Updated through 2023, here is a comparison of the “USA48” annual surface air temperature trend as computed by NOAA (+0.27 deg. C/decade, blue bar) to those in the CMIP6 climate models for the same time period and region (red bars). Following Gavin Schmidt’s concern that not all CMIP6 models should be included in such comparisons, I am only including those models having equilibrium climate sensitivities in the IPCC’s “highly likely” range of 2 to 5 deg. C for a doubling of atmospheric CO2.
Approximately 6 times as many models (23) have more warming than the NOAA observations than those having cooler trends (4). The model trends average 42% warmer than the observed temperature trends. As I allude to in the graph, there is evidence that the NOAA thermometer-based observations have a warm bias due to little-to-no adjustment for the Urban Heat Island effect, but our latest estimate of that bias (now in review at Journal of Applied Meteorology and Climatology) suggests the UHI effect in the U.S. has been rather small since about 1960.
Note I have also included our UAH lower tropospheric trend, even though I do not expect as good agreement between tropospheric and surface temperature trends in a regional area like the U.S. as for global, hemispheric, or tropical average trends. Theoretically, the tropospheric warming should be a little stronger than surface warming, but that depends upon how much positive water vapor feedback actually exists in nature (It is certainly positive in the atmospheric boundary layer where surface evaporation dominates, but it’s not obviously positive in the free-troposphere where precipitation efficiency changes with warming are largely unknown. I believe this is why there is little to no observational evidence of a tropical “hot spot” as predicted by models).
If we now switch to a comparison for just the summer months (June, July, August), the discrepancy between climate model and observed warming trends is larger, with the model trends averaging 59% warmer than the observations:
For the summer season, there are 26 models exhibiting warmer trends than the observations, and only 1 model with a weaker warming trend. The satellite tropospheric temperature trend is weakest of all.
Given that “global warming” is a greater concern in the summer, these results further demonstrate that the climate models depended upon for public policy should not be believed when it comes to their global warming projections.
Clintel’s Marcel Crok gave the Keynote Lecture at the Climate Realism conference: The Climate Emergency is Canceled. The conference was in Copenhagen, Denmark in their beautiful Parliament building. His presentation is in English (Marcel’s English is very good) and his presentation can be viewed in full here, to go directly to the Youtube video, click here.
Figure 1. Photo of Marcel Crok (left) and Wim Röst (right) in front of the Danish Parliament building. Photo: Hans-Henrik Juhl.
Marcel Crok:
“The errors we documented in [AR6] are so bad and so striking that one day [the IPCC must] react. … The IPCC should reform, or it should be dismantled. … [Currently] the IPCC is a really bad source…”Marcel Crok
Figure 2. Marcel speaking in the Danish Parliament at the conference. Photo taken by Hans-Henrik Juhl.
In my opinion, AR6 is the worst and most biased of the six major IPCC reports. Marcel’s message that there is no climate emergency and that the latest IPCC report, AR6, is deeply flawed and contains many critical errors. We document many of these errors in Clintel’s new book, The Frozen Climate Views of the IPCC, An analysis of AR6. There are errors of omission; that is, ignoring published evidence that is contrary to the IPCC hypothesis that climate change is dangerous, as well as errors of bias.
Marcel highlights the resurrection of the infamous hockey stick from the third IPCC report, and tells the story of the refutation of the original, which he was involved in. The new AR6 hockey stick is thoroughly debunked in Chapter one of the book by Javier Vinós. Marcel also highlights how AR6 cites one flawed paper that attributes disaster losses to climate change, while 52 others, which were not cited, did not find a connection between climate and disaster losses. This error of omission is discussed in detail in Chapter 12 of the book. The authors of the book chapters include Javier Vinós, Kip Hansen, Nicola Scafetta, Fritz Vahrenholt, Ross McKitrick, Ole Humlum, as well as Marcel and me and a number of helpful reviewers.
Figure 3. Marcel with our book. Photo by Hans-Henrik Juhl.
In his talk Marcel mentions that precisely that day protesters around the world demonstrated for the climate. Even so, the good news from the conference is that Clintel, Climate Realism, and our book show there is no climate emergency. This well-supported conclusion is ignored by the mainstream media. It seems that only the unlikely view that there is an impending climate catastrophe is presented to the public, which is, itself, a catastrophe.
Figure 4. An Extinction Rebellion protest in The Hague, The Netherlands. Photo: Mick Krever, CNN. May 27,2023.
Works Cited
Crok, M., & May, A. (2023). The Frozen Climate Views of the IPCC, An Analysis of AR6.
Download Marcel’s powerpoint slides from the conference here.
Arctic sea ice is lowest during the month of September, and its average extent during this month is a useful metric for measuring Arctic sea ice decline during the current period of global warming. During the 1980s and 1990s, September Arctic sea-ice extent (SIE) showed a moderate decline (Figure 1). After the 1997 climate shift, which involved a rather abrupt global atmospheric reorganization, the Arctic entered a period of rapid change that I call the Arctic Shift.[1] During this period, Arctic SIE declined more rapidly. Scientists noticed this change in trend about a decade later and became increasingly concerned about the prospect of an ice-free Arctic.[2]
Figure 1. September Arctic sea-ice extent since 1979. The blue area indicates the period of rapid change named the Arctic Shift.
The concern about the rapid decline of Arctic SIE in the early years of this century was due to the possibility of a runaway ice-albedo feedback. Loss of sea ice would reduce albedo, and additional solar energy would cause further sea ice loss. Models that reproduced the rapid loss predicted a tipping point that would lead to an ice-free Arctic by 2040, sparking public fears.[3] However, recent work suggests that up to 60% of the decline in September SIE since 1979 may be due to changes in atmospheric circulation.[4] In addition, the persistence of Arctic summer cloud cover significantly reduces the ice-albedo feedback.[5] The realization that internal variability is a more important factor than expected explains why the rate of decline of Arctic summer SIE has slowed so much since 2007, contrary to all expectations.
The Arctic Shift, a period of adjustment of Arctic climate variables to the new atmospheric regime induced by the 1997 climate shift, ended for Arctic SIE in 2007. Since then, the September Arctic SIE shows no significant trend. However, climate researchers are still unaware of the effects of climate shifts and regimes on climate change, and they were surprised by the recovery of sea ice in 2013 when it became clear that there had been no net loss since 2007. Using models, they calculated a 34% chance of a 7-year pause (Figure 2).[6]
However, the hiatus has now extended to 17 years and the probability has dropped to 10%. In other words, there is a 90% chance that climate scientists’ predictions about Arctic sea ice were wrong. If the hiatus continues until 2027, it will become statistically significant (p<0.05, or less than 5%) and no longer explainable by chance. For an explanation of the observed Arctic changes, see chapters 34 and 42 of my forthcoming book “Solving the Climate Puzzle. The Sun’s Surprising Role”.
Figure 2. Probability of a pause in September Arctic sea-ice extent as a function of pause length in the Historical-RCP4.5 experiment. It corresponds to the black curve in Figure 3c of Swart et al. 2015.
The current state of affairs has led society to be alarmed by model predictions that have been proven wrong by the time they are published, but this often goes unnoticed. A recent example of this phenomenon is shown in Figure 3. In June 2023, news headlines around the world highlighted a scientific study that warned of the possibility of ice-free summers in the Arctic by the 2030s, regardless of our efforts to reduce emissions.
Figure 3. Arctic sea ice projections and their implications. a) Results of a modeling study. The black line before 2020 is the observed change in September sea ice area, and after 2020 is the sea ice area projected in the study under the SSP2-4.5 scenario. They correspond to the orange curves in Figure 4b of Kim et al. 2023. The dashed red line is the mean Arctic sea ice area from the 6th Coupled-Model Intercomparison Project. The dotted blue line is the September sea ice extent (SIE), a related measure of sea ice, and the horizontal blue line shows the lack of trend over the past 16 years. b) Examples of media headlines following the June 6, 2023 press release.
The article presents projections based on observations of an ice-free Arctic even under a low emissions scenario.[7] However, it should be noted that the data in the article only cover observations through 2019, although data for 2020-22 were available at the time of publication. In addition, the model projections in the study begin in 2021. Figure 3 shows the results of the study under an intermediate emissions scenario similar to the current situation. However, a significant problem arises when considering the acceptance and publication of the paper, as the model projections for 2021 and 2022 differ greatly from the observed data, with a staggering difference of 1.3 million km2 (0.5 million square miles) or 33% lower. This obvious problem, which undermines the entire study, raises questions about how the paper was accepted for publication.
How could such a blatantly flawed, and provably incorrect, article successfully pass the peer-review process? Moreover, who determines its suitability for widespread dissemination in a global media landscape that seems incapable of questioning or scrutinizing these predictions? The data refuting the article are readily available to anyone with an Internet connection and can easily be located with a simple search engine query. The current method of communicating predictions from highly uncertain climate models to the public is undeniably inadequate, and it is truly surprising that no authoritative scientific voice has addressed this issue and voiced disapproval.
Note: Part of the text and some of the figures in this article are taken from several chapters of my forthcoming book, “Solving the Climate Puzzle. The Sun’s Surprising Role,” to be published in November 2023.
Outcome of an exchange of Comments at Geophysical Research Letters (GRL) on my paper regarding ECS of CMIP6 climate models
Back in March 2022 Gavin Schmidt on RealClimate.org critiqued one of my papers:
Scafetta, N., Advanced testing of low, medium, and high ECS CMIP6 GCM simulations versus ERA5-T2m, Geophysical Research Letters, 49, e2022GL097716, 2022, https://doi.org/10.1029/2022GL097716.
My GRL paper compared the warming of the global surface temperature data from 1980–1990 to 2011–2021 against the CMIP6 GCM hindcasts and found that only the GCM macro-ensemble made with the models with an Equilibrium Climate Sensitivity (ECS) ≤ 3 °C well agrees with the global surface temperature observations. The result is rather important because the GCMs with a low ECS are also those that project a moderate and nonalarming warming for the 21st century, in particular when the SSP2-4.5 scenario, which is the only SSP that seems to be realistic, is used for the climate projections.
Schmidt disliked my paper and claimed that it contains “numerous conceptual and statistical errors that undermine all of the conclusions”. Together with Gareth Jones and John Kennedy, he wrote a letter to the Editorial Board of GRL asking them to retract my paper. They claimed that (1) my GRL 2022 paper overlooked the error of the mean of the temperature data from 2011 to 2021, which they claimed to be 0.10 °C, and (2) they insisted that “the full ensemble for each model must be used” to test the models.
Their retraction request was rejected. GRL decided that a Comment-Reply exchange was more appropriate to clarify the subtle statistical issues that were being raised by their critiques and my rebuttals. Thus, Schmidt, Jones and Kennedy submitted their formal Comment, which essentially repeated the claims previously published on Real Climate. After their Comment was accepted on the 28th of January 2023, GRL asked me to write a formal Reply, which I submitted on the 21st of February 2023. My Reply was accepted on the 22nd of July 2023 and, finally, on the 21st of September both papers were published by GRL:
Schmidt, G.A., Jones, G.S., & Kennedy, J.J. (2023). Comment on “Advanced testing of low, medium, and high ECS CMIP6 GCM simulations versus ERA5-T2m” by N. Scafetta (2022). Geophysical Research Letters, 50, e2022GL102530. https://doi.org/10.1029/2022GL102530
Scafetta, N. (2023). Reply to “Comment on ‘Advanced testing of low, medium, and high ECS CMIP6 GCM simulations versus ERA5-T2m’ by N. Scafetta (2022)” by Schmidt et al. (2023). Geophysical Research Letters, 50, e2023GL104960. https://doi.org/10.1029/2023GL104960
My Reply demonstrates that Schmidt et al. made gross statistical and physical errors and that, in any case, their critiques do not change the conclusions of my 2022 GRL paper.
The Plain Language Summary of my Reply reads:
Schmidt, Jones, and Kennedy’s (SJK) (2023, GRL, link) assessment of the error of the ERA-T2m 2011–2021 mean (σμ,95% = 0.10 °C) incorrectly assumes that, during such a period, the global surface temperature was constant (T(t) = M) and that its interannual variability (ΔT i = T i – T (ti) = T i – M) was random noise. This is a nonphysical interpretation of the climate system that inflates the real error of the temperature mean by 5–10 times. In fact, the analysis of the ensemble of the global surface temperature members yields a decadal-scale error of about 0.01–0.02 °C, as reported in published records and deduced from the Gaussian error propagation formula (GEPF) of a function of several variables (such as the mean of a temperature sequence of 11 different years). Instead, SJK assessed such error using the standard deviation of the mean (SDOM), which is an equation that can only be used when there exists a distribution of repeated measurements of the same variable, which is not the present case. Furthermore, SJK misinterpreted Scafetta (2022, GRL, link) and ignored published literature such as Scafetta (2023, Climate Dynamics, link) that already contradicted their main claim about the role of the internal variability of the models and confirmed the results of Scafetta (2022, GRL,[link].
Both publications are open access, so interested readers can judge the scientific merits of both points of view for themselves. See also Schmidt’s latest post at RealClimate [link].
I found the Comment by Schmidt, Jones and Kennedy to be outdated and paradoxical because their main arguments had already been fully rebutted in another and much more extended paper of mine (Scafetta, N., CMIP6 GCM ensemble members versus global surface temperatures, Climate Dynamics 60, 3091–3120, 2023, [link], which they did not even cite. They also ignored other works (e.g. Lewis, N., Objectively combining climate sensitivity evidence, Climate Dynamics 60, 3139–3165, 2023 [link], first published on 18 September 2022) which essentially confirmed my main result that the actual ECS had to be ≤ 3 °C. The same result is now also confirmed by a third work (Spencer, R.W., Christy, J.R., Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021, Theoretical and Applied Climatology, 2023 [link]. My GRL Reply performs the calculations using the same data as in my GRL 2022 study, also considering Schmidt et al.’s main critiques outlined above, and once again validates the original finding in my 2022 GRL paper.
Herein, I would like to address only a major statistical and simple topic discussed in my Reply that might be of general interest: how to calculate the error of the mean of a temperature record.
The issue was to determine the error of the mean of the global surface temperature record from 2011 to 2021, that is an 11-year period. Schmidt, Jones, and Kennedy claimed that such an error must be calculated with an equation known as the Standard Deviation of the Mean (SDOM) and adopted the following equation:
where T i are the N = 11 annual temperature values from 2011 to 2021 and
is the mean over the 11-year period. As a result, they stated that the global surface temperature records from 2011 to 2021 are affected by a mean error of 0.10 °C.
However, such a result is clearly incorrect because the decadal uncertainty associated with the global surface temperature record from 2011 to 2021 (or even since 1980) has never been calculated to be 0.10 °C in scientific literature. Even on an annual scale, the global surface temperature data error has been reported to be much smaller than 0.10 °C, as also GISTEM (authored by Schmidt) and HadCRUT (authored by Kennedy) clearly show. For example, the Berkeley Earth’s global surface temperature record [link] reports a decadal scale error of about 0.02 °C (I reported the data version published in April 2023). Moreover, the claimed 0.10°C error is arbitrary calculated because Eq. 1 with the monthly temperature record (which has N = 132) yields an error of about 0.03 °C. As a result, utilizing the SDOM makes no sense because by simply interpolating the data and raising N, one may obtain an error as small as desired.
In fact, Schmidt, Jones, and Kennedy did not realize that, in our specific case, Eq. 2 is not the mean of a distribution of N repeated random measurements of one quantity, but a function of N different quantities. The 11 annual mean temperature data used for evaluating the mean from 2011 to 2021 are not 11 stochastic estimates of their 11-year mean and, therefore, they do not form a distribution of stochastic measurements of one quantity. When one just has a function of N different quantities, its error cannot be computed with the SDOM but only with a different equation known as the Gaussian Error Propagation Formula (GEPF) of a function of several quantities. In the case of the function called “mean”, the GEPF establishes that its error is given by the equation
where σzi2 is the variance of the single measurements zi, that is the reported experimental error of zi, and σzi,zj is the covariance of the individual measurement errors. When Eq. 3 is applied to the global surface temperature data from 2011 to 2021, it yields an error that varies between 0.01 and 0.02 °C according to whether the covariance of the errors is used or not.
The difference between the SDOM and the GEPF is covered in any 101 course of Statistics and Error Analysis in Physics and are detailed in popular textbooks (e.g. see Chapter 3 and Chapter 4 in Taylor, J.R., An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements (second edition), University Science Books, 1997; see also Chapter 4 and 5 in Evaluation of measurement data — Guide to the expression of uncertainty in measurement, JCGM 100:2008. [link]
In a nutshell, the GEPF must be used to assess the error of the mean weight between Mary and John (two different quantities) when using the same scale; the SDOM must be used to estimate the error of the weight of John (one quantity) when using two measurements from two separate scales. For example, every child knows that the mean between 10 and 20 (two different quantities) is 15. However, the SDOM adopted by Schmidt, Jones and Kennedy (Eq. 1) calculates 15±5 even when 10 and 20 indicate two different quantities and are error-free, which is clearly wrong because, for example, 11 or 17 are not be the mean of 10 and 20. The SDOM can be used only if 10 and 20 are two stochastic measures of the same thing of which one would like to estimate the best estimate.
Even more paradoxically, the erroneous adoption of the SDOM physically implies that Schmidt, Jones, and Kennedy assumed that the climate temperature of the Earth from 2011 to 2021 was constant, and that natural fluctuations such as ENSO and (natural and anthropogenic) trends are just errors of measure. To justify such a claim, Schmidt, Jones, and Kennedy even invented a new concept in climatology, the concept of “random nature” (perhaps derived from a parallel universe theory?). However, their interpretation of the temperature data is clearly nonphysical. Natural variability does not contribute to the error of measure of the data, but at most only to the error of a model regression coefficient of the data. However, here the issue was not to test an isothermal climate model of the type T (t) = M.
Then, Schmidt, Jones and Kennedy used their erroneous and inflated 0.10 °C error of the mean of the global surface temperature record from 2011 to 2021 to qualitatively claim that the conclusion of my GRL 2022 paper was wrong just because a very few GCM member simulations obtained with a very few GCM models with an ECS > 3 °C agree with the data within such erroneous interval, as Figure 1a shows. However, their own figure clearly shows that all the GCMs with ECS > 3 °C produce hindcasts that are statistically skewed toward temperature values larger than the warming reported by the data: see the green dots indicating the GCM average simulations. Thus, statistically speaking, such models run too hot. In fact, as my Figure 2 shows, when the right error of the mean is considered and the climate models are ensembled into three macro-GCM indicating the three ECS ranges (1.5–3.0 °C; 3.0–4.5 °C; and 4.5–6.0 °C) as did in my 2022 GRL paper and the proper statistics is evaluated also assuming some statistical dispersion due to their internal variability, the warm bias of the GCM groups with an ECS > 3.0 becomes evident. My figures are reported below.
In conclusion, the Comment by Schmidt, Jones, and Kennedy is flawed, both statistically and physically. Its publication, together with my Reply, is important only because pointing out such errors is also useful for educational purposes.
I need to add that this is not the first time that Schmidt has critiqued one of my works using severely flawed mathematics and logic. Some readers may remember that in 2009 Benestad and Schmidt published a paper in JGR (Benestad, R.E., and G.A. Schmidt, Solar trends and global warming, J. Geophys. Res. 114, D14101, 2009, [link], which was actually a kind of comment on some of my works. Here Schmidt made severe and naïve errors in the wavelet analysis and multilinear regression model, as I first demonstrated here [link]. Such errors obscured the empirically evident and significant solar contribution to climate change and might have misled the scientific community on this topic. For the interested readers, the detailed rebuttal of Benestad and Schmidt’s paper was later published here: Scafetta, N., Discussion on common errors in analyzing sea level accelerations, solar trends and global warming, Pattern Recognition in Physics 1, 37–57 [link]. Schmidt recently wrote other Real Climate flawed articles that critique papers that I have coauthored with Dr. Connolly, Dr. Soon, and many other colleagues which show the possibility that the sun can significantly contribute to climate change of the last century. The rebuttals of his critiques are found on [link].
In conclusion, these are cases that clearly demonstrate the necessity of having formal Comments and Replies published together to let the readers to properly evaluate both viewpoints. Thus, I am surprised that on RealClimate, Schmidt appears to complain that his Comment was not published by alone, before or even without my Reply. However, it is critical that professionally written Comments and Replies are published concurrently. Furthermore, for the sake of science, any form of political manipulation of journals behind the scenes (as the ClimateGate emails revealed link) must be abhorred. This must be done mostly for ethical reasons, notably to avoid potentially occurring instances of scientific disinformation campaigns promoted by the authors of the Comments and by various activist scientists.
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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.
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