The brief interlude presented in the previous post focused on the just released Doomsday Clock.
Now let's get back to the discussion concerning atmospheric gasses.
But first let me point out that we have been perusing the Lundstad et al. datasets,which do not include the record setting high global temperatures of recent years because they made their report circa 2020, and soon after that the warmest year on record took place in 2024 (see graph above).
Take a look at the dynamics of the atmospheric gases to get a grasp of trends in globally-averaged CH4, N2O, and SF6 determined from NOAA
Global Monitoring Laboratory measurements. Version 2026-01.
That pattern fits with the global temperature pattern of recent origin (see graphs below).
So, let's continue with recent concepts.
What are aerosols?
The trace gases such as water vapor can take up as much as 4% of the atmosphere, and that impacts aerosol content:
"Aerosols are small particles suspended in the atmosphere. They are often not or barely visible to the human eye, yet their impact on climate, weather, health, and ecology are significant. This page introduces the various major types of aerosols, and explains why researching them is important.
Aerosols range in size from a few tens of nanometers—less than the width of the smallest viruses—to several tens of micrometers—about the diameter of human hair. The size and composition of aerosol particles affects how far they can travel around the world, their interactions with solar and thermal radiation, and their potential effects on health. Aerosols injected into the atmosphere directly are known as 'primary aerosols'. Sea spray, mineral dust, smoke, and volcanic ash are all primary aerosols. Secondary aerosols are aerosols which were emitted in another form (e.g. gases), then become aerosol particles after going through chemical reactions in the atmosphere, such as sulfate aerosols from volcanoes or industrial emissions. All aerosols can also undergo further chemical changes, referred to as ‘aging effects’. Some more information about these various aerosol types is given below."
"Founded in 1945 by Albert Einstein, J. Robert Oppenheimer, and University of Chicago scientists who helped develop the first atomic weapons in the Manhattan Project, the Bulletin of the Atomic Scientists created the Doomsday Clock two years later, using the imagery of apocalypse (midnight) and the contemporary idiom of nuclear explosion (countdown to zero) to convey threats to humanity and the planet. The Doomsday Clock is set every year by the Bulletin’s Science and Security Board in consultation with its Board of Sponsors, which includes eight Nobel laureates. The Clock has become a universally recognized indicator of the world’s vulnerability to global catastrophe
caused by man-made technologies.
It is now 85 seconds to midnight
A year ago, we warned that the world was perilously close to global disaster and that any delay in reversing course increased the probability of catastrophe. Rather than heed this warning, Russia, China, the United States, and other major countries have instead become increasingly aggressive, adversarial, and nationalistic. Hard-won global understandings are collapsing, accelerating a winner-takes-all great power competition and undermining the international cooperation critical to reducing the risks of nuclear war, climate change, the misuse of biotechnology, the potential threat of artificial intelligence, and other apocalyptic dangers. Far too many leaders have grown complacent and indifferent, in many cases adopting rhetoric and policies that accelerate rather than mitigate these existential risks. Because of this failure of leadership, the Bulletin of the Atomic Scientists Science and Security Board today sets the Doomsday Clock at 85 seconds to midnight, the closest it has ever been to catastrophe."
Earth's lower atmosphere (dry air) is primarily composed of Nitrogen (78.08%) and Oxygen (20.95%), which together make up about 99% of its volume. The remaining 1% consists of Argon (0.93%), Carbon Dioxide (approx. 0.04%), and trace amounts of neon, helium, methane, and other gases. Water vapor content is highly variable, ranging from near 0% to 4%.
These key components of Earth's Atmosphere (Dry Air) Nitrogen (N2), Oxygen (O2), Argon (Ar), and Carbon Dioxide (CO2) are our next focus, including lesser quantities of trace gases Neon (Ne), Helium (He), Methane (CH4), Krypton (Kr), and Hydrogen (H2) to fill in to reach 100%.
While nitrogen and oxygen tend to be constant, water vapor (H2O), carbon dioxide (CO2), and methane (CH4) concentrations fluctuate based on location and time.
What impact do these changes in gas percentages have on atmospheric dynamics?
The graphs below show the impact on layers and zones in terms of "chemical potential" when those changes in t_si/p_si (temperature and humid air pressure/density) take place.
But what happens when the percent amounts change, but still add up to 100%?
If Nitrogen (N2) 78.08% drops to 75% and the remaining 3.08% is distributed into the other gases, what impact does that have on temperature, pressure, density and chemical potential?
I will begin to explore that in the next episode, but in the meantime let's ponder the event that scientists "got wrong", i.e., when the 2024 heat increase caught them by surprise ("Last" Doesn't Always Mean "Previous" - 4).
And let's ponder why Dr. James Hansen says it was caused by the removal of "aerosols" from the atmosphere (EPA regs changed concerning emissions by "container ships", etc.).
In a discussion with the Google AI, I stated "The high pressure 'H' on the weather maps yesterday was 1056. One of the meteorologists said that he had never seen one that high ..." The AI entity posted 'Thinking' ... then replied:
"A reading of 1056 mbar (approximately 31.18 inches of mercury) is exceptionally rare and would indeed be a career-first for many meteorologists. In the context of Winter Storm Fern (January 2026), this massive Arctic high-pressure cell is currently dominating the U.S. and is classified as "near record-breaking" for the lower 48 states.
Historical Context of a 1056 mbar High
To understand why your meteorologist was surprised, compare 1056 mbar to historical extremes:
Contiguous U.S. Record: The highest pressure ever recorded in the "Lower 48" is 1064 mbar, set in Miles City, Montana, in December 1983.
North American Record: Alaska holds the continent's record with 1078.6 mbar (Northway, 1989), recorded during an extreme cold wave where temperatures dropped to -62°F.
"Strong" Highs: Most powerful winter Arctic highs range between 1040 and 1050 mbar. Crossing the 1050 mbar threshold puts a system in the top 0.1% of intensity for most locations.
Why 1056 mbar Matters for Winter Storm Fern
While we often focus on low pressure for storms, this 1056 mbar high is the "engine" behind Winter Storm Fern:
Instrument Calibration: Pressures this high can actually exceed the display range of some home barometers and have historically required adjustment for aircraft altimeters to function properly.
The "Push" Factor: The intensity of this high creates a steep "pressure gradient." It acts like a massive plunger, forcing the frigid Arctic air deep into the Southern U.S., where it clashes with moisture to create the current ice and snow conditions.
Physical Effects: At 1056 mbar, the air is 4.2% denser than standard air. This extreme density is a hallmark of the "Polar Vortex" disruption that sent this air mass so far south."
(See Watching The Arctic Die - 7). I am wondering what a change in the mix of gases in the atmosphere at a particular Layer and Zone would have on that area's atmosphere.
I have been perusing the Lundstad et al. datasets, and putting them to the test in this series.
Today's graphs have been passed though the TEOS-10 SIA software I am converting from Fortran to C++.
After diagnosing the Lundstad dataset I ran it through some dry air functions in that library.
One of those functions is the "air_density_si (a_si, t_si, p_si)" in the "Air_3b" module.
Let's look at some Lundstad dataset graphs after passing that data
through that function which takes "a_si, t_si, and p_si" as parameters.
I used the temperature etc. sections of the dataset that were graphed previously in this series (see previous graphs).
The "Combined" graph below details the flow when all of the data in the individual zones are combined and averaged.
The graphs below the combined graph are individual layers in individual graphs.
Some layers are missing because some of the Lundstad data failed to make it through the allowable value checks.
But the clear message being shown is that the atmosphere is impacted not only by temperature, but also by the mixture of gases.
This is the description of the function that generated the data for the graphs:
=============================================
function air_density_si (a_si, t_si, p_si)
============================================= This function returns HUMID-AIR DENSITY as a function of AIR FRACTION, TEMPERATURE AND PRESSURE from numerical iteration of the HELMHOLTZ FUNCTION DERIVATIVE ...
OUTPUT: ... [density of humid air in kg/m3]
In other words more or less of gas X in the mixture causes one density phenomenon while others mixtures cause other density phenomenon.
I thing Dr. James Hansen has been criticized unfairly for his focus on the impact that the mixture quantities of aerosols changes atmospheric dynamics depending on the gases and their quantities in that mixture.
I am continuing research on this, but remember that adding more pollution by adding other gasses to the atmosphere is not an acceptable answer.
The answer is removing pollution from the atmosphere.
The next post in this series is here, the previous post in this series is here.
In today's post we take a first look at the atmospheric temperatures in the scientific work of Lundstad et al.
Previously we looked at the pressures in that same work.
The graphic to the left was also presented as a way of quickly determining the general location where measurement gathering took place.
In today's version both layer and zone are specified, and the zone locations are marked by red rectangles around their latitude and longitude boundaries.
This gives us an idea of how sparse or to the contrary closely covered the measurements are.
Satellites do not have thermometer characteristics however they cover a much wider area than thermometers do, so the two together can give reasonable information (NASA; Evidence).
The next episode of this series will deal with the TEOS-10 SIA processing of this data.
But I digress.
The following graphs detail the annual average temperature measurements (note that "Layer" and "Zone" refer to the layers (0-17) and zone numbers within those layers shown on the graphic above.
The next post in this series is here, the previous post in this series is here.
In the previous post I mentioned a source of information associated with a paper in a scientific journal (Lundstad, Elin; Brugnara, Yuri; Brönnimann, Stefan (2022): Early instrumental time series of global measurements of monthly precipitation between 1677-2021, part 1 [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.941263; In: Lundstad, E et al. (2022): Global Early Instrumental Monthly Meteorological Multivariable Database (HCLIM) [dataset bundled publication]. PANGAEA, https://doi.org/10.1594/PANGAEA.940724).
I have graphed the "pressure" data (called "d_si" in the previous post's graphs.
The difference in "d_si" in the previous graphs and in today's graphs is simply that the previous graphs used data generated by values calculated using anomaly values (anomalies are values that vary from values within a specified span of time).
The graphs today are generated using the values in the aforesaid Lundstad et al. dataset.
I used only values from 1850 on so as to make it easier to compare with the previous post's graphs which began in 1850.
Today's Lundstad graphs use "Layer" and "Zone" terms to describe the locations from which the d_si (pressure) values originated.
A "Layer" in this context is a Latitude band as shown in the graphic at the top of the page, and a zone is a rectangular area bounded by longitude lines.
One thing which I noticed that seems to validate some of the Lundstad data ("Climate change caused by human activities is influencing atmospheric
pressure, according to a new study. The research, published in this week’s Nature, is the first report of a human-induced effect on global climate that does not rely on measurements of temperature." - see previous post in this series) is that pressure does vary for various reasons.
Anyway, more to come after I process the Lundstad temperature data.
The next post in this series is here, the previous post in this series is here.
Computer languages not unlike human languages have nomenclature problems from small to large.
The title of this series brings to mind a small one, while this link brings to mind a deadly one.
This series is about software that analyzes the atmosphere using analysis techniques originally revealed by Josiah Gibbs.
The exhaustive coverage of the relevant software in Fortran and Visual Basic can be studied here. In my struggle to convert the Fortran version into C++ I have moved along enough to generate some graphs using the new C++ version (Graphs One thru Three)..
Graph Two
The mind-blowing science at work digs into the measurements of the atmosphere the way the TEOS-10 C++ software did while focusing on measurements of the ocean depths (World Ocean Database).
The global average atmospheric temperature anomalies (not ocean) can be viewed for Berkeley, NOAA, and GISSTEMP,
The graphs today use the Berkeley anomaly values. The CSV data for generating the graphs begins with a 2024 global climate temperature average in degrees Kelvin (K) as follows:
Global Average Temperature (2024) = 288.33 K
°C = K - 273.15
°C = 288.33 - 273.15
°C = 15.18 [NASA says 15.10]
°F = (°C)15.18 * (9/5) + 32
°F = 59.324
On 22 July 2024, the daily global average
temperature reached a new record high of
17.16°C (62.888 °F) (351.218 °K).
There is a seemingly strange characteristic that is noticeable in the graphs (Graph Three shows a decrease while Graph One and Graph Two show an increase).
Dr. Hansen argues that the software models do not properly recognize internal dynamics of the impact of varying mixes of atmospheric gasses (nitrogen, oxygen, trace amounts of argon, carbon dioxide, methane, water vapor,, ozone,, and other gases like neon, helium, and krypton),
Notice that in the three graphs above the temperature and density data are the same, however, as it turns out the results returned by the same function are a feature, not a bug:
"Climate change caused by human activities is influencing atmospheric pressure, according to a new study. The research, published in this week’s Nature, is the first report of a human-induced effect on global climate that does not rely on measurements of temperature.
Nathan Gillett of the University of Victoria in Canada and colleagues found that air pressure has decreased on average over the Arctic, Antarctic and North Pacific during the past five decades. In contrast, above the North Atlantic, southern Europe and North Africa, pressure has increased.
These changes in atmospheric pressure could have important consequences for future climate, owing to influences on patterns of rainfall, air temperatures, winds and storminess. This means that current climate predictions — which currently fail to take account of regional effects of air pressure changes — may be unreliable."
(SicDev). It boils down to: "Uncertainties in projected warming stem from three sources: uncertainty
in future emissions, uncertainty in the climate response to those
emissions, and internal variability" (Nature).
The just in case paths inside the function are like the gasses in the atmosphere::
ra = gas_constant_air_L2000; [SPECIFIC GAS CONSTANT OF AIR IN J KG-1 K-1 USED BY LEMON ET AL. 2000]
tr = 132.6312;
dr = 10447.7 * ma;
[ma = molar_mass_air_L2000 (MOLAR MASS OF AIR IN KG/MOL USED BY LEMMON et al. 2000]
init_iapws10();
tau = tr / t_si; del = d_si / dr;
case (drv_t 1, drv_d 1): a_d = 1 / del + alpha_res(0, 1, tau, del); a_td = alpha_res(1, 1, tau, del); f = ra * (a_d - tau * a_td) / dr;
case (drv_t 0, drv_d 1):: a_d = 1 / del + alpha_res(0, 1, tau, del); f = ra * t_si * a_d / dr;
case (drv_t 2, drv_d 0):: a_tt = alpha_ideal_tt(tau) + alpha_res(2, 0, tau, del); f = ra * pow(tau,2) * a_tt / t_si;
case (drv_t 0, drv_d : a_dd = -1 / pow(del,2) + alpha_res(0, 2, tau, del); f = ra * t_si * a_dd / pow(dr,2);
The variation in the graphs stems from the value of function parameters "drv_t" and "drv_d", because the other two parameters ("t_si" and "d_si") are the same in all three graphs (Note that the "f" in each case produces the "Result/Impact" line on the graphs).
Closing Comments
The d_si values are averages of atmospheric pressure from 1850 - 2024 recorded in over 404,000 weather station records from around the globe, and the t_si values are averages of global temperatures per Berkeley anomaly values.
Data is available for download: Lundstad, Elin; Brugnara, Yuri; Brönnimann, Stefan (2022): Early instrumental time series of global measurements of monthly precipitation between 1677-2021, part 1 [dataset]. PANGAEA, (2022): Global Early Instrumental Monthly Meteorological Multivariable Database (HCLIM) [dataset bundled publication]. PANGAEA).
This series is about TRENDS ... the trends of change that can be tricky and cause the best scientists to miss the bus, miss the train, miss the boat, and miss the mark (see video below).
The next post in this series is here, the previous post in this series is here.
