The great failure of the climate models

Computer models of the climate are at the heart of calls to ban the cheap, reliable energy that powers our thriving economy and promotes healthier, longer lives.

For decades, these models have projected dramatic warming from small, fossil-fueled increases in atmospheric concentrations of carbon dioxide, with catastrophic consequences.

Yet, the real-world data aren’t cooperating. They show only slight warming, mostly at night and in winter.

According to the United Nations’ Intergovernmental Panel on Climate Change, there has been no systematic increase in the frequency of extreme weather events, and the ongoing rise in sea level that began with the end of the ice age continues with no great increase in magnitude. The constancy of land-based records is obvious in data from the National Oceanic and Atmospheric Administration.

Should we trust these computer models of doom? Let’s find out by comparing the actual temperatures since 1979 with what the 32 families of climate models used in the latest U.N. report on climate science predicted they would be.

Atmospheric scientist John Christy developed a global temperature record of the lower atmosphere using highly accurate satellite soundings. NASA honored him for this achievement, and he was an author for a previous edition of the U.N. report. He told a House Science Committee hearing in March 2017 that the U.N. climate models have failed badly.

Christy compared the average model projections since 1979 to the most reliable observations — those made by satellites and weather balloons over the vast tropics. The result? In the upper levels of the lower atmosphere, the models predicted seven times as much warming as has been observed. Overprediction also occurred at all other levels. Christy recently concluded that, on average, the projected heating by the models is three times what has been observed.

This is a critical error. Getting the tropical climate right is essential to understanding climate worldwide. Most of the atmospheric moisture originates in the tropical ocean, and the difference between surface and upper atmospheric temperature determines how much of the moisture rises into the atmosphere. That’s important. Most of Earth’s agriculture is dependent upon the transfer of moisture from the tropics to temperate regions.

Christy is not looking at surface temperatures, as measured by thermometers at weather stations. Instead, he is looking at temperatures measured from calibrated thermistors carried by weather balloons and data from satellites. Why didn’t he simply look down here, where we all live? Because the records of the surface temperatures have been badly compromised.

Globally averaged thermometers show two periods of warming since 1900: a half-degree from natural causes in the first half of the 20th century, before there was an increase in industrial carbon dioxide that was enough to produce it, and another half-degree in the last quarter of the century.

The latest U.N. science compendium asserts that the latter half-degree is at least half manmade. But the thermometer records showed that the warming stopped from 2000 to 2014. Until they didn’t. In two of the four global surface series, data were adjusted in two ways that wiped out the “pause” that had been observed.

The first adjustment changed how the temperature of the ocean surface is calculated, by replacing satellite data with drifting buoys and temperatures in ships’ water intake. The size of the ship determines how deep the intake tube is, and steel ships warm up tremendously under sunny, hot conditions. The buoy temperatures, which are measured by precise electronic thermistors, were adjusted upwards to match the questionable ship data. Given that the buoy network became more extensive during the pause, that’s guaranteed to put some artificial warming in the data.

The second big adjustment was over the Arctic Ocean, where there aren’t any weather stations. In this revision, temperatures were estimated from nearby land stations. This runs afoul of basic physics.

Even in warm summers, there’s plenty of ice over much of the Arctic Ocean. Now, for example, when the sea ice is nearing its annual minimum, it still extends part way down Greenland’s east coast. As long as the ice-water mix is well-stirred (like a glass of ice water), the surface temperature stays at the freezing point until all the ice melts. So, extending land readings over the Arctic Ocean adds nonexistent warming to the record.

Further, both global and United States data have been frequently adjusted. There is nothing scientifically wrong with adjusting data to correct for changes in the way temperatures are observed and for changes in the thermometers. But each serial adjustment has tended to make the early years colder, which increases the warming trend. That’s wildly improbable.

In addition, thermometers are housed in standardized instrument shelters, which are to be kept a specified shade of white. Shelters in poorer countries are not repainted as often, and darker stations absorb more of the sun’s energy. It’s no surprise that poor tropical countries show the largest warming from this effect.

All this is to say that the weather balloon and satellite temperatures used in Christy’s testimony are the best data we have, and they show that the U.N.’s climate models just aren’t ready for prime time.

Patrick Michaels was a research professor of Environmental Sciences at the University of Virginia for 30 years and is the author of the upcoming book Scientocracy. Caleb Stewart Rossiter taught climate statistics and mathematical modeling at American University. They are currently with the CO2 Coalition in Arlington, Virginia.

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Comments (1)

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    tom0mason

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    As these models poorly model ocean current, atmospheric humidity (clouds, snow, precipitation, ice, etc.) as the basic science is so poorly known. The programmers ‘parameterize’ these and many other factors. The upshot of this is that ‘parameterize’ factors are then tuned to give the required output. No amount of statistical conjuring can resolve this deficiency. Until the basic science is done the models can NEVER be correct — END OF STORY. If you disagree with this please, scientifically explain (cite references) how all 1. How clouds form cite the mathematics for every type of cloud. 2. Explain every aspect governing formation evolution and dissipation of every type of clouds. 3. Explain the ‘energy balance’ of every type of clouds through it entire time it is evident. 4. Show where your ‘science is incorporated into the models.

    As a starting point of reference read V. Krishnamurthy say much in his report ‘Predictability of Weather and Climate’ ( https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019EA000586 )

    The paper highlights the FACT that all these models have a nasty tendency to multiply up the errors until their outputs are unusable, with results that rapidly head towards unreal and unstable weather and this leads to alarming climate predictions/projection.

    While the growing recognition of seamless prediction of weather and climate seems to be a desirable goal, some fundamental questions about the predictability of climate need to be addressed. With the present‐day models being incapable of predicting the instantaneous states beyond the weather time scale, there is a need to find answers for what is predictable at the climate scale and to identify the sources of long‐range predictability of climate. The optimism for long‐range predictability of climate comes from the existence of slowly varying components such as SST and soil moisture and more regularly varying oscillations such as monsoon ISOs, MJO, and ENSO. Instead of instantaneous states, the predictability of time‐averaged variability, the embedded nonlinear oscillations, and signals associated with persisting modes of climate must be investigated. The recent study employing the phase reconstruction method demonstrated that there is reliable long‐range predictability of monsoon ISO. Certain aspects of climate variability at intraseasonal and seasonal time scales can be predicted at extended range, and therefore, the predictability of phenomena such MJO and ENSO must be investigated. Ultimately, the prediction at the climate time scale must be made by global climate models. The demonstration of the predictability of climate by simple models brings optimism for better forecasts by operational models when they are capable of properly representing the sources of long‐range predictability. For example, a major problem with the operational model in predicting the monsoon ISO is the failure of the model to correctly capture the initial phase of the oscillation. Therefore, investigations with simple models and newer methods may shed light on the sources of climate predictability and the problems that need to be addressed by operational centers.

    [My bold, emphasizing some the deficiencies highlighted in this paper.]

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