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.

Now this is getting interesting!

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.

https://www.covidtrial.io/

…and countries with many COVID-19 cases have little to no malaria.

This subject has been making the rounds in recent days, much more in social media and lesser-known news outlets and not so much the mainstream media…

There is now considerable evidence from several countries (China, S. Korea, France, others?) that anti-malarial drugs, especially chloroquine, is effective at greatly reducing COVID-19 symptoms, and possibly preventing infection in the first place.

I downloaded the latest COVID-19 reported cases by country from the WHO as well as the incidence of malaria cases as of 2017. I calculated the COVID-19 incidence as the number per million total population, while the malaria numbers are reported per 1,000 “population at risk”.

It took a few hours to line everything up in Excel because of differences in naming of a few countries, no malaria data for countries where malaria has been essentially eradicated, and many countries where no COVID-19 cases have been reported.

I only have time to give some interesting bottom-line numbers. I encourage others to investigate this for themselves to see if the relationships are real.

If I sort all 234 countries by incidence of malaria, and compute the average incidence of malaria and the average incidence of COVID-19, the results are simply amazing: those countries with malaria have virtually no COVID-19 cases, and those countries with many COVID-19 cases have little to no malaria.

Here are the averages for the three country groupings:

Top 40 Malaria countries:

212.24 malaria per thousand = 0.2 COVID-19 cases per million

Next 40 Malaria countries:

7.30 malaria per thousand = 10.1 COVID-19 cases per million

Remaining 154 (non-)Malaria countries:

0.00 malaria per thousand = 68.7 COVID-19 cases per million

I tried plotting the individual country data on a graph but the relationship is so non-linear (almost all of the data lie on the horizontal and vertical axes) that the graph is almost useless.

This is based upon the total number of COVID-19 cases as of March 17, 2020 as tallied by the WHO.

Once again I am being drawn into defending the common explanation of Earth’s so-called “greenhouse effect” as it is portrayed by the IPCC, textbooks, and virtually everyone who works in atmospheric radiation and thermodynamics.

To be clear, I am not defending the IPCC’s predictions of future climate change… just the general explanation of the Earth’s greenhouse effect, which has a profound influence on global temperatures as well as on weather.

As we will see, much confusion arises about the greenhouse effect due to its complexity, and the difficulty in expressing that complexity accurately with words alone. In fact, the IPCC’s greenhouse effect “definition” quoted by Dr. Ollila is incomplete and misleading, as anyone who understands the greenhouse effect should know.

As we will see, in the case of something as complicated as the greenhouse effect, a simplified worded definition should never be the basis for quantitative calculations; instead, complicated calculations are sometimes only poorly described with words.

What is the “Greenhouse Effect”?

Descriptions of the Earth’s natural greenhouse effect are unavoidably incomplete due to its complexity, and even misleading at times due to ambiguous phrasing when trying to express that complexity.

The complexity arises because the greenhouse effect involves every cubic meter of the atmosphere having the ability to both absorb and emit infrared (IR) energy. (And almost never are the rates of absorption and emission the same, contrary to the claims of many skeptics – IR emission is very temperature-dependent, while absorption is not).

While essentially all the energy for this ultimately comes from absorbed sunlight, the infrared absorption and re-radiation by air (and by clouds in the atmosphere) makes the net impact of the greenhouse effect on temperatures somewhat non-intuitive. The emission of this invisible radiation by everything around us is obviously more difficult to describe than the single-source Sun.

The ability of air and clouds to absorb and emit IR radiation has profound impacts on energy flows and temperatures throughout the atmosphere, leading to the multiple infrared energy flow arrows (red) in the energy budget diagram originally popularized by Kiehl & Trenberth (Fig. 1).

[As an aside, contrary to the claims of the 2010 book Slaying the Sky Dragon: Death of the Greenhouse Gas Theory, this simplified picture of the average energy flows between the Earth’s surface, atmosphere, and space is NOT what is assumed by climate models. Climate models use the relevant physical processes at every point on three-dimensional grid covering the Earth, with day-night and seasonal cycles of solar illumination. The simplified energy budget diagram is instead the best-estimate of the global average energy flows based upon a wide variety of observations, model diagnostics, and the assumption of no natural long-term climate change.]

If the Earth had no atmosphere (like the Moon), the surface temperature at any given location would be governed by the balance between the rate of absorbed solar energy and the loss of thermally-emitted infrared (IR) radiation. The sun would heat the surface to a temperature where the emitted IR radiation balanced the absorbed solar radiation, and then the temperature would stop increasing. This general concept of energy balance between energy gain and energy loss is involved in determining the temperature of virtually anything you can think of.

But the Earth does have an atmosphere, and the atmosphere both absorbs and emits IR radiation in all directions. “Greenhouse gases” (primarily water vapor, but also carbon dioxide) provide most of this function, and any gain or loss of an IR photon by a GHG molecule is almost immediately felt by the non-radiatively active gases (like nitrogen and oxygen) through molecular collisions.

If we were to represent these infrared energy flows in Fig. 1 more completely, there would be a nearly infinite number of red arrows, both upward and downward, connecting every vanishingly-thin layer of atmosphere with every other vanishingly thin layer. Those are the flows that are happening continuously in the atmosphere.

The most important net impact of the greenhouse effect on terrestrial temperatures is this:

The net effect of a greenhouse atmosphere is that it keeps the lower atmospheric layers (and surface) warmer, and the upper atmosphere colder, than if the greenhouse effect did not exist.

I have often called this a “radiative blanket” effect.

Interestingly, without the greenhouse effect, the upper layers of the troposphere would not be able to cool to outer space, and weather as we know it (which depends upon radiative destabilization of the vertical temperature profile) would not exist. This was demonstrated by Manabe & Strickler (1964) who calculated that, without convective overturning, the pure radiative equilibrium temperature profile of the troposphere is very hot at the surface, and very cold in the upper troposphere. Convective overturning in the atmosphere reduces this huge temperature ‘lapse rate’ by about two-thirds to three-quarters, resulting in what we observe in the real atmosphere.

Dr. Ollila’s Claims

The latest installment of what I consider to be bad skeptical science regarding the greenhouse effect comes from emeritus professor of environmental science, Dr. Antero Ollila, who claims that the energy budget diagram somehow violates the 1st Law of Thermodynamics, i.e., conservation of energy, at least in terms of how the greenhouse effect is quantified.

His article is entitled, How The IPCC’s Greenhouse Definition Violates the Physical Law of Conservation of Mass & Energy. He uses a modified version (Fig. 2) of the Kiehl-Trenberth diagram:

It should be noted that these global average energy budget diagrams do indeed conserve energy in their total energy fluxes at the top-of-atmosphere (the climate system as a whole), as well as for the surface and atmosphere, separately. If you add up these energy gain and loss terms you will see they are equal, which must be the case for any system with a stable temperature over time.

But what Dr. Ollila seems to be confused about is what you can physically and quantitatively deduce about the greenhouse effect when you start combining energy fluxes in that diagram. Much of the first part of Dr. Ollila’s article is just fine. His objection to the diagram is introduced with the following statement, which those who hold similar views to his will be triggered by:

“The obvious reason for the GH effect seems to be the downward infrared radiation from the atmosphere to the surface and its magnitude is 345 W/m2. Therefore, the surface absorbs totally 165 (solar) + 345 (downward infrared from the atmosphere) = 510 W/m2.“

At this point some of my readers (you know who you are) will object to that quote, and say something like, “But the only energy input at the surface is from the sun! How can the atmosphere add more energy to the system, when the sun is the only source of energy?” My reading of Dr. Ollila’s article indicates that that is where he is going as well.

But this is where the problem with ambiguous wording comes in. The atmosphere is not, strictly speaking, adding more energy to the surface. It is merely returning a portion of the atmosphere-absorbed solar, infrared, and convective transport energy back to the surface in the form of infrared energy.

As shown in Fig. 2, the surface is still emitting more IR energy than the atmosphere is returning to the surface, resulting in net surface loss of [395 – 345 =] 50 W/m2 of infrared energy. And, as previously mentioned, all energy fluxes at the surface balance.

And this is what our intuition tells us should be happening: the surface is warmed by sunlight, and cooled by the loss of IR energy (plus moist and dry convective cooling of the surface of 91 and 24 W/m2, respectively.) But the atmosphere’s radiative blanket reduces the rate of IR cooling from the warmer lower layers of the atmosphere to the upper cooler layers. This alteration of average energy flows by greenhouse gases and clouds alters the atmospheric temperature profile.

A related but common misunderstanding is the idea that the rate of energy input determines a system’s temperature. That’s wrong.

Given any rate of energy input into a system, the temperature will continue to increase until temperature-dependent energy loss mechanisms equal the rate of energy input. If you don’t believe it, let’s look at an extreme example.

Believe it or not, the human body generates energy through metabolism at a rate that is 8,000 time greater than what the sun generates, per kg of mass. But the human body has an interior temperature of only 98.6 deg. F, while the sun’s interior temperature is estimated to be around 27,000,000 deg. F. This is a dramatic example that the rate of energy *input* does not determine temperature: it’s the balance between the rates of energy gain and energy loss that determines temperature.

If energy has no efficient way to escape, then even a weak rate of energy input can lead to exceedingly high temperatures, such as occurs in the sun. I have read that it takes thousands of years for energy created in the core of the sun from nuclear fusion to make its way to the sun’s surface.

Since this is meant to be a critique of Dr. Ollila’s specific arguments let’s return to them. I just wanted to first address his central concern by explaining the greenhouse effect in the best terms I can, before I confuse you with his arguments. Here I list the main points of his reasoning, in which I reproduce the first quote from above for completeness:

[begin quote]

The obvious reason for the GH effect seems to be the downward infrared radiation from the atmosphere to the surface and its magnitude is 345 Wm-2. Therefore, the surface absorbs totally 165 + 345 = 510 Wm-2….

The difference between the radiation to the surface and the net solar radiation is 510 – 240 = 270 Wm-2...

The real GH warming effect is right here: it is 270 Wm-2 because it is the extra energy warming the Earth’s surface in addition to the net solar energy.

The final step is that we must find out what is the mechanism creating this infrared radiation from the atmosphere. According to the IPCC’s definition, the GH effect is caused by the GH gases and clouds which absorb infrared radiation of 155 Wm-2 emitted by the surface and which they further radiate to the surface.

As we can see there is a problem – and a very big problem – in the IPCC’s GH effect definition: the absorbed energy of 155 Wm-2 cannot radiate to the surface 345 Wm-2 or even 270 Wm-2. According to the energy conversation law, energy cannot be created from the void. According to the same law, energy does not disappear, but it can change its form.

From Figure (2) it is easy to name the two other energy sources which are needed for causing the GH effect namely latent heating 91 Wm-2 and sensible heating 24 Wm-2, which make 270 Wm-2 with the longwave absorption of 155 Wm-2.

When the solar radiation absorption of 75 Wm-2 by the atmosphere will be added to these three GH effect sources, the sum is 345 Wm2. Everything matches without the violation of physics. No energy disappears or appears from the void. Coincidence? Not so.

Here is the point: the IPCC’s definition means that the LW absorption of 155 Wm-2 could create radiation of 270 Wm-2 which is impossible.“

[end quote]

Now, I have spent at least a couple of hours trying to follow his line of reasoning, and I cannot. If Dr. Ollila wanted to claim that the energy budget numbers violate energy conservation, he could have made all of this much simpler by asking the question, How can 240 W/m2 of solar input to the climate system cause 395 W/m2 of IR emission by the surface? Or 345 W/m2 of downward IR emission from the sky to the surface? ALL of these numbers are larger than the available solar flux being absorbed by the climate system, are they not? But, as I have tried to explain from the above, a 1-way flow of IR energy is not very informative, and only makes quantitative sense when it is combined with the IR flow in the opposite direction.

If we don’t do that, we can fool ourselves into thinking there is some mysterious and magical “extra” source of energy, which is not the case at all. All energy flows in these energy budget diagram have solar input as the energy source, and as energy courses through the climate system, they all end up balancing. There is no violation of the laws of thermodynamics.

Is There an Energy Flux Measure of the Greenhouse Effect?

One of the problems with Dr. Ollila’s reasoning is that there really isn’t any of these unidirectional energy fluxes (or combinations of energy fluxes, such as 155, or 270, or 345 W/m2) that can be called a measure of the greenhouse effect. The average unidirectional energy fluxes are what exist after the surface and atmosphere have readjusted their temperature and humidity structures (as well as after the sensible and latent convective heat transports get established).

Even the oft-quoted 33 deg. C of warming isn’t a measure of the greenhouse effect… it’s the resulting surface warming after convective heat transports have cooled the surface. As I recall, the true, pure radiative equilibrium greenhouse effect on surface temperature (without convective heat transports) would double or triple that number.

If the atmospheric radiative energy flows are too abstract for you, let’s use the case of a house heated in the winter. On an average cold winter day, I compute from standard sources that the heating unit in the average house leads to a loss of energy through the walls, ceiling, and floor of about 10 W/m2 (just take the heater input in Watts [around 5,000 Joules/sec] and divide by the surface area of all house exterior surfaces [ around 500 sq. meters]).

But compare that 10 W/m2 of energy flow though the walls, ceiling, and floor to the inward IR emission by the exterior walls, which (it is easy to show) emit an IR flux toward the center of the house that is about 100 W/m2 greater than the outward emission by the outside of the walls. That ~100 W/m2 difference in outward versus inward IR flux is still energetically consistent with the 10 W/m2 of heat flow outward through the walls.

This seeming contradiction is resolved (just as in the case of Earth’s surface energy budget) when we realize that the NET (2-way) infrared flux at the inside surface of the exterior walls is still outward, because that wall surface will be slightly colder than the interior of the house, which is also emitting IR energy toward the outside walls. Talking about the IR flux in only one direction is not very quantitatively useful by itself. There is no magical and law-violating creation of extra energy.

Concluding Comments

If you have managed to wade through the arguments above and understand most of them, congratulations. You now see how complicated the greenhouse effect is compared to, say, just sunlight warming the Earth’s surface. That complexity leads to imprecise, incomplete, and ambiguous descriptions of the greenhouse effect, even in the scientific literature (and the IPCC’s description).

The most accurate representation of the greenhouse effect is made through the relevant equations that describe the radiative (and convective) energy flows between the surface and the atmosphere. To express all of that in words would be nearly impossible, and the more accurate the wording, the more the reader’s eyes would glaze over.

So, we are left with people like me trying to inform the public on issues which I sometimes consider to be a waste of time arguing about. I only waste that time because I would like for my fellow skeptics to be armed with good science, not bad science.

[I still maintain that the simplest backyard demonstration of the greenhouse effect in action is with a handheld IR thermometer pointed at a clear sky at different angles, and seeing the warming of the thermometer’s detector as you scan from the zenith down to an oblique angle. That is the greenhouse effect in action.]

John Christy pointed out to me that our UAH lower stratosphere (“LS”) temperature product which has peak sensitivity at about 17 km (70 hPa pressure) has increased in the last 2 months to its warmest value since the post-Pinatubo period of warming (1991-93). This can be seen in the following plot of global average anomalies.

At first I though we might be seeing warming from the mid-January eruption of Taal volcano in the Philippines, but even the much more massive mid-June 1991 eruption of Pinatubo did not show up in the LS temperatures until the month following the eruption, while we see evidence of warming in Fig. 2 in the same month as the Taal eruption.

NASA had previously reported that the smoke from the Australian bushfires had been detected in January as as high as 20-25 km, well into the stratosphere (see here, here, and here). The measurements come from the CALIPSO spacecraft which has a lidar instrument capable of accurate altitude measurements of aerosols.

The mechanism for the warming of the lower stratosphere by the smoke is some combination of direct solar heating of the smoke particles, and infrared (“greenhouse”) warming of the smoke layer, the latter being the mechanism that caused the warming after the eruptions of El Chichon and Pinatubo. The aerosol layer is very cold, and it intercepts infrared radiation from below and so warms slightly.

I will try to examine the specific latitude band (30S-60S) being affected in more detail, including temperature measurements from higher up (which we do not produce official products for). The difficulty is that there is considerable natural variation in the tropical and extra-tropical temperatures in the stratosphere which have a see-saw behavior due to variations in the strength of the Brewer-Dobson circulation. As a result, these stratospheric aerosol effects on temperature tend to show up best in global or nearly-global averages (Fig. 2, above) where such circulation induced changes average out.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for February, 2020 was +0.76 deg. C, up considerably from the January, 2020 value of +0.57 deg. C.

This is the warmest monthly anomaly since March 2016 (+0.77 deg. C), and the warmest February since 2016 (+0.86 deg. C), both due to El Nino warmth. Continuing weak El Nino conditions are also likely responsible for the current up-tick in temperature, as I recently demonstrated here.

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 14 months are:

 YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
 2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.15 +1.15
 2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.04 +0.06
 2019 03 +0.35 +0.44 +0.25 +0.41 -0.55 +0.97 +0.59
 2019 04 +0.44 +0.38 +0.51 +0.54 +0.50 +0.92 +0.91
 2019 05 +0.32 +0.29 +0.35 +0.40 -0.61 +0.98 +0.39
 2019 06 +0.47 +0.42 +0.52 +0.64 -0.64 +0.91 +0.35
 2019 07 +0.38 +0.33 +0.44 +0.45 +0.11 +0.33 +0.87
 2019 08 +0.39 +0.38 +0.39 +0.42 +0.17 +0.44 +0.24
 2019 09 +0.62 +0.64 +0.59 +0.60 +1.14 +0.75 +0.57
 2019 10 +0.46 +0.64 +0.28 +0.31 -0.03 +0.99 +0.50
 2019 11 +0.55 +0.56 +0.54 +0.55 +0.21 +0.56 +0.38
 2019 12 +0.56 +0.61 +0.50 +0.58 +0.92 +0.66 +0.94
 2020 01 +0.57 +0.60 +0.53 +0.62 +0.73 +0.12 +0.66
 2020 02 +0.76 +0.96 +0.55 +0.76 +0.38 +0.02 +0.30

The UAH LT global gridpoint anomaly image for February, 2020 should be available in the next few days 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:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt

Last week I was privileged to present an invited talk (PDF here) to the Winter Roundtable of the the Pacific Pension & Investment Institute in Pasadena, CA. The PPI meeting includes about 120 senior asset managers representing about $25 Trillion in investments. Their focus is on long-term investing with many managing the retirement funds of private sector and state employees.

They had originally intended the climate change session to be a debate, but after numerous inquiries were unable to find anyone who was willing to oppose me.

Like most people, these asset managers represent a wide variety of views on climate change, but what they have in common is they are under increasing pressure to make “sustainable investing” a significant fraction of their portfolios. Some managers view this as an infringement on their fiduciary responsibility to provide the highest rates of return for their customers. Others believe that sustainable investing (e.g. in renewable energy projects) is a good long-term investment if not a moral duty. Nearly all have now divested from coal. Many investment funds now highlight their sustainable investments, as they cater to investors who (for a variety of reasons) want to be part of this new trend.

My understanding is that most investment managers have largely been convinced that climate change is a serious threat. My message was that this is not the case, and that at a minimum the dangers posed by human-caused climate change have been exaggerated. Furthermore, the benefits of more carbon dioxide in the atmosphere (e.g. increased agricultural productivity with no sign of climate change-induced agricultural harm) are seldom mentioned. I showed Bjorn Lomborg’s evidence for the 95% reduction in weather-related mortality over the last 100 years, as well as Roger Pielke, Jr’s Munich Re data showing no increase in insured damages as a fraction of GDP.

One meeting organizer took considerable professional risk in insisting that I be invited to provide a more balanced view of climate change than most of the attendees had been exposed to before, and there was considerable anxiety about my inclusion in the program. Fortunately, my message (a 30 minute PowerPoint presentation [pdf here] with a panel discussion afterward) was unexpectedly well-received. An e-mail circulated after the meeting claimed that I had “changed the dynamic of future meetings.” The Heartland Institute was also involved in making this happen.

Los Angeles Mayor Eric Garcetti gave a speech at the first night’s dinner, in which he (as you might expect) mentioned the challenge of climate change, reducing “carbon” emissions, and his young daughter’s anxiety over global warming.

The experience for me was gratifying. Even those few participants who disagreed with me were very polite, and we all got along very well. In what might be considered a bit of irony, on my flight to LAX we flew past the failed Ivanpah solar power facility southwest of Las Vegas, which produced a blinding white light for about 5 minutes.

Well, as I suspected (and warned everyone) in my blog post yesterday, a portion of my calculations were in error regarding how much CO2 is taken out of the atmosphere in the global carbon cycle models used for the RCP (Representative Concentration Pathway) scenarios. A few comments there said it was hard to believe such a discrepancy existed, and I said so myself.

The error occurred by using the wrong baseline number for the “excess” CO2 (atmospheric CO2 content above 295 ppm) that I divided by in the RCP scenarios.

Here is the corrected Fig. 1 from yesterday’s post. We see that during the overlap between Mauna Loa CO2 observations (through 2019) and the RCP scenarios (starting in 2000), the RCP scenarios do approximately match the observations for the fraction of atmospheric CO2 above 295 ppm.

But now, the RCP scenarios have a reduced rate of removal in the coming decades during which that same factor-of-4 discrepancy with the Mauna Loa observation period gradually develops. More on that in a minute.

First, I should point out that the CO2 sink (removal rate) in terms of ppm/yr in three of the four RCP scenarios does indeed increase in absolute terms from (for example ) the 2000-2005 period to the 2040-2050 period: from 1.46 ppm/year during 2000-2005 to 2.68 ppm/yr (RCP4.5), 3.07 ppm/yr (RCP6.0), and 3.56 ppm/yr (RCP8.5). RCP2.6 is difficult to compare to because it involves not only a reduction of emissions, but actual negative CO2 emissions in the future from enhanced CO2 uptake programs. So, the RCP curves in Fig.1 should not be used to infer a reduced rate of CO2 uptake; it is only a reduced uptake relative to the atmospheric CO2 “overburden” relative to more pre-Industrial levels of CO2.

How Realistic are the Future RCP CO2 Removal Fractions?

I have been emphasizing that the Mauna Loa data are extremely closely matched by a simple model (blue line in Fig. 1) that assumes CO2 is removed from the atmosphere at a constant rate of 2.3%/yr of the atmospheric excess over a baseline value of 295 ppm.

OK, now actually look at that figure I just linked to, because the fit is amazingly good. I’ll wait….

Now, if I reduce the model specified CO2 removal rate value from 2.3 to 2.0%/yr, I cannot match the Mauna Loa data. Yet the RCP scenarios insist that value will decrease markedly in the coming decades.

Who is correct? Will nature continue to remove 2.0-2.3%/yr of the CO2 excess above 295 ppm, or will that removal rate drop precipitously? If it stays fairly constant, then the future RCP scenarios are overestimating future atmospheric CO2 concentrations, and as a result climate models are predicting too much future warming.

Unfortunately, as far as I can tell, this situation can not be easily resolved. Since that removal fraction is MY metric (which seems physically reasonable to me), but is not how the carbon cycle models are built, it can be claimed that my model is too simple, and does not contain the physics necessary to address how CO2 sinks change in the future.

Which is true. All I can say is that there is no evidence from the past 60 years (1959-2019) of Mauna Loa data that the removal fraction is changing…yet.

There is no way for me to win that argument.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for January, 2020 was +0.56 deg. C, unchanged from the December 2019 value of +0.56 deg. C.

The linear warming trend since January, 1979 remains at +0.13 C/decade (+0.11 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 25 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST
 2018 01 +0.29 +0.51 +0.07 -0.10 +0.70 +1.39 +0.52
 2018 02 +0.25 +0.28 +0.21 +0.05 +0.99 +1.22 +0.35
 2018 03 +0.28 +0.43 +0.12 +0.08 -0.19 -0.32 +0.76
 2018 04 +0.21 +0.32 +0.10 -0.14 +0.06 +1.02 +0.84
 2018 05 +0.16 +0.38 -0.05 +0.02 +1.90 +0.14 -0.24
 2018 06 +0.20 +0.33 +0.06 +0.12 +1.10 +0.77 -0.41
 2018 07 +0.30 +0.38 +0.23 +0.28 +0.41 +0.24 +1.49
 2018 08 +0.18 +0.21 +0.16 +0.11 +0.02 +0.11 +0.37
 2018 09 +0.13 +0.14 +0.13 +0.22 +0.89 +0.23 +0.28
 2018 10 +0.20 +0.27 +0.12 +0.30 +0.20 +1.08 +0.43
 2018 11 +0.26 +0.24 +0.28 +0.46 -1.16 +0.68 +0.55
 2018 12 +0.25 +0.35 +0.15 +0.30 +0.24 +0.69 +1.20
 2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.15 +1.15
 2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.04 +0.05
 2019 03 +0.35 +0.44 +0.25 +0.41 -0.55 +0.96 +0.59
 2019 04 +0.44 +0.38 +0.51 +0.54 +0.50 +0.92 +0.91
 2019 05 +0.32 +0.29 +0.35 +0.40 -0.61 +0.98 +0.39
 2019 06 +0.47 +0.42 +0.52 +0.64 -0.64 +0.91 +0.35
 2019 07 +0.38 +0.33 +0.44 +0.45 +0.11 +0.33 +0.87
 2019 08 +0.39 +0.38 +0.39 +0.42 +0.17 +0.44 +0.24
 2019 09 +0.62 +0.64 +0.59 +0.60 +1.14 +0.75 +0.57
 2019 10 +0.46 +0.64 +0.28 +0.31 -0.03 +0.99 +0.50
 2019 11 +0.55 +0.56 +0.54 +0.55 +0.21 +0.56 +0.38
 2019 12 +0.56 +0.61 +0.50 +0.58 +0.92 +0.66 +0.94
 2020 01 +0.56 +0.60 +0.53 +0.62 +0.73 +0.12 +0.66

The UAH LT global gridpoint anomaly image for January, 2020 should be available in the next few days 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:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt

Note: What I present below is scarcely believable to me. I have looked for an error in my analysis, but cannot find one. Nevertheless, extraordinary claims require extraordinary evidence, so let the following be an introduction to a potential issue with current carbon cycle models that might well be easily resolved by others with more experience and insight than I possess.

UPDATE (2/6/2020): It turns out I made an error (as I feared) in my calculations that went into Fig. 1, below. You might want to instead read my corrected results here and what they suggest. The bottom line is that the IPCC carbon cycle models by 2100 reduce the fractional rate of removal of extra atmospheric CO2 by a factor of 3-4 versus what has actually been happening over the last 60 years of Mauna Loa CO2 data. That (as Fig. 2 in my previous post suggested) will have an effect on future CO2 projections and, in turn, global warming forecasts. But my previous claim that the discrepancy exists during the Mauna Loa record was incorrect.

Summary

Sixty years of Mauna Loa CO2 data compared to yearly estimates of anthropogenic CO2 emissions shows that Mother Nature has been removing 2.3%/year of the “anthropogenic excess” of atmospheric CO2 above a baseline of 295 ppm. When similar calculations are done for the RCP (Representative Concentration Pathway) projections of anthropogenic emissons and CO2 concentrations it is found that the carbon cycle models those projections are based upon remove excess CO2 at only 1/4th the observed rate. If these results are anywhere near accurate, the future RCP projections of CO2, as well as the resulting climate model projection of resulting warming, are probably biased high.

Introduction

My previous post from a few days ago showed the performance of a simple CO2 budget model that, when forced with estimates of yearly anthropogenic emissions, very closely matches the yearly average Mauna Loa CO2 observations during 1959-2019. I assume that a comparable level of agreement is a necessary condition of any model that is relied upon to predict future levels of atmospheric CO2 if it is have any hope of making useful predictions of climate change.

In that post I forced the model with EIA projections of future emissions (0.6%/yr growth until 2050) and compared it to the RCP (Representative Concentration Pathway) scenarios used for forcing the IPCC climate models. I concluded that we might never reach a doubling of atmospheric CO2 (2XCO2).

But what I did not address was the relative influence on those results of (1) assumed future anthropogenic CO2 emissions versus (2) how fast nature removes excess CO2 from the atmosphere. Most critiques of the RCP scenarios address the former, but not the latter. Both are needed to produce an RCP scenario.

I implied that the RCP scenarios from models did not remove CO2 fast enough, but I did not actually demonstrate it. That is the subject of this short article.

What Should the Atmospheric CO2 Removal Rate be Compared To?

The Earth’s surface naturally absorbs from, and emits into, the huge atmospheric reservoir of CO2 through a variety of biological and geochemical processes.

We can make the simple analogy to a giant vat of water (the atmospheric reservoir of CO2), with a faucet pouring water into the vat and a drain letting water out of the vat. Let’s assume those rates of water gain and loss are nearly equal, in which case the level of water in the vat (the CO2 content of the atmosphere) never changes very much. This was supposedly the natural state of CO2 flows in and out of the atmosphere before the Industrial Revolution, and is an assumption I will make for the purposes of this analysis.

Now let’s add another faucet that drips water into the vat very slowly, over many years, analogous to human emissions of CO2. I think you can see that there must be some change in the removal rate from the drain to offset the extra gain of water, otherwise the water level will rise at the same rate that the additional water is dripping into the vat. It is well known that atmospheric CO2 is rising at only about 50% the rate at which we produce CO2, indicating the “drain” is indeed flowing more strongly.

Note that I don’t really care if 5% or 50% of the water in the vat is exchanged every year through the actions of the main faucet and the drain; I want to know how much faster the drain will accomodate the extra water being put into the tank, limiting the rise of water in the vat. This is also why any arguments [and models] based upon atomic bomb C-14 removal rates are, in my opinion, not very relevant. Those are useful for determining the average rate at which carbon cycles through the atmospheric reservoir, but not for determining how fast the extra ‘overburden’ of CO2 will be removed. For that, we need to know how the biological and geochemical processes change in response to more atmospheric CO2 than they have been used to in centuries past.

The CO2 Removal Fraction vs. Emissions Is Not a Useful Metric

For many years I have seen reference to the average equivalent fraction of excess CO2 that is removed by nature, and I have often (incorrectly) said something similar to this: “about 50% of yearly anthropogenic CO2 emissions do not show up in the atmosphere, because they are absorbed.” I believe this was discussed in the very first IPCC report, FAR. I’ve used that 50% removal fraction myself, many times, to describe how nature removes excess CO2 from the atmosphere.

Recently I realized this is not a very useful metric, and as phrased above is factually incorrect and misleading. In fact, it’s not 50% of the yearly anthropogenic emissions that is absorbed; it’s an amount that is equivalent to 50% of emissions. You see, Mother Nature does not know how much CO2 humanity produces every year; all she knows is the total amount in the atmosphere, and that’s what the biosphere and various geochemical processes respond to.

It’s easy to demonstrate that the removal fraction, as is usually stated, is not very useful. Let’s say humanity cut its CO2 emissions by 50% in a single year, from 100 units to 50 units. If nature had previously been removing about 50 units per year (50 removed versus 100 produced is a 50% removal rate), it would continue to remove very close to 50 units because the atmospheric concentration hasn’t really changed in only one year. The result would be that the new removal fraction would shoot up from 50% to 100%.

Clearly, that change to a 100% removal fraction had nothing to do with an enhanced rate of removal of CO2; it’s entirely because we made the removal rate relative to the wrong variable: yearly anthropogenic emissions. It should be referenced instead to how much “extra” CO2 resides in the atmosphere.

The “Atmospheric Excess” CO2 Removal Rate

The CO2 budget model I described here and here removes atmospheric CO2 at a rate proportional to how high the CO2 concentration is above a background level nature is trying to “relax” to, a reasonable physical expectation that is supported by observational data.

Based upon my analysis of the Mauna Loa CO2 data versus the Boden et al. (2017) estimates of global CO2 emissions, that removal rate is 2.3%/yr of the atmospheric excess above 295 ppm. That simple relationship provides an exceedingly close match to the long-term changes in Mauna Loa yearly CO2 observations, 1959-2019 (I also include the average effects of El Nino and La Nina in the CO2 budget model).

So, the question arises, how does this CO2 removal rate compare to the RCP scenarios used as input to the IPCC climate models? The answer is shown in Fig. 1, where I have computed the yearly average CO2 removal rate from Mauna Loa data, and the simple CO2 budget model in the same way as I did from the RCP scenarios. Since the RCP data I obtained from the source has emissions and CO2 concentrations every 5 (or 10) years from 2000 onward, I computed the yearly average removal rates using those bounding years from both observations and from models.

The four RCP scenarios do indeed have an increasing rate of removal as atmospheric CO2 concentrations rise during the century, but their average rates of removal are much too low. Amazingly, there appears to be about a factor of four discrepancy between the CO2 removal rate deduced from the Mauna Loa data (combined with estimates of historical CO2 emissions) versus the removal rate in the carbon cycle models used for the RCP scenarios during their overlap period, 2000-2019.

Such a large discrepancy seems scarcely believable, but I have checked and re-checked my calculations, which are rather simple: they depend only upon the atmospheric CO2 concentrations, and yearly CO2 emissions, in two bounding years. Since I am not well read in this field, if I have overlooked some basic issue or ignored some previous work on this specific subject, I apologize.

Recomputing the RCP Scenarios with the 2.3%/yr CO2 Removal Rate

This raises the question of what the RCP scenarios of future atmospheric CO2 content would look like if their assumed emissions projections were combined with the Manua Loa-corrected excess CO2 removal rate of 2.3%/yr (above an assumed background value of 295 ppm). Those results are shown in Fig. 2.

Now we can see the effect of just the differences in the carbon cycle models on the RCP scenarios: those full-blown models that try to address all of the individual components of the carbon cycle and how it changes as CO2 concentrations rise, versus my simple (but Mauna Loa data-supportive) model that only deals with the empirical observation that nature removes excess CO2 at a rate of 2.3%/yr of the atmospheric excess above 295 ppm.

This is an aspect of the RCP scenario discussion I seldom see mentioned: The realism of the RCP scenarios is not just a matter of what future CO2 emissions they assume, but also of the carbon cycle model which removes excess CO2 from the atmosphere.

Discussion

I will admit to knowing very little about the carbon cycle models used by the IPCC. I’m sure they are very complex (although I dare say not as complex as Mother Nature) and represent the state-of-the-art in trying to describe all of the various processes that control the huge natural flows of CO2 in and out of the atmosphere.

But uncertainties abound in science, especially where life (e.g. photosynthesis) is involved, and these carbon cycle models are built with the same philosophy as the climate models which use the output from the carbon cycle models: These models are built on the assumption that all of the processes (and their many approximations and parameterizations) which produce a reasonably balanced *average* carbon cycle picture (or *average* climate state) will then accurately predict what will happen when that average state changes (increasing CO2 and warming).

That is not a given.

Sometimes it is useful to step back and take a big-picture approach: What are the CO2 observations telling us about how the global average Earth system is responding to more atmospheric CO2? That is what I have done here, and it seems like a model match to such a basic metric (how fast is nature removing excess CO2 from the atmosphere, as the CO2 concentration rises) would be a basic and necessary test of those models.

According to Fig. 1, the carbon cycle models do not match what nature is telling us. And according to Fig. 2, it makes a big difference to the RCP scenarios of future CO2 concentrations in the atmosphere, which will in turn impact future projections of climate change.