Contradictory Contrarian Claims, Part 2: Observational Data are Fudged to Match Models that Predict Too Much Warming

Another common contradiction I see among contrarian influencers is that observational data are manipulated to fit a global warming agenda that comes solely from fudged climate models that simultaneously predict far too much warming than observational data. Let's write this as two statements that make the contradiction even more obvious, where:

    A = Model Predictions
    B = Observational Data
    C = Climate Scam Agenda 

The contradiction can be stated in at least two ways. In the simpler statement, A = B and A ≠ B:

1. Observational data are manipulated to agree with model predictions.
2. Models are fudged to predict far more warming than is warranted from observational data. 

The logic can sometime be stated in a way that is slightly more complex. Here's another wording of the same contradiction. In this case A = C and B = C (so A = B) and A ≠ B:

1. Model predictions are fudged and observations are fabricated to agree with the same climate scam agenda.
2. Model predictions do not agree with observations because they show too much warming.

Clearly if observational data is manipulated to agree with a model-driven agenda, the observational data would agree with models. But in fact they say that models predict too much warming, disproving the model-driven agenda. Logically, either one or both claims are false. They can't both be true. If model predictions do not agree with observational data, the observational data is not fudged to agree with models. If observations agree with models, the models are not fudged to show more warming than observations.

It seems that contrarians try to space out these contradictory claims and word them so that the contradiction isn't as obvious, but it's not hard to show the same contrarians essentially making both of the above claims. I decided to show examples from John Shewchuk because he is one of the more prolific grifters on social media and content from him is pretty easy to find. Let's look at some examples. All of these below come from X, and I linked to the posts so that you can see them with context, which I would encourage you to do.

Models Are Fudged to Produce Too Much Warming

1. "Current failing models continue to produce unrealistically hot forecasts. In fact, not only have none ever been correct, but none can even replicate past climate change." (From X)
2. "...after 30+ years of consistent HOT error forecast biases -- it's clear the the IPCC models are designed with an agenda to scare. The IPCC is a global propaganda machine gun." (From X)
3. "I don't follow peers, or experts, unless they follow data using the scientific method. I also don't follow IPCC forecast models - because they stink." (From X)
4. "Folks frown when I say climate models have fudge factors. I was and still am a modeler, and I can tell you all weather/climate models have fudge factors, mainly because of our significant lack of knowledge of many, complex atmospheric processes. #ClimateScam models are fudged." (From X)
5. "Climate models, just like the Hurricanes models, only project the known properties and dynamics of the atmosphere. They are not data -- and they not capable of handling the unknowns and the unprecedented events like the Tonga volcano." (From X)
6. "The climate models dictate relentless CO2 warming ... yet the data shows absolutely NO change in 100 years of U.S. data for the month of May.   (Are the IPCC models tuned to only use 11 months of data?)" (From X)
7. "The oceans have been steadily cooling for 16 months following Tonga's global heating spike. How many of the multi-million dollar IPCC climate models predicted this?" (From X)
8. "Global models use global data - with 4 problems. 1 - data is insufficient. 2 - much data is altered (see graph). 3 - computer models are not data - but only guesses. 4 - no model can replicate past climate change - so they can't be trusted." (From X)
9. "IPCC climate model failure is accelerating - not global warming." (From X)

Here "none" of the models have been correct, and all have failed to replicate even past climate changes because they consistently show "hot error forecast biases" demonstrating that they are "designed with an agenda to scare" and are "fudged" to fit a "climate scam." Clearly it's fair to say that Shewchuk claims that models are fudged to fit an agenda and produce far more warming than is warranted by the observational data.

Observational Data is Manipulated to Fit a Global Warming Agenda

1. "Don't be fooled by these fake 'global' temperature graphs. They are concocted from a combination of altered and fabricated temperatures ... on top of steadily growing Urban Heat Island (UHI) effects that significantly contribute to the deceptive rise during the past 100 years." (From X)
2. "NOAA is expert at fabricating the hot stuff -- which is how the climate crisis is manufactured." (From X)
3. "Climate alarmists claim man is causing global warming. Well, they are right, because man keeps altering the temperature data to achieve the desired results -- just like a thermostat. But it's only on paper -- and not reality." (From X)
4. "My 'Global Warming' audience knows not to blindly believe climate alarmists' faked temperature graphs, because they now know they are contaminated with altered and fabricated, hot-biased, temperature data." (From X)
5. "No temperature data has been falsified. It's just been made up by people with an agenda. Their corrupt agenda is solid" (From X)
6. "A hallmark of #ClimateScam is how data is altered to fit an agenda." (From X)
7. "Notice how climate alarmism works -- by using altered data designed to inflate global warming trends and by using climate models which have NEVER been able to replicate past climate change..." (From X)
8. "Notice anything different about the trends in these 2 graphs of January - August temperature data for 1900 to 2023? Top is U.S. (observed data) while bottom is Global (altered data)." (From X)

Here Shewchuk claims observational data, especially surface thermometer data, is "concocted" and "altered" and "fabricated" in order to "manufacture a "climate crisis" agenda. It's been "made up by people with an agenda." Clearly for Shewchuk, models are "fudged" and "temperatures" are "fabricated" to agree with the same climate scam agenda AND models and temperatures don't agree with each other.

Now some may object that Shewchuk is saying models show too much warming with respect to UAH satellite data (which he says isn't manipulated), and thermometer temperature is manipulated to agree with models. This would be perhaps a fair rebuttal if Shewchuk limited his claims about models to their predictions about satellite observations. But he clearly doesn't. He clearly says that all models fail and have a warming bias, with the possible exception of the "Russian model." And he also explicitly says models produce much more warming than thermometer datasets. So he can't get out of this contradiction.

Both Claims Are Wrong

I've written a lot about each of these contradictory claims, so I don't see much reason to write new rebuttals. The point here is that Shewchuk and his fellow grifters freely contradict themselves on data manipulation. The following is a summary with links to other rebuttals I've written.

1. Models have pretty consistently predicted increases in global mean surface temperature accurately.
2. Model vs observation miss-match claims like those from Roy Spencer are often exaggerated by contrarians with deceitful plots and cherry-picked models and observational data.
3. Shewchuk's claim of a model-observation miss-match regarding the total troposphere temperatures (TTT) depends almost entirely on the fact that he incorrectly averaged UAH data.
4. I respond to challenges to GMST anomaly data here. There's no evidence that data is fabricated.
5. I respond to challenges of CONUS temperature data here. Again, there's no evidence that data is fabricated.
6. There is plenty of evidence that the UHI effect (while very real) does not affect either GMST or CONUS temperature trends in published datasets.