From Dr. Roy Spencer’s Global Warming Blog
by Roy W. Spencer, Ph. D.
Summary of Main Points
By choosing the “best” models and estimates of CO2 fluxes (those which best explain year-to-year changes in atmospheric CO2 content as measured at Mauna Loa, HI) for the period 1959-2023 as provided by the Global Carbon Project, a multiple linear regression of yearly Mauna Loa CO2 changes against those “best” estimates of sources and sinks leads to the following alterations to the “official ” Global Carbon Project estimates of the sources and sinks leading to the observed rise in atmospheric CO2. (NOTE: being a statistical exercise, this does not constitute “proof”… these are just some areas that carbon budget modelers might want to look into when tweaking their models):
- Global anthropogenic CO2 emissions appear to be 30% larger than reported (I find this hard to believe… again, statistics are not necessarily proof).
- The Land Sink of CO2 has been underestimated by an average of about 25%
- The Ocean Sink of CO2 has been overestimated by about 20% (I don’t know whether they include CO2 outgassing).
- The Land Use source of CO2 (primarily biomass burning) has been overestimated by about a factor of 2 (very uncertain)
- The cement carbonation sink has been underestimated by about a factor of 7 (very uncertain)
- There is a remaining unknown CO2 sink that has averaged 0.2 ppm/yr during 1959-2023 (this could just be a residual of other statistical errors).
Background
Many researchers have spent their careers trying to estimate the various global sources and sinks of atmospheric CO2. The main net sources are anthropogenic emissions (including cement production) and land use (mainly biomass burning). The main CO2 sinks are land (vegetation and soil storage), the ocean (mixing the “excess” atmospheric CO2 downward… biological uptake remains largely unknown), and cement carbonation (old cement absorbs atmospheric CO2).
The Global Carbon Project (GCP) periodically summarizes various estimates of these sources and sinks and produces easily-accessible spreadsheets of the data. I suppose for political expediency (don’t insult your peers), the GCP (like the IPCC does for climate models) just takes virtually all of the estimates of CO2 fluxes and averages them together to produce a single “best” estimate of specific fluxes on a yearly basis. For example, they average 20 (!) different land models results for yearly net CO2 fluxes into the land surface (I say “into” because the current atmospheric “excess” of CO2, around 50% above pre-Industrial levels, causes the land and ocean to be net sinks of CO2).
What I Did
But since I am not part of the global carbon budget research community, I can pick and choose which models and data-based estimates I use. Some of these models are better than others at explaining the yearly increase in atmospheric CO2 at Mauna Loa, Hawaii, and here I will provide an analysis using only the best estimates.
(Now, some researchers believe that an average of all estimates will be better than any individual estimates. I don’t believe that… and neither should you. As a simple example, you can’t make a better estimate of something by averaging a good estimate with a bad estimate.)
So, what I did was to examine how well each individual model estimate (or sometimes an observational estimate) helped to explain the yearly CO2 increases at Mauna Loa. I then chose the best ones, and averaged them together. Then I regresses the yearly CO2 changes at Mauna Loa against these averages. As Fig. 1 shows, this produces a much better estimate of the Mauna Loa CO2 record than the GCP estimates of CO2 fluxes based upon all available estimates from various sources.
Now, to be fair, part of this better agreement comes from the statistical regression. The GCP estimates (quite admirably) use all of the available estimates based upon physics and parameterizations, and then sees how well the results match the Mauna Loa record. And they even include the yearly “residual” in their spreadsheet to show how well (or how poorly) the models fit the data. Kudos.
But I used the best models and estimates, and then use multiple linear regression, to see how closely the data can be fit to the Mauna Loa observations. Again, the year-to-year changes in observed CO2 concentrations are statistically related to the sources and sinks of CO2 which come from (1) anthropogenic emissions, (2) land use emissions, (3) land vegetative and soil uptake, (4) ocean uptake, and (5) cement carbonation (old cement removes CO2 from the atmosphere).
The results give a total regression model explained variance of 81%. The regression coefficients tell us whether the individual CO2 budget terms (sources and sinks of CO2) have been underestimated or overestimated. If the terms equal +1 (for sources) or -1 (for sinks), then the model estimates of the yearly CO2 sources and sinks are (on average) unbiased in their explanation of yearly CO2 changes at Mauna Loa.
Again I emphasize that such statistical results can be misleading. Errors in one term’s regression coefficient can cause errors in other terms’ coefficients. But regression analysis can also sometimes can reveal insights into what physics might be missing. I have seen both in my 40 years of doing such calculations.
Here are the results:
Global Anthropogenic Emissions: Coefficient = 1.3 (+/-0.22) This suggests anthropogenic emissions have been underestimated by about 30%. I find this hard to believe. Energy use is pretty well known. Maybe the cement production source has been underestimated?
Global Land Use: Coefficient = 0.43 (+/-0.45) This suggests land use emissions have been overestimated (but the coefficient uncertainty is large). Also, if there is little skill in a term, a lower coefficient will result due to the “regression to the mean” effect. This result suggests to me that yearly land use as a source of CO2 remains very uncertain.
Global Land Sink: Coefficient = -1.26 (+/-0.16). This suggests the land (mainly vegetation) sink has been underestimated by maybe 25%. The error is the coefficient is pretty small, so I think this result is significant.
Global Ocean Sink: Coefficient = -0.80 (+/- 0.49) This suggests the ocean sink has been overestimated (but with rather large uncertainty) by about 20%. I haven’t looked at whether these ocean models include CO2 outgassing as the temperature rises (a small effect). I’m not convinced that this coefficient is significantly different than 1.0, which would be the case if the models are unbiased in their estimates of the ocean sink.
Cement Carbonation Sink: (-7.3 +/-4.9) This suggests the CO2 uptake by old cement has been greatly underestimated (but with large uncertainty). This is a surprisingly large number, and I don’t know what to make of it.
I’m not convinced of most of these conclusions, except maybe the vegetation sink of CO2 being underestimated by the models. There have been recent papers published finding some vegetation uptake processes have been underestimated by the models.
The global anthropogenic emissions source being underestimated is also intriguing. Being greater than 1, the 1.3 coefficient is the opposite of what we would get from regression if the yearly anthropogenic emissions estimates were poor. So, I’m inclined to believe this is real.
Anyway, this was an quick-and-dirty exercise. Maybe 4 hours of my time. You can access the GCP data spreadsheet here.
P.S. I’m sure someone will ask about adding various natural factors: for example, global surface temperature (land and/or ocean). Yes, that can be done.
I previously looked at a number of the source/sink study estimates. Some are clearly logically wrong (Murray Salby a while ago). All the others have such large uncertainties that I concluded it wasn’t a fruitful path for any further examination.
A similar comment applies to all the various atmospheric radiation budget charts. Trenberth solved the problem by NOT estimating uncertainty—which works only if you think you are certainly the expert.
NASA solved the problem by not using any numbers at all .
Graeme Stephens used both estimates and estimate uncertainties. The net sum TOA imbalance he derived was 0.6 +/- 0.4. That means his TOA imbalance could be anything from 0.2 to 1.0. Effectively useless.
I made an online spreadsheet / calculator, where you can fiddle with estimates of various climate parameters, and from them derive estimates of the Earth’s radiative energy imbalance. Here it is:
https://sealevel.info/radiative_imbalance_calc.htm
Always been bothered by the near linearity of the year to year increase
Many wish their bank accounts would do the same.
I always hope for compound interest rather than a linear increase.
Interesting.
But mainly I’m really glad to know that the Mauna Loa record still shows rising CO2 concentration. More margin against starvation is a good thing. Imagine what would be on the news if it were plummeting.
“Human carbon emissions have caused a steep decline in natural atmospheric carbonic anhydride, a gas essential to all life in earth.”
Of the 5 categories listed, and I might be missing something, are the relative magnitudes given? For example, a large error on a small factor would not be as meaningful as a large error on a large component, perhaps greater than 30% of the total. I don’t know the relative outputs of the five components listed.
As Dr. Roy said
You can access the GCP data spreadsheet here.
Yes, you are correct. I didn’t show those, and it’s useful information … the post was already approaching TL;DR status.
Dr Roy, when it comes to your work, TL doesn’t apply, and DR is unthinkable!
What a complete wasn’t of time … satellites have shown that CO2 is not a well mixed gas . PERIOD.. a CO2 measurement at a single location vs global sinks and sources is a waste of time …
Considering the M.L. is atop one of the world’s most active volcanos, I tend to agree.
BTW, they moved it due to local eruptions. It is some 200 miles away.
Also, there are no ocean temperatures included. The seasonal swings? Load of possibilities.
The Mauna Loa observatory started reporting atmospheric CO2 data in March 1958 and continues to do so today. There was a temporary move as reported by NOAA: “Due to the eruption of the Mauna Loa Volcano, measurements from Mauna Loa Observatory were suspended as of Nov. 29, 2022. Observations from December 2022 to July 4, 2023 are from a site at the Maunakea Observatories, approximately 21 miles north of the Mauna Loa Observatory. Mauna Loa observations resumed in July 2023.”
What is your source for a 200 mile move?
Indeed. I’m in agreement with those that find CO2 doesn’t cross the equator in any meaningful percentage due to correolis effect as is evident in many other physical observations.
At any specific location, atmospheric CO2 concentration varies daily, monthly and long term. Observatories such as Mauna Loa are sited to minimise the daily effects driven by ‘local’ photosynthesis and respiration, such that Mauna Loa primarily captures the latter two effects. The monthly changes are reflected in the seasonal cycle which is substantially different between high northern latitudes (e.g. Alert and Barrow) where they are at a maximum and the South Pole where they are barely discernible. However, these changes also incorporate the longer term global trend (as they must), which is virtually the same at all sites.
See: https://www.scrippsco2.ucsd.edu/graphics_gallery/other_stations/index.html
The permitting was also improper as Nantucket is on the East Coast Flyway, and adversely impacts migratory birds.
This would all be a little more interesting if CO2 were a climate control knob, which it isn’t. But the plants are happy, so that’s good.
Roy left out respiration from all vegetation and phytoplankton at night, respiration from the roots of boreal trees in the Winter, and CO2 from bacteria decomposing dead organic material in the Fall through early-Spring, all of which are increasing as a result of CO2 fertilization. These are not trivial contributions as shown by the seasonal ramp-ups, which are larger during warm El Niño years. With a warming climate, Henry’s Law also predicts a small annual out-gassing. Because the seasonal ramp-up lasts longer (7:5) than the photosynthetic draw-down, the net difference has been increasing over past decades. Because the net increase of CO2 is conveniently close to the anthropogenic contributions, it is tempting to attribute all of the net increase to humans. However, all of the sources should contribute in proportion to their partial pressure. That means that only about 4% is anthropogenic, suggesting that there is an abundance of CO2 that can contribute and if the anthro’ contributions were to disappear, the loss would be compensated from other sources. That is why we saw no difference in 2020. The decline was only about 7-10% of 4%. Also, I suspect that the oceanic flux is only a SWAG, with a bias to make sure that the mass-balance equations are balanced without anthropogenic contributions. Clearly, the influence of ‘niñas’ means that the atmosphere is always striving to be in balance. I’m reminded of Mark Twain’s quip that he had been on the verge of being an angel his whole life but never quite made it.
The Land Sink term includes all known and quantifiable components, including those which would be a source of CO2. The total of all of the vegetative sources and sinks are called a Land “Sink” because the net ends up being a sink every year. Regarding phytoplankton, as I mention in the post, my understanding is that the Ocean models don’t include changes in ocean biological activity, but yes, I would guess that is an additional sink (as is the land vegetation).
Population increases, too. We exhale CO2. Probably a minor factor in comparison, but the devil is in the details and if one is inclined to calculate temperatures to several decimal places, even a small source becomes significant.
When I first developed an interest in this topic I suspected that that humans might be a significant source of CO2. I did my homework and concluded that while, yes, it did contribute, the amount was so small as to be lost in the noise of uncertainty.
The critical point is that it is evident that the seasonal changes are driven by biology, with it being minimal at the South Pole, and maximal at the North Pole. That tells me that high-latitude out-gassing from the permafrost, and respiration from boreal tree roots and needles are probably the main contributors to the CO2 variance in the ramp-up. Leafing out of deciduous trees and annual plants, along with phytoplankton blooms, drive the seasonal withdrawal of atmospheric CO2.
What is really needed is a hydrogen (water) budget.
Asphalt.
It is pointless to estimate sinks and sources in large global lumps.
The oceans, for example, can be sinks in some places and sources in others. Vegetation at a place on land varies in its use of CO2 from hour to hour, day to night, season to season, from new growth to final growth.
On top of this, Mauna Loa CO2 is cherry picked each day with the aim of minimising the apparent year-to-year change difference.
Finally, the pre-industrial atmospheric CO2 assigned 280 ppm is synthetic and probably wrong, because it is arrived at by simply discarding many early chemical analysis numbers that are not close to 280 ppm.
Analytical chemists, if they are like me, find all this to be unacceptably bad.
Geoff S
Why are some of the coefficients negative, Roy?
And do you have a downloadable spreadsheet with your work?