{"id":411187,"date":"2025-11-01T16:11:25","date_gmt":"2025-11-01T15:11:25","guid":{"rendered":"https:\/\/climatescience.press\/?p=411187"},"modified":"2025-11-01T16:11:27","modified_gmt":"2025-11-01T15:11:27","slug":"hiding-the-endangerment-findings-systemic-biases-politicos-failed-attack-on-does-climate-science-report","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=411187","title":{"rendered":"Hiding the Endangerment Finding\u2019s Systemic Biases \u2013 Politico\u2019s Failed Attack on DOE\u2019s Climate Science Report"},"content":{"rendered":"\n
\"A<\/figure>\n\n\n\n

<\/p>\n\n\n\n

From Watts Up With That?<\/a><\/p>\n\n\n\n

Marlo Lewis, Ph.D., Senior Fellow in Energy and Environmental Policy, Competitive Enterprise Institute<\/p>\n\n\n\n

Politico<\/em> recently published an article by Benjamin Storrow, Chelsea Harvey, Scott Waldman, and Paula Friedrich titled \u201cHow a major DOE report hides the whole truth on climate change<\/a>.\u201d The reporters\u2019 objective is obvious and their strategy simple. They aim to discredit the Environmental Protection Agency\u2019s proposal to repeal<\/a> the December 2009 Greenhouse Gas Endangerment Finding<\/a> by discrediting a Department of Energy (DOE) draft report which is cited in the repeal proposal\u2019s climate science discussion.<\/p>\n\n\n\n

From a statutory perspective, that strategy is not a winner. The EPA\u2019s proposal to repeal the Endangerment Finding (plus motor vehicle emission standards<\/a> adopted by the agency in April 2024) relies chiefly on legal arguments<\/a> that do not presuppose specific climate change assessments.<\/p>\n\n\n\n

However, the Politico<\/em> article could sway the court of public opinion, which in turn could influence future litigation. Such influence would be undeserved. The article ignores foundational biases compromising the scientific basis of the 2009 Endangerment Finding. Further, its criticisms of the DOE report repeatedly misfire or backfire, and none comes close to refuting any of the report\u2019s conclusions.<\/p>\n\n\n\n

Background    <\/strong><\/p>\n\n\n\n

The 2009 Endangerment Finding purported to determine that carbon dioxide (CO2<\/sub>) and other greenhouse gas (GHG) emissions from new motor vehicles \u201ccause, or contribute to, air pollution which may reasonably be anticipated to endanger public health or welfare.\u201d The Finding was the impetus for the Obama administration EPA\u2019s adoption, in 2010, of GHG emission standards for model year 2012-2016 motor vehicles<\/a>. To one degree or another, the Finding undergirds all subsequent climate policy regulations proposed or promulgated by the Obama and Biden administrations.<\/p>\n\n\n\n

DOE\u2019s July 2025 draft report, A Critical Review of Impacts of Greenhouse Gas Emissions on the U.S. Climate<\/em><\/a>, does not opine on the Endangerment Finding, which is a legal document. However, the report\u2019s non-alarming assessment of climate change risks is heresy to legions of progressive policymakers, activists, academics, and journalists.<\/p>\n\n\n\n

The Politico<\/em> reporters accuse the DOE report\u2019s authors\u2014John Christy, Judith Curry, Steve Koonin, Ross McKitrick, and Roy Spencer\u2014of cherry picking, omitting context, relying on debunked or outmoded studies, and citing non-peer-reviewed analyses. They also contend the report is \u201covertly political\u201d and therefore not a \u201cscientific exercise.\u201d As shown below, those allegations are false, misleading, or unsubstantiated.<\/p>\n\n\n\n

This essay has two main parts. Part 1 summarizes the disqualifying cherry picks, omissions, and outmoded opinions fundamental to the \u201cvast scientific consensus\u201d the Politico<\/em> reporters invoke. It also rebuts their critique of the DOE report\u2019s discussion of climate models. Part 2 rebuts other objections they raise about the DOE report.<\/p>\n\n\n\n

Part 1: Realist perspective<\/strong>, (part 2 tomorrow)<\/strong><\/p>\n\n\n\n

Mainstream climate research has a deep scientific integrity problem due to its reliance on a triply biased methodology. For decades, the usual practice has been to run overheated models with inflated emission scenarios and ignore or depreciate humanity\u2019s remarkable capacity for adaptation. That approach is wired to exaggerate the physical impacts of GHG emissions and the harmfulness of such impacts.<\/p>\n\n\n\n

All three biases compromise the major assessment reports informing the 2009 Endangerment Finding as well as subsequent assessments touted as updating and strengthening it. However, studies that exposed those biases mostly examined the later assessments. Accordingly, the following sections on unrealistic models and emissions scenarios present the information in somewhat reverse chronological order.<\/p>\n\n\n\n

Warm-biased models<\/strong><\/p>\n\n\n\n

To project the physical impacts of climate change, the Intergovernmental Panel on Climate Change (IPCC), US Global Change Research Program (USGCRP), and other \u201cmainstream\u201d assessments use climate change projection models \u201cforced<\/a>\u201d with various GHG emission scenarios. The IPCC works with climate modeling groups around the world to develop and evaluate their products. This exercise is called the Coupled Model Intercomparison Project (CMIP<\/a>).<\/p>\n\n\n\n

There have been six CMIPs, the first one in 1996. The CMIP3 model ensemble was used in the IPCC\u2019s 2007 Fourth Assessment Report (AR4), the CMIP5 ensemble in the IPCC\u2019s 2013 Fifth Assessment Report (AR5) and USGCRP\u2019s 2017 Fourth National Climate Assessment (NCA4), and the CMIP6 ensemble in the IPCC\u2019s 2021 Sixth Assessment Report (AR6) and USGCRP\u2019s 2023 Fifth National Climate Assessment (NCA5).<\/p>\n\n\n\n

CMIP models make projections about the evolution of global annual average temperatures out to the year 2100 and beyond. There is no way to directly test the accuracy of those projections. However, the models can hindcast global temperature changes over recent decades, and those projections can be compared to observations. That is what atmospheric scientist John Christy and colleagues have done in a series of analyses since the early 2000s.<\/p>\n\n\n\n

The chart below compares CMIP5 warming projections in the tropical bulk atmosphere (mid-troposphere) to observations in three empirical datasets: satellites, balloons, and re-analyses<\/a>. On average, modeled warming exceeds observed warming by more than a factor of two during 1979-2016.<\/p>\n\n\n

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\"Graph
Source:<\/strong>\u00a0John Christy (2017)<\/em><\/a>. Solid red line\u2014average of all the CMIP5 climate models; thin colored lines\u2014individual CMIP-5 models; solid figures\u2014weather balloon, satellite, and reanalysis data for the tropical troposphere.<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

The next chart shows that only one CMIP5 model, the Russian INM-CM4, accurately tracks temperature change through the depth of the tropical troposphere.<\/p>\n\n\n\n

\"Graph
Source:\u00a0<\/strong>Updated from<\/em>\u00a0Christy and McNider (2017)<\/em><\/a>. Tropical atmosphere temperature trends (1979-2018) from 25 CMIP5 models compared to four radiosonde (weather balloon) datasets.<\/em><\/figcaption><\/figure>\n\n\n\n

The superior accuracy of INM-CM4 likely has something to do with its equilibrium climate sensitivity (ECS) estimate, which is the lowest of any CMIP5 model. ECS is customarily defined as the amount of warming that occurs after the climate system fully adjusts to a doubling of carbon dioxide-equivalent greenhouse gas concentration. INM-CM4<\/a> has an ECS of 1.8\u00b0C. In contrast, GFDL-CM3<\/a>, which has an ECS of 4.0\u00b0C (or higher)<\/a>, projects a warming trend that is literally off the chart.<\/p>\n\n\n\n

Readers may wonder why the comparisons focus on the tropical troposphere. After all, nobody lives there!<\/p>\n\n\n\n

As DOE report authors McKitrick and Christy explain in a peer-reviewed study<\/a> published in Earth and Space Science<\/em>, the tropical mid-troposphere is uniquely suited for testing the validity of climate models. That is because: (1) Nearly all models predict strong positive feedbacks (accelerated warming) in the tropical mid-troposphere; (2) the region is well-monitored by satellites and weather balloons; (3) the mid-troposphere is too distant from the surface to be influenced by land use changes; and (4) the models were not previously \u201ctuned\u201d to match the historical climatology in that region, hence are genuinely independent of the data used to test them.<\/p>\n\n\n\n

That last point is the most critical. Modelers try to make their models realistic by adjusting climate parameters (such as climate sensitivity) until hindcasts match historical temperature changes. Typically, 20th<\/sup> century land and sea surface temperatures are used to \u201ctrain\u201d the models. However, hindcasting data already used to tune a model is like peeking at the answers before taking a quiz.<\/p>\n\n\n\n

The only real way to test a climate model\u2019s predictive skill (other than waiting 30+ years to see how things evolve) is to compare the model\u2019s hindcasts to data that are \u201cout of sample\u201d\u2014data not used to adjust model parameters. That is Christy\u2019s procedure. The models are not trained to reproduce tropospheric data. The results speak for themselves. The models are not realistic. They run too hot.<\/p>\n\n\n\n

One might suppose the new and improved CMIP6 models used in AR6 would be more accurate. Not so\u2014instead, they are worse. In the tropical troposphere, all the models hindcast faster warming than observational average drawn from satellites, weather balloons, and re-analyses. Moreover, the CMIP6 models overshoot observed warming throughout the global troposphere, with projections rising about 2.3 times faster than observations.<\/p>\n\n\n\n

\"Graph
Source:<\/strong>\u00a0McKitrick and Christy (2025)<\/em><\/a>,\u00a0Draft DOE climate science report, p. 35<\/em>.<\/figcaption><\/figure>\n\n\n\n

A reasonable explanation for the persistent mismatch between models and observations is that the models overestimate climate sensitivity. The larger (global) mismatch in the CMIP6 ensemble is consistent with that explanation. A 2019 study by\u00a0Zeke Hausfather<\/a>\u00a0found that 14 of 40 CMIP6 models have higher ECS estimates than the warmest CMIP5 model.<\/p>\n\n\n

\n
\"Bar
Source:\u00a0<\/strong>Hausfather (2019)<\/em><\/a>. Yellow bars show CMIP6 models with higher sensitivity than any CMIP5 model. Blue bars show CMIP6 model sensitivities within the CMIP5 range.<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

But what about the 2009 Endangerment Finding\u2014did it also have a \u201chot model\u201d problem? Yes, as the next chart shows.<\/p>\n\n\n\n

The IPCC\u2019s 2007 AR4 was a key scientific basis for the Endangerment Finding. The most critical input to AR4 was the CMIP3 model ensemble. In the 2000s, it was still difficult to obtain tropospheric temperature projections from climate modelers. Christy, however, was able to obtain surface temperature projections from the models. He then compared those to the UK Climate Research Centre (HadCRUT<\/a>) surface record and satellite data adjusted to match surface temperatures. In the chart below, temperature trends start in the year indicated on the X-axis and end in 2009. The observations (squares) all fall much below the AR4 model average (diamonds), usually about half the magnitude of the modeled trend.<\/p>\n\n\n\n

\"Line
Source:\u00a0<\/strong>John Christy<\/em><\/figcaption><\/figure>\n\n\n\n

Another question arises: Did the Endangerment Finding\u00a0Technical Support Document<\/a>\u00a0(TSD) acknowledge the hot model problem? No, but the TSD makes a case for the models\u2019 realism. In a nutshell, the models are realistic because they can reproduce 20th century global-scale changes in surface temperature, but only if the models are run with both natural variability and anthropogenic GHG emissions.<\/p>\n\n\n\n

\"Graph
Source:\u00a0<\/strong>EPA 2009 TSD<\/em><\/a>,\u00a0<\/em>IPCC AR4<\/em><\/a><\/figcaption><\/figure>\n\n\n\n

As the chart shows, AR4 is the source of the TSD\u2019s assumption that models are realistic when run with both natural and anthropogenic \u201cforcings\u201d (perturbations that change the balance between incoming solar radiation and outgoing infrared radiation).<\/p>\n\n\n\n

The reasoning is circular, as it assumes all significant natural forcings that warm the planet are known and estimated correctly. If, instead, the models omit or underestimate such forcings, they might not track surface temperature trends unless forced with extra GHGs. The assumption of adequately known natural variability is problematic given the ongoing debate over the causes of early 20th<\/sup> century warming<\/a> and evidence of a widespread<\/a> Medieval Warm Period<\/a>.<\/p>\n\n\n\n

Moreover, as noted, because climate impact models are trained to simulate 20th century land and ocean temperatures, a model\u2019s ability to reproduce \u201cin sample\u201d data is no assurance of predictive skill.<\/p>\n\n\n\n

Christy may have been the first to challenge AR4\u2019s claim that model projections match observations when the models include both natural and anthropogenic forcings. However, he had to wait until the IPCC posted a hard-to-decipher chart in an online supplement to AR5 (Figure 10.8<\/a>).<\/p>\n\n\n\n

When enlarged and clarified, the AR5 chart reveals that model projections and observations almost entirely diverge<\/em> unless the models are run with natural variability alone<\/em>.<\/p>\n\n\n

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\"Graph
Source:\u00a0<\/strong>John Christy, Annotated version of IPCC AR5 Figure 10.8(b), vertical warming pattern for tropics (20S to 20N). Horizontal axis: \u00b0C\/decade. Draft DOE climate science report, p. 37.<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

According to the Politico<\/em> reporters, the DOE report\u2019s \u201cassertions about the models\u2019 track records are false.\u201d Citing Hausfather et al. (2019)<\/a>, they contend that 1970s climate models \u201caccurately predicted current global warming.\u201d However, that is a red herring, because the early 1970s models did not inform either the Endangerment Finding or subsequent IPCC and USGCRP assessments.<\/p>\n\n\n\n

As McKitrick<\/a> pointed out on Judith Curry\u2019s blog, Supporting Information published by Hausfather et al. (2019) reports the ECS estimates of eight early climate models. Those models and their ECS values are:<\/p>\n\n\n\n