Climatologists Don’t Know Clouds

at all.

As I wrote a couple days ago:

I deny that we understand the complex and chaotic interactions of the atmosphere, oceans and solar and other inputs sufficiently to model them with any confidence into the future, and I deny that it is unreasonable and unscientific to think that those who [believe they] do suffer from hubris.

There may be some time in the future that we understand climate as well as they think they do, but I don’t think it will be soon, and I wouldn’t bet that it will ever happen.

7 thoughts on “Climatologists Don’t Know Clouds”

  1. The problem is, there is no controlled experiment possible, no closed loop response to a known, persistently exciting input which can be measured. They think they can guess the form of the equations and, if they can replicate observed behavior, then their equations are right. But, you can always replicate behavior over a finite interval with a sufficiently complex model, often without gaining any predictive value or insightful perspective.

    Even worse than that, they have taken the models to be “truth”, and placed the burden of proof for any data which deviates from the model on the data. They massage and finagle the data until it matches the model, which is bass ackwards.

    Hubris led them down this road, and now they have backed themselves into a corner. It is now apparent that the run up in the average global temperature metric (AGTM whatever the average of an intensive variable like temperature even means), above the nominal trend since the LIA, from 1970 to 2000 is merely a natural cyclical repeat of the warming that occurred from 1910 to 1940.

    The underlying trend is about 1 degC/century, with a sinusoidal variation of +/- 0.4 degC with an approximately 60 year period. Those are the major components in the AGTM. They are natural, having very little change in character over the past century, and any anthropogenic impact is relatively small and not readily discernible.

    1. Even worse than that, they have taken the models to be “truth”, and placed the burden of proof for any data which deviates from the model on the data. They massage and finagle the data until it matches the model, which is bass ackwards.

      What they fail to accept is that when the model doesn’t agree with reality, it isn’t reality that’s wrong.

  2. Most of the models didn’t include clouds for many, many years, and when they did the clouds might as well have been floating sheep for all the accuracy they had. A few years ago some modelers just started cutting and pasting NASA satellite images of clouds because the GCM’s had no hope of reproducing the complex 3-D structures and spacings of natural clouds. They couldn’t even get the models to match up with each other in a one-dimensional air column run, with each model forming clouds of varying thickness and wildly varying heights. If you can’t model the atmosphere in a tall elevator shaft over a leaky basement, you don’t have a prayer to modeling an entire planet.

  3. I’m still stuck on calling the period 1850-now ‘the instrumental period’ for temperature measurements.

    Using two widely-separated thermometers with an instrumental error of ±0.1C to estimate by geographic interpolation values for ten intervening gridcells does not lead to having 12 measurements with an error of -better- than ±0.1C. Regardless of the fact that statistically they deviate from the interpolation by a miniscule amount. That’s a trivial “0=0” observation that’s not relevant to determining the true error. That’s a modelling error.

  4. Floating sheep. hah.

    I remember when I read the first batch of leaked Climate Gate emails. Wow. What a shock. Total scam, intentional scam; nothing, but nothing, to do with what I thought we meant by “science.”

    IIRC, one of the “scientists” couldn’t get the results he wanted from all of his tree rings, so he eliminated all the tree rings except one from someplace in Russia that showed the results he wanted. So his total research conclusion was extrapolated from one tree.

    Lawyers who engage falsification are supposed to get disbarred. What happens to scientist? Do they go on CNN as experts?

    btw, Rand, you use the word hubris. I think your point is true and even self-evident. In 2008, Newt said that the idea that humans cause the earth’s climate to change is a kind of hubris. Although he said he doesn’t think we know for sure what’s going on And he said he thinks we should be very good stewards of the earth. He also said the greatest cause of earth’s climate is the sun.
    [This is an excerpt from “THE BUSINESS OF GOING GREEN”- video below in its entirety]

    “THE BUSINESS OF GOING GREEN” [complete video]  –  March 20, 2008  – National Constitution Center – Philadelphia, Pennsylvania – 1:22:02

  5. The dirty little secret of climate models is that they are more or less the equivalent of convoluted fitted curves. No climate model is 100% derived from first principles, every single one of them includes at least one and sometimes several fudge factors, which have to be determined empirically. Worse yet, we only have detailed climate data in which to verify such models over an incredibly tiny time frame (a few decades), a time frame that is almost certainly too short to be of use to properly differentiate between actually accurate and only seemingly accurate models.

    The scary thing is that the climatology community has been striving more and more to shore up a particular result (that increased atmospheric CO2 will have devastating climatological effects in the 21st century) and have been doing a comparatively poorer job of simply trying to understand climate better.

  6. Clouds are actually the key element in creating optical depth that increases the time a photon spends in the atmosphere. High clouds tend to reflect half the heat photons back to the ground and let all the light through. Low clouds reflect the light back up to space increasing albedo. Without clouds, there is no global warming–the heat just radiates to space. Since emissivity goes as the fourth power of temperature, a 1% increase in temperature (3 degrees C) would result in a 4% increase in emissivity quickly canceling the temperature increase unless there were a lot more high clouds and/or a lot fewer low clouds.

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