauna Loa CO2 measurements, ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt, are well accepted and freely available to all researchers. Figure 1 shows a plot of the Ocean Temperature Anomaly from the satellite data shows a general rising trend. Shown along with the temperature data is a simple linear model showing the temperature rise as a linear function of CO2 concentration. This shown linear model is:
Temperature Anomaly = (CO2 -350)/180
No attempt has been made to optimize this model. Although it follows the general trend of the temperature data, it follows none of the details of the temperature anomaly curve. No amount of averaging or modification of the coefficients of the model would help it follow the details of the temperature anomaly.
Figure 1: Ocean Temperature Anomaly and linear CO2 model
Derivative approach
An alternate approach that does show these details is that the temperature anomaly is correlated with the rate of increase of CO2. I discovered this independently and roughly simultaneously with Michael Beenstock and Yaniv Reingewertz http://economics.huji.ac.il/facultye/beenstock/Nature_Paper091209.pdf.
Applying this model to the Mauna Loa data not only shows the overall trend, but also matches the many El Niño events that have occurred while satellite data has been available. The Figure 2, shows the derivative model along with the observed Ocean Temperature Anomaly. The model is simply
Temperature Anomaly = (CO2(n+6) – CO2(n-6))/(12*0.22) – 0.58
where ‘n’ is the month. Using the n+6 and n=6 values (CO2 levels six months before and six months after) cancels out the annual variations of CO2 levels that is seen in the Mauna Loa data, and provides some limited averaging of the data.
The two coefficients, (0.22 and 0.58) were chosen to optimize the fit. However, the constant 0.58 (degrees Celsius) corresponds to the offset needed to bring the temperature anomaly to the value generally accepted to be the temperature in the mid 1800’s when the temperature was considered to be relatively constant. The second coefficient also has a physical basis, and will be discussed later.
Figure 2: Ocean Temperature Anomaly and derivative CO2 model
There is a strong correlation between the measured anomaly and the Derivative model. It shows the strong El Niño of 1997-1998 very clearly, and also shows the other El Niño events during the plotted time period about as well as the satellite data does.
Discussion
El Niño events have been recognized from at least 1902, so it would seem inappropriate to claim that they are caused by the increase of CO2. Given the very strong correlation between the temperature anomaly and the rate of increase of CO2, and the inability to justify an increase of CO2 causing El Niño, it seems unavoidable that the causality is opposite from that which has been offered by the IPCC. The temperature increase is causing the change in the increase of CO2.
It is important to emphasize that this simple model only uses the raw Mauna Loa CO2 data for its input. The output of this model compares directly with the satellite data. Both of these data sets are readily available on the internet, and the calculations are trivially done on a spreadsheet.
Considering this reversed causality, it is appropriate to use the derivative model to predict the CO2 level given the temperature anomaly. The plot below shows the CO2 level calculated by using the same model. The CO2 level by summing the monthly CO2 level changes caused by the temperature anomaly.
Month(n) CO2 = Month(n-1) CO2 + 0.22*(Month(n) Anomaly + 0.58)
Figure 3: Modeled CO2 vs Observed CO2 over Time
Not surprisingly the model tracks the CO2 level well, though it does not show the annual variation. That it does not track the annual variations isn’t particularly surprising, since the ocean temperature anomaly is averaged over all the oceans, and the Mauna Loa observations are made at a single location. Careful inspection of the plot shows that it tracks the small inflections of the CO2 measurements.
The Mauna Loa data actually goes back to 1958, so one can use the model to calculate the temperature anomaly back before satellite data was available. The plot below shows the calculated temperature anomaly back to 1960, and may represent the most accurate available temperature measurement data set in the period between 1960 and 1978.
Figure 4: Calculated Temperature Anomaly from MLO CO2 data
Precise temperature measurements are not available in the time period before Satellite data. However, El Niño data is available at http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml making it possible to show the correlation between the calculated temperatures and the and El Niño strength. Note that the correlation between temperature anomaly and El Niño strength is strong throughout the time span covered.
Figure 5: Calculated Temp CO2 from CO2 and ENSO data
An Explanation for this Model
The second free parameter used to match the CO2 concentration and temperature anomaly, 0.22 ppm per month per degree C of temperature anomaly, has a clear physical basis. A warmer ocean can hold less CO2, so increasing temperatures will release CO2 from the ocean to the atmosphere.
The Atmosphere contains 720 billion tons of CO2 (http://eesc.columbia.edu/courses/ees/slides/climate/carbon_res_flux.gif), the ocean 36,000 billion tons of CO2. Raising the temperature of the ocean one degree reduces the solubility of CO2 in the ocean by about 4% (http://www.engineeringtoolbox.com/gases-solubility-water-d_1148.html)
Figure 6: Solubility of CO2 in water (While CO2 solubility in seawater is slightly different than in pure H2O shown above in Figure 6, it gives us a reasonably close fit.)
This releases about 1440 billion tons of CO2 to the atmosphere. This release would roughly triple the CO2 concentration in the atmosphere.
We have seen what appears to be about a 0.8 degree temperature rise of the atmosphere in the last century and a half, but nowhere near the factor of three temperature rise. There is a delay due to the rate of heat transfer to the ocean and the mixing of the ocean. This has been studied in detail by NOAA, http://www.oco.noaa.gov/index.jsp?show_page=page_roc.jsp&nav=universal, and they estimate that it would take 230 years for an atmospheric temperature change to cause a 63% temperature change if the ocean were rapidly mixed.
Using this we can make a back of the envelope calculation of the second parameter in the equation. This value will be approximately the amount of CO2 released per unit temperature rise (760 ppm/C)) divided by the mixing time (230 years). Using these values gives a value of 0.275 ppm /C/month instead of the observed 0.22 ppm/C/month, but not out of line considering that we are modeling a very complex transfer with a single time constant, and ignoring the mixing time of the ocean.
Conclusion
Using two well accepted data sets, a simple model can be used to show that the rise in CO2 is a result of the temperature anomaly, not the other way around. This is the exact opposite of the IPCC model that claims that rising CO2 causes the temperature anomaly.
We offer no explanation for why global temperatures are changing now or have changed in the past, but it seems abundantly clear that the recent temperature rise is not caused by the rise in CO2 levels.
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Lon Hocker describes himself as: “Undergrad physics at Princeton. Graduate School MIT. PhD under Ali Javan the inventor of the gas laser. Retired president of Onset Computer Corp., which I started over 30 years ago. Live in Hawaii and am in a band that includes two of the folks who work at MLO (Mauna Loa Observatory)!”
Data and calcs available on request
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Actually, Willis, I do present one data point for about 150 years ago. That’s where the 0.58 comes from.
With the exception of using Mauna Loa CO2 measurements to calculate temperatures between 1960 and 1980, I have no intervening points because I do not consider either the CO2 or temperature measurements in that time period to be reliable. The whole point of the exercise was to work only with data that is (almost) universally accepted. Plenty of posts on this site questioning data associated with tree rings, ice cores and the like in that time period.
Thanks for your interest.
Willis Eschenbach:
I have severe problems with several of your comments at June 12, 2010 at 4:25 pm and June 12, 2010 at 4:31 pm.
To begin, you say to me:
“First, the amount of human emissions to the atmosphere are not 0.02% of the total emissions to the atmosphere. They are currently on the order of 4% of total emissions to the atmosphere.”
I did NOT say “the amount of human emissions to the atmosphere are” “0.02% of the total emissions to the atmosphere”
At June 12, 2010 at 3:31 pm I said:
“that emission in each year is less than 0.02% of the carbon flowing around the carbon cycle”. IT IS.
Please dispute what I say. I do not appreciate straw man arguments.
Then you say;
“we can model the atmosphere as a basin with a hose filling it, and a hole at the bottom draining it out.”
True, we can do that, but it would be a mistake. If that model were to be made to work then the sizes of both the basin and the hole would have to vary in response to the flow from the hose and several other factors both known (e.g. global temperature, ENSO condition, etc.) and unknown.
Simply, your suggested model suffers from the same error that Joel Shore stated and I refuted; viz. that the sources and sinks of ‘natural’ CO2 do not vary, but there is no reason to suppose that they do not vary and there are known reasons why they do.
Hence, I reject your deductions from your model because your model is not valid.
It seems that you base your model on an untrue assertion that you state as being;
“I don’t see why this is so hard to understand. The size of the natural flows (the big hose) is not the issue, that doesn’t matter in an equilibrium. In an equilibrium situation, any addition of new matter will raise the level, by some amount which is less than the amount added. It will obey an exponential decay function, the decay factor of which (absent changes in the sequestration rate) will remain stable.
And that’s exactly what we are seeing in the atmosphere.”
NO! Nobody can see that in the atmosphere because it does not exist and is observed to not exist.
What happens “in an equilibrium” has no relevance to the situation of the real carbon cycle in the real world because the system is NOT in equilibrium. Indeed, the seasonal variation would not exist if it were in equilibrium.
The system is ‘hunting’ an equilibrium state that is constantly changing, but the system never achieves equilibrium. And the rate constants for several mechanisms (e.g. growth of live biota and decay of dead biota) are too slow for the system to ever achieve equilibrium. Indeed, effects of the thermohaline circulation take centuries while the seasonal variation occurs over months.
So, equilibrium never exists and any evaluations will be wrong if they are based on an assumption that it does exist.
But I agree with your point that
“The relevant quotation in this situation is “All models are wrong … but some models are useful.”
The problem is that we know much too little about the carbon cycle and its behaviour for us to make useful models of them.
As I said at June 12, 2010 at 3:31 pm :
“Please note that above, at June 11, 2010 at 7:05 pm, I referenced our six different models of the carbon cycle that each matches the Mauna Loa data perfectly without any data adjustment (i.e. each or our models is better than the so-called ‘mass balance’ or ‘budget models’ used by e.g. IPCC because those models require data smoothing to get them to match the Mauna Loa data) . But our models are each very different and each predicts a different future atmospheric CO2 concentration for the same anthropogenic CO2 emission.”
So, champion any one of our models and you have a 5:1 chance that you chose the wrong one. And other models – including your ‘hose pipe’ model’ – are also possible.
Please note – as I said – that “each or our models is better than the so-called ‘mass balance’ or ‘budget models’ used by e.g. IPCC because those models require data smoothing to get them to match the Mauna Loa data”. Your ’hose pipe’ model is merely a ‘mass balance’ model.
So, much more information (both quantitative and qualitative) is need before a useful model of the carbon cycle can be constructed. At present it is esay to construct a variety of different model that each matches the known behaviours of the carbon cycle but at most only one of them can be right.
Richard
Just for Reference:
From results obtained from the online MODTRAN interface provided by David and Jeremy Archer:
http://geoflop.uchicago.edu/forecast/docs/Projects/modtran.html
This model is supposed be a line-by-line simulation the emission and absorption of infrared radiation in the atmosphere. I have determined an ad hock formula that predicts the results obtained by this tool over a range of 0 to 100,000 ppm CO2 concentration for the default clear tropical atmosphere case.
First I introduce a Zero correction function that handles values near zero ppm and the log of zero math problem:
Z=1.2*CO2 + ((4.068 + 0.6227*(CO2)^(0.6)) / (1.0 + 0.1031*(CO2)^(1.2)) – 3.068
“CO2” in this equation is the CO2 concentration in ppm. Above about 35 ppm, the equation can be simplified to:
Z=1.2*CO2 – 3
For a fixed top of the atmosphere energy flow of 292.993 W/m2 and CO2 concentration less that 100,000 ppm, the estimated surface temperature returned by MODTRAN appeared to follow the formula:
T=293.55 +0.8495*(LOG2(Z)) + (1.538E-8)*(LOG2(Z))^7 within .05 deg K.
The seventh-power log term rises to a one-degree-effect around CO2 = 7143 ppm which is on the order of 18 times our current CO2 concentration. I do not know if this represents a real effect, possibly associated with the onset of earthshine-window pinch-off, or if it is a dynamic range limitation problem with the MODTRAN model data or calculations.
I believe the LOG2(Z) linear term, 0.8495 deg K, is the nominal predicted raw (no feedback) temperature increase for doubling of the CO2 concentration when the seventh-power term and the near zero-ppm effects can be ignored.
I do not know if this formula still represents the exact values being returned by the tool as it may be a work in progress. I have observed one apparent minor shift in the data returned by a fixed inquiry with default parameters.
Bart says:
Thanks for the reply, Bart. I did note that my model was oversimplified because it doesn’t include, for example, the exchange with the deep ocean. However, the point is that the fluxes there are smaller. My main point is that your notion that the fossil fuel flux is only a small percentage of the other fluxes of CO2 into the atmosphere is not relevant for determining how much the fossil fuel emissions can affect the atmospheric CO2 concentrations. This is because you are talking about fluxes from these other reservoirs (the ocean mixed layer and biosphere + soils) that together form a subsystem into which any new source of CO2 like that from fossil fuels rapidly partitions.
Let’s rewrite Lon Hocker’s equation in terms of CO2 rate of increase per year
dCO2/dt = 2.64T’ + 1.53
I think in this equation it is easy to see there is a dominant source given by 1.53 ppm/yr, which is somewhat like the accepted anthropogenic produced rate, plus an ocean-temperature dependent part. As I mention in my 10th June 9:04pm post, part of the T’ term can come from modulating the CO2 sink. I also notice now that part of it can come from a false correlation between T’ and the CO2 source because both are increasing with time. I say ‘false’ because there is no physical reason that anthropogenic emission should be related directly to ocean temperature, but there is nevertheless a correlation because both are increasing, so the T’ term also accounts for the fact that the current CO2 increase rate is nearer 2 ppm/yr than 1.53.
Joel Shore says:
June 13, 2010 at 7:10 am
“My main point is that your notion that the fossil fuel flux is only a small percentage of the other fluxes of CO2 into the atmosphere is not relevant for determining how much the fossil fuel emissions can affect the atmospheric CO2 concentrations.”
In your model. But, since your model does not match the real world, it’s rather a moot point. Under my model assumptions, it is very relevant.
Jim D. June 13, 2010 at 8:45 am
I could buy into that except that anthropogenic CO2 emissions have risen over 60% since 1980, so you can’t model that term as a constant. The data is all available, so use a spreadsheet, and see how well you can make your model work.
A example was done by Sylvain June 9, 2010 at 12:57 pm (an earlier comment)
using woodfortree.org
http://www.woodfortrees.org/plot/esrl-co2/mean:12/derivative/from:1979/normalise/plot/uah/from:1960/normalise
All: Another data set we are ignoring: http://www.youtube.com/watch?v=6-bhzGvB8Lo
This shows the time evolution of CO2 in the troposphere (Aqua/AIRS Carbon Dioxide with Mauna Loa Carbon Dioxide Overlaid), and gives a hint as to where the CO2 originates. A worthy model should fit this too, and I haven’t tried to do it yet with mine.
Willis Eschenbach says:
June 12, 2010 at 4:25 pm
Lots of people seem to miss the flaw in Willis’ argument at the above. The level of water you end up with is proportional to the total flow, because the pressure above the drain is proportional to height of the water column above the drain. Hence, by adding the small flow, you increase the level proportional to the small flow divided by the normal large flow.
The analogy with the climate system is, if we add 4% additional flow, we would reasonably expect to see only a 4% increase in atmospheric concentration.
There is a bit of a complication here in which the fountain analogy breaks down, and which Joel has brought to the fore, which basically comes down to a question of how much of the CO2 is sequestered versus how much is temporarily stored, as it all is in Joel’s ocean model. Willis, Joel and I and others had a discussion on the CO2 topic some months ago but did not make it quite this far, but I want to recall that the formula I gave them for my model then was similar to what I have above at June 10, 2010 at 10:15 am, except that I included an additional possible gain term Ko which would be inserted thusly:
Cdot = (C1[T-T0] – C)/tau + (1+K0)*adot
At the time, I ascribed Ko to “stimulated emissions,” but I now see it can also represent temporarily stored emissions. Ko was evaluated as the dc gain of a linear operator K[adot]. This term cannot be evaluated in Joel’s model because the integrator state means the dc gain tends to infinity. But, as I noted previously, Joel’s model is non-dissipative and does not match real world behavior. I will continue to look at this and draw conclusions, but for now, I must catch a plane.
Lon, yes, but I am actually saying you have absorbed that increase into your 2.64T’ term, because the increase from 1.53 can also be approximated as a linear function of T’.
OK, Jim D, you have the CO2, Temp and Emissions time series. Do the calculations based on your theory, and look at the results. For all I know, you will come up with a better match than I did, and that would be very significant. You won’t know unless you actually run the spread sheet. If you come up with a good match please let us know the equation and coefficients so we can see what you’ve done. Maybe next time I’ll be commenting on your writeup on WUWT!
In fact, Ko can be considered the ratio of stored emissions to sequestered emissions. Since Joel’s model has no sequestration, this term blows up. In the real world, the only question is the rate of sequestration. I mentioned the evidence of response to volcano eruptions, which argues that the rate is fairly swift, the operation K[adot] is well damped, and the gain Ko is small.
They’re calling me to board…
Lon, I am not disputing the formula, and it fits well, I agree. I just dispute the interpretation. Modifying my post from 9:04pm on the 10th slightly.
dCO2/dt = A(T) – B(T)
where A is a source and B is a sink, and T is the anomaly ocean temperature.
Let’s say the source is chosen as A(T)=a1 + a2T where a1 and a2 are constants, and the sink is B(T)=b1+b2T.
To get your formula, the constraints are
a1-b1=1.53 and a2-b2=2.64
However there are not enough constraints, so let’s define the source as
A=3+7.2T
This is chosen so that the source goes from 3 ppm/yr to 4.8 ppm/yr in the period that T increases from 0 to 0.25, i.e. a 60% increase as you stipulate. This is arbitrary, of course. A is emission rate, and is not really tied to T, but a correlation exists, so we can represent it like this. Others may have better numbers for the source in ppm/yr between 1980 and 2010, but the numbers chosen are not critical to the argument, just for illustration.
Now we have enough information to get b1 and b2
B=1.47+4.56T
Note that the sink also becomes stronger with T, but not as fast as the source.
Adding things up
dCO2/dt = 3+7.2T-1.47-4.56T=1.53+2.64T
So your formula is consistent with a source and sink that are both linearly related to T, but with slightly varying T coefficients. In terms of AGW, this would not be inconsistent with the idea.
Jim D:
“A is emission rate, and is not really tied to T, but a correlation exists, so we can represent it like this.” This is a dreadful assumption. They correlate only in that their ending point is above their starting points. Other than that there is no similarity.
Make your equation and test it with a spreadsheet. Until you do that, you are just waving your hands in the air.
I am not trying to make a new equation, but just to derive yours as it is. I agree my formulation for emission was not realistic, and will come up with a better one.
maksimovich:
Your assertion at June 12, 2010 at 6:02 pm is plain wrong. I and DirkH “misunderstood” nothing.
Please see what I wrote at June 12, 2010 at 3:50 pm and DirkH wrote at June 12, 2010 at 4:44 pm. Our different arguments are each correct, and a mere assertion that we “misundertood” does not change that. And nor does your statement of something that neither of us mentioned.
If you think either of us is wrong then please state why.
Joel Shore:
At June 12, 2010 at 7:26 pm you assert to me:
“It’s not the CO2 level but the rate of change that is most relevant. ”
Really? Prove it!
Anyway, if your assertion were true then it would disagree with the quotation you presented and that I pointed out is nonsense: i.e. at
“…Emiliania huxleyi, the current ubiquitous but one of the smallest
sized coccolithophore, may operate at less than 100% photosynthetic efficiency under modern ocean conditions of CO2(aq) (e.g. Rost and Riebesell, 2004; Rost et al., 2003).”
“Rate of change” has nothing to do with that.
Richard
Thanks for the reply Lon. Other thoughts:
1) Why does the literature say oceanic pH has fallen? Doesn’t that mean that carbon is being absorbed by the oceans?
2) Accumulation rate of CO2 in the atmosphere is ~45% of the rate that humans are emitting it (e.g. Knorr 2010). What’s happening to the human CO2?
3) It seems you implicitly assumed that CO2 is close to saturated in the ocean, yet there appears to be about 1.4E21 kg of seawater – CO2/water amount is 0.04 g CO2/kg water, less than 10% of the theoretical maximum. Doesn’t this throw a bit of a spanner in your calculations? That seems like a pretty big, unjustified assumption to me.
Lon Hocker:
This is a strawman. As I have pointed out, the fact that that temperatures affect the carbon cycle and hence the interannual variability in atmospheric CO2 rise is well-known and has been for at least 30 years. It is talked about in the IPCC report and there are papers on it that I referenced here: http://wattsupwiththat.com/2010/06/09/a-study-the-temperature-rise-has-caused-the-co2-increase-not-the-other-way-around/#comment-406435
The new thing you add is a claim that this somehow explains the trend in CO2 since the industrial revolution began, but for that your evidence is very thin…and your arguments contradict known facts: We know that in fact the oceans are not in net emitting CO2 but are absorbing CO2, as the rise in CO2 levels due to our emissions would be larger if this were not the case. We know that only a small fraction of the ocean (the so-called “mixed layer”) is effectively in contact with the atmosphere (and that the temperature increases do not affect most of the ocean deep down), in contradiction to what you imagine in your application of solubility. (We also know that the chemistry in sea water is much more complicated than CO2 just being absorbed in the water…There are various carbonate and bicarbonate ions. I forget the details but you can read up on them.) Finally, your model would imply extremely wild gyrations in CO2 levels would have happened in the past. After all, for each 1 C change in temperature, your model predicts that the change in atmospheric CO2 rise will be 2.64 ppm/year. So, if temperatures were more than 1 C lower than 1850 (as they surely were during the glacial period), you would eliminate all the CO2 in the atmosphere in about a century, according to your model.
Richard S Courtney says:
I recommend reading up on the chemistry of the ocean…in particular of dissolved CO2 and its reactions. Since I am not a chemist, my eyes tend to glaze over at the details. However, the simple point is that the acidification occurs because of the CO2 that invades the oceans but that this is neutralized by additional carbonate ions, e.g., leached from limestone. The problem is this leaching takes time.
Bart says:
You might recall that it your model, not mine, that is in contradiction with essentially all of the modern science on the carbon cycle. That science is based on a wealth of empirical data, theoretical understanding, and modeling. In proposing a new model, you would have to explain how it does a better job explaining all of this empirical data. That is a pretty heavy task. In the meantime, I’ll stick to models that are based on the reality as the scientists in the field understand it.
Joel:
Thanks for looking hard at the model. I present a simple model that breaks down the giga variabled world to find an excellent correlation between two variables over the period from now back to 1980, and possibly 1850. Jim D, seems convinced that the equation is right, or at least close, but the model is wrong. Your challenge is to find an equation that fits the data as well as mine does, and then make a model that explains it.
Forgive me for not writing down the equations for this model. Look back up the thread and you will see that Bart has done it.
Let’s return to my original model on the 10th at 9:04pm which resulted in
dCO2/dt = A – b1 + b2*T’
Remember, this assumes a source, A, and a temperature-dependent sink, B=b1-b2*T’.
I return to a simple source because in the period 1980-2010 you can fit quite a good straight line through the CO2 change with a mean gradient of about 1.7 ppm/yr.
We also noted before that Lon’s formula becomes
dCO2/dt = 1.53 + 2.64*T”
I deliberately use T” here as opposed to T’ above, as they differ by an offset constant. Let’s change T” to T’ by just subtracting 0.07 degrees leaving
dCO2/dt = 1.53 +2.64*(T”-0.07) +0.07*2.64 = 1.7 +2.64*T’
Now we see that if the sink can be expressed as B(T) = b1-b2*T’ and A-b1 = 1.7 ppm/yr, we just get Lon’s formula. We have evidence that A, the emission, grows with time, but apparently b1 also grows enough to keep the CO2 increase quite linear as observed. If we assume A=4 ppm/yr, b1=2.3 and b2=2.64, so B=2.3-2.64*(T”-0.07) where T” is the ocean temperature anomaly defined by Lon.
Bottom line: a temperature-dependent sink can account for the modulation of CO2 with temperature anomaly that Lon shows. A warmer ocean absorbs less leading to a faster rise of CO2 in the atmosphere for a steady source.
Jim D:
Pretty good ain’t good enough. Plot your model!
in reply to Joel Snore (June 12, 2010 at 8:02 pm
I am just as good at mispelling your name as you are with mine.
Joel, you asked where Hansen [in effect] said just use trend at location A and you have that at B even when they are 1,200 km apart (and possibly not on the same latitude) .. . “just add the word “trends” and then the local is according to said Hansen all that is needed” is what I said, and that is a reasonable paraphrase of Hansen.
For example, time and again Hansen’s GISStemp uses Anchorage Alaska as proxy for Barrow, well over 1,200 km to the north whenever the latter does NOT show the same warming trend as Anchorage to the south – and then GCHN has in any case expelled both Alaska and Hawaii from the USA, so it offers temperature data neitehr for Pt Barrow nor for Mauna Loa, the only 2 locations in the actual USA where atmopsheric CO2 is measured.
What an amazing coincidence! But down in Rochester NY you would not know about any of this would you – nor care?
And why should I answer you re Hansen when you have yet to provide the data I asked for of useable annual temperatures at Mauna Loa Slope Observatory from 1992 -2006, and any sort of weather data from there since 2006?
Anyway, here are Hansen, Ruedy, Sato and Lo (2009, 2010):
“The GISS analysis assigns a temperature anomaly to many gridboxes that do not contain measurement data, specifically all gridboxes located within 1200 km of one or more stations that do have defined temperature anomalies”. ..For example, if it is an unusually cold winter in New York, it is probably unusually cold in Philadelphia too. This fact suggests that it may be better to assign a temperature anomaly based on the nearest stations for a gridbox that contains no observing stations, rather than excluding that gridbox from the global analysis”.
You asked “Where does Hansen say this?” I reply, all over the place, eg (2009): “The distance over which temperature anomalies are highly correlated is of the order of 1000 kilometers at middle and high latitudes, as we illustrated in our 1987 paper. Hansen, J.E., and S. Lebedeff, 1987: Global trends of measured surface air temperature. J.Geophys. Res., 92, 13345-13372.
EM Smith has also documented how this GISS procedure is embedded in its Fortran codes: “And the way it fabricates those data are tuned. The code has clear parameters chosen to do that tuning. (In the code listings, look for the FORTRAN key word PARAMETER. Also look at the values passed in at run time from the scripts to the programs – like variously 1000 km or 1200 km; or sometimes 6 zones, sometimes more…)”
If that is science, Shore, you are a Dutchman.
Tim Curtin says:
No, Tim, what I asked you to justify is your statement:
I.e., I wanted you to tell me where Hansen said that the temperature trend from a single site can be used as a stand-in for the global temperature trend. The answer appears to be that Hansen never said that at all. You just made it up. What Hansen actually talked about is how the temperature trend is correlated over fairly long distances (out to about 1000 or 1200 km). Alas, the earth is a bit larger than 1200 km. In fact, the two most distant points on the earth are about 20,000 km apart!
I’m really not interested in your other paranoid rantings about where temperature data is or is not taken…But I am concerned about how you really seem to have difficulty keeping basic facts straight. Perhaps your strong feelings interfere with your ability to be at all objective about this subject?
So far, the term “temperature anomaly” has been used 95 times in this discussion. Sorry, but I hate this term. It means absolutely nothing.
NOAA defines the term as:
“The term “temperature anomaly” means a departure from a reference value or long-term average. A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value.”
What reference value? What long-term average? You see where I’m going with this…