Are Climate Models Built on Shaky Foundations?
Climate models are often hailed as the bedrock of climate science, the unshakeable foundation upon which policy, regulation, and global action are built.
Yet, these models rest on three critical assumptions: accurate population projections, a precise understanding of carbon sources and sinks, and the physics governing CO2’s behavior in the atmosphere.
Beneath their polished exterior lies a troubling reality: these assumptions are riddled with guesswork, speculation, and glaring uncertainties.
Are these models genuine tools for insight, or dangerously flawed frameworks that mislead us on climate policy?
What follows is a look at just how deep these flaws go, and a challenge to the so-called “settled science” of climate models. Are we really basing global decisions on science, or on little more than speculation?
Population Projections: The First Shaky Pillar
The first assumption underpinning climate models is population growth. The logic seems sound: more people mean more emissions.
But in reality, population projections are as much an exercise in guesswork as in science. According to analyses by experts like Roger Pielke Jr., these projections routinely overshoot, often by a significant margin.
Take South Korea, for instance, models project rising birth rates when, in reality, the country’s birth rate is plummeting.
This is not just a minor error; it’s a domino effect that skews emissions projections worldwide.
If climate models rely on inflated population figures, they inherently predict emissions that will never happen. And yet, this glaring flaw in demographic forecasting remains oddly tolerated within climate science.
By overestimating future population growth, climate models effectively overestimate future emissions, creating scenarios that are based on phantom carbon footprints.
Ignoring these inaccuracies throws the entire modeling framework off course. This is not a minor oversight; it’s a fundamental flaw that raises serious questions about the credibility of climate predictions.
Carbon Sources and Sinks: An Uncertain Balance
The second shaky pillar propping up climate models is our understanding of carbon sources and sinks.
We’re told that human activities contribute roughly 3% of total carbon emissions, yet this small fraction supposedly has a disproportionate impact on atmospheric CO2. Recent research paints a very different picture. A groundbreaking study published in Nature found…
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John V
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Yes.
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Herb Rose
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The models are based on bad data, bad theories, and bad reasoning.
#1: The temperature of a gas is nt an indication of the energy of the molecules and is in any way comparable to the temperature of the surface. To compare the 2 the thermometer should used in the same manner. To take the temperature at the surface you must submerge the entire thermometer so all the measuring liquid is absorbing energy as it does in the atmosphere. Then you must divide that reading by the number of molecules (density) to get the energy/ constant number of molecules. All matter absorbs radiated energy even if it doesn’t absorb visible light.
#2; The Earth is neither a black body or a green house. The surfaces loses energy almost exclusively by convection, not radiation. Radiation loss occurs in the thermosphere. How can something radiate and lose heat through a hotter medium?
#3: It is not possible for 442 CO2 molecules to heat a billion molecules on the surface when they are being heaTed by those same molecules.
Compared to the climate bullshit Jello is a granite rock.
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VOWG
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We saw how well computer models worked for the covid scam, why not the climate scam, people on average are not very smart.
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James Edward Kamis
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Scientific research studies should follow the process of “Scientific Methodology”. The steps are as follows. Gather of the relevant observations, data, ideas, and theories from many disciplines which may be connected to your study. Then develop numerous theories that fit part of information. Don’t ” Force Fit” all of the information into a theory formed prior reviewing all of the information! Always consider new information that proves your theory is false. Don’t try to get into groups or media that are consensus.
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sunsettommy
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Yes, researchers should be trying to beat their own hypothesis in order to learn if it is actually a viable one that survives to further research.
Nice to see you Dr. Kamis.
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