![ChrisMartzWX's tweet photo. Have you seen the figure on the left before?
Chances are you have if you’re actively involved in the climate change debate. If not, let me break it down.
The figure, adapted from Figure SPM.1 in the IPCC AR6 Summary for Policymakers, is often touted as the “smoking gun” that proves virtually all observed global warming since 1850 is due to mankind’s greenhouse gas (GHG) emissions. The figure implies that global climate models (GCMs) skillfully reproduce observed warming (see black curve) since the “pre-industrial” era. They claim that this chart is synthesized from “multiple lines of evidence,” and that GCMs cannot skillfully reproduce the observed temperature trends without extra GHGs.
🔗https://t.co/5lspFokYOu(p. 6)
However, this graph is not the “smoking gun” that many people think it is. So, let me explain why.
First, it’s important to understand exactly what Figure SPM.1 is showing.
⬛️ 𝗖𝘂𝗿𝘃𝗲: The observed global mean surface [air] temperature (GMST) anomaly (1850–2019) from four surface station-based datasets. It shows ~1.2°C of warming since 1850, though there is slight divergence between datasets (e.g., Berkley Earth, HadCRUT, NOAA, etc.). The likely range falls between 1.1–1.3°C, and I do not dispute these estimates, although I do not care much about what the GMST is.
🟩 𝗖𝘂𝗿𝘃𝗲: The CMIP6 multi-model mean (MMM) GMST response to natural forcings and internal variability only. These are primarily solar forcing (e.g., changes in total solar irradiance, which is a measure of the solar power over all wavelengths per unit area incident at the top of the atmosphere), stratospheric aerosol injection (SAI) from major volcanic eruptions (think Mount Tambora, 1815; Krakatoa, 1883; El Chichón, 1982; or Mount Pinatubo, 1991), and the El Niño Southern Oscillation (ENSO).
🟦 𝗖𝘂𝗿𝘃𝗲: Simulated GMST response due to aerosol forcing only. Aerosols are fine solid or liquid particles—natural or anthropogenic in origin—that are suspended in the air and generally have diameters of <1 μm (1 × 10⁻⁴ cm). Depending on type, aerosols can either act to warm or cool the Earth’s atmosphere. For instance, bright aerosols (e.g., sulfate particulates from coal burning) scatter incoming solar radiation back to space, which causes net cooling. Dark aerosols (e.g., black carbon from diesel exhaust), on the contrary, absorb incoming sunlight, resulting in net warming.
🟥 𝗖𝘂𝗿𝘃𝗲: Simulated ensemble GMST response to manmade GHG emissions only. These primarily are carbon dioxide (CO₂) and methane (CH₄), and to a lesser extent, include ozone (O₃) and nitrous oxides (NOx).
⬜️ 𝗖𝘂𝗿𝘃𝗲: Simulated GMST change due to natural forcings / variability + aerosols + GHGs. In other words, it’s the sum of the green, blue, and red curves.
As we can see below, the IPCC’s figure implies that GCMs have done remarkably well to explain why the globe has warmed [𝘴𝘭𝘪𝘨𝘩𝘵𝘭𝘺] since 1850. The verdict, so we’re told, is that GHG emissions from our energy consumption, industrial activities, and transportation are squarely to blame. And who’s going to argue with that? After all, scientists and climate activists routinely tell us that “It’s just basic physics” and that “The science is settled.”
There’s just one problem: the conclusion depends heavily on GCMs whose historical behavior (called hindcasting) is not an independent test in the way that the general public has been misled to believe by much of the scientific community.
GCMs are not simply pure radiative physics machines that can be turned on, run out to a certain date, and allowed to speak for themselves. They contain many parameterized processes such as cloud feedbacks, aerosol forcing, convection, land-surface exchanges, and ocean-atmosphere coupling that must be calibrated during model development.
1️⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗦𝗧𝗥𝗨𝗚𝗚𝗟𝗘 𝗪𝗜𝗧𝗛 𝗡𝗔𝗧𝗨𝗥𝗔𝗟 𝗩𝗔𝗥𝗜𝗔𝗕𝗜𝗟𝗜𝗧𝗬
Paleoclimate proxies (e.g., tree rings, ice cores, and sediments) show significant multidecadal to centennial temperature fluctuations in pre-industrial times. The Greenland and Antarctic ice core-based temperature reconstructions, for instance, make that abundantly clear.
🔗https://t.co/5PpWXs3cak
The models in CMIP5/6 smooth temperature curves and fail to reproduce the known amplitude of natural internal variability exhibited in multiple temperature reconstructions both regionally and globally (i.e., the more accurate pre-Hockey stick reconstructions). So, when modelers claim that natural forcing and internal variability cannot explain any warming since 1850, we should ask: Is that because nature truly could not have contributed much? Or is it possible that GCMs underrepresent natural variability?
That's not a trivial distinction.
2⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗔𝗥𝗘 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟𝗟𝗬 𝗧𝗨𝗡𝗘𝗗
Because GCMs struggle to independently reproduce observed historical temperature variations, modelers often pre-tune parameters (turn “knobs”) until the GMST output aligns with the observed GMST record.
This is not a conspiracy theory; it’s a demonstrable fact. The question is not whether tuning occurs. It does. The question is how much confidence we should place in attribution claims when models are essentially fudged to agree with the same historical record that is later used to demonstrate their alleged success.
Figure 3.8 on the right from IPCC AR6 WG1 illustrates this perfectly. It shows the assessed contributions to global warming from GHGs, aerosols, and natural forcings / internal variability over the period 2010–19 minus 1850–1900.
Let’s break it down:
🟩 The green boxes in Figure 3.8 show the spread in modeled GMST change ascribed to natural forcing / internal variability. The MMM is centered around the zero mark with little variation between the individual models in the ensemble. This is, once again, due to the fact that GCMs cannot hindcast well. So, the modelers assume that natural contributions to warming must be near zero.
🟦 Aerosol effective radiative forcing (ERF) carries enormous uncertainty: the MMM suggests a modest net cooling of ~0.5°C, but the range spans from a strong cooling of –0.9°C to a weak warming of +0.1°C over the period 1850–2019. The uncertainty arises because it is unknown whether the net warming from black carbon is offset by the cooling from sulfate aerosols (H₂SO₄). The MMM of 0.5°C thus has a 100% margin of error, which means that no scientists know exactly how much aerosol forcing has dampened (or enhanced) GMST change.
Does that sound like “settled science” to you? It sure doesn’t to me.
But modelers need to reproduce warming of ~0.9°C to 1.1°C. If aerosol emissions result in net cooling, then that cannot be the cause. So, what do these modelers td? They jack up the GHG forcing.
🟫 Extra GHGs also have uncertainty, though the margin of error is smaller in these estimates. The MMM suggests a strong warming of 1.5°C ± ~45%. All this tells us is that “the experts” have no clue just how sensitive the climate system is to extra GHGs in the Earth’s atmosphere.
Add these forcings together and the ensemble mean output lands near observations.
🔗https://t.co/6RS5rVXTRZ (p. 440)
There’s just one problem. Different models can get similar historical warming curves that resemble observations, but they use different combinations of aerosol cooling and GHG warming. That means the match to observed temperature trends does not uniquely prove the exact partitioning between GHGs, aerosols, and natural variability. For example, a model with strong aerosol cooling requires strong GHG warming to offset it. A different model with weak aerosol cooling requires a weaker GHG warming. Both will produce near-identical GMST curves that have high correlation to observations, but one cannot put much stock into these coefficients. This is a curve fitting exercise rather than real physics.
Mauritsen & Roeckner (2020), for example, openly describe tuning the Max Planck Institute model (MPI-ESM1.2) to match historical warming by adjusting cloud feedbacks and targeting an equilibrium climate sensitivity (ECS) of ~3°C (this is the IPCC's “best estimate”). With the known physics of radiative heat transfer, MPI-ESM1.2 produced an ECS of 7°C, more than double their target. Therefore, the modelers arbitrarily adjusted the cloud parameter scheme until the model output aligned with GMST observations.
In the closing remarks of the paper, the authors of Mauritsen & Roeckner (2020) say,
🗨️ “𝘞𝘦 𝘩𝘢𝘷𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘦𝘥 𝘩𝘰𝘸 𝙬𝙚 𝙩𝙪𝙣𝙚𝙙 𝙩𝙝𝙚 𝙈𝙋𝙄-𝙀𝙎𝙈𝟭.𝟮 𝙜𝙡𝙤𝙗𝙖𝙡 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙢𝙤𝙙𝙚𝙡 𝙩𝙤 𝙢𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙞𝙣𝙨𝙩𝙧𝙪𝙢𝙚𝙣𝙩𝙖𝙡 𝙧𝙚𝙘𝙤𝙧𝙙 𝘸𝘢𝘳𝘮𝘪𝘯𝘨; 𝘢𝘯 𝘦𝘯𝘥𝘦𝘢𝘷𝘰𝘳 𝙬𝙝𝙞𝙘𝙝 𝙝𝙖𝙨 𝙘𝙡𝙚𝙖𝙧𝙡𝙮 𝙗𝙚𝙚𝙣 𝙨𝙪𝙘𝙘𝙚𝙨𝙨𝙛𝙪𝙡. 𝘋𝘶𝘦 𝘵𝘰 𝘵𝘩𝘦 𝘩𝘪𝘴𝘵𝘰𝘳𝘪𝘤𝘢𝘭 𝘰𝘳𝘥𝘦𝘳 𝘰𝘧 𝘦𝘷𝘦𝘯𝘵𝘴, 𝙩𝙝𝙚 𝙘𝙝𝙤𝙞𝙘𝙚 𝙬𝙖𝙨 𝙩𝙤 𝙙𝙤 𝙩𝙝𝙞𝙨 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡𝙡𝙮 𝙗𝙮 𝙩𝙖𝙧𝙜𝙚𝙩𝙞𝙣𝙜 𝙖𝙣 𝙀𝘾𝙎 𝙤𝙛 𝘢𝘣𝘰𝘶𝘵 𝟯 𝙆 𝙪𝙨𝙞𝙣𝙜 𝙘𝙡𝙤𝙪𝙙 𝙛𝙚𝙚𝙙𝙗𝙖𝙘𝙠𝙨, 𝙖𝙨 𝙤𝙥𝙥𝙤𝙨𝙚𝙙 𝙩𝙤 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙝𝙚 𝙖𝙚𝙧𝙤𝙨𝙤𝙡 𝙛𝙤𝙧𝙘𝙞𝙣𝙜.”
🔗https://t.co/uBatWfdyvQ
🔗https://t.co/8Ntgszrz3a(p. 44 for IPCC ECS “best estimate” of 3 K)
The “basic physics” did not yield correct values, so the scientists arbitrarily turn knobs on various parameters until they get their desired result.
This transparency is useful, but it also exposes the problem. If a model is tuned to the instrumental GMST record, then its ability to reproduce the 20th-century temperature record cannot be treated as independent proof that the model has correctly identified the cause(s) of that warming. This is a circularity problem. It does not mean that GHGs have had no effect. They definitely have, at least to some extent. But one cannot confidently claim that all of the warming since 1850 has been anthropogenic because “the models match observations.”
Scientists also haven’t always been transparent about this. For decades, modeling centers refused to inform the public exactly how GCMs are calibrated. The IPCC subtly admitted this in Chapter 9 of AR5 WG1, stating,
🗨️ “𝘞𝘪𝘵𝘩 𝘷𝘦𝘳𝘺 𝘧𝘦𝘸 𝘦𝘹𝘤𝘦𝘱𝘵𝘪𝘰𝘯𝘴 (𝘔𝘢𝘶𝘳𝘪𝘵𝘴𝘦𝘯 𝘦𝘵 𝘢𝘭., 2012; 𝘏𝘰𝘶𝘳𝘥𝘪𝘯 𝘦𝘵 𝘢𝘭., 2013) 𝙢𝙤𝙙𝙚𝙡𝙡𝙞𝙣𝙜 𝙘𝙚𝙣𝙩𝙧𝙚𝙨 𝙙𝙤 𝙣𝙤𝙩 𝙧𝙤𝙪𝙩𝙞𝙣𝙚𝙡𝙮 𝙙𝙚𝙨𝙘𝙧𝙞𝙗𝙚 𝙞𝙣 𝙙𝙚𝙩𝙖𝙞𝙡 𝙝𝙤𝙬 𝙩𝙝𝙚𝙮 𝙩𝙪𝙣𝙚 𝙩𝙝𝙚𝙞𝙧 𝙢𝙤𝙙𝙚𝙡𝙨.. 𝘛𝘩𝘦𝘳𝘦𝘧𝘰𝘳𝘦, 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘰𝘣𝘴𝘦𝘳𝘷𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘤𝘰𝘯𝘴𝘵𝘳𝘢𝘪𝘯𝘵𝘴 𝘵𝘰𝘸𝘢𝘳𝘥 𝘸𝘩𝘪𝘤𝘩 𝘢 𝘱𝘢𝘳𝘵𝘪𝘤𝘶𝘭𝘢𝘳 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘵𝘶𝘯𝘦𝘥 𝘪𝘴 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘭𝘺 𝘯𝘰𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦.”
🔗https://t.co/z2jHU1IbkD (pp. 749-50)
This initial lack of transparency became the focus of an editorial in Science Magazine in October 2016,
🗨️ “𝘐𝘯𝘥𝘦𝘦𝘥, 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵𝘴 𝘭𝘪𝘬𝘦 𝘵𝘰 𝘢𝘥𝘮𝘪𝘵 𝘪𝘵 𝘰𝘳 𝘯𝘰𝘵, 𝙣𝙚𝙖𝙧𝙡𝙮 𝙚𝙫𝙚𝙧𝙮 𝙢𝙤𝙙𝙚𝙡 𝙝𝙖𝙨 𝙗𝙚𝙚𝙣 𝙘𝙖𝙡𝙞𝙗𝙧𝙖𝙩𝙚𝙙 𝙥𝙧𝙚𝙘𝙞𝙨𝙚𝙡𝙮 𝙩𝙤 𝙩𝙝𝙚 𝟮𝟬𝙩𝙝 𝙘𝙚𝙣𝙩𝙪𝙧𝙮 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙧𝙚𝙘𝙤𝙧𝙙𝙨—𝙤𝙩𝙝𝙚𝙧𝙬𝙞𝙨𝙚 𝙞𝙩 𝙬𝙤𝙪𝙡𝙙 𝙝𝙖𝙫𝙚 𝙚𝙣𝙙𝙚𝙙 𝙪𝙥 𝙞𝙣 𝙩𝙝𝙚 𝙩𝙧𝙖𝙨𝙝. ‘𝘐𝘵'𝘴 𝘧𝘢𝘪𝘳 𝘵𝘰 𝘴𝘢𝘺 𝘢𝘭𝘭 𝘮𝘰𝘥𝘦𝘭𝘴 𝘩𝘢𝘷𝘦 𝘵𝘶𝘯𝘦𝘥 𝘪𝘵,’ 𝘴𝘢𝘺𝘴 𝘐𝘴𝘢𝘢𝘤 𝘏𝘦𝘭𝘥, 𝘢 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵 𝘢𝘵 𝘵𝘩𝘦 𝘎𝘦𝘰𝘱𝘩𝘺𝘴𝘪𝘤𝘢𝘭 𝘍𝘭𝘶𝘪𝘥 𝘋𝘺𝘯𝘢𝘮𝘪𝘤𝘴 𝘓𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘺, 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘱𝘳𝘰𝘮𝘪𝘯𝘦𝘯𝘵 𝘮𝘰𝘥𝘦𝘭𝘪𝘯𝘨 𝘤𝘦𝘯𝘵𝘦𝘳, 𝘪𝘯 𝘗𝘳𝘪𝘯𝘤𝘦𝘵𝘰𝘯, 𝘕𝘦𝘸 𝘑𝘦𝘳𝘴𝘦𝘺.”
The Science Magazine article continues, stating that climate scientists had been reluctant to come clean about tuning practices for years because they feared that skeptics would use their practices against them to downplay global warming,
🗨️ “𝙁𝙤𝙧 𝙮𝙚𝙖𝙧𝙨, 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙨𝙘𝙞𝙚𝙣𝙩𝙞𝙨𝙩𝙨 𝙝𝙖𝙙 𝙗𝙚𝙚𝙣 𝙢𝙪𝙢 𝘪𝘯 𝘱𝘶𝘣𝘭𝘪𝘤 𝙖𝙗𝙤𝙪𝙩 𝘵𝘩𝘦𝘪𝘳 ‘𝘴𝘦𝘤𝘳𝘦𝘵 𝘴𝘢𝘶𝘤𝘦’: 𝙒𝙝𝙖𝙩 𝙝𝙖𝙥𝙥𝙚𝙣𝙚𝙙 𝙞𝙣 𝙩𝙝𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝘴𝘵𝘢𝘺𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭𝘴. 𝙏𝙝𝙚 𝙩𝙖𝙗𝙤𝙤 𝙧𝙚𝙛𝙡𝙚𝙘𝙩𝙚𝙙 𝙛𝙚𝙖𝙧𝙨 𝙩𝙝𝙖𝙩 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙘𝙤𝙣𝙩𝙧𝙖𝙧𝙞𝙖𝙣𝙨 𝙬𝙤𝙪𝙡𝙙 𝙪𝙨𝙚 𝙩𝙝𝙚 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙚 𝙤𝙛 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙤 𝙨𝙚𝙚𝙙 𝙙𝙤𝙪𝙗𝙩 𝙖𝙗𝙤𝙪𝙩 𝙢𝙤𝙙𝙚𝙡𝙨—𝙖𝙣𝙙, 𝙗𝙮 𝙚𝙭𝙩𝙚𝙣𝙨𝙞𝙤𝙣, 𝙩𝙝𝙚 𝙧𝙚𝙖𝙡𝙞𝙩𝙮 𝙤𝙛 𝙝𝙪𝙢𝙖𝙣-𝙙𝙧𝙞𝙫𝙚𝙣 𝙬𝙖𝙧𝙢𝙞𝙣𝙜. ‘𝘛𝘩𝘦 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺 𝘣𝘦𝘤𝘢𝘮𝘦 𝘥𝘦𝘧𝘦𝘯𝘴𝘪𝘷𝘦,’ [Bjorn] 𝘚𝘵𝘦𝘷𝘦𝘯𝘴[a director of the Max Planck Institute for Meteorology] 𝘴𝘢𝘺𝘴. ‘𝘐𝘵 𝘸𝘢𝘴 𝘢𝘧𝘳𝘢𝘪𝘥 𝘰𝘧 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘪𝘯𝘨𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘵𝘩𝘰𝘶𝘨𝘩𝘵 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘶𝘯𝘧𝘢𝘪𝘳𝘭𝘺 𝘶𝘴𝘦𝘥 𝘢𝘨𝘢𝘪𝘯𝘴𝘵 𝘵𝘩𝘦𝘮.’”
🔗https://t.co/PImzcNbUQu
A fair test of GCM skill uses temperature data that the models are not pre-tuned to match, such as satellite-based lower tropospheric temperatures (TLT). CMIP6 models, initialized in 1979, show roughly TWICE the warming rate observed in the University of Alabama in Huntsville (UAH v6.0) TLT dataset as shown in the bottom right time series (e.g., Christy & McNider, 2017; Christy et al., 2018) Recent events, such as the strong El Niño, reductions in low- and mid-altitude stratiform cloud cover, and perhaps the Hunga Tonga eruption have narrowed the gap temporarily, but the systematic warm bias persists in many model simulations.
🔗https://t.co/zueJLn9sY4
🔗https://t.co/vUJgmigsAe
3⃣ 𝗧𝗛𝗘𝗥𝗘 𝗜𝗦 𝗔𝗡𝗧𝗛𝗥𝗢𝗣𝗢𝗚𝗘𝗡𝗜𝗖 𝗖𝗢𝗡𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡; 𝗧𝗛𝗘 𝗠𝗔𝗚𝗡𝗜𝗧𝗨𝗗𝗘 𝗜𝗦 𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡
No serious analysis denies that there is some human influence on global warming. It is a scientific fact that CO₂ absorbs infrared radiation. Increasing CO₂ changes the radiative balance of Earth’s atmosphere. All else equal, that produces a warming tendency in the lower atmosphere.
The direct radiative forcing from doubled CO₂ concentrations is ~3.7 W/m², implying ~1°C direct warming before feedbacks act to amplify or dampen that change. However, the average radiation flux into the atmosphere is on the order of 239 ± 3.3 W/m² of absorbed solar radiation (ASR) averaged over a year (Trenberth et al., 2009; Stephens et al., 2012). When the error bars on baseline energy fluxes exceed the incremental forcing of increasing GHGs, precise attribution to GHGs becomes challenging.
🔗https://t.co/mlZbsIe8KQ
🔗https://t.co/5z5iMdb7H9 / open-access version: https://t.co/51Ys5w99LR
How much warming observed is due to increasing GHGs?
How much warming has been offset by aerosols?
How much warming is due to natural variability [which hasn’t been researched enough]?
What is the net effect of cloud feedbacks? Warming or cooling?
How do clouds respond to global warming, if at all? How do clouds change due to unforced internal variability? Are these changes observed in low-, mid-, or high-altitude clouds? Two of the three? All three? What about stratiform, cumuliform, and cirriform?
Have urbanization and land use changes been accurately removed from the GMST record as claimed by most government scientists and scholars?
These are all questions that remain unanswered, even if the scientific community at large refuses to acknowledge them for the sake of employment, to get research funding, or their choice to promote “the cause” for environmentalism.](https://pbs.twimg.com/media/HMk9OMFW0AACt_b.jpg)
![ChrisMartzWX's tweet photo. Have you seen the figure on the left before?
Chances are you have if you’re actively involved in the climate change debate. If not, let me break it down.
The figure, adapted from Figure SPM.1 in the IPCC AR6 Summary for Policymakers, is often touted as the “smoking gun” that proves virtually all observed global warming since 1850 is due to mankind’s greenhouse gas (GHG) emissions. The figure implies that global climate models (GCMs) skillfully reproduce observed warming (see black curve) since the “pre-industrial” era. They claim that this chart is synthesized from “multiple lines of evidence,” and that GCMs cannot skillfully reproduce the observed temperature trends without extra GHGs.
🔗https://t.co/5lspFokYOu(p. 6)
However, this graph is not the “smoking gun” that many people think it is. So, let me explain why.
First, it’s important to understand exactly what Figure SPM.1 is showing.
⬛️ 𝗖𝘂𝗿𝘃𝗲: The observed global mean surface [air] temperature (GMST) anomaly (1850–2019) from four surface station-based datasets. It shows ~1.2°C of warming since 1850, though there is slight divergence between datasets (e.g., Berkley Earth, HadCRUT, NOAA, etc.). The likely range falls between 1.1–1.3°C, and I do not dispute these estimates, although I do not care much about what the GMST is.
🟩 𝗖𝘂𝗿𝘃𝗲: The CMIP6 multi-model mean (MMM) GMST response to natural forcings and internal variability only. These are primarily solar forcing (e.g., changes in total solar irradiance, which is a measure of the solar power over all wavelengths per unit area incident at the top of the atmosphere), stratospheric aerosol injection (SAI) from major volcanic eruptions (think Mount Tambora, 1815; Krakatoa, 1883; El Chichón, 1982; or Mount Pinatubo, 1991), and the El Niño Southern Oscillation (ENSO).
🟦 𝗖𝘂𝗿𝘃𝗲: Simulated GMST response due to aerosol forcing only. Aerosols are fine solid or liquid particles—natural or anthropogenic in origin—that are suspended in the air and generally have diameters of <1 μm (1 × 10⁻⁴ cm). Depending on type, aerosols can either act to warm or cool the Earth’s atmosphere. For instance, bright aerosols (e.g., sulfate particulates from coal burning) scatter incoming solar radiation back to space, which causes net cooling. Dark aerosols (e.g., black carbon from diesel exhaust), on the contrary, absorb incoming sunlight, resulting in net warming.
🟥 𝗖𝘂𝗿𝘃𝗲: Simulated ensemble GMST response to manmade GHG emissions only. These primarily are carbon dioxide (CO₂) and methane (CH₄), and to a lesser extent, include ozone (O₃) and nitrous oxides (NOx).
⬜️ 𝗖𝘂𝗿𝘃𝗲: Simulated GMST change due to natural forcings / variability + aerosols + GHGs. In other words, it’s the sum of the green, blue, and red curves.
As we can see below, the IPCC’s figure implies that GCMs have done remarkably well to explain why the globe has warmed [𝘴𝘭𝘪𝘨𝘩𝘵𝘭𝘺] since 1850. The verdict, so we’re told, is that GHG emissions from our energy consumption, industrial activities, and transportation are squarely to blame. And who’s going to argue with that? After all, scientists and climate activists routinely tell us that “It’s just basic physics” and that “The science is settled.”
There’s just one problem: the conclusion depends heavily on GCMs whose historical behavior (called hindcasting) is not an independent test in the way that the general public has been misled to believe by much of the scientific community.
GCMs are not simply pure radiative physics machines that can be turned on, run out to a certain date, and allowed to speak for themselves. They contain many parameterized processes such as cloud feedbacks, aerosol forcing, convection, land-surface exchanges, and ocean-atmosphere coupling that must be calibrated during model development.
1️⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗦𝗧𝗥𝗨𝗚𝗚𝗟𝗘 𝗪𝗜𝗧𝗛 𝗡𝗔𝗧𝗨𝗥𝗔𝗟 𝗩𝗔𝗥𝗜𝗔𝗕𝗜𝗟𝗜𝗧𝗬
Paleoclimate proxies (e.g., tree rings, ice cores, and sediments) show significant multidecadal to centennial temperature fluctuations in pre-industrial times. The Greenland and Antarctic ice core-based temperature reconstructions, for instance, make that abundantly clear.
🔗https://t.co/5PpWXs3cak
The models in CMIP5/6 smooth temperature curves and fail to reproduce the known amplitude of natural internal variability exhibited in multiple temperature reconstructions both regionally and globally (i.e., the more accurate pre-Hockey stick reconstructions). So, when modelers claim that natural forcing and internal variability cannot explain any warming since 1850, we should ask: Is that because nature truly could not have contributed much? Or is it possible that GCMs underrepresent natural variability?
That's not a trivial distinction.
2⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗔𝗥𝗘 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟𝗟𝗬 𝗧𝗨𝗡𝗘𝗗
Because GCMs struggle to independently reproduce observed historical temperature variations, modelers often pre-tune parameters (turn “knobs”) until the GMST output aligns with the observed GMST record.
This is not a conspiracy theory; it’s a demonstrable fact. The question is not whether tuning occurs. It does. The question is how much confidence we should place in attribution claims when models are essentially fudged to agree with the same historical record that is later used to demonstrate their alleged success.
Figure 3.8 on the right from IPCC AR6 WG1 illustrates this perfectly. It shows the assessed contributions to global warming from GHGs, aerosols, and natural forcings / internal variability over the period 2010–19 minus 1850–1900.
Let’s break it down:
🟩 The green boxes in Figure 3.8 show the spread in modeled GMST change ascribed to natural forcing / internal variability. The MMM is centered around the zero mark with little variation between the individual models in the ensemble. This is, once again, due to the fact that GCMs cannot hindcast well. So, the modelers assume that natural contributions to warming must be near zero.
🟦 Aerosol effective radiative forcing (ERF) carries enormous uncertainty: the MMM suggests a modest net cooling of ~0.5°C, but the range spans from a strong cooling of –0.9°C to a weak warming of +0.1°C over the period 1850–2019. The uncertainty arises because it is unknown whether the net warming from black carbon is offset by the cooling from sulfate aerosols (H₂SO₄). The MMM of 0.5°C thus has a 100% margin of error, which means that no scientists know exactly how much aerosol forcing has dampened (or enhanced) GMST change.
Does that sound like “settled science” to you? It sure doesn’t to me.
But modelers need to reproduce warming of ~0.9°C to 1.1°C. If aerosol emissions result in net cooling, then that cannot be the cause. So, what do these modelers td? They jack up the GHG forcing.
🟫 Extra GHGs also have uncertainty, though the margin of error is smaller in these estimates. The MMM suggests a strong warming of 1.5°C ± ~45%. All this tells us is that “the experts” have no clue just how sensitive the climate system is to extra GHGs in the Earth’s atmosphere.
Add these forcings together and the ensemble mean output lands near observations.
🔗https://t.co/6RS5rVXTRZ (p. 440)
There’s just one problem. Different models can get similar historical warming curves that resemble observations, but they use different combinations of aerosol cooling and GHG warming. That means the match to observed temperature trends does not uniquely prove the exact partitioning between GHGs, aerosols, and natural variability. For example, a model with strong aerosol cooling requires strong GHG warming to offset it. A different model with weak aerosol cooling requires a weaker GHG warming. Both will produce near-identical GMST curves that have high correlation to observations, but one cannot put much stock into these coefficients. This is a curve fitting exercise rather than real physics.
Mauritsen & Roeckner (2020), for example, openly describe tuning the Max Planck Institute model (MPI-ESM1.2) to match historical warming by adjusting cloud feedbacks and targeting an equilibrium climate sensitivity (ECS) of ~3°C (this is the IPCC's “best estimate”). With the known physics of radiative heat transfer, MPI-ESM1.2 produced an ECS of 7°C, more than double their target. Therefore, the modelers arbitrarily adjusted the cloud parameter scheme until the model output aligned with GMST observations.
In the closing remarks of the paper, the authors of Mauritsen & Roeckner (2020) say,
🗨️ “𝘞𝘦 𝘩𝘢𝘷𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘦𝘥 𝘩𝘰𝘸 𝙬𝙚 𝙩𝙪𝙣𝙚𝙙 𝙩𝙝𝙚 𝙈𝙋𝙄-𝙀𝙎𝙈𝟭.𝟮 𝙜𝙡𝙤𝙗𝙖𝙡 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙢𝙤𝙙𝙚𝙡 𝙩𝙤 𝙢𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙞𝙣𝙨𝙩𝙧𝙪𝙢𝙚𝙣𝙩𝙖𝙡 𝙧𝙚𝙘𝙤𝙧𝙙 𝘸𝘢𝘳𝘮𝘪𝘯𝘨; 𝘢𝘯 𝘦𝘯𝘥𝘦𝘢𝘷𝘰𝘳 𝙬𝙝𝙞𝙘𝙝 𝙝𝙖𝙨 𝙘𝙡𝙚𝙖𝙧𝙡𝙮 𝙗𝙚𝙚𝙣 𝙨𝙪𝙘𝙘𝙚𝙨𝙨𝙛𝙪𝙡. 𝘋𝘶𝘦 𝘵𝘰 𝘵𝘩𝘦 𝘩𝘪𝘴𝘵𝘰𝘳𝘪𝘤𝘢𝘭 𝘰𝘳𝘥𝘦𝘳 𝘰𝘧 𝘦𝘷𝘦𝘯𝘵𝘴, 𝙩𝙝𝙚 𝙘𝙝𝙤𝙞𝙘𝙚 𝙬𝙖𝙨 𝙩𝙤 𝙙𝙤 𝙩𝙝𝙞𝙨 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡𝙡𝙮 𝙗𝙮 𝙩𝙖𝙧𝙜𝙚𝙩𝙞𝙣𝙜 𝙖𝙣 𝙀𝘾𝙎 𝙤𝙛 𝘢𝘣𝘰𝘶𝘵 𝟯 𝙆 𝙪𝙨𝙞𝙣𝙜 𝙘𝙡𝙤𝙪𝙙 𝙛𝙚𝙚𝙙𝙗𝙖𝙘𝙠𝙨, 𝙖𝙨 𝙤𝙥𝙥𝙤𝙨𝙚𝙙 𝙩𝙤 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙝𝙚 𝙖𝙚𝙧𝙤𝙨𝙤𝙡 𝙛𝙤𝙧𝙘𝙞𝙣𝙜.”
🔗https://t.co/uBatWfdyvQ
🔗https://t.co/8Ntgszrz3a(p. 44 for IPCC ECS “best estimate” of 3 K)
The “basic physics” did not yield correct values, so the scientists arbitrarily turn knobs on various parameters until they get their desired result.
This transparency is useful, but it also exposes the problem. If a model is tuned to the instrumental GMST record, then its ability to reproduce the 20th-century temperature record cannot be treated as independent proof that the model has correctly identified the cause(s) of that warming. This is a circularity problem. It does not mean that GHGs have had no effect. They definitely have, at least to some extent. But one cannot confidently claim that all of the warming since 1850 has been anthropogenic because “the models match observations.”
Scientists also haven’t always been transparent about this. For decades, modeling centers refused to inform the public exactly how GCMs are calibrated. The IPCC subtly admitted this in Chapter 9 of AR5 WG1, stating,
🗨️ “𝘞𝘪𝘵𝘩 𝘷𝘦𝘳𝘺 𝘧𝘦𝘸 𝘦𝘹𝘤𝘦𝘱𝘵𝘪𝘰𝘯𝘴 (𝘔𝘢𝘶𝘳𝘪𝘵𝘴𝘦𝘯 𝘦𝘵 𝘢𝘭., 2012; 𝘏𝘰𝘶𝘳𝘥𝘪𝘯 𝘦𝘵 𝘢𝘭., 2013) 𝙢𝙤𝙙𝙚𝙡𝙡𝙞𝙣𝙜 𝙘𝙚𝙣𝙩𝙧𝙚𝙨 𝙙𝙤 𝙣𝙤𝙩 𝙧𝙤𝙪𝙩𝙞𝙣𝙚𝙡𝙮 𝙙𝙚𝙨𝙘𝙧𝙞𝙗𝙚 𝙞𝙣 𝙙𝙚𝙩𝙖𝙞𝙡 𝙝𝙤𝙬 𝙩𝙝𝙚𝙮 𝙩𝙪𝙣𝙚 𝙩𝙝𝙚𝙞𝙧 𝙢𝙤𝙙𝙚𝙡𝙨.. 𝘛𝘩𝘦𝘳𝘦𝘧𝘰𝘳𝘦, 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘰𝘣𝘴𝘦𝘳𝘷𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘤𝘰𝘯𝘴𝘵𝘳𝘢𝘪𝘯𝘵𝘴 𝘵𝘰𝘸𝘢𝘳𝘥 𝘸𝘩𝘪𝘤𝘩 𝘢 𝘱𝘢𝘳𝘵𝘪𝘤𝘶𝘭𝘢𝘳 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘵𝘶𝘯𝘦𝘥 𝘪𝘴 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘭𝘺 𝘯𝘰𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦.”
🔗https://t.co/z2jHU1IbkD (pp. 749-50)
This initial lack of transparency became the focus of an editorial in Science Magazine in October 2016,
🗨️ “𝘐𝘯𝘥𝘦𝘦𝘥, 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵𝘴 𝘭𝘪𝘬𝘦 𝘵𝘰 𝘢𝘥𝘮𝘪𝘵 𝘪𝘵 𝘰𝘳 𝘯𝘰𝘵, 𝙣𝙚𝙖𝙧𝙡𝙮 𝙚𝙫𝙚𝙧𝙮 𝙢𝙤𝙙𝙚𝙡 𝙝𝙖𝙨 𝙗𝙚𝙚𝙣 𝙘𝙖𝙡𝙞𝙗𝙧𝙖𝙩𝙚𝙙 𝙥𝙧𝙚𝙘𝙞𝙨𝙚𝙡𝙮 𝙩𝙤 𝙩𝙝𝙚 𝟮𝟬𝙩𝙝 𝙘𝙚𝙣𝙩𝙪𝙧𝙮 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙧𝙚𝙘𝙤𝙧𝙙𝙨—𝙤𝙩𝙝𝙚𝙧𝙬𝙞𝙨𝙚 𝙞𝙩 𝙬𝙤𝙪𝙡𝙙 𝙝𝙖𝙫𝙚 𝙚𝙣𝙙𝙚𝙙 𝙪𝙥 𝙞𝙣 𝙩𝙝𝙚 𝙩𝙧𝙖𝙨𝙝. ‘𝘐𝘵'𝘴 𝘧𝘢𝘪𝘳 𝘵𝘰 𝘴𝘢𝘺 𝘢𝘭𝘭 𝘮𝘰𝘥𝘦𝘭𝘴 𝘩𝘢𝘷𝘦 𝘵𝘶𝘯𝘦𝘥 𝘪𝘵,’ 𝘴𝘢𝘺𝘴 𝘐𝘴𝘢𝘢𝘤 𝘏𝘦𝘭𝘥, 𝘢 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵 𝘢𝘵 𝘵𝘩𝘦 𝘎𝘦𝘰𝘱𝘩𝘺𝘴𝘪𝘤𝘢𝘭 𝘍𝘭𝘶𝘪𝘥 𝘋𝘺𝘯𝘢𝘮𝘪𝘤𝘴 𝘓𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘺, 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘱𝘳𝘰𝘮𝘪𝘯𝘦𝘯𝘵 𝘮𝘰𝘥𝘦𝘭𝘪𝘯𝘨 𝘤𝘦𝘯𝘵𝘦𝘳, 𝘪𝘯 𝘗𝘳𝘪𝘯𝘤𝘦𝘵𝘰𝘯, 𝘕𝘦𝘸 𝘑𝘦𝘳𝘴𝘦𝘺.”
The Science Magazine article continues, stating that climate scientists had been reluctant to come clean about tuning practices for years because they feared that skeptics would use their practices against them to downplay global warming,
🗨️ “𝙁𝙤𝙧 𝙮𝙚𝙖𝙧𝙨, 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙨𝙘𝙞𝙚𝙣𝙩𝙞𝙨𝙩𝙨 𝙝𝙖𝙙 𝙗𝙚𝙚𝙣 𝙢𝙪𝙢 𝘪𝘯 𝘱𝘶𝘣𝘭𝘪𝘤 𝙖𝙗𝙤𝙪𝙩 𝘵𝘩𝘦𝘪𝘳 ‘𝘴𝘦𝘤𝘳𝘦𝘵 𝘴𝘢𝘶𝘤𝘦’: 𝙒𝙝𝙖𝙩 𝙝𝙖𝙥𝙥𝙚𝙣𝙚𝙙 𝙞𝙣 𝙩𝙝𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝘴𝘵𝘢𝘺𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭𝘴. 𝙏𝙝𝙚 𝙩𝙖𝙗𝙤𝙤 𝙧𝙚𝙛𝙡𝙚𝙘𝙩𝙚𝙙 𝙛𝙚𝙖𝙧𝙨 𝙩𝙝𝙖𝙩 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙘𝙤𝙣𝙩𝙧𝙖𝙧𝙞𝙖𝙣𝙨 𝙬𝙤𝙪𝙡𝙙 𝙪𝙨𝙚 𝙩𝙝𝙚 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙚 𝙤𝙛 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙤 𝙨𝙚𝙚𝙙 𝙙𝙤𝙪𝙗𝙩 𝙖𝙗𝙤𝙪𝙩 𝙢𝙤𝙙𝙚𝙡𝙨—𝙖𝙣𝙙, 𝙗𝙮 𝙚𝙭𝙩𝙚𝙣𝙨𝙞𝙤𝙣, 𝙩𝙝𝙚 𝙧𝙚𝙖𝙡𝙞𝙩𝙮 𝙤𝙛 𝙝𝙪𝙢𝙖𝙣-𝙙𝙧𝙞𝙫𝙚𝙣 𝙬𝙖𝙧𝙢𝙞𝙣𝙜. ‘𝘛𝘩𝘦 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺 𝘣𝘦𝘤𝘢𝘮𝘦 𝘥𝘦𝘧𝘦𝘯𝘴𝘪𝘷𝘦,’ [Bjorn] 𝘚𝘵𝘦𝘷𝘦𝘯𝘴[a director of the Max Planck Institute for Meteorology] 𝘴𝘢𝘺𝘴. ‘𝘐𝘵 𝘸𝘢𝘴 𝘢𝘧𝘳𝘢𝘪𝘥 𝘰𝘧 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘪𝘯𝘨𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘵𝘩𝘰𝘶𝘨𝘩𝘵 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘶𝘯𝘧𝘢𝘪𝘳𝘭𝘺 𝘶𝘴𝘦𝘥 𝘢𝘨𝘢𝘪𝘯𝘴𝘵 𝘵𝘩𝘦𝘮.’”
🔗https://t.co/PImzcNbUQu
A fair test of GCM skill uses temperature data that the models are not pre-tuned to match, such as satellite-based lower tropospheric temperatures (TLT). CMIP6 models, initialized in 1979, show roughly TWICE the warming rate observed in the University of Alabama in Huntsville (UAH v6.0) TLT dataset as shown in the bottom right time series (e.g., Christy & McNider, 2017; Christy et al., 2018) Recent events, such as the strong El Niño, reductions in low- and mid-altitude stratiform cloud cover, and perhaps the Hunga Tonga eruption have narrowed the gap temporarily, but the systematic warm bias persists in many model simulations.
🔗https://t.co/zueJLn9sY4
🔗https://t.co/vUJgmigsAe
3⃣ 𝗧𝗛𝗘𝗥𝗘 𝗜𝗦 𝗔𝗡𝗧𝗛𝗥𝗢𝗣𝗢𝗚𝗘𝗡𝗜𝗖 𝗖𝗢𝗡𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡; 𝗧𝗛𝗘 𝗠𝗔𝗚𝗡𝗜𝗧𝗨𝗗𝗘 𝗜𝗦 𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡
No serious analysis denies that there is some human influence on global warming. It is a scientific fact that CO₂ absorbs infrared radiation. Increasing CO₂ changes the radiative balance of Earth’s atmosphere. All else equal, that produces a warming tendency in the lower atmosphere.
The direct radiative forcing from doubled CO₂ concentrations is ~3.7 W/m², implying ~1°C direct warming before feedbacks act to amplify or dampen that change. However, the average radiation flux into the atmosphere is on the order of 239 ± 3.3 W/m² of absorbed solar radiation (ASR) averaged over a year (Trenberth et al., 2009; Stephens et al., 2012). When the error bars on baseline energy fluxes exceed the incremental forcing of increasing GHGs, precise attribution to GHGs becomes challenging.
🔗https://t.co/mlZbsIe8KQ
🔗https://t.co/5z5iMdb7H9 / open-access version: https://t.co/51Ys5w99LR
How much warming observed is due to increasing GHGs?
How much warming has been offset by aerosols?
How much warming is due to natural variability [which hasn’t been researched enough]?
What is the net effect of cloud feedbacks? Warming or cooling?
How do clouds respond to global warming, if at all? How do clouds change due to unforced internal variability? Are these changes observed in low-, mid-, or high-altitude clouds? Two of the three? All three? What about stratiform, cumuliform, and cirriform?
Have urbanization and land use changes been accurately removed from the GMST record as claimed by most government scientists and scholars?
These are all questions that remain unanswered, even if the scientific community at large refuses to acknowledge them for the sake of employment, to get research funding, or their choice to promote “the cause” for environmentalism.](https://pbs.twimg.com/media/HMk9IpfW4AA1LuA.jpg)
Olivia
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Have you seen the figure on the left before?
Chances are you have if you’re actively involved in the climate change debate. If not, let me break it down.
The figure, adapted from Figure SPM.1 in the IPCC AR6 Summary for Policymakers, is often touted as the “smoking gun” that proves virtually all observed global warming since 1850 is due to mankind’s greenhouse gas (GHG) emissions. The figure implies that global climate models (GCMs) skillfully reproduce observed warming (see black curve) since the “pre-industrial” era. They claim that this chart is synthesized from “multiple lines of evidence,” and that GCMs cannot skillfully reproduce the observed temperature trends without extra GHGs.
🔗https://t.co/5lspFokYOu(p. 6)
However, this graph is not the “smoking gun” that many people think it is. So, let me explain why.
First, it’s important to understand exactly what Figure SPM.1 is showing.
⬛️ 𝗖𝘂𝗿𝘃𝗲: The observed global mean surface [air] temperature (GMST) anomaly (1850–2019) from four surface station-based datasets. It shows ~1.2°C of warming since 1850, though there is slight divergence between datasets (e.g., Berkley Earth, HadCRUT, NOAA, etc.). The likely range falls between 1.1–1.3°C, and I do not dispute these estimates, although I do not care much about what the GMST is.
🟩 𝗖𝘂𝗿𝘃𝗲: The CMIP6 multi-model mean (MMM) GMST response to natural forcings and internal variability only. These are primarily solar forcing (e.g., changes in total solar irradiance, which is a measure of the solar power over all wavelengths per unit area incident at the top of the atmosphere), stratospheric aerosol injection (SAI) from major volcanic eruptions (think Mount Tambora, 1815; Krakatoa, 1883; El Chichón, 1982; or Mount Pinatubo, 1991), and the El Niño Southern Oscillation (ENSO).
🟦 𝗖𝘂𝗿𝘃𝗲: Simulated GMST response due to aerosol forcing only. Aerosols are fine solid or liquid particles—natural or anthropogenic in origin—that are suspended in the air and generally have diameters of <1 μm (1 × 10⁻⁴ cm). Depending on type, aerosols can either act to warm or cool the Earth’s atmosphere. For instance, bright aerosols (e.g., sulfate particulates from coal burning) scatter incoming solar radiation back to space, which causes net cooling. Dark aerosols (e.g., black carbon from diesel exhaust), on the contrary, absorb incoming sunlight, resulting in net warming.
🟥 𝗖𝘂𝗿𝘃𝗲: Simulated ensemble GMST response to manmade GHG emissions only. These primarily are carbon dioxide (CO₂) and methane (CH₄), and to a lesser extent, include ozone (O₃) and nitrous oxides (NOx).
⬜️ 𝗖𝘂𝗿𝘃𝗲: Simulated GMST change due to natural forcings / variability + aerosols + GHGs. In other words, it’s the sum of the green, blue, and red curves.
As we can see below, the IPCC’s figure implies that GCMs have done remarkably well to explain why the globe has warmed [𝘴𝘭𝘪𝘨𝘩𝘵𝘭𝘺] since 1850. The verdict, so we’re told, is that GHG emissions from our energy consumption, industrial activities, and transportation are squarely to blame. And who’s going to argue with that? After all, scientists and climate activists routinely tell us that “It’s just basic physics” and that “The science is settled.”
There’s just one problem: the conclusion depends heavily on GCMs whose historical behavior (called hindcasting) is not an independent test in the way that the general public has been misled to believe by much of the scientific community.
GCMs are not simply pure radiative physics machines that can be turned on, run out to a certain date, and allowed to speak for themselves. They contain many parameterized processes such as cloud feedbacks, aerosol forcing, convection, land-surface exchanges, and ocean-atmosphere coupling that must be calibrated during model development.
1️⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗦𝗧𝗥𝗨𝗚𝗚𝗟𝗘 𝗪𝗜𝗧𝗛 𝗡𝗔𝗧𝗨𝗥𝗔𝗟 𝗩𝗔𝗥𝗜𝗔𝗕𝗜𝗟𝗜𝗧𝗬
Paleoclimate proxies (e.g., tree rings, ice cores, and sediments) show significant multidecadal to centennial temperature fluctuations in pre-industrial times. The Greenland and Antarctic ice core-based temperature reconstructions, for instance, make that abundantly clear.
🔗https://t.co/5PpWXs3cak
The models in CMIP5/6 smooth temperature curves and fail to reproduce the known amplitude of natural internal variability exhibited in multiple temperature reconstructions both regionally and globally (i.e., the more accurate pre-Hockey stick reconstructions). So, when modelers claim that natural forcing and internal variability cannot explain any warming since 1850, we should ask: Is that because nature truly could not have contributed much? Or is it possible that GCMs underrepresent natural variability?
That's not a trivial distinction.
2⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗔𝗥𝗘 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟𝗟𝗬 𝗧𝗨𝗡𝗘𝗗
Because GCMs struggle to independently reproduce observed historical temperature variations, modelers often pre-tune parameters (turn “knobs”) until the GMST output aligns with the observed GMST record.
This is not a conspiracy theory; it’s a demonstrable fact. The question is not whether tuning occurs. It does. The question is how much confidence we should place in attribution claims when models are essentially fudged to agree with the same historical record that is later used to demonstrate their alleged success.
Figure 3.8 on the right from IPCC AR6 WG1 illustrates this perfectly. It shows the assessed contributions to global warming from GHGs, aerosols, and natural forcings / internal variability over the period 2010–19 minus 1850–1900.
Let’s break it down:
🟩 The green boxes in Figure 3.8 show the spread in modeled GMST change ascribed to natural forcing / internal variability. The MMM is centered around the zero mark with little variation between the individual models in the ensemble. This is, once again, due to the fact that GCMs cannot hindcast well. So, the modelers assume that natural contributions to warming must be near zero.
🟦 Aerosol effective radiative forcing (ERF) carries enormous uncertainty: the MMM suggests a modest net cooling of ~0.5°C, but the range spans from a strong cooling of –0.9°C to a weak warming of +0.1°C over the period 1850–2019. The uncertainty arises because it is unknown whether the net warming from black carbon is offset by the cooling from sulfate aerosols (H₂SO₄). The MMM of 0.5°C thus has a 100% margin of error, which means that no scientists know exactly how much aerosol forcing has dampened (or enhanced) GMST change.
Does that sound like “settled science” to you? It sure doesn’t to me.
But modelers need to reproduce warming of ~0.9°C to 1.1°C. If aerosol emissions result in net cooling, then that cannot be the cause. So, what do these modelers td? They jack up the GHG forcing.
🟫 Extra GHGs also have uncertainty, though the margin of error is smaller in these estimates. The MMM suggests a strong warming of 1.5°C ± ~45%. All this tells us is that “the experts” have no clue just how sensitive the climate system is to extra GHGs in the Earth’s atmosphere.
Add these forcings together and the ensemble mean output lands near observations.
🔗https://t.co/6RS5rVXTRZ (p. 440)
There’s just one problem. Different models can get similar historical warming curves that resemble observations, but they use different combinations of aerosol cooling and GHG warming. That means the match to observed temperature trends does not uniquely prove the exact partitioning between GHGs, aerosols, and natural variability. For example, a model with strong aerosol cooling requires strong GHG warming to offset it. A different model with weak aerosol cooling requires a weaker GHG warming. Both will produce near-identical GMST curves that have high correlation to observations, but one cannot put much stock into these coefficients. This is a curve fitting exercise rather than real physics.
Mauritsen & Roeckner (2020), for example, openly describe tuning the Max Planck Institute model (MPI-ESM1.2) to match historical warming by adjusting cloud feedbacks and targeting an equilibrium climate sensitivity (ECS) of ~3°C (this is the IPCC's “best estimate”). With the known physics of radiative heat transfer, MPI-ESM1.2 produced an ECS of 7°C, more than double their target. Therefore, the modelers arbitrarily adjusted the cloud parameter scheme until the model output aligned with GMST observations.
In the closing remarks of the paper, the authors of Mauritsen & Roeckner (2020) say,
🗨️ “𝘞𝘦 𝘩𝘢𝘷𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘦𝘥 𝘩𝘰𝘸 𝙬𝙚 𝙩𝙪𝙣𝙚𝙙 𝙩𝙝𝙚 𝙈𝙋𝙄-𝙀𝙎𝙈𝟭.𝟮 𝙜𝙡𝙤𝙗𝙖𝙡 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙢𝙤𝙙𝙚𝙡 𝙩𝙤 𝙢𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙞𝙣𝙨𝙩𝙧𝙪𝙢𝙚𝙣𝙩𝙖𝙡 𝙧𝙚𝙘𝙤𝙧𝙙 𝘸𝘢𝘳𝘮𝘪𝘯𝘨; 𝘢𝘯 𝘦𝘯𝘥𝘦𝘢𝘷𝘰𝘳 𝙬𝙝𝙞𝙘𝙝 𝙝𝙖𝙨 𝙘𝙡𝙚𝙖𝙧𝙡𝙮 𝙗𝙚𝙚𝙣 𝙨𝙪𝙘𝙘𝙚𝙨𝙨𝙛𝙪𝙡. 𝘋𝘶𝘦 𝘵𝘰 𝘵𝘩𝘦 𝘩𝘪𝘴𝘵𝘰𝘳𝘪𝘤𝘢𝘭 𝘰𝘳𝘥𝘦𝘳 𝘰𝘧 𝘦𝘷𝘦𝘯𝘵𝘴, 𝙩𝙝𝙚 𝙘𝙝𝙤𝙞𝙘𝙚 𝙬𝙖𝙨 𝙩𝙤 𝙙𝙤 𝙩𝙝𝙞𝙨 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡𝙡𝙮 𝙗𝙮 𝙩𝙖𝙧𝙜𝙚𝙩𝙞𝙣𝙜 𝙖𝙣 𝙀𝘾𝙎 𝙤𝙛 𝘢𝘣𝘰𝘶𝘵 𝟯 𝙆 𝙪𝙨𝙞𝙣𝙜 𝙘𝙡𝙤𝙪𝙙 𝙛𝙚𝙚𝙙𝙗𝙖𝙘𝙠𝙨, 𝙖𝙨 𝙤𝙥𝙥𝙤𝙨𝙚𝙙 𝙩𝙤 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙝𝙚 𝙖𝙚𝙧𝙤𝙨𝙤𝙡 𝙛𝙤𝙧𝙘𝙞𝙣𝙜.”
🔗https://t.co/uBatWfdyvQ
🔗https://t.co/8Ntgszrz3a(p. 44 for IPCC ECS “best estimate” of 3 K)
The “basic physics” did not yield correct values, so the scientists arbitrarily turn knobs on various parameters until they get their desired result.
This transparency is useful, but it also exposes the problem. If a model is tuned to the instrumental GMST record, then its ability to reproduce the 20th-century temperature record cannot be treated as independent proof that the model has correctly identified the cause(s) of that warming. This is a circularity problem. It does not mean that GHGs have had no effect. They definitely have, at least to some extent. But one cannot confidently claim that all of the warming since 1850 has been anthropogenic because “the models match observations.”
Scientists also haven’t always been transparent about this. For decades, modeling centers refused to inform the public exactly how GCMs are calibrated. The IPCC subtly admitted this in Chapter 9 of AR5 WG1, stating,
🗨️ “𝘞𝘪𝘵𝘩 𝘷𝘦𝘳𝘺 𝘧𝘦𝘸 𝘦𝘹𝘤𝘦𝘱𝘵𝘪𝘰𝘯𝘴 (𝘔𝘢𝘶𝘳𝘪𝘵𝘴𝘦𝘯 𝘦𝘵 𝘢𝘭., 2012; 𝘏𝘰𝘶𝘳𝘥𝘪𝘯 𝘦𝘵 𝘢𝘭., 2013) 𝙢𝙤𝙙𝙚𝙡𝙡𝙞𝙣𝙜 𝙘𝙚𝙣𝙩𝙧𝙚𝙨 𝙙𝙤 𝙣𝙤𝙩 𝙧𝙤𝙪𝙩𝙞𝙣𝙚𝙡𝙮 𝙙𝙚𝙨𝙘𝙧𝙞𝙗𝙚 𝙞𝙣 𝙙𝙚𝙩𝙖𝙞𝙡 𝙝𝙤𝙬 𝙩𝙝𝙚𝙮 𝙩𝙪𝙣𝙚 𝙩𝙝𝙚𝙞𝙧 𝙢𝙤𝙙𝙚𝙡𝙨.. 𝘛𝘩𝘦𝘳𝘦𝘧𝘰𝘳𝘦, 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘰𝘣𝘴𝘦𝘳𝘷𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘤𝘰𝘯𝘴𝘵𝘳𝘢𝘪𝘯𝘵𝘴 𝘵𝘰𝘸𝘢𝘳𝘥 𝘸𝘩𝘪𝘤𝘩 𝘢 𝘱𝘢𝘳𝘵𝘪𝘤𝘶𝘭𝘢𝘳 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘵𝘶𝘯𝘦𝘥 𝘪𝘴 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘭𝘺 𝘯𝘰𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦.”
🔗https://t.co/z2jHU1IbkD (pp. 749-50)
This initial lack of transparency became the focus of an editorial in Science Magazine in October 2016,
🗨️ “𝘐𝘯𝘥𝘦𝘦𝘥, 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵𝘴 𝘭𝘪𝘬𝘦 𝘵𝘰 𝘢𝘥𝘮𝘪𝘵 𝘪𝘵 𝘰𝘳 𝘯𝘰𝘵, 𝙣𝙚𝙖𝙧𝙡𝙮 𝙚𝙫𝙚𝙧𝙮 𝙢𝙤𝙙𝙚𝙡 𝙝𝙖𝙨 𝙗𝙚𝙚𝙣 𝙘𝙖𝙡𝙞𝙗𝙧𝙖𝙩𝙚𝙙 𝙥𝙧𝙚𝙘𝙞𝙨𝙚𝙡𝙮 𝙩𝙤 𝙩𝙝𝙚 𝟮𝟬𝙩𝙝 𝙘𝙚𝙣𝙩𝙪𝙧𝙮 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙧𝙚𝙘𝙤𝙧𝙙𝙨—𝙤𝙩𝙝𝙚𝙧𝙬𝙞𝙨𝙚 𝙞𝙩 𝙬𝙤𝙪𝙡𝙙 𝙝𝙖𝙫𝙚 𝙚𝙣𝙙𝙚𝙙 𝙪𝙥 𝙞𝙣 𝙩𝙝𝙚 𝙩𝙧𝙖𝙨𝙝. ‘𝘐𝘵'𝘴 𝘧𝘢𝘪𝘳 𝘵𝘰 𝘴𝘢𝘺 𝘢𝘭𝘭 𝘮𝘰𝘥𝘦𝘭𝘴 𝘩𝘢𝘷𝘦 𝘵𝘶𝘯𝘦𝘥 𝘪𝘵,’ 𝘴𝘢𝘺𝘴 𝘐𝘴𝘢𝘢𝘤 𝘏𝘦𝘭𝘥, 𝘢 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵 𝘢𝘵 𝘵𝘩𝘦 𝘎𝘦𝘰𝘱𝘩𝘺𝘴𝘪𝘤𝘢𝘭 𝘍𝘭𝘶𝘪𝘥 𝘋𝘺𝘯𝘢𝘮𝘪𝘤𝘴 𝘓𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘺, 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘱𝘳𝘰𝘮𝘪𝘯𝘦𝘯𝘵 𝘮𝘰𝘥𝘦𝘭𝘪𝘯𝘨 𝘤𝘦𝘯𝘵𝘦𝘳, 𝘪𝘯 𝘗𝘳𝘪𝘯𝘤𝘦𝘵𝘰𝘯, 𝘕𝘦𝘸 𝘑𝘦𝘳𝘴𝘦𝘺.”
The Science Magazine article continues, stating that climate scientists had been reluctant to come clean about tuning practices for years because they feared that skeptics would use their practices against them to downplay global warming,
🗨️ “𝙁𝙤𝙧 𝙮𝙚𝙖𝙧𝙨, 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙨𝙘𝙞𝙚𝙣𝙩𝙞𝙨𝙩𝙨 𝙝𝙖𝙙 𝙗𝙚𝙚𝙣 𝙢𝙪𝙢 𝘪𝘯 𝘱𝘶𝘣𝘭𝘪𝘤 𝙖𝙗𝙤𝙪𝙩 𝘵𝘩𝘦𝘪𝘳 ‘𝘴𝘦𝘤𝘳𝘦𝘵 𝘴𝘢𝘶𝘤𝘦’: 𝙒𝙝𝙖𝙩 𝙝𝙖𝙥𝙥𝙚𝙣𝙚𝙙 𝙞𝙣 𝙩𝙝𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝘴𝘵𝘢𝘺𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭𝘴. 𝙏𝙝𝙚 𝙩𝙖𝙗𝙤𝙤 𝙧𝙚𝙛𝙡𝙚𝙘𝙩𝙚𝙙 𝙛𝙚𝙖𝙧𝙨 𝙩𝙝𝙖𝙩 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙘𝙤𝙣𝙩𝙧𝙖𝙧𝙞𝙖𝙣𝙨 𝙬𝙤𝙪𝙡𝙙 𝙪𝙨𝙚 𝙩𝙝𝙚 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙚 𝙤𝙛 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙤 𝙨𝙚𝙚𝙙 𝙙𝙤𝙪𝙗𝙩 𝙖𝙗𝙤𝙪𝙩 𝙢𝙤𝙙𝙚𝙡𝙨—𝙖𝙣𝙙, 𝙗𝙮 𝙚𝙭𝙩𝙚𝙣𝙨𝙞𝙤𝙣, 𝙩𝙝𝙚 𝙧𝙚𝙖𝙡𝙞𝙩𝙮 𝙤𝙛 𝙝𝙪𝙢𝙖𝙣-𝙙𝙧𝙞𝙫𝙚𝙣 𝙬𝙖𝙧𝙢𝙞𝙣𝙜. ‘𝘛𝘩𝘦 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺 𝘣𝘦𝘤𝘢𝘮𝘦 𝘥𝘦𝘧𝘦𝘯𝘴𝘪𝘷𝘦,’ [Bjorn] 𝘚𝘵𝘦𝘷𝘦𝘯𝘴[a director of the Max Planck Institute for Meteorology] 𝘴𝘢𝘺𝘴. ‘𝘐𝘵 𝘸𝘢𝘴 𝘢𝘧𝘳𝘢𝘪𝘥 𝘰𝘧 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘪𝘯𝘨𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘵𝘩𝘰𝘶𝘨𝘩𝘵 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘶𝘯𝘧𝘢𝘪𝘳𝘭𝘺 𝘶𝘴𝘦𝘥 𝘢𝘨𝘢𝘪𝘯𝘴𝘵 𝘵𝘩𝘦𝘮.’”
🔗https://t.co/PImzcNbUQu
A fair test of GCM skill uses temperature data that the models are not pre-tuned to match, such as satellite-based lower tropospheric temperatures (TLT). CMIP6 models, initialized in 1979, show roughly TWICE the warming rate observed in the University of Alabama in Huntsville (UAH v6.0) TLT dataset as shown in the bottom right time series (e.g., Christy & McNider, 2017; Christy et al., 2018) Recent events, such as the strong El Niño, reductions in low- and mid-altitude stratiform cloud cover, and perhaps the Hunga Tonga eruption have narrowed the gap temporarily, but the systematic warm bias persists in many model simulations.
🔗https://t.co/zueJLn9sY4
🔗https://t.co/vUJgmigsAe
3⃣ 𝗧𝗛𝗘𝗥𝗘 𝗜𝗦 𝗔𝗡𝗧𝗛𝗥𝗢𝗣𝗢𝗚𝗘𝗡𝗜𝗖 𝗖𝗢𝗡𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡; 𝗧𝗛𝗘 𝗠𝗔𝗚𝗡𝗜𝗧𝗨𝗗𝗘 𝗜𝗦 𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡
No serious analysis denies that there is some human influence on global warming. It is a scientific fact that CO₂ absorbs infrared radiation. Increasing CO₂ changes the radiative balance of Earth’s atmosphere. All else equal, that produces a warming tendency in the lower atmosphere.
The direct radiative forcing from doubled CO₂ concentrations is ~3.7 W/m², implying ~1°C direct warming before feedbacks act to amplify or dampen that change. However, the average radiation flux into the atmosphere is on the order of 239 ± 3.3 W/m² of absorbed solar radiation (ASR) averaged over a year (Trenberth et al., 2009; Stephens et al., 2012). When the error bars on baseline energy fluxes exceed the incremental forcing of increasing GHGs, precise attribution to GHGs becomes challenging.
🔗https://t.co/mlZbsIe8KQ
🔗https://t.co/5z5iMdb7H9 / open-access version: https://t.co/51Ys5w99LR
How much warming observed is due to increasing GHGs?
How much warming has been offset by aerosols?
How much warming is due to natural variability [which hasn’t been researched enough]?
What is the net effect of cloud feedbacks? Warming or cooling?
How do clouds respond to global warming, if at all? How do clouds change due to unforced internal variability? Are these changes observed in low-, mid-, or high-altitude clouds? Two of the three? All three? What about stratiform, cumuliform, and cirriform?
Have urbanization and land use changes been accurately removed from the GMST record as claimed by most government scientists and scholars?
These are all questions that remain unanswered, even if the scientific community at large refuses to acknowledge them for the sake of employment, to get research funding, or their choice to promote “the cause” for environmentalism.
![ChrisMartzWX's tweet photo. Have you seen the figure on the left before?
Chances are you have if you’re actively involved in the climate change debate. If not, let me break it down.
The figure, adapted from Figure SPM.1 in the IPCC AR6 Summary for Policymakers, is often touted as the “smoking gun” that proves virtually all observed global warming since 1850 is due to mankind’s greenhouse gas (GHG) emissions. The figure implies that global climate models (GCMs) skillfully reproduce observed warming (see black curve) since the “pre-industrial” era. They claim that this chart is synthesized from “multiple lines of evidence,” and that GCMs cannot skillfully reproduce the observed temperature trends without extra GHGs.
🔗https://t.co/5lspFokYOu(p. 6)
However, this graph is not the “smoking gun” that many people think it is. So, let me explain why.
First, it’s important to understand exactly what Figure SPM.1 is showing.
⬛️ 𝗖𝘂𝗿𝘃𝗲: The observed global mean surface [air] temperature (GMST) anomaly (1850–2019) from four surface station-based datasets. It shows ~1.2°C of warming since 1850, though there is slight divergence between datasets (e.g., Berkley Earth, HadCRUT, NOAA, etc.). The likely range falls between 1.1–1.3°C, and I do not dispute these estimates, although I do not care much about what the GMST is.
🟩 𝗖𝘂𝗿𝘃𝗲: The CMIP6 multi-model mean (MMM) GMST response to natural forcings and internal variability only. These are primarily solar forcing (e.g., changes in total solar irradiance, which is a measure of the solar power over all wavelengths per unit area incident at the top of the atmosphere), stratospheric aerosol injection (SAI) from major volcanic eruptions (think Mount Tambora, 1815; Krakatoa, 1883; El Chichón, 1982; or Mount Pinatubo, 1991), and the El Niño Southern Oscillation (ENSO).
🟦 𝗖𝘂𝗿𝘃𝗲: Simulated GMST response due to aerosol forcing only. Aerosols are fine solid or liquid particles—natural or anthropogenic in origin—that are suspended in the air and generally have diameters of <1 μm (1 × 10⁻⁴ cm). Depending on type, aerosols can either act to warm or cool the Earth’s atmosphere. For instance, bright aerosols (e.g., sulfate particulates from coal burning) scatter incoming solar radiation back to space, which causes net cooling. Dark aerosols (e.g., black carbon from diesel exhaust), on the contrary, absorb incoming sunlight, resulting in net warming.
🟥 𝗖𝘂𝗿𝘃𝗲: Simulated ensemble GMST response to manmade GHG emissions only. These primarily are carbon dioxide (CO₂) and methane (CH₄), and to a lesser extent, include ozone (O₃) and nitrous oxides (NOx).
⬜️ 𝗖𝘂𝗿𝘃𝗲: Simulated GMST change due to natural forcings / variability + aerosols + GHGs. In other words, it’s the sum of the green, blue, and red curves.
As we can see below, the IPCC’s figure implies that GCMs have done remarkably well to explain why the globe has warmed [𝘴𝘭𝘪𝘨𝘩𝘵𝘭𝘺] since 1850. The verdict, so we’re told, is that GHG emissions from our energy consumption, industrial activities, and transportation are squarely to blame. And who’s going to argue with that? After all, scientists and climate activists routinely tell us that “It’s just basic physics” and that “The science is settled.”
There’s just one problem: the conclusion depends heavily on GCMs whose historical behavior (called hindcasting) is not an independent test in the way that the general public has been misled to believe by much of the scientific community.
GCMs are not simply pure radiative physics machines that can be turned on, run out to a certain date, and allowed to speak for themselves. They contain many parameterized processes such as cloud feedbacks, aerosol forcing, convection, land-surface exchanges, and ocean-atmosphere coupling that must be calibrated during model development.
1️⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗦𝗧𝗥𝗨𝗚𝗚𝗟𝗘 𝗪𝗜𝗧𝗛 𝗡𝗔𝗧𝗨𝗥𝗔𝗟 𝗩𝗔𝗥𝗜𝗔𝗕𝗜𝗟𝗜𝗧𝗬
Paleoclimate proxies (e.g., tree rings, ice cores, and sediments) show significant multidecadal to centennial temperature fluctuations in pre-industrial times. The Greenland and Antarctic ice core-based temperature reconstructions, for instance, make that abundantly clear.
🔗https://t.co/5PpWXs3cak
The models in CMIP5/6 smooth temperature curves and fail to reproduce the known amplitude of natural internal variability exhibited in multiple temperature reconstructions both regionally and globally (i.e., the more accurate pre-Hockey stick reconstructions). So, when modelers claim that natural forcing and internal variability cannot explain any warming since 1850, we should ask: Is that because nature truly could not have contributed much? Or is it possible that GCMs underrepresent natural variability?
That's not a trivial distinction.
2⃣ 𝗖𝗟𝗜𝗠𝗔𝗧𝗘 𝗠𝗢𝗗𝗘𝗟𝗦 𝗔𝗥𝗘 𝗔𝗥𝗧𝗜𝗙𝗜𝗖𝗜𝗔𝗟𝗟𝗬 𝗧𝗨𝗡𝗘𝗗
Because GCMs struggle to independently reproduce observed historical temperature variations, modelers often pre-tune parameters (turn “knobs”) until the GMST output aligns with the observed GMST record.
This is not a conspiracy theory; it’s a demonstrable fact. The question is not whether tuning occurs. It does. The question is how much confidence we should place in attribution claims when models are essentially fudged to agree with the same historical record that is later used to demonstrate their alleged success.
Figure 3.8 on the right from IPCC AR6 WG1 illustrates this perfectly. It shows the assessed contributions to global warming from GHGs, aerosols, and natural forcings / internal variability over the period 2010–19 minus 1850–1900.
Let’s break it down:
🟩 The green boxes in Figure 3.8 show the spread in modeled GMST change ascribed to natural forcing / internal variability. The MMM is centered around the zero mark with little variation between the individual models in the ensemble. This is, once again, due to the fact that GCMs cannot hindcast well. So, the modelers assume that natural contributions to warming must be near zero.
🟦 Aerosol effective radiative forcing (ERF) carries enormous uncertainty: the MMM suggests a modest net cooling of ~0.5°C, but the range spans from a strong cooling of –0.9°C to a weak warming of +0.1°C over the period 1850–2019. The uncertainty arises because it is unknown whether the net warming from black carbon is offset by the cooling from sulfate aerosols (H₂SO₄). The MMM of 0.5°C thus has a 100% margin of error, which means that no scientists know exactly how much aerosol forcing has dampened (or enhanced) GMST change.
Does that sound like “settled science” to you? It sure doesn’t to me.
But modelers need to reproduce warming of ~0.9°C to 1.1°C. If aerosol emissions result in net cooling, then that cannot be the cause. So, what do these modelers td? They jack up the GHG forcing.
🟫 Extra GHGs also have uncertainty, though the margin of error is smaller in these estimates. The MMM suggests a strong warming of 1.5°C ± ~45%. All this tells us is that “the experts” have no clue just how sensitive the climate system is to extra GHGs in the Earth’s atmosphere.
Add these forcings together and the ensemble mean output lands near observations.
🔗https://t.co/6RS5rVXTRZ (p. 440)
There’s just one problem. Different models can get similar historical warming curves that resemble observations, but they use different combinations of aerosol cooling and GHG warming. That means the match to observed temperature trends does not uniquely prove the exact partitioning between GHGs, aerosols, and natural variability. For example, a model with strong aerosol cooling requires strong GHG warming to offset it. A different model with weak aerosol cooling requires a weaker GHG warming. Both will produce near-identical GMST curves that have high correlation to observations, but one cannot put much stock into these coefficients. This is a curve fitting exercise rather than real physics.
Mauritsen & Roeckner (2020), for example, openly describe tuning the Max Planck Institute model (MPI-ESM1.2) to match historical warming by adjusting cloud feedbacks and targeting an equilibrium climate sensitivity (ECS) of ~3°C (this is the IPCC's “best estimate”). With the known physics of radiative heat transfer, MPI-ESM1.2 produced an ECS of 7°C, more than double their target. Therefore, the modelers arbitrarily adjusted the cloud parameter scheme until the model output aligned with GMST observations.
In the closing remarks of the paper, the authors of Mauritsen & Roeckner (2020) say,
🗨️ “𝘞𝘦 𝘩𝘢𝘷𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘦𝘥 𝘩𝘰𝘸 𝙬𝙚 𝙩𝙪𝙣𝙚𝙙 𝙩𝙝𝙚 𝙈𝙋𝙄-𝙀𝙎𝙈𝟭.𝟮 𝙜𝙡𝙤𝙗𝙖𝙡 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙢𝙤𝙙𝙚𝙡 𝙩𝙤 𝙢𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙞𝙣𝙨𝙩𝙧𝙪𝙢𝙚𝙣𝙩𝙖𝙡 𝙧𝙚𝙘𝙤𝙧𝙙 𝘸𝘢𝘳𝘮𝘪𝘯𝘨; 𝘢𝘯 𝘦𝘯𝘥𝘦𝘢𝘷𝘰𝘳 𝙬𝙝𝙞𝙘𝙝 𝙝𝙖𝙨 𝙘𝙡𝙚𝙖𝙧𝙡𝙮 𝙗𝙚𝙚𝙣 𝙨𝙪𝙘𝙘𝙚𝙨𝙨𝙛𝙪𝙡. 𝘋𝘶𝘦 𝘵𝘰 𝘵𝘩𝘦 𝘩𝘪𝘴𝘵𝘰𝘳𝘪𝘤𝘢𝘭 𝘰𝘳𝘥𝘦𝘳 𝘰𝘧 𝘦𝘷𝘦𝘯𝘵𝘴, 𝙩𝙝𝙚 𝙘𝙝𝙤𝙞𝙘𝙚 𝙬𝙖𝙨 𝙩𝙤 𝙙𝙤 𝙩𝙝𝙞𝙨 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡𝙡𝙮 𝙗𝙮 𝙩𝙖𝙧𝙜𝙚𝙩𝙞𝙣𝙜 𝙖𝙣 𝙀𝘾𝙎 𝙤𝙛 𝘢𝘣𝘰𝘶𝘵 𝟯 𝙆 𝙪𝙨𝙞𝙣𝙜 𝙘𝙡𝙤𝙪𝙙 𝙛𝙚𝙚𝙙𝙗𝙖𝙘𝙠𝙨, 𝙖𝙨 𝙤𝙥𝙥𝙤𝙨𝙚𝙙 𝙩𝙤 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙝𝙚 𝙖𝙚𝙧𝙤𝙨𝙤𝙡 𝙛𝙤𝙧𝙘𝙞𝙣𝙜.”
🔗https://t.co/uBatWfdyvQ
🔗https://t.co/8Ntgszrz3a(p. 44 for IPCC ECS “best estimate” of 3 K)
The “basic physics” did not yield correct values, so the scientists arbitrarily turn knobs on various parameters until they get their desired result.
This transparency is useful, but it also exposes the problem. If a model is tuned to the instrumental GMST record, then its ability to reproduce the 20th-century temperature record cannot be treated as independent proof that the model has correctly identified the cause(s) of that warming. This is a circularity problem. It does not mean that GHGs have had no effect. They definitely have, at least to some extent. But one cannot confidently claim that all of the warming since 1850 has been anthropogenic because “the models match observations.”
Scientists also haven’t always been transparent about this. For decades, modeling centers refused to inform the public exactly how GCMs are calibrated. The IPCC subtly admitted this in Chapter 9 of AR5 WG1, stating,
🗨️ “𝘞𝘪𝘵𝘩 𝘷𝘦𝘳𝘺 𝘧𝘦𝘸 𝘦𝘹𝘤𝘦𝘱𝘵𝘪𝘰𝘯𝘴 (𝘔𝘢𝘶𝘳𝘪𝘵𝘴𝘦𝘯 𝘦𝘵 𝘢𝘭., 2012; 𝘏𝘰𝘶𝘳𝘥𝘪𝘯 𝘦𝘵 𝘢𝘭., 2013) 𝙢𝙤𝙙𝙚𝙡𝙡𝙞𝙣𝙜 𝙘𝙚𝙣𝙩𝙧𝙚𝙨 𝙙𝙤 𝙣𝙤𝙩 𝙧𝙤𝙪𝙩𝙞𝙣𝙚𝙡𝙮 𝙙𝙚𝙨𝙘𝙧𝙞𝙗𝙚 𝙞𝙣 𝙙𝙚𝙩𝙖𝙞𝙡 𝙝𝙤𝙬 𝙩𝙝𝙚𝙮 𝙩𝙪𝙣𝙚 𝙩𝙝𝙚𝙞𝙧 𝙢𝙤𝙙𝙚𝙡𝙨.. 𝘛𝘩𝘦𝘳𝘦𝘧𝘰𝘳𝘦, 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘰𝘣𝘴𝘦𝘳𝘷𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘤𝘰𝘯𝘴𝘵𝘳𝘢𝘪𝘯𝘵𝘴 𝘵𝘰𝘸𝘢𝘳𝘥 𝘸𝘩𝘪𝘤𝘩 𝘢 𝘱𝘢𝘳𝘵𝘪𝘤𝘶𝘭𝘢𝘳 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘵𝘶𝘯𝘦𝘥 𝘪𝘴 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘭𝘺 𝘯𝘰𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦.”
🔗https://t.co/z2jHU1IbkD (pp. 749-50)
This initial lack of transparency became the focus of an editorial in Science Magazine in October 2016,
🗨️ “𝘐𝘯𝘥𝘦𝘦𝘥, 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵𝘴 𝘭𝘪𝘬𝘦 𝘵𝘰 𝘢𝘥𝘮𝘪𝘵 𝘪𝘵 𝘰𝘳 𝘯𝘰𝘵, 𝙣𝙚𝙖𝙧𝙡𝙮 𝙚𝙫𝙚𝙧𝙮 𝙢𝙤𝙙𝙚𝙡 𝙝𝙖𝙨 𝙗𝙚𝙚𝙣 𝙘𝙖𝙡𝙞𝙗𝙧𝙖𝙩𝙚𝙙 𝙥𝙧𝙚𝙘𝙞𝙨𝙚𝙡𝙮 𝙩𝙤 𝙩𝙝𝙚 𝟮𝟬𝙩𝙝 𝙘𝙚𝙣𝙩𝙪𝙧𝙮 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙧𝙚𝙘𝙤𝙧𝙙𝙨—𝙤𝙩𝙝𝙚𝙧𝙬𝙞𝙨𝙚 𝙞𝙩 𝙬𝙤𝙪𝙡𝙙 𝙝𝙖𝙫𝙚 𝙚𝙣𝙙𝙚𝙙 𝙪𝙥 𝙞𝙣 𝙩𝙝𝙚 𝙩𝙧𝙖𝙨𝙝. ‘𝘐𝘵'𝘴 𝘧𝘢𝘪𝘳 𝘵𝘰 𝘴𝘢𝘺 𝘢𝘭𝘭 𝘮𝘰𝘥𝘦𝘭𝘴 𝘩𝘢𝘷𝘦 𝘵𝘶𝘯𝘦𝘥 𝘪𝘵,’ 𝘴𝘢𝘺𝘴 𝘐𝘴𝘢𝘢𝘤 𝘏𝘦𝘭𝘥, 𝘢 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵 𝘢𝘵 𝘵𝘩𝘦 𝘎𝘦𝘰𝘱𝘩𝘺𝘴𝘪𝘤𝘢𝘭 𝘍𝘭𝘶𝘪𝘥 𝘋𝘺𝘯𝘢𝘮𝘪𝘤𝘴 𝘓𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘺, 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘱𝘳𝘰𝘮𝘪𝘯𝘦𝘯𝘵 𝘮𝘰𝘥𝘦𝘭𝘪𝘯𝘨 𝘤𝘦𝘯𝘵𝘦𝘳, 𝘪𝘯 𝘗𝘳𝘪𝘯𝘤𝘦𝘵𝘰𝘯, 𝘕𝘦𝘸 𝘑𝘦𝘳𝘴𝘦𝘺.”
The Science Magazine article continues, stating that climate scientists had been reluctant to come clean about tuning practices for years because they feared that skeptics would use their practices against them to downplay global warming,
🗨️ “𝙁𝙤𝙧 𝙮𝙚𝙖𝙧𝙨, 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙨𝙘𝙞𝙚𝙣𝙩𝙞𝙨𝙩𝙨 𝙝𝙖𝙙 𝙗𝙚𝙚𝙣 𝙢𝙪𝙢 𝘪𝘯 𝘱𝘶𝘣𝘭𝘪𝘤 𝙖𝙗𝙤𝙪𝙩 𝘵𝘩𝘦𝘪𝘳 ‘𝘴𝘦𝘤𝘳𝘦𝘵 𝘴𝘢𝘶𝘤𝘦’: 𝙒𝙝𝙖𝙩 𝙝𝙖𝙥𝙥𝙚𝙣𝙚𝙙 𝙞𝙣 𝙩𝙝𝙚 𝙢𝙤𝙙𝙚𝙡𝙨 𝘴𝘵𝘢𝘺𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭𝘴. 𝙏𝙝𝙚 𝙩𝙖𝙗𝙤𝙤 𝙧𝙚𝙛𝙡𝙚𝙘𝙩𝙚𝙙 𝙛𝙚𝙖𝙧𝙨 𝙩𝙝𝙖𝙩 𝙘𝙡𝙞𝙢𝙖𝙩𝙚 𝙘𝙤𝙣𝙩𝙧𝙖𝙧𝙞𝙖𝙣𝙨 𝙬𝙤𝙪𝙡𝙙 𝙪𝙨𝙚 𝙩𝙝𝙚 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙚 𝙤𝙛 𝙩𝙪𝙣𝙞𝙣𝙜 𝙩𝙤 𝙨𝙚𝙚𝙙 𝙙𝙤𝙪𝙗𝙩 𝙖𝙗𝙤𝙪𝙩 𝙢𝙤𝙙𝙚𝙡𝙨—𝙖𝙣𝙙, 𝙗𝙮 𝙚𝙭𝙩𝙚𝙣𝙨𝙞𝙤𝙣, 𝙩𝙝𝙚 𝙧𝙚𝙖𝙡𝙞𝙩𝙮 𝙤𝙛 𝙝𝙪𝙢𝙖𝙣-𝙙𝙧𝙞𝙫𝙚𝙣 𝙬𝙖𝙧𝙢𝙞𝙣𝙜. ‘𝘛𝘩𝘦 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺 𝘣𝘦𝘤𝘢𝘮𝘦 𝘥𝘦𝘧𝘦𝘯𝘴𝘪𝘷𝘦,’ [Bjorn] 𝘚𝘵𝘦𝘷𝘦𝘯𝘴[a director of the Max Planck Institute for Meteorology] 𝘴𝘢𝘺𝘴. ‘𝘐𝘵 𝘸𝘢𝘴 𝘢𝘧𝘳𝘢𝘪𝘥 𝘰𝘧 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘪𝘯𝘨𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘵𝘩𝘰𝘶𝘨𝘩𝘵 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘶𝘯𝘧𝘢𝘪𝘳𝘭𝘺 𝘶𝘴𝘦𝘥 𝘢𝘨𝘢𝘪𝘯𝘴𝘵 𝘵𝘩𝘦𝘮.’”
🔗https://t.co/PImzcNbUQu
A fair test of GCM skill uses temperature data that the models are not pre-tuned to match, such as satellite-based lower tropospheric temperatures (TLT). CMIP6 models, initialized in 1979, show roughly TWICE the warming rate observed in the University of Alabama in Huntsville (UAH v6.0) TLT dataset as shown in the bottom right time series (e.g., Christy & McNider, 2017; Christy et al., 2018) Recent events, such as the strong El Niño, reductions in low- and mid-altitude stratiform cloud cover, and perhaps the Hunga Tonga eruption have narrowed the gap temporarily, but the systematic warm bias persists in many model simulations.
🔗https://t.co/zueJLn9sY4
🔗https://t.co/vUJgmigsAe
3⃣ 𝗧𝗛𝗘𝗥𝗘 𝗜𝗦 𝗔𝗡𝗧𝗛𝗥𝗢𝗣𝗢𝗚𝗘𝗡𝗜𝗖 𝗖𝗢𝗡𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡; 𝗧𝗛𝗘 𝗠𝗔𝗚𝗡𝗜𝗧𝗨𝗗𝗘 𝗜𝗦 𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡
No serious analysis denies that there is some human influence on global warming. It is a scientific fact that CO₂ absorbs infrared radiation. Increasing CO₂ changes the radiative balance of Earth’s atmosphere. All else equal, that produces a warming tendency in the lower atmosphere.
The direct radiative forcing from doubled CO₂ concentrations is ~3.7 W/m², implying ~1°C direct warming before feedbacks act to amplify or dampen that change. However, the average radiation flux into the atmosphere is on the order of 239 ± 3.3 W/m² of absorbed solar radiation (ASR) averaged over a year (Trenberth et al., 2009; Stephens et al., 2012). When the error bars on baseline energy fluxes exceed the incremental forcing of increasing GHGs, precise attribution to GHGs becomes challenging.
🔗https://t.co/mlZbsIe8KQ
🔗https://t.co/5z5iMdb7H9 / open-access version: https://t.co/51Ys5w99LR
How much warming observed is due to increasing GHGs?
How much warming has been offset by aerosols?
How much warming is due to natural variability [which hasn’t been researched enough]?
What is the net effect of cloud feedbacks? Warming or cooling?
How do clouds respond to global warming, if at all? How do clouds change due to unforced internal variability? Are these changes observed in low-, mid-, or high-altitude clouds? Two of the three? All three? What about stratiform, cumuliform, and cirriform?
Have urbanization and land use changes been accurately removed from the GMST record as claimed by most government scientists and scholars?
These are all questions that remain unanswered, even if the scientific community at large refuses to acknowledge them for the sake of employment, to get research funding, or their choice to promote “the cause” for environmentalism.](https://pbs.twimg.com/media/HMk9TgIXEAAZdC2.jpg)
@ChrisMartzWX Alarmist - “ranting is a leading cause of climate change!!!”
🤣
Love your work - keep fighting the good fight 👍🏻
Ever since I saw a photo like this in 2018 I wanted to do this.
It took me 8 years, but I finally got it.
These two images were taken by @astro_reid only minutes apart. The stark difference is the result of camera settings. In the first, a longer shutter speed let in much more light from Earth, while the shorter shutter speed in the second emphasizes our planet's nighttime glow.
Who to follow
Vanman 🏴☠️
@1crazyvanlife
🧡🧡Twitter is cheaper than therapy 🤷♂️ I used to be weird , now I’m magical 😜 Philly kid in found his way back home 👍🏼 🇺🇦 Go Birds 🦅
Chris A
@chchorizo
MUFC fan. Home brewer. Bought a Red Setter and named him Ollie (after Solskjaer). Like to cook at home but eat out often.
♛ ʍʀ ʍǟʀǟʋɨʟʟǟš ــہہ⭕️٨ــ٨ــــ.
@1mrmaravillas
“Exploring love's depth ~ one verse at a time”••• “Feeding love’s insatiable flames ~ one verse, one blaze at a time”•••Unleashing love’s wildfire•••❦
Welcome to the Tour.
Electromagnetic mass drivers on the Moon
Mission complete. After three months in orbit and 49 tests completed validating critical systems, components, and processes, we have successfully performed a controlled deorbit of Haven Demo, our in-space testbed for Haven-1 technologies.
Read more.
https://t.co/4oDDUC1im3
Very proud of my 'tool of hate' as the Government's new 'social cohesion strategy' has labelled our glorious flags.
Restore Britain's official response to their strategy:
SOD OFF.
Starship V3 SN1 headed for ground tests.
I am highly confident that the V3 design will achieve full reusability.
The first Starship V3 has left the build site to begin prelaunch testing
Oops 😂
Explained: Why serving halal meat across Britain on an industrial scale is a huge legal issue.
I don’t know who needs to hear this but you are enough just by existing. Who you are in this moment is enough to make you special and unique. Know that.
Have a good night. 👍👍
It’s the most wonderful time of the year - the self-assessment deadline.
That magical moment when many of Britain’s tax-paying men and women sit down trying to work out how much more of their hard-earned money the obese state is going to steal from them - and it is theft.
What’s the damage? A LOT.
For a moment, let’s ask ourselves for what…
To fund indolent Somalian families who refuse to work, living off benefits? To pay for this endless line of public sector bureaucrats who give nothing, but take so much? To provide laundry services to Eritrean conmen who have broken into our country so they can have their taxpayer-funded tracksuits washed twice a week? To send Pakistan £133 million in foreign aid whilst our own towns crumble away? To allow millions of healthy Brits to laze around all day, enjoying daytime television whilst the rest of us actually contribute?
What a crap deal. I am fed up of it. I am fed up of all of it.
Who picks up the bill?
Business owners, farmers, contractors, sole traders, publicans, plumbers, entrepreneurs, electricians, long-standing family businesses and so many more.
People who work and work and work to create real value. I see the effort, it is relentless and nobody in Government gives a toss about them. I really mean that. They don’t. They’re just another source of income to fund all this outrageous waste. Not forgetting the accountant fees we all have to pay to comply with the never-ending conveyor belt of laws and regulations…
These men and women are all now looking at their tax bills and thinking what the HELL is the point? Honestly? Why bother?
NOTHING WORKS. The roads are a mess, a chaotic NHS is failing and every organ of the state is utterly incompetent.
The entire Westminster establishment simply does not understand the brutal reality of what is needed. And it must be brutal.
There is ONE route out of this mess. Slash tax - income, corporation, dividends. Cut VAT. Everything. Brutalise the size of the state. Less Government, less regulation, less tax. Get it the hell out of our lives. And PLEASE stop pissing our tax away on all this nonsense. I want Government to do a small list of things, but do them very well.
Let these entrepreneurs and grafters do what they do best. Work, build success, generate opportunities and create wealth. Then actually, they’ll end up paying more tax. This is NOT complicated.
MPs are CLUELESS. I would honestly rather a flock of pigeons made these decisions, at least then there’s a random chance they might get one or two calls right.
The lack of business understanding from these clowns is PAINFUL.
Please know that there is at least one MP fighting for you all.
I thought this was Ai but it’s not.
Yesterday - Northern Lights aka Aurora Borealis over Stonehenge, England.
Simply unbelievable.
Truth truly is stranger than fiction.
Absolute beast of an Aurora over Stonehenge last night 🤩😍😲 Photo credit Nick Bull 🙏
#aurora #northernlights #auroraborealis #winter #January #astro #solarstorm
Just a heads up that an American startup called GRU Space is planning the first hotel on the moon by 2032, backed by Nvidia.
Meanwhile the UK government says it will take until 2035 to build a 3rd runway at Heathrow.
I'm 58.
If you're in your 20's, please read this:
They just want to suppress free speech
Dissolve Parliament and call a General Election now!
Sign and share. https://t.co/2aOOmZRkGi
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