Re: Whoops! The Atlantic Makes Trump Look EPIC In Cover Intended as a Smear

On Mon, 23 Sep 2024, D wrote:

These models have done a good job of simulating past climates, from the ice ages to Eocene warmth, to Pangean Monsoons and Holocene lake levels in east Africa.  Your source here is simply incorrect.
 They have also made the above correct predictions (do I have to repeat them for the tenth time?).  Neither of the ideas you propose above have made any correct predictions.
 "Flawed" has become a word which means nothing more than "I disagree".
It is meaningless without being able to point to an actual flaw.

>
Incorrect. They are just models, and past patterns do not guarantee
future performance. It is easy to create any model you want, to show any
result you need.
>
This reliance on models instead of proof, is another huge weakness of
climatologists and completely undermines their theories.

Good evening William, I found a great article on the unreliability of
models and why we cannot rely on them. I hope you enjoy the read!
hoover.org Flawed Climate Models September 20, 2024 11–14 minutes
The atmosphere is about 0.8˚ Celsius warmer than it was in 1850. Given
that the atmospheric concentration of carbon dioxide has risen 40
percent since 1750 and that CO2 is a greenhouse gas, a reasonable
hypothesis is that the increase in CO2 has caused, and is causing,
global warming.
But a hypothesis is just that. We have virtually no ability to run
controlled experiments, such as raising and lowering CO2 levels in the
atmosphere and measuring the resulting change in temperatures. What else
can we do? We can build elaborate computer models that use physics to
calculate how energy flows into, through, and out of our planet’s land,
water, and atmosphere. Indeed, such models have been created and are
frequently used today to make dire predictions about the fate of our
Earth.
The problem is that these models have serious limitations that
drastically limit their value in making predictions and in guiding
policy. Specifically, three major problems exist. They are described
below, and each one alone is enough to make one doubt the predictions.
All three together deal a devastating blow to the forecasts of the
current models.
     Measurement Error
Imagine that you’re timing a high school track athlete running 400
meters at the beginning of the school year, and you measure 56 seconds
with your handheld stopwatch that reads to ±0.01 seconds. Imagine also
that your reaction time is ±0.2 seconds. With your equipment, you can
measure an improvement to 53 seconds by the end of the year. The
difference between the two times is far larger than the resolution of
the stopwatch combined with your imperfect reaction time, allowing you
to conclude that the runner is indeed now faster. To get an idea of this
runner’s improvement, you calculate a trend of 0.1 seconds per week (3
seconds in 30 weeks). But if you try to retest this runner after half a
week, trying to measure the expected 0.05-second improvement, you will
run into a problem. Can you measure such a small difference with the
instrumentation at hand? No. There’s no point in even trying because
you’ll have no way of discovering if the runner is faster: the size of
what you are trying to measure is smaller than the size of the errors in
your measurements.
Scientists present measurement error by describing the range around
their measurements. They might, for example, say that a temperature is
20˚C ±0.5˚C. The temperature is probably 20.0˚C, but it could reasonably
be as high as 20.5˚C or as low as 19.5˚C.
Now consider the temperatures that are recorded by weather stations
around the world.
Patrick Frank is a scientist at the Stanford Synchrotron Radiation
Lightsource (SSRL), part of the SLAC National Accelerator Laboratory at
Stanford University. Frank has published papers that explain how the
errors in temperatures recorded by weather stations have been
incorrectly handled. Temperature readings, he finds, have errors over
twice as large as generally recognized. Based on this, Frank stated, in
a 2011 article in Energy & Environment, “…the 1856–2004 global surface
air temperature anomaly with its 95% confidence interval is 0.8˚C ±
0.98˚C.” The error bars are wider than the measured increase. It looks
as if there’s an upward temperature trend, but we can’t tell
definitively. We cannot reject the hypothesis that the world’s
temperature has not changed at all.
     The Sun’s Energy
Climate models are used to assess the CO2-global warming hypothesis and
to quantify the human-caused CO2 “fingerprint.”
How big is the human-caused CO2 fingerprint compared to other
uncertainties in our climate model? For tracking energy flows in our
model, we use watts per square meter (Wm–2). The sun’s energy that
reaches the Earth’s atmosphere provides 342 Wm–2—an average of day and
night, poles and equator—keeping it warm enough for us to thrive. The
estimated extra energy from excess CO2—the annual anthropogenic
greenhouse gas contribution—is far smaller, according to Frank, at 0.036
Wm–2, or 0.01 percent of the sun’s energy. If our estimate of the sun’s
energy were off by more than 0.01 percent, that error would swamp the
estimated extra energy from excess CO2. Unfortunately, the sun isn’t the
only uncertainty we need to consider.
     Cloud Errors
Clouds reflect incoming radiation and also trap it as it is outgoing. A
world entirely encompassed by clouds would have dramatically different
atmospheric temperatures than one devoid of clouds. But modeling clouds
and their effects has proven difficult. The Intergovernmental Panel on
Climate Change (IPCC), the established global authority on climate
change, acknowledges this in its most recent Assessment report, from
2013:
The simulation of clouds in climate models remains challenging. There is
very high confidence that uncertainties in cloud processes explain much
of the spread in modelled climate sensitivity. [bold and italics in
original]
What is the net effect of cloudiness? Clouds lead to a cooler atmosphere
by reducing the sun’s net energy by approximately 28 Wm–2. Without
clouds, more energy would reach the ground and our atmosphere would be
much warmer.  Why are clouds hard to model? They are amorphous; they
reside at different altitudes and are layered on top of each other,
making them hard to discern; they aren’t solid; they come in many
different types; and scientists don’t fully understand how they form. As
a result, clouds are modeled poorly. This contributes an average
uncertainty of ±4.0 Wm–2 to the atmospheric thermal energy budget of a
simulated atmosphere during a projection of global temperature. This
thermal uncertainty is 110 times as large as the estimated annual extra
energy from excess CO2. If our climate model’s calculation of clouds
were off by just 0.9 percent—0.036 is 0.9 percent of 4.0—that error
would swamp the estimated extra energy from excess CO2. The total
combined errors in our climate model are estimated be about 150 Wm–2,
which is over 4,000 times as large as the estimated annual extra energy
from higher CO2 concentrations. Can we isolate such a faint signal?
In our track athlete example, this is equivalent to having a reaction
time error of ±0.2 seconds while trying to measure a time difference of
0.00005 seconds between any two runs. How can such a slight difference
in time be measured with such overwhelming error bars? How can the faint
CO2 signal possibly be detected by climate models with such gigantic
errors?
Other Complications
Even the relationship between CO2 concentrations and temperature is
complicated.
The glacial record shows geological periods with rising CO2 and global
cooling and periods with low levels of atmospheric CO2 and global
warming. Indeed, according to a 2001 article in Climate Research by
astrophysicist and geoscientist Willie Soon and his colleagues,
“atmospheric CO2 tends to follow rather than lead temperature and
biosphere changes.”
A large proportion of the warming that occurred in the 20th century
occurred in the first half of the century, when the amount of
anthropogenic CO2 in the air was one quarter of the total amount there
now. The rate of warming then was very similar to the rate of warming
recently. We can’t have it both ways. The current warming can’t be
unambiguously caused by anthropogenic CO2 emissions if an earlier period
experienced the same type of warming without the offending emissions.
Climate Model Secret Sauce
It turns out that climate models aren’t “plug and chug.” Numerous inputs
are not the direct result of scientific studies; researchers need to
“discover” them through parameter adjustment, or tuning, as it is
called. If a climate model uses a grid of 25x25-kilometer boxes to
divide the atmosphere and oceans into manageable chunks, storm clouds
and low marine clouds off the California coast will be too small to
model directly. Instead, according to a 2016 Science article by
journalist Paul Voosen, modelers need to tune for cloud formation in
each key grid based on temperature, atmospheric stability, humidity, and
the presence of mountains. Modelers continue tuning climate models until
they match a known 20th century temperature or precipitation record. And
yet, at that point, we will have to ask whether these models are more
subjective than objective. If a model shows a decline in Arctic sea ice,
for instance—and we know that Arctic sea ice has, in fact, declined—is
the model telling us something new or just regurgitating its
adjustments?
Climate Model Errors
Before we put too much credence in any climate model, we need to assess
its predictions. The following points highlight some of the difficulties
of current models.
Vancouver, British Columbia, warmed by a full degree in the first 20
years of the 20th century, then cooled by two degrees over the next 40
years, and then warmed to the end the century, ending almost where it
started. None of the six climate models tested by the IPCC reproduced
this pattern. Further, according to scientist Patrick Frank in a 2015
article in Energy & Environment, the projected temperature trends of the
models, which all employed the same theories and historical data, were
as far apart as 2.5˚C.
According to a 2002 article by climate scientists Vitaly Semenov and
Lennart Bengtsson in Climate Dynamics, climate models have done a poor
job of matching known global rainfall totals and patterns.
Climate models have been subjected to “perfect model tests,” in which
the they were used to project a reference climate and then, with some
minor tweaks to initial conditions, recreate temperatures in that same
reference climate. This is basically asking a model to do the same thing
twice, a task for which it should be ideally suited. In these tests,
Frank found, the results in the first year correlated very well between
the two runs, but years 2-9 showed such poor correlation that the
results could have been random. Failing a perfect model test shows that
the results aren’t stable and suggests a fundamental inability of the
models to predict the climate.
The ultimate test for a climate model is the accuracy of its
predictions. But the models predicted that there would be much greater
warming between 1998 and 2014 than actually happened. If the models were
doing a good job, their predictions would cluster symmetrically around
the actual measured temperatures. That was not the case here; a mere 2.4
percent of the predictions undershot actual temperatures and 97.6
percent overshot, according to Cato Institute climatologist Patrick
Michaels, former MIT meteorologist Richard Lindzen, and Cato Institute
climate researcher Chip Knappenberger. Climate models as a group have
been “running hot,” predicting about 2.2 times as much warming as
actually occurred over 1998–2014. Of course, this doesn’t mean that no
warming is occurring, but, rather, that the models’ forecasts were
exaggerated.
Conclusions
If someone with a hand-held stopwatch tells you that a runner cut his
time by 0.00005 seconds, you should be skeptical. If someone with a
climate model tells you that a 0.036 Wm–2 CO2 signal can be detected
within an environment of 150 Wm–2 error, you should be just as
skeptical.
As Willie Soon and his coauthors found, “Our current lack of
understanding of the Earth’s climate system does not allow us to
determine reliably the magnitude of climate change that will be caused
by anthropogenic CO2 emissions, let alone whether this change will be
for better or for worse.”