Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
The New York Times (NYT) reports in โA New Idea to Save the Climate? Dam the Bering Strait.โ that scientists have proposed building a 50-mile-long dam between Alaska and Russia to stabilize the Atlantic Meridional Overturning Circulation (AMOC) in order to prevent climate catastrophe. This is a seriously risky idea.
TheNew York Times (NYT) reports in โA New Idea to Save the Climate? Dam the Bering Strait.โ that scientists have proposed building a 50-mile-long dam between Alaska and Russia to stabilize the Atlantic Meridional Overturning Circulation (AMOC) in order to prevent climate catastrophe. This is a seriously risky idea. The proposal is not an engineering plan grounded in observational need, but rather is computer-model thought experiment based on speculative tipping-point scenarios not justified by data concerning the AMOC or an understanding of what the possible future consequences might be for humans, the oceans, and sea life.
The author describes an article published in Science Advances where the idea of a cross continental dam across the Bering Strait is proposed as a โproof of concept,โ noting that the dam could, under certain modeled conditions, prevent an AMOC collapse. It further explains that the findings come from a computer model of Earthโs climate, not from direct evidence that the AMOC is about to fail. That distinction is critical.
As Climate Realism has reported in dozens of articles, the AMOC has been the subject of repeated alarmist headlines over the past decade. Some studies have projected weakening. Others have suggested relative stability. Still others have found mechanisms, such as Southern Ocean wind-driven upwelling, which may be strengthening the AMOC. The scientific literature has moved in three directions: collapse, steady-state persistence, and even partial strengthening depending on assumptions.
What has not emerged is observational evidence of imminent shutdown.
The NYT acknowledges that โthe uncertainty is very, very large,โ quoting a climate scientist who says researchers do not know how close the AMOC is to collapse.
The proposed intervention outlined in the study would block the Bering Strait to alter freshwater flows between the Pacific, Arctic, and Atlantic Oceans. The model simulations suggest that if the AMOC is still strong, closing the strait might help maintain salinity and stabilize circulation. But if the AMOC is already weak, the same intervention could accelerate collapse.
In other words, the intervention could help, harm, or do nothing depending on timing and initial conditions.
That is not a control mechanism; it is a Las Vegas style gamble on a global scale.
The proposal relies on climate model outputs run under specific forcing assumptions. Models are useful tools, but they are not reality. Ocean circulation at the scale of the AMOC involves complex thermohaline processes, wind forcing, stratification, and deep-water formation, most of which arenโt well understood and, at best if accounted for at all in climate models, are only imperfectly represented even in state-of-the-art systems.
Moreover, the underlying modeling framework uses coarse resolution that does not fully resolve the Bering Straitโs dynamics, instead parameterizing throughflow behavior. From that abstraction comes a proposal to physically block a major ocean gateway. The AMOC is critical to coastal communities and the health and lifecycles of sea life. There is no evidence the models accounted for ancillary impacts on these communities or species โ the only focus was keeping the AMOC from collapsing, though, it turns out, the proposed cure may, in fact, cause the collapse.
The scale of the proposal itself should give pause. An 80-kilometer barrier in Arctic conditions across an international boundary is not comparable to ordinary coastal infrastructure. The NYT notes that once built, such a structure โcouldnโt easily be taken down.โ Geoengineering does not come with an undo button.
The Bering Strait is also a biological choke point linking Pacific and Arctic ecosystems. Blocking it would alter nutrient transport, salinity gradients, and marine migration pathways. The ecological consequences are acknowledged only briefly in the NYT coverage, yet they could be profound.
There is also a glaring contradiction embedded in this narrative. For years, readers have been told that the AMOC is fragile, sensitive to freshwater perturbations, and prone to tipping points. If that is true, why would deliberately shutting off a major ocean exchange be considered a sane idea? If the system is robust enough to tolerate such intervention, then perhaps the entire AMOC collapse narrative deserves reconsideration.
The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) states that while AMOC weakening is likely under high-emissions scenarios โ scenarios which are not just improbable but likely impossible, there is low confidence in a collapse before 2100. That is a far cry from imminent shutdown requiring Arctic mega-dams. The NYT article concedes that scientists do not know how close the AMOC is to collapse.
What we are seeing is a pattern. As climate modeling grows more dramatic, proposed interventions grow more extreme: carbon capture and permanent storage; solar radiation blocking; and now ocean dams. Each rest on the assumption that models reliably predict nonlinear system behavior decades in advance.
Before entertaining planetary-scale geoengineering, a simpler question should be asked: where is the observational evidence of near-term failure? The RAPID array has been monitoring the AMOC since 2004 and shows variability but not collapse. Paleo records indicate multidecadal fluctuations long before industrial emissions.
Ocean circulation is complex. Uncertainty is high. Models disagree. And now, on that uncertain foundation, we are asked to consider blocking an ocean strait. This is mad scientists from the movies type stuff. The NYT shouldnโt have even given this proposal an audience.
This is not sober climate reporting by TheNew York Times. It amplifies a speculative modeling exercise as if it were visionary thinking. When the cure involves restructuring planetary oceanic circulation based on uncertain and likely flawed simulations, skepticism is not denial, it is prudence in the face of a crazy idea with unknown consequences. The evidence that humans actually control the climate is exceedingly weak, but this proposal, if enacted, would certainly cause unforetold and unpredictable climate disruptions that we havenโt even begun to consider.
Crazy plans donโt become reasonable just because some group of scientists propose them. And crazy plans, just because they are floated, donโt necessarily merit the attention of a major media outlet, promoting it as a reasonable idea.
โHeads I win, tails you loseโ is usually recognized as a rhetorical trick. In climate science, it has matured into a methodological principle. Observations are no longer allowed to contradict theory; they merely reveal which auxiliary explanation must be activated. Aerosols cool when cooling is needed, disappear when warming needs help, and reappear when projections drift off course. A recent paper on Chinese sulfur emissions offers a remarkably clear example of how this works in practice.
โHeads I win, tails you loseโ is usually recognized as a rhetorical trick. In climate science, it has matured into a methodological principle.
Observations are no longer allowed to contradict theory; they merely reveal which auxiliary explanation must be activated.
Aerosols cool when cooling is needed, disappear when warming needs help, and reappear when projections drift off course. A recent paper on Chinese sulfur emissions offers a remarkably clear example of how this works in practice.
The conclusion is reassuringly familiar: cleaner air reduces cooling aerosols, allowing warming to emerge more clearly. Models reproduce this effect. Warming follows. Confidence increases.
Abstract
Anthropogenic emissions over China have recently declined due to environmental actions.
This work estimates the sensitivity of sulfate aerosol () concentration to the amount ofย ย emissions reduction over China from 2010 to 2020 using an Earth system model with two different aerosol representations.
We find that a larger rate ofย ย emissions decline over 2010โ2020 from an updated Chinese emission inventory leads to improvement in modeledย ย andย ย concentrations when evaluated with targeted airborne observations from the Asian summer monsoon region from the 2022 Asian summer monsoon Chemical and Climate Impact Project.
Updated Chineseย ย emissions reduceย ย concentration by >20% at 200ย hPa over the North Pacific, and by >7% at 100ย hPa throughout the tropics.
Theseย ย reductions result in an increase to global net instantaneous radiative forcing ofย 0.10โ0.15ย Wย ย by 2020, with regional effects up toย 6 times greater.
Plain Language Summary
Recent environmental policies in China have reduced emissions of various chemical species. This study uses recent chemical observations from research aircraft to evaluate how global climate model simulations represent these recent Chinese emissions trends.
We find that configuring a model with a larger emission decline in China over the 2010โ2020 period improves simulated concentrations of chemical species compared to observations.
Model simulations using different Chinese emissions are compared to estimate how much global climate is impacted by the recent emissions reductions. The impacts of emissions differences on aerosols have a considerable effect on Earthโs radiation budget, which must be accounted for in future research including model intercomparison efforts.
The authors summarize the headline result succinctly:
โThese SOโ reductions result in an increase to global net instantaneous radiative forcing of โผ0.10โ0.15 W mโปยฒ by 2020, with regional effects up to โผ6 times greater.โ
That number is then framed as potentially important for explaining recent observed temperature increases. Aerosol reductions, we are told, may help reconcile models with reality. The implication is polite but unmistakable: the theory was right; the atmosphere was merely getting in the way.
This is the โheads I winโ side of the coin.
The โtails you loseโ side appears earlier, embedded in the evaluation section, where the authors compare their simulations to direct airborne measurements from the Asian Summer Monsoon Chemical and Climate Impact Project (ACCLIP). Here, the tone shifts from confidence to candor.
Discussing upper-tropospheric sulfate concentrations, the paper notes:
โMean modeled UT SOโ concentration for the MAM4-MEIC simulation is a factor of โผ2.2 higher than observations, and the CARMA-MEIC simulation is a factor of โผ3.7 higher.โ
This is a structural bias. The models produce two to four times more sulfate aerosol than is observed in the very region used for evaluation.
To their credit, the authors attempt to diagnose the problem. They run sensitivity experiments. They adjust wet removal efficiencies, convective depth, aerosol activation properties, and even vertical resolution. The results areโฆ underwhelming.
As the paper states plainly:
โChanges to SOโ in these experiments were relatively small compared to the existing biases with observations.โ
In other words, the problem stubbornly refuses to go away.
At this point, a skeptic might reasonably expect the analysis to slow down. If aerosol concentrations are significantly overestimated, then their cooling effect is also overestimated, and any warming attributed to their removal becomes correspondingly uncertain. One might expect strong caution in translating these results into climate-relevant conclusions.
Instead, the paper proceeds with admirable composure.
After acknowledging the bias, the authors reassure the reader:
โThe model high bias in ASM UTLS SOโ presented herein is not expected to have a significant quantitative impact on our global IRF estimates between the emissions scenarios.โ
And just like that, the difficulty is declared immaterial.
This is the critical maneuver. Aerosols are allowed to be deeply uncertain when they complicate matters, but suddenly well-behaved when they are needed to support a warming explanation. Overestimated concentrations do not undermine the forcing calculation; they are politely shown to the door.
The analysis then moves on to radiative forcing, carefully defined as instantaneous. Sea surface temperatures are fixed. The atmosphere is nudged toward reanalysis. Rapid adjustments and feedbacks are minimized by design.
The authors explain:
โThe calculated ToA shortwave (SW) and longwave (LW) radiation changes herein are analogous to โinstantaneousโ RFโฆ which minimizes rapid atmospheric adjustments and feedbacks.โ
This produces a clean number, free from messy responses by the real climate system. It is precise, controlled, and safely insulated from the possibility that something unexpected might happen.
The resulting forcing is then compared with other model-based estimates and found to be โbroadly consistent.โ This agreement is presented as reassuring.
Yet all of the cited estimates come from the same modeling ecosystem, using similar assumptions about aerosol physics, cloud interactions, and transport processes. Agreement within a closed loop is not independent confirmation; it is internal consistency. The system is congratulating itself for speaking with one voice.
An especially revealing moment comes in the discussion of the Asian Tropopause Aerosol Layer (ATAL). The paper notes that sulfate accounts for only about 30% of this layer, meaning that a roughly 10% reduction in sulfate corresponds to only about a 3% reduction in total aerosol burden.
The authors describe this as:
โa minor overall impact (โผ3%) of Chinese SOโ emissions reduction amount on total ATAL aerosol burden.โ
Minor overall impactโyet still sufficient to generate a globally relevant radiative forcing signal. Small where inconvenient, large where useful. Aerosols, once again, prove remarkably adaptable.
The conclusion section completes the narrative arc. The findings are said to be important for improving Earth system models, reproducing recent surface temperature increases, and even informing assessments of stratospheric aerosol injection as a potential intervention strategy.
The paper observes:
โEstimated global and regional radiation changes given herein are likely important for more precisely reproducing recently observed surface temperature increases in ESMs.โ
Which is to say: if the models and observations disagree, the models are not wrongโthey are merely awaiting better aerosol accounting.
There is a quiet irony here. For years, aerosols have been described as dangerously masking greenhouse warming. Now, their removal is invoked to explain why warming appears to be accelerating. And should warming ever slow again, aerosolsโor internal variabilityโwill no doubt be ready to assist.
The paper is careful, technical, and explicit about its limitations.
The problem lies not with what is admitted, but with how those admissions are treated.
Biases are acknowledged. Uncertainties are cataloged. Then conclusions proceed as though neither poses a serious obstacle.
This is what โheads I win, tails you loseโ looks like in a scientific framework. There is no observational outcome that forces a reconsideration of the underlying assumptions. Warming confirms the theory. Too much warming confirms aerosol masking. Too little warming confirms variability.ย
Aerosols can cool, warm, hide, reveal, or offsetโwhatever the moment requires.
An article by Nadir Jeevanjee, a Research Physical Scientist at the National Oceanic and Atmospheric Administration (NOAA),ย recently published inย The Conversationandย reprinted byย Space.Com, suggests that climate models are being given a bad rap. It cites a recentย Department of Energy reportย as using the complexity of climate models as the primary reason why these models cannot be trusted.
An article by Nadir Jeevanjee, a Research Physical Scientist at the National Oceanic and Atmospheric Administration (NOAA),ย recently published inย The Conversationandย reprinted byย Space.Com, suggests that climate models are being given a bad rap. It cites a recentย Department of Energy reportย as using the complexity of climate models as the primary reason why these models cannot be trusted. The article protests, โBut the history of climate science tells a different storyโ, because early climate models were instrumental in shaping our understanding of the Earthโs climate.
Okay, letโs revisit historyโbecause those who canโt remember history correctly are destined to get it wrong.
First, we need to get one fact correct. The recent Department of Energy report did not simply cite the complexity of climate models as the primary reason they cannot be trusted. It goes into detail as to why current climate models cannot be trusted: namely, they cannot reproduce current conditions.
For example, models are not capable of determining the equilibrium climate sensitivity to increasing carbon dioxide concentrations. As shown in this graph from the DOE report, they tend to โrun hot,โ or overstate surface warming.
Global surface air temperature trends (ยฐC/decade), 1979โ2024, from various CMIP6 climate models (red, 30-model average in orange); and the average of three thermometer datasets (HadCRUT5, NOAA Global Temp, and Berkeley 1 deg.) and two reanalysis datasets (ERA5 and NCEP/NCAR R1) in blue. Data source: https://climexp.knmi.nl/start.cgi.
They also overstate warming of the tropical troposphere, and their simulation of stratospheric cooling is inconsistent.
With regards to the tropical troposphere, the excessive warming was noted as a problem in the first United States National Climate Assessment and has been noted in every Intergovernmental Panel on Climate Change report since. This problem has become more extreme over timeโi.e., simulations from later models, despite decades of โimprovementsโ costing billions of dollars, deviate farther from observations than earlier modelsโand its spatial extent now encompasses the entire globe.
But the article asserts that early climate model forecasts about global warming were correct decades before the forecasts could be evaluated. It concludes, โit is this track record of success that gives us confidence in interpreting the changes weโre seeing now, as well as predicting changes to come.โ It then lists five forecasts the early models made that underscore how well even those early models could reproduce Earthโs climate.
Now, letโs give credit where credit is due, but letโs not reward models for getting the basics of climate correct. In other words, models should not be praised for figuring out that the Poles are cold and the Equator is warm. They should not be given credit for simulating the Hadley Cell circulation, or demonstrating that oceans have a moderating effect on climate, for examples. These basic premises of climate are obvious and must have been a product of the simulation; otherwise, the models would have been discarded before they were even published.
So, letโs test the articleโs evaluation of the models.
Forecast number 1: early models correctly simulated surface global warming from increased carbon dioxide. Yes, but this was obvious long before the first models were created. Carbon dioxide is a greenhouse gas, and thus the more of it added to the atmosphere, the higher surface temperature should rise. Way back in 1896, Svante Arrhennius (1859โ1927) showed that an increase in carbon dioxide would increase global temperatures.
The question always has been, and still is, how much should the temperature rise from, say, a doubling of carbon dioxide?
Early models argued that the temperature rise would be between 2.0 and 3.5 degrees Celsius. These numbers are actually closer to reality than estimates provided by later model simulations, but they are still higher than data-driven estimates using historical and paleoclimate data.
Verdict? Early models would have been discarded if they had simulated cooling or no effect. That they simulated warming from increased greenhouse gases is no proof of their reliability.
Forecast number 2: the models predicted stratospheric cooling from adding carbon dioxide to the atmosphere. This should also have been relatively obvious. If outgoing terrestrial radiation is being intercepted by more carbon dioxide at the surface, and some of it is radiated back to the surface, then less will reach the stratosphere and, consequently, the stratosphere should cool. Again, the question that demands an answer is, โHow much cooling?โโand even current climate models cannot simulate this correctly or consistently.
Forecast number 3: Arctic amplification. The Conversation suggests that it was surprising that the early models accurately predicted that rising carbon dioxide concentrations would warm the Arctic more than the Tropics. But that was no surprise at all. Given a warming planet, the Arctic will warm faster than the Equator due to six factors.
Colder air warms more than warmer air from the same amount of energy input due to the derivative of theย Stefan-Boltzmann Radiation Law.
Moist tropical air has a higher specific heat than dry polar air. (Specific heat is the amount of energy required to raise the temperature of one gram of a substance by one degree Celsius.) This is because water vapor has a higher specific heat than dry air and warm, tropical air has more moisture than cold, polar air.
The change in albedoโor surface reflectanceโis greater in the Arctic because of the melting of highly reflective ice and snow, uncovering darker soils and tundra.
Sea ice provides a layer of insulation between the unfrozen water beneath and the potentially much colder air above. Warming reduces the sea-ice coverage, which allows energy in the relatively warmer water to warm the air above it.
The lack of convection at the Poles keeps the warming closer to the surface. Unlike the tropics, where the atmosphere becomes unstable due to surface heating and rising air, the polar regions are not heated sufficiently to create rising air motions, and the creation of the Polar High inhibits vertical motion. Thus, the warmer air stays near the ground.
Finally, the evaporation of water stores energy asย latent heatโthat is, energy in the phase transition of water from liquid to gasโwhich then is transported poleward by global circulation. This latent energy is stored in equatorial regions and released as condensation occurs in higher latitudes, thereby transporting energy polewards.
Therefore, a greater warming of the Arctic was obvious to climatologists even before the first climate model was created. That models simulate it is no big feat.
Forecast number 4: land-ocean contrast. The article notes that the coupled atmosphere-ocean model โled to a slew of insights, including the observation that land generally warms more than ocean, by a factor of about 1.5.โ While this may sound impressive to the uninitiated, climatologists have long known the moderating effect of the oceans, again due to their high specific heat.
Consider San Diego and Dallas, at about the same latitude of 33 degrees north. The annual temperature range in San Diego is about 8 degrees Celsius, while that in Dallas is about 22 degreesโa factor of almost three. Extending any transect to both coasts indicates that the annual temperature rangeโand the diurnal temperature range, for that matterโincreases with distance from the coast.
To anyone who understands the Earthโs climate, the central Plains have a much greater temperature range than either coast, for example. So, why would this have been a novel finding back in 1979? Any competent climatologist would have known this, so it must have been a basic component of any climate model worth its salt at the time.
Forecast number 5: delayed warming of the Southern Ocean around Antarctica. This issue arises from the supposedly novel fact that the Arctic has a greater warming signal than that of the Southern Ocean.
But why is that surprising? The change in albedo is not as great in Antarctica, since there is little land area exposed by melting snow and ice, while there is much in the Arctic. Sea ice dynamics differ considerably in the Southern Hemisphere, and the loss of sea ice isnโt as dramatic to warming Antarctica. Moreover, the Southern Ocean is bounded by a hemisphere of ocean, which has a moderating effect on the climate as mentioned earlier. Thus, important contributions to Arctic warming are not present in the Southern Ocean, and so we would have expected the Southern Ocean to be different from the Arctic.
The take-home message? If early climate models missed any of these supposed climate novelties, climate modeling wouldnโt have gotten off the ground. What early climate modeling showed was that the basics of climate could be replicated with simple mathematical approximations. But thatโs the basics. The details are that climate is still very complex, and while the general patterns can be simulated accurately, specific details as to how climate varies spatially and how it is likely to change under various scenarios are still a far-off challenge.
The article concludes: โClimate models have their limitations, of course. For instance, they cannot predict regional climate change as well as people would like. But the fact that climate science, like any field, has significant unknowns should not blind us to what we do know.โ
I agree, but I would also note that we were not blind to these facts before the advent of climate models. Syukuro Manabe deserves credit for demonstrating that the basics of climate can be reproduced with a rudimentary computer program run on early computers. But the fact remains that current model simulations still deviate significantly from real-world observations. And letโs not lose sight of the fact that early climatologists understood quite a bit about how the Earthโs climate works. Yes, there were climatologists before the climate modelers, and they knew an awful lot about the Earthโs climate.
David R. Legates, Ph.D. (Climatology), is retired Professor of Climatology at the University of Delaware and Director of Research and Education for the Cornwall Alliance for the Stewardship of Creation.
Theย Politicoย reporters claim the DOE report โcherry-picks mainstream research and omits context,โ โrelies on outdated studies,โ โcites analyses that were not peer reviewed,โ and โrevives debunked arguments.โ
Marlo Lewis, Ph.D., Senior Fellow in Energy and Environmental Policy, Competitive Enterprise Institute
Part 2: Allegations and responses
The Politico reporters claim the DOE report โcherry-picks mainstream research and omits context,โ โrelies on outdated studies,โ โcites analyses that were not peer reviewed,โ and โrevives debunked arguments.โ
The reporters cite three examples of alleged cherry picking. First, as described by the reporters, the DOE authors claim that during the past 100 years, โthe U.S. saw its most intense heat in the 1930sโand that maximum temperatures have never been that hot again.โ The reporters do not dispute the veracity of that statement. However, they consider it cherry picking because average temperatures across the US and globally have been โclearly risingโ since the 1950s.
But the DOE authors do not hide or dispute that fact. Indeed, they quote the IPCC Sixth Assessment Report on that very point:
AR6: It is virtually certain that hot extremes (including heatwaves) have become more frequent and more intense across most land regions since the 1950s, while cold extremes (including cold waves) have become less frequent and less severe (SPM, A3.1).
The DOE report also quotes a relevant passage from the USGCRPโs Fourth National Climate Assessment:
NCA4: Since the mid-1960s, there has been only a very slight increase in the warmest daily temperature of the year (amidst large interannual variability). Heat waves (6-day periods with a maximum temperature above the 90th percentile for 1961โ1990) increased in frequency until the mid-1930s, became considerably less common through the mid-1960s, and increased in frequency again thereafter. As with warm daily temperatures, heat wave magnitude reached a maximum in the 1930s (NCA4, Vol. 1, p. 191).
So, heat waves and maximum temperatures in recent decades are increasing, but both reached higher levels in the 1930s. Summarizing the entire instrumental record of US heat waves and daily maximum temperatures is not cherry picking. Nor is it cherry picking to point out which decade set the most US records for extreme heat events.
The Politico reporters suggest the 1930s warming is climatologically irrelevant because โmost scientists say it was a statistical outlier that skewed heat trends for decades afterward.โ But assessing climate extremes means looking at outliers. It is ludicrous to chop past episodes of extreme heat out of the record and then depict todayโs heatwaves as unprecedented. Whatever caused the heatwaves of the 1920s and 1930s, it obviously wasnโt postwar greenhouse gases.
Another alleged cherry pick is the importance the DOE report places on the slow decadal warming rate in the US Corn Belt. The Politico reporters cry foul because โoutsideโ of this โwarming hole,โ the nation is experiencing โincreases in extreme summer heat.โ But the DOE authors do not suggest the Corn Belt is typical of all regions or has more reliable temperature data than other regions. The Politico reporters claim the DOE report โomits context,โ but thatโs what they do here. The context is a discussion of how well climate models reproduce the warming of recent decades.
The DOE report shows that, for a critically important climatic region in the US, 36 climate models used in AR6 hindcast significantly more warming than observed, with 30 models projecting two to eight times more warming than occurred during 1973-2022.
The DOE report contains other examples of model failure. It is only common sense to doubt the validity of climate models that cannot โpredictโ what has already occurred.
The third alleged cherry pick is the DOE authorsโ selection โof which model simulations and which observations they show.โ But as explained in Part 1, the DOE authors focus on model simulations of tropospheric temperatures because those data are out of sample and therefore suitable for scientific model testing. The DOE authorsโ choice of simulations and observations is sound experimental design, not cherry picking.
As for omitted context, the Politico reporters fault the DOE report for quoting one sentence in AR6 about hurricane intensity but not the next sentence. That omission is โclearly designed to mislead,โ they contend. Not so.
Hereโs the AR6 sentence quoted by the DOE report:
There is low confidence in most reported long-term (multi-decadal to centennial) trends in tropical cyclone frequency- or intensity-based metrics due to changes in the best track data.
Here is the next AR6 sentence, which the DOE report allegedly hides:
This should not be interpreted as implying that no physical (real) trends exist, but rather as indicating that either the quality or the temporal length of the data is not adequate to provide robust trend detection statements, particularly in the presence of multi-decadal variability.
According to the Politico reporters, omitting the second sentence is misleading because it โspecifically warns against making assumptions that no link exists between rising temperatures and stronger storms.โ Two responses are in order.
First, the quoted sentence does not imply there is no link between rising temperatures and stronger storms, and the omitted sentenceโs caution that absence of evidence is not evidence of absence adds nothing to the substantive discussion of why trend detection is not โrobust.โ Such admonitory verbiage is gratuitous except perhaps as political cover to mollify the climate thought police.
Second, the very next sentence of the DOE report quotes the following sentence from AR6: โIt is likely that the global proportion of major (Category 3โ5) tropical cyclone occurrence has increased over the last four decades โฆโ Thus, the DOE report does not hide anything or mislead anyone. It quotes AR6โs conclusion that a hurricane strength-warming link is โlikely.โ The Politico reporters do not quote or mention that statement.
Thus, it is the Politico reporters who omit context by quoting only one of two consecutive sentences. They commit the very journalistic malpractice they falsely ascribe to the DOE authors.
The Politico reporters additionally fault the DOE report for not addressing evidence that hurricanes are becoming more destructive by โdropping more rain.โ That is a โdead giveaway,โ MIT hurricane scientist Kerry Emanuel told them. Allow me to offer another conjecture of equal plausibility. Short data records and substantial natural variability continue to impede robust detection of the โincreasing destructiveness of tropical cyclonesโ two decades after Emanuel published his seminal paper on the topic. The recent emphasis on rainfall from slow-moving hurricanes is a โdead giveawayโ some scientists need a new rationale for climate alarm.
Litigation alleging procedural violations unrelated to the substance of the report resulted in DOE dissolving the five-person team of authors. Had the authors been allowed to finalize their draft report based on the numerous substantive comments DOE received, they would likely have addressed the hurricane-water issue more fully, as the Competitive Enterprise Instituteโs comments advised them to do.
The Politico reporters claim the DOE report โamplifies supposed conflicts among climate scientistsโeven when there isnโt any.โ They continue: โFor example, it is not true there is โsubstantial debateโ within climate science over whether the sun could be a primary driver of global warming.โ
However, the DOE report does not say the sun โcould be a primary driver.โ It more modestly states that the โimpact of solar variations on the climate is uncertain and subject to substantial debate.โ That is a reasonable conclusion from Connolly et al. (2021), a study cited in the DOE report.
The Politico reporters describe the study as a โ[Willie] Soon paper,โ and claim the DOE authors cite it โto suggest itโs the sun, not the burning of fossil fuels, that has contributed to the global temperature rise.โ That is sloppy reporting. The studyโs lead author is Ronan Connolly, who is joined by Soon and 21 other co-authors. The study does not purport to assess the sunโs contribution to โglobal temperature riseโ but its influence on โNorthern Hemisphere temperature trends.โ
The study examines 16 different estimates in the peer-reviewed literature of changes in total solar irradiance (TSI) going back to at least 1850 and reconstructions of Northern Hemisphere temperature trends using five largely independent estimation methods. Connelly et al. conclude: โFor all five Northern Hemisphere temperature series, different TSI estimates suggest everything from no role for the Sun in recent decades (implying that recent global warming is mostly human caused) to most of the recent global warming being due to changes in solar activity (that is, that recent global warming is mostly natural).โ
I challenge any fair-minded person to read the paper and conclude that a potentially significant solar contribution to Northern Hemisphere temperature trends has been โdebunkedโ or that no โsubstantial debateโ exists about the sunโs influence on those trends.
In any case, the DOE report does not โsuggest that itโs the sun, not the burning of fossil fuels, that has contributed to the global temperature rise.โ Moreover, although the so-called Soon paper reports evidence that the sun has a large role in global warming, it also reports evidence that the sun has no role. The Politico reporters hide half of the studyโs conclusion. Not fair or balanced!
As for the DOE report citing analyses that โwere not peer reviewed,โ that is misleading. The DOE report presents analyses of temperatures and precipitation using official US government datasets and peer-reviewed methods. In many cases, the authors updated analyses of results published a few years ago to avoid any accusation of relying on outdated findings. Where they presented updates of previous analyses, they also referred to the underlying peer-reviewed papers and published the new data. Furthermore, the entire report went through anonymous peer review by DOE technical staff. The Politico reportersโ insinuation of research impropriety is baseless.
The Politico reporters also raise the specter of โhidden connectionsโ between certain DOE authors and โconservative groups that oppose government efforts to address climate change,โ including groups that have received fossil fuel industry funding.
Newsflash: The validity of a study does not depend on the researchersโ โconnections.โ Regardless, the affiliations of Ross McKitrick, Roy Spencer, and Steve Koonin with certain conservative think tanks are not hidden; they are open and avowed.
The reporters acknowledge the authorsโ โacademic and government experienceโ but provide almost no specifics. They do not mention Kooninโs position as Under Secretary for Science in the Obama administration Department of Energy, John Christyโs work as a Lead Author of the 3rd IPCC Assessment Report (2001), Ross McKitrickโs work as an expert reviewer for the last three IPCC Assessment Reports (Working Groups I and II), Roy Spencerโs work as a NASA Senior Scientist for Climate Studies, or Judith Curryโs service on several top-tier advisory panels such as the National Research Councilโs Space Studies Board and Climate Research Council. Why are those โconnectionsโ considered less relevant than an association with a conservative think tank?
Finally, we come to the Politico reportersโ contention that the DOE report is โovertly politicalโ rather than a bona fide โscientific exercise.โ They ascribe that view to Massachusetts federal judge William Young. However, the judge does not use those phrases, and when the reporters do quote him, they take his words out of context.
Judge Young finds that the reportโs final paragraph is โarrangedโ to provide โadvice or recommendations for a renewed approach to climate policy.โ The Politico reporters claim that means the DOE report is a โpolitical plan.โ But the judge is addressing an issue of administrative law, not of scientific integrity. His point is simply that the final paragraphโs โadvice or recommendationsโ make the DOE authors an โadvisory committeeโ and, thus, subject to the procedural requirements of the Federal Advisory Committee Act (FACA).
More recently, Politico reporter Scott Waldman touts โthe revelationโ that four of the DOE authors have a history of challenging the Endangerment Finding. That means the report is โcolored byโ the authorsโ โpast views,โ which, according to him, is โfurther proofโ the report is โlittle more than a political document.โ
No, not even close. The Endangerment Finding expressly relies on โmainstreamโ climate science. So, it is a logical necessity for any scientist who finds serious flaws in such science to be critical of the Endangerment Finding as well. Only scientists who have produced substantial bodies of work critical of mainstream climate science could produce the wide-ranging literature reviews assembled in the DOE report. It is hardly newsโmuch less a revelationโthat four of the DOE authors had taken issue with the Endangerment Finding before they started working on the report.
So, in Waldmanโs view, contrarian scientists who have the courage to publish their research and speak candidly on the record are, by that very fact, unfit to produce a climate science report at the invitation of a cabinet secretary.
That argument backfires. Every mainstream scientist the Politico team cites to trash the DOE report has spoken or written in support of mainstream climate science, the Endangerment Finding, or the ensuing GHG regulations, which means they too are โcolored by past views.โ Indeed, so is Waldman himself, who has made something of a career of bashing contrarian scientists and decrying climate policy rollbacks.
By his logic, all scientists and journalists who have taken a position on the Endangerment Findingโwhether for or againstโare politically tainted and unfit to write or review climate science reports for the US government.
Conclusion
The Politico team denies the need for a critical review of mainstream climate science because, quoting IPCC coordinating author Camille Parmesan, the claim that dissenting views are silenced is โat best preposterous and arrogant and at worst a deliberate ruse to undermine decades of rigorous science.โ Sorry, that does not pass the laugh test.
Mainstream climate science is a multi-billion-dollar enterprise and almost wholly government-funded. Perceptions of a โclimate crisisโ are critical to the care and feeding of climate-focused agency budgets, climate-themed agency careers, and the self-transformation of environmental regulators into industrial policy czars. Consequently, agencies have a massive organizational interest in funding climate research that advances the crisis narrative.
Similarly, because university climate research programs heavily depend on federal funding, faculty chairs preferentially hire and promote researchers who toe the party line. Those same researchers supply most of the editors and peer reviewers of academic journals, who decide which papers to publish and which to reject. As the late climate scientist Patrick Michaels observed, reviewers whose careers depend on federal largesse need no external direction or backroom coordination to reject a study suggesting that โclimate change is a bit overblownโ or that โwe canโt do much about it anyway.โ
And letโs not forget the elephant in the room. Most of the nationโs climate research is conducted at prestige universities where political groupthink and viewpoint intolerance continually imperil freedom of speech and thought.
In 2019, Nature Communications published a study that says the quiet part out loud. The study advises researchers with โscientific authorityโ not to try refuting skeptics, as that would leave โthe counterproductive impression that there is something substantial in contrarian arguments to be debated.โ The article links to a list of 386 contrarians, so that โprofessional journalists and editorsโ can know whom not to interview or publish.
But such reflections are in a sense overkill. The Politico reporters are refuted by their own behavior. For what, pray tell, are they doing if not propagating the falsehood that the DOE authors have no arguments worth debating? And how is disqualifying a contrarian report because of its authorsโ โpast viewsโ not an attempt to silence their voices in the public square?
James Hansen is warning that the world isnโt just steadily warming but is dangerously accelerating, according to a study that some other scientists call overheated.
James Hansenโs latest paper โGlobal warming in the pipelineโ (Hansen et al. (2023)) has already been heavily criticized in a lengthy comment by Michael Mann, author of the original IPCC โhockey stickโ. However Mann does not deal with Hansenโs surprisingly high (4.8ยฐC) new estimate of equilibrium climate sensitivity (ECS)[1]. This ECS estimate is 60% above Hansenโs longstanding[2] previous estimate of 3ยฐC. It is Hansenโs new, very high ECS estimate drives, in conjunction with various questionable subsidiary assumptions, his paperโs dire predictions of high global warming and its more extreme concluding policy recommendations, such as โsolar radiation managementโ geoengineering.
Hansenโs new 4.8ยฐC ECS estimate is well above the best estimate of 3ยฐC reached in the IPCCโs latest scientific assessment report (AR6), lies outside the AR6 likely (66%) range of 2.5โ4ยฐC and is almost at the top of the AR6 90% uncertainty range of 2โ5ยฐC.[3]
Both Hansenโs new ECS estimate and his earlier estimate are based primarily on information about paleoclimate changes, particularly the extensively studied transition from the last glacial maximum (LGM) some 20,000 years ago to the preindustrial Holocene. But is his new estimate (or indeed his earlier estimate) justified by the evidence?
Hansenโs primary LGM to Holocene based ECS estimation.
For his LGM-based ECS estimate, Hansen assumes a 7.0ยฐC rise in global mean surface temperature (GMST) between the LGM and preindustrial Holocene. This value is 56% above the 4.5ยฐC rise that Hansen used previously2. Hansen cites three studies in support of his 7.0ยฐC LGM cooling estimate (which comes from the second of these): Tierney et al. (2020), Osman et al. (2021) and Seltzer et al. (2021).
I published in 2021 an article that was heavily critical of the Osman et al. data-assimilation (reanalysis) based temperature reconstruction. Their reconstruction uses only ocean sea surface temperature (SST) proxies and is based on a single climate model that simulates an unusually cold LGM state[4] and, strongly influenced by the model simulations, produced a 7ยฐC estimate of LGM cooling.[5] The proxy-only based reconstruction in the submitted version[6] of Osman et al. appeared more reasonable, and as shown in my article implied LGM to preindustrial GMST change of about 4.5ยฐC.[7]
The Tierney et al. estimate of LGM cooling (6.1ยฐC)[8] is based on a much larger set of SST proxies, which includes all those employed by Osman et al. used, but similarly uses no land temperature proxies. Tierney et al. uses the same single-model data-assimilation temperature reconstruction method as does Osman et al.[9] Therefore, my criticisms of Osman et al.โs reanalysis-derived LGM cooling estimate very largely apply also to Tierney et al.โs estimate.
The Seltzer et al. 5.8 ยฑ 0.6 ยฐC LGM cooling estimate is for land only and is limited to 45ยฐSโ35ยฐN, so does not represent GMST cooling. The authors use a groundwater-based proxy type and a complex model to convert proxy values to surface temperatures. I have concerns about some aspects of their methods.[10] Moreover, their 5.8ยฐC land cooling estimate is based on the error-weighted average of only 15 groundwater records, which show widely varying cooling[11] and may not be adequately representative of 45ยฐSโ35ยฐN land areas. Seltzer et al. completely ignore the uncertainty associated with these issues. Their estimate is inconsistent with most proxy-derived estimates of mean ocean cooling over 45ยฐSโ35ยฐN, which casts further doubt on its reliability.
I regard the recent Annan et al. (2022) LGM temperature reconstruction as producing a more reliable estimate of GMST cooling than the Tierney et al. (2020) and Osman et al. (2021) studies that Hansen et al. relies upon. The Annan et al. reconstruction uses a similar data-assimilation method to those studies, but with several key differences. Unlike them it uses a simulations from large set of acceptably dissimilar climate models, rather than a single model. Importantly, unlike those two studies, Annan et al. scale the model simulated temperature changes so that the initial guess model cooling estimates used are approximately centered on the data, to minimize model-generated bias. Moreover, Annan et al. use land temperature proxies as well as a larger set of ocean SST proxies to adjust the initial guess. They use the same SST proxy dataset as Tierney et al., but extend its coverage with data from a widely used earlier SST dataset, which in particular reduces Tierney et al.โs huge gaps in coverage of the Pacific ocean.
As a result of the much better proxy coverage, the use of multiple models and the debiasing step, the 4.5ยฐC estimate of GMST cooling at the LGM that the Annan et al. reconstruction produces should be much more realistic than the Tierney et al. (2020) and Osman et al. (2021) estimates that Hansen uses. Indeed, Annan et al. criticize Tierney et al.โs approach, using very cold simulations from a single model, pointing out that it will produce a cool reconstruction regardless of whether the data points to a milder LGM climate.
By coincidence[12], Annan et al.โs 4.5ยฐC GMST LGM cooling estimate is the same value as I adopted for the LGM derived climate sensitivity estimation in Lewis (2023)[13]. That study reworked Sherwood et al. (2020), a very influential World Climate Research Programme linked review of climate sensitivity evidence, which had assessed the LGM to be 5ยฐC cooler than preindustrial.
Hansenโs ECS estimate is increased not only by adoption of an unrealistically large LGM cooling value, but also by very low estimates[14] for the various changes in forcing between the LGM and the preindustrial Holocene[15]. Hansenโs central estimate for the total Holocene to LGM forcing difference is โ5.75 W/m2, much smaller than the โ8.63 W/m2 assessed by Sherwood et al.(2020) and even further below the โ8.93 W/m2 best estimate adopted in Lewis (2023). The LGM section of Table 1 in the Appendix gives details of all the data-variable estimates used to form the Sherwood et al. (2020), Lewis (2023) and Hansen et al. (2023) LGMโpreindustrial Holocene based ECS estimates.
The combination of the large GMST change and small forcing change that Hansen adopts for the LGM results in a central ECS estimate of 4.8ยฐC. By comparison, the Lewis (2023) LGM-based ECS estimate was 2.2ยฐC, somewhat less than the corresponding ECS estimate from Sherwood et al. of 2.8ยฐC.[16] Both these estimates are well below the lower bound of the 3.6 โ 6.0 ยฐC ECS 95% uncertainty range for ECS that Hansen estimates.
Hansen supports his LGM-based ECS estimate with one based on the difference in GMST between the LGM and the previous, Eemian, interglacial period. His estimate is therefore inflated by his assumption that the LGM was very cool. Moreover, compared to the LGM there is even greater uncertainty in Eemian GMST, ice-sheet forcing, and other non-CO2 forcing. IPCC AR6 mentions all these drawbacks and additionally points outs that accounting for varying orbital forcing is challenging for this period. Sherwood et al. (2020) did not attempt to estimate climate sensitivity from the Eemian to LGM or any other pre-LGM climate transition because the data available are far more limited than for the much more extensively studied LGM. I likewise did not do so in Lewis (2023), nor do I attempt such an estimate here.
Hansenโs analysis of the PETM event and its implications.
Hansen also considers the Paleocene-Eocene Thermal Maximum (PETM) warming event some 56 million years ago, adopting a best estimate for the warming involved of 5.6ยฐC and assuming, as did Sherwood et al. (2020) and Lewis (2023), that greenhouse gases alone produced the increase in forcing that caused the warming.
Rather than estimating ECS using an estimate of the PETM forcing change, Hansen calculates what the PETM CO2 level would have had to be to cause warming of 5.6ยฐC, based on his ECS estimate of 4.8ยฐC, assuming a pre-PETM CO2 level of 910 ppm and that non-CO2 greenhouse gases contributed 25% as much forcing as CO2 did. Hansen thereby calculates a PETM CO2 level of 1630 ppm. In fact, the correct figure seems to be approximately1565 ppm; Hansen appears to have gone wrong somewhere in his calculations.
Although Hansenโs 910 ppm pre-PETM CO2 concentration value is closely in line with other estimates, the circa 1600 ppm value for the PETM CO2 concentration implied by his 4.8ยฐC ECS and his other assumptions is much lower than the estimate of 2400 ppm, with a ยฑ1 standard deviation range of 1700โ3100 ppm, that Sherwood et al. (2020) reached after assessing the available evidence. It is also near the bottom of the 1400โ3150 ppm uncertainty range given in AR6.
Moreover, Sherwood et al. assessed the best estimate GMST rise at the PETM to be slightly lower than did Hansen, at 5.0ยฐC, and the non-CO2 greenhouse gas forcing to be higher, at 40% of CO2 forcing. All these differences contribute to Sherwood et al.โs PETM-based ECS estimate being, at 2.5ยฐC, barely half Hansenโs assumed 4.8ยฐC. Moreover, Lewis (2023), using identical data-variable assumptions as Sherwood et al. (2020) but the AR6 formula for CO2 forcing, which is more accurate at high concentrations than the simple formula Sherwood et al. used, obtains an even lower PETM-based ECS estimate of 2.2ยฐC.
The PETM section of Table 1 in the Appendix gives details of all the data-variable estimates used to form the Sherwood et al. (2020)and Lewis (2023) PETM based ECS estimates, and the Hansen et al. (2023) estimate of CO2 concentration at the PETM. Figure 1 shows what the central estimate of the total change in forcing involved was, as a multiple of the forcing from a doubling of preindustrial CO2 concentration, for all three studiesโ LGM and PETM estimates.
Although there are much greater uncertainties involved when estimating ECS from the PETM event than for the LGM โ preindustrial Holocene transition, the available PETM evidence is closely consistent with the lower, 2.2ยฐC and 2.8ยฐC, ECS estimates derived from the LGM data-values used respectively by Lewis (2023) and Sherwood et al. (2020), but not with Hansenโs very high 4.8ยฐC estimate of ECS.
It is also clear that Hansenโs claim that todayโs human-made greenhouse gas forcing is, at 4.6 W/m2, at least comparable to the PETM forcing (which his assumptions imply was 4.67 W/m2) is strongly at variance with the evidence as assessed by Sherwood et al.[17]ย That evidence implies a best estimate of PETM forcing of 1.98ร that for a doubling of preindustrial CO2ย concentration[18]ย (versus 1.17ร on Hansenโs assumptions), double or more the latest (2022) AR6-basis estimate, inย Forster et al. (2023), of 0.88ร for greenhouse gas forcing (1.00ร when including that from ozone, a short lived greenhouse gas).
Figure 1.The forcing change between the LGM and preindustrial Holocene, and between before and during the PETM, implicit in each studyโs assumptions. The forcing changes are expressed as a multiple of that from a doubling of preindustrial CO2ย concentration. The associated ECS estimate equals in each case the corresponding assumed GMST change divided by the forcing change shown, so a higher forcingย change implies a lower estimated ECS value.
Conclusions
I do not consider that Hansenโs climate sensitivity estimation properly assesses and fairly reflects all the available relevant evidence. Unfortunately, unlike Sherwood et al.(2020), IPCC AR6 and Lewis (2023), Hansen et. al. (2023) estimates ECS using only paleoclimate proxy-derived evidence, which generally varies considerably according to the proxies involved and to the methods used to interpret them. This opens the door for biased (cherry picked) assessments. For instance, Hansen et. al. do not even mention any studies (e.g. Annan and Hargreaves (2013) and (2022)) that find a much lower LGM โ preindustrial warming than their chosen value.
Although I respect Hansenโs ability and considerable scientific contributions, in my view papers he leads are increasingly strongly biased towards overheated projections and dire conclusions.[19] The โpolitical recommendationsโ with great impact on the society in Hansen et al. (2023) cannot be justified because their foundation is very shaky, as shown here for climate sensitivity and, in relation to warming-in-the-pipeline and ocean heating, in Michael Mannโs critique.
Nicholas Lewis 6 November 2023
Appendix
Table 1. Paleoclimate evidence data-variable best-estimate valuesa used to estimate S and ECS
Notes: a Data-values for Sherwood et al.(2020) and Lewis (2023) are medians that have been extracted from Table 3 of Lewis (2023), the notes to which are incorporated here by reference. See those papers for details of the sources used to derive their respective data-variable estimates. b Using the Hansen et al. (2023) Table 1 formula for CO2 forcing
[1] ECS is defined as the global mean surface warming for a doubled CO2 concentration after so-called fast-feedbacks have been fully activated and the ocean has reached equilibrium (which it approaches within a thousand or so years). Earth system sensitivity (ESS), which also includes very slow feedbacks such as those from changes in ice sheets, represents global warming arising over much longer timescales. ESS is relevant when studying paleoclimate changes, but not when projecting changes over the next few centuries.
[3] Some of the latest generation (CMIP6) global climate models do have ECS values above 5ยฐC, but they are generally regarded by climate scientists as being too sensitive.
[4] Actually two slightly different versions, iCESM1.2 and iCESM1.3, of the same underlying CESM1 GCM.
[5] Supporting my critique of their reanalysis, I showed that its co-located estimates were uncorrelated with values from the cave speleothem proxies it used. Moreover, unlike proxy-based reconstructions (e.g., Caufman et al. (2020)), which show early Holocene GMST 5,000โ9,000 years ago being circa 0.5ยฐC higher than late preindustrial (1750) GMST, their reanalysis showed that period as being significant cooler than late preindustrial GMST.
[6] The final, published version of Osman et al. was slightly different, with no mention of the change in the peer review files, and cooled marginally more near and at the LGM.
[7] Using a more recent model generation than the older ones Osman et al. used to derive the ratio used to convert their ex-polar sea surface temperature (SST) proxy-based estimates to GMST.
[8] No explanation is available of why, disconcertingly, this differs from the 5.9ยฐC in their submitted manuscript.
[9] The Osman et al. cooling estimate may be greater because they use only a subset of Tierney et al.โs proxies, and have no proxy coverage at all in the central Pacific ocean.
[10] While Seltzer et al. validated their model on modern proxy data, it may not be valid for conditions at the LGM, when factors such as vegetation cover, rainfall patterns, and seasonal temperature and precipitation cycles were different. An additional concern is that Seltzer use two alternative methods to select LGM samples, which give noticeably different LGM cooling estimates (5.8ยฐC and 4.8ยฐC). They prefer the method giving the higher estimate, however it will tend to pick out particularly cold temperatures from datasets, and so may overestimate LGM cooling. Interestingly, the average of the Annan et al. LGM reconstructionโs cooling estimates for grid cells co-located with the 15 proxies that Seltzer et al. use is close to the 4.8ยฐC estimate that Seltzer et al. obtain using their second method.
[11] Thus, excluding two particularly cool, low assessed uncertainty proxy estimates would reduce the estimate by 0.4ยฐC.
[12] Lewis (2023) was submitted earlier than was Annan et al (2022).
[14] Hansenโs estimate of the greenhouse gas forcing change, relative to that from a doubling of CO2 concentration, is 27% lower than the value assessed by Sherwood et al. and also adopted in Lewis (2023). Moreover, Hansen ignores the significant dust aerosol forcing change assessed by Sherwood et al., likewise also adopted in Lewis (2023).
[15] In the formula for estimating ECS, the change in forcing (ฮF) forms the denominator: ECS = F2รCO2 ร ฮT/ฮF, where ฮT is the GMST change in equilibrium and F2รCO2 is the forcing from a doubling of atmospheric CO2 concentration. All forcing changes given in this article are for effective radiative forcing (ERF), the principal forcing metric used in AR6.
[16] Sherwood et al. (2020) and Lewis (2023) focused on a climate sensitivity measure, S, an easier to estimate approximation to ECS that is normally derived for global climate models instead of ECS. They accordingly converted their underlying paleoclimate ECS estimates into estimates of S.
[17] Hansenโs claim here that todayโs human-made GHG forcing is 4.6 W/m2 is moreover inconsistent with the statement earlier in his paper that it is 4.13 W/m2 for 2022 (1.03ร his value for a doubling of CO2 concentration, close to the AR6-basis ratio), and growing by 0.5 W/m2 per decade.
[19] Moreover, Iโm not sure that Hansen is fully up with recent developments in climate science. For instance, when discussing different measures of radiative forcing, a topic on which Hansen wrote a seminal paper in 2005, he wrongly claims that (unlike for the formulae in his equation (3)) AR6 uses the biased-low Fo measure for its long-lived (non-ozone) greenhouse gas effective radiative forcing (ERF) estimates, and on that basis adjusts the AR6 forcing values. The 2013 (AR5) IPCC assessment report did use Fo, but AR6 uses a measure that, like Hansenโs preferred Fs, excludes the effect of surface temperature change.
[20] Hansen estimates ECS from changes in greenhouse gases (GHG) between the LGM and the mid-Holocene. The GMST estimate he uses is the same for that period as for the immediately preindustrial period (circa 1750). However, changes in non-GHG forcing agents are more uncertain for the mid-Holocene than for the preindustrial period (which most forcing change estimates relate to). In particular, aerosols and land surface albedo could have been significantly affected by deforestation starting well before the preindustrial period, a possibility mentioned in Hansenโs paper. This makes ECS estimates based on LGM to mid-Holocene changes more uncertain than those based on LGM to preindustrial changes.
Forcing Efficacy is related to how quickly a related flux imbalance at the Top of the Atmosphere (TOA) is restored to equilibrium and could potentially make an enormous difference to how we calculate Equilibrium Climate sensitivity (ECS).
Forcing Efficacy is related to how quickly a related flux imbalance at the Top of the Atmosphere (TOA) is restored to equilibrium and could potentially make an enormous difference to how we calculate Equilibrium Climate sensitivity (ECS).
Is it possible that the Effective Radiative Forcing (ERF) of a small change in GHG forcing is a lot lower than the ERF for a similar change in solar forcing? This essay attempts to make this case.
Iโm not saying here that the inputs to the model, Figure 1, are accurate, they almost certainly arenโt, Iโm simply trying to show what is possible if ERF, as discussed below, is taken into account.
If, for example, a one W/M^2 change in GHG forcing only had a quarter of the surface temperature effect in total and over time as say a one W/M^2 change in solar forcing then it is possible to create a model that tracks past temperature with surprising accuracy. This has been done in Figure 1 below.
Figure 1 โ The model (Red) is compared to the NOAA measured temperature (Blue) and assumes that each W/M^2 of GHG forcing has one quarter of the temperature effect of a similar solar forcing. Lag times are allowed for, and an estimate is made for aerosols and natural internal variability. The cooling effect of volcanic activity is not included in this model and may explain some of the inconsistency in 1980 and 1990.
The model in Figure 1 compares favourably with the IPCCโs solution to the same problem as shown in Figure 2 below.
The blue line in Figure 2 is the IPCCโs expected equilibrium temperature response as stated in their reports, AR5 and AR6 for the dates as shown. The relative positions of these two lines is not important and relates to the complex area of transient temperature response as compared to equilibrium temperature response.
The important thing to notice in Figure 2 is that the IPCCโs modelled equilibrium temperature response (Blue) is diverging rapidly from the NOAA measured temperature (Orange). The IPCCโs equilibrium temperature has increased on average at a rate of 0.26C/Decade since 1945 while the HadCrut temperature series has increased at an average rate of 0.11C/Decade since 1945. Importantly, this discrepancy appears to be getting worse indicating that human influences on climate forcing are not the whole story.
Figure 2 โ Forcing is taken directly from the IPCC reports and converted to Equilibrium climate response temperature (Blue) then compared to the actual HadCrut temperature as recorded. The blue line is fitted using 4 points from the IPCC reports. The IPCCโs AR5 states that in 1950 humans had added 0.57 W/M2 to the global energy balance. In 1980 1.25 W/m2. In 2011 2.29 W/M2. The AR6 states that we had added 2.72 W/M2 in 2019. These are multiplied by 0.81 (3.0/3.7) to convert to Equilibrium Climate response temperature according to the IPCCโs (most likely) ECS of 3.0C.
THE CASE FOR โERFโ BEING SIGNIFICANT
Forster [1], make the case that โEffective Radiative Forcingโ (ERF) is a much more useful way of estimating climate sensitivity than conventional; one size fits all, Radiative Forcing (RF). They make their case succinctly in the following quote;
โImagine, for example, that the atmosphere alone (perhaps through some cloud change unrelated to any surface temperature response) quickly responds to a large Radiative Forcing to restore the flux imbalance at the TOA (Top Of Atmosphere), yielding a small effective climate forcing. In this case the ocean would never get a chance to respond to the initial Radiative Forcing, so the resulting climate response would be small, and this would be consistent with our diagnosed โEffective Climate Forcingโ rather than the conventional โRadiative Forcing.โ
It follows that a shorter response time at the โTop of the Atmosphereโ (TOA) produces a lower climate sensitivity. Hansen [2] confirm and support this by saying โOn a planet with no ocean or only a mixed layer ocean, the climate response time is proportional to climate sensitivity. โฆโฆโฆ..Hansen et al (1985) show analytically, with ocean mixing approximated as a diffusive process, that the response time increases as the square of climate sensitivity.โ
ERF is the metric we should be using to evaluate any warming from increased GHGs in the atmosphere and this is directly calculated from energy residence time in the earthโs climate system. Importantly this system includes the worldโs oceans.
It is established physics that the worldโs oceans are opaque to energy with the wavelength generally reemitted by CO^2. This energy is almost totally absorbed in the first 0.15mm skin of the oceans. The top 1.0 mm of the ocean is generally referred to as the evaporation layer, so accordingly energy reemitted by the GHGs and striking the ocean is largely returned to the atmosphere immediately as latent heat of evaporation. It is then returned to space relatively quickly.
Solar energy, on the other hand, is overwhelmingly responsible for the temperature profile of the worldโs oceans. It is readily absorbed to a depth of up to 100 meters in clear water and can remain in the ocean for centuries or sometimes thousands of years.
It follows that equilibrium restoration times at the top of the atmosphere (TOA) are likely to be significantly faster, on average, for a change in GHG forcing than for a similar change in solar forcing.
Ignoring these factors, the IPCC AR4 states that ERF for solar forcing is actually lower than ERF for the GHGs. They then modified this position in later reports and now believe that ERF for solar and the GHGs is similar.
The IPCC position appears to be erroneously supported by the models.
It is clear that the latest models treat all energy that is absorbed in the first 10m grid of the ocean in the same way. The โTechnical Guide to MOM 4.0, GFDL Ocean Group Technical Report 5, 2008) divides solar penetration into the water column into three exponentials. The assumption and quote from MOM Guide 8.3.2 is copied below;
โThe first exponential is for wavelengths >750nm (i.e., IIR) and assumes a single attenuation of 0.267 meterโฆโ
This assumption means that all long wave solar energy with wavelength greater than 750 nm will be modelled as being subject to significant turbulent mixing.
My understanding is that the models treat the long wave GHG energy the same way that they treat the long wave solar component, by simply including it in the first, nominally 10-meter, grid by assuming an attenuation of 0.267 meter. They simply do not distinguish or account for the fact that long wave energy reemitted by CO^2 and centred at 15 microns is almost totally absorbed in the evaporation layer and is proportionately returned to the atmosphere almost immediately by evaporation when compared to solar energy. To treat this accurately the models would need the first ocean grid to be 0.2mm thick. This is something they appear not to do for reasons of complexity.
CONCLUSION
In general, if a photon of energy is absorbed by a water molecule it increases the velocity of that molecule. This increase in velocity may cause the molecule to break the surface tension of the water body or it may not. If the surface tension of the water is broken, then evaporation occurs and both the introduced and original energy of the molecule is lost to the water body and that body is evaporatively cooled.
If, on the other hand, the molecule does not break the surface tension of the water body for any reason, including that it is too far from the surface, then the introduced energy remains in the water and warms that water.
If this is an accurate representation of what happens then it follows that down Long Wave Infrared Radiation (LWIR) could have a slight cooling effect in warmer water, and for colder water a slight warming effect.
The overall effect of this on the earthโs Ocean Heat Content (OHC) is difficult to quantify. What can be said is that LWIR from GHGs will have a different and smaller effect on OHC than a similar amount of solar radiation as the LWIR is nearly totally absorbed in the evaporation layer while nearly all short-wave solar radiation is not.
It follows that the efficacy of GHG Forcing is likely to be significantly lower than the efficacy of a similar sized solar forcing. A change in GHG forcing is likely, therefore, to have a significantly smaller effect on global temperature, in total and over time, than a similar change in solar forcing to the point where the effect of recent rises in GHG forcing may be negligible.
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