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Orwell2024🏒 Profile picture
Orwell2024🏒
@orwell2022
If it disagrees with the expert, it’s wrong. In that simple statement is the key to consensus. https://t.co/MYLncQl3Uh
11 subscribers
Nov 13 • 8 tweets • 4 min read
1/ This brigheting since 1980 is known in prof. circles.

Here’s the data from Germany (Potsdam).

+15 W/m² 🌞since the 1980s. ✅

Yet the public narrative claims climate change/CO₂ (~+1.4 W/m² over the same period). ❌

The gap between evidence and narraitive is 10x....off. Image 2/ The analysis is already done. DWD and peer-reviewed literature.
It matches what we saw from JMA and KNMI raw data:
a +10–20 W/m² increase in surface solar radiation.

So the question:
How did they get away with knowing this and selling the story of ~1.4 W from CO₂ instead? Image
Aug 6 • 9 tweets • 4 min read
A +14 W/m² total solar increase over 50 years is realistic. Japan alone shows +20 W/m². That’s 10× larger than the minuscule additional CO₂ forcing (~1W). And nearly 50× greater than the impact of sunspot cycles (±0.5 W).

So why is the climate scam still lying? Image
Image Japan has one of the best measurement data. The analysis is clear. The brightening amount to almost 20 W. That is a lot. But the main and dominant effect is still urbanization, which makes up to 6°. Image
Jul 6 • 4 tweets • 2 min read
UAH is a model inference, not a measurement. It can’t be tested, yet many treat it like real raw. Calling that a ‘measurement’ is wrong. Neither Lindzen nor us take it seriously. It starts in a cold period, with no long-term data — adjusted, multi mission stitched SW composite.🚮 UAH is not measurement — it’s model-driven inference. Satellites detect radiance, not temperature. The ‘trend’ is built through weighting functions, drift corrections, and stitched instruments. It’s untestable, synthetic, and not suitable for long-term climate baselines. Image
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Jul 1 • 6 tweets • 3 min read
London is glowing today. Wide urban heat plume. Not “climate change.” Just real estate and concrete. The effect is visible. Quantifiable. Known. This should be a good study day to quantify UHI in more detail once the IR satellite pictures come in. Image
Image 2/ We start low tech. Actually nothing more is needed. There is over 6°C urban heat. It's embarrassing to pretend today's 33°C are comparable to 100 years ago. Subtract 6–8°C for UHI and you get... 25–27°C. Welcome back to reality. Image
Jun 29 • 5 tweets • 3 min read
Stockholm downtown: Where’s the UHI correction?

QCU = raw data
QCF = adjusted data

Same numbers.

C A N -- Y O U -- S E E -- IT

Fredrik? Show us.
We can’t see it.

Where exactly is the downward adjustment of 2C-4C? That's the bias you have in 2025. Image We can also do from SE raw. And we can also show how rural stations look. Frederik does like them. Climate agenda is measured in downtowns of the capitals? Image
Jun 27 • 6 tweets • 4 min read
1/ +++🚨BREAKING🚨+++

Yes. Hausfather & Berkeley Earth are pushing it.
But it’s not a measurement. Not one station shows that.
It’s what you get when you aggregate rot over time.

On the left: 8 pristine USCRN sites. Same y-scale.
Now look what they did.👇 Image 2/ 🚨 BREAKING 🚨

We overlay. Do you see it now?

👉 They erased the real past.
👉 They had no data to do so.
👉 It's pure statistical deception—and wrong.

How? All exposed in the Wickham et al. (2013) audit thread.🧵

Image
Jun 23 • 12 tweets • 6 min read
1/ Weil’s beliebt ist: Hohenpeissenberg-Daten – fallen zwar schon nach BU-Filter raus, aber gut: Dr. Connolly war schlau.

Oben: Wie stark sich die Temperatur verändert hat.
Unten: Wie „unrund“ die Messwerte wurden – Entropie der Kommazahlen (h/t Connolly) Image 2/ Was wir hier sehen: Die Datenreihe ist ein Komposit (sehr beliebt, wenig seroes, in der Klima-„Wissenschaft“).
Die Messmethode (und mehr) hat sich verändert – von analogen zu digitalen Sensoren. Die Entropie der Nachkommastellen zeigt das – deutlich.
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Jun 22 • 6 tweets • 3 min read
1/ The result is simply wrong.
There are 2 stations there — we can compare.
🟥Red: Carlwood
🟩Green: Gatewick
We clearly see the overshoot.
Moreover: They’re using subhourly spikes (error) from a single, low-inertia sensor.
Total incompetence. Image 2/ Using TMAX from a low-quality single urban sensor is already peak incompetence.

But they go further — they take the spikes.
Even top-tier stations like USCRN show 2–3°C error at peak forcing.
USCRN uses triple sensors — worst spikes get voted out.

Image
Jun 12 • 17 tweets • 7 min read
1/ Back to Japan temperature trends.

We now add a smaller town: Suttsu. Still not truly rural nor stable site, but better.

We now show:
🟥 Kyoto
⬛️ Tokyo
🟦 Suttsu

Can you see it?

They then aggregate urban-biased data (rot) like this and call it “global temperature.” Image 2/ Here it is: Suttsu.

Not a high-quality reference site like
Valentia Observatory (Ireland) or h-USCRN sites.

But: Lower urban bias than cities like Kyoto or Tokyo. It starts to show the well known flatliner we see at stable sites. Image
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May 27 • 6 tweets • 3 min read
The red areas are fully man-made—built or cultivated.
You cannot measure climate anywhere near them.
And MODIS still misses a lot.
In reality, it’s worse.

When we inspected what @BerkeleyEA calls “rural”?
Almost all those stations are worthless Image Imagine a field looks like it does on the left…alive.
And later, like the right. Dead and brown.
Still think you'll measure the same 2m temperature?
Or might that just—possibly—have a major impact as the surroundings changed? GPT estimates 3C. It's not wrong. Image
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May 23 • 9 tweets • 4 min read
Maybe some still don’t grasp the novelty here. This shows what the “climate consensus” is built on: MODIS — a coarse, fully outdated DB.

Now compare that to P2023A.

So—@BerkeleyEarth @hausfath—still think your pick is better? Let me know. And explain why. Image Thanks to this trick, they labeled urban sites as “rural”—then obviously saw no difference.
That paper must be retracted.
Back then: resolution limits.
Today? Ignoring P2023A is agenda.
Anyone can open Google Earth and see houses where MODIS finds none.

Image
May 9 • 6 tweets • 3 min read
1/ April resists warming.
Remember: warming causes cooling.
If you’re freezing, you're actually warming.
Colder weather confirms it’s warmer.
We must prevent cooling to stop warming.
Yes, it still was the warmest April in SW models.
Now pay your CO2 tax please and eat vegan. Image 2/ We check ourselves. The ClimDiv curve is even cooling 1.37C compared with the stable USCRN sites. Image
May 5 • 6 tweets • 4 min read
1/ This proxy is the most dishonest narrative in the entire climate agenda.
Anyone pushing it isn’t doing science — they’re signaling allegiance.
If you still treat them seriously, that’s on you.
They’re not analysts. They’re ideological fools. #ClimateScam Image
Image 2/ Last week of April 2025. Rural Nagano. ~700m elevation. Full bloom.
I challenge the town-proxy scammers to show us blooming in late May or June a hundred years ago.
Go ahead— make fools out of yourself by failing.
👉The consensus now = defund climate activists (“academics”). Image
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Apr 12 • 5 tweets • 4 min read
1/ I was told non US GHCN “raw” is adjusted already.

-----TRUE-----

Now I see it. Gosh.

Composite. 2x adjusted. NOAA doesn’t even know where non-US stations are—or what they’re measuring. Their own US data (USCRN) is light-years better. But for “global”? It’s clown-tier level. Image 2/ And here it is—the DOUBLE-adjusted COMPOSITE.
Not raw. I doubted @connolly_s at first—like someone denying their 2nd-hand car is stolen, crash-salvaged, and repainted twice. Turns out he was right.
NOAA’s “global” QCU (non-US): not raw.
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Apr 10 • 27 tweets • 15 min read
1/ The WMO’s temperature station classification study isn’t a glamorous reading —but it’s the bare minimum anyone aggregating climate data should know about every single station. They don’t.

Scandal hiding in plain sight Image 2/ Class 1 is “bare minimum” for climate-grade weather station suitability. One means maybe ok.
met.no/publikasjoner/…
I’ll be counting impressions. I’ll know if you didn’t read.
(you’re allowed to LLM TlDR it.)
Next up: NOAA climate site requirements (HLR). 👇 x.com/orwell2022/sta…
Mar 28 • 43 tweets • 22 min read
1/ Digging deeper, we find 3 USCRN sites with 2 IDs — a legacy historical one and a USCRN. That’s big. It means we can stitch together long-term time series for 3 “golden” stations. Why haven’t @NOAA or @hausfath done this? Not the “right” narrative result? 🙃 Let’s take a look Image 2/ Here is an example of such a pair. STILLWATER. Note that you can see the wind fence around the precipitation gauge on satellite picture — that round structure.
ncei.noaa.gov/access/crn/pdf…Image
Mar 19 • 15 tweets • 9 min read
1/ Mr. @hausfath packed multiple fallacies into one graph. We replicate: he used homogenized data. We get the same.

Bottom right shows the raw. His fallacy: claiming that USCRN-ClimDiv agreement in the modern era (where adjustments are ~zero) validates strong past adjustments. Image
Image 3/ His fallacy is blatant bad faith. Measurement validation isn't done by induction. He claims adjustments are valid because USCRN-ClimDiv align from 2008-2024—yet no adjustments were made in that period. Then he asserts past adjustments are proven. Exceptional level of malice. Image
Mar 19 • 6 tweets • 4 min read
1/ New tool - let's test with VALENTIA (hourly) overlay: solid agreement. A model (ERA5) is only as good as its ground truth measurements constraints it. We saw good US results before, but obvious heat bias in polar regions—nothing measured to compare with there anyway. Image 2/ Now we match the 1940-2024 range. Note temp vs. anomaly scale—same curve, just shifted. A trick to amplify range. Few notice. Climate stripes? Perfect for manipulation—e.g. add offset (ECMWF) to make it red “=warm"= behavior science (manipulative).
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Mar 15 • 7 tweets • 4 min read
1/ Absolutely my worldview. But I haven’t found a trace of it in temperature measurements. Accuracy doesn’t seem to be a factor at all. Instead, they rely on bizarre software that arbitrarily alters the data. No station audits. No QMS existing. Nothing.
2/ This magic software even adjusts in various directions from day to day—without any explicit justification beyond it doing so. Is the sensor accuracy changing day to day?? No.

This finding by @connolly_s is important and exposes PHA being unrelated to measurement principles.
Mar 14 • 14 tweets • 7 min read
1/ The temperature (USCRN) since the 2014/2015 El Niño has been stable and slightly declining (cooling). Yet, we’re witnessing an unprecedented surge in mania. Interesting, isn’t it? Let’s demonstrate this by exposing the bias in GPT. We’ll trick it. Ready? Image 2/ To force it to be honest, we’ll deactivate ideological filters by labeling USCRN anomalies into portfolio value (adding 30 to shift upward of zero). This way, it will think it’s analyzing the fund performance from an automated trading product from my bank.

Hah. Gotcha. Down☺️ Image
Mar 11 • 8 tweets • 4 min read
Revisiting Las Vegas: The observed warming trend is likely driven primarily by urbanization. Unfortunately, there is no available data from before the 1970s, and the period of overlap with USCRN station records is short, which limits ability to analyze long-term. Image We look closer and see that for 10 years, there's no warming—flat. To make the Urban Heat Island (UHI) effect in Vegas clearer, we compare it with Gallup Muni AP🔵, a highly rural spot (BU 2020 <<1%). This highlights urbanization's impact in VEGAS🔴.
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