The Earth and the Moon have been neighbors for a very long time. We should expect long term steady state heat transfer from Earth’s internal energy (without Sun) to the Moon to be equal to Earth’s geothermal heat flux. To my knowledge, no one has ever made this claim. Sound outrageous? Let’s see…

According to [Davies 2010], the geothermal flux is 46.7 TW:

We conclude by discussing our preferred estimate of 47 TW, (rounded from 46.7 TW given that our error estimate is ± 2 TW)

Amateur scientist Willis Eschenbach developed a thought experiment to demonstrate how the greenhouse effect “works”:

It’s been refuted many times before, but I’ll make it even simpler.

The main claim is that the outer shell’s presence will force the inner core to warm up and radiate twice as much compared to no shell at all.

We start with 235 W/m² emerging from core and going to shell. I’ll use an inner and outer surface area of 1, and consider what is going on every second to make things simple.

235 Joules emerges from the core in the 1st second and goes to the shell . Willis reminds us:

In order to maintain its thermal equilibrium, the whole system must still radiate 235 W/m² out to space.

However … the first thing Willis does is break this rule, and sends 235 J back to the core. Nothing to space.

Now a new 235 J emerges in the 2nd second from the core which gets added with the 235 J that’s coming back from the shell from the 1st second.

The core now sends 470 J to shell. This 470 J now gets split into 235 J back to core and 245 J to space.

Second 3 and on just repeats. So you see what he did there? By violating a rule, he gets to cycle in an extra 235 J every second.

There’s a more accurate variation where the rule is violated several times but with less offending Joules each cycle. It goes like this:

Second 1: 235 core->shell, 117.5 shell->core, 117.5 -> space
Second 2: 352.5 core->shell, 176.25 shell->core, 176.25 -> space
Second 3: 411.25 ... 205.625 ... 205.625
Second 4: 440.625 ... 220.3125 ... 220.3125
Second 5: 455.3125 ... 227.65625 ... 227.65625
...
Second X: 470 ... 235 ... 235

I think you get the idea. I wrote a program to do all this, here. The first variation is easier to describe. Here’s some fun satire to illustrate the main point:

Imagine you’re the head manager of a sugar factory.

Every minute, a bag filled with 235 grams of sugar slides down a chute and lands in a basket. You take this bag and walk across the factory to place it inside a truck for later delivery.

You’ve been trying to figure out how to cheaply increase your production for a while now, and one day you finally got a great idea …

You decide to place a table at a halfway point between basket and truck.

In the first minute of implementing your great idea, you move the bag from basket to table. You decide not to then carry the bag to the truck, but back to the basket. You drop the bag in the basket a second before a new bag comes down the chute. When that new bag drops in the basket, and you see two bags, you say to yourself: “I’m a genius! I just doubled production!”.

You now carry two bags to the table. Then one bag to the truck, and one bag back to the basket. You then repeat this over and over.

You convince yourself that seeing two bags in the basket and carrying it to the table means that you’ve doubled production. The proof is self-evident. Congratulations!

Unfortunately not everyone agreed with you. Many thought you are crazy. So you fired them and hired those that agreed with you. You wanted consensus, and you got it!

Now I’m going to illustrate the greenhouse gas fallacy in the most primitive way, using only 2 water molecules:

We’re at second 0, before any greenhouse magic begins, so the shell is still at 0 J, but the nuclear core is at 235 J. The intensity of motion represents the amount of energy present.

Energy is in fact motion. The universe has only two things: things and motion of them. I’m excluding space.

Willis (and all greenhouse gas junkies in general) believe that energy is just like matter, and you can pass it back to where it came from to have more of it.

What Willis et al end up doing is adding motion to existing motion to intensify motion. They believe this is science, but it’s actually a false philosophy.

Philosophy – core vibrates at twice the intensity

In actual science, we know that the max energy into a system is the max energy THROUGHOUT the system. But in Willis’ philosophy, you can create a feedback loop that causes more energy (motion) somewhere in the system, but it’s all fine as long as just the final output (to space) obeys conservation of energy in regard to original input. This is completely false. Conservation of energy must be followed at every boundary.

Science – shell achieves vibrational resonance with core

In reality, the shell will just come to resonate with the core. There will never be a molecule that vibrates more intensely than what the original energy supplied into the system allows.

This is all just 220 year old basic science. Hopefully, climate scientists might learn some basic experimental thermodynamics rather than relying on a falsified thought experiment.

Summary: You can’t make something vibrate more vigorously by confining it with another thingvibrating at an equal or lower rate.

Fourier is considered a direct predecessor to mainstream climatology. Mainstream climatology follows him and purposefully neglects geothermal energy in Earth’s energy budget due to the belief that it is too small. This then allows them to make the outrageous claim that it is IR-absorbing gases in the atmosphere that boosts surface temperatures to what we measure with thermometers.

The effect of the primitive heat which the globe has retained has therefore become essentially imperceptible at the Earth’s surface …

the effect of the interior heat is no longer perceptible at the surface of the Earth

– Temperatures of the Terrestrial Sphere, Page 15

Well that looks settled. Doesn’t it? Let’s see the whole context:

This is a very curious paragraph, for it admits too much.

The only way to melt ice is to provide at least 0°C worth of energy. Right?

0°C is not “negligible”, now is it?

I can already hear my critics saying: “But Zoe, he said over a century!”

Sure. It’s so marginally over 0°C, that it takes a century to melt 3 cubic meters of ice. So what? It’s still at least 0°C. And it’s coming from the Earth.

Fourier contradicts himself when he claims Earth’s internal heat is imperceptible. Is ice melting not perceptible? What if he chose dry ice? More perceptible. What about nitrogen or oxygen “ice”? Even more perceptible!

Is 0°C correct? What do modern geophysicists think?

Same thing! 0°C is still the convention.

The radiative equivalent of 0°C at emissivity=1 is 315.6 W/m²

Can this really be excluded from the energy budget? No.

What’s the significance of this?

It means the greenhouse effect is junk science. The surface has enough energy from geothermal and solar to explain surface temperatures.

I have two previous articles describing how the geothermal contribution can be computed more accurately using two different methods:

It’s nice to know that the geothermal hypothesis was accidently scientifically supported by the very guy that unfortunately rejected it. A guy who modern academics follow uncritically. The answer was right beneath his feet, but unfortunately his head was in the clouds. Because of him, modern academics truly believe that it is the atmosphere that provides raw energy to the surface, rather than geothermal. What a colossal mistake. They flipped reality completely upside down.

While my critics like to claim that geothermal can only provide ~36 Kelvin because they applied Stefan-Boltzmann formula to the small conductive heat flux of 91.6 mW/m², actual scientists know that geothermal can melt ice. And this knowledge is 200 years old! When are climate scientists going to wake up?

-Zoe

Update 10/02/2020

My critics point out that Fourier meant to add that 318 mW/m² over a course of a century; 3 centuries by today’s known geothermal heat flux: 91 mW/m².

That’s not the point. The point was to expose Fourier’s own confusion over the difference between heat and energy. Fourier’s conduction formula applies to HEAT flow, not energy. 318 mW/m² or 91 mW/m² of total emissive energy will NEVER melt ice. But 318 or 91 mW/m² of HEAT flow might, depending on the temperature the ice is sitting on.

Bottom line: Did Fourier claim geothermal could melt ice? YES. Did he give a good explanation? NO.

Is Fourier a good choice to be a father of climate science? That’s a big NO.

But … since Fourier claimed geothermal could melt ice, I will take his word for it, because in this case he is absolutely right.

Many people believe you can compare the Geothermal Heat Flux to Insolation, see that it’s pitiful and then exclude Geothermal from the energy budget. I have touched on this subject several times: here, here, and here. Today I will again show that this idea is plain nonsense.

Let’s start with the basics of radiation:

The radiation emerging out of a plane in the (x,y) dimension is proportional to the fourth power of its temperature. The choice of variable names x,y is arbitrary. Now what about conduction?

Geothermal Heat Flux has been globally measured to be ~ 91.6 mW/m²; a very small number. Many people claim that you can convert this figure into a value that tells you what the surface temperature would be in the absence of the sun.

What they do is equate the radiation emerging out of a plane with the internal conductive heat flux. In the language of my previous articles, they equate Cold Side Radiation to Conductive Heat Flux: CSR = CHF. Then they solve for T_cold.

This is kind of funny, because even though we have proof that geothermal will deliver ~273 K, they still think geothermal can only deliver ~36 K.

They believe their argument is reasonable because both CSR and CHF are in units W/m², and therefore they can be equated to one another.

What they don’t understand is that the meters squared (m²) are in completely different dimensions.

In radiative flux, the m² comes from the surface plane. But in conductive flux, the m² comes from dividing the thermal conductivity constant (k) by the depth (L).

The depth is orthogonal (perpendicular) to the surface plane!

How much sense does it make to compare emergent radiation to something based on a 90 degree angle to it? None at all.

I derived the proper relationship between CSR and CHF in my previous articles:

Now I do proper dimensional analysis:

Yes, their idea doesn’t make any sense at all, but it does make for great rhetorical pseudoscience.

Now for some satire …

Question: How much rain falls on a flat roof top?

Answer: It depends on the building material and height of the building.

Normal Person: Say what?

This is the best analogy I could come up with what their idea represents. Maybe someone else could come up with a better one. Main point: they’re 90 degrees wrong.

I hope to repeat this for the last time: the Geothermal Heat Flux is NOT enough information to say what radiation emerges out of the surface. There are many possibilities with the same heat flux value, as shown here. CHF divided by k (thermal conductivity) yields a temperature gradient. A gradient measure tells you nothing about what’s at the top.

Today I analyze COVID19 data for my home state of Georgia. I thought it would be interesting because there is an anomaly. Let’s see the anamoly:

You see it? The largest density of cases does not match the largest density of population. We would expect most cases per 100K to be in the 9th largest metropolis in the US (Atlanta), but it’s not!

How could this be? What could cause such an anomaly?

It might have something to do with foreign labor? Georgia is the 2nd largest recipient of temporary agricultural H-2A visas in 2019 (Source). Trend:

There’s no data as to which counties migrant workers go to, but we can take a logical leap: The most agriculturally productive counties probably have the most migrant workers.

We would expect those counties with the largest share of agriculture to be those disproportionately affected by COVID19. Let’s see …

It’s not a perfect match, but I think there’s something to it. Maybe I am wrong, but I haven’t found a better explanation from my local media. In fact, the issue was not even addressed by anyone.

Other states also have low density counties with high COVID19 densities, but they seldom surpass the rates in their major metro areas. Georgia is anomalous in this regard.

With the global economic response to the COVID19 epidemic, we would expect global CO2 to be rising much less than other years, if the theory of man-made global warming is indeed true.

I use data from NOAA to see what’s going on.

The estimated daily global seasonal cycle and trend value for CO2 are determined from the daily averaged CO2 data from the four NOAA/ESRL/GMD Baseline observatories. A smoothed seasonal cycle and a smoothed de-seasonalized trend curve are determined for each observatory record at daily intervals. An estimated global seasonal cycle and trend are computed by averaging the four individual observatory seasonal cycle and trend curves at each daily interval.

I chose the most official processed data there is, so I can’t be accused of cherrypicking. What I do is compare May 1st to Jan 1st of every year from 2010 to 2020. Results:

You’ve probably heard it before: the geothermal heat flux is so small (91.6 mW/m²) that it can be effectively ignored in Earth’s energy budget. The first part is true, the heat flux is small, but this fact is completely irrelevant. And what is relevant is popularly denied and masked as something else.

I’ve already explained the problem here and here. Unfortunately not everyone understood the point I was trying to make, so I made a visualization:

CF (Conductive Flux) is the Geothermal Heat Flux, EF is the Emergent Geothermal Flux, Th and Tc are the temperatures of the hot side and cold side. d is depth. Compatibility with my previous terminology: CF = CHF and EF = CSR.

As you can see all of these profiles have the same geothermal heat flux (CF), and all of them produce a very different emergent flux (EF) out of the surface. The popularly stated geothermal heat flux is NOT a value that you can compare to insolation. The value itself gives you NO clue as to what can emerge at the top. Anyone telling you otherwise is stupid or lying.

The geothermal heat flux and the thermal conductivity factor determines the temperature gradient. A gradient can never tell you either what kinetic energy is at the bottom or the top. Never.

So what really emerges at the top on Earth? In this visualization, the closest answer is ~5°C or ~340 W/m² – what was calculated and observed here and here. ~340 W/m² is what is claimed for the total greenhouse gas backradiation effect, as shown in the “official” energy budget here. That’s not surprising, because the greenhouse gas effect is secretly just geothermal flipped upside down. It’s the biggest scam in climate science, and you heard it here first.

Geothermal provides a tremendous amount of energy, even more than the sun, but climate scientists ignore it because they are looking at a component of a gradient/slope measure, rather than the temperature (kinetic energy) it delivers to the surface.

I invite everyone to give this some serious thought and not just dismiss it using sophistry.

Love, -Zoe

Extra

Geothermal Heat Flux (CF) is a very useful value for commercial geothermal energy prospectors, but not for atmospheric scientists creating an energy budget. EF is what they need to use. They do use it, but they flip it upside down and call it GHE.

The temperature gradient value used is 27.5 °C/km, which I got from here: “it is about 25–30 °C/km”. This makes k = 0.333 W/(m*K).

Mimas is a small moon of Saturn. It is most famous for being the inspiration for the Death Star in the popular movie Star Wars.

But from this day it will be famous for refuting mainstream climate science.

How you ask?

Well … let’s examine its external energy sources:

1) Insolation. The insolaton at Mimas should be approximately the same as that for Saturn.

2) Saturn. Radiation received from Saturn should equal the emission from Saturn diluted by the square of the radius of Saturn divided by distance from Saturn to Mimas and divided by 4.

Let’s convert that back to a temperature (assuming emissivity = 1, by [Howett 2003]):

(1.5344/5.67e-8)^0.25 = 72.1

According to mainstream climate science, only special gases in the atmosphere can boost the surface temperature beyond what external radiation (the sun) alone can do. On Earth, they claim these gases boost the surface temperature by ~33°K.

Mimas has no greenhouse gases or even an atmosphere, so its average temperature should never exceed 72.1 K.

But in reality …

It looks like PacMan is powering the Death Star and the surface temperature is boosted from 2 to 24 K beyond what external radiation alone can do. There is nothing below 74 K?

Isn’t it obvious that Mimas is geothermally boosted?

Neither the greenhouse effect theory of mainstream climate science or the atmospheric pressure theory of Nikolov & Zeller, et al can explain this!

Nothing else can explain PacMan and the thermal boost other than geothermal.

And if a tiny planetoid like Mimas has its own oddly distributed internal energy, maybe the Earth, which is 158,730 times more massive could as well?

In a previous article, I examined the average moon temperature (AMT). You may have noticed that there’s been about ~3 degree K warming in the last decade.

According to [Vasavada 2012], the mean equatorial temperature between 2009 and 2011 was about 213K, whereas the 2017-2018 data from UCLA and WUSTL shows that to be about 216K.

For AMT, the increase has been from ~197K to ~200K.

Perhaps there is some error in the exactness, but that the moon has warmed is not actually controversial; it is accepted by mainstream scientists. I wanted to share with you today their theory as to the cause. Are you ready?

According to the new study, the 12 Apollo astronauts who walked on the moon between 1969 and 1972 kicked aside so much dust that they revealed huge regions of darker, more heat-absorbing soil that may not have seen the light of day in billions of years. Over just six years, this newly exposed soil absorbed enough solar radiation to raise the temperature of the entire moon’s surface by up to 3.6 degrees F (2 degrees C), the study found.

You got that? They didn’t just raise the temperature where they walked but the ENTIRE moon!

You buy it? I hope not. Great laugh, right?

What is the ratio of surface area walked to the entire moon? I don’t know, but it’s ultra tiny. Seems like heat capacity calculations were ignored. The walked surface area might have to be millions (if not billions) of degrees to raise the entire surface area of the moon by a single degree – ASSUMING there’s horizontal heat transfer via conduction.

Now why would they say something that absurd?

I’ll tell you. Scientists have known that Total Solar Irradiance has been decreasing since the 1950s, and the moon has virtually no atmosphere. Because there is no atmosphere there can’t be any stupid greenhouse effect at work.

That would leave geothermal (lunathermal, I guess) warming as the only culprit!

And if the surface of the moon can warm up due to more internal energy coming up from beneath the surface, perhaps the same thing can be at work on Earth …

Believers of the Greenhouse Effect all use the same analogy to get you to believe in their junk science. The site Skeptical Science sets the standard in this article:

So have climate scientists made an elementary mistake? Of course not! The skeptic is ignoring the fact that the Earth is being warmed by the sun, which makes all the difference.

To see why, consider that blanket that keeps you warm. If your skin feels cold, wrapping yourself in a blanket can make you warmer. Why? Because your body is generating heat, and that heat is escaping from your body into the environment. When you wrap yourself in a blanket, the loss of heat is reduced, some is retained at the surface of your body, and you warm up. You get warmer because the heat that your body is generating cannot escape as fast as before.

To summarise: Heat from the sun warms the Earth, as heat from your body keeps you warm. The Earth loses heat to space, and your body loses heat to the environment. Greenhouse gases slow down the rate of heat-loss from the surface of the Earth, like a blanket that slows down the rate at which your body loses heat. The result is the same in both cases, the surface of the Earth, or of your body, gets warmer.

The greenhouse effect is the way in which heat is trapped close to the surface of the Earth by “greenhouse gases.” These heat-trapping gases can be thought of as a blanket wrapped around the Earth, which keeps it toastier than it would be without them.

You got that? Blankets warm you! Their logic is so sound that they couldn’t possibly be wrong, could they?

What empirical evidence do they provide for such an assertion? None!

Do they even attempt to predict what temperature a blanket could force? No!

Any such attempt would be very embarrassing for them, so instead they just leave it to the reader’s imagination.

First a note: there is no doubt that a blanket can make you warmer by blocking convection. The issue at hand is whether there is a warming due to radiative heat transfer, as is claimed for the greenhouse effect by analogy.

Let’s consider the case of a typical cotton blanket, whose emissivity ranges from 0.81 to 0.88 [Bellivieu 2019], depending on humidity. I will choose 0.85 for an average humidity condition; The exactness hardly matters. According to the verified program provided in my article The Dumbest Math Theory Ever, a blanket with an emissivity of 0.85 placed on a human being whose normal temperature is at 37°C, should produce a final skin temperature of …

$ ALB=0 TSI=2090.8 bash gheffect 0.85
Sec | Upwelling | Temp | GH Effect | Trapped | To Space
1 | 522.700 W | 36.701 C | 444.295 W | 222.148 W | 300.553 W
2 | 744.848 W | 65.389 C | 410.973 W | 94.413 W | 428.287 W
3 | 839.260 W | 75.642 C | 396.811 W | 40.125 W | 482.575 W
4 | 879.386 W | 79.738 C | 390.792 W | 17.053 W | 505.647 W
5 | 896.439 W | 81.436 C | 388.234 W | 7.248 W | 515.452 W
6 | 903.687 W | 82.151 C | 387.147 W | 3.080 W | 519.620 W
7 | 906.767 W | 82.453 C | 386.685 W | 1.309 W | 521.391 W
8 | 908.076 W | 82.582 C | 386.489 W | 0.556 W | 522.144 W
9 | 908.632 W | 82.636 C | 386.405 W | 0.236 W | 522.464 W
10 | 908.869 W | 82.659 C | 386.370 W | 0.100 W | 522.600 W
11 | 908.969 W | 82.669 C | 386.355 W | 0.043 W | 522.657 W
12 | 909.012 W | 82.673 C | 386.348 W | 0.018 W | 522.682 W
13 | 909.030 W | 82.675 C | 386.345 W | 0.008 W | 522.692 W
14 | 909.038 W | 82.676 C | 386.344 W | 0.003 W | 522.697 W
15 | 909.041 W | 82.676 C | 386.344 W | 0.001 W | 522.699 W
16 | 909.042 W | 82.676 C | 386.344 W | 0.001 W | 522.699 W
17 | 909.043 W | 82.676 C | 386.344 W | 0.000 W | 522.700 W

82.6°C ! Really hot!

Note that I set the albedo to zero. This is because I figure any scattering of photons between human and blanket will find its path back to the human (and thus “should” cause warming), with very little leakage at the edges of the blanket. But let us be as generous as possible to climate alarmists and say the blanket has an albedo of 0.22 (The highest value found for cotton in scientific literature: Source 1, Source 2). What then?

$ ALB=0.22 TSI=2090.8 bash gheffect 0.85
Sec | Upwelling | Temp | GH Effect | Trapped | To Space
1 | 407.706 W | 18.040 C | 346.550 W | 173.275 W | 234.431 W
2 | 580.981 W | 44.999 C | 320.559 W | 73.642 W | 334.064 W
3 | 654.623 W | 54.635 C | 309.513 W | 31.298 W | 376.408 W
4 | 685.921 W | 58.484 C | 304.818 W | 13.302 W | 394.404 W
5 | 699.222 W | 60.081 C | 302.823 W | 5.653 W | 402.053 W
6 | 704.875 W | 60.752 C | 301.975 W | 2.403 W | 405.303 W
7 | 707.278 W | 61.036 C | 301.614 W | 1.021 W | 406.685 W
8 | 708.299 W | 61.157 C | 301.461 W | 0.434 W | 407.272 W
9 | 708.733 W | 61.208 C | 301.396 W | 0.184 W | 407.522 W
10 | 708.918 W | 61.230 C | 301.368 W | 0.078 W | 407.628 W
11 | 708.996 W | 61.239 C | 301.357 W | 0.033 W | 407.673 W
12 | 709.029 W | 61.243 C | 301.352 W | 0.014 W | 407.692 W
13 | 709.043 W | 61.245 C | 301.349 W | 0.006 W | 407.700 W
14 | 709.049 W | 61.245 C | 301.349 W | 0.003 W | 407.703 W
15 | 709.052 W | 61.246 C | 301.348 W | 0.001 W | 407.705 W
16 | 709.053 W | 61.246 C | 301.348 W | 0.000 W | 407.706 W
17 | 709.054 W | 61.246 C | 301.348 W | 0.000 W | 407.706 W

61.2°C ! Still very hot.

OK, I’m now going to be extremely generous, and use an emissivity value of 0.5, which is not even scientifically justifiable, but let’s give the alarmists a huge advantage. What then?

$ ALB=0.22 TSI=2090.8 bash gheffect 0.5
Sec | Upwelling | Temp | GH Effect | Trapped | To Space
1 | 407.706 W | 18.040 C | 203.853 W | 101.927 W | 305.780 W
2 | 509.633 W | 34.746 C | 152.890 W | 25.482 W | 382.224 W
3 | 535.114 W | 38.525 C | 140.149 W | 6.370 W | 401.336 W
4 | 541.485 W | 39.448 C | 136.964 W | 1.593 W | 406.113 W
5 | 543.077 W | 39.678 C | 136.167 W | 0.398 W | 407.308 W
6 | 543.475 W | 39.735 C | 135.968 W | 0.100 W | 407.606 W
7 | 543.575 W | 39.750 C | 135.919 W | 0.025 W | 407.681 W
8 | 543.600 W | 39.753 C | 135.906 W | 0.006 W | 407.700 W
9 | 543.606 W | 39.754 C | 135.903 W | 0.002 W | 407.704 W
10 | 543.607 W | 39.754 C | 135.902 W | 0.000 W | 407.706 W
11 | 543.608 W | 39.754 C | 135.902 W | 0.000 W | 407.706 W

Now we get only 39.8°C, for a total warm up of 2.8°C – by a blanket that can only be heated by the human, and starts off colder (or same) as the human.

So is there any evidence to support the heating of human skin by a passively heated blanket via backradiation ?

However, if a cotton blanket heated to 90°C is in contact with skin the patient does not experience the same tissue injuries, because the blanket has less than one third the specific heat of skin. In addition, the blanket has less than 1/1000 the density of skin (the density of a blanket is about 1 kg/m³ because it is roughly half cotton and half air.) The blanket can give up all of its heat to the skin yet raise the temperature no more than 1/80th of the 70°C temperature difference, or about 1°C.

This scientist rightfully does not acknowledge warming by radiative effect. The blanket must be theoretically warmed to 90°C to achieve a rise of about 1°C. A table of empirical results is also provided in [House 2011]:

Body Part

Unheated Blankets

Blankets Warmed to 43.3°C

Blankets Warmed to 65.6°C

Abdomen

0.17°C

1.11°C

2.39°C

Lower Legs

0.33°C

0.89°C

1.11°C

[ House 2011], Table 2, Converted to Celcius

Though there is obviously a tiny amount of warming due to blocking convection, we don’t see any warming as predicted by GH effect radiative heat transfer theory. We should’ve seen a very generous 2.8°C warming as predicted by such a theory in the column Unheated Blankets. We don’t even see such a high number with blankets externally heated to 65.6°C !

Now we move onto [Kabbara 2002]. In this paper we see how expensive equipment can be used to maintain a patient’s temperature. Figure 6 shows how externally heated air prevents a patient’s temperature from falling. But one may ask: What is the purpose of this expensive equipment when climate “scientists” already know that a non-externally heated blanket should raise skin temperature by at least the very generous 2.8°C?

Would you trust these climate “scientists” with your health? Do you think they really believe what they claim?

The blanket A has a maximum power draw of 6.5 amps. With fully charged batteries, the blanket will reach its target temperature (i.e. 100 degrees Fahrenheit or 38 degrees Celsius) approximately 5 minutes and will remain heated for five to eight hours.

Why need external power or even a patent when a simple blanket ought to do the trick?

Please do not object to this article because I based this off a normal temperature of 37°C. Even a hypothermic temperature of 33°C should be raised by 2.72°C, IF the GH effect blanket analogy held any merit.

A search on google scholar for “hospital blankets temperature” should convince anyone with integrity that blankets don’t raise your skin temperature in accordance to radiative transfer theory. For if they did, most of the discussion and science in that search would be moot: human-only heated blankets would solve the problems and special technology would not be necessary.

Skeptical Science finishes off their article:

So global warming does not violate the second law of thermodynamics. And if someone tells you otherwise, just remember that you’re a warm human being, and certainly nobody’s dummy.

I’ll translate that for you: If you believe their sophistry, you are a dummy!

While using poetic license it is alright to say that blankets warm you, but using actual science, it is not correct. The best a blanket can do is keep you warm, but never make you warmer.

Enjoy 🙂 -Zoe

Addendum

Blanket(s) can suppress your perspiration and make you sick from your own urea, thus causing your temperature to go up. However, this could never be a proper analogy for the greenhouse effect.

I will use 41 years of NCEP Reanalysis Data. Create a new file fluxchange.sh, and paste:

# source fluxchange.sh
# Zoe Phin 2020/03/10
F=(0 ulwrf dswrf uswrf lhtfl shtfl)
O=(0 3201.5 3086.5 3131.5 856.5 2176.5)
require() { sudo apt install nco gnuplot; } # Linux Only
download() {
b="ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis2.derived/gaussian_grid"
for i in ${F[*]}; do wget -O $i.nc -c $b/$i.sfc.mon.mean.nc; done
}
extract() {
rm -f .fx*
for i in {1..5}; do echo $i of 5 >&2
for t in {000..491}; do
ncks --trd -HC ${F[$i]}.nc -v ${F[$i]} -d time,$t | sed \$d | awk -F[=\ ] -vO=${O[$i]} -vt=$t '{
W[$4]+=$8/10+O } END { for (L in W) { T += W[L]/192*cos(L*atan2(0,-1)/180) }
printf "%04d %02d %7.3f\n", t/12+1979, t%12+1, T/60.1647 }'
done | tee -a .fx$i
done
}
annualize() {
for i in {1..5}; do
awk '{ T[$1]+=$3 } END { for (y in T) printf "%04d %7.3f\n", y, T[y]/12 }' .fx$i > .af$i
done
}
change() {
paste .af1 .af2 .af3 .af4 .af5 | awk '{
printf "%s %s %s %s %s %s | %7.3f %7.3f %7.3f\n",
$1, $2, $4, $6, $8, $10, $2+$8+$10, $4-$6, $2-($4-$6)+$8+$10 }' | tee fluxchg.csv | awk '
NR==1 { Ui=$2; Ni=$5+$6; Si=$9; Gi=$10 } END {
dU=$2-Ui; dN=$5+$6-Ni; dS=$9-Si; dG=$10-Gi
printf "Upwelling Change:\t%7.3f W/m^2\n", dU
printf "NonRadiative Change:\t%7.3f W/m^2\n\n", dN
printf "Net Solar Change:\t%7.3f W/m^2\n", dS
printf "Geothermal Change:\t%7.3f W/m^2\n", dG
}'
}
plot() {
echo "set term png size 740,550 font 'arial,12'; unset key; set grid
plot 'fluxchg.csv' u 1:9 t 'Net Solar' w lines lw 3 lc rgb 'orange'" | gnuplot > slrchg.png
echo "set term png size 740,550 font 'arial,12'; unset key; set grid
plot 'fluxchg.csv' u 1:10 t 'Geothermal' w lines lw 3 lc rgb 'green'" | gnuplot > geochg.png
}

Run it:

$ source fluxchange.sh
$ require # linux only
$ download
$ extract
$ annualize
$ change
Upwelling Change: 3.401 W/m^2
NonRadiative Change: 4.784 W/m^2
Net Solar Change: 1.419 W/m^2
Geothermal Change: 6.766 W/m^2

The results are changes for years 1979 to 2019 (inclusive). The upwelling radiation flux and non-radiative flux equivalent has changed 3.401+4.784 = 8.185 W/m², and the attribution is properly divided among

The change in insolation (primarily due to reduced cloud cover) – 1.419 W/m²

Internal geothermal changes within the Earth – 6.766 W/m²

The crackpot mainstream greenhouse gas theory lacks empirical evidence, and yet its followers have the nerve to claim that humans are mostly responsible for recent warming. Nonsense. The cause was always #1 and #2.

Mainstream climate scientists believe in the dumbest math theory ever devised to try and explain physical reality. It is called the Greenhouse Effect. It’s so silly and unbelievable that I don’t even want to give it the honor of calling it a scientific theory, because it is nothing but ideological mathematics that has never been empirically validated. In fact it is nothing but a post hoc fallacy: the surface is hotter than what the sun alone can do, therefore greenhouse gases did it!

Today we will play with this silly math theory called the greenhouse effect. Here are two examples of its typical canonical depiction:

Let’s get started. Please create a new file called gheffect, and paste the following into it:

# bash gheffect
# Zoe Phin, 2020/03/03
[ -z $TSI ] && TSI=1361
[ -z $ALB ] && ALB=0.306
echo $1 | awk -vALB=$ALB -vTSI=$TSI 'BEGIN {
SIG = 5.67E-8 ; CURR = LAST = SUN = TSI*(1-ALB)/4
printf "Sec | Upwelling | Temp | GH Effect | Trapped | To Space\n"
} {
for (i=1 ;; i++) {
printf "%3d | %7.3f W | %7.3f C ", i, CURR, (CURR/SIG)^0.25-273.16
CURR = SUN + $1*LAST/2 ; GHE = SUN - (LAST*(1-$1))
printf "| %7.3f W | %7.3f W | %07.3f W\n", GHE, CURR-LAST, CURR-GHE
if ( sprintf("%.3f", CURR) == sprintf("%.3f", LAST) ) break
#if ( CURR==LAST ) break
LAST = CURR
}
}'

Now run it with atmospheric emissivity = 0.792:

$ bash gheffect 0.792
Sec | Upwelling | Temp | GH Effect | Trapped | To Space
1 | 236.133 W | -19.125 C | 187.018 W | 93.509 W | 142.625 W
2 | 329.642 W | 2.971 C | 167.568 W | 37.030 W | 199.104 W
3 | 366.672 W | 10.419 C | 159.866 W | 14.664 W | 221.470 W
4 | 381.336 W | 13.212 C | 156.816 W | 5.807 W | 230.327 W
5 | 387.142 W | 14.296 C | 155.608 W | 2.300 W | 233.834 W
6 | 389.442 W | 14.722 C | 155.130 W | 0.911 W | 235.223 W
7 | 390.352 W | 14.890 C | 154.940 W | 0.361 W | 235.773 W
8 | 390.713 W | 14.957 C | 154.865 W | 0.143 W | 235.991 W
9 | 390.856 W | 14.983 C | 154.835 W | 0.057 W | 236.077 W
10 | 390.912 W | 14.994 C | 154.824 W | 0.022 W | 236.111 W
11 | 390.935 W | 14.998 C | 154.819 W | 0.009 W | 236.125 W
12 | 390.944 W | 14.999 C | 154.817 W | 0.004 W | 236.130 W
13 | 390.947 W | 15.000 C | 154.816 W | 0.001 W | 236.132 W
14 | 390.949 W | 15.000 C | 154.816 W | 0.001 W | 236.133 W

As you can see, by delaying outgoing radiation for 14 [¹] seconds [²], we have boosted surface up-welling radiation by an additional ~66% (154.8/236.1 W/m²). Amazing, right? That’s what my program shows, and that’s what is claimed:

This is zero in the absence of any long‐wave absorbers, and around 155 W/m² in the present‐day atmosphere [Kiehl and Trenberth, 1997]. This reduction in outgoing LW flux drives the 33°C greenhouse effect …

The main prediction of the theory is that as the atmosphere absorbs more infrared radiation, the surface will get warmer. Let’s rerun the program with a higher atmospheric emissivity = 0.8

$ bash gheffect 0.8
Sec | Upwelling | Temp | GH Effect | Trapped | To Space
1 | 236.133 W | -19.125 C | 188.907 W | 94.453 W | 141.680 W
2 | 330.587 W | 3.168 C | 170.016 W | 37.781 W | 198.352 W
3 | 368.368 W | 10.746 C | 162.460 W | 15.113 W | 221.021 W
4 | 383.481 W | 13.614 C | 159.437 W | 6.045 W | 230.088 W
5 | 389.526 W | 14.738 C | 158.228 W | 2.418 W | 233.715 W
6 | 391.944 W | 15.184 C | 157.745 W | 0.967 W | 235.166 W
7 | 392.911 W | 15.361 C | 157.551 W | 0.387 W | 235.747 W
8 | 393.298 W | 15.432 C | 157.474 W | 0.155 W | 235.979 W
9 | 393.453 W | 15.461 C | 157.443 W | 0.062 W | 236.072 W
10 | 393.515 W | 15.472 C | 157.431 W | 0.025 W | 236.109 W
11 | 393.539 W | 15.477 C | 157.426 W | 0.010 W | 236.124 W
12 | 393.549 W | 15.478 C | 157.424 W | 0.004 W | 236.130 W
13 | 393.553 W | 15.479 C | 157.423 W | 0.002 W | 236.132 W
14 | 393.555 W | 15.479 C | 157.423 W | 0.001 W | 236.133 W

A 1% rise in atmospheric emissivity (0.8/0.792) predicts a 0.479 °C rise in surface temperature.

You would think such intelligent and “correct” mathematics would be based on actual experiments, but you would be wrong; it is not based on anything other than its presuppositions, and has been so for more than a century by name, and two centuries by concept.

Let’s outline a very simple experiment to test whether the greenhouse effect is true:

Solid Surface
v
1) Person => | IR Camera
2) Person <- | -> IR Camera
And repeats until "equilibrium"

Radiation leaves the body and strikes a screen. After absorption some radiation will go out to the IR camera, and the rest will go back to the person, thereby warming them up further, according to greenhouse effect theory. Note that we don’t even need absorption, merely reflecting back a person’s radiation should warm them up.

Let’s assume the human body emits 522.7 W/m² (37 °C) (Emissivity: 0.9961, [Sanchez-Marin 2009]). For compatibility with my program, we multiply this figure by 4, and call it TSI. Let’s assume the screen and air in between together has a total emissivity of 0.9. Now run:

$ TSI=2090.8 bash gheffect 0.9
Sec | Upwelling | Temp | GH Effect | Trapped | To Space
1 | 362.754 W | 9.658 C | 326.478 W | 163.239 W | 199.515 W
2 | 525.993 W | 37.188 C | 310.154 W | 73.458 W | 289.296 W
3 | 599.451 W | 47.498 C | 302.809 W | 33.056 W | 329.698 W
4 | 632.507 W | 51.830 C | 299.503 W | 14.875 W | 347.879 W
5 | 647.382 W | 53.725 C | 298.016 W | 6.694 W | 356.060 W
6 | 654.076 W | 54.566 C | 297.346 W | 3.012 W | 359.742 W
7 | 657.088 W | 54.943 C | 297.045 W | 1.356 W | 361.398 W
8 | 658.443 W | 55.112 C | 296.909 W | 0.610 W | 362.144 W
9 | 659.053 W | 55.188 C | 296.848 W | 0.274 W | 362.479 W
10 | 659.328 W | 55.222 C | 296.821 W | 0.124 W | 362.630 W
11 | 659.451 W | 55.238 C | 296.809 W | 0.056 W | 362.698 W
12 | 659.507 W | 55.244 C | 296.803 W | 0.025 W | 362.729 W
13 | 659.532 W | 55.248 C | 296.801 W | 0.011 W | 362.743 W
14 | 659.543 W | 55.249 C | 296.799 W | 0.005 W | 362.749 W
15 | 659.548 W | 55.250 C | 296.799 W | 0.002 W | 362.752 W
16 | 659.550 W | 55.250 C | 296.799 W | 0.001 W | 362.753 W
17 | 659.552 W | 55.250 C | 296.799 W | 0.000 W | 362.753 W

We see that the screen is “trapping” a lot of human radiation from reaching the IR camera, and we expect an extra 296.8 W/m² greenhouse effect, bringing us up to 55°C. Merely placing a screen in front of us should make us feel as if we’re stepping inside a sauna.

These people must be really feeling the heat. But they don’t, and for good reason: preventing radiation from reaching a colder place does not cause heating back at the source. Had these people had thermometers strapped to them, they would note the virtually zero temperature rise (due to blocked convection). Look very closely at the videos. Note the seconds the screens are placed in front of their faces and notice the lack of any thermal reading changes. None!

All empirical evidence shows the opposite of the claims of the greenhouse effect.

So the question remains, why is the surface hotter than the sun can make it alone?

If we look at the energy budget, we can see a dependency loop between surface and atmosphere: Surface -> Atmo = 350 and Atmo -> Surface = 324. So which came first, the chicken or the egg? This is nonsense. You can’t have a dependency loop for heat flow. Let’s try a theory that does not cause mental anguish and lacks empirical evidence. For this, we ignore the climate “scientists”, and go to the geophysicists:

Here we see that Earth’s geothermal energy is capable of delivering 0 °C to the surface; This is equivalent to 315.7 W/m². We add the sun and subtract latent+sensible heat:

Now we get a figure that that’s 390 – 381.7 = 8.3 W/m² off, but that’s OK because latent and sensible heat are not directly measured but estimated with certain physical assumptions, and/or the 0 °C geothermal is an approximation too.

Now we finally realize that the greenhouse effect is a hoax, and nothing but geothermal flipped up-side down. There is no Downwelling Radiation, there is only Upwelling-from-measurement-instrument Radiation (See here). Those who read Why is Venus so hot?, probably already saw where I was going. Now doesn’t it make more sense than backradiation temperature raising? Reality shows abolutely normal geothermal and solar combining to produce what we observe. We see all normal heating, and no ugly backwards zig-zag heating.

[¹] We only care about matching 3 decimal places. If we want to extend it to IEEE754 64-bit precision, it takes 40 seconds. Not that this matters much; Most work is accomplished in the first 5 seconds.

[²] I debated with myself whether to use the term seconds or iterations. Real physical calculations would take mass and heat capacity into account, but since greenhouse theorists don’t use these, I won’t either. Their simple model is in seconds.

You can run all the code at this blog on Windows, rather than Linux. I will show you how. Preferred Windows is version 10. That’s the only one I’ve set up. Any Windows >7.0 should work in theory.

Run Windows Command Line (<Win>+R; type “cmd” then <enter>)

cd \
mkdir Zoe

Zoe is your variable. Make it whatever you want, remember it, then “exit” <enter>

I used to be a fan of Joseph Postma before I realized he’s very stubborn and on the wrong track headed for a dead end. I hope he turns around.

I highly recommend that everyone read his great series … The Fraud of the Greenhouse Effect (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19). You will learn a lot!

Today I will be critiquing Joseph Postma’s paper A Discussion on the Absence of a Measurable Greenhouse Effect in light of what I know about his current view. I take no issue with his central proposition that there is no Greenhouse Effect, but I do take issue with his current personal conclusion that the sun, and the sun alone, is enough to explain the surface temperature. His latent heat doesn’t occur until there’s energy to evaporate water first, and the only energy he has is from the sun. My goal is to show that in fact the sun is not enough, and Postma’s implicit hypothesis is in error, though his paper is still very good and highly recommended reading.

The hard way performs the actual integration (approximation), while easy way just uses divisor (1/2 for sphere and 1/4 for hemisphere). The hard way is needed to create data of average of insolation across all latitudes as a function of local time. An average location on Earth will see this kind of daily insolation, averaged for the year. Keep in mind that this is an average value for a typical year and therefore my code purposefully disregards the math for Earth’s tilt and resulting seasonal daylight variation. It’s just not necessary. Resulting graph:

It should be noted that we can’t use the standard albedo of ~0.3, because this albedo is an average for the entire surface+atmosphere ensemble. We need to know the actual portion of solar radiation that reaches the surface. This is different from the official standard albedo.

> absorbs
Surface Absorption: 0.4874

Flux data from NASA’s ISCCP Project (1983-2004 data here), yields a surface absorption of 0.4874, while NASA’s “official” energy budget [2008] shows: 163.3 / 340.3 = 0.4799. I am just going to choose an “albedo” of 0.52 (1-0.4799). We re-run with an ALB parameter set to 0.52:

Now we’re ready to move on to serious analysis. Postma has provided the only formula we will need:

where C(t) is literally a climate term which could be either positive or negative (adding heat or taking heat away) in total, or composed of several unique contributions depending on if there is an additional heat source such as the “greenhouse effect”, or chemical and geologic sources, etc.

“emis = 1.0; % emissivity, using 1.0 for surface” [ I will use the same ]

— Same, Page 63

The following code snippets (see addendum) summarize this formula (emis = 1):

$1+ADD # As in Current Flux += ADD # ADD = C(t)
T = T + ($1 - SIG*T^4)/TAU # As in Tnew = Told + (Current Flux - SIG*Told^4)/TAU

We must choose a value for τ (TAU) that will produce a diurnal difference of 10°K (or °C) as shown in NASA’s Earth Fact Sheet above. The needed TAU equals 12940.

> TAU=12940; ADD=0; manydays; lastday
24HR Sun ... 163.32 W/m2
Day Sun ... 326.64 W/m2
Day Max ... 236.64 K -36.52 C
Night Min ... 226.64 K -46.52 C
Max - Min ... 10.00 K 10.00 C
24HR Avg ... 231.59 K -41.57 C
24HR Flux ... 163.10 W/m2

We start off at T=0, and after 40 days we get to a stable typical day:

Note the result: Max – Min … 10.00 K. We have satisfied one of the criteria, but notice that our 24HR average is : –41.57°C. That’s not the 15°C we need. Obviously the sun is not enough! And the diurnal temperature does not go from a night average of -46.5°C to a day average of -36.5°C. We must satisfy all 4 temperature criteria, not just 1.

Postma spends a part of the paper analyzing a “C(t)” value of 324 W/m² (what is claimed for GHG backradiation) along with arbitrary (though intelligently guessed) τ values. He then dismisses the results for fairly good reasons. However he missed the crucial point: the sun is not enough. I’m going to show you what values he should have used. I have a parameter in my program (ADD) that is equivalent to C(t). I have found the necessary parameters to be TAU=12895, ADD=227.66. ADD is what I believe to be the radiative component of geothermal.

> TAU=12895; ADD=227.66; manydays; lastday
24HR Sun ... 390.98 W/m2
Day Sun ... 781.96 W/m2
Day Max ... 293.21 K 20.05 C
Night Min ... 283.21 K 10.05 C
Max - Min ... 10.00 K 10.00 C
24HR Avg ... 288.11 K 14.95 C
24HR Flux ... 390.67 W/m2

Notice that we satisfied all criteria set forth in NASA’s Earth Fact Sheet (with only 0.05°C error):

Day Max … 20.05 C Night Min … 10.05 C Max – Min … 10.00 K 24HR Avg … 14.95 C

But we’re not done. ADD is just the radiative component of geothermal. Let’s add Sensible and Latent Heat from NASA’s “official” energy budget [2008]:

227.66 + 18.4 + 86.4 = 332.46 W/m²

This result is not much different than the 335.64 W/m² result I got here: Measuring Geothermal, using NCEP Reanalysis data.

332.46 vs 335.64 ! What’s the significance of this? I was able to approximately get the same geothermal emergent radiative flux from a very simple model! I don’t know about you, but I’m impressed.

How about Postma? Where’s his mind today?

Yah…that’s definitely my Zoe…one of my most attractive stalkers for sure [Zoe: How sweet, TY] . She thinks that the flat Earth theory model is all totally fine [Zoe: strawman; no one presents an actual flat earth model, including those that print maps on flat paper rather than globes]…but backradiation isn’t from the atmosphere “because that’s impossible, but it is from geothermal.”

So…she wants to keep the flat Earth theory [Zoe: Two hemisphere 24hr heat capacity theory, actually] accounting where the Sun can’t heat the Earth or create and sustain the weather/climate, and where there’s some additional energy source which provides twice more energy than the Sun…but instead of it being “backradiation” she wants it to be geothermal….providing twice the energy than the Sun. She went on about this here for months…finally banned her…because the heat from geothermal is known and measured…and flat Earth theory would be the WRONG way to try to incorporate it anyway!

These people are sick, sick demented freaks, and they seem to really want to keep their flat Earth theory no matter what mechanisms need to change to make it work.

Stalker means you are criticizing a public scientist for their ideas. I agree with him that there is no Greenhouse Effect, but he goes too far – way too far. As you can see he still thinks the sun, and the sun alone, is enough to explain surface temperatures and their diurnal variation – and everyone who disagrees is a flat earther!

One thing from his publication I found very interesting:

Solar forcing acts directly only on the top few millimeters of surface soil itself (the penetration depth is larger for ocean water and some heating occurs directly in the atmosphere via extinction), and this is where the incoming short wave radiant energy performs work and raises the temperature. This heat energy will then conduct its way down into the subsurface until it merges with the geothermal temperature at a depth of somewhere around, say, 5 to 10 meters and temperature of approximately 5°C to 10°C [Zoe: I found 0 to 10] … and this much larger thermal-mass system will respond much more slowly, in aggregate, to the solar variation. This low-frequency [Zoe: Goes to nil frequency] aggregate response will provide a baseline upon which the daily variations will oscillate at the top …

A baseline you say? Maybe this baseline would exist without the sun? Maybe this baseline is capable of its own thermal action and emission? Too bad he did not pursuit this line of thought. But thanks for reaffirming my intuition at the time.

I hope you’ve enjoyed this article.

Love, -Zoe

Update

Guys I just had several dozen notifications come through of Zoe trying to link to my blog from what is apparently her new blog.

WHAT A FN STALKER! [Zoe: You made yourself a public scientist so stop playing the victim]

“Zoe’s geothermal insights” or some retardation.

“Flat Earth is OK! [Zoe: usual strawman]. We just need to use geothermal to make up the temperature instead!”

Nice! Joseph, your solar-only theory can’t explain observations. It would be great if you could rejoin reality and continue to make contributions to science, as you did in the past.

Addendum

Code gmodel.sh:

# source gmodel.sh
# Zoe Phin, 2020/02/20
plothead="set term png size 740,550 font 'arial,12'; unset key
set xtics 360 out nomirror; set mxtics 6; set grid xtics ytics
set xtics add ('0h' 0,'6h' 360,'12h' 720,'18h' 1080,'24h' 1440)
set yrange [0 to 800]; set ytics 100,100,800
"
require() { sudo apt-get install gnuplot; }
download() {
wget -O sdn.bin -c https://isccp.giss.nasa.gov/pub/data/FC/FDAVGANN__SWFLSRFDW
wget -O sup.bin -c https://isccp.giss.nasa.gov/pub/data/FC/FDAVGANN__SWFLSRFUW
}
absorbs() {
od='od -An -f -w4 --endian=big'; $od sup.bin > .sup; $od sdn.bin > .sdn
paste .sdn .sup | awk '{S+=$1-$2} END {
printf "Surface Absorption: %.4f\n", (S/NR)/(1361/4) }'
}
justsun() {
[ -z $ALB ] && ALB=0.306
seq -89.875 0.25 89.875 | awk -vA=$ALB 'BEGIN {pi = atan2(0,-1); r=pi/180} {
CONVFMT="%.8f"; if ($1 < 0) $1 = 0 - $1
a = sin(r*($1+0.125))-sin(r*($1-0.125))
for (m=0; m<1440; m++) {
y = cos((m-720)*pi/720)
print m" "$1" "a/2" "1361*(1-A)*cos($1*pi/180)*((y<0)?0:y)
}
}' | awk '{ M[$1]+=$3*$4 } END {
for (i in M) print M[i]
}' | tee solar.dat | awk -vA=$ALB '$1>0 { S+=$1 } END { CONVFMT="%.4f"
HW = S/720; EW = 1361*(1-A)/2
print "Sphere ... Hard Way: " HW/2 " W/m^2 , Easy Way: " EW/2 " W/m^2"
print "Hemisphere ... Hard Way: " HW " W/m^2 , Easy Way: " HW " W/m^2" }'
echo "$plothead set title 'Solar Flux (W/m²)'
plot 'solar.dat' u (\$1==0?-1:\$1) w filledcu above fc 'yellow',\\
513.085*cos((x-720)*pi/720) w lines lc rgb 'orange' lt 8 lw 3
# 741.835*cos((x-720)*pi/720) w lines lc rgb 'orange' lt 8 lw 3
" | gnuplot > justsun.png
}
manydays() {
[ -z $ADD ] && ADD=0; [ -z $DAYS ] && DAYS=15
for n in `seq $DAYS`; do cat solar.dat | awk -vADD=$ADD '{
printf "%7.3f\n", $1+ADD }'
done | awk -vTAU=$TAU 'BEGIN { SIG=5.67e-8 } {
T=T+($1-SIG*T^4)/TAU
printf "%10.6f %10.6f %10.6f\n", $0, T, SIG*T^4
}' > many.dat
echo "$plothead set key samplen 0; set title '$DAYS Days of Flux (W/m²)'
set xtics format ''; set xtics 1440 in mirror; unset mxtics
plot 'many.dat' u (\$1==0?-1:\$1) title '' w filledcurves above y=$ADD fc 'yellow',\\
$ADD t '' w filledcu above y=0 fc 'dark-green' fs solid 0.8 border lt 2 lw 4,\\
'' u 3 t 'τ = $TAU' w lines lw 2 lc 8" | gnuplot > many-t${TAU}.png
}
lastday() {
tail -n 1440 many.dat | nl | tee last.dat | awk 'BEGIN { MIN=999 }
$3 > MAX { MAX=$3 }
$3 < MIN { MIN=$3 }
$2>0 { S+=$2 }
NR >360 && NR <=1080 { D+=$3 }
NR<=360 || NR > 1080 { N+=$3 }
END { SIG=5.67e-8; D24 = (D+N)/1440; C=273.16
printf "24HR Sun ... %6.2f W/m2 \n", S/1440
printf "Day Sun ... %6.2f W/m2 \n", S/720
printf "Day Max ... %6.2f K %9.2f C \n", MAX, MAX-C
printf "Night Min ... %6.2f K %9.2f C \n", MIN, MIN-C
printf "Max - Min ... %6.2f K %9.2f C \n", MAX-MIN, MAX-MIN
# printf "Day Avg ... %6.2f K %9.2f C \n", D/720, D/720-C
# printf "Night Avg ... %6.2f K %9.2f C \n", N/720, N/720-C
# printf "D-N Delta ... %6.2f K \n", (D-N)/720
printf "24HR Avg ... %6.2f K %9.2f C \n", D24, D24-C
printf "24HR Flux ... %6.2f W/m2 \n", SIG*D24^4
}'
echo "$plothead set key samplen 0
set ylabel 'Flux (W/m²)'; set y2label 'Temperature (K)'
set ytics out 50 nomirror; set mytics 5;
set y2tics 210,10,800; set my2tics 2
set link y2 via (y/5.67e-8)**0.25 inverse 5.67e-8*(y**4)
plot $ADD t '' w filledcu above y=0 fc 'dark-green' fs solid 0.8 border lt 2 lw 4,\\
'last.dat' u 1:2 t '' w filledcu above y=$ADD+1 fc 'yellow' fs solid 1 border lt 5 lw 4,\\
'' u 1:3 t 'Surface T (τ=$TAU)' w lines lw 3 lc 8 axes x1y2" | gnuplot > last.png
}

About a dozen people who have read my article, the case of two different fluxes, have dismissed my central argument by invoking a silly theory. The most famous critic, Willis Eschenbach (of WUWT fame), thus writes:

Zoe, I just took a look at your page. I fear that you’ve made a mathematical mistake. The problem is that you have over-specified the equation. Let me explain by a parallel example:

It is a physical impossibility for there to be more water flowing out of the end of a hose than there is flowing through the hose. Can’t happen. The flow through the hose must be equal to the flow out the end.

In the same way, It is a physical impossibility for there to be more energy flowing out of the end of a block of concrete than there is flowing through the block. It is logically impossible. The flow through the block must be equal to the flow out the end.

— Willis Eschenbach

Willis then went on to resolve my equations using his key “insight” that the radiation emerging out of an object “must” equal its conductive heat flux. In the language of my article, the assertion is: CSR = CHF (Conductive Heat Flux = Cold Side Radiation [radiation at interested end] ).

The emission is at any moment εσT⁴. If the emission is not balanced by absorption or heat flux the temperature and consequently the emission will drop.

— Dirk Visser

This is essentially the same as Willis’ argument.

Other critics write:

If the heat flux is only 92 mW/m², then obviously geothermal can only make the surface about 36 kelvin.

Sun is more than 500 times as powerful as geothermal.

— Unnamed

Both of these comments implicitly assume CSR = CHF.

All other critiques are just variations on the same theme. Only difference is how many implicit logical leaps they are from the core assumption that CSR “must equal” CHF.

In my article I clearly explained that there is a difference between conductive heat flux within a medium and the emergent electromagnetic radiation out of the medium, but it’s been lost on deaf ears for some people. I don’t why (their denial), but I feel the need to shame them a little.

What is the conductive heat flux (CHF) of an object at thermal equilibrium (a uniform temperature)?

The conduction formula is:

CHF = Q/(A*Δt)

Obviously with a uniform temperature, ΔT equals 0, and thus CHF is also ZERO!

And what did Ludwig Boltzmann and Max Planck discover emitted from their radiation cavities which had a CHF of zero? Was it also zero as my critics assert with their CHF=CSR theory? No, of course not! What comes out of an object with CHS=0 is CSR=εσT⁴ , and not CHF=εσT⁴ [ as my 2nd critic evaluated ]. Nor is this CSR transient and headed for zero, as Willis and Dirk would have you believe.

Just as the wikipedia snippet above implies: ONLY the TEMPERATURE on the edge matters.

Now wikipedia is not always right about everything, but this is so commonly well known that I don’t need any other source. You can find essentially the same thing in every high school or college textbook. Every experiment since Gustav Kirchoff [1859] has invalidated the CHF=CSR hypothesis, and reaffirmed my hypothesis: CHF and CSR are completely different and their relationship is inverse:

CSR = εσ(T-CHF*L/k)⁴

The greatest external emission is achieved at the lowest internal heat flux, assuming the hot side temperature is the same.

At thermal equilibrium (CHF=0), this formula drops to:

If my critics were correct, then all (even one!) experiments since 1859 would show their claim to be true. Yet none of them do, because my critics are … merely engaging in ideological mathematics and not real physics.

Summary:

CHF = CSR

CSR = εσ(T-CHF*L/k)⁴

Ideological Mathematics

Physics

Geothermal is more than capable of delivering 0°C (CSR=~315 W/m²), despite the fact that its near surface CHF is ~92 mW/m². In fact, assuming same temperature at same depth, a smaller CHF yields a higher CSR. The CHF (~92 mW/m²) alone is not even enough information to determine the final temperature, and hence radiation out of the medium. Quoting CHF and comparing it to insolation is nothing but junk science.

Sincerely Yours, -Zoe

Update 2020/03/03

This video shows CHF through the water approaching zero. Gets to ~0.01 W/m² at the end.

This video shows CHF through the pan get to zero. See time 01:53.

The value all the way on the bottom right: 335.643, is our geothermal emission in W/m² (for whole Earth). This means that geothermal supplies a temperature of:

(335.643/5.67e-8)^0.25-273.16 = 4.22°C

Hmm, If only there was some way to reaffirm this via the scientific literature…

Sure looks like geothermal delivers at least 0 degrees celsius. Our calculation is slightly off, but keep in mind that Sensible and Latent Heat is not directly measured by satellite but approximated via satellite data and some physical assumptions. Also, these geotherm diagram have been around for decades and it’s possible they are just sticking to a convention, while the actual measured surface T has changed.

It appears that everyone in geophysics already knows the truth. It’s only climate “scientists” who think greenhouse gases raise temperature, and without them the surface would be ~-18°C. Nope, without GHGs or even the Sun, it would be at least 0°C.

Geothermal and Solar completely explain the surface temperature and the remaining energy that goes into the atmosphere. No silly greenhouse effect necessary.

This is just a teaser to get people thinking. More to follow. Subscribe and stay tuned.

The Diviner Lunar Radiometer Experiment onboard the Lunar Reconnaissance Orbiter (LRO) has been acquiring moon data since July 2009. A very detailed paper [Williams 2017] was written, yet nowhere does it answer the simple question: what is the average temperature of the moon?

Today I will be using derived diviner data to calculate the average temperature on the moon. My data has two sources: UCLA and WUSTL. The first has hourly data, and the other has 15 minute data (although many fields are missing). The final result of the two should not differ highly.

First column is latitude, 2nd is average temperature for that latitude +/- 0.5 degrees, and 3rd is the surface area proportion of that latitude +/- 0.5 degrees. 3rd column adds up to 1. MoonT is the area-weighted mean of all latitudes.

The standard mean sea level pressure is defined as 101.325 kPa. This is the standard used in US and International Standard Atmosphere. This value is all over the place. In reality this value was agreed upon by committee and at no time represented the true mean sea level pressure. Most certainly it does not represent the true value today. Today I will try to calculate what the real value should be. I will be using data from NOAA’s ESR Lab.

You will need gnuplot:

sudo apt install gnuplot

pres.sh:

# bash pres.sh
# Zoe Phin, 2020/02/05
wget -qO- 'https://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Pressure&level=2000&lat1=-90&lat2=90&lon1=0&lon2=360&iseas=1&mon1=0&mon2=11&iarea=1&typeout=1&Submit=Create+Timeseries' | awk '$1>1947&&$1<2020{print $1" "$2/10}' | sed 1d > surpres.txt
wget -qO- 'https://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Sea+Level+Pressure&level=2000&lat1=-90&lat2=90&lon1=0&lon2=360&iseas=1&mon1=0&mon2=11&iarea=1&typeout=1&Submit=Create+Timeseries' | awk '$1>1947&&$1<2020{print $1" "$2/10}' | sed 1d > seapres.txt
echo 'set term png size 740,740; set key below
set title "Pressure (kPa)"; set xrange [1947 to 2020]
set yrange [98.58 to 98.46]; set format y "%5.2f"
set y2range [101.24 to 101.12]; set format y2 "%6.2f"
set grid xtics mxtics ytics y2tics mytics my2tics
set xtics 10; set mxtics 2; set ytics 0.02; set y2tics 0.02; set mytics 2; set my2tics 2
plot "surpres.txt" u 1:2 axes x1y1 t "Surface (Left)" w lines lt 1 lw 2 lc rgb "red",\
"seapres.txt" u 1:2 axes x1y2 t "Sealevel (Right)" w lines lt 1 lw 2 lc rgb "blue"' | gnuplot > pres.png

Run it:

> bash pres.sh

Result is three files: surpres.txt, seapres.txt, and pres.png

I have trouble believing global data before the 1979 full global satellite era. In any case we see that both surface and sea-level pressure have been decreasing. This is odd considering that temperatures have been going up, but I will not go into that today.

To figure out the real mean sea-level pressure I will simply average the data between 1979 and 2019 (inclusive):

Today I will analyze some differences between the north and south hemisphere. I’ll be using NCEP‘s Long Term Mean Air Surface Temperature for 1979-2017, and NASA’s ISCCP Project Insolation data from 1983-2009. Sure the years don’t overlap, but we are using long term averages anyway and don’t care about the time trend. First we need one tool:

$ apt get install nco

Create a new file called hemi.sh, with the following:

# source hemi.sh
# Zoe Phin, 2020/01/24
download() {
wget -O air.nc -c ftp://ftp.cdc.noaa.gov/Datasets/ncep/air.sfc.day.ltm.nc
wget -O wtr.nc -c http://research.jisao.washington.edu/data_sets/elevation/fractional_land.1-deg.nc
wget -O ele.nc -c http://research.jisao.washington.edu/data_sets/elevation/elev.1-deg.nc
wget -O sup.fl -c https://isccp.giss.nasa.gov/pub/data/FC/FDAVGANN__SWFLSRFDW
wget -O sdn.fl -c https://isccp.giss.nasa.gov/pub/data/FC/FDAVGANN__SWFLSRFUW
}
water() { # Arg: 1 - Min Latitude, 2 - Max Latitude
ncks --trd -HC wtr.nc -v data | awk -F[=\ ] -vm=$1 -vM=$2 '
$4!=NIL && $4>m && $4 <M {
a = 6378138; b = 6356753; e = 1-(b/a)^2; r = atan2(0,-1)/180
A = (a*r)^2*(1-e)*cos(r*$4)/(1-e*sin(r*$4)^2)^2/510072e9
printf "%6.2f %5.1f %.9f %6.2f\n", $4, $6, A, $8/10000
}' | awk '
{ A+=$3; L+=$3*$4 } END { printf "Water Fraction: %7.4f\n", 1-L/A }'
}
elev() { # Arg: 1 - Min Latitude, 2 - Max Latitude
ncks --trd -HC ele.nc -v data | awk -F[=\ ] -vm=$1 -vM=$2 '
$4!=NIL && $4>m && $4 <M {
a = 6378138; b = 6356753; e = 1-(b/a)^2; r = atan2(0,-1)/180
A = (a*r)^2*(1-e)*cos(r*$4)/(1-e*sin(r*$4)^2)^2/510072e9
if ($8 < 0) $8 = 0
printf "%6.2f %5.1f %.9f %6.2f\n", $4, $6, A, $8
}' | awk '
{ A+=$3; E+=$3*$4 } END { printf "Avg Elevation: %7.4f\n", E/A }'
}
solar() { # Arg: 1 - N or S, Empty Arg = All
od -An -w4 -f --endian=big sup.fl > .sup
od -An -w4 -f --endian=big sdn.fl > .sdn
H='1,6596p'; D=3298;
[[ -z $1 ]] && D=6596
[[ $1 = "S" ]] && H='1,3298p'
[[ $1 = "N" ]] && H='3299,$p'
paste .sup .sdn | sed -n $H | awk '{
printf "%7.3f %7.3f %7.3f %7.3f\n", $1, $2, $1-$2, 1-$2/$1
}' | awk -vD=$D '{UP+=$1;DN+=$2;NT+=$3;AB+=$4} END {
print "Averages:"
printf "%7.3f %7.3f %7.3f %7.3f\n", UP/D, DN/D, NT/D, AB/D
}'
}
temp() { # Arg: 1 - Min Latitude, 2 - Max Latitude
for d in `seq 0 364`; do
ncks --trd -HC air.nc -v air -d time,$d |\
awk -F[=\ ] -vm=$1 -vM=$2 '$4!=NIL && $4>=m && $4<=M {
if ($4 < 0) { $4 += 1.25 } else { $4 -= 1.25 }
a = 6378138; b = 6356753; e = 1-(b/a)^2; r = atan2(0,-1)/180
A = (a*2.5*r)^2*(1-e)*cos(r*$4)/(1-e*sin(r*$4)^2)^2/510072e9
printf "%6.2f %5.1f %.9f %6.2f\n", $4, $6, A, $8/100+477.65-273.16
}' | awk -vd=$d '
{ A+=$3; T+=$3*$4 } END { printf "%03d %7.4f\n", d+1, T/A }'
done
}
tempsavg() { # Not generic
for f in ans nor sou; do
cat $f.csv | awk '{S+=$2}END{print S/NR}'
done
}
tempsplot() { # Not generic
echo "set term png size 740,370;set grid;set key below;set xrange [0:365]
set title 'Long Term Day of the Year Mean (°C)'; set xtics 30
plot 'ans.csv' u 1:2 t 'Whole' w lines lw 2 lc rgb 'black',\
'nor.csv' u 1:2 t 'North' w lines lw 2 lc rgb 'orange',\
'sou.csv' u 1:2 t 'South' w lines lw 2 lc rgb 'blue'" | gnuplot > allhemi.png
}

We will source the code to have its functions run as separate command-line commands:

We can see that there’s a lot more fluctuation in the north than south hemisphere. This is most likely due to more ocean in the south having a moderating influence. Let’s see what the actual averages are:

$ tempsavg
# Result:
# 14.9809 - Whole
# 15.6322 - North
# 14.3295 - South

The north is actually over a degree warmer than the south. I did not expect that. I would’ve thought that more ocean would have made it warmer. Let’s move on to elevation analysis.

The nothern hemisphere is on average 81.7 meters higher. From my previous article Air Temperatures and Average Lapse Rate, we learned that the average lapse rate is ~0.0056 °C/m. 81.7 * 0.0056 = 0.458 °C advantage for the south.

Now let’s take a look at the water fraction:

$ water -90 90 # Whole Earth
Water Fraction: 0.7110
$ water 0 90 # North Hemisphere
Water Fraction: 0.6092
$ water -90 0 # South Hemisphere
Water Fraction: 0.8127

The southern hemisphere has 0.8127/0.6092 = 33 % MORE water than the northern hemisphere.

Now we do insolation analysis. I expect that the south will receive more insolation given that perihelion occurs while the sun is in the south, and aphelion occurs while the sun is in the north.

$ solar # Whole Earth
Averages:
189.141 23.309 165.832 0.854
$ solar N # North hemisphere
Averages:
187.909 24.750 163.159 0.847
$ solar S # South hemisphere
Averages:
190.373 21.868 168.504 0.861

The results are best explained in a table:

Downwelling

Surface Albedo

Net Solar

Absorption

Whole

189.1 W/m²

23.3 W/m²

165.8 W/m²

85.4 %

North

187.9 W/m²

24.8 W/m²

163.2 W/m²

84.7 %

South

190.4 W/m²

21.9 W/m²

168.5 W/m²

86.1 %

Insolation

The southern hemisphere has a higher absorption fraction, higher net insolation, more water and lower elevation. It has every advantage to be hotter than the northern hemisphere and yet it is not, it is 15.6322 °C (North) – 14.3295 °C (South) = 1.3 °C cooler. How come?

Who can solve this mystery?

Enjoy 🙂 -Zoe

Update 2020/01/31

A 110 views and 2 dozen comments later nobody has solved the mystery. The best answer was the heat capacity difference of land and water, but as pointed out, heat capacity controls both the heating and cooling rate.

Astute readers of this blog may have guessed where I was going: geothermal. Indeed, I do think it is geothermal, and I have thought this for about half a year now.

I have a simple formula for guessing the radiative component of geothermal. It is:

RadGeo = Longwave Upwelling IR - (Shortwave Downwelling - Shortwave Upwelling)
RadGeo = Longwave Upwelling IR - Net Solar

Precipitable water is a measure of how high water would stack up if all the water vapor in the atmosphere would rain down, right now! It typically ranges between 22.4 and 24.2 millimeters. All the water vapor raining down would add up to about 0.9 inches.

Now a little bit of logic: the amount of water vapor in the atmosphere depends on how hot the oceans/lakes/rivers and whatever water is on/in the ground is. The hotter, the more evaporation. Simple. Therefore precipitable water should be a good proxy for surface water temperature. Let’s see what the history of precipitable water looks like. For that we go to NOAA’s ESRL.

We fill out the form, like this:

And this is what we get:

One would think that with constant warming, we should see the precipitable water always going up. But we don’t see that. We clearly see a CYCLE here, an invisible letter U or V. In fact, it reminds me of something we discovered here:

Let’s combine the two, while shifting temperatures forward 7 years:

Now that makes sense. You know what doesn’t make sense? The “consensus” temperature data. Here it is:

It is clear that Berkeley (and other similar outfits) do not perform proper latitude drift adjustment and so their result does not match what we should expect to happen to precipitable water level.

What we have here is a great confirmation that mainstream climate science has gone off the rails.

#!/usr/bin/bash
# Zoe Phin, 2019/12/17
P=(1000 925 850 700 600 500 400 300 250 200 150 100 70 50 30 20 10) # Size: 17
aircsv() {
for t in `seq 0 479`; do
for p in `seq 0 16`; do
for l in `seq 0 72 | sed s/36//`; do
ncks -v air -d time,$t,$t -d lat,$l,$l -d level,$p,$p air.nc |\
sed -n "/air =/,/^$/p" | egrep -o '[-0-9].*[0-9]' | tr -s ', ' '\n' | awk -v l=$l '
function rad(x) { return x*atan2(0,-1)/180 } {
lat = l * 2.5 - 90; lon = n * 2.5; n += 1
if (lat < 0) { lat += 1.25 } else { lat -= 1.25 }
a = 6378137.678; b = 6356752.964; E = 1-b^2/a^2; r = rad(lat)
A = (a*rad(2.5))^2*(1-E)*cos(r)/(1-E*sin(r)^2)^2/510065728777854
printf "%5.2f %5.1f %6.2f %12.10f\n", lat, lon, $1/100+465.15, A}'
done | awk -v T=$t -v P=${P[p]} '
{S+=$3*$4} END {printf "%3d %4d %6.2f\n", T, P, S}'
done
done
}
avgair() {
for p in ${P[*]}; do
awk -v P=$p '$2==P{S+=$3;N+=1} END {printf "%8.1f %6.2f\n",P*100,S/N}' air.old
done
}
plotavgair() {
avgair > avgair.csv
echo 'set term png size 740,740; unset key;
set mxtics 2; set mytics 2; set ytics 10000
set grid mytics ytics xtics
set title "Temperature (K) vs. Pressure (Pa)"
set yrange [101325 to 0]
plot "avgair.csv" u 2:1 w linespoints ps 1 pt 3 lw 3 lc rgb "red"' | gnuplot
}
slopes() {
for p in ${P[*]}; do
printf "%4d: " $p; sed 's/\... / /' .p$p.dat |\
awk '{T[$1]+=$2}END{for (i in T) printf "%s %6.2f\n",i,T[i]/12}' |\
gmt gmtregress -Fp -o5 | awk '{printf "%9.6f\n", $1}'
done
}
plotair() {
for p in ${P[*]}; do
awk -v P=$p '$2==P{printf "%7.2f %6.2f\n", $1/12+1979+1/24, $3}' air.old > .p$p.dat
done
(echo 'set term png size 740,1000; set key below;
set mxtics 5; set mytics 5; set grid mytics ytics xtics
set title "Temperature (K) at Pressure (hPa)"
set xrange [1978 to 2020]; set yrange [300 to 200]; plot \';
for p in ${P[*]}; do
echo "'.p$p.dat' u 1:2 t '$p' w points,\\"
done ) | sed '$s/..$//' | gnuplot
}
lapse(){
avgair > avgair.csv
I=(`head -1 avgair.csv`)
awk -v Ps=${I[0]} -v Ts=${I[1]} 'BEGIN{
# g = 9.7977074331 # Based on EGM2008
g = 9.7977115 # Account for using Atmosphereless GM constant
g = g - 0.16 # Adjust g to middle of the troposphere
R = 8.31446261815324 # Gas Constant
M = 0.0289644 # from US Standard Atmosphere
}
$1 >= 10000 && $1 < 100000 {
P = $1; T = $2
L = (g*M/R)*log(T/Ts)/log(P/Ps)
printf "%s %10.6f\n", $1, L
}' avgair.csv |\
awk '{S+=$2; print} END {printf "\nAVG = %12.10f\n", S/NR}'
}

We source the code, to allow its functions to act like command-line commands we can run in parts.

$ source air.sh

The first thing we must do is generate the data for all the other commands:

$ aircsv > air.csv

This will take several hours to run on an average laptop. Afterwards we are free to run various things quickly. When complete, let’s plot the air data:

The above is the trend in °K/year. You’ll notice that the lower atmosphere is getting hotter while the upper atmosphere is getting colder.

This is a signature of reduced insolation with a greater reduction of cloud cover. In simple terms: there is less sun power but even less clouds, allowing MORE of that sun to get through.

This is NOT a signature of greenhouse gas forcing, as the literature has the magical pivot point at either the beginning of the tropopause (P~100 hPa) or in the middle of the troposphere (P~500 hPa) or where T~242°K (P~410 hPa), and certainly not at P~225 hPa as we see here.

What’s the average for the entire 40 years for each pressure? (Now in Pascals)

Don’t let the precision of the average fool you, but the 99% confident answer is between 0.0055 and 0.0057 °C/meter. This is an average for a mixed wet/dry troposphere, i.e the actual troposphere. It is what is actually observed with the average water vapor level being what it actually is. All those using 0.0065 °C/m as used in the US/International Standard Atmosphere will not be reflecting reality (only an ideal dry atmosphere model), and should abandon doing so.

It would be interesting to know the average surface gravity of Earth. I looked over at Wikipedia: Standard Gravity and found:

The standard acceleration due to gravity (or standard acceleration of free fall), sometimes abbreviated as standard gravity, usually denoted by ɡ_{0} or ɡ_{n}, is the nominal gravitational acceleration of an object in a vacuum near the surface of the Earth. It is defined by standard as 9.80665 m/s^{2}

The value of ɡ_{0} defined above is a nominal midrange value on Earth, originally based on the acceleration of a body in free fall at sea level at a geodetic latitude of 45°.

Nominal midrange? What is that? That’s not an average for the whole Earth. We will have to figure it out by other means.

A National Geospatial Intelligence Agency document tells us how to do it. Appendix B gives us all the math we need. We will however use the latest values for a and b derived from here. Create a new file called efacts.sh with:

#!/usr/bin/bash
# Zoe Phin 2019/12/15
echo ' function __(s, p, n) { printf "%-3s\t%25.*f\n", s, p, n }
BEGIN { π = atan2(0,-1)
# Given
GM = 3.9860046055e14 # Earth Mass * G
a = 6378137.678 # Semi-major axis
b = 6356752.964 # Semi-minor axis
ω = 7.292115e-5 # Angular Velocity
G = 6.67428e-11 # Gravity "Constant"
Mₐ = 5.148e18 # Mass of the Atmosphere
# Derived
Mₑ = GM / G # Mass of the Earth (with Atmosphere)
Mₒ = Mₑ − Mₐ # Mass of the Earth (w/o Atmosphere)
GMₒ = G × Mₒ # Atmosphere-less Constant
F = (a − b) / b # Flattening Factor
f = 1 / F # Inverse Flattening
e = √(1 − b²/a²) # 1st Eccentricity
E = √(a²/b² − 1) # 2nd Eccentricity
Rₚ = a²/b # Polar Radius of Curvature
R₁ = a × (1 − F/3) # Mean Radius of the Three Semi-Axes
R₂ = Rₚ × (1 − (⅔)E² + \# Radius of a Sphere of Equal Area
(26/45)E⁴ − \
(100/189)E⁶ + \
(7034/14175)E⁸ − \
(220652/467775)E¹⁰ )
R₃ = ³√(a² × b) # Radius of a Sphere of Equal Volume
t = tan⁻¹(E)
q = ½ × ( (t + 3×t/E²) − 3/E )
Q = 3 × ( (1 + 1/E²) × (1 − t/E) )-1
__( "GM" , 00 , GM )
GM = GMₒ # Warning ! Switching to Atmosphere-less Constant
m = (ω² × a ² × b) / GM # Normal Gravity Formula Constant
J₂ = (e²/3) × (1−(2×m×E)/(15×q)) # Dynamical Form Factor
U₀ = t*GM/(a×e) + ⅓×ω²×a² # Normal Gravity Potential
Gₑ = GM/(a×b) × (1 − m − (m×E×Q)/(6×q)) # Normal Gravity at the Equator
Gₚ = GM/a² × (1 + m × (E×Q)/(3×q)) # Normal Gravity at the Poles
k = (b × Gₚ)/(a × Gₑ) − 1
Gₘ = Gₑ × (1 + (⅙)e² + (⅓)k + \# Mean Value of Normal Gravity
(59/360)e⁴ + (5/18)ke² + \
(2371/15120)e⁶ + (259/1080)ke⁴ + \
(270229/1814400)e⁸ + (9623/45360)ke⁶) \
__( "a" , 03 , a )
__( "b" , 03 , b )
__( "ω" , 11 , ω )
__( "G" , 16 , G )
__( "Mₑ" , 00 , Mₑ )
__( "Mₐ" , 00 , Mₐ )
__( "Mₒ" , 00 , Mₒ )
__( "GMₒ", 03 , GMₒ)
__( "F" , 16 , F )
__( "f" , 09 , f )
__( "e" , 15 , e )
__( "E" , 15 , E )
__( "Rₚ" , 04 , Rₚ )
__( "R₁" , 04 , R₁ )
__( "R₂" , 04 , R₂ )
__( "R₃" , 04 , R₃ )
__( "q" , 12 , q )
__( "Q" , 12 , Q )
__( "m" , 14 , m )
__( "J₂" , 15 , J₂ )
__( "U₀" , 04 , U₀ )
__( "Gₑ" , 10 , Gₑ )
__( "Gₚ" , 10 , Gₚ )
__( "k" , 15 , k )
__( "Gₘ" , 10 , Gₘ )
}'| sed 's/#.*//' | sed '/__/s/,/,\\\n/' | sed -r '
/__/! { y,πωₚₑₐₒₘ₀₁₂₃₄×−,pWpeaom01234*-,;
s,³√(.*),(\1)^(1/3),g; s,tan⁻¹\(E\),atan2(E\,1),g;
s,½,1/2,g; s,⅓,1/3,g; s,⅙,1/6,g; s,⅔,2/3,g; s,²,^2,g;
s,⁴,^4,g; s,⁶,^6,g; s,⁸,^8,g; s,¹⁰,^10,g; s,√,sqrt,g;
s,\)ke,\)*k*e,g; s,\)([Eke]),\)*\1,g }' | awk -f -

The program is written in unicode to make reading it and following Appendix B much easier. Sorry if your browser doesn’t render the code properly. I just noticed it doesn’t on my smartphone.

Make this file executable and run it:

> chmod +x efacts.sh
> ./efacts.sh
GM 398600460550000
a 6378137.678
b 6356752.964
ω 0.00007292115
G 0.0000000000667428
Mₑ 5972186671071637111046144
Mₐ 5148000000000000000
Mₒ 5972181523071637240414208
GMₒ 398600116958065.625
F 0.0033640939204509
f 297.256861326
e 0.081819240444396
E 0.082094488053751
Rₚ 6399594.3322
R₁ 6370985.4599
R₂ 6371007.8495
R₃ 6371001.4586
q 0.000073346392
Q 0.002688044572
m 0.00344979040432
J₂ 0.001082631230267
U₀ 62636794.1768
Gₑ 9.7803152684
Gₚ 9.8321748731
k 0.001931854280022
Gₘ 9.7976331557

Gₘ (mean normal gravity) is the value we’re looking for, and it’s 9.7976331557.

Now if only the actual earth was a model, then we’d already be done. We want the most accurate answer though, and so we need measured data. We will use the EGM2008 standard, as it’s the most popular. We’ll be using this great resource to seek our answer. But first we’ll need to generate a table of latitudes and longitudes:

> for x in `seq -89.5 89.5`; do for y in `seq -179.5 179.5`; do echo $x $y; done; done > latlon.csv

We will upload this file to get the results. Now go there and fill out the form exactly as shown here (note Gm is our atmosphere-less value GMₒ, not GM)

This will take a while …

But Zoe, Why are we interested in Normal Gravity and not Gravity? Because we’re interested in the acceleration towards the surface and not the center of the earth.

To find the average for the whole Earth we will have to go through each latitude,longitude pair and find out their surface area using the formula from here:

dA = a^{2} cos[ø (1-e^{2})] dø dl / (1-e^{2} sin^{2}ø)^{2}

This will generate an error of ~0.00001281 when we add up all the pairs, but that’s alright because we’ll just divide by a surface area 1.00001281 times as large.

Click on “Download results” when the site is finished, and save to EGM2008.csv in the same folder as the following code.

egm.sh:

#!/usr/bin/bash
# Zoe Phin 2019/12/17
sed 1,32d EGM2008.csv | awk '
function r(x) { return x*atan2(0,-1)/180 } {
e = 0.081819240444396; printf "%.12f %.12f\n", $5/1e5, \
(6378137.678*r(1))^2*(1-e^2)*cos(r($3))/(1-e^2*sin(r($3))^2)^2
}' | awk '
{ S += $1*$2; } END { printf "%.10f\n", S/510072261022076.375 }'

Run it

> bash egm.sh
9.7977074331

The best estimate for Earth’s average surface gravity using the most popular reference model is: 9.7977074331

Note how little difference there is between this number and the previous mathematically derived value; It’s only 0.0000742774.

No one should use the “Standard Gravity” value of 9.80655 if they are interested in the whole earth. Such use will lead to a geometric error of ~0.0009025 and an arithmetic error of ~0.0088426.

Now these errors might be small, but why not make them smaller? Use my number. Thank you.

Berkeley Earth is a popular resource among climate alarmists. Today I will examine their most popular data, available here. This data is the basis of a very popular meme, the global warming color stripes. But how valid is it? Could it be a misinterpretation?

Here is coverage for January of years: 1850, 1875, 1900, 1925, 1950, and 2019

As you can see, we obviously did not have global coverage for most of the interested period. Could it possibly be that the “global” warming reported by Berkeley Earth and similar outfits is actually a statistical artifact due to increased coverage and incompatible comparison? In other words, is it the whole globe that’s warming or is it just the shifting and increasing subset of the globe that’s warming?

Good question! Let’s investigate …

We need elevation data so that we can see if that’s a factor. We get that from here. Download using:

The Berkeley Earth data file has information about the baseline temperatures for Jan 1950 through Dec 1980. We examine this first. Create a new file called base.sh, and paste in the following text:

#!/usr/bin/bash
# Zoe Phin, 2019/12/29
ncks --trd -HC elev.1-deg.nc -v data | sed \$d | awk -F[=\ ] '$8>=0{print $8} $8<0{print 0}' > .elev
onemonth() {
for l in `seq 0 179`; do
ncks -v climatology -d month_number,$1,$1 -d latitude,$l,$l temps.nc |\
sed -n '/climatology =/,/;/p' | sed 1d | tr -d ' ;' | tr ',' '\n' |\
awk -v l=$l 'function rad(x) { return x*atan2(0,-1)/180 } {
lat = l - 89.5; lon = n - 179.5; n += 1
a = 6378137.678; b = 6356752.964; E = 1-b^2/a^2;
x = rad(lat)
A = (a*rad(1))^2*(1-E)*cos(x)/(1-E*sin(x)^2)^2
printf "%5.1f %6.1f %10.6f %20.18f\n", lat, lon, $1, A/510072261022077
}'
done > .t$1
paste .t$1 .elev
}
allmonths() {
for t in `seq 0 11`; do
printf "%2d " $(($t+1)); onemonth $t |\
awk '$3!="nan"{T+=$3*$4;L+=$1*$4;A+=$4;E+=$5*$4}END{
printf "%10.6f %5.3f %6.3f %7.2f\n",T,A,L/A,E/A}'
done | awk '{print;S+=$2}END{print "AVG: "S/12}'
}
allmonths

Here we see the month number, the average area-weighted temperature, coverage (100%), average latitude (equator), and average elevation. Average elevation will come in handy later. At the end is an annual average temperature. So far so good. Now we do latitude analysis. Create a new file latitudes.sh, and paste in the following:

This tells us that in the tropics, for every degree latitude away from the equator, the temperature drops 0.133 °C, according to 1950-1980 baseline data. This fact will come in handy later.

Now we are ready to analyze the temporal data. Create a new file called temps.sh and paste the following text into it:

#!/usr/bin/bash
# Zoe Phin, 2019/12/29
ncks -v data -lat,0,0 elev.1-deg.nc |\
sed -n '/data =/,/;/p' | sed 1d |\
tr -d ' ;' | tr ',' '\n' | sed '/^$/d' |\
awk '$1>=0{print $1}$1<0{print 0}' > .elev
onetime() {
for l in `seq 0 179`; do
ncks -v temperature -d time,$1,$1 -d latitude,$l,$l temps.nc |\
sed -n '/temperature =/,/;/p' | sed 1d | tr -d ' ;' | tr ',' '\n' |\
awk -v l=$l 'function rad(x) { return x*atan2(0,-1)/180 } {
lat = l - 89.5; lon = n - 179.5; n += 1
a = 6378137.678; b = 6356752.964; E = 1-b^2/a^2;
x = rad(lat)
A = (a*rad(1))^2*(1-E)*cos(x)/(1-E*sin(x)^2)^2
printf "%5.1f %6.1f %10.6f %20.18f\n", lat, lon, $1, A/510072261022077
}'
done > .tmp
paste .tmp .elev
}
alltimes() {
for t in `seq 0 2027`; do
printf "%4d " $t
onetime $t | awk '
$3!="nan"{T+=$3*$4;L+=$1*$4;A+=$4;E+=$5*$4}END{printf "%10.6f %5.3f %6.3f %7.2f\n",T,A,L/A,E/A}'
done
}
alltimes

Make new file executable, and run it. Then wait hours. lol

> chmod +x temps.sh
> ./temps.sh | tee temps.csv

While you’re waiting for temps.csv to be completely filled, create a new program that will analyze and adjust the results. Call it analyze.sh

Notice that I’m using two parameters we discovered earlier: 0.133022 and 232.86 (mean elevation). A third parameter is 0.0057, which is just the average lapse rate in °C/meter.

Make the file executable and run it after temps.csv is completely generated.

From 1850 to 2019, we’ve been warming up by 0.0013 °C/year.

We’ve warmed up by 0.22 °C since 1850. I wouldn’t worry about it! A linear regression is inappropriate anyway. The data is obviously cyclical.

Summary: After adjusting Berkeley Earth’s data for latitude and elevation, we found no serious global warming.

Enjoy 🙂 -Zoe

Addendum:

In case it wasn’t obvious: The so-called “global warming” is primarily due to incompleteness of data, and the average latitude drifting south towards equator in the data we do have.

We should rename Climate Change to Historic Mean Latitude Change.

Who’s with me?

Update 2020/01/03

A youtube channel operator claims that Berkeley already performed a latitude and elevation adjustment. This is absolutely bogus. He has since censored our discussion thread from the public. Let’s address this anyway…

In the Berkeley grid data , there are 16635 cells (out of 64800) that have complete data from 1850 to 2018. I compare these 16635 cells to Berkeley’s global summary (first chart on this post). Result:

There ought to have been a huge difference between 1850 and ~1980, if they really accounted for latitude. There is hardly a difference. It’s also painfully obvious that Berkeley stuffs the missing data with neighboring data and model interpolations – and the result is hardly different than just taking a plain area-weighted average of time-persistent locations, which have a VERY Northern bias.

Update 2020/01/04

Below is a very long term weather station in the Netherlands; one of the oldest continuous stations that exist.

What do you see? Uhuh

Before you accuse me of cherrypicking, consider that this cherry has been getting wrapped in more CO2 and it didn’t make a lick of difference. Why is that? Uhuh

Update 2020/01/17

More confirmation that what I’ve done here is correct, available here.

To plot this curve, create an empty file called hypso.plot and paste text below:

set term png size 740,370
unset key
set xtics 0.1
set mxtics 2
set grid mxtics xtics ytics
set xrange [-0.01 to 1.01]
plot "hypso.csv" u 2:1 w filledcurves above y=0 fc "orange",\
"hypso.csv" u 2:1 w filledcurves below y=1 fc "blue"

What is the average depth of the ocean? What is the average elevation on land? And what is the average elevation of the Earth? I will answer all of these questions.

ETOPO1 is a 1 arc-minute global relief model of Earth’s surface that integrates land topography and ocean bathymetry. Built from global and regional data sets, it is available in “Ice Surface” (top of Antarctic and Greenland ice sheets) and “Bedrock” (base of the ice sheets).

Now here is my main program. Save the following to a new file etop.sh.

#!/usr/bin/bash
# Zoe Phin, 2019/12/17
for l in `seq 0 10799`; do
ncks -v z -d y,$l,$l etop01$1.nc | sed -n '/z =/,/^$/p' | egrep -o '[-0-9].*[0-9]' | tr -s ', ' '\n' | awk -v l=$l '
function rad(x) { return x*atan2(0,-1)/180 }
{
a = 6378137.678; b = 6356752.964; E = 1-b^2/a^2;
lon = n - 180 + 1/120; n+=1/60
lat = l/60 - 90 + 1/120
x = rad(lat)
A = rad(1/60)^2*a^2*(1-E)*cos(x)/(1-E*sin(x)^2)^2
printf "%7.3f %8.3f %5.0f %6.3f\n", lat, lon, $1, A
}'
done

This program takes 1 argument: b for bedrock, and i for ice. Make the program executable and run it: (This will take quite a bit of time, and generates two 8.5GB files)

> chmod +x etop.sh
> ./etop.sh b > b.csv
> ./etop.sh i > i.csv

I will use the Surface Area from this article. Now I write an additional program: etop2.sh

echo -n 'Over land, Bedrock, Water surface = 0 ... '
awk '$3>0 {S+=$3*$4} END {printf "%8.3f\n", S/510065728777854.5264}' b.csv
echo -n 'Over land, Bedrock, No water included ...'
awk '$3>0 {S+=$3*$4;N+=$4} END {printf "%8.3f\n", S/N}' b.csv
echo -n 'Under water, Bedrock, No land Included ... '
awk '$3<=0{S+=$3*$4;N+=$4} END {printf "%8.3f\n", S/N}' b.csv
echo -n 'Bedrock, Overall Average ... '
awk ' {S+=$3*$4} END {printf "%8.3f\n", S/510065728777854.5264}' b.csv
echo -n 'Over land, Ice Top, Water surface = 0 ... '
awk '$3>0 {S+=$3*$4} END {printf "%8.3f\n", S/510065728777854.5264}' i.csv
echo -n 'Over land, Ice Top, No water included ...'
awk '$3>0 {S+=$3*$4;N+=$4} END {printf "%8.3f\n", S/N}' i.csv
echo -n 'Under water, Ice Top, No land Included ... '
awk '$3<=0{S+=$3*$4;N+=$4} END {printf "%8.3f\n", S/N}' i.csv
echo -n 'Ice Top, Overall Average ... '
awk ' {S+=$3*$4} END {printf "%8.3f\n", S/510065728777854.5264}' i.csv

Make it executable and run it: (This will take >30 minutes on an average laptop)

> chmod +x etop2.sh
> ./etop2.sh
Over land, Bedrock, Water surface = 0 ... 180.573
Over land, Bedrock, No water included ... 651.100
Under water, Bedrock, No land Included ... -3624.931
Bedrock, Overall Average ... -2439.039
Over land, Ice Top, Water surface = 0 ... 231.404
Over land, Ice Top, No water included ... 796.560
Under water, Ice Top, No land Included ... -3683.895
Ice Top, Overall Average ... -2382.302

It’s interesting to note that total ice stacks up to an average of ~51 meters globe wide.

Using Ice Top data:

The average depth of the ocean is: 3683.9 meters

The average elevation on land is 796.6 meters

Average height above sea level is 231.4 meters

Treating the ocean bottom and land top as a surface, the average height is 2382.3 meters BELOW sea level.

It would be interesting to know Earth’s surface area, including its topographic features. Unfortunately, I haven’t been able to find this out. But what I can do is provide an answer for a reference oblate spheroid Earth.

We first need to find out Earth’s equatorial and polar radii. Best to use the latest information. We get that from this excellent paper:

semi-major and minor axes of the MEE, a = 6378137.678 ± 0.0003 m and b = 6356752.964 ± 0.0005 m

Next we need the proper formula. That is found here:

Now we do the math straight on a linux command line, but we substitue b for c, because that’s how geoscientists do it:

> bc -l <<< 'pi=4*a(1);a=6378137.678;b=6356752.964;e=sqrt(1-b^2/a^2);2*pi*a^2+pi*b^2/e*l((1+e)/(1-e))'
510065728777854.52641416583885417771

The answer is 510,065,728,777,855 m² – for Mean Sea Level.

Wikipedia and a few other sources use 510,072,000 km², but since there is no reference to any scholarly source, it’s meaningless. That result is equivalent to adding ~40 meters to equatorial and polar radii.

Enjoy 🙂 -Zoe

Update – 01/01/2020:

I think I have figured out where Wikipedia et al gets that number. They use an approximation technique and add up latitude,longitude pairs of a 1×1 degree grid.

area.sh:

for x in `seq -89.5 89.5`; do for y in `seq -179.5 179.5`; do echo $x $y; done; done | awk '
function r(x) { return x*atan2(0,-1)/180 } {
e=0.08182; printf "%20.8f\n", (6378137*r(1))^2*(1-e^2)*cos(r($1))/(1-e^2*sin(r($1))^2)^2
}' | awk '{ S += $1; } END { printf "%.4f\n", S }'

> bash area.sh
510072131344033.4375

The number 510,072,131.34 km² is too close to 510,072,000 km² to be a coincidence, I think. Either way, their number is wrong 🙂

There’s a [stupid] idea popular among climate scientists that there is an extra warming effect from water vapor due to the fact that CO2 and water vapor have overlapping spectral bands. Is this true? Certainly CO2 and Water Vapor “do” have overlapping “bands”, but as I will show all these “bands” are artificially created from the raw line-by-line spectral data. There is only one official source from which we can get line-by-line spectral data: HITRAN. While there’s no easy way to download the data directly, HITRAN provides a programming interface. First we need the requisite software:

Now we are ready to write a program to fetch the necessary data. Save the following to getdata.py:

from hapi import *
db_begin('data')
fetch('co2',2,1,1,10000)
fetch('h2o',1,1,1,10000)

Now run it:

> python3 getdata.py

The data is now available in a folder called data as two files: data/co2.data & data/h2o.data. Each file is a table of properties for given wavelengths (cm⁻¹) – the line-by-line data. How many lines are there?

> wc -l data/*.data
170103 data/co2.data
74478 data/h2o.data
244581 total

The 2nd column in the table contains all the wavelengths that the substance can absorb in. We will extract the 2nd column from each file.

Everyone who claims there is an overlap MUST have conjured it up from manipulating the raw data. Charlatans call this ‘modeling’.

It is true that some lines are close to each other. Here is what happens when we start removing decimal digits:

0.XXXXXX

0 Matches

0.XXXXX

35 Matches

0.XXXX

337 Matches

0.XXX

3233 Matches

0.XX

23012 Matches

The “scientists” must believe that close is good enough, but in reality, there’s a big difference between between a 123.456789 cm⁻¹ photon and a 123.45678 cm⁻¹ photon, and no amount of manipulation can get around this fact.

Since the premise of water vapor feedback is false, so is the conclusion.

The prime meridian is at an unsatisfactory location. Placing the prime meridian in the center of a map produces a very ugly map:

If you ask a petite woman (me) where the new meridian should pass through, naturally the answer would be the islands of Askø, Lilleø, Femø.

They belong to Denmark, and they all have a spot on land 11.5° to the east of the current meridian. What does moving the meridian 11.5° to the east do?

Much better!

The very center is now in the country of Gabon.

So let’s gab on about this until it becomes a reality!

Yours Truly, -Zoe

P.S. This article is written in jest. I’m dead serious tho. grrr

In my previous article I promised to measure how much energy is produced from geothermal. I will make a first attempt today. I downloaded a big collection of borehole data from U of Michigan for us to analyze. I am interested in data as close to the surface as possible while being below 10 meters deep. Why 10 meters? It is commonly well known that the sun does not penetrate below 10 meters. Of the 1012 locations available, 312 have data for 50m and 100m depth. This is what we will be using.

Number of locations per country, and the country’s geographic middle latitude:

1 AL 41.153332
29 AU -25.274398
5 BR -14.235004
3 BW -22.328474
9 BY 53.709807
37 CA 56.130366
28 CN 35.86166
2 CU 21.521757
32 CZ 49.817492
1 IE 53.41291
2 IN 20.593684
2 IT 41.87194
22 JP 36.204824
20 KR 35.907757
2 MN 46.862496
1 NA -22.95764
1 NE 17.607789
3 PE -9.189967
3 PL 51.919438
27 RU 61.52401
8 SI 46.151241
4 TZ -6.369028
4 UA 48.379433
2 UK 55.378051
42 US 37.09024
13 ZA -30.559482
3 ZM -13.133897
6 ZW -19.015438

The average latitude weighted by number of locations is 30.7607. Here is all the data:

The 2nd column is the # of locations per country. The 5th column is CHF. The average CHF for all locations is 48.9 mW/m² (0.0489157 W/m²).

Now we will calculate what the surface (Depth = 0m) temperature would be assuming no solar influence, and a continuation of the trend that existed between -100 and -50 meters. The formula is:

The 7th column is our predicted temperature. The average value for all locations is 13.08 °C at an average latitude of 30.7607 N.

Mainstream climate science argues that the sun only provides the surface of the earth 163.3 W/m² (-41.5°C), and the rest of the energy is provided by greenhouse gases which boosts the global average temperature to ~15°C.

And yet here we have evidence that 312 locations don’t need greenhouse gases at all, because they get enough energy from geothermal.

Today I will be investigating the difference between two different types of fluxes that elude almost every climate scientist. Let’s take a typical simple conduction problem you can find in every high school or college textbook:

We need to figure out the temperature and radiative emission on the colder side (Tc and Ec).

The formula for conduction is:

Q k * A * (Th-Tc)
Power = --- = q = -----------------
t L

As you can see we have two different flux (W/m²) figures: 2.5 and 557 W/m². We need appropriate labels for them so we don’t confuse them:

Conductive Heat Flux through the medium (CHF)

2.5 W/m²

Cold-Side Radiation from the medium (CSR)

557 W/m²

We can easily see that despite the small CHF through the medium, the emergent CSR is 557/2.5= 223 times larger.

Let’s now combine Equation I and II to summarize what is going on:

/ q * L \ 4
Ec = ε*σ*| Th - ------- |
\ A * k /

Remembering that CHF is just q/A, we reduce further:

CSR = εσ(Th-CHF*L/k)⁴

It becomes obvious now that CHF and CSR have an inverse relationship. The higher the CHF, the lower the CSR, and the lower the CHF, the higher the CSR.

Why is the distinction between CHF and CSR important?

Professors Davies and Davies have done a good job in measuring Earth’s heat flux:

We present a revised estimate of Earth’s surface heat flux that is based upon a heat flow data-set with 38 347 measurements, which is 55% more than used in previous estimates.

We conclude by discussing our preferred estimate of 47 TW, (rounded from 46.7 TW given that our error estimate is ± 2 TW)

It’s unfortunate that they call their measurement “surface heat flux”, when in reality they measure heat fluxes at various depth ranges which they don’t disclose, and then average that. They measure CHF. The value they get for averaged CHF is 46.7 TW divided by Earth’s surface area: 47×10¹² / 510.1×10¹² = 91.6 mW/m².

91.6 mW/m² is a very small number when compared to the average insolation we receive at the surface, which according to NASA’s official energy budget is 163.3 W/m². 0.0916/163.3 = 1783 times smaller. Perhaps you’ve heard of the idea that the sun supplies 99.95% of our energy? Well guess what? All of this is nonsense, because CHF is irrelevant!!! What is relevant is CSR!

What Davies & Davies should have done is measure all the averaged parameters (Th, k, L) and not just q or q/A (CHF). We need to know the actual CSR before we can start comparing it to insolation. This is key, because without CSR, climate scientists have a completely erroneous view of the way things really are.

Assuming k = 1 and A=1, we examine all the possible temperatures that produce Davies’ CHF (q/A) of 91.6 mW/m².

Depth = 110 meters

Depth = 10 meters

°C/m

10°C

0.84°C

-0.0916

100°C

90.84°C

-0.0916

1000°C

990.84°C

-0.0916

10000°C

9990.84°C

-0.0916

All result in Conductive Heat Flux of 91.6 mW/m²

Let’s think about this: Does it matter whether it’s 0.84°C or 9990.84°C 10 meters below your feet? Of course it does! But you can’t tell the difference using CHF. Only using CSR can we tell the true radiation emerging out of the earth!

We will now transform the above table into CSR, using emissivity = 0.93643 (why this number?).

Depth = 110 meters

Depth = 0 meters

CSR

10°C

-0.076°C

295.31 W/m²

100°C

89.924°C

922.81 W/m²

1000°C

989.924°C

135,150 W/m²

10000°C

9989.924°C

589,112,205 W/m²

Makes a big difference, right? It would be interesting to know the actual Earth-wide averaged CSR, but we will leave that for another day. (Update: That day came: Measuring Geothermal, CSR = 294 W/m²)

In short summary, most climate scientists are clueless about the difference between CHF and CSR and therefore erroneously greatly underestimate the power of geothermal. It may even be the biggest scientific scandal of our time!

I created a program to calculate the temperature, pressure, density, gravity, mols/m³, and root-mean squared velocity profile for the troposphere. It is based on the US/International Standard. The height used is the real geometric height, and not the geopotential height.

It would be useful to figure out what percentage of Earth’s outgoing longwave radiation CO2 absorbs.

It’s necessary to have line-by-line CO2 spectrum data. I got my data here.

I cleaned up the data, and uploaded it to pastebin. Should you download it or copy/paste it, you need to save it co2.csv .

I made a program to analyze the data and plot a pretty chart. You will need gnuplot (sudo apt install gnuplot). Copy and paste the following text into a new file called gas.sh.

#!/usr/bin/bash
# Zoe Phin, 2019/11/13
gas=$2; GAS=`echo $gas | tr [:lower:] [:upper:]`
declare -A color; color[co2]=green; color[h2o]=blue
awk -v FLUX=$1 ' BEGIN { PI=atan2(0,-1);
c=299792458; h=6.62607015E-34; k=1.38106485E-23; s=5.670367e-8
}
function P(T,w) { return (2*PI*h*c^2/(w/1e6)^5*1/(exp(h*c/(k*T*w/1e6))-1))/1e7 }
!/^#/{ SR = P((FLUX/s)^0.25, $1)
if (SR > 0.005) printf "%.4f %.4f %.4f\n", $1, SR, SR*$3
}END{
for (w = 40.005; w <= 104.12; w+=0.005) {
printf "%.4f %.4f 0\n", w, P((FLUX/s)^0.25, w)
}
}' ${gas}.csv > ${gas}.dat
awk '{D=$1-LW; printf "%.9f\n", D*LG; LW=$1;LG=$3}' co2.dat |\
awk -v FLUX=$1 '{G+=$1}END{print G*10" "G*10/FLUX}'
echo "set term png size 740,460
unset key; set title '${GAS} Absorption'
set ylabel 'Spectral Radiance (W/m²/μm)'
set xlabel 'Wavelength (μm)'
set xrange [2.8:104.2]
set logscale x
set mxtics 10; set mytics 5; set xtics out
plot '${gas}.dat' using 1:2 w boxes fs solid 1.0 fc 'black',\
'' using 1:3 w boxes fs solid 1.0 fc '${color[$gas]}'
" | gnuplot > ${gas}-spectrum.png

The program takes 2 arguments. The first is Earth’s outgoing surface flux. I will use the value found in the latest NASA’s Energy Budget. The second argument is the type of gas. I intend to make other gases available in the future, but for now only “co2” is available. Now we run:

> bash gas.sh 398.2 co2

The result is:

20.4858 0.051446

The first number is in W/m², and the second is a proportion to the flux you used. Here is what the plot looks like:

The answer is to our question is 5.14% (20.49 W/m²)

Mainstream climate scientists believe there’s such a thing as Downwelling Longwave Radiation (DLWR) originating primarily from Greenhouse Gases. Does it actually exist? Maybe, but I haven’t seen any compelling evidence for it.

Professor Claes Johnson has done a great job exposing their confusion here. Essentially, they confused up for down!

Isn’t it obvious what they did? How could there be two radiation dead ends? Nothing works like this in reality. If you were to place a resister into a circuit, would the electrons suddenly flow from two sides into the resister? No, the flow is one way. The flow is always from the generally hotter surface to the colder atmosphere. Downwelling Longwave Radiation is ACTUALLY Upwelling-from-Pyrgeometer Longwave Radiation. There are two Ups, not one Up/one Down.

You may have seen something like this “proving” DLWR exists:

Why is this evidence no good? Because they use special instruments that are super-cooled! (Paper):

“Both instruments incorporated a liquid-nitrogen-cooled, narrow-band, MCT detector …”

Well yes, heat flows from hot to cold, so naturally, when you use super-cooled instruments and pretend that represents Earth’s surface, you will see a lot of Downwelling Radiation – otherwise it doesn’t exist.

Do people spontaneously get swallowed up by the Earth? No, but if you pretend the Grand Canyon represents typical Earth, then sure, people will get “swallowed up” as they try to move across it.

I think most rational people can agree that there is generally no Downwelling radiation from atmosphere to surface. So why does NASA pretend that the atmosphere provides the surface with 158 or 340 W/m² (contradiction exposed here), when it usually provides NOTHING?

In a previous post, I presented a program for generating global insolation data. In this post I kick it up a notch and do the same thing using higher resolution graphics. It turned out making a graphical version was quite easy thanks to gnuplot.

To get gnuplot on a Debian Linux-based system (I use Linux Lite), type command:

> sudo apt install gnuplot

After installation, copy the following code to a new text file (I called it gtoa.sh):

#!/usr/bin/bash
# Zoe Phin, 2019/11/04
DAY=`printf "%03d" $1`
(echo "set term png size 740,460
set title 'Day ${DAY} : W/m²'; unset key
set xtics out 3; set mxtics 3
set ytics out 15; set mytics 3
set palette defined (0 0 0 0, 1 0 0 1, 2 0.58 0 0.83, 3 1 0 0, 4 1 0.65 0, 5 1 1 0, 6 1 1 1)
plot '-' using 1:2:3 with dots palette"
awk -v day=$1 '
function asin(x) { return atan2(x, sqrt(1-x*x)) }
function rad(x) { return x*PI/180 }
function abs(x) { if (x<0) x=-1*x; return x }
BEGIN { day--
PI = atan2(0,-1)
TSI = 1361; YEAR = 365.2422; TILT = 23.45; ECC = 0.017
VEQ = 79.915; PHA = 2.013 # Vernal Equinox & Perihelion Adjustment
for (lat = -90; lat <= 90; lat += 0.5) {
for (min = -720; min <= 720; min += 2) {
dm = day + min/1440
ORB = 1-ECC*cos(2*PI*(dm-PHA)/YEAR)
DEC = asin(sin(rad(TILT)*sin(2*PI*(dm-VEQ)/YEAR)))
LAT = rad(lat); LON=rad(min*360/1440)
INS = (sin(LAT)*sin(DEC)+cos(LAT)*cos(DEC)*cos(LON))
if (INS<0) { INS = 0 } else { INS = TSI/ORB*INS }
printf "%f %f %f\n", (min+720)/60, lat, INS
}
}
}') | gnuplot > Day${DAY}.png

Now make the program executable:

> chmod +x gtoa.sh

The program takes one argument: day of the year. To generate a plot for the 123rd day of the year:

> ./gtoa.sh 123

A file called Day123.png will be generated in the same directory as your program. It will look like this:

I automated the generation of every day of the year ( ask me how 🙂 ) and created an animated gif:

I reduced the color depth, otherwise the gif becomes 33MB. Now it’s 6MB.

Gavin Schmidt is the current head of NASA GISS. In 2010, he wrote:

We quantify the impact of each individual absorber in the total effect by examining the net amount of long‐wave radiation absorbed in the atmosphere (G, global annual mean surface upwelling LW minus the TOA LW upwelling flux) [Raval and Ramanathan, 1989; Stephens and Greenwald, 1991]. This is zero in the absence of any long‐wave absorbers, and around 155 W/m^{2} in the present‐day atmosphere [Kiehl and Trenberth, 1997]. This reduction in outgoing LW flux drives the 33°C greenhouse effect defined above …

Kiehl & Trenberth (the creators of the “official” energy budget), in 1997, wrote (PDF) that:

values for the net surface shortwave flux range from 154 to 174 W/m^{2}

Clearly both are saying that in their conception of the greenhouse effect, greenhouse gases (GHGs) add something like 155 W/m^{2} beyond what the sun can do.

If that is the case, then why does NASA’s official Energy Budget (also developed by Kiehl & Trenberth) try to imply something else … something extra?

As you can see in the above diagram from NASA, they are trying to imply that greenhouse gases provide 340.3 W/m^{2}. Isn’t that what they are doing? Why else have the label “Greenhouse Gases” in green hovering over “back radiation 340.3 W/m^{2}” with green arrows pointing down?

Indeed, they are trying to perpetrate a subtle fraud. How do they accomplish this?

Very simple! They add the values encircled in red to the true value encircled in green.

340.3 – 77.1 – 18.4 – 86.4 = 158.4 (i.e, around 155 W/m^{2}) is the true value.

So which is it, NASA? Do GHGs emit 158.4 W/m^{2} or do they emit 340.3 W/m^{2}? Clearly the former. Now why would NASA attempt such a blatant obfuscation? If they are lying about such small things, what else could they be lying about?

Where does this number come from? It comes from a very narrow wavelength range, typically 8-14 microns. Not “broadband” at all.

When other scientists wish to specify an emissivity factor, they will choose a value between 0.95 and 1, also based on a narrow band range.

This is all wrong! We actually do have a very good broad measure of emissivity covering 5-200 microns (over 99% of Earth’s emission)! This data was collected by NASA’s ISCCP Project, and is available here:

The relevant file is called D1AVGANN__BBEMIS. It consists of 6530 equal-area sections of the earth, thus we can just average all the numbers in the file.

Here is a one-liner to get the answer we’re looking for:

This data was collected from 1983 to 2004, so one would think most climate scientists would just use the right number. But of course we are assuming they are interested in science.

Continuing from Part 1, I further show why the mainstream Greenhouse Effect is a Fraud.

First we must understand that math is not physics. Physics uses math, but math is not bound by the laws of physics. In other words, you can use math to fabricate the unreal. For example: say we want to know how much electricity is needed to power a neighborhood that is being planned for development; We can easily do that. Does that mean that there is already electricity doing that? No.

But … in Eq. 7.12 and 7.13, it is already presumed that the surface output (390 W/m²) is already a given, yet in reality they completely failed to show the causality of it. What I’m trying to say is that their equations fuse the available and the needed together to produce what is observed (390 W/m²).

What is available? If the sun is considered the only input, then only 240 W/m² is available. Let’s see what that produces:

A 24-hour average input of 240 W/m² simply produces a 24-hour average output of 240 W/m²

Notice that the Earth “keeps” nothing in step 5. There is another problem with their physics. In Figure 1 of Part 1 they “kept” 390 W/m². The Stefan-Boltzmann Law is for emission and emission only. An object can’t “keep” a flux and be at a given temperature. It has to emit to be said to be at a given temperature. I should have shown a step 6 with 390 W/m² being emitted to space. Doing so would show that they emit 240+390=630 W/m² to space … from a solar input of only 240 W/m²!

It is becoming even more clear that they can’t derive the observable surface temperature directly from the sun, and so they cheat. Here is what they end up doing:

They simply treat Greenhouse Gases (GHGs) as if they are a raw source of energy providing 150 W/m² in addition to the sun’s 240 W/m² (But there is another contradiction I expose in a later article).

Now their math works! So what’s the problem? The needed became GHGs-provide-raw energy! This is a huge affirming-the-consequent fallacy.

In most popular literature on this topic it is claimed that GHGs trap solar energy, but in reality they fabricate energy from GHGs because they want to show that they are responsible for why the Earth is warmer than what the sun alone can do. In reality they can’t actually show the causation in a scientific manner, they can only describe/declare/insist on a process using a logical fallacy.

The radiation-based Greenhouse Effect is nothing but rhetoric devoid of any scientific merit.

The atmospheric greenhouse effect is a fraud, and I will show you why. Let’s take a typical example of a model greenhouse effect used by climate “scientists” as found in a free online book from Harvard. Here is the relevant formulas, text and diagram:

I will assume their assumption that f=0.77, although this number is not based on any observations, but is merely a fudge factor to make their formulas match real observations. The surface temperature is indeed somewhere around 288°K.

In Eq 7.13 I already see a problem. The surface temperature is dependent on the atmosphere temperature, and yet the atmosphere temperature is dependent on the surface temperature. This is a lifting-yourself-by-your-bootstraps type of problem.

Eq 7.15 summarizes their fraud. We see that they magically enhanced global average solar input at the surface by a factor of 1/(1-0.77/2)=1.626. This is a complete violation of the laws of thermodynamics. If the sun is considered the only input, then you can never get more radiation out of the sun then what originally came in. Going from 1 to 1.626 is over-unity, and is not allowed in physics.

So how did it come to this?

Let’s first work out some of the numbers. Fs=1361, A=0.3, f=0.77.

Fs(1-A)/4 =~ 240 W/m²

Now what do they do with this incoming radiation?

A diagram will help. Below is an accurate representation of what they want to happen. This diagram is in joules per second per square meter. Each dot represents 10 joules. Half dot is 5

1) The sun sends 340J

2) The surface (and lower atmosphere in reality) receives only 240J due to albedo and then sends it out to the atmosphere

3) The radiation gets split. 195J goes up and 195J goes down

4) Earth now has 435J, and will emit an additional 45J to space

5) Space was sent a total of 240J, and Earth “keeps” 390J.

Everything seems to be in order according to their math. So what’s the problem?

The problem is they cheated. How?

In Step #2, the surface sent 240J to the atmosphere, while still retaining it! It takes energy to create radiation, and thus sending to the atmosphere must drain the energy at the surface, but they don’t do that. They mathematically cloned energy.

In Step #3, they emit 2*195J=390J from only having received 240J.

They have a serious problem with the order in which things must happen.

I wrote a little program to help people figure out top-of-the-atmosphere insolation year-round all over the globe. You can modify it for more specific purposes. It is written mostly in awk – the best programming language for processing columnated data tables.

#!/usr/bin/bash
# Zoe Phin, 2019/10/30
awk '
function asin(x) { return atan2(x, sqrt(1-x*x)) }
function rad(x) { return x*PI/180 }
function abs(x) { if (x<0) x=-1*x; return x }
function color(n) { return sprintf("\033[0;%sm", n) }
BEGIN {
PI = atan2(0,-1)
TSI = 1361; YEAR = 365.2422; TILT = 23.45; ECC = 0.017
VEQ = 79.915; PHA = 2.013 # Vernal Equinox & Perihelion Adjustment
for (day = 0; day < 365; day++) {
for (lat = 85; lat >= -85; lat -= 10) {
printf "%03d ", day+1
if (lat < 0) { printf "%02dS ", 0-lat }
else { printf "%02dN ", lat }
for (hour = -12; hour <= 11; hour++) {
dh = day + hour/24
ORB = 1-ECC*cos(2*PI*(dh-PHA)/YEAR)
DEC = asin(sin(rad(TILT)*sin(2*PI*(dh-VEQ)/YEAR)))
LAT = rad(lat); LON=rad(15*hour)
INS = (sin(LAT)*sin(DEC)+cos(LAT)*cos(DEC)*cos(LON))
if (INS<0) { INS = 0 } else { INS = TSI/ORB*INS }
c = color(36) # Cyan
if (INS > 315) { c = color(34) } # Blue
if (INS > 500) { c = color(32) } # Green
if (INS > 700) { c = color(33) } # Orange
if (INS > 1000) { c = color(31) } # Red
printf "%s%4d\033[0m ", c, INS
avg += INS/24
}
area = 2*PI*abs((sin(rad(lat+5)))-sin(rad(lat-5)))
contrib = avg*area/(4*PI)
dayavg += contrib
printf "| %3d %.2f %2d\n", avg, area, contrib
avg = 0
}
printf "Day Global Average: %.3f\n", dayavg
annavg += dayavg/365
dayavg = 0
}
print "Annual Global Average: "annavg
}'

Copy above text to a new text file. Save it as toa.sh (for example). Make it executable and run it:

> chmod +x toa.sh
> ./toa.sh

The beginning output should look like this:

The first column is the day #. Second column is the latitude. The next 24 columns are hours, starting from midnight. The 3rd to last column is the average insolation for the latitude. The 2nd to last column is the surface area weight of the latitude for a sphere with radius 1. The vertical total for this column should add up to 4π. The last column is just the previous two columns multiplied with each other and then divided by 4π. The vertical sum of this column adds up to the Day Global Average.

The last line is the average for the whole year

Annual Global Average: 339.926

Now here is something really neat. I animated a whole year!

Enjoy! -Zoe

Edit: I don’t like how wordpress makes things complicated. To see the animation in full size, right click here, and select “Open in New Tab”.