Tag Archives: AR6

Modeling HadCRUT5 with CO2 and without CO2

From Watts Up With That?

By Andy May

I hate statistics, as many of you know. Some people think statistics and/or statistical models that meet standard statistical criteria are facts. The IPCC can be like that. They statistically model global surface temperatures with models of volcanic and anthropogenic forcing and compare the model to one with only volcanic forcing. Then they turn to us, with a straight face, and say the comparison shows anthropogenic forcing is driving all the warming. What about solar? Oh, they considered that they say, the Sun makes no difference, see their chart in figure 1 from AR6.[1] Solar is assumed to be zero and volcanism is small, thus the model assumes all recent warming is due to humans, then draws the same conclusion in a perfect example of circular reasoning. But what if the solar forcing is not zero? What difference does that make?

Figure 1. The IPCC AR6 assumed forces affecting global surface warming translated to degrees C. From AR6 WG1, page 961.

Numerous papers have been published that show the Sun could have more impact on global temperatures and climate change than assumed by the IPCC.[2] We must remember that statistical models are not evidence or theories, they aren’t even proper hypotheses. They are just a tool to test the validity of ideas and a hypothesis might come out of a statistical model, but proof never will. If a model repeatedly predicts the future accurately, then it is evidence the hypothesis is correct, it isn’t proof. The IPCC presents their statistical climate model with the plots shown in figure 2.

Figure 2 is quite busy, but what it says, in brief, is that they assume that natural warming (heavy green line) is zero, which makes, under their assumptions, all warming due to human activities. The WG1 AR6 report is 2,391 pages long, but figure 2, modified slightly from what they display on page 441, really encapsulates everything it proposes. The rest is filler.

Figure 2. The IPCC model shows their greenhouse gas warming hypothesis with this graph. This is after IPCC AR6 WG1 figure 3.9b (page 441). The vertical axis is the temperature anomaly relative to 1850-1900.

There are numerous problems with figure 2, but we will focus on the comparison between the anthropogenic + natural models, in orange, and the observations in black. First of all, the orange is not one model, but the average of many selected models. The range of model calculations (5 to 95th percentile) is shown with light orange shading. The range is quite large, if they had confidence in their models wouldn’t they choose the best one and use it? If they don’t trust the models, why try to use them as evidence that the Sun has no influence, and all the warming is due to human activities? Why use the models to confidently predict a man-made climate catastrophe? AR6 WGII Summary for Policymakers (p 12-20) reports high confidence in many future catastrophes based on model results. Why high confidence, if the models are so imprecise, that they must be averaged? Second, they use thick lines to try and obscure the differences between the black and orange lines, but the differences are significant, especially between 1935 and 1976 and 1980 to 2000. The model average between 1920 and 1960 looks almost hand-drawn because it is so straight relative to rising temperatures until 1944 and falling temperatures afterward.

So, let’s take a different approach. The classical paleoclimate literature, pre-IPCC, mostly thought that solar variability dominated climate change.[3] Over time the study of the cosmogenic isotopes 14C[4] in tree rings and 10Be[5] in ice cores has led to accepted proxy records of the Sun’s output that go back thousands of years (see the discussion of Carbon-14 and Beryllium-10 here).[6] These isotopes are created in the atmosphere when galactic cosmic rays make it through the solar magnetic field and impact the atmosphere. When solar output is high, its magnetic field is stronger than when it is low. Thus, low concentrations of 14C[7] and 10Be[8] suggest a strong solar output and vice versa. Since 1700 sunspot records provide a more accurate view of solar activity.[9]

Studies of 14C, 10Be, and sunspot records have uncovered four major long-term solar cycles. These are the Hallstatt (or Bray) cycle of about 2,400 years,[10] the Eddy Cycle of about 1,000 years,[11] the de Vries (or Suess) cycle of about 210 years, Feynman (or Gleissberg) cycle of about 105 years,[12] and the Pentadecadal cycle of about 50 years.[13] All the cycle periods are approximate, further, they may vary over geological time.[14] Some may not like my use of the term “cycles,” since our understanding of the cycle periods and the strength or power of each cycle is poor. Perhaps the term oscillation would be better but understand that I fully appreciate how poorly we understand these cycles and use the term only for convenience and not necessarily according to the precise definition of the word.

The Sun is a dynamo and generates a magnetic field that controls the variations in its output over time. Such a dynamo will have cycles, we have shown they exist and affect Earth’s climate, but the details are sketchy. What astrophysicists and paleoclimatologists have done is observe the Sun and solar impacts on Earth’s climate and recognized in-phase patterns of both solar activity and climate impacts. We discuss these observed (but only approximate) patterns in the post and correlate them to HadCRUT5. Cycles are also observed in other stars that are like our Sun.[15]

There are also shorter periods of solar variability, like the sunspot cycle which has a varying period and asymmetrical shape that averages about 11 years.[16] Finally, we have the ENSO cycle, also with a varying period, that is driven, in part, by solar activity.[17] To cover the shorter solar cycles we include the SILSO sunspot record[18] and the ERSST Niño 3.4 (ENSO) record from KNMI.[19]

If we ignore the IPCC assumption that solar activity has played no role in climate change since 1750, as suggested in figure 1, it is possible to investigate the correlation of these well-established cycles or oscillations and one of the global surface temperature records used in AR6, the HadCRUT5[20] record. Unfortunately, the HadCRUT5 global surface temperature record only goes back to 1850, but it is an instrumental record, and preferable to proxies. The data used to build HadCRUT5 is poor prior to 1958,[21] so we will also investigate the even shorter period of more accurate data from 1958 to 2023.

We used statistical multiple regression to see how well these cycles and data can predict HadCRUT5. We understand going in, that even if we can build a multiple regression model with a high R2 (Coefficient of determination or the square of the correlation coefficient), we haven’t proven anything. We also understand that while global average surface temperature is an important metric of climate change, it is not the only important metric. Other metrics, such as mid-latitude wind speed and direction, as well as surface temperature trends at the poles, and in the tropics (especially in the middle troposphere[22]) are also important. The purpose of this post is simply to show that the IPCC’s choice to characterize the correlation of the trends in the logarithm of CO2 concentration and global average surface temperature as “proof” or “evidence” that CO2 and other human greenhouse gas emissions drive climate change is not very solid. In fact, it is probably wrong. Other reasonable correlations are possible, and arguably better.

Figure 3 is a plot of the independent or predictor variables used in our regression study. They have been normalized to scales of -3 to +3 by dividing the larger variables (Log(CO2) and sunspots) by their mean to better compare the variables to one another. In addition, we divided the sunspot number by its standard deviation to help make it comparable in scale to other variables.

Figure 3. The input series used in this multiple regression study. The y axis scale is an index, and the curves cannot be compared quantitatively.

Unfortunately, our period is too short to properly evaluate some of the stronger climate cycles, like the Hallstatt (light blue) and Eddy (orange) cycles. These two cycles bottomed in the Little Ice Age and their periods are so long they almost appear as straight lines, but they are increasing like the HadCRUT5 record. The logarithm of CO2[23] is also nearly a straight line, and very slightly increasing. The CO2 data are interpolated yearly averages to avoid the seasonal wiggles.

The ENSO 3.4, sunspots (SN Norm), and CO2 (Log CO2 Norm) records used in the study are from well-known datasets.[24] The longer-term solar cycles are created using a sinusoid function[25] of the form:

  • Cycle (t) = cos(2πft – offset)

Where the cosine argument is in radians, f=frequency, t=time, and the offset is used to align the sine wave with assumed cycle lows (cold periods) from Ilya Usoskin[26] and Joan Feynman.[27] For more on this transform, used in Fourier analysis, see David Evans’ paper here.[28] These lows are not precise and must be estimated from the available data. The actual values used, and the precise functions are in the supplementary materials which are linked at the end of this post.

The Multiple Regression Model

I performed a number of regressions with the variables plotted in figure 3 and various subsets of them. In every case where I could tell, the statistically most important single variable, judging from AIC,[29] sum of squares, and R2, was the logarithm of CO2. However, all the variables were significant, and CO2 compared to the impact of all the others combined was small, as we will see. AIC ranks the input predictors for the 1958 case rank as follows: Log_CO2, Nino_3_4, Hallstatt, Eddy, Pentadecadal, sunspots, and finally de Vries. AIC is based on the sum of squares, so it can be problematic in autocorrelated series[30] like these. The plots below give you feel for the relative importance of the main variables, which is hard (maybe impossible) to calculate statistically with any precision, mainly due to the brief period of our instrumental data and the long periods of the important solar cycles. The next four plots are for the whole instrumental record, 1850 to 2023. Figure 4 includes all the variables in the study.

Figure 4. A model with all series, including log(CO2). The fine gray line is the monthly HadCRUT5 data, and the blue line is smoothed with an 11-year moving average. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990. The orange line is the model.

Figure 5 uses all the variables except Log_CO2. In both figures the blue line is the smoothed HadCRUT5 record, and the fine gray line is the monthly HadCRUT5 data. The orange line is the model. We can see that Log_CO2 visually adds little to the match between observations and the model. Significant improvement is visible around 1940, otherwise the two models are about the same.

Figure 5. Plot of the regression with log(CO2) removed from the list of predictors. The R2 drops to 0.84 and there is noticeable deterioration in the fit between 1935 and 1947. The fine gray line and the blue line are as before. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 6 compares the model that uses Log_CO2 to the model that only uses the solar related variables. The two models are similar. The only noticeable differences are before 1940 when CO2 was supposedly not very important. It is possible that the differences are due to data quality. As we will see, the data prior to 1958 was lower in quality than the data after that date.

Figure 5. Plot of the regression with log(CO2) removed from the list of predictors. The R2 drops to 0.84 and there is noticeable deterioration in the fit between 1935 and 1947. The fine gray line and the blue line are as before. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 6 compares the model that uses Log_CO2 to the model that only uses the solar related variables. The two models are similar. The only noticeable differences are before 1940 when CO2 was supposedly not very important. It is possible that the differences are due to data quality. As we will see, the data prior to 1958 was lower in quality than the data after that date.

Figure 6. A comparison of the “no CO2” versus “with CO2” models. All other predictors are in both models. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

In figure 7 we model HadCRUT5 with only CO2. While the R2 is 0.8 and the model generally follows HadCRUT5, the model lacks the granularity and detail that is apparent in figures 5 and 6. The IPCC calls the granularity natural variability and dismisses it as statistical “noise” that is random. Notice the P-value doesn’t change, the P-value is of little use in models like this that have a lot of observations and produce good matches. It is not a good measure of model quality.

Figure 7. HadCRUT5 is modeled with only CO2. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Next, we repeat the above four plots using a new model that only uses the data between 1958 and the present day. This is the largest period possible with good data. To get another upward step change in data quality we would need to move to 2005 when the ARGO array became sufficiently large to produce better data on ocean temperatures than we can get from ships. But only 17 years of good ocean data is not long enough to judge the influence of the longer solar cycles.

Figure 8 shows a good visual match between observations and a model with all the variables. It also has an R2 of 0.9, which would be impressive if the variables were independent and not autocorrelated. The mismatch between 1992 and 1995 is probably due to the Pinatubo eruption in 1991, which was not incorporated into this model.

Figure 8. A model with all predictors, including CO2, from 1958 to the present day. The Pinatubo eruption is identified. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 9. A model from 1958 with all predictors except CO2. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 9 is the model with all variables except for CO2. The match is still good, but there are differences in detail suggesting that adding CO2 makes a difference. The large difference just after 1992 is probably due to the influence of the Mt. Pinatubo eruption in the summer of 1991. The effect of the eruption lasted several years. With the exception of the Pinatubo eruption, the model is almost as good as the model that includes CO2, at least visually.

Figure 10. Models with and without CO2 over the 1958 to present period. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 10 compares the models with and without CO2 directly, and except for the period right around the Mt. Pinatubo eruption, the match is excellent. I’m not saying that Pinatubo had an effect before it erupted, just that the large impact of the eruption on the HadCRUT5 record (see Figure 11) could have distorted the two regressions differently in that period. Possibly the addition of CO2 makes a small difference, but it isn’t apparent in this plot anywhere except around the eruption.

Figure 11. A model with only CO2 as a predictor from 1958 to the present. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 11 shows a model using only the logarithm of CO2, there is a general correspondence of temperature and CO2, but a great deal of detail is missing that we see in the other models. We can argue that the variation of the HadCRUT5 record around the orange model in figure 11 is not random noise if it can be modeled with solar cycles.

A word on statistics

The risk in evaluating regression statistics of models of autocorrelated series is most easily seen by considering that any two monotonically increasing time series, for example CO2 and temperature since 1850, will appear to correlate, even if they are unrelated. This is why I often hate statistics, too often statistical measures of fit, like R2, or computed statistical probabilities are used to gaslight readers into believing something that isn’t true. Your first judgment of a correlation should be made with a plot of the data versus the model, second should be a plot of the residuals. Are the residuals evenly dispersed about zero, or do they have a trend? All the residual plots for the top models in this post are trendless, as they should be.

The main point is to trust your eyes, not statistical measures of the fits, they are secondary. Sometimes the obvious is correct. To illustrate this point, I used a stepwise regression to order the models. To generate these four models, I removed the top variable (according to its AIC) and reran the regression with the remaining variables until the visual model did not match HadCRUT5 very well. The procedure suggests the most important variables are Log_CO2, Hallstatt, and Eddy. The four acceptable stepwise regression models are plotted in figure 12.

Figure 12. The four best forward stepwise regression models. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

The first stepwise model (All) chose the variables listed in the figure. The variables are listed in order of importance according to their AIC scores. The best models, visually, are “All” and “no CO2,” and it is hard to tell the difference between the two. Notice that when CO2 was removed from the selection list, more variables were chosen.

After Hallstatt is removed, the list of chosen variables shrinks, but the model visually degraded a lot. Once Eddy was removed the model becomes very poor. The top variable, by AIC, is Log_CO2, but when Log_CO2 is removed from the model (the green curve) the match to HadCRUT5 is still good. Other models were also evaluated in this fashion, but these three are the best.

The variables that came out consistently on the bottom, according to AIC, were the Pentadecadal cycle and sunspots. However, removing these variables always caused the model to visually deteriorate unacceptably. Thus, AIC, while useful, is not a good sole criterion for the value of variables or models. Always look at the plots.


There are several logical conclusions from this study.

  1. A successful model can be built using only solar cycles, ENSO, and the sunspot record.
  2. Adding CO2 to the model described in (1) above adds a little to the fit, mostly in short intervals, like from 1935 to 1940 and in the middle 1990s around the Pinatubo eruption.
  3. Standard statistical measures, like AIC, R2 or the P test, cannot be used as the sole measure of the success of the model. Evaluating the plots is critical.

This study shows that solar variability, at least statistically, correlates to HadCRUT5 at least as well as CO2. Since HadCRUT5 is one of the main global average surface temperature records used by the IPCC to measure climate change, their conclusion, as stated in the AR6 Technical Summary is:

“Taken together with numerous formal attribution studies across an even broader range of indicators and theoretical understanding, this underpins the unequivocal attribution of observed warming of the atmosphere, ocean, and land to human influence.”AR6 TS, page 63, emphasis added.

This is incorrect, and the result of their unsupported assertion that the Sun has no influence on climate. They should seriously investigate the influence of solar variability on climate change. I expected to have to deal with lagged solar effects on climate in this study going in. Possible multi-year lags between solar events and related climatic effects are mentioned in many papers (example here, other examples are cited in Eichler, et al.), but the observation/model matches in this post were all achieved with no lags.

I would like to thank Charley May and David Evans for their help with this post, although if there are any errors, they are mine alone.

Download the bibliography here.

Download the supplementary material here. You will find the R code to create all the models and Excel code to make the main models, not all the R models can be made in Excel. To run the models in Excel you will need to the “Analysis ToolPak” and the “Solver” Add-in. These are found under File/Options/Add-ins.

  1. (IPCC, 2021, p. 961) 
  2. See especially: (Connolly et al., 2021), (Hoyt & Schatten, 1997), (Soon, Connolly, & Connolly, 2015), (Usoskin I. , 2017), (Usoskin, Gallet, Lopes, Kovaltsov, & Hulot, 2016), (Scafetta N. , 2023), (Vahrenholt & Lüning, 2015), and (Judge, Egeland, & Henry, 2020). 
  3. (Hoyt & Schatten, 1997) and (Bray, 1968) 
  4. 14C is the Carbon-14 isotope, except for nuclear bombs it is only created in the atmosphere by galactic cosmic rays, which increase when the Sun is less active. It has been used a proxy for solar activity for many decades. It is stored in tree rings, which provide a convenient and accurate date for each 14C concentration. (Cain & Suess, 1976) and (Cain W. , 1975). 
  5. 10Be is an isotope of Beryllium that is created by cosmic rays and is also inversely correlated with solar activity. It is stored in ice cores. (Beer, Blinov, Bonani, & al., 1990). 
  6. (Beer, Blinov, Bonani, & al., 1990) and (Hoyt & Schatten, 1997, p. 174) 
  7. (Bray, 1968) 
  8. (Delaygue & Bard, 2011) 
  9. https://www.sidc.be/SILSO/datafiles 
  10. (Bray, 1968) 
  11. (Abreu, Beer, & Ferriz-Mas, 2010) 
  12. Joan Feynman studied this centennial cycle and the pentadecadal cycle for many years. She called it the Gleissberg cycle, but since many have used the name Feynman cycle, we continue with that name here (Feynman & Ruzmaikin, 2014). See also (Peristykh & Damon, 2003). 
  13. The Pentadecadal cycle was first recognized by Rudolf Wolf in 1862 (Peristykh & Damon, 2003). He recognized that two or three high cycles were often followed by two or three low cycles. More formal recognition of the cycle was made by (Feynman & Ruzmaikin, 2014) and (Clilverd, Clarke, Ulich, Rishbeth, & Jarvis, 2006). 
  14. (Peristykh & Damon, 2003) 
  15. (Judge, Egeland, & Henry, 2020) and (Baliunas, et al., 1995) 
  16. (Peristykh & Damon, 2003) 
  17. (Roy, 2014) 
  18. https://www.sidc.be/SILSO/datafiles 
  19. https://climexp.knmi.nl/getindices.cgi?WMO=NCDCData/ersst_nino3.4a&STATION=NINO3.4&TYPE=i&id=someone@somewhere 
  20. https://www.metoffice.gov.uk/hadobs/hadcrut5/ 
  21. 1958 was the International Geophysical Year (IGY), which led to gathering much higher quality climate and climate-related data. It is notable that the late S. Fred Singer was one of the organizers of this project and that it was organized in James Van Allen’s living room in 1950. According to Van Allen, it was his wife’s (Abigail) chocolate cake that sealed the deal that day. (Korsmo, 2007). 
  22. (McKitrick & Christy, 2018) and (McKitrick & Christy, 2020) 
  23. CO2 concentration varies as the logarithm to the base 2 with temperature, which means as CO2 doubles, temperature increases linearly. As CO2 concentration increases, its effect on surface temperature decreases. (Romps, Seeley, & Edman, 2022) and (Wijngaarden & Happer, 2020) 
  24. ENSO 3.4 is from ERSST, which only goes back to 1854. 1850 through 1853 are filled in with the Webb, 2022 ONI. The sunspot number is from SILSO, and the CO2 concentration data are from NASA and NOAA. The CO2 record is interpolated yearly averages to avoid the seasonal changes. 
  25. (Evans, 2013) 
  26. (Usoskin, Gallet, Lopes, Kovaltsov, & Hulot, 2016) and (Usoskin I. , 2017) 
  27. (Feynman & Ruzmaikin, 2014) 
  28. (Evans, 2013) 
  29. AIC stands for Akaike Information Criterion. It estimates the information lost by using the regression model in the place of the measurements. Like R2, it is based on the sum of squares and is susceptible to inflation (making variables and models look better than they actually are) due to autocorrelation. The Wikipedia article on this metric is helpful, see here. The lower the AIC value the better the model. 
  30. All the input time series used in these multiple regression models are autocorrelated, which simply means each value in the series is highly dependent on its previous values not independent of one another as required by the rules of regression. This artificially inflates the statistical measures often used to evaluate the quality of a regression, such as the R2 value shown in some of the plots. 

The Buzz about The Frozen Climate Views of the IPCC

From Watts Up With That?

Our new book, The Frozen Climate Views of the IPCC: An Analysis of AR6, is creating a lot of attention and excitement around the world. It is the first authoritative critique of the new IPCC (Intergovernmental Panel on Climate Change) climate change report, “AR6.” AR6 ignores all climate change research that goes against their narrative and agenda, we show you what they are hiding in this book.

Click on the image to purchase the book at Amazon or to leave a nice review, it is also available at KoboBarnes and Noble, or purchase the book directly from Clintel and get a signed copy.

Below are links to some of the interviews that Marcel Crok and I have had so far on the book:

Marcel’s interview on the Laughland Report, in English. “Do the cost/benefit analysis on climate expenditures!”

Marcel explaining our critique of AR6 on the Laughland Report TV Show.

My interview on the Heartland Institute Climate Change Roundtable TV show, in English. “The IPCC RCP 8.5 CO2 emissions scenario is not just improbable, it is impossible!”

The Heartland Institute Climate Roundtable and me.

Marcel Crok speaks at the Heartland Institute Climate Reality Forum, before publication of the book, in English. “The IPCC is rewriting history with their new hockey stick graph.”

My podcast interview with H. Sterling Burnett, in English, with no video.

My interview on Tom Nelson’s podcast, in English. “The 13 of us that worked on this book come from seven different countries around the world, eight of us were chapter authors and five were expert reviewers.”

The title slide for my presentation to Tom Nelson.

Marcel Crok speaks at the EIKE (European Institute for Climate and Energy) meeting in Germany, in English. “[AR6] hides good news about disaster losses and climate-related deaths. [AR6] wrongly claimed the estimate of climate sensitivity is above 2.5 deg. C, it is more likely below 2 deg. C.”

Marcel shows the authors of The Frozen Climate Views of the IPCC to EIKE.

Jurgen Tiekstra interviews Marcel Crok and Guido van der Werf on De Nieuwe Wereld TV in Dutch. Guido van der Werf is a professor of the carbon cycle and expert in the field of forest fires. Marcel and Guido are opponents in the interview and also friends.

Jurgen Tiekstra (center) interviews Marcel (right) and Guido van der Werf (left). Image from De Nieuwe Wereld TV.

Marcel Crok is interviewed on Ongehoord TV Nieuws, in Dutch.

Marcel speaks to the panel on Ongehoord TV Nieuws

Marcel Crok is interviewed on blckbx in Dutch.

A Twitter Debate on Clintel’s IPCC AR6 Critique

From Watts Up With That?

By Andy May

In May 2023, Clintel published a book (see figure 1) criticizing AR6 (IPCC, 2021), a publication that was supposed to summarize climate science research to date. We found that AR6 was biased in its reporting of recent developments in climate science, and they ignored published research contrary to their narrative that humans have caused all the warming since the Little Ice Age (the so called “preindustrial”), and that recent warming is somehow dangerous. Comments and reviews of the Clintel volume can be seen here and on Judith Curry’s website here.

Figure 1. The Clintel critique of the IPCC AR6 report. More details here or click on the image.

This post discusses a twitter debate about possible mistakes in the Clintel volume, specifically the Chapter 6 (written by Nicola Scafetta and Fritz Vahrenholt) discussion of the evidence that changes in the Sun affect Earth’s climate. We argue that recent evidence supports a role for the Sun in modern climate change, and the IPCC argues that the Sun has not contributed to recent (since 1750, see AR6, page 959, figure 7.6) warming or recent climate change.

We will see that Theodosios Chatzistergos, who also argues for no contribution from the Sun seems to confuse opinions with facts, and considers opinions different from his own as “mistakes.” This is a common problem with younger scientists, and undoubtably it is a product of poor scientific training in universities today. Opinions, regardless of who holds them, are not facts. Differing opinions, based on the same pool of evidence, are not mistakes, they are just different opinions. It is easy to see how “climate science” has devolved into “climate politics.”

Dr. Judith Curry praised the Clintel volume on twitter, which led to criticism from Dr. Theodosios Chatzistergos. Chatzistergos claims that Scafetta and Vahrenholt’s Chapter 6 had several errors, claims that I discuss in detail below.

Chatzistergos Point 1:

Chatzistergos points out that most TSI (total solar irradiance) composites agree with the IPCC favored PMOD composite and that all TSI composites show a declining trend since the mid-1990s. These points are questionable, because I would argue that RMIB (sometimes abbreviated IRMB) and NOAA composites are similar to ACRIM, see below and here, for more details on comparing the three composites. You can decide for yourself. All the composites are very similar, the differences are quite small and below the uncertainty in the data, see figures 5 and 6 here, and figure 2 below.

Figure 2. A comparison of the RMIB, PMOD, and ACRIM TSI composites with the uncertainty shown as gray shading. Notice the large reduction in uncertainty after 1996. The uncertainty assumes all TSI records are equally certain, allowing for the possibility that any of the composites could represent the longer-term secular trend. After­ (Coddington, et al., 2019).

Chatzistergos point is that the longer-term trend in solar activity can only be detected during solar cycle minima because solar cycle maxima are highly variable, yet the uncertainty in TSI does not drop enough to detect a possible trend until after 1996, all records more-or-less agree after that time. There are only two fully resolved solar cycle minima after 1996, including the most recent one. Two are not enough to resolve a trend with any confidence. Besides the critical difference in the longer-term trends occurs between 1985 and 1996 when the data during the ACRIM Gap (1989.5-1991.75) are very uncertain due to the trend difference between the Nimbus7 and ERBS data. See Scafetta et al. (2019) for a detailed discussion.

In any case, consensus, that is the majority of the TSI reconstructions, has little to do with science, and if more composites are similar to PMOD than ACRIM, that simply means there are more opinions that PMOD is preferred. This does not mean that the opinions expressed by Scafetta and Vahrenholt in the Clintel volume chapter 6 are mistaken. Nor do these opinions invalidate Connolly, et al., 2021 or Soon, Connoly, and Connolly, 2015. The truth is, the data we have on TSI is so poor prior to 1996, that any of the various TSI reconstructions could be correct, as Chatzistergos himself admits in his 2023 paper, quoted below:

Measurements of total solar irradiance (TSI) exist since 1978, but this is too short compared to climate-relevant time scales. Coming from a number of different instruments, these measurements require a cross-calibration, which is not straightforward, and thus several composite records have been created. All of them suggest a marginally decreasing trend since 1996. Most composites also feature a weak decrease over the entire period of observations, which is also seen in observations of the solar surface magnetic field and is further supported by Ca ii K data. Some inconsistencies, however, remain and overall the magnitude and even the presence of the long-term trend remain uncertain. Emphasis added.(Chatzistergos, Krivova, & Yeo, 2023)

Chatzistergos Point 2:

Chatzistergos claims that the analysis of the NRLTSI2 (Coddington O. , Lean, Pilewskie, Snow, & Lindholm, 2016) and SATIRE (Krivova, Solanki, & Unruh, 2011) data performed by Nicola Scafetta is incorrect. For my discussion of Scafetta’s paper see here. Since 1996, the trends in all the TSI constructions match, the differences are in the period from 1978 to 1996 where the data is quite poor. Extrapolations of TSI into the past rely on solar models (such as SATIRE). As Scafetta and many others have pointed out, these models are based upon many speculative assumptions that are not consistent with the satellite data, particularly during the critical ACRIM data gap (see figure 3). Chatzistergos offers no evidence that Scafetta’s analysis is incorrect, just his opinion, which is contradicted by the quote above from Chatzistergos’ own 2023 paper.

Figure 3. The critical ACRIM gap is plotted in red. Plot (a) uses the original TSI calculated by the satellite teams and TSI is rising during the gap. The PMOD record is shown in plot (b), as modified with their solar model, and it is flat to declining. Source: (Scafetta, Willson, Lee, & Wu, 2019).

Figure 3 highlights the critical portion of the early TSI record. Figure 3a shows how the TSI satellite composite appears when the original TSI satellite records published by their original experimental teams are adopted (it looks more like ACRIM); Figure 3b shows how the TSI satellite composite appears when one adopts the TSI satellite modified by PMOD. Both figures were published by Dudok de Wit (Dudok de Wit, Kopp, Fröhlich, & Schöll, 2017) using the same composite methodology. The differences appear tiny, but when extrapolated back to the Little Ice Age Maunder Solar Grand Minimum, they make a big difference in the level of solar activity then versus now. Data does not exist at this time that can determine whether Chatzistergos or Scafetta are correct about the long-term trend in solar activity or how well it correlates with climate changes in the past.

Chatzistergos Point 3:

Chatzistergos claims that the following sentence in our book is incorrect.

“The main difference between the ACRIM and PMOD TSI satellite composites is that while the former uses the original raw satellite TSI records, the latter is based on TSI satellite records modified with a model.”(Crok & May, 2023, Ch 6)

ACRIM uses the satellite data as interpreted by the respective satellite teams to compute TSI and prefers to bridge the ACRIM-gap using the Nimbus7 record because it is considered more accurate than the ERBS record from an experimental point of view, then the ACRIM team splices the data, as described here, and similar to the RMIB and Dudok de Wit reconstructions illustrated in figures 2 and 3a. One could nitpick, as Chatzistergos does, and claim that Dudok de Wit, the ACRIM team, and the RMIB team used a simple model to splice the satellite data together. But when we consider that the PMOD team changes the Nimbus7 satellite data to conform to their solar models, his nitpicking looks weak. The weak justification for the data changes made by the PMOD team is explained by Douglas Hoyt, the leader of the Nimbus 7 satellite team:

“[The NASA Nimbus7/ERB team] concluded there was no internal evidence in the [Nimbus7/ERB] records to warrant the correction that [PMOD] was proposing. Since the result was a null one, no publication was thought necessary. Thus, Fröhlich’s PMOD TSI composite is not consistent with the internal data or physics of the [Nimbus7/ERB] cavity radiometer.”(Scafetta and Willson 2014, Appendix A)

One could quibble over the language of the contested statement from our book, but the bottom line is that the ACRIM adjustments can be justified by solid engineering data from the satellite teams, whereas the PMOD adjustments are not consistent with the satellite data according to the satellite teams. Our sentence, while possibly poorly worded, is correct.

Chatzistergos Point 4:

Chatzistergos complains that our book points out that the IPCC has progressively downgraded their estimate of the influence of the Sun, then admits that we are correct, but adds that the IPCC did nothing wrong. That is his opinion, ours is different. He claims again that “we” understand the Sun better today than in the 1980s and now “know” the Sun has little influence on climate change, nearly opposite of what he says in his own 2023 paper as quoted above. The truth is that a considerable amount of evidence exists that the Sun plays a role in recent climate change, but how the Sun accomplishes this is still debated and poorly understood. For a comprehensive discussion see here and here, Scafetta’s recent paper here, or Javier Vinós’ book (Vinós, 2022).

Chatzistergos Point 5&6:

Chatzistergos claims that the statement below, from Chapter 6 of our book, is incorrect:

“[The IPCC] TSI record is a combination of two TSI records (NRLTSI2 and SATIRE) that show a very small secular variability while many other TSI reconstructions show a much larger, up to about 10 times, larger secular variability and also slightly different patterns.”(Crok & May, 2023, Ch 6)

Then confusingly, writes: “There are indeed many models reconstructing TSI in different ways…” He never explains how the statement from our book is incorrect, it just seems to be his opinion. On the face of it, the statement above is clearly accurate and well written.

Chatzistergos Point 7:

Here he claims that we listed the evidence that the Sun influences the number of cosmic rays that strike the Earth, which affects cloud cover and thus the climate, but that we ignored the evidence against this hypothesis. He did not read very carefully. The following is also from our book:

“During the period 1983-2002 global cloud cover developed synchronously with the eleven-year solar cycle (see Figure 3). After then, however, the relationship broke down, which led to criticism from Svensmark’s scientific opponents.”(Crok & May, 2023, Ch 6, p 87)

The evidence against the hypothesis is the breakdown in the cloud/cosmic ray correlation during the 1990s, as described in our book, nothing was ignored.

Chatzistergos Point 8:

His opinion is that figure 2 in chapter 6 of our book is cherry picked and that the series shown in the figure are somehow inferior. Our opinion is different, and he does not present any evidence to support his opinion. The correlation between the long-term (century or more) trends in solar activity and the long-term trends in climate is clear and has been recognized by paleoclimatologists for centuries, see here and here. However, a proper explanation or model of the mechanics of the solar influence on climate eludes us.

Chatzistergos Point 9:

His point is that we “mislead with the grand solar maximum of the 20th century by conveniently failing to mention that solar activity peaked during late 50’s…” Here Chatzistergos makes the implicit assumption that solar changes affect climate in some linear and instantaneous way. If that were true, the mechanism would have been discovered long ago. The Modern Solar Maximum lasted from around 1935 to 2005, it was the longest solar maximum in at least 600 years, as described here. Figure 4 shows the Modern Solar Maximum, as reconstructed from sunspot records.

Figure 4. The Modern Solar Maximum, the longest solar maximum in 600 years. Source: (Vinós, 2022) and here.

Chatzistergos Point 10:

Chatzistergos’ 10th point is that we mislead when we state that “the increase in solar activity correlates well with the current global warming” referencing Connolly et al 2021. He claims this is incorrect, even though more than 50 paleoclimatologists have written that the solar modulation of climate is obvious in the data and that research should focus on finding out how it occurs, as reported by Vinós and myself here.


Chatzistergos inability to see the difference between opposing opinions and actual mistakes is not surprising given the appalling level of scientific training today and the politicization of climate science. That is why I took the time to write this post defending our book.

His tweets confuse facts with opinions. This is also commonly seen in supposed “fact checks” by Climate Feedback and other organizations of that ilk, as we discuss here. Clearly our universities are not training our young scientists very well, this is a real problem that should be addressed.

Download the bibliography here.

A Critique of AR6

From Watts Up With That?

By Andy May

After more than two years of hard work, Marcel Crok, I, and 11 other scientists have finally published our critique of the Intergovernmental Panel on Climate Change (IPCC) sixth report (AR6). The entire book has been extensively peer reviewed and a low-resolution pdf of a nearly final draft of the book has been available for weeks at clintel.org. All comments received on this draft have been carefully considered and incorporated, if approved by the team, in the final book. We are a bit hard on AR6, but our criticisms are well deserved. Only the eBook is out now, the print edition should be along in a week or two. The Kindle edition is text-to-speech enabled. Available at AmazonKobo, and Barnes and Noble.

A Brief Summary of the Contents

The IPCC has completed its sixth climate change assessment cycle consisting of seven reports in total, collectively known as “AR6.” A team of eight scientists, in addition to several anonymous expert reviewers, from the Clintel network, have analyzed several claims from the Working Group 1 (The Physical Science Basis) and Working Group 2 (Impacts, Adaptation and Vulnerability) reports. The team and reviewers are from Spain, Canada, Italy, Germany, Norway, The Netherlands, the U.K., and the U.S. In every chapter, this book documents biases and errors in the IPCC assessment. The errors are worse in the WG2 report but are also present in the WG1 report. 

For example, the IPCC ignored 52 highly relevant peer-review articles showing that “normalised disaster losses” saw no increase attributable to climate change yet highlighted one, out of 53 papers, that claimed there is an increase in losses. That one paper is – not surprisingly – flawed, but apparently its conclusions were so appealing to the IPCC that they fell for it. The strategy of the IPCC seems to be to hide any good news about climate change. 

We are on a highway to climate hell”, said UN-boss Guterres recently. But an in-depth look at mortality data shows that climate-related deaths are at an all-time low. Well-known economist Bjorn Lomborg published this excellent news in a 2020 peer-reviewed paper, but the IPCC chose to ignore it, see figure 17 here

Back in 2010, errors in the fourth WG2 report led to the investigation of the IPCC by the InterAcademy Council. This IAC Review recommended, among other recommendations, that “[h]aving author teams with diverse viewpoints is the first step toward ensuring that a full range of thoughtful views are considered.” This important recommendation is still ignored by the IPCC. One of the key recommendations in IAC Review that the AR6 authors ignored, as documented in our book, is:

“The IPCC should encourage Review Editors to fully exercise their authority to ensure that reviewers’ comments are adequately considered by the authors and that genuine controversies are adequately reflected in the report.”InterAcademy Council Review of the IPCC, page xiv

Numerous very well documented reviewer’s comments were completely ignored in AR6, our book documents many of the more egregious of these. The AR6 Working Group 1 report is not free from bias and misleading conclusions either. The IPCC tries to rewrite climate history by erasing the existence of the Holocene Climatic Optimum, a warm period between 10,000 and 6000 years ago, by embracing a new hockey stick graph, that is the result of cherry-picked temperature proxies. They ignore temperature reconstructions that show significantly more variability in the past.

The IPCC claims there is an acceleration in the rate of sea level rise in recent decades. We show this claim is flawed because the IPCC ignores decadal natural variability in the sea level rate. We also show that the IPCC sea level tool – made available for the first time – shows a mysterious and unlikely jump upward in 2020.

Canadian economist Ross McKitrick, pointed out that all models used by the IPCC, show too much warming in the troposphere, both globally and in the tropics (where models predict a ‘hot spot’). Observed warming indicates a moderate climate sensitivity between 1 and 2.5 degrees Celsius, while the IPCC claims a climate sensitivity of 3 degrees.

On top of that, the IPCC is ‘addicted’ to its highest greenhouse gas emission scenario, the so-called RCP8.5 or now SSP5-8.5 scenario. In recent years, several papers have demonstrated that this scenario is simply not plausible and should not be used for policy purposes. Deep inside the WG1 report the IPCC acknowledges that this scenario has a ‘low likelihood’, but this very important remark was not highlighted in the Summary for Policy Makers, so the media and policy makers are unaware of this. This implausible scenario is commonly used in the report.

Our conclusions are quite harsh. We document biases and errors in almost every chapter we reviewed. In some cases, of course, one can quibble endlessly about our criticism and how relevant it is for the overall ‘climate narrative’ of the IPCC. In some cases, though, we document such blatant cherry picking by the IPCC, that even ardent supporters of the IPCC should feel embarrassed.

The AR6 report reveals that they have ignored the very important multi-decadal ocean oscillations discovered in the 1990s and 2000s (see Vinos, 2022 Ch. 11 and Wyatt and Curry, 2014) long after the IPCC had focused exclusively on anthropogenic causes. These ocean oscillations, collectively, have a large effect on our climate, but are unrelated to “non-condensing greenhouse gases.” AR6 states that:

“there has been negligible long-term influence from solar activity and volcanoes”AR6, page 67

Yet, they acknowledge no other natural influence on multidecadal climate change despite the recent discoveries suggesting significant natural climate change, a true case of tunnel vision.

We were promised IPCC reports that would objectively report on the peer-reviewed scientific literature, yet we find numerous examples where important research was ignored. In Ross McKitrick’s chapter on the “hot spot,” he lists many important papers that are not even mentioned in AR6. Marcel Crok gives examples where unreasonable emissions scenarios are used to frighten the public in his chapter on scenarios, and examples of bias and hiding good news in his chapters on extreme weather and snowfall. Nicola Scafetta and Fritz Vahrenholt document that over 100 papers showing solar activity correlates with climate change have been ignored by the IPCC. Numerous other examples are documented in other chapters. These deliberate omissions and distortions of the truth do not speak well for the IPCC, reform of the institution is desperately needed.

Perhaps this is why, after 47 reports and 32 years, they have yet to convince a majority of the people on Earth, or in the United States, that manmade climate change is our most important and serious societal problem. Other problems are always considered more important and urgent. In a 2018 Pew Research poll climate change ranked 18th, of 19 issues in importance, in a similar 2014 poll, climate change ranked 14th in a list of priorities. A 2022 poll by the Pew Research Center also found climate change ranked 14th. In the UN My World 2015 Report, a poll of 10 million people around the world, climate change ranked dead last of 16 issues in importance. Minds are not being changed.

Are we at a fork in the road? Will the United Nations, the IPCC, and politicians finally realize that their 50-year-old hypothesis is out of date and incorporate the new natural warming forces discovered in the past thirty years into their work and projections? In the past the IPCC has fought off attempts to independently review their work. We hope our documentation of the problems in AR6 eventually leads to the necessary changes in their organization and procedures.

The Data Shows Sea Levels Have Been Falling For Over 100 Years in Scandinavia. But the IPCC Decides They’re Rising

Adventure Caravans Scandinavian Countries RV Tour

From The Daily Sceptic


For over 100 years the North European cities of Oslo, Stockholm and Helsinki have been steadily rising from the sea with no suggestion from observed evidence that local sea levels will not continue to drop by a few millimetres a year. That is until 2020, when the IPCC’s new AR6 Sea Level Projection Tool suddenly promoted substantial sea level rises all round. The discovery appears to baffle Ole Humlum, Emeritus Professor of Physical Geography at the University of Oslo. It seems that this tool was not produced to test the validity of a scientific idea. It was instead an attempt to “alarm the user”, he said.

Alarm it has. Since this Intergovernmental Panel on Climate Change (IPCC) computer model was first made publicly available in 2020, there has been a rush of unchecked ‘flood’ stories in the mainstream media. The Daily Sceptic has reported on the activities of a US-based green agitprop operation called Climate Central that is backed by billionaire Foundations and uses the IPCC data to promote custom-made flood catastrophe stories in local media. Recently the Mirror reported that much of London could be gone within 80 years, while large area along the Humber and the Midlands could also disappear beneath the waves. Local politicians such as London mayor Sadiq Khan pick up on these fantastical stories and use them to justify harsh ‘climate’ polices, including an assault on private transport.

This is what Humlum found when he interrogated the IPCC’s new sea level change computer projection model for the coastal city of Oslo. The Norwegian capital, in common with other Scandinavian cities, was buried under a massive ice sheet that only started to lift 20,000 years ago. Even today, the area experiences an ongoing ‘isostatic’ land rise of several millimetres a year as it bounces back from underlying layers. The observed rate of sea level decline can be seen in the above graph in purple. If the 100-year plus observational trend continues, the sea level will fall by 28cm by the end of the century. The IPCC model forecasts a rise in sea level by 2100 of 17cm. Humlum found similar IPCC patterns and disconnects for the capitals of Sweden and Finland. Copenhagen was at the margin of the ice sheet, and a very small annual increase has become a substantial uplift of 45cm by 2100.

It is “extremely surprising”, observes Humlum, that this modelled change should first appear in 2020 as a rather marked step change in the relative sea level. Humlum suggests that if the modellers had produced data going back to 1950, “the conflict between measured and modelled data would immediately have become apparent”. In Humlum’s view, “it is highly disappointing that such a simple quality – or sanity check – was apparently never requested or performed by the IPCC”.

Humlum’s work features in the recently-published Clintel Report – The Frozen Climate Views of the IPCC – and is part of a detailed and critical examination of the UN organisation’s Sixth Assessment Report (AR6). As we noted recently, the scientific authors are damning about much of the IPCC’s work. In addition to emphasising worst-case scenarios, it rewrites climate history, has a “huge” bias in favour of bad news, and keeps good news out of the widely-distributed Summary for Policy Makers. The worst case scenario is called SSP5-8.5 and it assumes temperatures will rise by up to 5°C within less than 80 years. Given that temperatures increased by barely 0.1°C in the first two decades of this century, almost nobody believe these scenarios are remotely plausible. Nevertheless, Clintel notes that 43% of IPCC predictions of drastic and damaging climate change, and around half the climate science literature, are based on these scenarios.

Humlum’s graph uses only a moderate SSP2-4.5 scenario. The more extreme scenarios are available to use on the IPCC Tool, a fact that might explain how, with the help of Climate Central, the Wilshire Times reported last year that by 2050 the waters could be lapping around Gloucester Cathedral, sited at an elevation of 19 metres. For its part, Climate Central notes that it provides “authoritative information to help the public and policymakers make sound decisions about climate change and energy”.

The level of the sea is very difficult to measure and despite recent advances in satellite altimetry, tidal gauges still offer a consistent record. Humlum has noted that these gauges located around the world suggest an average sea level rise of 1-2mm a year. Recent modelled attempts to incorporate satellite measurements produce a rise that that is said to be over 3mm. The IPCC is claiming a recent acceleration in sea level rise, but Humlum says the evidence for this is “thin”. The tide gauge records are said to show “remarkably linear behaviour for more than a century”.

Humlum says it is likely that the IPCC conflates what it sees as a recent “acceleration” in sea level with ocean multidecadal variability. “This should become clear in the next 10-20 years,” he writes. “Right now, it is very preliminary to claim there is an acceleration of the sea level rise.”

The starting point for the IPCC’s work is the assumption that all warming from about 1850 was caused by humans burning fossil fuel. Its 1988 founding principles told it to determine the “scientific basis of risk of human-induced climate change”. Given this dogma, the IPPC has become uniquely unsuited to considering all aspects of climate change, whether arising from the activities of humans or due to natural causes. Since 1988, there is considerably more understanding about the natural forces that cause the climate to change. Humlum feels that the IPCC’s blinkered view may have led to its latest errors in sea level modelling. “The fundamental IPCC finding of no significant influence of natural variations since about 1850 should therefore be reconsidered,” he argues.

The more cynical might note that the IPCC’s primary purpose is to promote the idea that the sole cause of global warming since 1850 is human activity. Any deviation from this line will cause considerable financial hardship and widespread unemployment in the climate science community.

Chris Morrison is the Daily Sceptic Environment Editor.

Is AR6 the worst and most biased IPCC Report?

From Watts Up With That?

By Andy May

This is the text of my presentation on Tom Nelson’s podcast which can be viewed here. The question and answers start at about 18:15 into the interview.

The first IPCC Physical Science Basis report is called “FAR” and was first published in 1990. An updated 1992 version of the report contains this statement:

“global-mean surface air temperature has increased by 0.3°C to 0.6°C over the last 100 years … The size of this warming is broadly consistent with predictions of climate models, but it is also of the same magnitude as natural climate variability. … The unequivocal detection of the enhanced greenhouse effect from observations is not likely for a decade or more.”(IPCC, 1992, p. 6).

This was an accurate statement at the time, and it is mostly accurate to this day. In the past century (since 1920) temperatures have increased about one degree and I’m not sure we will be able to detect a human enhanced greenhouse effect in ten years, or ever, but otherwise the quote is still accurate. One degree of global warming in a century is well within natural climate variability according to historical records and records of glacier advances and retreats (Vinós, 2022, pp. 89-107).

Glaciers exist today, where no glaciers existed during the Medieval Warm Period from about 800 to 1200AD and during the Holocene Climatic Optimum from about 7500 to 4500BC. In addition, the Vikings farmed parts of Greenland where permafrost exists today. Ötzi, the Tyrolean iceman, who was frozen into a glacier about 5,000 years ago, and only recently discovered in his glacier tomb, can attest to the fact that modern glaciers are more advanced than they were before 3000BC.

The second report, called SAR was published in 1996 and 1997. Chapter 8 was a major issue when it came out because in the original draft, the scientists who wrote it all agreed to include this statement:

“no study to date has both detected a significant climate change and positively attributed all or part of that change to anthropogenic causes.”(Final draft, approved by all 36 authors, SAR, July 1995)

Yet, in the final meeting of the IPCC supervising committee of government politicians, the editors and lead authors of the IPCC on November 29th, 1995, which went very late and into the early morning of November 30th, this statement was changed to read:

“The balance of evidence suggests a discernible human influence on global climate.”(IPCC, 1996, p. 4).

This change was agreed by the lead authors and political representatives of the participating countries, and without consulting the scientists who wrote and approved the final draft months earlier (May, 2020c, pp. 230-235). The change caused an uproar in the scientific community with Frederick Seitz, the 17th president of the United States National Academy of Sciences, writing about it in the Wall Street Journal (1996), under the headline “A Major Deception On Global Warming.”

In the article, Seitz writes:

“In my more than 60 years as a member of the American scientific community, including service as president of both the National Academy of Sciences and the American Physical Society, I have never witnessed a more disturbing corruption of the peer-review process than the events that led to this IPCC report.”Frederick Seitz, the 17th president of the United States National Academy of Sciences

He did not choose the word “corruption” lightly.

The third report “TAR” was published in 2001. It was seriously tarnished by the inclusion and promotion of the notorious “hockey stick” graph that was later shown to be seriously flawed due to major statistical errors and the inclusion of seriously flawed data.

Even so, the IPCC included the following statement that was based on the flawed hockey stick:

“In the light of new evidence and taking into account the remaining uncertainties, most of the observed warming over the last 50 years is likely to have been due to the increase in greenhouse gas concentrations.”(IPCC, 2001, p. 699).

Numerous reports, peer-reviewed articles, most notably by Stephen McIntyre and Ross McKitrick, Edward Wegman, and the U.S. National Research Council, detailed the numerous flaws in the graph (May, 2020c, pp. 164-198). Analysis showed that random red noise could be fed into the statistical algorithm that was used to create the hockey stick and it still produced hockey sticks.

The fourth report “AR4” issued this statement:

“Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.”(IPCC, 2007b, p. 10).

This was very much like what was written in TAR where the same conclusion was based on the now discredited hockey stick. AR4 backed away from the hockey stick, admitting it was flawed, but it also claimed that there was a very high chance that the Himalayan glaciers would melt by 2035. This is an impossibility it turned out, and the head of the AR4 effort, Rajendra Pachauri had to back down and apologize for the error.

This and other problems with the report led to a U.N. InterAcademy Council investigation that found that the IPCC guidelines for their reports had not been followed and that serious bias had crept into AR4. They also found that a full range of peer-reviewed views were not included.

AR5, published in 2013, included the following statement:

“More than half of the observed increase in global mean surface temperature (GMST) from 1951 to 2010 is very likely due to the observed anthropogenic increase in greenhouse gas (GHG) concentrations.”(IPCC, 2013, p. 869)”

This is very similar to the conclusions of TAR and AR4, but no new evidence is included in the report. Importantly, John Christy, Ross McKitrick, and others had warned the authors of the report that the climate models they were using predicted much more warming in the tropical troposphere than was observed (see figure 1). Still later, Ross McKitrick and John Christy showed that nearly all the AR5 models predicted too much warming at a statistically significant level (McKitrick & Christy, 2018) and this excess warming was dubbed the “hot spot.”

Figure 1. The data is from (McKitrick & Christy, 2018), the plot is from John Christy.

The hot spot still exists in AR6 and has gotten worse (McKitrick & Christy, 2020). It is notable that if the human greenhouse gas emissions are removed from the climate models the fictitious hot spot goes away and the models move much closer to observations.

In AR6 we read the following:

“The likely range of human-induced change in global surface temperature in 2010–2019 relative to 1850–1900 is 0.8°C to 1.3°C, with a central estimate of 1.07°C, encompassing the best estimate of observed warming for that period, which is 1.06°C with a very likely range of [0.88°C to 1.21°C], while the likely range of the change attributable to natural forcing is only –0.1°C to +0.1°C.”(AR6, page 59).

Thus, they now claim that it is likely all the warming since the 19th century is due to humans. And this is despite the fact that in the tropical troposphere their climate models are statistically invalidated if they include human greenhouse gas emissions in the model.

They were warned to avoid confirmation bias and that the AR5 models were running too hot.

Yet, in AR6, they made the models run even hotter than in AR5 and they ignored dissenting opinions by Richard Lindzen, Roger Pielke Jr., John Christy, Ross McKitrick and many other prominent climate scientists. This is illustrated in figure 2.

Figure 2. The AR6 graph is from AR6, page 444. The AR5 graph is from AR5, page 892. For more on this comparison, go here.

Notice the range of AR5 model results do not touch 0.6, yet in AR6 they do.

In AR6, notice the coupled ocean/atmosphere models (red boxes) produce higher sea surface temperatures than the observed sea surface temperatures (blue boxes). The model/observation mismatch in sea surface temperatures in the Pacific Ocean is a very serious problem.

Besides sea surface temperatures, the IPCC/CMIP climate models have a serious problem with clouds. They cannot model clouds. It is well known and accepted that clouds are net cooling, but how do they respond when surface temperatures rise? What is the net feedback of clouds when the world warms? They don’t know and the uncertainty in the cloud response to warming is nearly as large as the total uncertainty in all modeled surface warming feedbacks.

We find this in AR6 on the subject:

“… CMIP6 models have higher mean ECS and TCR [climate sensitivity to greenhouse gases] values than the CMIP5 generation of 50 models. They also have higher mean values and wider spreads than the assessed best estimates and very likely ranges within this [AR6] Report. These higher ECS and TCR values can, in some models, be traced to changes in extra-tropical cloud feedbacks that have emerged from efforts to reduce biases in these clouds compared to satellite observations (medium confidence). The broader ECS and TCR ranges from CMIP6 also lead the models to project a range of future warming that is wider than the assessed warming range”(AR6, p 927).

Translation: We adjusted our cloud feedback parameters to try and fix the mismatch with the real world and when we did that, the already too-warm models got worse. They are clearly in that stage of their modeling effort that every time they try and fix a mismatch, they break something else. It is a sign that their models are missing some vital component of climate.

Figure 3 is a plot of model climate feedback to model calculated ECS or equilibrium climate sensitivity to a doubling of CO2 (Ceppi, Brient, Zelinka, & Hartmann, 2017). Remember cloud feedback cannot be modeled, it must be input to the model via user adjustable parameters. The plot tells us that 71% of the model computed ECS is determined by these user input parameters. The models can literally produce almost any ECS the modeler desires.

Figure 3. Modeled cloud feedback to surface temperature versus model calculated ECS (climate sensitivity to a doubling of CO2 or 2xCO2). Data from (Ceppi, Brient, Zelinka, & Hartmann, 2017).

As previously mentioned, the IPCC climate models have a hard time with sea surface temperatures. They not only predict higher sea surface temperatures than observed, they also get the pattern of warming and cooling oceans wrong. It seems they have decided that their models must be correct, so they have assumed that the feedbacks must be changing, and this has screwed them up.

They are fundamentally changing their models such that they cannot be refuted by observations. By hypothesizing a continually changing climate state, they are making their already unfalsifiable ideas even more unfalsifiable. As Karl Marx and his followers found out, if your hypothesis is fluid enough, you can conclude whatever you want, and no one can challenge you. From Karl Popper, 1962, page 37:

“The Marxist theory of history, in spite of the serious efforts of some of its founders and followers, ultimately adopted [a] soothsaying practice. In some of its earlier formulations (for example in Marx’s analysis of the character of the ‘coming social revolution’) their predictions were testable, and in fact falsified. Yet instead of accepting the refutations the followers of Marx re-interpreted both the theory and the evidence in order to make them agree. In this way they rescued the theory from refutation; but they did so at the price of adopting a device which made it irrefutable. They … destroyed its much-advertised claim to scientific status.”(Popper, 1962, p. 37).

So now AR6 claims that as surface temperatures rise, the feedbacks to that warming change. In one fell swoop, they both explain why their models do not match observations and they invalidate those pesky observation-based calculations of climate sensitivity that are so much less than their model-based estimates.

As you can see in the AR6 maps in figure 4, the modeled ocean temperatures are much simpler than the observed pattern. Further, the cloud cover over South America is increasing, not decreasing as predicted. The models expect the eastern Pacific to warm much more than observed and the western Pacific is warming much more than predicted. The pattern is wrong.

Figure 4. A comparison of observed sea surface temperature changes from 1870 to 2019 to modeled changes. The scales are different because the actual change in CO2, in the top map, is smaller than the model scenario used. But the colors in the maps are compatible.

They claim that the models are OK, they just need to adjust their feedbacks. Richard Seager and his colleagues write:

“The tropical Pacific Ocean response to rising GHGs impacts all of the world’s population. State-of-the-art climate models predict that rising GHGs reduce the west-to-east warm-to-cool sea surface temperature gradient across the equatorial Pacific.

In nature, however, the gradient has strengthened in recent decades as GHG concentrations have risen sharply. This stark discrepancy between models and observations has troubled the climate research community for two decades. … The failure of state-of-the-art models to capture the correct response introduces critical error into their projections of climate change in the many regions sensitive to tropical Pacific sea-surface-temperatures.”(Seager, Cane, & Henderson, 2019)

AR6 has its own version of the TAR hockey stick, and it is just as flawed as the first one. They also published a refutation of their own version on page 316 of their report, as shown in figure 5. They want us to believe that the last decade was warmer than any decade in the past 125,000 years. The data they rely on from 10,000 years ago to 2,000 years ago only has century (10 decades!) resolution by their own admission. I added the red circle, arrows, and brackets to their figure 2.11.

Figure 5. Modified after AR6, figure 2.11, page 316.

Notice especially the bracket. The uncertainty bars in their plot of temperatures from 10,000 years ago to 2000 years ago are larger than all the recent warming. In other words, their own data does not support their statement. They can’t possibly tell us anything about how the most recent decade compares to any decade prior to around 1850, at the end of the Little Ice Age.

In closing, I could go on and on, but the bottom line is that AR6 is the worst and most biased IPCC Physical Science Basis report ever. SAR through AR5 were bad, but AR6 is beyond help.

Take this from one of the few who has read all of them.

It is very clear that the IPCC is losing the public, polls repeatedly show the world population does not believe global warming is a priority. Recent polls show that skepticism about human-caused climate change is increasing around the world. A recent University of Chicago poll found that the belief that humans have caused all or most climate change slumped to 49% from 60% just five years ago. Seventy percent of the U.S. public are unwilling to spend more than $2.50 a week to combat climate change.

Figure 6. The number of pages in each major IPCC Physical Science Basis Report.

60% of U.S voters believe that climate change has become a religion that has nothing to do with climate. Billions of dollars, six major reports that total 6,543 pages (2,391, or nearly half of them are in AR6 as shown in figure 6) and a total of 47 reports of all kinds, and the public has not been convinced that climate change is important. It’s time for the IPCC to reform or give up, in my opinion.

For more details about the flaws in AR6 read the Clintel report. It was created by an international team of scientists, from seven countries around the world. It has been extensively peer-reviewed by some of the world’s top climate scientists. The cover is shown below as figure 7. It is available as a low resolution pdf for download at clintel.org and will be for sale as a proper ebook or paperback at Amazon, Kobo, and Barnes and Noble on May 29th.

Figure 7. Cover of the new Clintel assessment of the IPCC AR6 report. Download at clintel.org or purchase at your favorite bookseller after May 29th.

Download the bibliography here.

Andy May: Is AR6 the worst and most biased IPCC report? | Tom Nelson Pod #105

Tom Nelson

Tom Nelson Podcast

Andy May is a writer, blogger, and author living in The Woodlands, Texas; and enjoys golf and traveling in his spare time.

He is the author of three books on climate change issues and one on Kansas history.

Andy is the author or co-author of seven peer-reviewed papers on various geological, engineering and petrophysical topics. He retired from a 42-year career in petrophysics in 2016.

You can find many of his posts on the popular climate change blog Wattsupwiththat.com, where he is an editor.

Andy’s previous appearance on this podcast:

00:00 Introduction

00:06 First Assessment Report (FAR)

01:56 SAR

04:01 TAR

05:14 AR4

06:30 AR5

07:48 AR6

16:12 Conclusion

18:11 Q and A

CLINTEL’s critical evaluation of the IPCC AR6

From Climate Etc.

by Judith Curry

Clintel has published a new report entitled “The Frozen Climate Views of the IPCC: Analysis of the AR6.”

“The new Report provides an independent assessment of the most important parts of AR6. We document biases and errors in almost every chapter we reviewed. In some cases, of course, one can quibble endlessly about our criticism and how relevant it is for the overall ‘climate narrative’ of the IPCC. In some cases, though, we document such blatant cherry picking by the IPCC, that even ardent supporters of the IPCC should feel embarrassed.”

Climate Intelligence (CLINTEL) is an independent foundation that operates in the fields of climate change and climate policy. CLINTEL was founded in 2019 by emeritus professor of geophysics Guus Berkhout and science journalist Marcel Crok.

The CLINTEL Report is edited by Marcel Crok and Andy May, with contributions from Javier Vinos, Ross McKitrick, Ole Humlum, Nicola Scafetta, and Fritz Vahrenholt.

The Chapter topics are:

  1.  No confidence that the present is warmer than the mid-Holocene
  2. The resurrection of the Hockey Stick
  3. Measuring global surface temperature
  4. Controversial Snow Trends
  5. Accelerated sea level rise: not so fast
  6. Why does the IPCC downplay the Sun?
  7. Misty climate sensitivity
  8. AR6: more confidence that models are unreliable
  9. Extreme scenarios
  10. A miraculous sea level jump in 2020
  11. Hiding the good news on hurricanes and floods
  12. Extreme views on disasters
  13. Say goodbye to climate hell, welcome climate heaven

The key issue is this:  the IPCC focuses on “dangerous anthropogenic climate change,” which leads to ignoring natural climate change, focusing on extreme emissions scenarios, and cherry picking the time periods and the literature to make climate change appear “dangerous.”

“The IPCC ignored crucial peer-reviewed literature showing that normalised disaster losses have decreased since 1990 and that human mortality due to extreme weather has decreased by more than 95% since 1920. The IPCC, by cherry picking from the literature, drew the opposite conclusions, claiming increases in damage and mortality due to anthropogenic climate change.” 

With regards to IPCC AR6’s error ridden assessment of extreme weather events, see also this analysis by Roger Pielke Jr that demonstrated egregious errors in incorrectly reporting the conclusions from papers that were actually cited by the IPCC.

With regards to ignoring natural climate variability, Chapters 1 (mid-Holocene), 2 (Hockey Stick) and 6 (the sun) are excellent.

I’ve looked at the AR6 WGI Report fairly thoroughly, focusing mainly on specific material that was relevant for my new book Climate Uncertainty and Risk.   I am familiar with nearly all of the issues raised in the CLINTEL Report, but the material in Chapters 2 (Hockey Stick) and 4 (snow trends) was new to me.  The next section focuses on the Hockey Stick.

Zombie Hockey Stick

Shortly after publication of AR6 WGI, I spotted some comments in twitter regarding the resurrection of the Hockey Stick.  After wondering “what fresh new Hockey Stick hell is this?”, I didn’t investigate further.

Well the Clintel Report did the work for me.  Subtitle for Chapter 2:

“A big surprise in the new IPCC report is the publication of a brand new hockey stick. The IPCC once again has to cherry pick and massage proxy data in order to fabricate it. Studies that show larger natural climate variations are ignored.”

Excerpts from the Chapter:

<begin quotes>

The PAGES 2k group is specialised in climate reconstructions and back in 2013 was comprised of the majority of all active paleoclimatologists. The PAGES 2k Consortium (2013) published a reconstruction in which parts of the first millennium were occasionally as warm as present-day

In 2019, PAGES 2k published a new version of the temperature development of the past 2000 years (PAGES 2k Consortium, 2019)11. Surprisingly, it differed greatly from the predecessor version. Even though the database had only mildly changed, the pre-industrial part was now suddenly nearly flat again. The hockey stick was reborn.

The new hockey stick was immediately incorporated into the AR6 report (IPCC, 2021). Among the lead authors of AR6 chapter 2 is Darrell S. Kaufman who is a co-author of the new hockey stick in the PAGES 2k Consortium (2019). This is probably not a coincidence.

Evidence suggests that a significant part of the original PAGES 2k researchers could not technically support the new hockey stick and seem to have left the group in dispute. Meanwhile, the dropouts published a competing temperature curve with significant pre-industrial temperature variability (Büntgen et al., 2020). On the basis of thoroughly verified tree rings, the specialists were able to prove that summer temperatures had already reached today’s levels several times in the pre-industrial past. However, the work of Ulf Büntgen and colleagues was not included in the IPCC report, although it was published well before the editorial deadline.

Like its predecessor, the new hockey stick by PAGES 2k 2019 is based on a large variety of proxy types and includes a large number of poorly documented tree ring data. In many cases, the tree rings‘ temperature sensitivity is uncertain. For example, both PAGES 2k Consortium (2013) and PAGES 2k Consortium (2019) used tree ring series from the French Maritime Alps, even though tree ring specialists had previously cautioned that they are too complex to be used as overall temperature proxies.

In contrast, Büntgen et al. (2020) were more selective, relied on one type of proxy (in this case tree rings) and validated every tree ring data set individually. Their temperature composite for the extra-tropical northern hemisphere differs greatly from the studies that use bulk tree ring input.

In some cases, PAGES 2k composites have erroneously included proxies that later turned out to reflect hydroclimate and not temperature. In other cases, outlier studies have been selected in which the proxies exhibit an anomalous evolution that cannot be reproduced in neighbouring sites (e.g. MWP data from Pyrenees and Alboran Sea in PA13). Outliers can have several reasons, e.g. a different local development, invalid or unstable temperature proxies, or sample contamination.

Steve McIntyre has studied the PAGES 2k proxy data base in great detail and summarized his criticism in a series of blog posts on his website Climate Audit.  For example, the PAGES 2k Consortium (2019) integrated a tree ring chronology from northern Pakistan near Gilgit (“Asia_207”) which shows an extreme closing uptick. Incorporation of data series like this strongly promote the hockey stick geometry of the resulting temperature composite.  McIntyre analysed the original tree ring data and found that the steep uptick in the Asia_207 chronology is the result of questionable data processing. When calculating the site chronology using the rcs function from Andy Bunn’s dplR package, the uptick surprisingly disappears. In fact, the series declines over the 20th century.

Conclusion: The resurrected hockey stick of AR6 shows how vulnerable the IPCC process is to scientific bias. Cherry picking, misuse of the peer review process, lack of transparency, and likely political interference have led to a gross misrepresentation of the pre-industrial temperature evolution.

<end quotes>

JC reflections

The CLINTEL Report provides a much needed critical evaluation and intellectual counterpoint to the IPCC AR6.

There is a lot of good material in the AR6 WG1 Report, but there is also a lot of cherry picking and flat out errors in the Report (the AR6 WG2 Report is just flat out bad).  With any kind of serious review, or if the author teams have been sufficiently diverse, we would not see so many of these kinds of errors.  Unfortunately, the IPCC defines “diversity” in terms of gender, race and developed versus underdeveloped countries; actual diversity of thought and perspective is dismissed in favor of promoting the politically mandated narrative from the UN. 

The consensus disease that that was caught by the IPCC following publication of the First Assessment Report in 1990, combined with pressures from policy makers, is resulting in documents that don’t reflect the broad disagreement and uncertainties on these complex topics.  The IPCC’s mandated narrative has become very stale.  Worse yet, it is becoming increasingly irrelevant to policy making by continuing to focus on extreme emissions scenarios and the embarrassing cherry picking that is required to support the “climate crisis” narrative that is so beloved by UN officials.

In any event, UN-driven climate policy has moved well past any moorings in climate science, even the relatively alarming version reported by the IPCC.  The insane policies and deadlines tied to greenhouse gas emissions are simply at odds with the reality of our understanding of climate change and the uncertainties, and with broader considerations of human well being.

The Mysterious AR6 ECS, Part 1

From Watts Up With That?

By Andy May

The climate sensitivity to CO2 and other greenhouse gases (GHGs) is arguably the most important number in the climate change debate. AR6[1] claims the sensitivity, which they call “ECS” or the equilibrium climate sensitivity, is three degrees per doubling of CO2, or 3°C/2xCO2 (“/2xCO2” simply means per doubling of the atmospheric CO2 concentration). They claim the very likely (10% to 90%) range of possible values is from 2 to 5°C/2xCO2 and the likely (66%) range has narrowed to 2.5 to 4°C. Since 1979, with the publication of 1979 Charney Report,[2] the range of possible ECS values has normally been about 1.5 to 4.5°C for a total range of 3°C, how has it now narrowed to 2.5 to 4°C, a full likely uncertainty range of only 1.5°C? It is generally accepted that the direct warming effect of CO2 and other greenhouse gases is small, only about one degree per doubling of CO2,[3] so the debate is all about the feedbacks, especially cloud feedback to the greenhouse gas warming.[4]

The real-world effect of changing the CO2 and GHG atmospheric concentration on climate, whether natural or emitted by humans, has never been measured, only modeled. ECS is defined as the ultimate warming due to an instantaneous doubling of the atmospheric CO2 concentration. The ultimate climate response to that doubling will not occur for hundreds or thousands of years and everything else affecting the climate, like cloudiness, and insolation will not stay static for that long, so it is an artificial quantity that cannot be measured. Importantly, the IPCC estimate of ECS can probably not be falsified through real world measurements, which means it is not a proper scientific hypothesis. Even with a climate model it is difficult, in Sherwood, et al., they write:

“To calculate the ECS in a fully coupled climate model requires very long integrations (>1,000 years).”[5]

A more relevant measure of climate sensitivity is the transient climate response, or TCR, which is also calculated by the AR6/CMIP6 climate models. This is the climate response to a steady increase in CO2 concentration of about 1% per year, to the point where CO2 doubles, roughly 70 years.[6] Thus, it is a more realistic and, given the short time frame, it is possibly measurable in the real world. Sherwood, et al.,6 a source relied upon in AR6 (Chapter 7 mentions the Sherwood paper 43 times), defines a term called “effective climate sensitivity” that is the climate response to an instantaneous doubling of CO2, or more specifically half of the climate response to an instantaneous quadrupling of CO2. By adding “effective” to the name rather than “equilibrium” they cut the time frame to 150 years.

AR6 constrains their estimates of TCR and ECS using four lines of evidence: process (mostly feedbacks) understanding, climate model simulations, historical observations, and paleoclimatic observations, plus a fifth category they call a synthesis of evidence, in explaining this new ECS evaluation they write:

“All four lines of evidence rely, to some extent, on climate models, and interpreting the evidence often benefits from model diversity and spread in modelled climate sensitivity. … unlike in previous assessments, climate models are not considered a line of evidence in their own right in the IPCC Sixth Assessment Report.” AR6, page 1024.

As explained above, ECS is not measurable since it is derived from an unreal model scenario. In AR6 ECS and TCR are referred to as “idealized quantities … that can be inferred from [observations] or estimated directly using climate [model] simulations.”[7] Thus, all attempts to estimate them in nature require some sort of model to transform the measurements to the modeled ECS or TCR scenario given in AR6.

Nic Lewis and Judith Curry try to simplify the conversion from observations of temperature and CO2 by carefully selecting periods of time when natural forces are as comparable as possible. However, they only consider volcanism and major ocean oscillations, like ENSO and the Atlantic Multidecadal Oscillation (AMO). We have more to say about this idea in Part 4.

The world was cooler in the 19th century and the Little Ice Age was just ending. For this reason, there were fewer El Niños then than now. El Niños occur due to excess heat buildup in the Pacific Ocean that must be expelled to the atmosphere. They warm Earth’s surface for a few years, but longer term they act as a cooling agent.[8] The number of strong El Niños and their strength increases as warm periods end and the world grows cooler, as happened at the end of Medieval Warm Period when the Earth dipped into the Little Ice Age from ~1050AD to ~1400AD. Once the world became colder, their strength and number reduced, as observed until the late 20th century.[9]

While there is considerable debate on the subject, it is likely that solar variability also plays a role in climate change and the Sun was less active in the 19th century than during the Modern Solar Maximum from ~1935 to ~2005.[10] While the IPCC believes that solar variability and other natural factors, except for volcanos over short periods, play no role in climate change over the past 270 years,[11] Javier Vinós and Ronan Connolly, et al.[12] have presented considerable evidence that this is not the case. Thus, the calculations Lewis and Curry make to convert their measurements to the modeled quantities of ECS and TCR might be contaminated by natural factors that they did not take into account. Even so, their calculations of ECS and TCR are considerably below the IPCC likely lower limits of 2.5°C and 1.4°C, respectively. Tables of estimates of ECS and TCR will be presented in part 3.

Besides TCR and ECS, the classical climate sensitivity quantity, which we simply call “climate sensitivity to CO2,” is totally evidence based and determined from observations. The classical quantity is best defined as the surface air temperature sensitivity (SATS) to an increase in CO2.[13] The units used for SATS are degrees C per Watts per square meter of forcing. By assuming all the forcing is due to CO2, the value can be converted into °C/2xCO2. The further conversion of this value to ECS or TCR requires making assumptions about the time required for Earth’s surface (mainly the oceans) to come into equilibrium from the change in forcing inclusive of any feedbacks to the CO2-caused warming. In the tables shown in part 3, we list many of these observation-based estimates of climate sensitivity. Some of them, including Lewis and Curry, use simple models to translate the measurements into pseudo ECS and TCR. When this is done, typically the same assumptions made by the IPCC are used for the conversion model. Other estimates of the classical climate sensitivity just present the measurements, but they assume a forcing for the CO2 changes observed.

Mauna Loa[14] measured atmospheric CO2 is increasing at about 2 ppm (0.5%) per year and is not far off from the TCR defined 1% per year. Since the preindustrial era, or the Little Ice Age, CO2 has increased about 50% (one-half of a doubling), thus we are in a time when TCR is relevant.

As mentioned above, AR6 does not use models to directly compute ECS and TCR as they did in the past. Instead, they use five lines of evidence to constrain the final ECS and TCR model calculations.[15] This process is laid out in considerable detail in Sherwood, et al.[16] and in AR6 section 7.5. Sherwood’s analysis tries to show that all values used in the current calculation of ECS are narrowly constrained except for the cloud feedback to surface warming, and in particular, the feedback due to lower-level clouds. It is important to understand that all the methods that AR6 uses to constrain their estimates of ECS and TCR rely, to some extent, on climate models. We show the cloud feedback relationship to ECS in part 2.

The statistical analysis methods that Sherwood, et al. use to integrate various estimates of climate sensitivity into one range are subjective and seriously flawed, as shown by Nic Lewis.[17] Lewis corrected their errors and lowered their estimate of climate sensitivity from 3.23 to 2.16K, about 33%. Nic Lewis points out that “Climate sensitivity has been estimated from various types of evidence, but none of these has narrowly constrained its value.”

AR6 isolates the processes that they think contribute to warming and constrain them with observations, this is a reasonable approach if the full range of possible processes affecting warming are considered. The authors of Connolly, et al. [18] believe that AR6 have not properly considered the potential influence of solar variability. Connolly, et al. demonstrate that insolation and other solar variability may be much more important than assumed by the IPCC. As shown in figure 1, the AR6 estimate of natural warming (volcanism and solar variability) is zero, or slightly negative.

Figure 1. AR6 estimated temperature change contributions from 1750 to 2019, with uncertainties. The assumed natural contribution is zero, or slightly negative, plus or minus a small amount. Source: AR6, chapter 7, page 961.

Global surface warming from 1971 through 2018 is about 0.85°C, according to HadCRUT4.[19] According to AR6[20] this corresponds to a top of the atmosphere (TOA) energy imbalance of +0.57 W/m2 (+0.2% of the incoming ~340 W/m2 from the Sun) for the same period. For Earth’s surface to warm, it must retain more thermal energy than it emits to space. When this surface energy imbalance, which is measured in Watts per square meter of surface (W/m2), causes warming, it is positive by convention. Some of the excess energy warms the surface, and the warmer surface and lower atmosphere emit more energy to space, resulting in the positive 0.57 W/m2 increase in emissions at the TOA.

Thus, if we were to assume all the surface warming is due to increasing CO2 and other greenhouse gases (GHGs), the surface air temperature sensitivity (SATS) to GHGs is about 1.6°C/W/m2, if nothing else changes. This is an extraordinarily large number. The classical values, based on observations,[21] are typically between 0.1°C/W/m2 to 0.5°C/W/m2. This suggests that all recent warming is not entirely due to GHGs.

The oceans are not really a factor in the short term, since IR (Infrared Radiation) emitted by CO2 and other GHGs cannot penetrate far below the ocean surface, like sunlight does. Most incident IR is absorbed in the first millimeter of the ocean and re-emitted or evaporated away shortly after. IR does warm the sea surface and some of this heat will go into the deeper ocean through conduction and turbulent mixing, but IR is not as transmissible to the deep ocean as visible light, especially blue light.[22]

If the AR6 estimate of the radiative imbalance is correct, the 48-year period from 1971 to 2018 had an average imbalance of 0.01 W/m2 per year. This is tiny and far below what we can measure today. The accuracy of our satellite measurements of Earth’s incoming and outgoing radiation is no better than ±2 W/m2.[23] Besides the contribution of GHGs, there are other natural factors, such as changes in cloud cover and type, that can play a large role in either increasing or decreasing the radiative imbalance at Earth’s surface.

In part 2 of this series, we will examine the largest uncertainty in the AR6 ECS estimate, cloud feedback. In part 3 of the series, we will compare the AR6 ECS and TCR estimates to observation-based estimates. Some of the observation-based estimates are considered by AR6, and some are not. We will see that many observation-based estimates of climate sensitivity are considerably lower than the AR6 likely lower bound of 2.5°C.

Finally, in part 4 we examine how Lewis and Curry[24] convert their selected observations into a value that can be compared to the totally model-based value called “ECS.” It is unusual to convert measurements to model values, usually it is done the other way around, but is their conversion valid? What assumptions do they make? The Lewis and Curry ECS is significantly lower than the AR6 likely lower bound, how do we interpret that difference?

The bibliography can be downloaded here.

  1. (IPCC, 2021) or AR6. 
  2. Charney, J., Arakawa, A., Baker, D., Bolin, B., Dickinson, R., Goody, R., . . . Wunsch, C. (1979). Carbon Dioxide and Climate: A Scientific Assessment. National Research Council. Washington DC: National Academies Press. doi:https://doi.org/10.17226/12181&nbsp;
  3. (Charney, et al., 1979, p. 8) 
  4. Dessler, A. E. (2013). Observations of Climate Feedbacks over 2000-10 and Comparisions to Climate Models. J of Climate, 333-342. 
  5. Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., J., P. M., Hargreaves, C., . . . Knutti, R. (2020, July 22). An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence. Reviews of Geophysics, 58
  6. AR6, p 992 
  7. AR6, p 992 
  8. Vinós, J. (2022). Climate of the Past, Present and Future, A Scientific Debate. Spain: Critical Science Press. Pages 53-54. Link
  9. (Moy, Seltzer, & Rodbell, 2002) 
  10. Vinós, J. (2022). Climate of the Past, Present and Future, A Scientific Debate. Spain: Critical Science Press. Page 192 and Connolly et al., R. (2021). How much has the Sun influenced Northern Hemisphere temperature trends? Research in Astronomy and Astrophysics, 21(6). Link
  11. AR6, page 961. 
  12. Connolly et al., R. (2021). How much has the Sun influenced Northern Hemisphere temperature trends? Research in Astronomy and Astrophysics, 21(6). 
  13. Newell, R., & Dopplick, T. (1979). Questions Concerning the Possible Influence of Anthropogenic CO2 on Atmospheric Temperature. J. Applied Meterology, 18, 822-825 and (Idso S. , 1998). 
  14. Global Monitoring Laboratory – Carbon Cycle Greenhouse Gases (noaa.gov) 
  15. AR6, page 993 
  16. Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., J., P. M., Hargreaves, C., . . . Knutti, R. (2020, July 22). An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence. Reviews of Geophysics, 58. doi:https://doi.org/10.1029/2019RG000678&nbsp;
  17. Lewis, N. (2022). Objectively combining climate sensitivity evidence. Climate Dynamics
  18. Connolly et al., R. (2021). How much has the Sun influenced Northern Hemisphere temperature trends? Research in Astronomy and Astrophysics, 21(6). 
  19. (Met Office Hadley Centre, 2017) 
  20. AR6 p 937 
  21. Newell, R., & Dopplick, T. (1979). Questions Concerning the Possible Influence of Anthropogenic CO2 on Atmospheric Temperature. J. Applied Meterology, 18, 822-825. and Idso, S. (1998). CO2-induced global warming: a skeptic’s view of potential climate change. Climate Research, 10(1), 69-82. 
  22. Homewood, P. (2015, May 28). Yes, The Ocean Has Warmed; No, It’s Not Global Warming. Retrieved from Not a Lot of People Know That. Also see Britannica here
  23. Loeb, N. G., Doelling, D., Wang, H., Su, W., Nguyen, C., Corbett, J., & Liang, L. (2018). Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product. Journal of Climate, 31(2). 
  24. (Lewis & Curry, The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity, 2018)