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The Greenhouse Defect - The most disruptive site on climate science

The Greenhouse Defect

The most disruptive site on climate science

Whatever you think to know about clouds within the climate system, this would be about the right time forget it all. Common data products on cloud forcing are nothing but junk science. Here is why.

 

This is one of those things people just can not imagine and even if you happen to see it with your own eyes, you have a hard time trusting your senses. And once you realize it is true, you sink into your chair and think OMG! It is certainly redefining reality and alienating from society, or at least "science".

If you go out and explore you might find the unexpected. If you are smart and explore, you might find what you expect, but no one else can imagine. Ok, what I did was to start climate research from the scratch and do the most reasonable, logical thing one could only imagine. I looked up weather records for statistical analysis. How are clouds and temperatures statistically correlated?

 

In meteorology cloud cover is usually described with oktas as a unit of measurement.12 Accordingly 0 oktas represent a completely clear sky, while 8 means overcast. In the (usual) case of doubt, the rule is to…

First of all let us simplify a couple of terms, which might otherwise cause confusion. There is albedo, reflectivity, absorptivity and emissivity. In climate science they somehow all mean the same thing. Albedo literally means the same thing as reflectivity, it really makes no difference. Then according to Kirchhoff's law absorptivity = emissivity at any respective wavelength. And since transmissivity is usually and rightfully ignored in this discipline, both absorptivity and emissivity respectively are just the inversion of albedo or reflectivity. So absorptivity = emissivity = 1 - albedo = 1…

While the data itself may be sound, the samples published are usually hand-picked to support a certain, deceiving narrative. Then with the sub text, the explanations hereto, the gloves are off and there are no more limits to deception and lies. Let us discuss another example, this time with a desert climate.

Emission spectra as seen from satellites, or modelled with modtran, are probably among the most profound evidence for Earth's greenhouse effect. They visualize not just how greenhouse gases reduce emissions, but also which GHGs do that and to what extent. However, these data are highly deceptive as they get matched against the erroneous benchmark of a perfectly emitting surface. And it seems everyone falls for the trick.

A straight forward answer would be, we do not really know. There are a couple of issues which make it hard to simply measure emissivity, and that is not just true for Earth, but also for the Moon for instance. Just like within the visible light, the optical properties of the surface within the LWIR range can be very diverse. Just think of the different colors you see when you look outside. What we perceive as colors are effectively different wavelengths of electromagnetic radiation within the relatively tight spectrum of visible light. The fact that surfaces have all these different colors…

I was a bit reluctant to write this article, since it is certainly a horribly annoying issue. There are no insights to be gained here, at least not with regard to climate science. It is only about cleaning up a horrible mess. The only thing making it worthwhile is in pointing out how stupid people are. And with people I mean "experts".

The position of the IPCC on the cloud radiative effect (CRE, also frequently named cloud forcing CF) is simple and straight forward: By enhancing the planetary albedo, cloudy conditions exert a global and annual short-wave cloud radiative effect (SWCRE) of approximately –50 W m–2 and, by contributing to the greenhouse effect, exert a mean longwave effect (LWCRE) of approximately +30 W m–2, with a range of 10% or less between published satellite estimates

There is a simple formula our understanding of the greenhouse effect (GHE) is based on. ((1-0.3) * 342 / 5.67e-8) ^0.25 = 255K

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