Summary:Atmospheric levels of carbon dioxide (CO2) continue to increase with no sign of the global economic slowdown in response to the spread of COVID-19. This is because the estimated reductions in CO2 emissions (around -11% globally during 2020) is too small a reduction to be noticed against a background of large natural variability. The reduction in economic activity would have to be 4 times larger than 11% to halt the rise in atmospheric CO2.
Changes in the atmospheric reservoir of CO2 occur when there is an imbalance between surface sources and sinks of CO2. While the global land and ocean areas emit approximately 30 times as much CO2 into the atmosphere as humans produce from burning of fossil fuels, they also absorb about an equal amount of CO2. This is the global carbon cycle, driven mostly by biological activity.
There are variations in the natural carbon cycle, such as during El Nino (more CO2 accumulation in the atmosphere) and La Nina (more CO2 removed from the atmosphere). Greater wildfire activity releases more CO2, while major volcanic eruptions (paradoxically) lead to greater photosynthesis from more diffuse sunlight and extra removal of CO2 from the air. The most dramatic variations are seasonal, as the land-dominated Northern Hemisphere experiences an annual cycle of vegetation growth (CO2 removal) and decay (CO2 release).
The increase in atmospheric CO2 observed since the 1950s is most likely dominated by anthropogenic CO2 emissions, which are twice as large as that needed to explain the observed rise. As I have shown before, a simple CO2 budget model driven by (1) estimates of global yearly anthropogenic CO2 emissions, (2) El Nino and La Nina activity, and (3) a CO2 removal rate that is proportional to how much “extra” CO2 is in the atmosphere compared to a “preferred baseline” CO2 level, yields an excellent fit to yearly CO2 observations at Mauna Loa, Hawaii.
Fig. 1. Yearly Mauna Loa, HI CO2 observations since 1959 (red) versus a simple CO2 budget model (blue).
But those are yearly measurements, and we are now interested in whether the recent global economic slowdown is showing up in the monthly Mauna Loa CO2 data. If we remove the large seasonal variations (driven by the seasonal growth and decay of Northern Hemisphere vegetation), we see no evidence of the economic slowdown through April, 2020.
Fig. 2. Monthly CO2 data since 2015 from Mauna Loa, HI after the average seasonal cycle is statistically removed.
As can be seen in Fig. 2, there are some pretty large month-to-month jumps and dips around the long-term increase (represented by the dotted line). These are probably natural variations due to fluctuations in the average seasonal variations in vegetation growth and decay, wildfire activity, and El Nino and La Nina activity (which are imperfectly removed in the solid blue line in Fig. 2). Variations in economic activity might also be involved in these fluctuations.
The point is that given the large month-to-month variations in natural CO2 sources and sinks seen in Fig. 2, it would be difficult to see a downturn in the anthropogenic source of CO2 unless it was very large (say, over 50%) and prolonged (say over a year or longer).
Instead, the U.S. Energy Information Administration (EIA) estimates that the global economic slowdown this year due to the spread of the novel coronavirus will amount to only about an 11% reduction in global CO2 emissions. This is simply too small of a decrease in CO2 emissions to show up against a background of considerable monthly and yearly natural variability in the atmospheric CO2 budget.
That relatively small 11% reduction also illustrates how dependent humanity is on energy, since the economic disruption is leading to U.S. unemployment rates not seen since the Great Depression of the 1930s. Everything that humans do requires access to abundant and affordable energy, and even the current economic downturn is not enough to substantially reduce global CO2 emissions.
ADDENDUM: How much of a decrease in CO2 emissions would be required to stop the atmospheric rise in CO2?
An interesting aspect of the observed rise of atmospheric CO2 is that it indicates the greater the CO2 concentration, the faster the “extra” CO2 is removed by biological activity. The observed annual rate of removal is 2.3% of the excess above a baseline of 295 ppm. The greater the “excess”, the faster the rate of removal.
Because of this rapid rate of removal, the anthropogenic CO2 emissions do not have to go to zero to stop the observed rise in atmospheric CO2. Using my simple model (blue line in Fig. 1, above), I find that a 43% reduction in anthropogenic CO2 emissions in 2020 would — in the absence of natural fluctuations in the carbon cycle — lead to a halt in the observed rise of atmospheric CO2 in 2020 over 2019 levels. This is about 4 times larger than the EIA estimate of an 11% reduction in CO2 emissions for the year 2020.
UPDATE: Changed emphasis from Northern Hemisphere extratropics to entire Northern Hemisphere (h/t John Christy)
In April, 2020, the Northern Hemisphere experienced its 2nd largest 2-month drop in temperature in the 497-month satellite record.
The Version 6.0 global average lower tropospheric temperature (LT) anomaly for April, 2020 was +0.38 deg. C, down from the March, 2020 value of +0.48 deg. C.
The Northern Hemisphere temperature anomaly fell from +0.96 deg. C to 0.43 deg. C from February to April, a 0.53 deg. C drop which is the 2nd largest 2-month drop in the 497-month satellite record. The largest 2-month drop was -0.69 deg. C from December 1987 to February 1988.
The linear warming trend since January, 1979 has now increased to +0.14 C/decade (but remains statistically unchanged at +0.12 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).
Various regional LT departures from the 30-year (1981-2010) average for the last 16 months are:
The UAH LT global gridpoint anomaly image for April, 2020 should be available within the next week here.
The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:
The COVID-19 disease spread is causing a worldwide shutdown in economic activity as business close, airlines cancel flights, and people shelter in their homes. For example, there was a 28% decline in global commercial air traffic in March 2020 compared to March of last year.
Last month I described a simple method for removing the large seasonal cycle from the Mauna Loa CO2 data, and well as the average effects from El Nino and La Nina (the removal is noisy and imperfect), in an effort to capture the underlying trend in CO2 and so provide a baseline to compare future months’ measurements too.
What we are looking for is any evidence of a decline in the atmospheric CO2 content that would be strong enough to attribute to the economic downturn. As can be seen, the latest CO2 data show a slight downturn, but it’s not yet out of the ordinary compare to previous month-to-month downturns.
I personally doubt we will see a clear COVID-19 effect in the CO2 data in the coming months, but I would be glad to be proved wrong. As I mentioned last month, those who view the economic downturn as an opportunity to reduce atmospheric CO2 would have to wait many years — even decades — before we would see the impact of a large economic downturn on global temperatures, which would occur at great cost to humanity, especially the poor.
I am seeing an increasing number of people on social media pointing to the weekly CDC death statistics which show a unusually low number of total deaths for this time of year, when one would expect the number to be increasing from COVID-19. But what most people don’t realize is that this is an artifact of the late arrival of death certificate data as gathered by the National Center for Health Statistics (NCHS).
This first came to my attention as a tweet by some researchers who were using the CDC weekly death data in a research paper pointing out the downturn in deaths in early 2020 and had to retract the paper because of the incomplete data problem. A disclaimer at the CDC website points out the incomplete nature of recent data. While they say that the new totals could be adjusted either upward or downward, it appears that the adjustments are almost always upward (i.e. recent data have a low bias in reported deaths).
As a first attempt to possibly correct for this under-reporting problem, I downloaded the data two weeks in a row (approximately March 30 and April 5, 2020) to examine how the recent data changes as new death certificate data are obtained. I realize this is only one week’s worth of changes, and each week would provide additional statistics. But the basic methodology could be applied with additional weeks of data added.
I first use the 4.5 years of reported weekly death data to compute an average seasonal cycle in deaths, with the slow upward trend included (red line in the following figure). Also shown are the total deaths reported on 2 successive weeks, showing the increase in reported deaths from late reports coming in.
Although it is not obvious in the above plot, there were new deaths reported as much as 1 year late. If we use the difference between the two successive weeks’ reports as an estimate of how many new reports will come in each week as a percentage of the average seasonal cycle, and sum them up for 52 weeks, we can get a rough estimate of what the totals will look like a year from now (the blue line in the following figure).
The blue line shows behavior quite close to that seen last year at this time. Keep in mind that Week 10 is only through early March, at which point there were only 30 COVID-19 deaths reported, which is too small a number to show up on these plots. I’m posting this as just a suggestion for those who want to analyze recent weekly death data and make some sense out of it.
It is also of interest how bad the 2017-18 flu season was compared to this season. I’m sure many medical people are aware of this, but I don’t recall it being a huge news story two years ago.
Last month I noted how the global average lower stratospheric temperature had warmed considerably in recent months, especially in February, and tentatively attributed it to smoke from the Australian bushfires entering the lower stratosphere. You can read more there about my reasoning that the effect was unlikely to be due to the recent Taal volcanic eruption.
Here’s the March 2020 update, showing continued warming.
The effect cannot be as clearly seen in regional averages (e.g. tropics or Southern Hemisphere) because those regions routinely see large changes which are compensated for by changes of the opposite sign in other regions, due to strong adiabatic warming (sinking motion) or cooling (rising motion) in the statically stable stratosphere. Thus, global averages show the best signal of something new going on, even if that something new is only occurring in a specific region.
The Version 6.0 global average lower tropospheric temperature (LT) anomaly for March, 2020 was +0.48 deg. C, down substantially from the February, 2020 value of +0.76 deg. C.
The northern extratropics (poleward of 20 deg. N) experienced the 12th largest drop in tropospheric temperature out of the 495 months of the satellite record. For those interested in speculating regarding reasons for this, it could not be from reduced CO2 emissions from the response to the spread of COVID-19; to the extent that recent warming has been due to more CO2 in the atmosphere, the radiative forcing from extra CO2 would not change substantially even if all CO2 emissions stopped for a full year.
Another possibility is reduced air travel reducing the amount of jet contrails in the upper troposphere, which I am not going to discount at this point.
The linear warming trend since January, 1979 remains at +0.13 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).
Various regional LT departures from the 30-year (1981-2010) average for the last 15 months are:
The UAH LT global gridpoint anomaly image for March, 2020 should be available within the next week here.
The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:
Today (Monday, March 30) is the 30th anniversary of our publication in Science describing the first satellite-based dataset for climate monitoring.
While much has happened in the last 30 years, I thought it might be interesting for people to know what led up to the dataset’s development, and some of the politics and behind-the-scenes happenings in the early days. What follows is in approximate chronological order, and is admittedly from my own perspective. John Christy might have somewhat different recollections of these events.
Some of what follows might surprise you, some of it is humorous, and I also wanted to give credit to some of the other players. Without their help, influence, and foresight, the satellite temperature dataset might never have been developed.
Spencer & Christy Backgrounds
In the late 1980s John Christy and I were contractors at NASA/MSFC in Huntsville, AL, working in the Atmospheric Sciences Division where NASA managers and researchers were trying to expand beyond their original mission, which was weather support for Space Shuttle launches. NASA/MSFC manager Gregory S. Wilson was a central figure in our hiring and encouragement of our work.
I came from the University of Wisconsin-Madison with a Ph.D. in Meteorology, specializing in the energetics of African easterly waves (the precursors of most Atlantic hurricanes). I then did post-doc work there in the satellite remote sensing of precipitation using microwave radiometers. John Christy received his Ph.D. in Atmospheric Science from the University of Illinois where he did his research on the global surface atmospheric pressure field. John had experience in analyzing global datasets for climate research, and was hired to assist Pete Robertson (NASA) to assist in data analysis. I was hired to develop new microwave satellite remote sensing projects for the Space Shuttle and the Space Station.
James Hansen’s Congressional Testimony, and Our First Data Processing
In 1988, NASA’s James Hansen testified for then-Senator Al Gore, Jr., testimony which more than any single event thrust global warming into the collective consciousness of society. We were at a NASA meeting in New Hampshire. As I recall, UAH’s Dick McNider on the plane ride up had just read a draft of a paper by Kevin Trenberth given to him by John Christy (who had been a Trenberth student) that discussed many issues with the sparse surface temperature data for detecting climate change.
During lunch Dick asked, given all the issues with the lack of global coverage and siting issues with surface data sets discussed by Trenberth, if there wasn’t satellite data that could be used to investigate Hansen’s global claims? NASA HQ manager James Dodge was there and expressed immediate interest in funding such a research project.
I said, yes, such data existed from the NOAA/NASA/JPL Microwave Sounding Unit (MSU) instruments, but it would be difficult to access approximately 10 years of global data. Note that this was before there was routine internet access to large digital datasets, and ordering data from the government had a very large price tag. No one purchased many years of global data; it came on computer 6250 bpi computer tapes each containing approximately 100 MB of data, and computers then were pretty slow. The data we wanted was from NOAA satellites, and NOAA would reuse these large (10.5 inch) IBM tapes rather than to keep the old data tapes around using up storage space.
It turns out that Roy Jenne who worked data systems at the NSF’s National Center for Atmospheric Research (NCAR) in Boulder had years before taken it upon himself to archive the old NOAA satellite data before it was lost altogether. He kept the data on a “mass storage system” (very large and inefficient by today’s standards) and I believe it was Greg Wilson who John Christy made the connection to gain us access to those data.
We obtained somewhat less than 10 years of data from NCAR, and I decided how to best calibrate it and average it into a more manageable space/time resolution. I had frequent contact with JPL engineers who built the MSU instruments, Fred Soltis in particular, who along with Norman Grody at NOAA provided me with calibration data for the MSU instruments flying on different satellites.
We enlisted John Christy to analyze those data since he brought considerable experience with diagnosing global datasets for climate purposes. One of the first things John discovered was from comparing global averages from different satellites in different orbits: They gave surprisingly similar answers in terms of year-to-year temperature variability. This was quite unexpected and demonstrated that the MSU instruments had high calibration stability, at least over a few years. It also demonstrated that NOAA’s practice of adjusting satellite data with radiosondes (weather balloons) was backwards: the differences others had seen between the two systems were due to poor spatial sampling by the radiosondes, not due to changes in the satellite calibration stability.
In addition to the critical historical data archived by Roy Jenne at NCAR, we would some of the more recent satellite data that was kept at NOAA. We didn’t have quite ten years of data, and an editor at Science magazine wanted ten full years of data before they would publish our first findings. We were able to order more data from NOAA to get the first 10 years’ worth (1979 through 1988), and Science accepted our paper.
The NASA Press Conference
On March 29, 1990 we held a “media availability” at the communications center at NASA/MSFC. For some reason, NASA would not allow it to be called a full-fledged “press conference”. As I recall, attendance was heavy (by Huntsville standards) and there was no place for me to park but in the grass, for which I was awarded a parking ticket by NASA security. JPL flew a remaining copy of the MSU instrument in as a prop; it had its own seat on a commercial flight from Pasadena.
Jay Leno would later mention our news conference in his monologue, and Joan Lunden covered it on Good Morning America. While we watched Ms. Lunden on a monitor the next morning, a NASA scientist remarked that he was too distracted by her long, slender legs to listen to what she was saying.
Our 1990 Senate Testimony for Gore
After we published our first research results on March 30,1990, we received an invitation to testify for Al Gore in a Senate committee hearing in October, 1990 on the subject of coral bleaching. Phil Jones from the University of East Anglia was also there to testify.
As people filed into the hearing room, I saw a C-SPAN camera being set up, and having noticed that Al Gore seemed to be the only committee member in attendance, I asked the cameraman about the lack of interest from other senators. He said something like, “Oh, Senator Gore likes it this way… he gets all the media attention.”
We still used overhead projectors back then with view graphs, and I thought I’d better check out the equipment. The projector turned out to be seriously out-of-focus, and the focus adjustment on the arm would not fix it. I remember thinking to myself, “this seems pretty shoddy for Congress”.
Senator Gore launched into some introductory remarks while looking at me as I struggled with the projector. From his comments, he was obviously assuming I was Phil Jones (who was supposed to go first, and who Gore said he had previously visited in England). I thought to myself, this is getting strange. Just in time, I realized the projector arm was bent slightly out of alignment, I bent it back, and took my seat while Phil Jones presented his material.
Our testimony, which was rather uneventful, led to the traditional letter of thanks from Gore for supporting the hearing. In that letter, Gore expressed interest in additional results as they became available.
So, when it came time to get the necessary additional satellite data out of NOAA, I dropped Gore’s name to a manager at NOAA who suddenly became interested in providing everything they had to us at no charge… rather than us having to pay tens of thousands of our research dollars.
Hundreds of Computer Tapes and an Old Honda Civic
It might seem absurd to today’s young scientists, but it was not an easy task to process large amounts of digital data in the late 1980s. I received box after box of 9-track computer tapes in the mail from NOAA. Every few days, I would load them up in my old, high-mileage, barely-running 2-door Honda Civic and cart them over to the computer center at MSFC.
NASA’s Greg Wilson had gotten permission to use the computer facility for the task. At that time, most of the computer power was being taken up by engineers modeling the fuel flows within the Space Shuttle main engines. As I added more data and processed it, I would pass the averages on to John Christy who would then work his analysis magic on them.
I don’t recall how many years we would use this tape-in-the-mail ordering system. Most if not all of those tapes now reside in a Huntsville landfill. After many years of storage and hauling them from one office location to another during our moves, I decided there was no point in keeping them any longer.
A Call from the White House, and the First Hubble Space Telescope Image
Also in 1990, John Sununu, White House Chief of Staff to President George H. W. Bush, had taken notice of our work and invited us to come up to DC for a briefing.
We first had to bring the NASA Administrator V. Adm. Richard Truly up to speed. Truly was quite interested and was trying to make sure he understood my points by repeating them back to me. In my nervousness, I was apparently interrupting him by finishing his sentences, and he finally told me to “shut up”. So, I shut up.
The next stop was the office of the Associate Administrator, Lennard Fisk. While we were briefing Fisk, an assistant came in to show him the first image just collected by the Hubble Space Telescope (HST). This was before anyone realized the HST was miss-assembled and was out of focus. In retrospect, it was quite a fortuitous coincidence that we were there to witness such an event.
As the day progressed, and no call was coming in from the White House, Dr. Fisk seemed increasingly nervous. I was getting the impression he really did not want us to be briefing the White House on something as important as climate change. In those days, before NASA’s James Hansen made it a habit, no scientists were allowed to talk to politicians without heavy grooming by NASA management.
As the years went by, we would learn that the lack of substantial warming in the satellite data was probably hurting NASA’s selling of ‘Mission to Planet Earth’ to Congress. The bigger the perceived problem, the more money a government agency can extract from Congress to research the problem. At one point a NASA HQ manager would end up yelling at us in frustration for hurting the effort.
Late in the afternoon the call finally came in from the White House for us to visit, at which point Dr. Fisk told them, “Sorry, but they just left to return to Huntsville”, as he ushered us out the door. Dr. Wilson swore me to secrecy regarding the matter. (I talked with John Sununu at a Heartland Institute meeting a few years ago but forgot to ask him if he remembered this course of events). This would probably be – to me, at least – the most surreal and memorable day of our 30+ years of experiences related to the satellite temperature dataset.
After 1990
In subsequent years, John Christy would assume the central role in promoting the satellite dataset, including extensive comparisons of our data to radiosonde data, while I spent most of my time on other NASA projects I was involved in. But once a month for the next 30 years, we would process the most recent satellite data with our separate computer codes, passing the results back and forth and posting them for public access.
Only with our most recent Version 6 dataset procedures would those codes be entirely re-written by a single person (Danny Braswell) who had professional software development experience. In 2001, after tiring of being told by NASA management what I could and could not say in congressional testimony, I resigned NASA and continued my NASA projects as a UAH employee in the Earth System Science Center, for which John Christy serves as director (as well as the Alabama State Climatologist).
At this point, neither John nor I have retirement plans, and will continue to update the UAH dataset every month for the foreseeable future.
Given the global hysteria over the spread COVID-19, you might be excused if you are very surprised to learn that the most recent week of mortality data in the EU shows an actual decline from what is expected for this time of year.
In the coming months there will be an increasing debate over whether the virtual shutdown of our economy was warranted given the threat of the latest form of the coronavirus, SARS-CoV-2. While there are still large uncertainties about how fast it spreads and how lethal it is (statistically, those are inversely related), I suspect we will ultimately realize that our response might well have done more harm than good to society as a whole.
This is mainly because poverty is the leading cause of premature death in the world, and shutting down the economy leads to premature death for a multitude of reasons related to poverty. In the extreme example, you could save lives in the short run by keeping everyone at home, but in the long run we would all starve to death.
But that is not the main subject of this post.
A couple weeks ago I started expressing the opinion on social media that if our reaction to the spread of COVID-19 turns out to be overdone, it might end up having the unexpected consequence of reducing total virus-related mortality.
Let me explain.
As I am sure you are aware, seasonal flu is a global killer, with 300,000 to 650,000 deaths on average each year, mainly among the elderly and those with pre-existing health conditions. At this writing, COVID-19 has killed 10% or less of that number. (Yes, I realize that number might have been considerably higher if not for our response).
Here’s the point: It might well be that the increased level of hand-washing, sanitizing, and social distancing we have exercised might save more lives from reducing influenza-A and -B that were lost to COVID-19, and that net virus-related mortality might go down this season.
I personally became more careful about not spreading germs several years ago. No so much for myself (I have a pretty strong immune system) but so I would not carry disease home to my family members. I carry antibacterial wipes in my car and use them religiously. We are hearing more and more now about how such habits can help prolong the lives of those around us who are elderly or have compromised immune systems.
Now, recent results from Europe suggest that the COVID-19 response might be having the unintended benefit of saving total lives. This is all very preliminary, I realize, and that coming weeks might see some change in that picture. But it is worth thinking about.
Early Results from Europe
Every week (on Thursday) the Euro MOMO project (European MOnitoring of excess MOrtality) publishes a report of mortality statistics across the EU, including stratification by age group. The latest report (which I believe includes data through March 24, but I am not sure) shows (green line) no uptick in total mortality from the assumed baseline (red line). In fact, it’s a little below that line (they also account for missing and late reports).
Amazingly, this flu season is seen to be surprisingly mild compared to previous flu seasons in the EU. On the chart I have also indicated the number of reported COVID-19 deaths in the most recent week, around 7,000.
Why do we not see an uptick on the chart? The charts for individual countries do show an up-tick for Italy (for example), but not unlike what was seen in previous flu seasons.
The report itself provides two or three possible explanations, none of which are particularly satisfying. Read it yourself and tell me it doesn’t sound like the people writing the report are also somewhat mystified. They don’t mention what I am discussing here.
So, the chart begs at least two questions: 1) Are the effects of practicing increased hygiene in response to COVID-19 saving more lives that would have been lost to seasonal flu deaths, than are being lost to COVID-19 itself? 2) Why are we not outraged and deathly afraid of the seasonal flu (-A and -B), given the widespread death that routinely occurs from those viruses that come around each season?
You might claim, “It’s because COVID-19 can kill anyone, not just the elderly.” Well, that’s true of the seasonal flu, as well. The case of an apparently healthy 44-year-old Texas man who recently died of COVID-19 probably scares many people, but according to the CDC approximately 5 “healthy” young people a day in the U.S. under the age of 25 die from sudden cardiac arrest. Maybe that Texas man had an underlying health condition that was previously undiagnosed. Unless they do an autopsy, and the family reveals the results, we will never know.
And, you might well think of other reasons why EU deaths have not experienced an uptick yet. Human behavior involves many confounding variables. I’m just mentioning one potential reason I am not seeing discussed.
I am not trying to minimize the deaths due to COVID-19. I’m trying to point out that if we are fearful of death from COVID-19, we should be even more concerned about the seasonal flu (many people are saying this), and that one benefit of the current experience might be that people will be more mindful about avoiding the spread of viruses in the future.
Some global warming alarmists are celebrating the current economic downturn as just what is needed to avert climate catastrophe. I’ve seen a couple estimates that China’s manufacturing and commerce might have seen up at 40% reduction recently.
The current global crisis will be a test of just how much economic pain is required to substantially reduce CO2 emissions (assuming there is no reasonably affordable and practical replacement for fossil fuels).
I already know that some of my “deep skeptic” acquaintances (you know who you are) who believe the global CO2 increase is mostly natural will claim a continuing CO2 rise in the face of a decrease in economic activity supports their case. I have previously shown that a simple model of the CO2 variations since 1959 forced with anthropogenic emissions accurately explain the Mauna Loa observations (see Fig. 2 , explanation here). It will take considerable evidence to convince me that the long-term rise is not anthropogenic, and maybe the current “coronavirus experiment” will provide some contrarian evidence.
Of course, for anthropogenic CO2 emissions reductions to have any effect, they actually have to show up in the atmosphere. The most widely cited monitoring location for CO2 is on Mauna Loa in Hawaii. It is at high elevation in a persistent subtropical high pressure zone that should be able to detect large emissions changes in several weeks time as weather systems move around the world.
I’ve had several requests, and seen numerous social media comments, suggesting this is something that should be looked at. So, I’ve analyzed the Mauna Loa CO2 data (updated monthly) through February 2020 to see if there is any hint of a CO2 concentration downturn (or, more accurately, reduced rate of rise).
The short answer is: No… at least not yet.
The Mauna Loa Data: Removing Seasonal and ENSO Effects
While an anthropogenic source of CO2 can explain the long-term rise in CO2, the trouble with finding an anthropogenic signal on time scale of a few months to a couple years is that natural variations swamp any anthropogenic changes on short time scales.
The monthly data (arbitrarily starting 1996, below) shows a continuing long-term rise that has been occurring since monitoring began in 1958. Also seen is the strong seasonal cycle as the vegetation in the Northern Hemisphere goes through its normal seasonal variations in growth and decay.
Obviously, not much can be discerned from the raw monthly average data in the above plot because the seasonal cycle is so strong. So, the first step is to remove the seasonal cycle. I did this by subtracting out a 4th order polynomial fit before removing the average seasonal cycle, then adding that statistical fit back in:
Next, there are some wiggles in the data due to El Nino and La Nina (ENSO) activity, and if we remove an average statistical estimate of that (a time lag and averaging is involved to increase signal), we can get a little better idea of whether the most recent month (February 2020) is out of the ordinary. I have zeroed in on just the most recent 5 years for clarity.
The polynomial fit to the data (thin dotted line) shows what we might expect for the coming months, and we can see that February is not yet departing from the expected values.
Of course, there are a variety of natural variations that impact global average CO2 on a month-to-month basis: Interannual variations in wildfire activity, land vegetation and sea surface temperatures, variations in El Nino and La Nina effects, and short-term fluctuations in anthropogenic emissions immediately come to mind. (The Pinatubo and El Chichon volcano eruptions actually caused a reduction in global CO2, probably due to post-eruption vegetation effects from an increase in diffuse sunlight penetration of forest canopies).
I will try to update this analysis every month as long as the issue is of sufficient interest.
Tucker Carlson is, as I type, interviewing one of the COVID-19 researchers from Stanford, who is quoting the new French study that shows (he says) a 100% cure rate using hydroxychloroquine. (I don’t know if my pestering of my contact at FoxNews helped instigate the coverage, I sent him the earlier Stanford-led research report that use China and S. Korea results with the drug).
The Stanford researcher said that Trump has authorized mass buys (I think that’s what he said) of the drug.
Here’s the website with the latest results. Could be a Big Let-Down for Big Pharma, which I’m sure wants to produce a variety of treatments and vaccines.
I expect this story will evolve rapidly in the coming days.
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