[UPDATE: The study is available here — w.]
UNIVERSITY OF EAST ANGLIA
A team of scientists led by the University of East Anglia (UEA) has made a major breakthrough in detecting changes in fossil fuel carbon dioxide emissions more quickly and frequently.
In a study published today they quantified regional fossil fuel CO2 emissions reductions during the Covid-19 lockdowns of 2020-2021, using atmospheric measurements of CO2 and oxygen (O2) from the Weybourne Atmospheric Observatory, on the north Norfolk coast in the UK.
The estimate uses a new method for separating CO2 signals from land plants and fossil fuels in the atmosphere. Previously it has not been possible to quantify changes in regional-scale fossil fuel CO2 emissions with high accuracy and in near real-time.
Existing atmospheric-based methods have largely been unsuccessful at separating fossil fuel CO2 from large natural CO2 variability, so that estimates of changes, such as those occurring in response to the lockdowns, must rely on indirect data sources, which can take months or years to compile.
The atmospheric O2-based method, published in the journal Science Advances, is in good agreement with three lower frequency UK emissions estimates produced during the pandemic by the Department for Business, Energy and Industrial Strategy, the Global Carbon Budget and Carbon Monitor, which used different methods and combinations of data, for example those based on energy usage.
Crucially, as well as being completely independent of the other estimates, this approach can be calculated much more quickly.
The researchers are also able to detect changes in emissions with higher frequency, such as daily estimates, and can clearly see two periods of reductions associated with two UK lockdown periods, separated by a period of emissions recovery when Covid restrictions were eased, during the summer of 2020.
Researchers at UEA – home of the UK’s only high-precision atmospheric O2 measurement laboratory – worked with colleagues at Wageningen University in the Netherlands and the Max Planck Institute for Biogeochemistry, Germany.
The study’s lead author, Dr Penelope Pickers, of UEA’s Centre for Ocean and Atmospheric Sciences, said: “If humans are to reduce our CO2 emissions from fossil fuels and our impact on the climate, we first need to know how much emissions are changing.
“Our study is a major achievement in atmospheric science. Several others, based solely on CO2 data, have been unsuccessful, owing to large emissions from land plants, which obscure fossil fuel CO2 signals in the atmosphere.
“Using atmospheric O2 combined with CO2 to isolate fossil fuel CO2 in the atmosphere has enabled us to detect and quantify these important signals using a ‘top-down’ approach for the first time. Our findings indicate that a network of continuous measurement sites has strong potential for providing this evaluation of fossil fuel CO2 at regional levels.”
Currently, fossil fuel CO2 emissions are officially reported with a ‘bottom-up’ approach, using accounting methods that combine emission factors with energy statistics to calculate emissions.
These are then compiled into national inventories of estimated greenhouse gas (GHG) emissions to the atmosphere from anthropogenic sources and activities, such as domestic buildings, vehicles, and industrial processes.
However, inventories can be inaccurate, especially in less developed countries, which makes it more difficult to meet climate targets.
It can also take years for the inventory assessments to be completed, and at the regional scale, or on a monthly or weekly basis, the uncertainties are much larger.
An alternative method of estimating GHG emissions is to use a ‘top-down’ approach, based on atmospheric measurements and modelling.
The UK emissions inventory is already successfully informed and supported by independent top-down assessments for some key GHGs, such as methane and nitrous oxide.
But for CO2, the most important GHG for climate change, this has never before been feasible, because of the difficulties distinguishing between CO2 emissions from fossil fuels and land plant sources in the atmosphere.
Dr Pickers said: “The time taken for inventories to be completed makes it hard to characterise changes in emissions that happen suddenly, such as the reductions associated with the Covid pandemic lockdowns.
“We need reliable fossil fuel CO2 emissions estimates quickly and at finer scales, so that we can monitor and inform climate change policies to prevent reaching 2°C of global warming.
“Our O2-based approach is cost-effective and provides high frequency information, with the potential to provide fossil fuel CO2 estimates quickly and at finer spatial scales, such as for counties, states or cities.”
The team used 10 years of high-precision, hourly measurements of atmospheric O2 and CO2 from Weybourne Atmospheric Observatory, which are supported by the UK’s National Centre for Atmospheric Science. Having long-term measurements of these climatically important gases was crucial to the success of the study.
To detect a Covid signal, they had to first remove the effects of atmospheric transport on their O2 and CO2 datasets, using a machine learning model.
They trained the machine learning model on pre-pandemic data, to estimate the fossil fuel CO2 they would have expected to observe at Weybourne if the pandemic had never occurred.
They then compared this estimate to the fossil fuel CO2 that was actually observed during 2020-2021, which revealed the relative reduction in CO2 emissions.
‘Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements’, Penelope A. Pickers et al., is published in Science Advances on Friday, April 22, 2022.
JOURNAL
Science Advances
METHOD OF RESEARCH
Data/statistical analysis
ARTICLE TITLE
Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements
ARTICLE PUBLICATION DATE
22-Apr-2022
Next thing you know they will be able to determine which electrons come from FF power plants and those which come from wind on the grid. Amazing stuff.
I think it is worthwhile to read the original study report:
https://www.science.org/doi/10.1126/sciadv.abl9250
My assessment is that they have made a case for a reduction in fossil fuel CO2 reductions during 2020-21:
This can be seen in their graphic, Fig. 1D. However, their Fig. 1A does not show a similar reduction in total atmospheric CO2; it appears to me to be similar to previous measurements, showing a continuing upward trend. Neither result is any great surprise.
I have previously demonstrated that the monthly data for 2020 do not show any obvious influence on the shape of the seasonal curves during the pandemic closures, being essentially indistinguishable from 2019, despite previous bottom-up, and their top-down approach to estimating reductions in anthropogenic emissions.
https://wattsupwiththat.com/2021/06/11/contribution-of-anthropogenic-co2-emissions-to-changes-in-atmospheric-concentrations/
I think that what they have inadvertently demonstrated is that despite a decline in anthropogenic CO2 emissions, the system has compensated by releasing (and/or not absorbing) an amount that essentially balances the anthropogenic reductions.
What is still missing is a compelling argument to justify a draconian reduction in the use of fossil fuels.
With both India & Chima putting lots of CO2 into the atmosphere, why are we, the West spending lots of money in trying to remove it ?.
Anyway according to both David Coe & William Harper, it does not matter how much CO2 we have in the atmosphere.
Because compared to the H2O water vapour , CO2 in its parts per million is just a bit player.
Bring up Analysis of the gas CO2 by both those well known scientists, it’s very interesting.
Michael VK5ELL
The article talked a lot about methodology & nothing about results. Makes me suspect the results & implications were not in line with pre-ordained dogma.
Has anyone read the research & if yes, can you report on results ?
The first paragraph quoted in the above article/PR, which is attributed to the University of East Anglia is:
“A team of scientists led by the University of East Anglia (UEA) has made a major breakthrough in detecting changes in fossil fuel carbon dioxide emissions more quickly and frequently.” (my underling emphasis added).
Makes it sounds like the scientists are actually measuring an objective signature, doesn’t it?
However, after repeated read-throughs of the entire article, I find instead that the referenced scientists are actually employing “a ‘top-down’ approach, based on atmospheric measurements and modelling“, wherein specifically they had to “first remove the effects of atmospheric transport on their O2 and CO2 datasets, using a machine learning model” and subsequently “They trained the machine learning model on pre-pandemic data, to estimate the fossil fuel CO2 they would have expected to observe at Weybourne if the pandemic had never occurred. . . .They then compared this estimate to the fossil fuel CO2 that was actually observed during 2020-2021, which revealed the relative reduction in CO2 emissions.”
So, the announced “major breakthrough” is based on atmospheric modelling, removing effects of atmospheric transport from selected datasets using a machine learning model, then “training” that machine learning model so that it could output an estimate they would have expected to observe that is, finally, compare to actual measured data.
Got that?
Gee, I wonder what could go possibly wrong with that convoluted process?
So why the mention of O2? There must be more to it than that?
JF
Amazing!
They spend all this time and money to (supposedly) be able to measure the difference between “fossil” CO2 and natural CO2.
Yet they still can’t tell the difference between theoretical “man-made climate change” and actual natural climate change.
PS How could the “science” have been “settled” before they could accurately measure what they claim is the main driver of “climate change”, Man’s CO2.
PPS “CO2 emissions from fossil fuels and land plant sources”? Why don’t they consider non-land and non-plant sources? (And plants breath CO2.)
Ya see Dr. Pickers, even if global warming from added CO2 was a real thing, it matters not where it comes from. Like, the ‘bad’ CO2 behaves exactly the same as the ‘good’ CO2! Sheesh
As soon asI saw the Headline, I thought… Oh! I bet this is gonna be good. Then they mention trained machine learning to meet their expectations…blah blah blah…another load of BS rubbish.
Woohoo! A breakthrough in guessing!
Another computer game. Measure and model, the question is how much tweaking is being done.
Charles, I’ve been searching for a description of how this study actually does the ‘this molecule is natural, this is caused by burning fossil fuel’ distinction. Its not often WUWT is no more informative than the MSM, with the same vague references to O2 and machine learning.
I hope this piece is just a place-holder while someone writes a proper analysis of their method. If it actually works it will be very useful – – it. must be pretty clever stuff.
JF
It’s another model. The scientist’s middle name is Cherry BTW.
A new and untested method. What could be better? Trust us; we’re scientists.
First off, from the institution involved I know this is just a steaming load of BS. It appears they are very much into using computer games and simulations, having taught their system to use the metadata attached to the CO2 molecules to tell if they are from new plants or very old plants. Why does everyone have to bandy about the term “machine learning” to make it sound like they are doing something high tech?
Does this make any sense to anyone? They “compared this estimate” to “that (which) was actually observed”. Exactly why would one create an estimate if you could actually observe a quantity of fossil fuel CO2?
Is this circular logic at its best?
anything generatedjby or created at eau is garbage!
” However, inventories can be inaccurate
(…)
An alternative method of estimating GHG emissions is to use a ‘top-down’ approach, based on atmospheric measurements and modelling. ”
More models.
And… models are better (more accurate) than data (inventories)!
Enough said.