Steve Fitzpatrick writes in with a short essay:
On May 11 you reposted a blog from Dr. Roy Spencer, where he suggests that much of the increase in atmospheric CO2 could be due to warming of the oceans, and where he presents a few graphs that he claims are consistent with ocean surface temperature change contributing more than 80% of the measure increase in CO2 since 1958. Dr. Spencer’s suggestion is contradicted by many published studies of absorption of CO2 by the ocean, with some studies dating from the early 1960’s, long before “global warming” was a political issue. In this post I offer a simple model that shows why net absorption of CO2 by the ocean is most likely the main ocean effect.
If the rise in CO2 is being driven by human emissions, then the year-on-year increase in atmospheric CO2 ought to be a function of the rate of release of CO2, less any increase in the rate of removal of CO2 by increased plant growth and by absorption and chemical neutralization of CO2 by the ocean. Both ocean absorption and plant growth rates should increase with increased CO2 concentration in the atmosphere. To simplify things, I focus here only on ocean absorption.
On the other hand, surface temperature changes ought to have a relatively rapid effect, because the surface of the ocean is in contact with the atmosphere and so can quickly absorb or desorb CO2 as the water temperature changes. In fact, the ocean surface continuously absorbs CO2 where the temperature is falling, mostly at high latitudes, and emits CO2 where the water is warming, mostly at lower latitudes. Cold upwelling water from the deep ocean warms at the surface and desorbs CO2, while very cold water at high latitudes absorbs CO2 before it falls to the deep ocean. An increase in average ocean surface temperature will cause more CO2 to be emitted from surface water, but this effect is limited to a very small volume fraction of the ocean. Effects due to rapid temperature changes (annual time scale and less) are limited to a relatively thin layer, while the gradual absorption/neutralization process takes place at a rate controlled by ocean circulation and replacement of the surface water with upwelling (and “very old”) deep ocean water.
Any change in sea surface temperature should add to or subtract from the atmosphere’s CO2.
Annual change = (Annual emissions) – K1 * (CO2 – 285) + K2 * (delta SST)
Where “CO2” is the atmospheric concentration, K1 is a unitless “ocean uptake constant”, and K2 is a sea surface absorption/temperature constant, with units of PPM per decree C. Delta SST is the year-on-year change in average sea surface temperature. K1 is related to how quickly surface water is replaced by deeper water, and it should be a relatively small number, since ocean circulation and mixing are slow. K2 should be a relatively large number, since surface water temperature changes are relatively fast and we know that there is a strong short-term correlation between the rate of change of CO2 concentration and SST changes.
The model performs an iterative calculation (a step-wise approximation of integration) of the evolution of CO2 in the atmosphere. Each year a change in CO2 is calculated using the above equation, that change is added to the atmospheric CO2 concentration from the previous year, and the process is then repeated. The calculation starts with 1959, using a starting CO2 concentration of 315 (the value from Mauna Loa in 1958).
Measured CO2 values and measured year-on-year changes are from Mauna Loa. Average SST’s are from GISS. CO2 emissions, expressed as PPM potential increase in CO2 in the atmosphere, are based on worldwide carbon emissions (according to CDIAC at Oak Ridge) converted to an equivalent weight of CO2, divided by an assumed atmosphere weight of 5.3 X 10^9 million tons. This result was scaled by a constant factor of 0.7232, which is 28.96/44 = 0.6582 (to convert weight fraction CO2 to volume fraction), multiplied by 1.099 to match up with the range of CO2 emissions that Dr. Spencer used in his May 11 blog post. Note that nobody really knows the total carbon emissions, so different sources offer different estimates of total emissions. The final two years of CO2 emissions I had to estimate beacause the CDIAC data ended in 2006. I assumed an equilibrium ocean CO2 level of 285 PPM. I optimized K1 and K2 by hand so that the model had a reasonable fit with the data; the values were 0.0215 for K1 and 5.0 for K2. So the model equation is:
Annual change = (Annual emissions) – 0.0215 * (CO2 – 285) + 5.0 * (delta SST)
The graph titled “Annual Increase in CO2” compares the measured and calculated year-on-year changes along with the potential increase from fossil fuels.
The graph titled “Correlation: Model Increase vs. Mauna Loa Increase” shows that the model does a decent job of capturing the year-on-year temperature driven change in atmospheric CO2.
I suspect that if the model used monthly data and the 6-month lag between SST changes and CO2 changes that Dr. Spencer used, then the model fit would be better.
The graph titled “Measured CO2 versus Ocean Uptake Model” shows the final result of the calculation.
The evolution of CO2 in the atmosphere calculated by the model between 1958 and 2008 is reasonably close to the Mauna Loa record. The model suggests that about 2.15 PPM equivalent of emitted CO2 is currently being absorbed, or about half the total emissions.
My only objective is to show that the CO2 released by human activities, combined with slow ocean absorption/neutralization and sea surface temperature variation, is broadly consistent with the measured historical trend in atmospheric CO2, including the effect of changing average SST on short term variation in the rate of CO2 increase. Temperature changes in ocean surface waters cause shifts of a few PPM up and down in the rate of increase, but surface temperature changes do not explain 80% to 90% of the increase in atmospheric CO2 since 1958, as suggested in Dr. Spencer’s May 11 post. Because of its relatively high pH, high buffering capacity, enormous mass, and slow circulation, the ocean is, and will be for a very long time, a significant net sink for atmospheric CO2.
With a bit of luck, continuing flat-to-falling average surface temperatures and ocean heat content will discredit the model predictions before too much economic damage is done.
anna v (07:00:00) :
Ferdinand Engelbeen (04:36:17) :
We will not agree on this. I do not defend Beck’s time series. Just that he has illuminated the differences in CO2 values form the politically correct ones.
Humans live below 500 meters and below the inversion layer. Either we are interested for temperature and CO2 at what is happening where we live, or we go to 5000 meters with satellites and measure temperature and CO2.
To take temperatures below 500 meters and CO2 at 5000meters is completely mixing apples and oranges for global averages.
The satellite temperatures as seen in http://discover.itsc.uah.edu/amsutemps/ have little to do with the temperatures on the land, the ones we live through. The same is true for CO2 up so high.
That is your fundamental error, the temperature where we live is governed by what happens at the top of the atmosphere which in turn does depend on the CO2 concentration there.
As I said, global quantities have to be rethought from the beginning.
It is you who needs to do some rethinking since your understanding of the physics of the atmosphere seems to be lacking.
Phil. (09:49:41) :
Can you estimate the change in the total CO2 optical thickness due to the changes at the top of the atmosphere?
I do happen to be a physicist, [sarcasm on] and I am sure that the 1 molecule of anthropogenic CO2 in in the rest of the million , at the densities of air at the top of the atmosphere surely will heat up and boil the earth.[ sarcasm off] It is the CO2 in the column of air that has any real greenhouse effect, and it is denser at the lower parts not up high in the sky.
Now of course if you mean clouds and the albedo or trapping clouds generate, that is another story but has nothing to do with CO2 at the top of the atmosphere. Temperatures at 5km are correlated but not the cause of temperatures at surface, either.
anna v (10:42:08) :
I do happen to be a physicist, […] It is the CO2 in the column of air that has any real greenhouse effect, and it is denser at the lower parts not up high in the sky.
Anna, as said (and calculated) before, the variability of CO2 in the first few hundred meters above land has negligible influence on the IR absorbance of CO2 in the total air column at the same place. And it has negligible influence on the yearly average CO2 levels in 95% of the atmosphere.
I don’t understand why you insist that it is necessary to measure and average all garbage levels of CO2 near ground over land, if the influence is negligible on near global levels, where the yearly average mixing ratio is near the same from sea surface to 12,000 m (and higher) and above 1,000 m over land, from the north pole to the south pole?
Steve Fitzpatrick (20:02:54) :
Those of us who honestly doubt the catastrophic climate forecasts made by climate models/modelers must be willing to acknowledge that some of the data and assumptions that go into the climate models are reasonable (CO2 will continue rising due to burning fossil fuels), and some are rubbish (half of future warming is already “in the pipeline”). Some of the predictions are reasonable (CO2 and other infrared absorbing trace gases should increase the average temperature of the earth by some amount due to their radiative effects), and some are rubbish (the average temperature will increase 4C in 90 years, and sea level will rise by several meters).
I think it more important to discredit the rubbish, not the reasonable.
I fully agree! Too many discussions in sceptic circles in recent times are focused on the reasonable like that humans are/aren’t responsible for the increase of CO2 in the atmosphere, while there is a lot of evidence that humans are responsible (and no evidence at all of the contrary). That undermines the credibility of all sceptics on items where the whole AGW theory is on shaky grounds like the (weak) influence of aerosols, and thus the (weak) influence of GHGs and the negative feedback caused by clouds, while all models see clouds as a positive feedback…
Re: anna v (10:53:51)
Anna, the truth is you can copy, paste, & plot data in Excel in mere seconds. I’ve guided 100s of online-students through the basics. It’s a breeze — the students never believe it will be initially – but they get all the basics (for all the basic types of graphs) in a single sitting. I don’t mind coaching in small increments…
Scatterplot example:
1) Find the data-webpage you want. For example:
ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_gl.txt
2) Scroll down to the data.
3) Select & copy the data (just like you select & copy text).
4) In Excel – using menus: Edit > Paste Special > Text > OK.
5) Data > Text-to-Columns > Next > Finish.
6) Highlight columns C & D (i.e. date & CO2, in this example).
7) Insert > Chart > XY (Scatter) > Finish.
This will produce a (rather unaesthetic) graph in less than one minute.
By double-clicking and right-clicking all over the graph (in every conceivable place) you will discover that cosmetic adjustments are an absolute breeze.
Re: Philip Mulholland (01:48:09)
Very stimulating post – thank you.
Cautionary Notes:
1) The arctic is ringed by a massive boreal forest.
2) Since a number of relevant variables follow seasonal cycles, it will be tricky ascribing proportions of causation (due to all of the confounding).
Certainly this is an interesting puzzle to work on …
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Re: Steve Fitzpatrick (20:02:54) & Ferdinand Engelbeen (14:13:44)
I share your concerns about attacking the real flaws rather than unreal ones. With power comes responsibility.
– – –
Ferdinand Engelbeen (04:36:17) “[…] there is very little variation even over a short time span for CO2 levels for any point on earth, as long as you are sufficiently far away from huge sources and sinks. In general that is everywhere above the inversion layer and above sea level.”
“above sea level” includes “above the inversion layer”
This is the whole atmosphere.
Did you intend something more like, “In general that is everywhere not at the inversion layer and not at sea level.”?
Clarification (via web-link(s) perhaps) of the vertical structure of mixing dynamics will be appreciated.
Raising temperatures means retaining more heat. To do that one has to raise the heat capacity. Since the atmosphere is a macrosopic phenomenon, I refuse to believe that in energy terms it is not following thermodynamics. Total heat retained depends on density and volume. That is the only way in thermodynamic terms CO2/H2O can increase temperatures in the lower atmosphere. Hand waving photons and practically 1 anthropogenic CO2 in a million up high in the rare atmosphere is just that, hand waving, and the reason that, as expounded in another thread, theGCM models’ predictions for behavior of troposphere and stratosphere do not work. Thermodynamics trumps in volumes.
Paul Vaughan (14:34:32) :
Thanks for the tutorial.
In my youth I was known as the fastest histogram producer in the lab, and that was when we carried cards to the computer room.
My reluctance to get hands on the data probably stems from laziness in getting into the real nitty gritty of errors and such.
Paul Vaughan (14:34:32) :
Re: anna v (10:53:51)
Anna, the truth is you can copy, paste, & plot data in Excel in mere seconds. I’ve guided 100s of online-students through the basics. It’s a breeze — the students never believe it will be initially – but they get all the basics (for all the basic types of graphs) in a single sitting. I don’t mind coaching in small increments…
Scatterplot example:
1) Find the data-webpage you want. For example:
ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_gl.txt
2) Scroll down to the data.
3) Select & copy the data (just like you select & copy text).
4) In Excel – using menus: Edit > Paste Special > Text > OK.
5) Data > Text-to-Columns > Next > Finish.
6) Highlight columns C & D (i.e. date & CO2, in this example).
7) Insert > Chart > XY (Scatter) > Finish.
Finally I was tempted. Step 7 did not work. It just gives two points, with irrelevant values .
Paul, it worked in my 2000 office excel on my laptop. On the home recent office my daughter is still trying to see what is wrong.
Anna The steps are all correct. However excel has a nasty habit of chosing the type of plot itself if it finds data it doesn’t like.
Step 7 would better be
Select the data you want to plot (NOT the whole columns). Ensure that there are no text entries apart from the column titles in your selection. Also there should be no blank entries immediately under the titles.
Sometimes excel will swap what is plotted against what. Right click the graph, select [select data]. The click swap row/col. this may fix it!
anna v (22:05:11) :
Raising temperatures means retaining more heat. To do that one has to raise the heat capacity. Since the atmosphere is a macrosopic phenomenon, I refuse to believe that in energy terms it is not following thermodynamics. Total heat retained depends on density and volume. That is the only way in thermodynamic terms CO2/H2O can increase temperatures in the lower atmosphere. Hand waving photons and practically 1 anthropogenic CO2 in a million up high in the rare atmosphere is just that, hand waving, and the reason that, as expounded in another thread, theGCM models’ predictions for behavior of troposphere and stratosphere do not work. Thermodynamics trumps in volumes.
As I stated above you appear to have no understanding of the physics of the atmosphere, your being a physicist notwithstanding. The influence of CO2 has nothing to do with its contribution to the atmosphere’s heat capacity, rather it is due to the enhanced rate of radiative heat transfer to/from the atmosphere.
Paul Vaughan, 24-05-2009 (16:58:48) :
“above sea level” includes “above the inversion layer”
This is the whole atmosphere.
Did you intend something more like, “In general that is everywhere not at the inversion layer and not at sea level.”?
Clarification (via web-link(s) perhaps) of the vertical structure of mixing dynamics will be appreciated.
Sorry, with my school English, I sometimes mix up things… “Sea level” in this case means from the surface on, everywhere on the oceans, except if there is an inversion, which is quite seldom over the oceans. But even then, the exchange rate between the oceans and atmosphere, positive in the tropics, negative near the poles, is slow enough to show little variation in the atmosphere from the poles to the equator (about 10 ppmv measured by ships). With the slightest wind, there is fast mixing with the air layers above.
That is different over land: at night there is often an inversion at a few tens to a few hundreds meters, especially in shielded valleys and at low wind speed. Then we see a buildup of CO2 from plant respiration and human sources (heating, traffic, industry), while during the day more sunshine/wind increases turbulence and we see that the CO2 levels drop to or slightly below “background”. Averaging continuous measurements for daily values thus gives a positive bias, as the buildup at night is fully seen, but the uptake by plants during the day is dispersed over more air layers…
In Diekirch, Luxemburg, the local weather station made CO2 measurements averaged over half hours during a few years. The results are here:
http://meteo.lcd.lu/papers/co2_patterns/co2_patterns.html with a lot of interesting findings.
Vertical profiles were done in Scandinavia and other places (even pre-Mauna Loa, but with physical impossibilities: higher levels at higher altitude than near ground) by e.g. Bert Bolin over the oceans (including regular commercial flights), see:
http://www.icsu-scope.org/downloadpubs/scope13/chapter03.html Fig. 3.2
This Scope paper includes more interesting items.
Modern flights for vertical profiling (mostly over land) were done at several places at the same place as tall towers (200 m), a good start is at:
http://www.esrl.noaa.gov/gmd/ccgg/iadv/ where you can compare ground level and tall tower/airplane data
Over the ocean, there is one in the above at American Samoa at ground level and nearby Cook Islands for regular flight measurements.
Individual projects are in The Netherlands and other places:
http://www.chiotto.org/cabauw.html
And of course, we have the satellite data, averaging over mid-troposphere:
http://airs.jpl.nasa.gov/story_archive/Pre-Release_CO2_Data_Available/
It would be interesting to compare the calculations of Bolin and Keeling of 1963 (Fig. 3.3 in with the Scope paper) with the AIRS satellite data…
The increase in CO2 prior to AIRS (and even now) is the result of model output with some of the data literally coming from receipts at the gas pump. The sinks are nearly ALL modeled inputs. Therefore the outcome of this notion of increased CO2 is an assumption, not data. The AIRS data does indeed show that CO2 has increased overall since it first started measurements. However, it is a HUGE jump to say that CO2 is increasing. It could just be in an oscillated stage. It is another HUGE jump to say that the increase is human-caused. The validity is weak. The current model of increasing CO2 driven by humans has yet to be proven valid (does it measure what it says it measures). The AIRS satellite doesn’t five a rat’s ass about the source of CO2 increases (or decreases). The reliability (can it be replicated) is also weak in that sinks are not totally understood and that different models of sources and sinks can produce different CO2 numbers. The number often quoted as showing that human-driven CO2 is increasing is a hypothesis, not a theory. It may become one but I don’t see proof of it yet.
Let us HOPE beyond hope that the CO2 numbers being spit out by AIRS is not “massaged” data to include modeled sinks prior to its publication. We kinda don’t like that here at WUWT.
Pamela Gray (15:25:59) :
Yes.
Let us HOPE beyond hope that the CO2 numbers being spit out by AIRS is not “massaged” data to include modeled sinks prior to its publication. We kinda don’t like that here at WUWT.
At this day and age of computers and grants flowing for climate change models one becomes suspicious, particularly as it took so long for the data to come out.
It reminds me of a children’s shadow play , Karagiozis, killing the dragon:
“Come forth curse’d snake
If you don’t come forth
I’ll come forth and come forth you”
Phil. (07:27:26) :
anna v (22:05:11) :
Raising temperatures means retaining more heat. To do that one has to raise the heat capacity. Since the atmosphere is a macrosopic phenomenon, I refuse to believe that in energy terms it is not following thermodynamics. Total heat retained depends on density and volume. That is the only way in thermodynamic terms CO2/H2O can increase temperatures in the lower atmosphere. Hand waving photons and practically 1 anthropogenic CO2 in a million up high in the rare atmosphere is just that, hand waving, and the reason that, as expounded in another thread, theGCM models’ predictions for behavior of troposphere and stratosphere do not work. Thermodynamics trumps in volumes.
As I stated above you appear to have no understanding of the physics of the atmosphere, your being a physicist notwithstanding. The influence of CO2 has nothing to do with its contribution to the atmosphere’s heat capacity, rather it is due to the enhanced rate of radiative heat transfer to/from the atmosphere.
I have to reply to this, because it is climate modelers who do not understand physics.
Physics theory has many axiomatic systems based on solid data. Partially they overlap.
Classical mechanics and quantum mechanics.
Neutonian mechanics and general relativity.
Thermodynamics , statistical mechanics,and quantum statistical mechanics.
Thermodynamics knows nothing of statistical mechanics or quantum statistical mechanics. It works very well macroscopically, (which is what weather and climate are. macroscopic observations) and includes all macroscopic radiative effects, viz black body radiation etc. Thus within thermodynamics the atmosphere can be fully described because there is no need it involve quantum mechanics ( no coherence and it is macroscopic). Heat capacity is the way in thermodynamics that ability of matter in bulk to retain energy in kinetic and radiative form is described.
Theoretically one could describe a bulk system by quantum statistical dynamics, but this means that the whole caboodle should be consistently described with statistical ensembles and probability functions and minimizations, an impossible task.
Climate theorists have confused the knowledge gained by quantum studies of how molecules interact with energy, useful to know, useless in bulk, to make a mixture of pure thermodynamic background with quantum statistical joints. This cannot be done consistently, and it is why the models fail among other things. CO2 is distributed in the bulk, and is not sitting up in the troposphere or where have you playing ball with photons, neither H2O at that. The photons it plays ball with are from the bottom up, and there is very much less matter the higher one goes.
Would you make a hot water bottle filled with CO2 at atmospheric pressure?
at troposphere pressure?
Pamela Gray (15:25:59) :
Pamela and Anna V,
Sorry, but what you are telling now is pure fiction. The CO2 data at Barrow, Mauna Loa, south pole and some 70+ other places on earth, plus flight measurements, buoiys and ships over the oceans, far away from huge sources and sinks are measurements not the outcome of any model. The average measurement error is better than 0.1 ppmv for one series and parallel series from flask measurements at the same place are within 0.12 ppmv. The increase at the south pole (the oldest series) since 1959 is 60+ ppmv. All other stations and flights, representing over 95% of the atmosphere show the same increase over time. Why do you think that the increase is the result of a model?
The only place where a simple “model” is used is when at any station there is a lack of data, due to mechanical problems or volcanic eruptions, etc. Then one uses a curve fitting algorithm that uses the seasonal curve of the previous three years + the remaining good data of a month to estimate the monthly average. In that case the hourly data are flagged with an *A* flag. In all cases both the real measurements (if available) and the calculated trend are presented in the tables, but when possible only real data are used for daily, monthly and yearly averages. Even if at one station there is a problem with the data, the other stations simply go on with monitoring and show the usual, emissions related increase, modulated by temperature variations.
The emissions all are based on fossil fuel production/sales inventories, which are kept under supervision of the tax income departments. I don’t think that these are interested in underestimating the sales, but some under the counter sales may give a slight underestimate… Every type of fuel has its own efficiency when burned. That is used to estimate the CO2 emissions + cement manufacturing + forest clearing. All together, the human emissions are about twice the measured increase of CO2 in the atmosphere over the past 150 years.
This all has nothing to do with any model or any detailed knowledge of the carbon cycle: if you add twice the amount which is seen as increase in the atmosphere, one can be sure that it is the addition which is responsible and nature as a whole is a net sink for human CO2, no matter where it is absorbed.
In addition,
I have plotted both the raw hourly data of Mauna Loa without any selection criteria, thus including all outliers for 2004:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/mlo2004_hr_raw.jpg
and with the usual selection criteria:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/mlo2004_hr_selected.gif
For 2004, 8784 hourly average data should have been sampled, but:
1102 have no data, due to instrumental errors (including several weeks in June).
1085 were flagged, due to upslope diurnal winds (which have lower values), not used in daily, monthly and yearly averages.
655 had large variability within one hour, were flagged, but still are used in the official averages.
866 had large hour-by-hour variability > 0.25 ppmv, were flagged and not used.
As one can see in the trends, despite the exclusion of (in the above second graph) all outliers, the difference in trend with or without flagged data is minimal (less than 0.1 ppmv), only the number of outliers around the seasonal trend is reduced and the overall increase in 2004 in both cases is about 1.5 ppmv.
Further, no need to hope that the AIRS (or any new satellite) data will give different results: the satellite data are calibrated with… station data like Mauna Loa, expanded with in-flight and balloon measurements. As good as the satellite temperature data were calibrated with surface and balloon data… Once the calculation method is established, the trends can differ, as satellites span (near) the whole world. But in the case of CO2, that will show much better regional resolution of sources and sinks (good for modelling the carbon cycle), but will not change the overall trends, as that is, averaged over a year, the same everywhere in 95% of the atmosphere…
Paul Vaughan (17:09:00) : 23 05
This is a rather trivial answer to a serious question.
If you read the early Keeling reports you will find that even on Mauna Loa there are huge differences in CO2 concentration as winds change direction etc. These are thrown out to give a “pure” background reading that might be specific to Mauna Loa. Yet, the lower altitude CO2 concentrations, to my knowledge, have no fine structure on an annual basis. So how the heck can a diverse mixture of miscellanous-sourced CO2 go globally to the South Pole (where there is no land vegetation for a radius of 3500 km), without annual bumps getting mixed out of existence?
Have you ever seen movies of the winds that can blow in the Antarctic and wondered how those delicate little annual wriggles can be preserved?
I’d call them an artistic licence, to make them more credible by resembling the (filtered) Mauna Loa set.
Taking it further, to say that the CO2 level at the Soth Pole, at Barrow and at Mauna Loa is essentially the same concentration (after purification) might simply mean that places of high variability have been excluded from the data. Because CO2 is a rather dense gas and because a lot is cycled near the ground surface, this is where I’d expect the highest CO2 concentrations to be found, volcanos excluded, in the atmosphere. This is where there will be the greatest “greenhouse effect”, this is where CO2 levels should be measured for correlation with temperature if you have idle time to do that. Next thing, someone will be measuring CO2 at commercial jet cruise levels and correlating that with global temperatures.
Unreal, man.
Geoff Sherrington (04:28:34) :
Geoff,
The variability of CO2 is mainly over land in the first tens of meters, up to a few hundred meters. Not above the sea surface and not over land above 500-1000 m. See e.g. the measurements of the tall tower of Cabauw (The Netherlands) at different heights:
http://www.chiotto.org/cabauw.html
Even if you double the current CO2 levels in the first 1,000 m, the influence of the doubling is only 10% of the IR absorption (0.4 W/m2) over the full air column at the same place. Thus the typical bias of average 30-60 ppmv over land in the first tens of meters has little to no influence on local, regional or total land warming, and none over the oceans.
Again, throwing out the outliers of Mauna Loa (to both sides) doesn’t influence the average increase, it only smooths the variability around the seasonal trend (see the graphs in the previous message). You can do the same work for Barrow, Samoa and the south pole data, as all the unfiltered hourly averages (calculated from 40 minutes of 10-second raw voltage sampling) are available at: ftp://ftp.cmdl.noaa.gov/ccg/co2/in-situ/
And there is a good correlation between inflight (and station) CO2 data and SST: SST governs the variability around the increase rate of CO2. The opposite is more difficult to estimate/prove… Inflight data of commercial flights (Scandinavia-Boston) showed in the begin 60-ies the same values as Mauna Loa, south pole and other places, be it more smoothed for seasonal variation than Mauna Loa.
Because CO2 is a rather dense gas and because a lot is cycled near the ground surface, this is where I’d expect the highest CO2 concentrations to be found, ……
Unreal, man.
Certainly, anyone referring to the density of CO2 gas and implying that means that it should therefore be more concentrated near the ground should automatically be disqualified from a serious scientific discussion.
Ferdinand Engelbeen (08:00:46) “[…] except if there is an inversion”
That’s what I figured you meant — I wanted to be sure. Thanks not only for the clarification, but also for the great links. I am very much interested in ALL of the spatiotemporal variability at all scales …and busy digging into covariates …
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Re: anna v (23:37:49) & (05:45:25)
A nice thing about Excel is that it provides a number of ways to do the same thing …
Another way to access the “Chart Wizard” (graphing) dialog-box is via a menu-button with an icon that looks like a mini-barchart.
[If you hover your mouse over it – & pause movement – Excel will display “Chart Wizard”.]
If you hover your mouse over Excel graphs – & pause movement – Excel will display the name of the graph feature over which you are hovering.
If you right-click while hovering over your graph’s “Chart Area”, you can access a variety of dialog-boxes.
To check what type of graph Excel has made:
1) Right-click while hovering over your graph’s “Chart Area”.
2) From the menu that appears, choose “Chart Type”.
[You want “XY (Scatter)” for the CO2 vs. Date scatterplot.]
A few side-notes:
Note that there are tabs across the top of Excel dialog-boxes —- Suggested: Spend a few minutes checking out the various adjustables on each tab when you have a minute.
And note that Excel uses letters to represent columns and numbers to represent rows.
Next:
To check what data Excel has (actually) graphed:
1) Right-click while hovering over your graph’s “Chart Area”.
2) From the menu that appears, choose “Source Data”.
3) In the dialog-box that appears, select the “Series” tab.
If you (accidentally) made the mistake of (for example) only highlighting cells C1 & D1 (instead of cells C1 through D351, which is what you want), you will see a blank “X Values” box and something like “=Sheet1!$C$1:$D$1” in the “Y Values” box ….when what you want to see are:
…in the X Values box:
=Sheet1!$C$1:$C$351
…in the Y Values box:
=Sheet1!$D$1:$D$351
Suggested:
Check to see if you have a “XY (Scatter)” plot with the preceding X & Y Values.
Just small steps… I can share some more tips later. With patience, it all works out – it always does.
Re: Geoff Sherrington (04:28:34)
We need to keep in mind that “‘background’ CO2 concentration at Mauna Loa” is just that.
Questions:
a) Have you watched the AIRS movies?
b) Are you suggesting the South Pole CO2 record is pure fabrication?
I have been trying to warn people that this was coming, now certain foods and beer are on the eco chopping block:
http://www.timesonline.co.uk/tol/news/environment/article6350237.ece