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.
“How does your model cope with the warming between 1910 and 1945. Can anyone else shed any light on this strange effect.”
The only thing “strange” about this well- within-normal warming phase is that anyone should think it “strange”.
Air ρC = 1,200 J/m^3 K
Oceans ρC = 4,190,000 J/m^3 K
Dry Clay Ground ρC = 1,420,000 J/m^3 K
Specific Heat Capacity is not the same as Heat Capacity.
very cool post
Dave Middleton 15:38:18
“There are two common ways to estimate CO2 concentrations in past atmospheres (before instrumental records began in 1959): 1) Measuring CO2 content in air bubbles trapped in ice cores and 2) measuring the density of stoma in plants.”
As a physicist I may be missing something, but the work done in Germany over the past 180 years seems to give a reasonable assessment of atmospheric CO2 in the northern hemisphere.
This web site is a good place to look
http://www.biokurs.de/treibhaus/180CO2_supp.htm
Ronaldo (02:42:02) :
The same problem with the work of Ernst Beck as with the stomata data: high variations within a day and over the months due to huge local/regional sources and sinks. Biased to too high values as extreme high levels at night (early morning / late evening) are not fully compensated by more photosynthesis during the day. Several measurements which are instrumental to the 1942 “peak” are within rice fields, near agriculture/towns/factories…
The values measured over/near the oceans in general are far lower and near all ranges of measurements encompass the ice core values. Neither ice core data (8 years resolution), nor stomata data (5 year resolution) show the 1935-1950 peak value found by Beck. For a comprehensive comment on Beck’s interpretation of the historical measurements, see:
http://www.ferdinand-engelbeen.be/klimaat/beck_data.html
The Engineer (00:29:20) :
I have compared the pre-Mauna Loa CO2 data (from different ice cores) with the emission values in the period 1900-1959. The ratio between increase in the atmosphere and emissions is about 58%:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/acc_co2_1900_1959.jpg
Compare that to the influence of temperature on CO2 increase in the same period:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/temp_co2_1900_1959.jpg
The conclusion only can be that temperature is not the cause of the CO2 increase: even with a cooling about halve the temperature scale (due to the included part of the 1945-1975 period), CO2 levels still go up, while the emissions and increase are nicely coupled.
For the full 1900-2004 period it is even more clear:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/acc_co2_1900_2004.jpg
and
http://www.ferdinand-engelbeen.be/klimaat/klim_img/temp_co2_1900_2004.jpg
Temperature modulates the increase speed (hence the nice correlation between the derivative of the increase and temperature variations), thus the “noise” around the trend, but the bulk of the trend is caused by the emissions…
Steve Fitzpatrick (08:50:48) : on 22 05
Can you point me to a reference or two where people have actually titrated natural seawater with air with a known CO2 and SO2 concentration and measured the pH change? Granted, it migh take some time to equilibrate and biota might die in the flask, but I simply cannot see that the minute amount of CO2 in the air has the capability of lowering pH by 0.1, as is often claimed in lterature.
These claims are often based on phase diagrams using bicarbonate, calcium etc. Having used similar phase diagrams in other fields, I know that small errors in the determination of coefficients can accumulate to major errors. So I regard them as indicative rather than quantitative, especially with natural systems as opposed to synthetic look-alikes.
Ronaldo (02:42:02) :
Dave Middleton 15:38:18
“There are two common ways to estimate CO2 concentrations in past atmospheres (before instrumental records began in 1959): 1) Measuring CO2 content in air bubbles trapped in ice cores and 2) measuring the density of stoma in plants.”
As a physicist I may be missing something, but the work done in Germany over the past 180 years seems to give a reasonable assessment of atmospheric CO2 in the northern hemisphere.
This web site is a good place to look
http://www.biokurs.de/treibhaus/180CO2_supp.htm
Yes, Beck’s compilation.
During these threads I always ask the question and no satisfactory answer is given:
Ice cores are taken in regions where there is snow and ice, i.e. very few CO2 sources. AIRS animations show that contrary to the “well mixed” theory, CO2 follows wind patterns. Beck’s compilation shows that over land where we have many accurate measurements, even away from cities and populations, CO2 has large natural variations, as large as we see now in the homogenized Mauna Loa Data.
Why to we believe the 280 ppms given in the ice cores as anything other than the measuremnt of CO2 in ice regions? Where there are no or few CO2 sources?
Also I have never seen a preprint or publication open to the public with the raw data from Mauna Loa and all the corrections introduced by hand. What are the raw monthly values before sanitization?
@ur momisugly Ronaldo (02:42:02)
Thanks for the link to Beck’s paper. I knew there were other chemical studies of pre-Keeling CO2; but I was unaware of Beck’s work.
So it looks like there are three ways to estimate a pre-Keeling CO2 background…Plant SI and chemical methods show that the modern CO2 level is not anomalous and only the ice core data suggest a background below 320-340ppm.
So the 270-280ppm from the ice cores is the annual average and the 320-340ppm from the plant SI data represent the springtime bloom? So the annual CO2 seasonal range was +/- 100ppm?
What is the seasonal amplitude variation of the Keeling Curve? It’s less than 10ppm.
SI studies have been performed on samples within the time span of the Keeling data and they match the Keeling data.
See: “Stomatal frequency responses in hardwood-swamp vegetation from Florida during a 60-year continuous CO2 increase1” by Wagner (American Journal of Botany, March 2004)…
The SI data match the current observations…The ice core data do not significantly overlap the Keeling data. So they can’t really be directly compared. The SI data and the chemical data both show past atmospheric CO2 levels to be far closer to the modern observations than the ice core data do. And both can be directly compared with and calibrate well with the modern observations.
Added La Jolla to the timing of CO2 ,minima plot expecting to see a delay from Barrow to La Jolla. But from the data I would suggest that the two values are not connected.
La Jolla frequency of measurement improves in 1985 so before then accuracy of minima is suspect.
Until 1999 it may be possible to say the delay is about 8 days. After 1999 the delay is possibly zero!
No-one has yet suggested a reasonable cause for this sharp dip. And the dip is greater than the yearly increment.
It occurs mid summer in NH too late for spring too early for autumn.
It does not correspond to plankton blooms.
It occurs (currently) 218 days after jan 1st in both locations 4850km apart (north/south)
Without any comment:
1. http://www.rightsidenews.com/200905224868/energy-and-environment/northern-ireland-climate-change-committee-hears-evidence-no-man-made-global-warming.html
2. http://icecap.us/images/uploads/GlobalWarmingMyth.pdf
anna v (05:11:26) :
Ice cores are taken in regions where there is snow and ice, i.e. very few CO2 sources. AIRS animations show that contrary to the “well mixed” theory, CO2 follows wind patterns. Beck’s compilation shows that over land where we have many accurate measurements, even away from cities and populations, CO2 has large natural variations, as large as we see now in the homogenized Mauna Loa Data.
Why to we believe the 280 ppms given in the ice cores as anything other than the measuremnt of CO2 in ice regions? Where there are no or few CO2 sources?
Also I have never seen a preprint or publication open to the public with the raw data from Mauna Loa and all the corrections introduced by hand. What are the raw monthly values before sanitization?
Anna, the yearly average CO2 data in 95% of the atmosphere (over the oceans and above about 1,000 m over land) don’t differ more than 5 ppmv of each other. Within one hemisphere the difference is less than 2 ppmv. Between Barrow (87N) and the south pole (90S) less than 5 ppmv:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/co2_trends.jpg
The delay between NH and SH points to a source in the NH, while the ITCZ delays the mixing of CO2 into the SH. The increase in the atmosphere is tightly coupled (at about 55%) with the cumulative emissions:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/acc_co2_1960_2006.jpg
This looks like causation, as I don’t know of any natural process which follows the emissions at such an incredible constant ratio…
Many of the historical data were taken near (even in) agricultural projects (rice ans soja fields), near and in towns, etc. Callendar rejected all these data, just because of that reason, as these show the local variability within about 5% of the atmosphere, not the more or less global values. Seasonal variations at that time were near undetectable (most methods were accurate to +/- 10 ppmv). His selection (pre-defined, not post-defined!) resulted in mainly sealevel and coastal data which match with the ice core data, measured 40-60 years later than Callendar’s work.
The problem with Beck’s interpretation is the same as with many temperature measurements: wrong place, heavely contaminated by local sources/sinks. Ice cores simply reflect the (smoothed!) variability of global CO2 levels over time, most of the historical data only reflect local variability.
Uncorrected hourly averages of 40 minute 10-second voltage measurements are online available for four baseline stations at:
ftp://ftp.cmdl.noaa.gov/ccg/co2/in-situ/ from N to S including Barrow, Mauna Loa, Samoa and the south pole.
For daily, monthly and yearly averages only selected data are used, as one is interested in background data, not contaminated by local vegetation or volcanic degassing (Mauna Loa) or mechanical problems (south pole). All deselected data still are available, but “flagged” for different reasons. Again the rules for deselection (and calibration) are strict and predefined. The only case where post-data correction is applied is when problems are encountered with the calibration gases.
For one year (2004), I have used all available data and only selected data: there is no difference in average and slope…
A comprehensive explanation of the calibration and selection procedures at Mauna Loa (and other baseline stations) is available here:
http://www.esrl.noaa.gov/gmd/ccgg/about/co2_measurements.html
Ron de Haan 07:23:25
“Without any comment”
No comment needed!!!
Dave Middleton (05:49:16) :
So the 270-280ppm from the ice cores is the annual average and the 320-340ppm from the plant SI data represent the springtime bloom? So the annual CO2 seasonal range was +/- 100ppm?
That is possible at any location on land, but doesn’t represent the global CO2 levels… The very first measurements that Keeling made in his long CO2 measurements life, were at Big Sur state park (California). The diurnal variation was about 60 ppmv. He measured d13C levels of the same (flask) samples and could conclude that the change was caused by respiration/photosynthesis of vegetation. That was the main reason for him to look at less contaminated places like the south pole and Mauna Loa (the latter with some caution…).
Stomata data are by definition at places where a lot of vegetation is present. While the resolution is moderate (+/- 10 ppmv), the spring bias (according to one of the authors, the SI index is predefined by the CO2 levels of the previous growing season, then we have a summer/autumn bias…) can be compensated for by calibrating the SI data with the ice cores/atmospheric data over the recent period. The main problem is that one need to have a good indication of what happened with local/regional CO2 levels if the local/regional landscape changed from swamp to forest or increasing/shrinking forest/forest area ratio over longer periods, related to temperature. And that information is lacking.
See the SI calibration of querqus (oak) in the Netherlands:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/van_Hoof.jpg
Still some problems to solve before that method is robust enough to give reliable figures of the past…
I have many comments today.
First, Steve: nice post, a very useful model for thinking about the role of the oceans. The model may be right or wrong, but I think it helps move our thinking forward on the matter, and I think that is a material service. My gut says this model will actually hold up pretty well over time.
Second: On NevadaFACE. Yes, they track ambient CO2, but for purposes of fertilization, not climate tracking, and their recent measurements are close to Mauna Loa. I see no reason to demote Mauna Loa based on FACE data, useful as it may be as an additional data point.
Third: Change in atmospheric CO2 cannot be explained by SST alone. If we allow that the ocean temps drive CO2, then atmospheric CO2 should have been stable over the last decade as temperatures moderated. Instead, CO2 continues to increase, suggesting that human activity is most probably a material driver of atmospheric CO2.
Fourth: CO2 emissions have soared in the last ten years, Oil consumption is up about 10% decade over decade and Chinese coal consumption has increased by more than US total consumption in just the last four years. So CO2 is entering the atmosphere at a record pace. However, rather than seeing an exponential growth in temperature, which an AGW argument might posit, we see flat to declining temperatures, which suggests that either CO2 is not a dominant force or immaterial.
Fifth: I personally am not too concerned the need for peer-reviewed journals. Over time, blogs like this one–properly managed–will carry more weight than academic journals (odd, you may think), because they have certain distinct advantages: i) they are timely, ii) they are relevant (because they are timely); iv) open and therefore amenable to google searches (which means they are available for incorporation into public presentations at conferences and the like, and iv) they are visible and read, which makes their contributors and editors public figures and authorities.
Finally: This was an excellent, excellent thread today. Great contributions showing real thought. No name calling, all quality.
Is there any way to use actual AIMS CO2 data instead of model derived CO2 data? The noisy data in both sets (CO2 vs temp) would lend itself much better to analysis and removes the assumptions that global atmospheric CO2 is increasing each and every day based on modeled output of Mauna Loa data input.
Steven, why do you believe CO2 is increasing? If you look at the AIMS data, there are seasonal variations that do not appear to correlate well at all with the step function we see from Mauna Loa (IE the noise is different than the starting and end points between the two sets of data). If the noise is different at multiple points, the fact that starting and end points are similar (4 data points) loses its strength as a correlation that CO2 is increasing because of human influences. Most people don’t realize that the typical number quoted as the amount of CO2 in our atmosphere is a derived statistic partially based on data input then modeled heavily to get output.
AIRS! meant AIRS! still…need…more…coffee
Maybe with a bit o’ Irish Cream.
Pamela Gray (10:03:14) :
Why do you think that “global” CO2 data are based on a model? The global data used are the simple averages of a few baseline stations at sealevel, but every station that is situated in a surrounding with a limited amount of huge local/regional sources/sinks is suitable. All baseline CO2 levels over the globe, representing 95% of the atmosphere are within 5 ppmv for yearly averages, but all show near the same slope over the past 50+ years, only with a slight delay for the NH-SH lag.
The AIMS data at specific points like Mauna Loa or Barrow show the same values in the same period. That means lower values at Barrow in summer and higher in winter. The local yearly averages are less than 2 ppmv of each other. The variation you see in AIMS simply is the seasonal variation, far larger in the NH than in the SH, but the trend in both hemispheres is nearly the same.
Indeed AIRS (my memory is aging rapidely…).
Steve; “The CO2 from fossil fuels has the same residence time as the radioactive C14 generated by atomic bomb tests, as you said, between 5 and 10 years, no longer.”
Why than IPCC defines it to be “50-200 years”? What you basically are saying is that although natural fluxes are so large, we should accept that fossil fuels drive little increments in overall CO2 because C12/C13 ratio is changing what can theoretically be caused by fossil fuel burning, but we don’t know, because many other things can cause that (as Spencer and many others point out).
“The effect of a continuous relatively small addition of CO2 to the large natural pool will never be evident over the noise of natural variation in the very short term, but it should cause a gradual increase over time.”
In the light of previous discussion that sounds more like a statement of faith, not science, doesn’t it?
Ferdinand Engelbeen (08:30:34) :
Thank you for the links, except they are not really in a digestible form, are they?
I am too old a dog to learn the new tricks of making plots with excell or whatnot.
I think that the argument that one should measure CO2 where there are no CO2 sources is specious, and defeated by the Mauna Loa location itselfwhich is right on top of a volcano which emmits CO2. I cannot see any volcano measurements, for example, in the link you gave for supposedly raw data. Vegetation and plankton are contamination but volcanoes not?
I am patiently waiting for the near surface measurements from the Japanese satellite.
As you know I think that the whole CO2 measurements is dominated by the Keeling mentality and they will probably correct away until they get the same Keeling curve. We are in great need of independent measurements all over the globe and analysis that does not presuppose “well mixed”. The Airs animation does not show “well mixed”
continuing.
I think the way they measure in Mauna Loa and consequently all other mimicking spots, is crazy:
http://www.esrl.noaa.gov/gmd/ccgg/about/co2_measurements.html
At Mauna Loa we use the following data selection criteria:
1. The standard deviation of minute averages should be less than 0.30 ppm within a given hour. A standard deviation larger than 0.30 ppm is indicated by a “V” flag in the hourly data file, and by the red color in Figure 2.
OK translate this to a temperature measurement and see what it means: throw out measurements that have a 1C difference from day to day( a cloudy day)
2. The hourly average should differ from the preceding hour by less than 0.25 ppm. A larger hour-to-hour change is indicated by a “D” flag in the hourly data file, and by the green color in Figure 2.
a cloudy day
3. There is often a diurnal wind flow pattern on Mauna Loa driven by warming of the surface during the day and cooling during the night. During the day warm air flows up the slope, typically reaching the observatory at 9 am local time (19 UTC) or later. The upslope air may have CO2 that has been lowered by plants removing CO2 through photosynthesis at lower elevations on the island, although the CO2 decrease arrives later than the change in wind direction, because the observatory is surrounded by miles of bare lava. In Figure 2 the downslope wind changed to upslope during hour 18. Upslope winds can persist through ~7 pm local time (5 UTC, next day, or hour 29 in Figure 2). Hours that are likely affected by local photosynthesis are indicated by a “U” flag in the hourly data file, and by the blue color in Figure 2. The selection to minimize this potential non-background bias takes place as part of step 4. At night the flow is often downslope, bringing background air. However, that air is sometimes contaminated by CO2 emissions from the crater of Mauna Loa. As the air meanders down the slope that situation is characterized by high variability of the CO2 mole fraction. In Figure 2, downslope winds resumed in hour 28. Hour 33 in Figure 2 is the first of an episode of high variability lasting 7 hours.
The wind is from the Sahara, throw out the temperature measurement
4. In keeping with the requirement that CO2 in background air should be steady, we apply a general “outlier rejection” step, in which we fit a curve to the preliminary daily means for each day calculated from the hours surviving step 1 and 2, and not including times with upslope winds. All hourly averages that are further than two standard deviations, calculated for every day, away from the fitted curve (“outliers”) are rejected. This step is iterated until no more rejections occur. These hours are indicated by an “A” flag in the hourly data file, and by the purple color in Figure 2, also indicated as “spline” in the legend. Spline is a curve fitting technique. Rejected hours occurring during times with upslope winds are given a “U” character in the data file.
Any large temperature fluctuations should be thrown out.
Don’t I wish they had done that in the surface temperature measurements !! We would have no AGW !!
anna v (10:53:51) :
The hourly average data are more a question of volume (8,600 lines per year) than difficult to see. Mauna Loa only has serious trouble if there is a real eruption, in general if the wind is from the venting places, that translates in a huge variability of measurements within an hour (average +4 ppmv), which is one of the several reasons to reject the data. Real background data have no detectable change over a day…
The same for frequent upslope winds in the afternoon: depleted (at -4 ppmv) of CO2 by lower based vegetation. In both cases the average difference with real “background” CO2 levels is less than 4 ppmv and the yearly average is not more than 0.1 ppmv different if you include or exclude the outliers. Thus there is no correction applied at all, only good data where we are interested in are retained. With a few exceptions: if too many days are lost (for any reason) within a month, the remaining days are used with a correction based on the seasonal slope of the previous years at the same days to calculate the monthly average (see the discussion on this blog some time ago).
Further, the continuous Mauna Loa data are confirmed by three independent flask measurements at the same spot and a fourth series at the bottom of one of the Hawai islands. Measured by different persons in different labs from different organisations by different methods. The series are in average within +/- 0.12 ppmv of each other. The Keeling curve meanwhile is near the same, measured from near the north pole to the south pole in 10 baseline stations, 70+ similar stations at least contaminated places, flight measurements, buoiys and ships on sea. See:
http://www.esrl.noaa.gov/gmd/ccgg/iadv/
Besides that, some 400+ stations which monitor over land for local/regional CO2 fluxes, a quite impossible task, which may be better done by the Japanese satellite…
The global number for CO2 takes into account modeled sinks. It is a modeled number, not measured data, although measured data at sources is put into the calculation.